├── .github
    ├── FUNDING.yml
    ├── ISSUE_TEMPLATE
    │   ├── bug_report.md
    │   └── feature_request.md
    ├── PULL_REQUEST_TEMPLATE.yml
    ├── dependabot.yml
    ├── labeler.yml
    └── workflows
    │   ├── code_quality_control.yml
    │   ├── cos_integration.yml
    │   ├── docs.yml
    │   ├── docs_test.yml
    │   ├── label.yml
    │   ├── lints.yml
    │   ├── pr_request_checks.yml
    │   ├── pull-request-links.yml
    │   ├── pylint.yml
    │   ├── python-publish.yml
    │   ├── quality.yml
    │   ├── ruff.yml
    │   ├── run_test.yml
    │   ├── stale.yml
    │   ├── test.yml
    │   ├── testing.yml
    │   ├── unit-test.yml
    │   └── welcome.yml
├── .gitignore
├── CONTRIBUTING.md
├── LICENSE
├── README.md
├── cookbook
    ├── enterprise
    │   ├── accounting
    │   │   └── multi_agent
    │   │   │   ├── accounting_agent_rearrange_book.ipynb
    │   │   │   └── accounting_agents_for_moa.ipynb
    │   ├── finance
    │   │   └── multi_agent
    │   │   │   ├── Swarms_Cookbook_CryptoAgent.ipynb
    │   │   │   ├── Swarms_Cookbook_Tickr_Agent.ipynb
    │   │   │   ├── swarms_finance_10k_analysis_agentrearrange.ipynb
    │   │   │   └── swarms_finance_10k_analysis_custom.ipynb
    │   ├── marketing
    │   │   ├── content_generation
    │   │   │   └── swarms_spreadsheet_analysis_walkthrough.ipynb
    │   │   └── news
    │   │   │   ├── Swarms_Cookbook_NewsAgent.ipynb
    │   │   │   └── main.py
    │   └── medical
    │   │   └── physical_therapy
    │   │       ├── Swarms_Cookbook_MedInsight_Pro.ipynb
    │   │       └── swarms_diagnosis_treatment_extreme_athletics.ipynb
    └── workshops
    │   └── sep_6_workshop
    │       ├── 305_fights_tv.py
    │       ├── agent_example.py
    │       ├── agent_workspace
    │           ├── Financial-Analysis-Agent_sas_chicken_eej_state.json
    │           ├── Spreedsheet-Swarm
    │           │   └── 305FightsTV-Social-Media-Swarm
    │           │   │   └── spreedsheet-swarm-spreadsheet_swarm_run_2024-09-06T19:25:59.463407_metadata.json
    │           └── error.txt
    │       ├── art_therapy_agent.py
    │       ├── data.csv
    │       ├── discord_tool.py
    │       ├── fight_night.csv
    │       ├── gptuesday_swarm.py
    │       ├── groq_agent.py
    │       ├── ig_tool.py
    │       ├── model.py
    │       └── test_rearrange.py
└── swarms_logo.png


/.github/FUNDING.yml:
--------------------------------------------------------------------------------
 1 | # These are supported funding model platforms
 2 | 
 3 | github: [kyegomez]
 4 | patreon: # Replace with a single Patreon username
 5 | open_collective: # Replace with a single Open Collective username
 6 | ko_fi: # Replace with a single Ko-fi username
 7 | tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
 8 | community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
 9 | liberapay: # Replace with a single Liberapay username
10 | issuehunt: # Replace with a single IssueHunt username
11 | otechie: # Replace with a single Otechie username
12 | lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry
13 | custom: #Nothing
14 | 


--------------------------------------------------------------------------------
/.github/ISSUE_TEMPLATE/bug_report.md:
--------------------------------------------------------------------------------
 1 | ---
 2 | name: Bug report
 3 | about: Create a detailed report on the bug and it's root cause. Conduct root cause error analysis
 4 | title: "[BUG] "
 5 | labels: bug
 6 | assignees: kyegomez
 7 | 
 8 | ---
 9 | 
10 | **Describe the bug**
11 | A clear and concise description of what the bug is and what the main root cause error is. Test very thoroughly before submitting.
12 | 
13 | **To Reproduce**
14 | Steps to reproduce the behavior:
15 | 1. Go to '...'
16 | 2. Click on '....'
17 | 3. Scroll down to '....'
18 | 4. See error
19 | 
20 | **Expected behavior**
21 | A clear and concise description of what you expected to happen.
22 | 
23 | **Screenshots**
24 | If applicable, add screenshots to help explain your problem.
25 | 
26 | **Additional context**
27 | Add any other context about the problem here.
28 | 


--------------------------------------------------------------------------------
/.github/ISSUE_TEMPLATE/feature_request.md:
--------------------------------------------------------------------------------
 1 | ---
 2 | name: Feature request
 3 | about: Suggest an idea for this project
 4 | title: ''
 5 | labels: ''
 6 | assignees: 'kyegomez'
 7 | 
 8 | ---
 9 | 
10 | **Is your feature request related to a problem? Please describe.**
11 | A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
12 | 
13 | **Describe the solution you'd like**
14 | A clear and concise description of what you want to happen.
15 | 
16 | **Describe alternatives you've considered**
17 | A clear and concise description of any alternative solutions or features you've considered.
18 | 
19 | **Additional context**
20 | Add any other context or screenshots about the feature request here.
21 | 


--------------------------------------------------------------------------------
/.github/PULL_REQUEST_TEMPLATE.yml:
--------------------------------------------------------------------------------
 1 | <!-- Thank you for contributing to Zeta!
 2 | 
 3 | Replace this comment with:
 4 |   - Description: a description of the change, 
 5 |   - Issue: the issue # it fixes (if applicable),
 6 |   - Dependencies: any dependencies required for this change,
 7 |   - Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
 8 |   - Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
 9 | 
10 | If you're adding a new integration, please include:
11 |   1. a test for the integration, preferably unit tests that do not rely on network access,
12 |   2. an example notebook showing its use.
13 | 
14 | Maintainer responsibilities:
15 |   - nn / Misc / if you don't know who to tag: kye@apac.ai
16 |   - tokenizers: kye@apac.ai
17 |   - training / Prompts: kye@apac.ai
18 |   - models: kye@apac.ai
19 | 
20 | If no one reviews your PR within a few days, feel free to kye@apac.ai
21 | 
22 | See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/kyegomez/zeta


--------------------------------------------------------------------------------
/.github/dependabot.yml:
--------------------------------------------------------------------------------
 1 | # https://docs.github.com/en/code-security/supply-chain-security/keeping-your-dependencies-updated-automatically/configuration-options-for-dependency-updates
 2 | 
 3 | version: 2
 4 | updates:
 5 |   - package-ecosystem: "github-actions"
 6 |     directory: "/"
 7 |     schedule:
 8 |       interval: "weekly"
 9 | 
10 |   - package-ecosystem: "pip"
11 |     directory: "/"
12 |     schedule:
13 |       interval: "weekly"
14 | 
15 | 


--------------------------------------------------------------------------------
/.github/labeler.yml:
--------------------------------------------------------------------------------
 1 | # this is a config file for the github action labeler
 2 | 
 3 | # Add 'root' label to any root file changes
 4 | # Quotation marks are required for the leading asterisk
 5 | root:
 6 | - changed-files:
 7 |   - any-glob-to-any-file: '*'
 8 | 
 9 | # Add 'Documentation' label to any changes within 'docs' folder or any subfolders
10 | Documentation:
11 | - changed-files:
12 |   - any-glob-to-any-file: docs/**
13 | 
14 | # Add 'Tests' label to any file changes within 'docs' folder
15 | Tests:
16 | - changed-files:
17 |   - any-glob-to-any-file: tests/*
18 | 
19 | # Add 'Documentation' label to any file changes within 'docs' or 'guides' folders
20 | ghactions:
21 | - changed-files:
22 |   - any-glob-to-any-file:
23 |     - .github/workflows/*
24 |     - .github/*
25 | 
26 | # Add 'Scripts' label to any file changes within 'docs' folder
27 | Scripts:
28 | - changed-files:
29 |   - any-glob-to-any-file: scripts/*
30 |   
31 | ## Equivalent of the above mentioned configuration using another syntax
32 | Documentation:
33 | - changed-files:
34 |   - any-glob-to-any-file: ['docs/*', 'guides/*']
35 | 
36 | # Add 'Documentation' label to any change to .md files within the entire repository 
37 | Documentation:
38 | - changed-files:
39 |   - any-glob-to-any-file: '**/*.md'
40 | 
41 | # Add 'source' label to any change to src files within the source dir EXCEPT for the docs sub-folder
42 | source:
43 | - all:
44 |   - changed-files:
45 |     - any-glob-to-any-file: 'src/**/*'
46 |     - all-globs-to-all-files: '!src/docs/*'
47 | 
48 | # Add 'feature' label to any PR where the head branch name starts with `feature` or has a `feature` section in the name
49 | feature:
50 |  - head-branch: ['^feature', 'feature']
51 | 
52 | # Add 'release' label to any PR that is opened against the `main` branch
53 | release:
54 |  - base-branch: 'main'
55 | 


--------------------------------------------------------------------------------
/.github/workflows/code_quality_control.yml:
--------------------------------------------------------------------------------
 1 | name: Linting and Formatting
 2 | 
 3 | on:
 4 |   push:
 5 |     branches:
 6 |       - main
 7 | 
 8 | jobs:
 9 |   lint_and_format:
10 |     runs-on: ubuntu-latest
11 | 
12 |     steps:
13 |       - name: Checkout code
14 |         uses: actions/checkout@v4
15 | 
16 |       - name: Set up Python
17 |         uses: actions/setup-python@v5
18 |         with:
19 |           python-version: '3.10'
20 | 
21 |       - name: Install dependencies
22 |         run: pip install --no-cache-dir -r requirements.txt
23 | 
24 |       - name: Find Python files
25 |         run: find swarms_torch -name "*.py" -type f -exec autopep8 --in-place --aggressive --aggressive {} +
26 | 
27 |       - name: Push changes
28 |         uses: ad-m/github-push-action@master
29 |         with:
30 |           github_token: ${{ secrets.GITHUB_TOKEN }}


--------------------------------------------------------------------------------
/.github/workflows/cos_integration.yml:
--------------------------------------------------------------------------------
 1 | name: Continuous Integration
 2 | 
 3 | on:
 4 |   push:
 5 |     branches:
 6 |       - main
 7 | 
 8 | jobs:
 9 |   test:
10 |     runs-on: ubuntu-latest
11 |     steps:
12 |       - name: Checkout code
13 |         uses: actions/checkout@v4
14 | 
15 |       - name: Set up Python
16 |         uses: actions/setup-python@v5
17 |         with:
18 |           python-version: '3.10'
19 | 
20 |       - name: Install dependencies
21 |         run: pip install --no-cache-dir -r requirements.txt
22 | 
23 |       - name: Run unit tests
24 |         run: pytest tests/unit
25 | 
26 |       - name: Run integration tests
27 |         run: pytest tests/integration
28 | 
29 |       - name: Run code coverage
30 |         run: pytest --cov=swarms tests/
31 | 
32 |       - name: Run linters
33 |         run: pylint swarms
34 | 
35 |       - name: Build documentation
36 |         run: make docs
37 | 
38 |       - name: Validate documentation
39 |         run: sphinx-build -b linkcheck docs build/docs
40 | 
41 |       - name: Run performance tests
42 |         run: pytest tests/performance


--------------------------------------------------------------------------------
/.github/workflows/docs.yml:
--------------------------------------------------------------------------------
 1 | name: Docs WorkFlow
 2 | 
 3 | on:
 4 |   push:
 5 |     branches:
 6 |       - master
 7 |       - main
 8 |       - develop
 9 | jobs:
10 |   deploy:
11 |     runs-on: ubuntu-latest
12 |     steps:
13 |       - uses: actions/checkout@v4
14 |       - uses: actions/setup-python@v5
15 |         with:
16 |           python-version: '3.10'
17 |       - run: pip install mkdocs-material
18 |       - run: pip install "mkdocstrings[python]"
19 |       - run: mkdocs gh-deploy --force


--------------------------------------------------------------------------------
/.github/workflows/docs_test.yml:
--------------------------------------------------------------------------------
 1 | name: Documentation Tests
 2 | 
 3 | on:
 4 |   push:
 5 |     branches:
 6 |       - master
 7 | 
 8 | jobs:
 9 |   test:
10 |     runs-on: ubuntu-latest
11 | 
12 |     steps:
13 |       - name: Checkout code
14 |         uses: actions/checkout@v4
15 | 
16 |       - name: Set up Python
17 |         uses: actions/setup-python@v5
18 |         with:
19 |           python-version: '3.10'
20 | 
21 |       - name: Install dependencies
22 |         run: pip install --no-cache-dir -r requirements.txt
23 | 
24 |       - name: Build documentation
25 |         run: make docs
26 | 
27 |       - name: Validate documentation
28 |         run: sphinx-build -b linkcheck docs build/docs


--------------------------------------------------------------------------------
/.github/workflows/label.yml:
--------------------------------------------------------------------------------
 1 | # This workflow will triage pull requests and apply a label based on the
 2 | # paths that are modified in the pull request.
 3 | #
 4 | # To use this workflow, you will need to set up a .github/labeler.yml
 5 | # file with configuration.  For more information, see:
 6 | # https://github.com/actions/labeler
 7 | 
 8 | name: Labeler
 9 | on: [pull_request_target]
10 | 
11 | jobs:
12 |   label:
13 | 
14 |     runs-on: ubuntu-latest
15 |     permissions:
16 |       contents: read
17 |       pull-requests: write
18 | 
19 |     steps:
20 |     - uses: actions/labeler@v5.0.0
21 |       with:
22 |         repo-token: "${{ secrets.GITHUB_TOKEN }}"
23 | 


--------------------------------------------------------------------------------
/.github/workflows/lints.yml:
--------------------------------------------------------------------------------
 1 | name: Linting
 2 | 
 3 | on:
 4 |   push:
 5 |     branches:
 6 |       - master
 7 | 
 8 | jobs:
 9 |   lint:
10 |     runs-on: ubuntu-latest
11 | 
12 |     steps:
13 |       - name: Checkout code
14 |         uses: actions/checkout@v4
15 | 
16 |       - name: Set up Python
17 |         uses: actions/setup-python@v5
18 |         with:
19 |           python-version: '3.10'
20 | 
21 |       - name: Install dependencies
22 |         run: pip install --no-cache-dir -r requirements.txt
23 | 
24 |       - name: Run linters
25 |         run: pylint swarms_torch


--------------------------------------------------------------------------------
/.github/workflows/pr_request_checks.yml:
--------------------------------------------------------------------------------
 1 | name: Pull Request Checks
 2 | 
 3 | on:
 4 |   pull_request:
 5 |     branches:
 6 |       - master
 7 | 
 8 | jobs:
 9 |   test:
10 |     runs-on: ubuntu-latest
11 | 
12 |     steps:
13 |       - name: Checkout code
14 |         uses: actions/checkout@v4
15 | 
16 |       - name: Set up Python
17 |         uses: actions/setup-python@v5
18 |         with:
19 |           python-version: '3.10'
20 | 
21 |       - name: Install dependencies
22 |         run: pip install --no-cache-dir -r requirements.txt
23 | 
24 |       - name: Run tests and checks
25 |         run: |
26 |           pytest tests/
27 |           pylint swarms_torch


--------------------------------------------------------------------------------
/.github/workflows/pull-request-links.yml:
--------------------------------------------------------------------------------
 1 | name: readthedocs/actions
 2 | on:
 3 |   pull_request_target:
 4 |     types:
 5 |       - opened
 6 |     paths:
 7 |       - "docs/**"
 8 | 
 9 | permissions:
10 |   pull-requests: write
11 | 
12 | jobs:
13 |   pull-request-links:
14 |     runs-on: ubuntu-latest
15 |     steps:
16 |       - uses: readthedocs/actions/preview@v1
17 |         with:
18 |           project-slug: swarms_torch


--------------------------------------------------------------------------------
/.github/workflows/pylint.yml:
--------------------------------------------------------------------------------
 1 | name: Pylint
 2 | 
 3 | on: [push]
 4 | 
 5 | jobs:
 6 |   build:
 7 |     runs-on: ubuntu-latest
 8 |     strategy:
 9 |       matrix:
10 |         python-version: ["3.9", "3.10"]
11 |     steps:
12 |     - uses: actions/checkout@v4
13 |     - name: Set up Python ${{ matrix.python-version }}
14 |       uses: actions/setup-python@v5
15 |       with:
16 |         python-version: ${{ matrix.python-version }}
17 |     - name: Install dependencies
18 |       run: |
19 |         python -m pip install --no-cache-dir --upgrade pip
20 |         pip install pylint
21 |     - name: Analysing the code with pylint
22 |       run: |
23 |         pylint $(git ls-files '*.py')
24 | 


--------------------------------------------------------------------------------
/.github/workflows/python-publish.yml:
--------------------------------------------------------------------------------
 1 | 
 2 | name: Upload Python Package
 3 | 
 4 | on:
 5 |   release:
 6 |     types: [published]
 7 | 
 8 | permissions:
 9 |   contents: read
10 | 
11 | jobs:
12 |   deploy:
13 | 
14 |     runs-on: ubuntu-latest
15 | 
16 |     steps:
17 |     - uses: actions/checkout@v4
18 |     - name: Set up Python
19 |       uses: actions/setup-python@v5
20 |       with:
21 |         python-version: '3.10'
22 |     - name: Install dependencies
23 |       run: |
24 |         python -m pip install --no-cache-dir --upgrade pip
25 |         pip install build
26 |     - name: Build package
27 |       run: python -m build
28 |     - name: Publish package
29 |       uses: pypa/gh-action-pypi-publish@ec4db0b4ddc65acdf4bff5fa45ac92d78b56bdf0
30 |       with:
31 |         user: __token__
32 |         password: ${{ secrets.PYPI_API_TOKEN }}


--------------------------------------------------------------------------------
/.github/workflows/quality.yml:
--------------------------------------------------------------------------------
 1 | name: Quality
 2 | 
 3 | on:
 4 |   push:
 5 |     branches: [ "main" ]
 6 |   pull_request:
 7 |     branches: [ "main" ]
 8 | 
 9 | jobs:
10 |   lint:
11 |     runs-on: ubuntu-latest
12 |     strategy:
13 |       fail-fast: false
14 |     steps:
15 |       - name: Checkout actions
16 |         uses: actions/checkout@v4
17 |         with:
18 |           fetch-depth: 0
19 |       - name: Init environment 
20 |         uses: ./.github/actions/init-environment 
21 |       - name: Run linter
22 |         run: |
23 |           pylint `git diff --name-only --diff-filter=d origin/main HEAD | grep -E '\.py$' | tr '\n' ' '`


--------------------------------------------------------------------------------
/.github/workflows/ruff.yml:
--------------------------------------------------------------------------------
1 | name: Ruff
2 | on: [ push, pull_request ]
3 | jobs:
4 |   ruff:
5 |     runs-on: ubuntu-latest
6 |     steps:
7 |       - uses: actions/checkout@v4
8 |       - uses: chartboost/ruff-action@v1
9 | 


--------------------------------------------------------------------------------
/.github/workflows/run_test.yml:
--------------------------------------------------------------------------------
 1 | name: Python application test
 2 | 
 3 | on: [push]
 4 | 
 5 | jobs:
 6 |   build:
 7 | 
 8 |     runs-on: ubuntu-latest
 9 | 
10 |     steps:
11 |     - uses: actions/checkout@v4
12 |     - name: Set up Python 3.10
13 |       uses: actions/setup-python@v5
14 |       with:
15 |         python-version: '3.10'
16 |     - name: Install dependencies
17 |       run: |
18 |         python -m pip install --no-cache-dir --upgrade pip
19 |         pip install pytest
20 |         if [ -f requirements.txt ]; then pip install --no-cache-dir -r requirements.txt; fi
21 |     - name: Run tests with pytest
22 |       run: |
23 |         pytest tests/
24 | 


--------------------------------------------------------------------------------
/.github/workflows/stale.yml:
--------------------------------------------------------------------------------
 1 | # This workflow warns and then closes issues and PRs that have had no activity for a specified amount of time.
 2 | #
 3 | # You can adjust the behavior by modifying this file.
 4 | # For more information, see:
 5 | # https://github.com/actions/stale
 6 | name: Mark stale issues and pull requests
 7 | 
 8 | on:
 9 |   schedule:
10 |   - cron: '26 12 * * *'
11 | 
12 | jobs:
13 |   stale:
14 | 
15 |     runs-on: ubuntu-latest
16 |     permissions:
17 |       issues: write
18 |       pull-requests: write
19 | 
20 |     steps:
21 |     - uses: actions/stale@v9
22 |       with:
23 |         repo-token: ${{ secrets.GITHUB_TOKEN }}
24 |         stale-issue-message: 'Stale issue message'
25 |         stale-pr-message: 'Stale pull request message'
26 |         stale-issue-label: 'no-issue-activity'
27 |         stale-pr-label: 'no-pr-activity'


--------------------------------------------------------------------------------
/.github/workflows/test.yml:
--------------------------------------------------------------------------------
 1 | name: test
 2 | 
 3 | on:
 4 |   push:
 5 |     branches: [master]
 6 |   pull_request:
 7 |   workflow_dispatch:
 8 | 
 9 | env:
10 |   POETRY_VERSION: "1.4.2"
11 | 
12 | jobs:
13 |   build:
14 |     runs-on: ubuntu-latest
15 |     strategy:
16 |       matrix:
17 |         python-version:
18 |           - "3.9"
19 |           - "3.10"
20 |           - "3.11"
21 |         test_type:
22 |           - "core"
23 |           - "extended"
24 |     name: Python ${{ matrix.python-version }} ${{ matrix.test_type }}
25 |     steps:
26 |       - uses: actions/checkout@v4
27 |       - name: Set up Python ${{ matrix.python-version }}
28 |         uses: "./.github/actions/poetry_setup"
29 |         with:
30 |           python-version: ${{ matrix.python-version }}
31 |           poetry-version: "1.4.2"
32 |           cache-key: ${{ matrix.test_type }}
33 |           install-command: |
34 |               if [ "${{ matrix.test_type }}" == "core" ]; then
35 |                 echo "Running core tests, installing dependencies with poetry..."
36 |                 poetry install
37 |               else
38 |                 echo "Running extended tests, installing dependencies with poetry..."
39 |                 poetry install -E extended_testing
40 |               fi
41 |       - name: Run ${{matrix.test_type}} tests
42 |         run: |
43 |           if [ "${{ matrix.test_type }}" == "core" ]; then
44 |             make test
45 |           else
46 |             make extended_tests
47 |           fi
48 |         shell: bash


--------------------------------------------------------------------------------
/.github/workflows/testing.yml:
--------------------------------------------------------------------------------
 1 | name: Unit Tests
 2 | 
 3 | on:
 4 |   push:
 5 |     branches:
 6 |       - master
 7 | 
 8 | jobs:
 9 |   test:
10 |     runs-on: ubuntu-latest
11 | 
12 |     steps:
13 |       - name: Checkout code
14 |         uses: actions/checkout@v4
15 | 
16 |       - name: Set up Python
17 |         uses: actions/setup-python@v5
18 |         with:
19 |           python-version: '3.10'
20 | 
21 |       - name: Install dependencies
22 |         run: pip install --no-cache-dir -r requirements.txt
23 | 
24 |       - name: Run unit tests
25 |         run: pytest tests/


--------------------------------------------------------------------------------
/.github/workflows/unit-test.yml:
--------------------------------------------------------------------------------
 1 | name: build
 2 | 
 3 | on:
 4 |   push:
 5 |     branches: [ main ]
 6 |   pull_request:
 7 |     branches: [ main ]
 8 | 
 9 | jobs:
10 | 
11 |   build:
12 | 
13 |     runs-on: ubuntu-latest
14 | 
15 |     steps:
16 |     - uses: actions/checkout@v4
17 | 
18 |     - name: Setup Python
19 |       uses: actions/setup-python@v5
20 |       with:
21 |         python-version: '3.10'
22 | 
23 |     - name: Install dependencies
24 |       run: pip install --no-cache-dir -r requirements.txt
25 | 
26 |     - name: Run Python unit tests
27 |       run: python3 -m unittest tests/
28 | 
29 |     - name: Verify that the Docker image for the action builds
30 |       run: docker build . --file Dockerfile
31 | 
32 |     - name: Verify integration test results
33 |       run: python3 -m unittest tests/
34 | 


--------------------------------------------------------------------------------
/.github/workflows/welcome.yml:
--------------------------------------------------------------------------------
 1 | name: Welcome WorkFlow
 2 | 
 3 | on:
 4 |   issues:
 5 |     types: [opened]
 6 |   pull_request_target:
 7 |     types: [opened]
 8 | 
 9 | jobs:
10 |   build:
11 |     name: 👋 Welcome
12 |     permissions: write-all
13 |     runs-on: ubuntu-latest
14 |     steps:
15 |       - uses: actions/first-interaction@v1.3.0
16 |         with:
17 |           repo-token: ${{ secrets.GITHUB_TOKEN }}
18 |           issue-message: "Hello there, thank you for opening an Issue ! 🙏🏻 The team was notified and they will get back to you asap."
19 |           pr-message:  "Hello there, thank you for opening an PR ! 🙏🏻 The team was notified and they will get back to you asap."


--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
  1 | # Byte-compiled / optimized / DLL files
  2 | __pycache__/
  3 | *.py[cod]
  4 | *$py.class
  5 | 
  6 | # C extensions
  7 | *.so
  8 | .vscode/
  9 | .vscode
 10 | 
 11 | # Distribution / packaging
 12 | .Python
 13 | build/
 14 | develop-eggs/
 15 | dist/
 16 | downloads/
 17 | eggs/
 18 | .eggs/
 19 | lib/
 20 | lib64/
 21 | parts/
 22 | .ruff_cache/
 23 | sdist/
 24 | var/
 25 | wheels/
 26 | share/python-wheels/
 27 | *.egg-info/
 28 | .installed.cfg
 29 | *.egg
 30 | MANIFEST
 31 | 
 32 | # PyInstaller
 33 | #  Usually these files are written by a python script from a template
 34 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 35 | *.manifest
 36 | *.spec
 37 | 
 38 | # Installer logs
 39 | pip-log.txt
 40 | pip-delete-this-directory.txt
 41 | 
 42 | # Unit test / coverage reports
 43 | htmlcov/
 44 | .tox/
 45 | .nox/
 46 | .coverage
 47 | .coverage.*
 48 | .cache
 49 | nosetests.xml
 50 | coverage.xml
 51 | *.cover
 52 | *.py,cover
 53 | .hypothesis/
 54 | .pytest_cache/
 55 | cover/
 56 | 
 57 | # Translations
 58 | *.mo
 59 | *.pot
 60 | 
 61 | # Django stuff:
 62 | *.log
 63 | local_settings.py
 64 | db.sqlite3
 65 | db.sqlite3-journal
 66 | 
 67 | # Flask stuff:
 68 | instance/
 69 | .webassets-cache
 70 | 
 71 | # Scrapy stuff:
 72 | .scrapy
 73 | 
 74 | # Sphinx documentation
 75 | docs/_build/
 76 | 
 77 | # PyBuilder
 78 | .pybuilder/
 79 | target/
 80 | 
 81 | # Jupyter Notebook
 82 | .ipynb_checkpoints
 83 | 
 84 | # IPython
 85 | profile_default/
 86 | ipython_config.py
 87 | 
 88 | # pyenv
 89 | #   For a library or package, you might want to ignore these files since the code is
 90 | #   intended to run in multiple environments; otherwise, check them in:
 91 | # .python-version
 92 | 
 93 | # pipenv
 94 | #   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
 95 | #   However, in case of collaboration, if having platform-specific dependencies or dependencies
 96 | #   having no cross-platform support, pipenv may install dependencies that don't work, or not
 97 | #   install all needed dependencies.
 98 | #Pipfile.lock
 99 | 
100 | # poetry
101 | #   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
102 | #   This is especially recommended for binary packages to ensure reproducibility, and is more
103 | #   commonly ignored for libraries.
104 | #   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
105 | #poetry.lock
106 | 
107 | # pdm
108 | #   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
109 | #pdm.lock
110 | #   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
111 | #   in version control.
112 | #   https://pdm.fming.dev/#use-with-ide
113 | .pdm.toml
114 | 
115 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
116 | __pypackages__/
117 | 
118 | # Celery stuff
119 | celerybeat-schedule
120 | celerybeat.pid
121 | 
122 | # SageMath parsed files
123 | *.sage.py
124 | 
125 | # Environments
126 | .env
127 | .venv
128 | env/
129 | venv/
130 | ENV/
131 | env.bak/
132 | venv.bak/
133 | 
134 | # Spyder project settings
135 | .spyderproject
136 | .spyproject
137 | 
138 | # Rope project settings
139 | .ropeproject
140 | 
141 | # mkdocs documentation
142 | /site
143 | 
144 | # mypy
145 | .mypy_cache/
146 | .dmypy.json
147 | dmypy.json
148 | 
149 | # Pyre type checker
150 | .pyre/
151 | 
152 | # pytype static type analyzer
153 | .pytype/
154 | 
155 | # Cython debug symbols
156 | cython_debug/
157 | 
158 | # PyCharm
159 | #  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
160 | #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
161 | #  and can be added to the global gitignore or merged into this file.  For a more nuclear
162 | #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
163 | #.idea/
164 | 


--------------------------------------------------------------------------------
/CONTRIBUTING.md:
--------------------------------------------------------------------------------
 1 | # Welcome, Swarm Chef
 2 | 
 3 | The Swarms Cookbook is a community-driven resource aimed at sharing knowledge in a way that is accessible, engaging, and enriching for all AI builders.
 4 | 
 5 | Before contributing, read through the existing issues and pull requests to see if someone else is already working on something similar. That way you can avoid duplicating efforts.
 6 | 
 7 | ## What makes a good contribution?
 8 | 
 9 | Generally, we have found that the best contributions to the Cookbook are **useful**, **novel** or **creative**, or a combination of these.
10 | 
11 | - **Useful:** Involves concepts or techniques that can be applied broadly and often, and can translate to practical use-cases and solving real-world problems. If you're doing something often, chances are others are too, and having reusable examples to reference can be very helpful.
12 | - **Novel:** Showcases new developments or techniques. Look out for new research on how to best use LLMs, or new models and capabilities in the API.
13 | - **Creative:** Uses LLMs in creative and innovative ways, or combines multiple APIs and tools in novel ways.
14 | 
15 | Additionally, we strive to maintain a **neutral** tone, and aim for **high quality** writing.
16 | 
17 | - **Neutral:** Maintains a neutral stance on tools and products. While it's natural to have preferences for particular tools, a good guide avoids over-evangelizing or marketing specific products, ensuring integrity and inclusivity.
18 | - **High quality:** Well structured, clear and complete. Writing good content ensures others can fully benefit from it. See the rubric below for more details on how we assess the quality of submissions to the Cookbook.
19 | 
20 | ## Rubric
21 | 
22 | To ensure the quality of submissions, we have established a rubric that assesses each contribution on various areas. The purpose of this rating system is to maintain a high standard of quality, relevance, and uniqueness. Each area is rated on a scale from 1 to 4. Contributions that score lower than a 3 in any of the areas will generally be rejected.
23 | 
24 | We encourage contributors to familiarize themselves with this rubric before writing content. Understanding the criteria not only increases the chances of your contribution being accepted, but also helps in creating a resource that is comprehensive, clear, and beneficial for all users.
25 | 
26 | 
27 | | Criteria     | Description                                                                                         | Score |
28 | | ------------ | --------------------------------------------------------------------------------------------------- | ----- |
29 | | Relevance    | Is the content related to building with Swarms technologies? Is it useful to others?                |       |
30 | | Uniqueness   | Does the content offer new insights or unique information compared to existing documentation?       |       |
31 | | Clarity      | Is the language easy to understand? Are things well-explained? Is the title clear?                  |       |
32 | | Correctness  | Are the facts, code snippets, and examples correct and reliable? Does everything execute correctly? |       |
33 | | Conciseness  | Is the content concise? Are all details necessary? Can it be made shorter?                          |       |
34 | | Completeness | Is the content thorough and detailed? Are there things that weren’t explained fully?                |       |
35 | | Grammar      | Are there grammatical or spelling errors present?                                                   |       |
36 | 
37 | ### Breakdown
38 | 
39 | | Criteria     | 4                                             | 3                                         | 2                                             | 1                                          |
40 | | ------------ | --------------------------------------------- | ----------------------------------------- | --------------------------------------------- | ------------------------------------------ |
41 | | Relevance    | Relevant and useful.                          | Relevant but not very useful.             | Tangentially relevant.                        | Not relevant.                              |
42 | | Uniqueness   | Completely unique with fresh insights.        | Unique with minor overlaps.               | Some unique aspects, but significant overlap. | Many similar guides/examples.              |
43 | | Clarity      | Clear language and structure.                 | Clear language, unclear structure.        | Some sections unclear.                        | Confusing and unclear.                     |
44 | | Correctness  | Completely error free.                        | Code works, minor improvements needed.    | Few errors and warnings.                      | Many errors, code doesn't execute.         |
45 | | Conciseness  | Cannot be reduced in any section, or overall. | Mostly short, but could still be reduced. | Some long sections, and/or long overall.      | Very long sections and overall, redundant. |
46 | | Completeness | Complete and detailed.                        | Mostly complete, minor additions needed.  | Lacks some explanations.                      | Missing significant portions.              |
47 | | Grammar      | Perfect grammar.                              | Correct grammar, few typos.               | Some spelling/grammatical errors.             | Numerous spelling/grammatical errors.      |


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2023 The Galactic Swarm Corporation
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | [![Multi-Modality](swarms_logo.png)](https://discord.com/servers/agora-999382051935506503)
 2 | 
 3 | # Swarms CookBook
 4 | 
 5 | [![Join our Discord](https://img.shields.io/badge/Discord-Join%20our%20server-5865F2?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/agora-999382051935506503) [![Subscribe on YouTube](https://img.shields.io/badge/YouTube-Subscribe-red?style=for-the-badge&logo=youtube&logoColor=white)](https://www.youtube.com/@kyegomez3242) [![Connect on LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/kye-g-38759a207/) [![Follow on X.com](https://img.shields.io/badge/X.com-Follow-1DA1F2?style=for-the-badge&logo=x&logoColor=white)](https://x.com/kyegomezb)
 6 | 
 7 | 
 8 | Example code and guides for accomplishing common tasks with the [Swarms Framework](https://docs.swarms.world) 
 9 | To run these examples, you'll need an OpenAI account and associated API key ([create a free account here](https://beta.openai.com/signup)) or an API key from your designated provider like Anthropic, Gemini, etc or you can leverage your own local models!
10 | Set an environment variable called `OPENAI_API_KEY` with your API key. Alternatively, in most IDEs such as Visual Studio Code, you can create an `.env` file at the root of your repo containing `OPENAI_API_KEY=<your API key>`, which will be picked up by the notebooks.
11 | 
12 | Most code examples are written in Python, though the concepts can be applied in any language.
13 | 
14 | ## Contributing
15 | 
16 | The Swarms Cookbook is a community-driven resource. Whether you're submitting an idea, fixing a typo, adding a new guide, or improving an existing one, your contributions are greatly appreciated!
17 | 
18 | Before contributing, read through the existing issues and pull requests to see if someone else is already working on something similar. That way you can avoid duplicating efforts.
19 | 
20 | If there are examples or guides you'd like to see, feel free to suggest them on the [issues page](https://github.com/The-Swarm-Corporation/Cookbook/issues).
21 | 
22 | If you'd like to contribute new content, make sure to read through our [contribution guidelines](/CONTRIBUTING.md). We welcome high-quality submissions of new examples and guides, as long as they meet our criteria and fit within the scope of the cookbook.
23 | 
24 | [![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=468576060&machine=basicLinux32gb&location=EastUs)
25 | 
26 | 
27 | # License 
28 | MIT
29 | 


--------------------------------------------------------------------------------
/cookbook/enterprise/accounting/multi_agent/accounting_agent_rearrange_book.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "d4af840b",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "\n",
  9 |     "# Building a Swarm of Agents for Accounting Tasks\n",
 10 |     "\n",
 11 |     "This notebook will guide you through the process of creating a swarm of agents using the `swarms` framework. \n",
 12 |     "We will focus on analyzing cash flow statements and balance sheets, demonstrating how these agents can \n",
 13 |     "collaborate to produce a comprehensive financial report.\n",
 14 |     "\n",
 15 |     "### Objectives\n",
 16 |     "- **Learn** how to initialize and configure the model.\n",
 17 |     "- **Understand** the purpose of each agent in the swarm.\n",
 18 |     "- **Interact** with the agents by running the system and analyzing the outputs.\n",
 19 |     "- **Explore** the potential applications of such systems in real-world scenarios.\n",
 20 |     "\n",
 21 |     "Let's get started!\n"
 22 |    ]
 23 |   },
 24 |   {
 25 |    "cell_type": "markdown",
 26 |    "id": "c546142a",
 27 |    "metadata": {},
 28 |    "source": [
 29 |     "# Installation\n",
 30 |     "Ensure you have swarms installed with:\n",
 31 |     "\n",
 32 |     "```bash\n",
 33 |     "$ pip3 install -U swarms\n",
 34 |     "```"
 35 |    ]
 36 |   },
 37 |   {
 38 |    "cell_type": "markdown",
 39 |    "id": "7c029fdc",
 40 |    "metadata": {},
 41 |    "source": [
 42 |     "\n",
 43 |     "## Step 1: Model Initialization\n",
 44 |     "\n",
 45 |     "In this step, we initialize the language model that will power our agents. We will use the `OpenAIChat` model, \n",
 46 |     "configured with specific parameters to suit our needs. \n",
 47 |     "\n",
 48 |     "**Note:** Replace `\"your_api_key_here\"` with your actual API key before running the cell.\n"
 49 |    ]
 50 |   },
 51 |   {
 52 |    "cell_type": "code",
 53 |    "execution_count": null,
 54 |    "id": "667aa292",
 55 |    "metadata": {},
 56 |    "outputs": [],
 57 |    "source": [
 58 |     "# -*- coding: utf-8 -*-\n",
 59 |     "# pip install swarms\n",
 60 |     "from swarms import Agent, AgentRearrange, OpenAIChat\n",
 61 |     "\n",
 62 |     "\n",
 63 |     "# Create an instance of the OpenAIChat class\n",
 64 |     "model = OpenAIChat(\n",
 65 |     "    api_key=\"your_api_key_here\", model_name=\"gpt-4o-mini\", temperature=0.1\n",
 66 |     ")\n",
 67 |     "\n",
 68 |     "print(\"Model initialized successfully!\")\n"
 69 |    ]
 70 |   },
 71 |   {
 72 |    "cell_type": "markdown",
 73 |    "id": "c3dce36a",
 74 |    "metadata": {},
 75 |    "source": [
 76 |     "\n",
 77 |     "## Step 2: Agent Creation\n",
 78 |     "\n",
 79 |     "Now, we will create the agents that will perform specific tasks related to accounting. Each agent is configured with a unique \n",
 80 |     "role and system prompt. \n",
 81 |     "\n",
 82 |     "We'll start with the `AccountingDirector`, who will orchestrate the tasks, followed by agents specialized in analyzing balance sheets, cash flow statements, and generating reports.\n",
 83 |     "\n",
 84 |     "80% of an agent's performance is dependent on the quality of your prompts, so be mindful and if issues arise with performance, upgrade your prompt with better instructions or more examples"
 85 |    ]
 86 |   },
 87 |   {
 88 |    "cell_type": "code",
 89 |    "execution_count": null,
 90 |    "id": "d0775298",
 91 |    "metadata": {},
 92 |    "outputs": [],
 93 |    "source": [
 94 |     "\n",
 95 |     "# Initialize the AccountingDirector agent\n",
 96 |     "director = Agent(\n",
 97 |     "    agent_name=\"AccountingDirector\",\n",
 98 |     "    system_prompt=\"Directs the tasks for the workers in analyzing cashflow statements, balance sheets, and more.\",\n",
 99 |     "    llm=model,\n",
100 |     "    max_loops=1,\n",
101 |     "    dashboard=False,\n",
102 |     "    streaming_on=True,\n",
103 |     "    verbose=True,\n",
104 |     "    stopping_token=\"<DONE>\",\n",
105 |     "    state_save_file_type=\"json\",\n",
106 |     "    saved_state_path=\"accounting_director.json\",\n",
107 |     ")\n",
108 |     "\n",
109 |     "# Initialize BalanceSheetAnalyzer agent\n",
110 |     "balance_sheet_analyzer = Agent(\n",
111 |     "    agent_name=\"BalanceSheetAnalyzer\",\n",
112 |     "    system_prompt=\"Analyzes balance sheets, identifying key metrics like assets, liabilities, and equity.\",\n",
113 |     "    llm=model,\n",
114 |     "    max_loops=1,\n",
115 |     "    dashboard=False,\n",
116 |     "    streaming_on=True,\n",
117 |     "    verbose=True,\n",
118 |     "    stopping_token=\"<DONE>\",\n",
119 |     "    state_save_file_type=\"json\",\n",
120 |     "    saved_state_path=\"balance_sheet_analyzer.json\",\n",
121 |     ")\n",
122 |     "\n",
123 |     "# Initialize CashFlowAnalyzer agent\n",
124 |     "cash_flow_analyzer = Agent(\n",
125 |     "    agent_name=\"CashFlowAnalyzer\",\n",
126 |     "    system_prompt=\"Analyzes cash flow statements, identifying cash inflows, outflows, and net cash flow.\",\n",
127 |     "    llm=model,\n",
128 |     "    max_loops=1,\n",
129 |     "    dashboard=False,\n",
130 |     "    streaming_on=True,\n",
131 |     "    verbose=True,\n",
132 |     "    stopping_token=\"<DONE>\",\n",
133 |     "    state_save_file_type=\"json\",\n",
134 |     "    saved_state_path=\"cash_flow_analyzer.json\",\n",
135 |     ")\n",
136 |     "\n",
137 |     "# Initialize ReportGenerator agent\n",
138 |     "report_generator = Agent(\n",
139 |     "    agent_name=\"ReportGenerator\",\n",
140 |     "    system_prompt=\"Summarizes the analyses provided by BalanceSheetAnalyzer and CashFlowAnalyzer, generating a cohesive report.\",\n",
141 |     "    llm=model,\n",
142 |     "    max_loops=1,\n",
143 |     "    dashboard=False,\n",
144 |     "    streaming_on=True,\n",
145 |     "    verbose=True,\n",
146 |     "    stopping_token=\"<DONE>\",\n",
147 |     "    state_save_file_type=\"json\",\n",
148 |     "    saved_state_path=\"report_generator.json\",\n",
149 |     ")\n",
150 |     "\n",
151 |     "print(\"Agents initialized successfully!\")\n"
152 |    ]
153 |   },
154 |   {
155 |    "cell_type": "markdown",
156 |    "id": "c73f82fd",
157 |    "metadata": {},
158 |    "source": [
159 |     "\n",
160 |     "## Step 3: Defining the Agent Flow\n",
161 |     "\n",
162 |     "In this step, we define how the agents will interact with each other. The `AccountingDirector` will assign tasks to the `BalanceSheetAnalyzer` \n",
163 |     "and `CashFlowAnalyzer`, and then the `ReportGenerator` will compile the results.\n",
164 |     "\n",
165 |     "This flow ensures that each agent focuses on its specialized task, and the overall process is efficient and organized.\n"
166 |    ]
167 |   },
168 |   {
169 |    "cell_type": "code",
170 |    "execution_count": null,
171 |    "id": "4482b9c7",
172 |    "metadata": {},
173 |    "outputs": [],
174 |    "source": [
175 |     "\n",
176 |     "# Create a list of agents\n",
177 |     "agents = [director, balance_sheet_analyzer, cash_flow_analyzer, report_generator]\n",
178 |     "\n",
179 |     "# Define the flow pattern\n",
180 |     "flow = \"AccountingDirector -> BalanceSheetAnalyzer -> CashFlowAnalyzer -> ReportGenerator\"\n",
181 |     "\n",
182 |     "# Using AgentRearrange class\n",
183 |     "agent_system = AgentRearrange(agents=agents, flow=flow)\n",
184 |     "\n",
185 |     "print(\"Agent flow defined successfully!\")\n"
186 |    ]
187 |   },
188 |   {
189 |    "cell_type": "markdown",
190 |    "id": "8fa46276",
191 |    "metadata": {},
192 |    "source": [
193 |     "\n",
194 |     "## Step 4: Running the Agent System\n",
195 |     "\n",
196 |     "Now it's time to see our agents in action! In this step, we will run the system on a hypothetical scenario, \n",
197 |     "where the agents will analyze the Q4 financial data and generate a summary report.\n"
198 |    ]
199 |   },
200 |   {
201 |    "cell_type": "code",
202 |    "execution_count": null,
203 |    "id": "34d8e8a8",
204 |    "metadata": {},
205 |    "outputs": [],
206 |    "source": [
207 |     "\n",
208 |     "# Run the agent system\n",
209 |     "output = agent_system.run(\n",
210 |     "    \"Analyze the cashflow statement and balance sheet for Q4 and provide a summary report.\"\n",
211 |     ")\n",
212 |     "\n",
213 |     "print(output)\n"
214 |    ]
215 |   },
216 |   {
217 |    "cell_type": "markdown",
218 |    "id": "7dafd262",
219 |    "metadata": {},
220 |    "source": [
221 |     "\n",
222 |     "## Step 5: Results and Interpretation\n",
223 |     "\n",
224 |     "The output generated by the agents provides a comprehensive analysis of the Q4 financial data. \n",
225 |     "Here, we'll break down the results and discuss how they can be interpreted and applied in a real-world context.\n",
226 |     "\n",
227 |     "Feel free to experiment by modifying the inputs or adding more agents to see how the system adapts to different scenarios.\n"
228 |    ]
229 |   },
230 |   {
231 |    "cell_type": "markdown",
232 |    "id": "7f76a0d1",
233 |    "metadata": {},
234 |    "source": [
235 |     "\n",
236 |     "## Conclusion\n",
237 |     "\n",
238 |     "Congratulations! You've successfully created and run a swarm of agents to perform accounting tasks. \n",
239 |     "This system showcases the power of multi-agent collaboration, particularly in automating complex processes \n",
240 |     "like financial analysis. \n",
241 |     "\n",
242 |     "### Next Steps\n",
243 |     "- Try integrating more specialized agents, such as those focused on tax calculations or budget forecasting.\n",
244 |     "- Experiment with different models and parameters to see how they impact the results.\n",
245 |     "- Consider applying this approach to other domains, such as marketing, sales, or operations.\n",
246 |     "\n",
247 |     "Thank you for following along, and happy coding!\n"
248 |    ]
249 |   }
250 |  ],
251 |  "metadata": {
252 |   "language_info": {
253 |    "name": "python"
254 |   }
255 |  },
256 |  "nbformat": 4,
257 |  "nbformat_minor": 5
258 | }
259 | 


--------------------------------------------------------------------------------
/cookbook/enterprise/accounting/multi_agent/accounting_agents_for_moa.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "36630b0c",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "\n",
  9 |     "# Getting Started with Accounting Agents using Swarms\n",
 10 |     "\n",
 11 |     "This notebook will guide you through the process of setting up and running a swarm of accounting agents using the `swarms` framework. \n",
 12 |     "These agents will work together to perform tasks like preparing financial projections and conducting risk assessments. By the end of this tutorial, \n",
 13 |     "you'll understand how to create and orchestrate these agents, and you'll be able to adapt this process to other accounting or business tasks.\n",
 14 |     "\n",
 15 |     "For more detailed information, please refer to the [Swarms Documentation](https://docs.swarms.world/en/latest/).\n"
 16 |    ]
 17 |   },
 18 |   {
 19 |    "cell_type": "markdown",
 20 |    "id": "7e2326e0",
 21 |    "metadata": {},
 22 |    "source": [
 23 |     "# Installation\n",
 24 |     "Ensure you have swarms installed with:\n",
 25 |     "\n",
 26 |     "```bash\n",
 27 |     "$ pip3 install -U swarms\n",
 28 |     "```"
 29 |    ]
 30 |   },
 31 |   {
 32 |    "cell_type": "markdown",
 33 |    "id": "8f39e8f8",
 34 |    "metadata": {},
 35 |    "source": [
 36 |     "\n",
 37 |     "## Step 1: Setting Up the Model\n",
 38 |     "\n",
 39 |     "We begin by initializing the language model that will power our agents. In this case, we're using the `OpenAIChat` model, which is part of the `swarms` framework. \n",
 40 |     "This model will handle the natural language processing tasks that the agents need to perform their roles.\n",
 41 |     "\n",
 42 |     "**Note:** Replace `\"your_api_key_here\"` with your actual API key before running the cell.\n"
 43 |    ]
 44 |   },
 45 |   {
 46 |    "cell_type": "code",
 47 |    "execution_count": null,
 48 |    "id": "dadf2352",
 49 |    "metadata": {},
 50 |    "outputs": [],
 51 |    "source": [
 52 |     "\n",
 53 |     "# Import necessary classes\n",
 54 |     "from swarms import OpenAIChat\n",
 55 |     "\n",
 56 |     "# Initialize the model\n",
 57 |     "model = OpenAIChat(\n",
 58 |     "    api_key=\"your_api_key_here\", model_name=\"gpt-4o-mini\", temperature=0.1\n",
 59 |     ")\n",
 60 |     "\n",
 61 |     "print(\"Model initialized successfully!\")\n"
 62 |    ]
 63 |   },
 64 |   {
 65 |    "cell_type": "markdown",
 66 |    "id": "ba7a96c9",
 67 |    "metadata": {},
 68 |    "source": [
 69 |     "\n",
 70 |     "## Step 2: Creating the Agents\n",
 71 |     "\n",
 72 |     "Next, we create the agents that will perform specific accounting tasks. Each agent is configured with a detailed system prompt that defines its role. \n",
 73 |     "The agents include:\n",
 74 |     "- **Director**: Oversees the entire process and coordinates the tasks.\n",
 75 |     "- **FinancialProjectionPreparer**: Prepares detailed financial projections.\n",
 76 |     "- **RiskAssessmentSpecialist**: Conducts a comprehensive risk assessment.\n",
 77 |     "- **ReportConsolidator**: Consolidates the projections and assessments into a final report.\n",
 78 |     "\n",
 79 |     "Let's start by creating each agent.\n"
 80 |    ]
 81 |   },
 82 |   {
 83 |    "cell_type": "code",
 84 |    "execution_count": null,
 85 |    "id": "93ed1fca",
 86 |    "metadata": {},
 87 |    "outputs": [],
 88 |    "source": [
 89 |     "\n",
 90 |     "from swarms import Agent\n",
 91 |     "\n",
 92 |     "# Initialize the Director agent\n",
 93 |     "director = Agent(\n",
 94 |     "    agent_name=\"Director\",\n",
 95 |     "    system_prompt=(\n",
 96 |     "        \"As the Director of Accounting, your responsibility is to oversee the entire accounting process, \"\n",
 97 |     "        \"ensuring that all financial projections, risk assessments, and related reports are prepared with \"\n",
 98 |     "        \"the highest level of accuracy and detail. You will coordinate between various accounting agents, \"\n",
 99 |     "        \"delegating tasks such as preparing financial projections, conducting risk assessments, and consolidating \"\n",
100 |     "        \"these into comprehensive financial reports. You will ensure that all tasks are completed on time and meet \"\n",
101 |     "        \"the company's standards for accuracy and thoroughness.\"\n",
102 |     "    ),\n",
103 |     "    llm=model,\n",
104 |     "    max_loops=1,\n",
105 |     "    dashboard=False,\n",
106 |     "    streaming_on=True,\n",
107 |     "    verbose=True,\n",
108 |     "    stopping_token=\"<DONE>\",\n",
109 |     "    state_save_file_type=\"json\",\n",
110 |     "    saved_state_path=\"director.json\",\n",
111 |     ")\n",
112 |     "\n",
113 |     "# Initialize FinancialProjectionPreparer agent\n",
114 |     "financial_projection_preparer = Agent(\n",
115 |     "    agent_name=\"FinancialProjectionPreparer\",\n",
116 |     "    system_prompt=(\n",
117 |     "        \"Your task is to prepare detailed financial projections based on current financial data, market trends, \"\n",
118 |     "        \"and economic indicators. You will analyze historical financial statements, evaluate current financial conditions, \"\n",
119 |     "        \"and use advanced forecasting techniques to project future revenues, expenses, and profitability. Your projections \"\n",
120 |     "        \"must be thorough, realistic, and supported by clear assumptions. You will also prepare multiple scenarios (best case, \"\n",
121 |     "        \"worst case, and most likely case) to help the company plan for various potential outcomes.\"\n",
122 |     "    ),\n",
123 |     "    llm=model,\n",
124 |     "    max_loops=1,\n",
125 |     "    dashboard=False,\n",
126 |     "    streaming_on=True,\n",
127 |     "    verbose=True,\n",
128 |     "    stopping_token=\"<DONE>\",\n",
129 |     "    state_save_file_type=\"json\",\n",
130 |     "    saved_state_path=\"financial_projection_preparer.json\",\n",
131 |     ")\n",
132 |     "\n",
133 |     "# Initialize RiskAssessmentSpecialist agent\n",
134 |     "risk_assessment_specialist = Agent(\n",
135 |     "    agent_name=\"RiskAssessmentSpecialist\",\n",
136 |     "    system_prompt=(\n",
137 |     "        \"Your task is to conduct a comprehensive risk assessment for the company's financial projections. You will identify \"\n",
138 |     "        \"and evaluate potential risks that could impact the financial forecasts, including market volatility, changes in regulation, \"\n",
139 |     "        \"and operational risks. You will assess the likelihood and potential impact of these risks, and provide recommendations \"\n",
140 |     "        \"for mitigating them. Your assessment should be detailed, covering all aspects of the company's operations and external \"\n",
141 |     "        \"environment that could influence financial performance. You will also prepare a risk matrix to visually represent the risks \"\n",
142 |     "        \"and their potential impact.\"\n",
143 |     "    ),\n",
144 |     "    llm=model,\n",
145 |     "    max_loops=1,\n",
146 |     "    dashboard=False,\n",
147 |     "    streaming_on=True,\n",
148 |     "    verbose=True,\n",
149 |     "    stopping_token=\"<DONE>\",\n",
150 |     "    state_save_file_type=\"json\",\n",
151 |     "    saved_state_path=\"risk_assessment_specialist.json\",\n",
152 |     ")\n",
153 |     "\n",
154 |     "# Initialize ReportConsolidator agent\n",
155 |     "report_consolidator = Agent(\n",
156 |     "    agent_name=\"ReportConsolidator\",\n",
157 |     "    system_prompt=(\n",
158 |     "        \"Your task is to consolidate the financial projections and risk assessments into a comprehensive financial report. \"\n",
159 |     "        \"You will carefully review the projections and assessments provided by other agents, ensuring that all data is accurate \"\n",
160 |     "        \"and that the report is logically structured and easy to understand. Your report should include an executive summary, \"\n",
161 |     "        \"detailed financial projections, risk assessments, and recommendations for mitigating identified risks. The report should \"\n",
162 |     "        \"be formatted professionally and be ready for presentation to senior management.\"\n",
163 |     "    ),\n",
164 |     "    llm=model,\n",
165 |     "    max_loops=1,\n",
166 |     "    dashboard=False,\n",
167 |     "    streaming_on=True,\n",
168 |     "    verbose=True,\n",
169 |     "    stopping_token=\"<DONE>\",\n",
170 |     "    state_save_file_type=\"json\",\n",
171 |     "    saved_state_path=\"report_consolidator.json\",\n",
172 |     ")\n",
173 |     "\n",
174 |     "print(\"Agents initialized successfully!\")\n"
175 |    ]
176 |   },
177 |   {
178 |    "cell_type": "markdown",
179 |    "id": "9fc0bcd1",
180 |    "metadata": {},
181 |    "source": [
182 |     "\n",
183 |     "## Step 3: Defining the Agent Swarm\n",
184 |     "\n",
185 |     "With our agents ready, we now define how they will work together in a swarm. The `MixtureOfAgents` class allows us to organize the agents into layers, \n",
186 |     "with a final agent overseeing the entire process. In this case, the `Director` will act as the final agent, ensuring all tasks are completed efficiently.\n",
187 |     "\n",
188 |     "Let's define the swarm and set up the flow of tasks.\n"
189 |    ]
190 |   },
191 |   {
192 |    "cell_type": "code",
193 |    "execution_count": null,
194 |    "id": "0e341194",
195 |    "metadata": {},
196 |    "outputs": [],
197 |    "source": [
198 |     "\n",
199 |     "from swarms import MixtureOfAgents\n",
200 |     "\n",
201 |     "# Create a list of agents\n",
202 |     "agents = [director, financial_projection_preparer, risk_assessment_specialist, report_consolidator]\n",
203 |     "\n",
204 |     "# Swarm setup\n",
205 |     "swarm = MixtureOfAgents(\n",
206 |     "    name=\"Accounting Swarm for Financial Projections and Risk Assessment\",\n",
207 |     "    agents=agents,\n",
208 |     "    layers=3,\n",
209 |     "    final_agent=director,\n",
210 |     ")\n",
211 |     "\n",
212 |     "print(\"Swarm defined successfully!\")\n"
213 |    ]
214 |   },
215 |   {
216 |    "cell_type": "markdown",
217 |    "id": "95fcc037",
218 |    "metadata": {},
219 |    "source": [
220 |     "\n",
221 |     "## Step 4: Running the Swarm\n",
222 |     "\n",
223 |     "Now it's time to see our swarm of agents in action! We'll run the swarm on a task that involves preparing detailed financial projections and performing a comprehensive risk assessment. The output will be a consolidated financial report.\n",
224 |     "\n",
225 |     "You can modify the task description to suit your specific needs or experiment with different scenarios.\n"
226 |    ]
227 |   },
228 |   {
229 |    "cell_type": "code",
230 |    "execution_count": null,
231 |    "id": "5d46a33d",
232 |    "metadata": {},
233 |    "outputs": [],
234 |    "source": [
235 |     "\n",
236 |     "# Run the swarm\n",
237 |     "out = swarm.run(\"Prepare detailed financial projections and perform a comprehensive risk assessment.\")\n",
238 |     "print(out)\n"
239 |    ]
240 |   },
241 |   {
242 |    "cell_type": "markdown",
243 |    "id": "793c4f15",
244 |    "metadata": {},
245 |    "source": [
246 |     "\n",
247 |     "## Conclusion and Next Steps\n",
248 |     "\n",
249 |     "You've successfully set up and run a swarm of accounting agents! This approach demonstrates how multi-agent systems can be used to automate complex accounting tasks, making the process more efficient and accurate.\n",
250 |     "\n",
251 |     "### Next Steps:\n",
252 |     "- Explore the [Swarms Documentation](https://docs.swarms.world/en/latest/) for more details and advanced features.\n",
253 |     "- Experiment with different agents, tasks, and scenarios to see how the system adapts.\n",
254 |     "- Consider how this framework can be applied to other domains such as marketing, sales, or operations.\n",
255 |     "\n",
256 |     "Thank you for following along!\n"
257 |    ]
258 |   }
259 |  ],
260 |  "metadata": {
261 |   "language_info": {
262 |    "name": "python"
263 |   }
264 |  },
265 |  "nbformat": 4,
266 |  "nbformat_minor": 5
267 | }
268 | 


--------------------------------------------------------------------------------
/cookbook/enterprise/finance/multi_agent/Swarms_Cookbook_CryptoAgent.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "029606c8",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "# Swarms Cookbook: CryptoAgent Real-Time Cryptocurrency Data Analysis\n",
  9 |     "\n",
 10 |     "Welcome to the Swarms Cookbook! In this guide, we will walk through the steps of using **CryptoAgent**, an enterprise-grade solution for real-time cryptocurrency data fetching, analysis, and summarization.\n",
 11 |     "\n",
 12 |     "CryptoAgent integrates with CoinGecko's API to retrieve the latest cryptocurrency metrics and leverages OpenAI's language models to provide insightful reports for crypto investors and analysts.\n",
 13 |     "\n",
 14 |     "### Key Features:\n",
 15 |     "- **Real-Time Data Fetching**: Retrieves up-to-date cryptocurrency prices, market capitalization, and trading volume.\n",
 16 |     "- **Advanced Analysis**: Uses AI to summarize complex data into clear, actionable insights.\n",
 17 |     "- **Enterprise-Grade**: Designed with robust error handling and scalability for professional environments.\n",
 18 |     "\n",
 19 |     "Follow the step-by-step guide below to set up **CryptoAgent** for your cryptocurrency analysis needs.\n",
 20 |     "\n",
 21 |     "Be sure to visit the **[Swarms GitHub](https://github.com/kyegomez/swarms)**, **[Discord](https://discord.com/servers/agora-999382051935506503)**, and **[Swarms Marketplace](https://swarms.xyz)** for more resources and to connect with the community.\n"
 22 |    ]
 23 |   },
 24 |   {
 25 |    "cell_type": "markdown",
 26 |    "id": "d81accd1",
 27 |    "metadata": {},
 28 |    "source": [
 29 |     "## Installation\n",
 30 |     "\n",
 31 |     "First, install the **CryptoAgent** package by running the following command:\n",
 32 |     "\n",
 33 |     "```bash\n",
 34 |     "pip3 install -U cryptoagent\n",
 35 |     "```\n",
 36 |     "\n",
 37 |     "You will also need to create a `.env` file with the necessary environment variables for API access:\n",
 38 |     "\n",
 39 |     "```bash\n",
 40 |     "OPENAI_API_KEY=\"your-openai-api-key\"\n",
 41 |     "```\n"
 42 |    ]
 43 |   },
 44 |   {
 45 |    "cell_type": "code",
 46 |    "execution_count": null,
 47 |    "id": "dd3ceea4",
 48 |    "metadata": {},
 49 |    "outputs": [],
 50 |    "source": [
 51 |     "# Import necessary libraries\n",
 52 |     "import os\n",
 53 |     "from cryptoagent.main import OpenAIChat, CryptoAgent\n",
 54 |     "from swarms import Agent\n",
 55 |     "from dotenv import load_dotenv\n",
 56 |     "\n",
 57 |     "# Load environment variables from the .env file\n",
 58 |     "load_dotenv()\n",
 59 |     "\n",
 60 |     "# Ensure API key is loaded\n",
 61 |     "openai_api_key = os.getenv(\"OPENAI_API_KEY\")\n"
 62 |    ]
 63 |   },
 64 |   {
 65 |    "cell_type": "markdown",
 66 |    "id": "14e5a9e7",
 67 |    "metadata": {},
 68 |    "source": [
 69 |     "## Setting Up CryptoAgent\n",
 70 |     "\n",
 71 |     "Now, let's initialize the **CryptoAgent** for real-time cryptocurrency data analysis. We will create an **OpenAIChat** instance for LLM integration and an input agent for running the analysis.\n",
 72 |     "\n",
 73 |     "In this example, we'll analyze multiple cryptocurrencies including Bitcoin, Ethereum, Dogecoin, and XRP.\n"
 74 |    ]
 75 |   },
 76 |   {
 77 |    "cell_type": "code",
 78 |    "execution_count": null,
 79 |    "id": "40c11472",
 80 |    "metadata": {},
 81 |    "outputs": [],
 82 |    "source": [
 83 |     "# Create an instance of OpenAIChat class for LLM integration\n",
 84 |     "model = OpenAIChat(\n",
 85 |     "    openai_api_key=openai_api_key,\n",
 86 |     "    model_name=\"gpt-4o-mini\",\n",
 87 |     "    temperature=0.1\n",
 88 |     ")\n",
 89 |     "\n",
 90 |     "# Create the input agent\n",
 91 |     "input_agent = Agent(\n",
 92 |     "    agent_name=\"Crypto-Analysis-Agent\",\n",
 93 |     "    system_prompt=\"You are a financial analysis agent that provides crypto analysis with live data.\",\n",
 94 |     "    llm=model,\n",
 95 |     "    max_loops=1,\n",
 96 |     "    autosave=True,\n",
 97 |     "    dashboard=False,\n",
 98 |     "    verbose=True,\n",
 99 |     "    dynamic_temperature_enabled=True,\n",
100 |     "    saved_state_path=\"crypto_agent.json\",\n",
101 |     "    user_name=\"swarms_corp\",\n",
102 |     "    retry_attempts=2,\n",
103 |     "    context_length=10000,\n",
104 |     ")\n",
105 |     "\n",
106 |     "# Create CryptoAgent instance and pass the input agent\n",
107 |     "crypto_analyzer = CryptoAgent(agent=input_agent)\n",
108 |     "\n",
109 |     "# Define the coins to be analyzed\n",
110 |     "coin_ids = [\"bitcoin\", \"ethereum\", \"dogecoin\", \"xrp\"]\n",
111 |     "\n",
112 |     "# Fetch and summarize crypto data for multiple coins in parallel\n",
113 |     "summaries = crypto_analyzer.run(coin_ids)\n",
114 |     "\n",
115 |     "# Print the summaries\n",
116 |     "for summary in summaries:\n",
117 |     "    print(summary)\n"
118 |    ]
119 |   },
120 |   {
121 |    "cell_type": "markdown",
122 |    "id": "582fcd74",
123 |    "metadata": {},
124 |    "source": [
125 |     "## System Architecture\n",
126 |     "\n",
127 |     "CryptoAgent uses a modular system architecture that combines real-time data retrieval from CoinGecko's API and powerful AI-based summarization:\n",
128 |     "\n",
129 |     "- **CryptoAgent**: Fetches cryptocurrency data from the CoinGecko API, including price, market cap, and trading volume.\n",
130 |     "- **OpenAI Integration**: Uses GPT-4 to summarize and analyze complex data, providing tailored insights for crypto investors and financial analysts.\n",
131 |     "- **Agent Framework**: Built on the **Swarms** framework, ensuring flexibility and scalability for enterprise-grade deployments.\n"
132 |    ]
133 |   },
134 |   {
135 |    "cell_type": "markdown",
136 |    "id": "54d0320a",
137 |    "metadata": {},
138 |    "source": [
139 |     "## Next Steps and Resources\n",
140 |     "\n",
141 |     "You've successfully set up and run a real-time cryptocurrency analysis using **CryptoAgent**!\n",
142 |     "\n",
143 |     "To explore more:\n",
144 |     "- Track different cryptocurrencies and customize your analysis.\n",
145 |     "- Adjust the **system_prompt** and **temperature** settings for tailored insights.\n",
146 |     "- Integrate CryptoAgent into your own financial analysis workflows.\n",
147 |     "\n",
148 |     "For more resources and to contribute, visit:\n",
149 |     "- **[Swarms GitHub](https://github.com/kyegomez/swarms)**\n",
150 |     "- **[Swarms Discord](https://discord.com/servers/agora-999382051935506503)**\n",
151 |     "- **[Swarms Marketplace](https://swarms.xyz)**\n",
152 |     "\n",
153 |     "Stay connected with the Swarms community and continue building advanced financial agents!\n"
154 |    ]
155 |   }
156 |  ],
157 |  "metadata": {},
158 |  "nbformat": 4,
159 |  "nbformat_minor": 5
160 | }
161 | 


--------------------------------------------------------------------------------
/cookbook/enterprise/finance/multi_agent/Swarms_Cookbook_Tickr_Agent.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "209b4525",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "# Swarms Cookbook: Tickr-Agent Financial Analysis\n",
  9 |     "\n",
 10 |     "Welcome to the Swarms Cookbook! In this guide, we will walk you through the steps of running **Tickr-Agent**, a Python library for building swarms of financial agents that perform stock analysis at an enterprise scale.\n",
 11 |     "\n",
 12 |     "Tickr-Agent is built to handle real-time financial data analysis, multithreaded processing, and seamless AI integration, powered by OpenAI models. \n",
 13 |     "\n",
 14 |     "### Key Features:\n",
 15 |     "- **Multithreaded Stock Data Fetching**: Efficiently fetch and analyze data for multiple stocks.\n",
 16 |     "- **Advanced Logging**: Utilize loguru for comprehensive logging.\n",
 17 |     "- **AI-Integrated**: Integrate OpenAI models for financial insights.\n",
 18 |     "- **Pydantic Validation**: Ensure reliable and validated stock data.\n",
 19 |     "  \n",
 20 |     "Follow the step-by-step guide below to get started with Tickr-Agent and automate your stock analysis tasks.\n",
 21 |     "\n",
 22 |     "Be sure to visit the **[Swarms GitHub](https://github.com/kyegomez/swarms)**, **[Discord](https://discord.com/servers/agora-999382051935506503)**, and **[Swarms Marketplace](https://swarms.xyz)** for more resources and to connect with the community.\n"
 23 |    ]
 24 |   },
 25 |   {
 26 |    "cell_type": "markdown",
 27 |    "id": "dd7ad131",
 28 |    "metadata": {},
 29 |    "source": [
 30 |     "## Installation\n",
 31 |     "\n",
 32 |     "Before we begin, make sure to install the required packages. Run the following command to install **tickr-agent**:\n",
 33 |     "\n",
 34 |     "```bash\n",
 35 |     "pip3 install -U tickr-agent\n",
 36 |     "```\n",
 37 |     "\n",
 38 |     "You will also need to create an `.env` file with the following environment variables:\n",
 39 |     "\n",
 40 |     "```bash\n",
 41 |     "WORKSPACE_DIR=\"\"\n",
 42 |     "OPENAI_API_KEY=\"\"\n",
 43 |     "```\n"
 44 |    ]
 45 |   },
 46 |   {
 47 |    "cell_type": "code",
 48 |    "execution_count": null,
 49 |    "id": "d902146a",
 50 |    "metadata": {},
 51 |    "outputs": [],
 52 |    "source": [
 53 |     "# Import necessary libraries\n",
 54 |     "from tickr_agent.main import TickrAgent\n",
 55 |     "from loguru import logger\n",
 56 |     "\n",
 57 |     "# Ensure proper logging setup\n",
 58 |     "logger.add(\"tickr_agent.log\", rotation=\"500 MB\")\n"
 59 |    ]
 60 |   },
 61 |   {
 62 |    "cell_type": "markdown",
 63 |    "id": "680ec04a",
 64 |    "metadata": {},
 65 |    "source": [
 66 |     "## Setting Up the Tickr Agent\n",
 67 |     "\n",
 68 |     "Now, let's set up the `TickrAgent` to perform stock analysis on a set of tickers. For this example, we'll analyze two stocks: **NVDA** and **CEG**.\n",
 69 |     "\n",
 70 |     "We can configure the agent with the following parameters:\n",
 71 |     "- `stocks`: A list of stock tickers to analyze.\n",
 72 |     "- `max_loops`: How many times the agent should run.\n",
 73 |     "- `workers`: Number of threads for concurrent processing.\n",
 74 |     "- `retry_attempts`: Number of retry attempts for failed fetches.\n",
 75 |     "- `context_length`: Context window length for AI-powered analysis.\n",
 76 |     "\n",
 77 |     "Below is an example of setting up and running the agent.\n"
 78 |    ]
 79 |   },
 80 |   {
 81 |    "cell_type": "code",
 82 |    "execution_count": null,
 83 |    "id": "893c800d",
 84 |    "metadata": {},
 85 |    "outputs": [],
 86 |    "source": [
 87 |     "# Define stock tickers\n",
 88 |     "stocks = [\"NVDA\", \"CEG\"]\n",
 89 |     "\n",
 90 |     "# Initialize the TickrAgent\n",
 91 |     "agent = TickrAgent(\n",
 92 |     "    stocks=stocks,\n",
 93 |     "    max_loops=1,\n",
 94 |     "    workers=10,\n",
 95 |     "    retry_attempts=1,\n",
 96 |     "    context_length=16000,\n",
 97 |     ")\n",
 98 |     "\n",
 99 |     "# Run the analysis and fetch results\n",
100 |     "result = agent.run(\"Conduct an analysis on this stock and show me if it's a buy or not and why\")\n",
101 |     "\n",
102 |     "# Output the result\n",
103 |     "print(result)\n"
104 |    ]
105 |   },
106 |   {
107 |    "cell_type": "markdown",
108 |    "id": "e461591d",
109 |    "metadata": {},
110 |    "source": [
111 |     "## How It Works\n",
112 |     "\n",
113 |     "- **Stock Data Fetching**: The agent fetches real-time stock data from the financial APIs using the `yfinance` library. It computes technical indicators such as RSI and moving averages.\n",
114 |     "- **Multithreading**: Stock tickers are processed concurrently using Python’s ThreadPoolExecutor, enhancing performance.\n",
115 |     "- **Data Validation**: All stock data is validated using `pydantic` to ensure accuracy and consistency.\n",
116 |     "- **AI-Driven Insights**: The processed data is sent to OpenAI's API for in-depth analysis and insights, such as whether a stock is a buy or not.\n",
117 |     "\n",
118 |     "Tickr-Agent makes the whole process seamless and enterprise-ready.\n"
119 |    ]
120 |   },
121 |   {
122 |    "cell_type": "markdown",
123 |    "id": "5fbb5848",
124 |    "metadata": {},
125 |    "source": [
126 |     "## Next Steps and Resources\n",
127 |     "\n",
128 |     "Congratulations! You've successfully run a stock analysis using Tickr-Agent.\n",
129 |     "\n",
130 |     "To explore more, you can:\n",
131 |     "- Experiment with different stock tickers.\n",
132 |     "- Adjust the agent configuration (e.g., `max_loops`, `workers`).\n",
133 |     "- Integrate Tickr-Agent into your own workflows.\n",
134 |     "\n",
135 |     "For more examples and to contribute to the project, check out:\n",
136 |     "- **[Swarms GitHub](https://github.com/kyegomez/swarms)**\n",
137 |     "- **[Swarms Discord](https://discord.com/servers/agora-999382051935506503)**\n",
138 |     "- **[Swarms Marketplace](https://swarms.xyz)**\n",
139 |     "\n",
140 |     "Stay connected with the community and continue automating with agent swarms!\n"
141 |    ]
142 |   }
143 |  ],
144 |  "metadata": {},
145 |  "nbformat": 4,
146 |  "nbformat_minor": 5
147 | }
148 | 


--------------------------------------------------------------------------------
/cookbook/enterprise/finance/multi_agent/swarms_finance_10k_analysis_agentrearrange.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "a3c4a08b",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "\n",
  9 |     "# Analyzing 10-K Reports using AgentRearrange with Mixed Sequential and Parallel Swarm\n",
 10 |     "\n",
 11 |     "This notebook will guide you through the process of setting up a mixed sequential and parallel workflow to analyze 10-K reports filed with the SEC. \n",
 12 |     "Using the `swarms` framework and the `AgentRearrange` class, we'll create specialized agents that focus on different aspects of the 10-K reports, \n",
 13 |     "such as financial statements, management discussion, and risk factors. The workflow will process some tasks sequentially while others will run in parallel.\n",
 14 |     "\n",
 15 |     "For more detailed information, please refer to the [Swarms Documentation](https://docs.swarms.world/en/latest/).\n"
 16 |    ]
 17 |   },
 18 |   {
 19 |    "cell_type": "markdown",
 20 |    "id": "a6006eee",
 21 |    "metadata": {},
 22 |    "source": [
 23 |     "# Installation\n",
 24 |     "Ensure you have swarms and other packages installed with:\n",
 25 |     "\n",
 26 |     "```bash\n",
 27 |     "$ pip3 install -U swarms python-dotenv PyPDF2\n",
 28 |     "```\n"
 29 |    ]
 30 |   },
 31 |   {
 32 |    "cell_type": "markdown",
 33 |    "id": "cb28fed0",
 34 |    "metadata": {},
 35 |    "source": [
 36 |     "\n",
 37 |     "## Step 1: Setting Up the Environment\n",
 38 |     "\n",
 39 |     "First, we need to load our environment variables and initialize the model. Make sure you have your OpenAI API key stored \n",
 40 |     "in a `.env` file in the same directory as this notebook.\n"
 41 |    ]
 42 |   },
 43 |   {
 44 |    "cell_type": "code",
 45 |    "execution_count": null,
 46 |    "id": "838e3a6c",
 47 |    "metadata": {},
 48 |    "outputs": [],
 49 |    "source": [
 50 |     "\n",
 51 |     "import os\n",
 52 |     "from dotenv import load_dotenv\n",
 53 |     "from swarms import OpenAIChat\n",
 54 |     "\n",
 55 |     "# Load environment variables\n",
 56 |     "load_dotenv()\n",
 57 |     "\n",
 58 |     "# Get the API key from the environment variables\n",
 59 |     "api_key = os.environ.get(\"OPENAI_API_KEY\")\n",
 60 |     "\n",
 61 |     "# Initialize the OpenAIChat model\n",
 62 |     "llm = OpenAIChat(\n",
 63 |     "    temperature=0.5, \n",
 64 |     "    model_name=\"gpt-4o-mini\", \n",
 65 |     "    openai_api_key=api_key, \n",
 66 |     "    max_tokens=4000\n",
 67 |     ")\n",
 68 |     "\n",
 69 |     "print(\"Environment set up and model initialized successfully!\")\n"
 70 |    ]
 71 |   },
 72 |   {
 73 |    "cell_type": "markdown",
 74 |    "id": "99affc24",
 75 |    "metadata": {},
 76 |    "source": [
 77 |     "\n",
 78 |     "## Step 2: Creating the Agents\n",
 79 |     "\n",
 80 |     "Next, we'll create the agents that will analyze different sections of the 10-K report. Each agent is assigned a specific task \n",
 81 |     "with a detailed system prompt. The agents include:\n",
 82 |     "- **FinancialStatementsExpert**: Analyzes the financial statements section.\n",
 83 |     "- **ManagementDiscussionReviewer**: Reviews the management's discussion and analysis.\n",
 84 |     "- **RiskAssessmentAnalyst**: Examines the risk factors section.\n",
 85 |     "\n",
 86 |     "Let's create each agent with its specific prompt.\n"
 87 |    ]
 88 |   },
 89 |   {
 90 |    "cell_type": "code",
 91 |    "execution_count": null,
 92 |    "id": "6b433163",
 93 |    "metadata": {},
 94 |    "outputs": [],
 95 |    "source": [
 96 |     "\n",
 97 |     "from swarms import Agent\n",
 98 |     "\n",
 99 |     "# Initialize FinancialStatementsExpert agent\n",
100 |     "financial_statements_expert = Agent(\n",
101 |     "    agent_name=\"FinancialStatementsExpert\",\n",
102 |     "    llm=llm,\n",
103 |     "    max_loops=1,\n",
104 |     "    autosave=True,\n",
105 |     "    system_prompt=(\n",
106 |     "        \"You are an expert in financial analysis. Your task is to thoroughly analyze the financial statements section \"\n",
107 |     "        \"of the 10-K report. Pay close attention to the balance sheet, income statement, and cash flow statement. Identify \"\n",
108 |     "        \"key financial metrics such as revenue growth, profit margins, debt levels, and cash flow trends. Provide a detailed \"\n",
109 |     "        \"summary of your findings, highlighting any potential red flags or areas of concern.\"\n",
110 |     "    )\n",
111 |     ")\n",
112 |     "\n",
113 |     "# Initialize ManagementDiscussionReviewer agent\n",
114 |     "management_discussion_reviewer = Agent(\n",
115 |     "    agent_name=\"ManagementDiscussionReviewer\",\n",
116 |     "    llm=llm,\n",
117 |     "    max_loops=1,\n",
118 |     "    autosave=True,\n",
119 |     "    system_prompt=(\n",
120 |     "        \"You specialize in understanding and interpreting management's discussion and analysis. Your task is to critically \"\n",
121 |     "        \"review this section of the 10-K report. Identify the key themes discussed by management, such as company performance, \"\n",
122 |     "        \"strategic initiatives, and forward-looking statements. Analyze the tone and language used to assess management's confidence \"\n",
123 |     "        \"in the company's future prospects. Summarize the key points and provide insights into potential risks or opportunities.\"\n",
124 |     "    )\n",
125 |     ")\n",
126 |     "\n",
127 |     "# Initialize RiskAssessmentAnalyst agent\n",
128 |     "risk_assessment_analyst = Agent(\n",
129 |     "    agent_name=\"RiskAssessmentAnalyst\",\n",
130 |     "    llm=llm,\n",
131 |     "    max_loops=1,\n",
132 |     "    autosave=True,\n",
133 |     "    system_prompt=(\n",
134 |     "        \"As an expert in risk management, your task is to analyze the risk factors section of the 10-K report. Carefully read through \"\n",
135 |     "        \"the risks identified by the company, and assess their potential impact on the company's operations and financial performance. \"\n",
136 |     "        \"Prioritize the risks based on their severity and likelihood, and provide recommendations on how the company might mitigate these risks.\"\n",
137 |     "    )\n",
138 |     ")\n",
139 |     "\n",
140 |     "# Initialize SummaryReportGenerator agent\n",
141 |     "summary_report_generator = Agent(\n",
142 |     "    agent_name=\"SummaryReportGenerator\",\n",
143 |     "    llm=llm,\n",
144 |     "    max_loops=1,\n",
145 |     "    autosave=True,\n",
146 |     "    dashboard=False,\n",
147 |     "    system_prompt=(\n",
148 |     "        \"You are a specialist in creating executive summaries. Your task is to consolidate the findings from the financial statements, \"\n",
149 |     "        \"management discussion, and risk factors into a cohesive summary report. Highlight the most important insights and provide \"\n",
150 |     "        \"a clear and concise overview that can be presented to senior management.\"\n",
151 |     "    )\n",
152 |     ")\n",
153 |     "\n",
154 |     "print(\"Agents created successfully!\")\n"
155 |    ]
156 |   },
157 |   {
158 |    "cell_type": "markdown",
159 |    "id": "c3b5453d",
160 |    "metadata": {},
161 |    "source": [
162 |     "\n",
163 |     "## Step 3: Defining the Workflow with AgentRearrange\n",
164 |     "\n",
165 |     "Now that we have our agents, we'll set up a workflow using the `AgentRearrange` class. This workflow will process the tasks \n",
166 |     "sequentially and in parallel, as defined by the flow: `\"FinancialStatementsExpert -> ManagementDiscussionReviewer, RiskAssessmentAnalyst, SummaryReportGenerator\"`.\n"
167 |    ]
168 |   },
169 |   {
170 |    "cell_type": "code",
171 |    "execution_count": null,
172 |    "id": "c1b640fb",
173 |    "metadata": {},
174 |    "outputs": [],
175 |    "source": [
176 |     "\n",
177 |     "from swarms import AgentRearrange\n",
178 |     "\n",
179 |     "# Create a list of agents\n",
180 |     "agents = [financial_statements_expert, management_discussion_reviewer, risk_assessment_analyst, summary_report_generator]\n",
181 |     "\n",
182 |     "# Define the flow pattern\n",
183 |     "flow = \"FinancialStatementsExpert -> ManagementDiscussionReviewer, RiskAssessmentAnalyst, SummaryReportGenerator\"\n",
184 |     "\n",
185 |     "# Using AgentRearrange class to create the workflow\n",
186 |     "agent_system = AgentRearrange(agents=agents, flow=flow, max_loops=1)\n",
187 |     "\n",
188 |     "print(\"Workflow defined successfully!\")\n"
189 |    ]
190 |   },
191 |   {
192 |    "cell_type": "markdown",
193 |    "id": "2c0f2aa7",
194 |    "metadata": {},
195 |    "source": [
196 |     "\n",
197 |     "## Step 4: Running the Workflow\n",
198 |     "\n",
199 |     "Now it's time to execute the workflow. The agents will analyze the assigned sections of the 10-K report, following the defined \n",
200 |     "flow where the financial statements are analyzed first, followed by parallel analyses of the management discussion, risk factors, \n",
201 |     "and then generating the summary report.\n",
202 |     "\n",
203 |     "First, we're going to parse and load the PDF into the swarm, and then the pdf is going to be sent to all of the agents at once."
204 |    ]
205 |   },
206 |   {
207 |    "cell_type": "code",
208 |    "execution_count": null,
209 |    "id": "372c8c9c",
210 |    "metadata": {},
211 |    "outputs": [],
212 |    "source": [
213 |     "\n",
214 |     "# pip install PyPDF2 \n",
215 |     "import PyPDF2\n",
216 |     "\n",
217 |     "def load_pdf_to_string(file_path: str):\n",
218 |     "    # Open the PDF file in read-binary mode\n",
219 |     "    with open(file_path, 'rb') as file:\n",
220 |     "        # Create a PDF file reader object\n",
221 |     "        pdf_reader = PyPDF2.PdfFileReader(file)\n",
222 |     "        \n",
223 |     "        text = \"\"\n",
224 |     "        # Loop through all the pages (if multiple) and extract text\n",
225 |     "        for page_number in range(pdf_reader.getNumPages()):\n",
226 |     "            page = pdf_reader.getPage(page_number)\n",
227 |     "            text += page.extractText()\n",
228 |     "        \n",
229 |     "        # Return the extracted text\n",
230 |     "        return text\n",
231 |     "\n",
232 |     "ten_k_data = load_pdf_to_string(\"sample_10k.pdf\")\n",
233 |     "\n",
234 |     "# Run the workflow\n",
235 |     "output = agent_system.run(f\"Analyze the 10-K report and generate a summary report: {ten_k_data}\")\n",
236 |     "print(output)\n"
237 |    ]
238 |   },
239 |   {
240 |    "cell_type": "markdown",
241 |    "id": "d35a34ec",
242 |    "metadata": {},
243 |    "source": [
244 |     "\n",
245 |     "## Conclusion and Next Steps\n",
246 |     "\n",
247 |     "You've successfully set up and run a mixed sequential and parallel workflow to analyze 10-K reports using specialized agents. \n",
248 |     "This approach demonstrates how multi-agent systems can be used to automate complex financial analysis tasks, providing valuable \n",
249 |     "insights from detailed reports.\n",
250 |     "\n",
251 |     "### Next Steps:\n",
252 |     "- Explore the [Swarms Documentation](https://docs.swarms.world/en/latest/) for more details and advanced features.\n",
253 |     "- Experiment with different agents, tasks, and scenarios to see how the system adapts.\n",
254 |     "- Consider applying this framework to other types of financial reports or even different domains.\n",
255 |     "\n",
256 |     "Thank you for following along!\n"
257 |    ]
258 |   }
259 |  ],
260 |  "metadata": {
261 |   "language_info": {
262 |    "name": "python"
263 |   }
264 |  },
265 |  "nbformat": 4,
266 |  "nbformat_minor": 5
267 | }
268 | 


--------------------------------------------------------------------------------
/cookbook/enterprise/finance/multi_agent/swarms_finance_10k_analysis_custom.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "f918e126",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "\n",
  9 |     "# Analyzing 10-K Reports using Swarms\n",
 10 |     "\n",
 11 |     "This notebook will guide you through the process of setting up a workflow to analyze 10-K reports filed with the SEC. \n",
 12 |     "Using the `swarms` framework, we'll create specialized agents that focus on different aspects of the 10-K reports, \n",
 13 |     "such as financial statements, management discussion, and risk factors. By the end of this tutorial, you'll understand \n",
 14 |     "how to create and orchestrate these agents to perform detailed analyses of 10-K reports.\n",
 15 |     "\n",
 16 |     "For more detailed information, please refer to the [Swarms Documentation](https://docs.swarms.world/en/latest/).\n"
 17 |    ]
 18 |   },
 19 |   {
 20 |    "cell_type": "markdown",
 21 |    "id": "1bc8966d",
 22 |    "metadata": {},
 23 |    "source": [
 24 |     "# Installation\n",
 25 |     "Ensure you have swarms installed with:\n",
 26 |     "\n",
 27 |     "```bash\n",
 28 |     "$ pip3 install -U swarms\n",
 29 |     "```"
 30 |    ]
 31 |   },
 32 |   {
 33 |    "cell_type": "markdown",
 34 |    "id": "89d25e9b",
 35 |    "metadata": {},
 36 |    "source": [
 37 |     "\n",
 38 |     "## Step 1: Setting Up the Environment\n",
 39 |     "\n",
 40 |     "First, we need to load our environment variables and initialize the model. Make sure you have your OpenAI API key stored \n",
 41 |     "in a `.env` file in the same directory as this notebook.\n"
 42 |    ]
 43 |   },
 44 |   {
 45 |    "cell_type": "code",
 46 |    "execution_count": null,
 47 |    "id": "92da376b",
 48 |    "metadata": {},
 49 |    "outputs": [],
 50 |    "source": [
 51 |     "\n",
 52 |     "import os\n",
 53 |     "from dotenv import load_dotenv\n",
 54 |     "from swarms import OpenAIChat\n",
 55 |     "\n",
 56 |     "# Load environment variables\n",
 57 |     "load_dotenv()\n",
 58 |     "\n",
 59 |     "# Get the API key from the environment variables\n",
 60 |     "api_key = os.environ.get(\"OPENAI_API_KEY\")\n",
 61 |     "\n",
 62 |     "# Initialize the OpenAIChat model\n",
 63 |     "llm = OpenAIChat(\n",
 64 |     "    temperature=0.5, openai_api_key=api_key, max_tokens=4000\n",
 65 |     ")\n",
 66 |     "\n",
 67 |     "print(\"Environment set up and model initialized successfully!\")\n"
 68 |    ]
 69 |   },
 70 |   {
 71 |    "cell_type": "markdown",
 72 |    "id": "b2fd4ee8",
 73 |    "metadata": {},
 74 |    "source": [
 75 |     "\n",
 76 |     "## Step 2: Creating the Agents\n",
 77 |     "\n",
 78 |     "Next, we'll create the agents that will analyze different sections of the 10-K report. Each agent is assigned a specific task \n",
 79 |     "with a detailed system prompt. The agents include:\n",
 80 |     "- **FinancialStatementsExpert**: Analyzes the financial statements section.\n",
 81 |     "- **ManagementDiscussionReviewer**: Reviews the management's discussion and analysis.\n",
 82 |     "- **RiskAssessmentAnalyst**: Examines the risk factors section.\n",
 83 |     "\n",
 84 |     "Let's create each agent with its specific prompt.\n"
 85 |    ]
 86 |   },
 87 |   {
 88 |    "cell_type": "code",
 89 |    "execution_count": null,
 90 |    "id": "b4a54e53",
 91 |    "metadata": {},
 92 |    "outputs": [],
 93 |    "source": [
 94 |     "\n",
 95 |     "from swarms import Agent\n",
 96 |     "\n",
 97 |     "# Initialize FinancialStatementsExpert agent\n",
 98 |     "financial_statements_expert = Agent(\n",
 99 |     "    agent_name=\"FinancialStatementsExpert\",\n",
100 |     "    llm=llm,\n",
101 |     "    max_loops=1,\n",
102 |     "    autosave=True,\n",
103 |     "    dashboard=False,\n",
104 |     "    system_prompt=(\n",
105 |     "        \"You are an expert in financial analysis. Your task is to thoroughly analyze the financial statements section \"\n",
106 |     "        \"of the 10-K report. Pay close attention to the balance sheet, income statement, and cash flow statement. Identify \"\n",
107 |     "        \"key financial metrics such as revenue growth, profit margins, debt levels, and cash flow trends. Provide a detailed \"\n",
108 |     "        \"summary of your findings, highlighting any potential red flags or areas of concern.\"\n",
109 |     "    )\n",
110 |     ")\n",
111 |     "\n",
112 |     "# Initialize ManagementDiscussionReviewer agent\n",
113 |     "management_discussion_reviewer = Agent(\n",
114 |     "    agent_name=\"ManagementDiscussionReviewer\",\n",
115 |     "    llm=llm,\n",
116 |     "    max_loops=1,\n",
117 |     "    autosave=True,\n",
118 |     "    dashboard=False,\n",
119 |     "    system_prompt=(\n",
120 |     "        \"You specialize in understanding and interpreting management's discussion and analysis. Your task is to critically \"\n",
121 |     "        \"review this section of the 10-K report. Identify the key themes discussed by management, such as company performance, \"\n",
122 |     "        \"strategic initiatives, and forward-looking statements. Analyze the tone and language used to assess management's confidence \"\n",
123 |     "        \"in the company's future prospects. Summarize the key points and provide insights into potential risks or opportunities.\"\n",
124 |     "    )\n",
125 |     ")\n",
126 |     "\n",
127 |     "# Initialize RiskAssessmentAnalyst agent\n",
128 |     "risk_assessment_analyst = Agent(\n",
129 |     "    agent_name=\"RiskAssessmentAnalyst\",\n",
130 |     "    llm=llm,\n",
131 |     "    max_loops=1,\n",
132 |     "    autosave=True,\n",
133 |     "    dashboard=False,\n",
134 |     "    system_prompt=(\n",
135 |     "        \"As an expert in risk management, your task is to analyze the risk factors section of the 10-K report. Carefully read through \"\n",
136 |     "        \"the risks identified by the company, and assess their potential impact on the company's operations and financial performance. \"\n",
137 |     "        \"Prioritize the risks based on their severity and likelihood, and provide recommendations on how the company might mitigate these risks.\"\n",
138 |     "    )\n",
139 |     ")\n",
140 |     "\n",
141 |     "print(\"Agents created successfully!\")\n"
142 |    ]
143 |   },
144 |   {
145 |    "cell_type": "markdown",
146 |    "id": "ef7f181c",
147 |    "metadata": {},
148 |    "source": [
149 |     "\n",
150 |     "## Step 3: Building the Workflow Graph\n",
151 |     "\n",
152 |     "Now that we have our agents, we'll set up a workflow using the `GraphWorkflow` class. This workflow will define how the agents \n",
153 |     "interact with each other and the tasks they need to complete. We'll use nodes to represent the agents and tasks, and edges to \n",
154 |     "define the flow of the workflow.\n"
155 |    ]
156 |   },
157 |   {
158 |    "cell_type": "code",
159 |    "execution_count": null,
160 |    "id": "2376b6cd",
161 |    "metadata": {},
162 |    "outputs": [],
163 |    "source": [
164 |     "\n",
165 |     "from swarms import GraphWorkflow, Node, NodeType, Edge\n",
166 |     "\n",
167 |     "# Define a sample task function\n",
168 |     "def sample_task():\n",
169 |     "    print(\"Running sample task\")\n",
170 |     "    return \"Task completed\"\n",
171 |     "\n",
172 |     "# Initialize the workflow graph\n",
173 |     "wf_graph = GraphWorkflow()\n",
174 |     "\n",
175 |     "# Add nodes for each agent\n",
176 |     "wf_graph.add_node(Node(id=\"financial_statements_expert\", type=NodeType.AGENT, agent=financial_statements_expert))\n",
177 |     "wf_graph.add_node(Node(id=\"management_discussion_reviewer\", type=NodeType.AGENT, agent=management_discussion_reviewer))\n",
178 |     "wf_graph.add_node(Node(id=\"risk_assessment_analyst\", type=NodeType.AGENT, agent=risk_assessment_analyst))\n",
179 |     "\n",
180 |     "# Add a sample task node\n",
181 |     "wf_graph.add_node(Node(id=\"task1\", type=NodeType.TASK, callable=sample_task))\n",
182 |     "\n",
183 |     "# Define the edges (flow of tasks)\n",
184 |     "wf_graph.add_edge(Edge(source=\"financial_statements_expert\", target=\"task1\"))\n",
185 |     "wf_graph.add_edge(Edge(source=\"management_discussion_reviewer\", target=\"task1\"))\n",
186 |     "wf_graph.add_edge(Edge(source=\"risk_assessment_analyst\", target=\"task1\"))\n",
187 |     "\n",
188 |     "# Set entry and end points for the workflow\n",
189 |     "wf_graph.set_entry_points([\"financial_statements_expert\", \"management_discussion_reviewer\", \"risk_assessment_analyst\"])\n",
190 |     "wf_graph.set_end_points([\"task1\"])\n",
191 |     "\n",
192 |     "# Visualize the workflow\n",
193 |     "print(wf_graph.visualize())\n",
194 |     "\n",
195 |     "print(\"Workflow graph created successfully!\")\n"
196 |    ]
197 |   },
198 |   {
199 |    "cell_type": "markdown",
200 |    "id": "5ee202d4",
201 |    "metadata": {},
202 |    "source": [
203 |     "\n",
204 |     "## Step 4: Running the Workflow\n",
205 |     "\n",
206 |     "Now it's time to execute the workflow. The agents will analyze the assigned sections of the 10-K report, and the workflow \n",
207 |     "will ensure that all tasks are completed in the correct order. Let's run the workflow and see the results.\n"
208 |    ]
209 |   },
210 |   {
211 |    "cell_type": "code",
212 |    "execution_count": null,
213 |    "id": "a194c985",
214 |    "metadata": {},
215 |    "outputs": [],
216 |    "source": [
217 |     "\n",
218 |     "# pip install PyPDF2 \n",
219 |     "import PyPDF2\n",
220 |     "\n",
221 |     "def load_pdf_to_string(file_path: str):\n",
222 |     "    # Open the PDF file in read-binary mode\n",
223 |     "    with open(file_path, 'rb') as file:\n",
224 |     "        # Create a PDF file reader object\n",
225 |     "        pdf_reader = PyPDF2.PdfFileReader(file)\n",
226 |     "        \n",
227 |     "        text = \"\"\n",
228 |     "        # Loop through all the pages (if multiple) and extract text\n",
229 |     "        for page_number in range(pdf_reader.getNumPages()):\n",
230 |     "            page = pdf_reader.getPage(page_number)\n",
231 |     "            text += page.extractText()\n",
232 |     "        \n",
233 |     "        # Return the extracted text\n",
234 |     "        return text\n",
235 |     "\n",
236 |     "ten_k_data = load_pdf_to_string(\"sample_10k.pdf\")\n",
237 |     "\n",
238 |     "# Run the workflow\n",
239 |     "results = wf_graph.run(f\"Analyze the 10-K report below and provide a detailed summary of your findings: {ten_k_data}\")\n",
240 |     "print(\"Execution results:\", results)\n"
241 |    ]
242 |   },
243 |   {
244 |    "cell_type": "markdown",
245 |    "id": "64d8dbbd",
246 |    "metadata": {},
247 |    "source": [
248 |     "\n",
249 |     "## Conclusion and Next Steps\n",
250 |     "\n",
251 |     "You've successfully set up and run a workflow to analyze 10-K reports using specialized agents. This approach demonstrates how \n",
252 |     "multi-agent systems can be used to automate complex financial analysis tasks, providing valuable insights from detailed reports.\n",
253 |     "\n",
254 |     "### Next Steps:\n",
255 |     "- Explore the [Swarms Documentation](https://docs.swarms.world/en/latest/) for more details and advanced features.\n",
256 |     "- Experiment with different agents, tasks, and scenarios to see how the system adapts.\n",
257 |     "- Consider applying this framework to other types of financial reports or even different domains.\n",
258 |     "\n",
259 |     "Thank you for following along!\n"
260 |    ]
261 |   }
262 |  ],
263 |  "metadata": {
264 |   "language_info": {
265 |    "name": "python"
266 |   }
267 |  },
268 |  "nbformat": 4,
269 |  "nbformat_minor": 5
270 | }
271 | 


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/cookbook/enterprise/marketing/content_generation/swarms_spreadsheet_analysis_walkthrough.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "965c183b",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "\n",
  9 |     "# Comprehensive Walkthrough: Managing Multiple Agent Outputs with SpreadSheetSwarm\n",
 10 |     "\n",
 11 |     "In this notebook, we'll explore how to manage and analyze outputs from multiple agents using the `SpreadSheetSwarm` architecture. \n",
 12 |     "When dealing with multiple agents, each producing different outputs, it can become challenging to manage and analyze these results efficiently. \n",
 13 |     "The `SpreadSheetSwarm` helps solve this problem by organizing the outputs into a spreadsheet format, making it easier to compare, analyze, \n",
 14 |     "and optimize the results.\n",
 15 |     "\n",
 16 |     "### Objectives:\n",
 17 |     "- Understand the challenges of managing multiple agent outputs.\n",
 18 |     "- Learn how to create custom agents for different tasks.\n",
 19 |     "- Use `SpreadSheetSwarm` to organize and analyze agent outputs in a spreadsheet.\n",
 20 |     "- Explore how this approach simplifies the analysis and optimization of outputs.\n",
 21 |     "\n",
 22 |     "For more detailed information, please refer to the [Swarms GitHub Repository](https://github.com/kyegomez/swarms) and join the [Swarms Community](https://discord.com/servers/agora-999382051935506503).\n"
 23 |    ]
 24 |   },
 25 |   {
 26 |    "cell_type": "markdown",
 27 |    "id": "5431acab",
 28 |    "metadata": {},
 29 |    "source": [
 30 |     "# Installation\n",
 31 |     "Ensure you have swarms installed with the following command!\n",
 32 |     "\n",
 33 |     "```bash\n",
 34 |     "$ pip3 install -U swarms pandas\n",
 35 |     "```"
 36 |    ]
 37 |   },
 38 |   {
 39 |    "cell_type": "markdown",
 40 |    "id": "6c89a841",
 41 |    "metadata": {},
 42 |    "source": [
 43 |     "\n",
 44 |     "## Step 1: Setting Up the Environment\n",
 45 |     "\n",
 46 |     "First, we'll load our environment variables and initialize the model. Ensure that your OpenAI API key is stored in a `.env` file in the same directory as this notebook.\n"
 47 |    ]
 48 |   },
 49 |   {
 50 |    "cell_type": "code",
 51 |    "execution_count": null,
 52 |    "id": "3e0b261d",
 53 |    "metadata": {},
 54 |    "outputs": [],
 55 |    "source": [
 56 |     "\n",
 57 |     "import os\n",
 58 |     "from dotenv import load_dotenv\n",
 59 |     "from swarms import OpenAIChat\n",
 60 |     "\n",
 61 |     "# Load environment variables\n",
 62 |     "load_dotenv()\n",
 63 |     "\n",
 64 |     "# Get the API key from the environment variables\n",
 65 |     "api_key = os.environ.get(\"OPENAI_API_KEY\")\n",
 66 |     "\n",
 67 |     "# Initialize the OpenAIChat model\n",
 68 |     "model = OpenAIChat(\n",
 69 |     "    openai_api_key=api_key, model_name=\"gpt-4o-mini\", temperature=0.1\n",
 70 |     ")\n",
 71 |     "\n",
 72 |     "print(\"Environment set up and model initialized successfully!\")\n"
 73 |    ]
 74 |   },
 75 |   {
 76 |    "cell_type": "markdown",
 77 |    "id": "b159e5a1",
 78 |    "metadata": {},
 79 |    "source": [
 80 |     "\n",
 81 |     "## Step 2: Creating Custom Agents\n",
 82 |     "\n",
 83 |     "Next, we'll create custom agents for different social media platforms. Each agent will be configured with a specific system prompt tailored \n",
 84 |     "to the requirements of that platform. These agents will generate content for Twitter, Instagram, Facebook, and Email marketing.\n"
 85 |    ]
 86 |   },
 87 |   {
 88 |    "cell_type": "code",
 89 |    "execution_count": null,
 90 |    "id": "8c82ebf0",
 91 |    "metadata": {},
 92 |    "outputs": [],
 93 |    "source": [
 94 |     "\n",
 95 |     "from swarms import Agent\n",
 96 |     "\n",
 97 |     "# Define custom system prompts for each social media platform\n",
 98 |     "TWITTER_AGENT_SYS_PROMPT = '''\n",
 99 |     "You are a Twitter marketing expert. Your task is to create engaging, concise tweets and analyze trends to maximize engagement. Consider hashtags, timing, and content relevance.\n",
100 |     "'''\n",
101 |     "\n",
102 |     "INSTAGRAM_AGENT_SYS_PROMPT = '''\n",
103 |     "You are an Instagram marketing expert. Your task is to create visually appealing and engaging content, including captions and hashtags, tailored to a specific audience.\n",
104 |     "'''\n",
105 |     "\n",
106 |     "FACEBOOK_AGENT_SYS_PROMPT = '''\n",
107 |     "You are a Facebook marketing expert. Your task is to craft posts that are optimized for engagement and reach on Facebook, including using images, links, and targeted messaging.\n",
108 |     "'''\n",
109 |     "\n",
110 |     "EMAIL_AGENT_SYS_PROMPT = '''\n",
111 |     "You are an Email marketing expert. Your task is to write compelling email campaigns that drive conversions, focusing on subject lines, personalization, and call-to-action strategies.\n",
112 |     "'''\n",
113 |     "\n",
114 |     "# Initialize your agents for different social media platforms\n",
115 |     "agents = [\n",
116 |     "    Agent(\n",
117 |     "        agent_name=\"Twitter-Marketing-Agent\",\n",
118 |     "        system_prompt=TWITTER_AGENT_SYS_PROMPT,\n",
119 |     "        llm=model,\n",
120 |     "        max_loops=1,\n",
121 |     "        dynamic_temperature_enabled=True,\n",
122 |     "        saved_state_path=\"twitter_agent.json\",\n",
123 |     "        user_name=\"swarms_corp\",\n",
124 |     "        retry_attempts=1,\n",
125 |     "    ),\n",
126 |     "    Agent(\n",
127 |     "        agent_name=\"Instagram-Marketing-Agent\",\n",
128 |     "        system_prompt=INSTAGRAM_AGENT_SYS_PROMPT,\n",
129 |     "        llm=model,\n",
130 |     "        max_loops=1,\n",
131 |     "        dynamic_temperature_enabled=True,\n",
132 |     "        saved_state_path=\"instagram_agent.json\",\n",
133 |     "        user_name=\"swarms_corp\",\n",
134 |     "        retry_attempts=1,\n",
135 |     "    ),\n",
136 |     "    Agent(\n",
137 |     "        agent_name=\"Facebook-Marketing-Agent\",\n",
138 |     "        system_prompt=FACEBOOK_AGENT_SYS_PROMPT,\n",
139 |     "        llm=model,\n",
140 |     "        max_loops=1,\n",
141 |     "        dynamic_temperature_enabled=True,\n",
142 |     "        saved_state_path=\"facebook_agent.json\",\n",
143 |     "        user_name=\"swarms_corp\",\n",
144 |     "        retry_attempts=1,\n",
145 |     "    ),\n",
146 |     "    Agent(\n",
147 |     "        agent_name=\"Email-Marketing-Agent\",\n",
148 |     "        system_prompt=EMAIL_AGENT_SYS_PROMPT,\n",
149 |     "        llm=model,\n",
150 |     "        max_loops=1,\n",
151 |     "        dynamic_temperature_enabled=True,\n",
152 |     "        saved_state_path=\"email_agent.json\",\n",
153 |     "        user_name=\"swarms_corp\",\n",
154 |     "        retry_attempts=1,\n",
155 |     "    ),\n",
156 |     "]\n",
157 |     "\n",
158 |     "print(\"Custom agents created successfully!\")\n"
159 |    ]
160 |   },
161 |   {
162 |    "cell_type": "markdown",
163 |    "id": "28362499",
164 |    "metadata": {},
165 |    "source": [
166 |     "\n",
167 |     "## Step 3: Defining the SpreadSheetSwarm\n",
168 |     "\n",
169 |     "Now, we'll create a `SpreadSheetSwarm` that can run multiple agents in parallel and collect their outputs in a spreadsheet format. \n",
170 |     "This makes it easy to analyze the results from each agent side by side.\n"
171 |    ]
172 |   },
173 |   {
174 |    "cell_type": "code",
175 |    "execution_count": null,
176 |    "id": "37551cc6",
177 |    "metadata": {},
178 |    "outputs": [],
179 |    "source": [
180 |     "\n",
181 |     "from swarms.structs.spreadsheet_swarm import SpreadSheetSwarm\n",
182 |     "\n",
183 |     "# Create a Swarm with the list of agents\n",
184 |     "swarm = SpreadSheetSwarm(\n",
185 |     "    name=\"Social-Media-Marketing-Swarm\",\n",
186 |     "    description=\"A swarm that processes social media marketing tasks using multiple agents on different threads.\",\n",
187 |     "    agents=agents,\n",
188 |     "    autosave_on=True,\n",
189 |     "    save_file_path=\"social_media_marketing_spreadsheet.csv\",\n",
190 |     "    run_all_agents=False,\n",
191 |     "    repeat_count=2,\n",
192 |     ")\n",
193 |     "\n",
194 |     "print(\"SpreadSheetSwarm defined successfully!\")\n"
195 |    ]
196 |   },
197 |   {
198 |    "cell_type": "markdown",
199 |    "id": "90fdaef3",
200 |    "metadata": {},
201 |    "source": [
202 |     "\n",
203 |     "## Step 4: Running the Swarm\n",
204 |     "\n",
205 |     "With our agents and `SpreadSheetSwarm` set up, we'll now run the swarm on a sample task. This task will involve creating content \n",
206 |     "to promote a series of hack nights in Miami Beach for developers, engineers, and tech enthusiasts. The agents will generate content \n",
207 |     "for Twitter, Instagram, Facebook, and an Email campaign, and the outputs will be saved in a spreadsheet.\n"
208 |    ]
209 |   },
210 |   {
211 |    "cell_type": "code",
212 |    "execution_count": null,
213 |    "id": "e1b005a5",
214 |    "metadata": {},
215 |    "outputs": [],
216 |    "source": [
217 |     "\n",
218 |     "# Run the swarm\n",
219 |     "swarm.run(\n",
220 |     "    task=\"Create posts to promote hack nights in Miami Beach for developers, engineers, and tech enthusiasts. Include relevant hashtags, images, and engaging captions.\"\n",
221 |     ")\n",
222 |     "\n",
223 |     "print(\"Swarm run completed. Check the 'social_media_marketing_spreadsheet.csv' file for the results.\")\n"
224 |    ]
225 |   },
226 |   {
227 |    "cell_type": "markdown",
228 |    "id": "bd9ef4e9",
229 |    "metadata": {},
230 |    "source": [
231 |     "\n",
232 |     "## Step 5: Analyzing the Results\n",
233 |     "\n",
234 |     "The outputs from each agent have been saved in a spreadsheet file named `social_media_marketing_spreadsheet.csv`. \n",
235 |     "This structured format allows you to easily compare the outputs from each agent, analyze the effectiveness of the content, \n",
236 |     "and make informed decisions on how to optimize your social media marketing strategy.\n"
237 |    ]
238 |   },
239 |   {
240 |    "cell_type": "code",
241 |    "execution_count": null,
242 |    "id": "b0dfaf8e",
243 |    "metadata": {},
244 |    "outputs": [],
245 |    "source": [
246 |     "\n",
247 |     "import pandas as pd\n",
248 |     "\n",
249 |     "# Load the results into a DataFrame\n",
250 |     "df = pd.read_csv(\"social_media_marketing_spreadsheet.csv\")\n",
251 |     "\n",
252 |     "# Display the DataFrame\n",
253 |     "df.head()\n"
254 |    ]
255 |   },
256 |   {
257 |    "cell_type": "markdown",
258 |    "id": "88204160",
259 |    "metadata": {},
260 |    "source": [
261 |     "\n",
262 |     "## Conclusion and Next Steps\n",
263 |     "\n",
264 |     "In this notebook, we've demonstrated how to manage and analyze outputs from multiple agents using the `SpreadSheetSwarm` architecture. \n",
265 |     "This approach simplifies the process of handling multiple agent outputs by organizing them into a structured spreadsheet format, \n",
266 |     "making it easier to compare and optimize your content.\n",
267 |     "\n",
268 |     "### Next Steps:\n",
269 |     "- Explore the [Swarms GitHub Repository](https://github.com/swarms-corp/swarms) to learn more about the Swarms framework.\n",
270 |     "- Join the [Swarms Community](https://discord.gg/swarms) for support, discussions, and updates.\n",
271 |     "- Experiment with different tasks, agents, and scenarios to see how this architecture can be applied to other use cases.\n",
272 |     "\n",
273 |     "Thank you for following along!\n"
274 |    ]
275 |   }
276 |  ],
277 |  "metadata": {
278 |   "language_info": {
279 |    "name": "python"
280 |   }
281 |  },
282 |  "nbformat": 4,
283 |  "nbformat_minor": 5
284 | }
285 | 


--------------------------------------------------------------------------------
/cookbook/enterprise/marketing/news/Swarms_Cookbook_NewsAgent.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "1e7d476c",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "# Swarms Cookbook: NewsAgent - Real-Time News Aggregation and Summarization\n",
  9 |     "\n",
 10 |     "Welcome to the Swarms Cookbook! In this guide, we will walk through the steps of using **NewsAgent**, an enterprise-grade solution for real-time news aggregation, querying, and summarization.\n",
 11 |     "\n",
 12 |     "NewsAgent helps businesses stay informed by automating news monitoring from multiple sources, summarizing relevant content, and providing timely updates.\n",
 13 |     "\n",
 14 |     "### Key Features:\n",
 15 |     "- **News Aggregation**: Fetch news from various sources via NewsAPI.\n",
 16 |     "- **Advanced Summarization**: Leverages AI to generate concise summaries of complex news stories.\n",
 17 |     "- **Enterprise-Grade**: Built with robust error handling and scalability for enterprise deployments.\n",
 18 |     "\n",
 19 |     "Follow this step-by-step guide to set up **NewsAgent** and integrate it into your news monitoring workflows.\n",
 20 |     "\n",
 21 |     "Be sure to visit the **[Swarms GitHub](https://github.com/kyegomez/swarms)**, **[Discord](https://discord.com/servers/agora-999382051935506503)**, and **[Swarms Marketplace](https://swarms.xyz)** for more resources and to connect with the community.\n"
 22 |    ]
 23 |   },
 24 |   {
 25 |    "cell_type": "markdown",
 26 |    "id": "8e2c95c9",
 27 |    "metadata": {},
 28 |    "source": [
 29 |     "## Installation\n",
 30 |     "\n",
 31 |     "First, install the **NewsAgent** package by running the following command:\n",
 32 |     "\n",
 33 |     "```bash\n",
 34 |     "pip3 install -U news-swarm\n",
 35 |     "```\n",
 36 |     "\n",
 37 |     "Ensure that you have Python 3.10+ installed.\n",
 38 |     "\n",
 39 |     "You will also need to create a `.env` file with the following environment variables for API access:\n",
 40 |     "\n",
 41 |     "```bash\n",
 42 |     "OPENAI_API_KEY=\"your-openai-api-key\"\n",
 43 |     "NEWSAPI_API_KEY=\"your-newsapi-api-key\"\n",
 44 |     "WORKSPACE_DIR=\"agent_workspace\"\n",
 45 |     "```\n"
 46 |    ]
 47 |   },
 48 |   {
 49 |    "cell_type": "code",
 50 |    "execution_count": null,
 51 |    "id": "a9480d48",
 52 |    "metadata": {},
 53 |    "outputs": [],
 54 |    "source": [
 55 |     "# Import necessary libraries\n",
 56 |     "import os\n",
 57 |     "from dotenv import load_dotenv\n",
 58 |     "from swarm_models import OpenAIChat\n",
 59 |     "from swarms import Agent\n",
 60 |     "from news_swarm.main import NewsAgent\n",
 61 |     "\n",
 62 |     "# Load environment variables from the .env file\n",
 63 |     "load_dotenv()\n",
 64 |     "\n",
 65 |     "# Ensure API keys are loaded\n",
 66 |     "openai_api_key = os.getenv(\"OPENAI_API_KEY\")\n",
 67 |     "newsapi_api_key = os.getenv(\"NEWSAPI_API_KEY\")\n"
 68 |    ]
 69 |   },
 70 |   {
 71 |    "cell_type": "markdown",
 72 |    "id": "948f87de",
 73 |    "metadata": {},
 74 |    "source": [
 75 |     "## Setting Up NewsAgent\n",
 76 |     "\n",
 77 |     "Now, let's initialize **NewsAgent** for real-time news aggregation and summarization. We will create an **OpenAIChat** instance for language model integration and set up the news agent to query and summarize the latest news from multiple sources.\n",
 78 |     "\n",
 79 |     "In this example, we'll fetch news related to **multi-agent collaboration** and display the results.\n"
 80 |    ]
 81 |   },
 82 |   {
 83 |    "cell_type": "code",
 84 |    "execution_count": null,
 85 |    "id": "7da9002b",
 86 |    "metadata": {},
 87 |    "outputs": [],
 88 |    "source": [
 89 |     "# Create an instance of OpenAIChat class for LLM integration\n",
 90 |     "model = OpenAIChat(\n",
 91 |     "    openai_api_key=openai_api_key,\n",
 92 |     "    model_name=\"gpt-4o-mini\",\n",
 93 |     "    temperature=0.1\n",
 94 |     ")\n",
 95 |     "\n",
 96 |     "# Initialize the base agent\n",
 97 |     "base_agent = Agent(\n",
 98 |     "    agent_name=\"News-Agent-V1\",\n",
 99 |     "    llm=model,\n",
100 |     "    max_loops=1,\n",
101 |     "    autosave=True,\n",
102 |     "    dashboard=False,\n",
103 |     "    verbose=True,\n",
104 |     "    dynamic_temperature_enabled=True,\n",
105 |     "    saved_state_path=\"news_agent.json\",\n",
106 |     "    user_name=\"swarms_corp\",\n",
107 |     "    retry_attempts=1,\n",
108 |     "    context_length=200000,\n",
109 |     ")\n",
110 |     "\n",
111 |     "# Create the NewsAgent instance\n",
112 |     "news_agent = NewsAgent(\n",
113 |     "    agent_name=\"news-agent-v1\",\n",
114 |     "    agent=base_agent,\n",
115 |     "    newsapi_api_key=newsapi_api_key,\n",
116 |     "    system_prompt=None,\n",
117 |     "    return_json=True,\n",
118 |     ")\n",
119 |     "\n",
120 |     "# Run the agent to fetch news related to \"multi-agent collaboration\"\n",
121 |     "print(news_agent.run(\"multi-agent collaboration\"))\n",
122 |     "\n",
123 |     "# Fetch news for multiple topics concurrently\n",
124 |     "print(news_agent.run_concurrently([\"OpenAI\", \"Anthropic\"]))\n"
125 |    ]
126 |   },
127 |   {
128 |    "cell_type": "markdown",
129 |    "id": "ebaadd82",
130 |    "metadata": {},
131 |    "source": [
132 |     "## System Architecture\n",
133 |     "\n",
134 |     "**NewsAgent** operates by combining real-time data fetching from **NewsAPI** with advanced AI-based summarization. Here’s how it works:\n",
135 |     "\n",
136 |     "- **News Fetching**: NewsAgent queries **NewsAPI** to fetch the latest news articles based on the provided keywords or topics.\n",
137 |     "- **OpenAI Integration**: Uses **GPT-4** to summarize complex news stories into concise reports that are easy to understand and act upon.\n",
138 |     "- **Agent Framework**: Powered by the **Swarms** framework, NewsAgent is scalable, flexible, and reliable for enterprise use.\n"
139 |    ]
140 |   },
141 |   {
142 |    "cell_type": "markdown",
143 |    "id": "5f8c3abb",
144 |    "metadata": {},
145 |    "source": [
146 |     "## Next Steps and Resources\n",
147 |     "\n",
148 |     "You've successfully set up and run a real-time news aggregation and summarization using **NewsAgent**!\n",
149 |     "\n",
150 |     "To explore more:\n",
151 |     "- Try different queries for a wide range of topics.\n",
152 |     "- Adjust the agent’s configuration for more tailored results.\n",
153 |     "- Integrate **NewsAgent** into your existing platforms for automated news monitoring.\n",
154 |     "\n",
155 |     "For more resources and to contribute, visit:\n",
156 |     "- **[Swarms GitHub](https://github.com/kyegomez/swarms)**\n",
157 |     "- **[Swarms Discord](https://discord.com/servers/agora-999382051935506503)**\n",
158 |     "- **[Swarms Marketplace](https://swarms.xyz)**\n",
159 |     "\n",
160 |     "Stay connected with the Swarms community and keep building advanced agents!\n"
161 |    ]
162 |   }
163 |  ],
164 |  "metadata": {},
165 |  "nbformat": 4,
166 |  "nbformat_minor": 5
167 | }
168 | 


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/cookbook/enterprise/marketing/news/main.py:
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1 | 
2 | 


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/cookbook/enterprise/medical/physical_therapy/Swarms_Cookbook_MedInsight_Pro.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "dfd2e7ff",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "# Swarms Cookbook: MedInsight Pro Medical Research Summarization\n",
  9 |     "\n",
 10 |     "Welcome to the Swarms Cookbook! In this guide, we will demonstrate how to use **MedInsight Pro**, an AI-driven agent that extracts actionable insights from medical research. \n",
 11 |     "\n",
 12 |     "**MedInsight Pro** is designed for healthcare professionals, researchers, and innovators to quickly summarize complex medical papers, discover breakthrough treatments, and stay updated on clinical trials and emerging trends.\n",
 13 |     "\n",
 14 |     "### Key Features:\n",
 15 |     "- **Advanced NLP**: Powered by GPT-4 for understanding dense medical research.\n",
 16 |     "- **Real-Time Data Retrieval**: Integrated with PubMed, Semantic Scholar, and other research databases.\n",
 17 |     "- **Actionable Insights**: Extracts key findings from large volumes of research.\n",
 18 |     "- **Scalable**: Suitable for enterprise-scale deployments.\n",
 19 |     "  \n",
 20 |     "Follow the guide below to get started with **MedInsight Pro** for your medical research needs.\n",
 21 |     "\n",
 22 |     "Be sure to visit the **[Swarms GitHub](https://github.com/kyegomez/swarms)**, **[Discord](https://discord.com/servers/agora-999382051935506503)**, and **[Swarms Marketplace](https://swarms.xyz)** to learn more and connect with the community.\n"
 23 |    ]
 24 |   },
 25 |   {
 26 |    "cell_type": "markdown",
 27 |    "id": "fb328770",
 28 |    "metadata": {},
 29 |    "source": [
 30 |     "## Installation\n",
 31 |     "\n",
 32 |     "First, install the **MedInsight Pro** package by running the following command:\n",
 33 |     "\n",
 34 |     "```bash\n",
 35 |     "pip install -U medinsight\n",
 36 |     "```\n",
 37 |     "\n",
 38 |     "Additionally, you will need to create a `.env` file with the following environment variables for API access:\n",
 39 |     "\n",
 40 |     "```bash\n",
 41 |     "OPENAI_API_KEY=\"your-openai-api-key\"\n",
 42 |     "PUBMED_API_KEY=\"your-pubmed-api-key\"  # Optional, for enhanced rate limits\n",
 43 |     "SEMANTIC_SCHOLAR_API_KEY=\"your-semantic-scholar-api-key\"\n",
 44 |     "WORKSPACE_ID=\"your-workspace-id\"\n",
 45 |     "ENTREZ_EMAIL=\"your-email@example.com\"\n",
 46 |     "```\n"
 47 |    ]
 48 |   },
 49 |   {
 50 |    "cell_type": "code",
 51 |    "execution_count": null,
 52 |    "id": "26c32ba6",
 53 |    "metadata": {},
 54 |    "outputs": [],
 55 |    "source": [
 56 |     "# Import necessary libraries\n",
 57 |     "from medinsight.agent import MedInsightPro\n",
 58 |     "from loguru import logger\n",
 59 |     "\n",
 60 |     "# Ensure proper logging setup\n",
 61 |     "logger.add(\"medinsight_agent.log\", rotation=\"500 MB\")\n",
 62 |     "\n",
 63 |     "# Load environment variables from the .env file\n",
 64 |     "import os\n",
 65 |     "from dotenv import load_dotenv\n",
 66 |     "\n",
 67 |     "load_dotenv()\n",
 68 |     "\n",
 69 |     "# Ensure API keys are loaded\n",
 70 |     "openai_api_key = os.getenv(\"OPENAI_API_KEY\")\n",
 71 |     "pubmed_api_key = os.getenv(\"PUBMED_API_KEY\")\n",
 72 |     "semantic_scholar_api_key = os.getenv(\"SEMANTIC_SCHOLAR_API_KEY\")\n"
 73 |    ]
 74 |   },
 75 |   {
 76 |    "cell_type": "markdown",
 77 |    "id": "722614f0",
 78 |    "metadata": {},
 79 |    "source": [
 80 |     "## Setting Up MedInsight Pro\n",
 81 |     "\n",
 82 |     "Now, let's initialize the **MedInsight Pro** agent for summarizing the latest medical research.\n",
 83 |     "\n",
 84 |     "MedInsight Pro uses API integrations with PubMed and Semantic Scholar to fetch real-time data and GPT-4 for generating accurate summaries.\n",
 85 |     "\n",
 86 |     "Below, we will set up the agent and summarize recent research on **COVID-19 treatments**.\n"
 87 |    ]
 88 |   },
 89 |   {
 90 |    "cell_type": "code",
 91 |    "execution_count": null,
 92 |    "id": "7e0b2e3d",
 93 |    "metadata": {},
 94 |    "outputs": [],
 95 |    "source": [
 96 |     "# Initialize the MedInsight Pro agent\n",
 97 |     "agent = MedInsightPro()\n",
 98 |     "\n",
 99 |     "# Run the query to summarize recent medical research on COVID-19 treatments\n",
100 |     "output = agent.run(\"Summarize the latest medical research on COVID-19 treatments\")\n",
101 |     "\n",
102 |     "# Output the result\n",
103 |     "print(output)\n"
104 |    ]
105 |   },
106 |   {
107 |    "cell_type": "markdown",
108 |    "id": "30922804",
109 |    "metadata": {},
110 |    "source": [
111 |     "## How It Works\n",
112 |     "\n",
113 |     "- **Advanced NLP Models**: MedInsight Pro leverages GPT-4 to read and summarize complex medical research papers, producing clear and concise summaries.\n",
114 |     "- **Real-Time Data Retrieval**: Fetches medical research papers from PubMed and Semantic Scholar, automatically processing the most recent studies.\n",
115 |     "- **Actionable Insights**: Extracts valuable information on medical breakthroughs, clinical trials, and treatment options, providing concise reports for healthcare professionals.\n",
116 |     "- **Scalable Integration**: MedInsight Pro can handle large-scale operations, processing thousands of medical papers across different platforms with support for API rate limits and retries.\n"
117 |    ]
118 |   },
119 |   {
120 |    "cell_type": "markdown",
121 |    "id": "3e3e4fc2",
122 |    "metadata": {},
123 |    "source": [
124 |     "## Next Steps and Resources\n",
125 |     "\n",
126 |     "Congratulations! You've successfully set up and run a medical research summarization using **MedInsight Pro**.\n",
127 |     "\n",
128 |     "To explore more:\n",
129 |     "- Try different queries for various medical topics.\n",
130 |     "- Adjust settings like context length and summarization depth to customize the output.\n",
131 |     "- Integrate MedInsight Pro into your own medical research platform.\n",
132 |     "\n",
133 |     "For more examples and contributions, check out:\n",
134 |     "- **[Swarms GitHub](https://github.com/kyegomez/swarms)**\n",
135 |     "- **[Swarms Discord](https://discord.com/servers/agora-999382051935506503)**\n",
136 |     "- **[Swarms Marketplace](https://swarms.xyz)**\n",
137 |     "\n",
138 |     "Stay informed and connected with the Swarms community as you continue advancing medical research automation!\n"
139 |    ]
140 |   }
141 |  ],
142 |  "metadata": {},
143 |  "nbformat": 4,
144 |  "nbformat_minor": 5
145 | }
146 | 


--------------------------------------------------------------------------------
/cookbook/enterprise/medical/physical_therapy/swarms_diagnosis_treatment_extreme_athletics.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "48042c23",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "\n",
  9 |     "# Diagnosis and Treatment Swarm for Extreme Athletics using AgentRearrange\n",
 10 |     "\n",
 11 |     "This notebook will guide you through the process of setting up a swarm of agents designed to diagnose and treat issues related to extreme athletics, \n",
 12 |     "such as nausea and fatigue. Using the `swarms` framework and the `AgentRearrange` class, we'll create customized agents that focus on different aspects \n",
 13 |     "of diagnosis and treatment. We'll also include a human in the loop to verify and adjust the recommendations.\n",
 14 |     "\n",
 15 |     "For more detailed information, please refer to the [Swarms Documentation](https://docs.swarms.world/en/latest/).\n"
 16 |    ]
 17 |   },
 18 |   {
 19 |    "cell_type": "markdown",
 20 |    "id": "9086c365",
 21 |    "metadata": {},
 22 |    "source": [
 23 |     "# Installation\n",
 24 |     "To ensure you have swarms downloaded, run the following command in your bash\n",
 25 |     "\n",
 26 |     "```bash\n",
 27 |     "$ pip3 install -U swarms\n",
 28 |     "```"
 29 |    ]
 30 |   },
 31 |   {
 32 |    "cell_type": "markdown",
 33 |    "id": "35861e51",
 34 |    "metadata": {},
 35 |    "source": [
 36 |     "\n",
 37 |     "## Step 1: Setting Up the Environment\n",
 38 |     "\n",
 39 |     "First, we need to load our environment variables and initialize the model. Make sure you have your OpenAI API key stored \n",
 40 |     "in a `.env` file in the same directory as this notebook.\n"
 41 |    ]
 42 |   },
 43 |   {
 44 |    "cell_type": "code",
 45 |    "execution_count": null,
 46 |    "id": "443c085a",
 47 |    "metadata": {},
 48 |    "outputs": [],
 49 |    "source": [
 50 |     "\n",
 51 |     "import os\n",
 52 |     "from dotenv import load_dotenv\n",
 53 |     "from swarms import OpenAIChat\n",
 54 |     "\n",
 55 |     "# Load environment variables\n",
 56 |     "load_dotenv()\n",
 57 |     "\n",
 58 |     "# Get the API key from the environment variables\n",
 59 |     "api_key = os.environ.get(\"OPENAI_API_KEY\")\n",
 60 |     "\n",
 61 |     "# Initialize the OpenAIChat model\n",
 62 |     "llm = OpenAIChat(\n",
 63 |     "    temperature=0.5, openai_api_key=api_key, model_name = \"gpt-4o-mini\", max_tokens=4000\n",
 64 |     ")\n",
 65 |     "\n",
 66 |     "print(\"Environment set up and model initialized successfully!\")\n"
 67 |    ]
 68 |   },
 69 |   {
 70 |    "cell_type": "markdown",
 71 |    "id": "fd7ab1d7",
 72 |    "metadata": {},
 73 |    "source": [
 74 |     "\n",
 75 |     "## Step 2: Creating the Agents\n",
 76 |     "\n",
 77 |     "Next, we'll create the agents that will diagnose and treat issues related to extreme athletics. Each agent is assigned a specific task \n",
 78 |     "with a detailed system prompt. The agents include:\n",
 79 |     "- **SymptomAnalyzer**: Diagnoses symptoms such as nausea and fatigue.\n",
 80 |     "- **TreatmentAdvisor**: Provides treatment recommendations to recover faster.\n",
 81 |     "- **RecoveryPlanner**: Creates a detailed recovery plan.\n",
 82 |     "\n",
 83 |     "Let's create each agent with its specific prompt.\n"
 84 |    ]
 85 |   },
 86 |   {
 87 |    "cell_type": "code",
 88 |    "execution_count": null,
 89 |    "id": "7cfc4ef1",
 90 |    "metadata": {},
 91 |    "outputs": [],
 92 |    "source": [
 93 |     "\n",
 94 |     "from swarms import Agent\n",
 95 |     "\n",
 96 |     "# Initialize SymptomAnalyzer agent\n",
 97 |     "symptom_analyzer = Agent(\n",
 98 |     "    agent_name=\"SymptomAnalyzer\",\n",
 99 |     "    llm=llm,\n",
100 |     "    max_loops=1,\n",
101 |     "    autosave=True,\n",
102 |     "    system_prompt=(\n",
103 |     "        \"You are a specialist in sports medicine, particularly in diagnosing symptoms related to extreme athletics. \"\n",
104 |     "        \"Your task is to diagnose symptoms such as nausea, fatigue, and other physical or mental conditions that athletes might experience \"\n",
105 |     "        \"after intense activities. Provide a detailed analysis of the symptoms and suggest potential causes.\"\n",
106 |     "    )\n",
107 |     ")\n",
108 |     "\n",
109 |     "# Initialize TreatmentAdvisor agent\n",
110 |     "treatment_advisor = Agent(\n",
111 |     "    agent_name=\"TreatmentAdvisor\",\n",
112 |     "    llm=llm,\n",
113 |     "    max_loops=1,\n",
114 |     "    autosave=True,\n",
115 |     "    system_prompt=(\n",
116 |     "        \"You are an expert in sports recovery and treatment. Based on the symptoms provided by the SymptomAnalyzer, your task is to recommend \"\n",
117 |     "        \"the most effective treatments to help the athlete recover quickly. Consider factors such as hydration, nutrition, rest, and any specific \"\n",
118 |     "        \"medical interventions that may be necessary. Provide a detailed list of recommendations.\"\n",
119 |     "    )\n",
120 |     ")\n",
121 |     "\n",
122 |     "# Initialize RecoveryPlanner agent\n",
123 |     "recovery_planner = Agent(\n",
124 |     "    agent_name=\"RecoveryPlanner\",\n",
125 |     "    llm=llm,\n",
126 |     "    max_loops=1,\n",
127 |     "    autosave=True,\n",
128 |     "    system_prompt=(\n",
129 |     "        \"You are a recovery planning specialist. Your task is to create a comprehensive recovery plan based on the recommendations provided by \"\n",
130 |     "        \"the TreatmentAdvisor. The plan should include a timeline for recovery, daily routines, nutritional guidelines, and any other relevant \"\n",
131 |     "        \"advice to ensure the athlete returns to optimal performance as quickly as possible.\"\n",
132 |     "    )\n",
133 |     ")\n",
134 |     "\n",
135 |     "\n",
136 |     "print(\"Agents created successfully!\")\n"
137 |    ]
138 |   },
139 |   {
140 |    "cell_type": "markdown",
141 |    "id": "a3935121",
142 |    "metadata": {},
143 |    "source": [
144 |     "\n",
145 |     "## Step 3: Defining the Workflow with AgentRearrange\n",
146 |     "\n",
147 |     "Now that we have our agents, we'll set up a workflow using the `AgentRearrange` class. This workflow will process the tasks \n",
148 |     "sequentially, with a human in the loop to verify the outputs. The flow is defined as:\n",
149 |     "`\"SymptomAnalyzer -> HumanVerifier -> TreatmentAdvisor, RecoveryPlanner\"`\n"
150 |    ]
151 |   },
152 |   {
153 |    "cell_type": "code",
154 |    "execution_count": null,
155 |    "id": "bd6c5d8a",
156 |    "metadata": {},
157 |    "outputs": [],
158 |    "source": [
159 |     "\n",
160 |     "from swarms import AgentRearrange\n",
161 |     "\n",
162 |     "# Create a list of agents\n",
163 |     "agents = [symptom_analyzer, treatment_advisor, recovery_planner]\n",
164 |     "\n",
165 |     "# Define the flow pattern\n",
166 |     "flow = \"SymptomAnalyzer -> H -> TreatmentAdvisor, RecoveryPlanner\"\n",
167 |     "\n",
168 |     "# Using AgentRearrange class to create the workflow\n",
169 |     "agent_system = AgentRearrange(agents=agents, flow=flow, max_loops=1)\n",
170 |     "\n",
171 |     "print(\"Workflow defined successfully!\")\n"
172 |    ]
173 |   },
174 |   {
175 |    "cell_type": "markdown",
176 |    "id": "8e250ca6",
177 |    "metadata": {},
178 |    "source": [
179 |     "\n",
180 |     "## Step 4: Running the Workflow\n",
181 |     "\n",
182 |     "Now it's time to execute the workflow. The agents will diagnose the symptoms, a human will verify the diagnosis and adjust if necessary, \n",
183 |     "and then the treatment and recovery plan will be provided.\n"
184 |    ]
185 |   },
186 |   {
187 |    "cell_type": "code",
188 |    "execution_count": null,
189 |    "id": "b29026ed",
190 |    "metadata": {},
191 |    "outputs": [],
192 |    "source": [
193 |     "\n",
194 |     "# Run the workflow\n",
195 |     "output = agent_system.run(\"Diagnose symptoms related to extreme athletics and provide a recovery plan.\")\n",
196 |     "print(output)\n"
197 |    ]
198 |   },
199 |   {
200 |    "cell_type": "markdown",
201 |    "id": "c8dcf55d",
202 |    "metadata": {},
203 |    "source": [
204 |     "\n",
205 |     "## Conclusion and Next Steps\n",
206 |     "\n",
207 |     "You've successfully set up and run a diagnosis and treatment swarm for extreme athletics using specialized agents and a human-in-the-loop process. \n",
208 |     "This approach demonstrates how multi-agent systems can be used to automate complex tasks, while still allowing human oversight for critical decisions.\n",
209 |     "\n",
210 |     "### Next Steps:\n",
211 |     "- Explore the [Swarms Documentation](https://docs.swarms.world/en/latest/) for more details and advanced features.\n",
212 |     "- Experiment with different agents, tasks, and scenarios to see how the system adapts.\n",
213 |     "- Consider applying this framework to other domains, such as mental health or general medicine.\n",
214 |     "\n",
215 |     "Thank you for following along!\n"
216 |    ]
217 |   }
218 |  ],
219 |  "metadata": {
220 |   "language_info": {
221 |    "name": "python"
222 |   }
223 |  },
224 |  "nbformat": 4,
225 |  "nbformat_minor": 5
226 | }
227 | 


--------------------------------------------------------------------------------
/cookbook/workshops/sep_6_workshop/305_fights_tv.py:
--------------------------------------------------------------------------------
  1 | import os
  2 | from swarms import OpenAIChat, Agent, SpreadSheetSwarm
  3 | from dotenv import load_dotenv
  4 | 
  5 | load_dotenv()
  6 | 
  7 | api_key = os.getenv("OPENAI_API_KEY")
  8 | # Ensure your .env file contains: OPENAI_API_KEY="sk-..."
  9 | 
 10 | model = OpenAIChat(
 11 |     model_name="gpt-4o-mini",  # Corrected model name
 12 |     openai_api_key=api_key,
 13 |     max_tokens=4000,
 14 |     temperature=0.1,
 15 | )
 16 | 
 17 | # Define system prompts for each social media platform
 18 | system_prompts = {
 19 |     "Facebook-Agent": """
 20 |     You are a Social Media Marketing Agent specialized in promoting 305FightsTV on Facebook.
 21 |     Your tasks include creating engaging posts, managing Facebook Ads, interacting with followers, and organizing live events for amateur fighters in Miami.
 22 |     Target Audience: Individuals aged 17-65 who love fighting and have an interest or background in martial arts.
 23 |     Goals: Increase page followers, boost post engagement, promote events, and enhance community interaction.
 24 |     Utilize Facebook's features such as groups, events, and live streaming to maximize reach and engagement.
 25 |     Stay updated with Facebook's latest trends and algorithm changes to optimize content performance.
 26 |     """,
 27 |     "Instagram-Agent": """
 28 |     You are a Social Media Marketing Agent specialized in promoting 305FightsTV on Instagram.
 29 |     Your responsibilities include creating visually appealing posts and stories, managing Instagram Ads, engaging with followers through comments and DMs, and showcasing highlights from amateur fighting events in Miami.
 30 |     Target Audience: Individuals aged 17-65 who love fighting and have practiced or studied martial arts.
 31 |     Goals: Grow the follower base, enhance post engagement, promote upcoming events, and build a strong visual brand presence.
 32 |     Leverage Instagram features like Reels, IGTV, and Stories to maximize content visibility and engagement.
 33 |     Stay informed about Instagram's latest features and best practices to optimize content strategy.
 34 |     """,
 35 |     "Twitter-Agent": """
 36 |     You are a Social Media Marketing Agent specialized in promoting 305FightsTV on Twitter.
 37 |     Your duties include crafting engaging tweets, managing Twitter Ads, interacting with followers, and providing real-time updates during amateur fighting events in Miami.
 38 |     Target Audience: Individuals aged 17-65 who are passionate about fighting and have an interest in martial arts.
 39 |     Goals: Increase follower count, boost tweet engagement, promote events, and participate in relevant conversations and hashtags.
 40 |     Utilize Twitter's features such as threads, polls, and live tweeting to enhance engagement and visibility.
 41 |     Stay updated with Twitter trends and algorithm changes to ensure content remains relevant and effective.
 42 |     """,
 43 |     "TikTok-Agent": """
 44 |     You are a Social Media Marketing Agent specialized in promoting 305FightsTV on TikTok.
 45 |     Your tasks include creating short, engaging videos, managing TikTok Ads, interacting with followers through comments and duets, and showcasing highlights from amateur fighting events in Miami.
 46 |     Target Audience: Individuals aged 17-65 who enjoy fighting content and have an interest in martial arts.
 47 |     Goals: Grow the follower base, enhance video engagement, promote events, and build a dynamic and entertaining brand presence.
 48 |     Leverage TikTok's trends, challenges, and viral content strategies to maximize reach and engagement.
 49 |     Stay informed about TikTok's latest features and best practices to optimize video content strategy.
 50 |     """,
 51 |     "YouTube-Agent": """
 52 |     You are a Social Media Marketing Agent specialized in promoting 305FightsTV on YouTube.
 53 |     Your responsibilities include creating and uploading high-quality videos, managing YouTube Ads, engaging with viewers through comments, and showcasing full-length amateur fighting events in Miami.
 54 |     Target Audience: Individuals aged 17-65 who love fighting and have a background or interest in martial arts.
 55 |     Goals: Increase channel subscribers, boost video views and engagement, promote events, and establish a strong video content library.
 56 |     Utilize YouTube's features such as playlists, live streaming, and community posts to enhance content reach and viewer interaction.
 57 |     Stay updated with YouTube's latest trends, algorithm changes, and best practices to optimize video performance.
 58 |     """,
 59 |     "Event-Hosting-Agent": """
 60 |     You are an Event Hosting Agent responsible for organizing and managing amateur fighting events for 305FightsTV in Miami.
 61 |     Your tasks include planning event logistics, coordinating with fighters and venues, promoting events across all social media platforms, and ensuring a high-quality experience for participants and attendees.
 62 |     Target Audience: Amateur fighters aged 17-65 and fighting enthusiasts in Miami who have studied or practiced martial arts.
 63 |     Goals: Successfully host engaging and well-attended events, promote fighter participation, and enhance 305FightsTV's reputation in the fighting community.
 64 |     Utilize social media marketing, local partnerships, and community engagement to maximize event visibility and participation.
 65 |     Stay informed about the latest trends in event management and fighting sports to ensure events are competitive and appealing.
 66 |     """,
 67 | }
 68 | 
 69 | # Create agents for each social media platform
 70 | agents = []
 71 | 
 72 | for agent_name, system_prompt in system_prompts.items():
 73 |     agent = Agent(
 74 |         agent_name=agent_name,
 75 |         description=f"Agent responsible for managing and promoting 305FightsTV on {agent_name.replace('-Agent', '')}.",
 76 |         system_prompt=system_prompt,
 77 |         llm=model,
 78 |         max_loops=1,
 79 |         dashboard=False,
 80 |         stopping_token="<DONE>",
 81 |     )
 82 |     agents.append(agent)
 83 | 
 84 | task = "November 2nd Manuel art time theater Little Havana 6pm "
 85 | 
 86 | # Create a Swarm with the list of agents
 87 | swarm = SpreadSheetSwarm(
 88 |     name="305FightsTV-Social-Media-Swarm",
 89 |     description="Swarm of agents responsible for managing and promoting 305FightsTV across various social media platforms.",
 90 |     agents=agents,
 91 |     autosave_on=True,
 92 |     save_file_path="fight_night.csv",
 93 |     run_all_agents=False,
 94 |     repeat_count=2,
 95 | )
 96 | 
 97 | prompt = f"Create posts to advertise the upcoming fight night event: {task} "
 98 | 
 99 | swarm.run(prompt)
100 | 


--------------------------------------------------------------------------------
/cookbook/workshops/sep_6_workshop/agent_example.py:
--------------------------------------------------------------------------------
 1 | import os
 2 | from swarms import OpenAIChat, Agent
 3 | from dotenv import load_dotenv
 4 | 
 5 | load_dotenv()
 6 | 
 7 | api_key = os.getenv("OPENAI_API_KEY")
 8 | # .env OPENAI_API_KEY="sk-"
 9 | 
10 | model = OpenAIChat(
11 |     model_name="gpt-4o-mini", openai_api_key=api_key, max_tokens=4000, temperature=0.1
12 | )
13 | 
14 | 
15 | agent = Agent(
16 |     agent_name="Financial-Advisor-Agent",
17 |     description="Your task is to provide financial advice to clients. You will help them with their financial planning, investment strategies, and retirement planning. You will also provide advice on tax planning, estate planning, and insurance planning. You will need to understand the client's financial goals, risk tolerance, and investment preferences to provide the best advice. You will need to stay up-to-date on the latest financial products, market trends, and regulations to provide the best advice to your clients.",
18 |     system_prompt="",
19 |     llm=model,
20 |     max_loops=1,
21 |     dashboard=False,
22 |     stopping_token="<DONE>",
23 | )
24 | 
25 | 
26 | # Run the agent
27 | out = agent.run("What are interesting ways to deduct taxes for a small business?")
28 | print(out)
29 | 


--------------------------------------------------------------------------------
/cookbook/workshops/sep_6_workshop/agent_workspace/Financial-Analysis-Agent_sas_chicken_eej_state.json:
--------------------------------------------------------------------------------
  1 | {
  2 |     "agent_id": "2781504b9a5f469fad1138fac7836f5e",
  3 |     "id": "0505700c46bb4a13882f6ad807b4d290",
  4 |     "llm": {
  5 |         "name": "ChatOpenAI",
  6 |         "doc": "[*Deprecated*] `OpenAI` Chat large language models API.\n\nTo use, you should have the ``openai`` python package installed, and the\nenvironment variable ``OPENAI_API_KEY`` set with your API key.\n\nAny parameters that are valid to be passed to the openai.create call can be passed\nin, even if not explicitly saved on this class.\n\nExample:\n    .. code-block:: python\n\n        from langchain_community.chat_models import ChatOpenAI\n        openai = ChatOpenAI(model_name=\"gpt-3.5-turbo\")\n\nNotes\n-----\n.. deprecated:: 0.0.10\n   "
  7 |     },
  8 |     "template": null,
  9 |     "max_loops": 2,
 10 |     "stopping_condition": null,
 11 |     "loop_interval": 0,
 12 |     "retry_attempts": 1,
 13 |     "retry_interval": 1,
 14 |     "task": null,
 15 |     "stopping_token": "<DONE>",
 16 |     "interactive": false,
 17 |     "dashboard": false,
 18 |     "return_history": false,
 19 |     "dynamic_temperature_enabled": true,
 20 |     "dynamic_loops": false,
 21 |     "user_name": "swarms_corp",
 22 |     "context_length": 200000,
 23 |     "sop": null,
 24 |     "sop_list": null,
 25 |     "tools": null,
 26 |     "system_prompt": "\n\n### System Prompt for an Agent Specializing in Analyzing Financial and Accounting Statements\n\n---\n\n#### Introduction\n\nWelcome! You are an advanced AI agent designed to analyze financial and accounting statements, extracting and summarizing key statistics and insights. Your primary goal is to provide structured knowledge that highlights the financial health, performance, and trends within an organization. Below, we will detail how you should approach this task, including how to think, reason, and structure your analyses, followed by several examples to illustrate the process.\n\n#### How to Think and Reason\n\n1. **Understand the Document:**\n   - Begin by identifying the type of financial statement you are analyzing. Common types include balance sheets, income statements, cash flow statements, and statements of shareholders' equity.\n   - Determine the reporting period and the currency used.\n\n2. **Identify Key Sections:**\n   - For balance sheets, focus on assets, liabilities, and shareholders' equity.\n   - For income statements, focus on revenues, expenses, and net income.\n   - For cash flow statements, focus on operating, investing, and financing activities.\n   - For statements of shareholders' equity, focus on changes in equity, including retained earnings and issued shares.\n\n3. **Extract Key Metrics:**\n   - Calculate and highlight important financial ratios such as liquidity ratios (current ratio, quick ratio), profitability ratios (gross profit margin, net profit margin, return on equity), and solvency ratios (debt-to-equity ratio, interest coverage ratio).\n   - Identify trends by comparing current figures with those from previous periods.\n   - Highlight significant changes, unusual items, and potential red flags.\n\n4. **Summarize Clearly and Concisely:**\n   - Use plain language to explain the financial health and performance of the organization.\n   - Organize your summary logically, mirroring the structure of the original document.\n   - Include visual aids like charts or graphs where applicable to illustrate trends and comparisons.\n\n#### Examples\n\n---\n\n**Example 1: Income Statement Analysis**\n\n**Original Text:**\n\"ABC Corporation's income statement for the fiscal year ended December 31, 2023, reports total revenues of $5,000,000, cost of goods sold (COGS) of $3,000,000, operating expenses of $1,200,000, and net income of $600,000. The previous fiscal year's total revenues were $4,500,000, with a net income of $500,000.\"\n\n**Summary:**\n- **Revenues:** $5,000,000 (up from $4,500,000 in the previous year, an increase of 11.1%)\n- **Cost of Goods Sold (COGS):** $3,000,000\n- **Operating Expenses:** $1,200,000\n- **Net Income:** $600,000 (up from $500,000 in the previous year, an increase of 20%)\n- **Gross Profit Margin:** 40% (calculated as (Revenues - COGS) / Revenues)\n- **Net Profit Margin:** 12% (calculated as Net Income / Revenues)\n- **Key Observations:** Revenue growth of 11.1%, with a significant improvement in net income (20% increase), indicating improved profitability.\n\n---\n\n**Example 2: Balance Sheet Analysis**\n\n**Original Text:**\n\"As of December 31, 2023, XYZ Ltd.'s balance sheet reports total assets of $10,000,000, total liabilities of $6,000,000, and shareholders' equity of $4,000,000. The previous year's total assets were $9,000,000, total liabilities were $5,500,000, and shareholders' equity was $3,500,000.\"\n\n**Summary:**\n- **Total Assets:** $10,000,000 (up from $9,000,000 in the previous year, an increase of 11.1%)\n- **Total Liabilities:** $6,000,000 (up from $5,500,000 in the previous year, an increase of 9.1%)\n- **Shareholders' Equity:** $4,000,000 (up from $3,500,000 in the previous year, an increase of 14.3%)\n- **Current Ratio:** 1.67 (calculated as Total Assets / Total Liabilities)\n- **Debt-to-Equity Ratio:** 1.5 (calculated as Total Liabilities / Shareholders' Equity)\n- **Key Observations:** Healthy increase in both assets and equity, indicating growth and improved financial stability. The debt-to-equity ratio suggests a moderate level of debt relative to equity.\n\n---\n\n**Example 3: Cash Flow Statement Analysis**\n\n**Original Text:**\n\"For the fiscal year ended December 31, 2023, DEF Inc.'s cash flow statement shows net cash provided by operating activities of $700,000, net cash used in investing activities of $300,000, and net cash used in financing activities of $200,000. The beginning cash balance was $100,000, and the ending cash balance was $300,000.\"\n\n**Summary:**\n- **Net Cash Provided by Operating Activities:** $700,000\n- **Net Cash Used in Investing Activities:** $300,000\n- **Net Cash Used in Financing Activities:** $200,000\n- **Net Increase in Cash:** $200,000 (calculated as $700,000 - $300,000 - $200,000)\n- **Beginning Cash Balance:** $100,000\n- **Ending Cash Balance:** $300,000\n- **Key Observations:** Positive cash flow from operating activities indicates strong operational performance. The company is investing in growth while maintaining a healthy cash balance. The ending cash balance shows a significant increase, indicating improved liquidity.\n\n---\n\n**Example 4: Statement of Shareholders' Equity Analysis**\n\n**Original Text:**\n\"GHI Corporation's statement of shareholders' equity for the fiscal year ended December 31, 2023, shows common stock of $1,000,000, retained earnings of $2,000,000, and additional paid-in capital of $500,000. The previous year's retained earnings were $1,500,000.\"\n\n**Summary:**\n- **Common Stock:** $1,000,000\n- **Retained Earnings:** $2,000,000 (up from $1,500,000 in the previous year, an increase of 33.3%)\n- **Additional Paid-in Capital:** $500,000\n- **Total Shareholders' Equity:** $3,500,000\n- **Key Observations:** Significant growth in retained earnings indicates strong profitability and reinvestment in the business. The overall increase in shareholders' equity reflects the company's robust financial health and potential for future growth.\n\n---\n\nBy following this structured approach, you will be able to provide thorough and accurate analyses of financial and accounting statements, ensuring that all key metrics and insights are clearly understood.\n\n",
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111 |         "system_prompt": "\n\n### System Prompt for an Agent Specializing in Analyzing Financial and Accounting Statements\n\n---\n\n#### Introduction\n\nWelcome! You are an advanced AI agent designed to analyze financial and accounting statements, extracting and summarizing key statistics and insights. Your primary goal is to provide structured knowledge that highlights the financial health, performance, and trends within an organization. Below, we will detail how you should approach this task, including how to think, reason, and structure your analyses, followed by several examples to illustrate the process.\n\n#### How to Think and Reason\n\n1. **Understand the Document:**\n   - Begin by identifying the type of financial statement you are analyzing. Common types include balance sheets, income statements, cash flow statements, and statements of shareholders' equity.\n   - Determine the reporting period and the currency used.\n\n2. **Identify Key Sections:**\n   - For balance sheets, focus on assets, liabilities, and shareholders' equity.\n   - For income statements, focus on revenues, expenses, and net income.\n   - For cash flow statements, focus on operating, investing, and financing activities.\n   - For statements of shareholders' equity, focus on changes in equity, including retained earnings and issued shares.\n\n3. **Extract Key Metrics:**\n   - Calculate and highlight important financial ratios such as liquidity ratios (current ratio, quick ratio), profitability ratios (gross profit margin, net profit margin, return on equity), and solvency ratios (debt-to-equity ratio, interest coverage ratio).\n   - Identify trends by comparing current figures with those from previous periods.\n   - Highlight significant changes, unusual items, and potential red flags.\n\n4. **Summarize Clearly and Concisely:**\n   - Use plain language to explain the financial health and performance of the organization.\n   - Organize your summary logically, mirroring the structure of the original document.\n   - Include visual aids like charts or graphs where applicable to illustrate trends and comparisons.\n\n#### Examples\n\n---\n\n**Example 1: Income Statement Analysis**\n\n**Original Text:**\n\"ABC Corporation's income statement for the fiscal year ended December 31, 2023, reports total revenues of $5,000,000, cost of goods sold (COGS) of $3,000,000, operating expenses of $1,200,000, and net income of $600,000. The previous fiscal year's total revenues were $4,500,000, with a net income of $500,000.\"\n\n**Summary:**\n- **Revenues:** $5,000,000 (up from $4,500,000 in the previous year, an increase of 11.1%)\n- **Cost of Goods Sold (COGS):** $3,000,000\n- **Operating Expenses:** $1,200,000\n- **Net Income:** $600,000 (up from $500,000 in the previous year, an increase of 20%)\n- **Gross Profit Margin:** 40% (calculated as (Revenues - COGS) / Revenues)\n- **Net Profit Margin:** 12% (calculated as Net Income / Revenues)\n- **Key Observations:** Revenue growth of 11.1%, with a significant improvement in net income (20% increase), indicating improved profitability.\n\n---\n\n**Example 2: Balance Sheet Analysis**\n\n**Original Text:**\n\"As of December 31, 2023, XYZ Ltd.'s balance sheet reports total assets of $10,000,000, total liabilities of $6,000,000, and shareholders' equity of $4,000,000. The previous year's total assets were $9,000,000, total liabilities were $5,500,000, and shareholders' equity was $3,500,000.\"\n\n**Summary:**\n- **Total Assets:** $10,000,000 (up from $9,000,000 in the previous year, an increase of 11.1%)\n- **Total Liabilities:** $6,000,000 (up from $5,500,000 in the previous year, an increase of 9.1%)\n- **Shareholders' Equity:** $4,000,000 (up from $3,500,000 in the previous year, an increase of 14.3%)\n- **Current Ratio:** 1.67 (calculated as Total Assets / Total Liabilities)\n- **Debt-to-Equity Ratio:** 1.5 (calculated as Total Liabilities / Shareholders' Equity)\n- **Key Observations:** Healthy increase in both assets and equity, indicating growth and improved financial stability. The debt-to-equity ratio suggests a moderate level of debt relative to equity.\n\n---\n\n**Example 3: Cash Flow Statement Analysis**\n\n**Original Text:**\n\"For the fiscal year ended December 31, 2023, DEF Inc.'s cash flow statement shows net cash provided by operating activities of $700,000, net cash used in investing activities of $300,000, and net cash used in financing activities of $200,000. The beginning cash balance was $100,000, and the ending cash balance was $300,000.\"\n\n**Summary:**\n- **Net Cash Provided by Operating Activities:** $700,000\n- **Net Cash Used in Investing Activities:** $300,000\n- **Net Cash Used in Financing Activities:** $200,000\n- **Net Increase in Cash:** $200,000 (calculated as $700,000 - $300,000 - $200,000)\n- **Beginning Cash Balance:** $100,000\n- **Ending Cash Balance:** $300,000\n- **Key Observations:** Positive cash flow from operating activities indicates strong operational performance. The company is investing in growth while maintaining a healthy cash balance. The ending cash balance shows a significant increase, indicating improved liquidity.\n\n---\n\n**Example 4: Statement of Shareholders' Equity Analysis**\n\n**Original Text:**\n\"GHI Corporation's statement of shareholders' equity for the fiscal year ended December 31, 2023, shows common stock of $1,000,000, retained earnings of $2,000,000, and additional paid-in capital of $500,000. The previous year's retained earnings were $1,500,000.\"\n\n**Summary:**\n- **Common Stock:** $1,000,000\n- **Retained Earnings:** $2,000,000 (up from $1,500,000 in the previous year, an increase of 33.3%)\n- **Additional Paid-in Capital:** $500,000\n- **Total Shareholders' Equity:** $3,500,000\n- **Key Observations:** Significant growth in retained earnings indicates strong profitability and reinvestment in the business. The overall increase in shareholders' equity reflects the company's robust financial health and potential for future growth.\n\n---\n\nBy following this structured approach, you will be able to provide thorough and accurate analyses of financial and accounting statements, ensuring that all key metrics and insights are clearly understood.\n\n",
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118 |                 "content": "\n\n### System Prompt for an Agent Specializing in Analyzing Financial and Accounting Statements\n\n---\n\n#### Introduction\n\nWelcome! You are an advanced AI agent designed to analyze financial and accounting statements, extracting and summarizing key statistics and insights. Your primary goal is to provide structured knowledge that highlights the financial health, performance, and trends within an organization. Below, we will detail how you should approach this task, including how to think, reason, and structure your analyses, followed by several examples to illustrate the process.\n\n#### How to Think and Reason\n\n1. **Understand the Document:**\n   - Begin by identifying the type of financial statement you are analyzing. Common types include balance sheets, income statements, cash flow statements, and statements of shareholders' equity.\n   - Determine the reporting period and the currency used.\n\n2. **Identify Key Sections:**\n   - For balance sheets, focus on assets, liabilities, and shareholders' equity.\n   - For income statements, focus on revenues, expenses, and net income.\n   - For cash flow statements, focus on operating, investing, and financing activities.\n   - For statements of shareholders' equity, focus on changes in equity, including retained earnings and issued shares.\n\n3. **Extract Key Metrics:**\n   - Calculate and highlight important financial ratios such as liquidity ratios (current ratio, quick ratio), profitability ratios (gross profit margin, net profit margin, return on equity), and solvency ratios (debt-to-equity ratio, interest coverage ratio).\n   - Identify trends by comparing current figures with those from previous periods.\n   - Highlight significant changes, unusual items, and potential red flags.\n\n4. **Summarize Clearly and Concisely:**\n   - Use plain language to explain the financial health and performance of the organization.\n   - Organize your summary logically, mirroring the structure of the original document.\n   - Include visual aids like charts or graphs where applicable to illustrate trends and comparisons.\n\n#### Examples\n\n---\n\n**Example 1: Income Statement Analysis**\n\n**Original Text:**\n\"ABC Corporation's income statement for the fiscal year ended December 31, 2023, reports total revenues of $5,000,000, cost of goods sold (COGS) of $3,000,000, operating expenses of $1,200,000, and net income of $600,000. The previous fiscal year's total revenues were $4,500,000, with a net income of $500,000.\"\n\n**Summary:**\n- **Revenues:** $5,000,000 (up from $4,500,000 in the previous year, an increase of 11.1%)\n- **Cost of Goods Sold (COGS):** $3,000,000\n- **Operating Expenses:** $1,200,000\n- **Net Income:** $600,000 (up from $500,000 in the previous year, an increase of 20%)\n- **Gross Profit Margin:** 40% (calculated as (Revenues - COGS) / Revenues)\n- **Net Profit Margin:** 12% (calculated as Net Income / Revenues)\n- **Key Observations:** Revenue growth of 11.1%, with a significant improvement in net income (20% increase), indicating improved profitability.\n\n---\n\n**Example 2: Balance Sheet Analysis**\n\n**Original Text:**\n\"As of December 31, 2023, XYZ Ltd.'s balance sheet reports total assets of $10,000,000, total liabilities of $6,000,000, and shareholders' equity of $4,000,000. The previous year's total assets were $9,000,000, total liabilities were $5,500,000, and shareholders' equity was $3,500,000.\"\n\n**Summary:**\n- **Total Assets:** $10,000,000 (up from $9,000,000 in the previous year, an increase of 11.1%)\n- **Total Liabilities:** $6,000,000 (up from $5,500,000 in the previous year, an increase of 9.1%)\n- **Shareholders' Equity:** $4,000,000 (up from $3,500,000 in the previous year, an increase of 14.3%)\n- **Current Ratio:** 1.67 (calculated as Total Assets / Total Liabilities)\n- **Debt-to-Equity Ratio:** 1.5 (calculated as Total Liabilities / Shareholders' Equity)\n- **Key Observations:** Healthy increase in both assets and equity, indicating growth and improved financial stability. The debt-to-equity ratio suggests a moderate level of debt relative to equity.\n\n---\n\n**Example 3: Cash Flow Statement Analysis**\n\n**Original Text:**\n\"For the fiscal year ended December 31, 2023, DEF Inc.'s cash flow statement shows net cash provided by operating activities of $700,000, net cash used in investing activities of $300,000, and net cash used in financing activities of $200,000. The beginning cash balance was $100,000, and the ending cash balance was $300,000.\"\n\n**Summary:**\n- **Net Cash Provided by Operating Activities:** $700,000\n- **Net Cash Used in Investing Activities:** $300,000\n- **Net Cash Used in Financing Activities:** $200,000\n- **Net Increase in Cash:** $200,000 (calculated as $700,000 - $300,000 - $200,000)\n- **Beginning Cash Balance:** $100,000\n- **Ending Cash Balance:** $300,000\n- **Key Observations:** Positive cash flow from operating activities indicates strong operational performance. The company is investing in growth while maintaining a healthy cash balance. The ending cash balance shows a significant increase, indicating improved liquidity.\n\n---\n\n**Example 4: Statement of Shareholders' Equity Analysis**\n\n**Original Text:**\n\"GHI Corporation's statement of shareholders' equity for the fiscal year ended December 31, 2023, shows common stock of $1,000,000, retained earnings of $2,000,000, and additional paid-in capital of $500,000. The previous year's retained earnings were $1,500,000.\"\n\n**Summary:**\n- **Common Stock:** $1,000,000\n- **Retained Earnings:** $2,000,000 (up from $1,500,000 in the previous year, an increase of 33.3%)\n- **Additional Paid-in Capital:** $500,000\n- **Total Shareholders' Equity:** $3,500,000\n- **Key Observations:** Significant growth in retained earnings indicates strong profitability and reinvestment in the business. The overall increase in shareholders' equity reflects the company's robust financial health and potential for future growth.\n\n---\n\nBy following this structured approach, you will be able to provide thorough and accurate analyses of financial and accounting statements, ensuring that all key metrics and insights are clearly understood.\n\n",
119 |                 "timestamp": "2024-09-06 18:02:53"
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123 |                 "content": "How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria",
124 |                 "timestamp": "2024-09-06 18:02:53"
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--------------------------------------------------------------------------------
 1 | {
 2 |     "run_id": "spreadsheet_swarm_run_2024-09-06T19:25:59.463407",
 3 |     "name": "305FightsTV-Social-Media-Swarm",
 4 |     "description": "Swarm of agents responsible for managing and promoting 305FightsTV across various social media platforms.",
 5 |     "agents": [
 6 |         "Facebook-Agent",
 7 |         "Instagram-Agent",
 8 |         "Twitter-Agent",
 9 |         "TikTok-Agent",
10 |         "YouTube-Agent",
11 |         "Event-Hosting-Agent"
12 |     ],
13 |     "start_time": "2024-09-06T19:25:59.463407",
14 |     "end_time": "2024-09-06T19:25:59.463407",
15 |     "tasks_completed": 12,
16 |     "outputs": [
17 |         {
18 |             "agent_name": "Facebook-Agent",
19 |             "task": "Create posts to advertise the upcoming fight night event: November 2nd Manuel art time theater Little Havana 6pm  ",
20 |             "result": "### Post 1: Event Announcement\n\n🗓️ **Mark Your Calendars! Fight Night is Coming!** 🥊\n\nJoin us on **November 2nd** at the **Manuel Art Time Theater** in **Little Havana** for an electrifying night of amateur fights! 🎉\n\n📍 **Location:** Manuel Art Time Theater, Little Havana  \n🕕 **Time:** 6 PM  \n\nGet ready to witness some of the fiercest fighters in Miami battle it out in the ring! Whether you're a die-hard fan or new to the fight scene, this is an event you won't want to miss! \n\n👉 **Tag your friends** and let us know who you're rooting for in the comments! \n\n🎟️ **Tickets are limited!** Grab yours now and support local talent! [Link to tickets]\n\n#305FightsTV #FightNight #MiamiFights #SupportLocalFighters\n\n---\n\n### Post 2: Fighter Spotlight\n\n🔥 **Fighter Spotlight: Meet the Warriors of November 2nd!** 🔥\n\nAs we gear up for Fight Night at the **Manuel Art Time Theater**, we want to introduce you to some of the incredible fighters who will be stepping into the ring! 🥋\n\n💪 **[Fighter Name 1]** - Known for their lightning-fast kicks and relentless spirit!  \n💪 **[Fighter Name 2]** - A grappling expert with a heart of a lion!  \n💪 **[Fighter Name 3]** - The crowd favorite with a knockout punch!\n\nWho are you most excited to see? Drop their name in the comments! 👇\n\n📅 **Don't forget:** November 2nd, 6 PM, Little Havana!  \n🎟️ **Get your tickets now!** [Link to tickets]\n\n#305FightsTV #FighterSpotlight #FightNight #MiamiFights\n\n---\n\n### Post 3: Countdown to Fight Night\n\n⏳ **Countdown to Fight Night: Just 1 Week to Go!** ⏳\n\nThe excitement is building as we approach **November 2nd**! Are you ready for an unforgettable night of action at the **Manuel Art Time Theater**? 🥊\n\n🎉 **What to Expect:**  \n- Thrilling matches  \n- Local talent showcasing their skills  \n- An electric atmosphere with fellow fight fans!  \n\n📍 **Location:** Manuel Art Time Theater, Little Havana  \n🕕 **Time:** 6 PM  \n\n👉 **Share this post** and invite your friends to join the fight community! Let’s make this a night to remember! \n\n#305FightsTV #FightNightCountdown #MiamiFights #SupportLocalFighters\n\n---\n\n### Post 4: Live Event Reminder\n\n🚨 **Fight Night is Almost Here!** 🚨\n\nJust a few days left until we take over the **Manuel Art Time Theater** in **Little Havana** for an epic night of fights! 🥋🔥\n\n📅 **Date:** November 2nd  \n🕕 **Time:** 6 PM  \n\n🎥 **Can't make it in person?** Don’t worry! We’ll be live streaming the event on our Facebook page! Stay tuned for all the action! \n\n👉 **Comment below** if you’ll be joining us live or watching from home! \n\n#305FightsTV #FightNight #LiveStreaming #MiamiFights\n\n---\n\nFeel free to customize the fighter names and ticket links as needed! Let’s get the buzz going for Fight Night! 🥊🎉",
21 |             "timestamp": "2024-09-06T19:25:59.463407"
22 |         },
23 |         {
24 |             "agent_name": "Instagram-Agent",
25 |             "task": "Create posts to advertise the upcoming fight night event: November 2nd Manuel art time theater Little Havana 6pm  ",
26 |             "result": "### Instagram Post Ideas for Upcoming Fight Night Event\n\n#### Post 1: Event Announcement\n**Image/Video:** A dynamic graphic featuring the event details with a background of fighters in action or a vibrant shot of the Manuel Art Time Theater.\n\n**Caption:**\n🥊 **Get Ready for Fight Night!** 🥊  \nJoin us on **November 2nd** at the **Manuel Art Time Theater** in Little Havana for an electrifying night of amateur fighting! 💥  \n🕕 **Time:** 6 PM  \n🎟️ **Tickets:** [Link in Bio]  \n\nTag your fight buddies and let’s make some noise! 🔥  \n#305FightsTV #FightNight #MiamiFights #AmateurFighting #LittleHavana\n\n---\n\n#### Post 2: Fighter Spotlight\n**Image/Video:** A short video clip or a carousel post featuring fighters who will be competing, with their stats and a brief bio.\n\n**Caption:**\n👊 **Fighter Spotlight: [Fighter Name]**  \nThis November 2nd, watch [Fighter Name] take the ring at the Manuel Art Time Theater!  \n💪 **Record:** [Wins-Losses]  \n🔥 **Style:** [Martial Arts Style]  \n🌟 **Why You Should Watch:** [A brief reason why this fighter is exciting to watch]\n\nDon’t miss out on the action! Grab your tickets now! 🎟️  \n#305FightsTV #FighterSpotlight #FightNight #MiamiFights\n\n---\n\n#### Post 3: Countdown to Fight Night\n**Image/Video:** A countdown graphic or a short video montage of past fights leading up to the event.\n\n**Caption:**\n⏳ **Countdown to Fight Night!** Only [X days] to go!  \nJoin us on **November 2nd** at the **Manuel Art Time Theater** for an unforgettable evening of adrenaline and excitement!  \nWho are you most excited to see? Let us know in the comments! 👇  \n#305FightsTV #Countdown #FightNight #MiamiFights\n\n---\n\n### Instagram Story Ideas\n\n#### Story 1: Event Reminder\n- **Slide 1:** “Fight Night is Coming!” (with a countdown sticker)\n- **Slide 2:** Event details: “November 2nd | Manuel Art Time Theater | 6 PM”\n- **Slide 3:** “Tag your fight crew!” (with a poll: “Who’s coming?” Yes/No)\n\n#### Story 2: Behind the Scenes\n- **Slide 1:** “Preparing for Fight Night!” (show clips of fighters training)\n- **Slide 2:** “Meet the Fighters!” (short clips of fighters introducing themselves)\n- **Slide 3:** “Get Your Tickets Now!” (swipe up link or link sticker)\n\n#### Story 3: Engagement\n- **Slide 1:** “What’s your favorite fighting style?” (poll: “MMA / Boxing / Muay Thai / Other”)\n- **Slide 2:** “Who do you think will win on November 2nd?” (question sticker for responses)\n\n### Instagram Ads\n- **Ad Type:** Carousel Ad featuring event details, fighter spotlights, and a call-to-action for ticket purchases.\n- **Target Audience:** Individuals aged 17-65 interested in martial arts, combat sports, and local events in Miami.\n\nBy utilizing these posts and stories, you can effectively promote the upcoming fight night event and engage with your audience on Instagram!",
27 |             "timestamp": "2024-09-06T19:25:59.463407"
28 |         },
29 |         {
30 |             "agent_name": "Twitter-Agent",
31 |             "task": "Create posts to advertise the upcoming fight night event: November 2nd Manuel art time theater Little Havana 6pm  ",
32 |             "result": "Sure! Here are some engaging tweets to promote the upcoming fight night event on November 2nd at the Manuel Art Time Theater in Little Havana:\n\n---\n\n**Tweet 1:**\n🥊🔥 Get ready, fight fans! The most electrifying night of amateur fighting is coming to Little Havana! Join us on November 2nd at the Manuel Art Time Theater for an unforgettable evening of action! 🎉💥 #305FightsTV #FightNight\n\n---\n\n**Tweet 2:**\n📅 Mark your calendars! 🗓️ November 2nd is the date you don’t want to miss! Witness the best amateur fighters battle it out at the Manuel Art Time Theater in Little Havana! Doors open at 6 PM! 🥋👊 #305FightsTV #MiamiFights\n\n---\n\n**Tweet 3:**\n💥 Who's ready for some adrenaline-pumping action? Join us on November 2nd at the Manuel Art Time Theater for an epic fight night! 🥊 Tag your fight buddies and let’s make some noise! 🙌 #305FightsTV #FightNight #LittleHavana\n\n---\n\n**Tweet 4 (Poll):**\n🤔 Who are you rooting for at the upcoming fight night on November 2nd? \n1️⃣ Team A \n2️⃣ Team B \n3️⃣ Team C \n4️⃣ I’m just here for the action! \nVote and let us know! 🥊💪 #305FightsTV #FightNight\n\n---\n\n**Tweet 5 (Thread):**\n🎉 Excitement is building for our Fight Night on November 2nd! Here’s what you need to know: \n1️⃣ Location: Manuel Art Time Theater, Little Havana \n2️⃣ Time: 6 PM \n3️⃣ Tickets: [Link to purchase] \nStay tuned for fighter highlights and more! 🥋🔥 #305FightsTV #MiamiFights\n\n---\n\n**Tweet 6 (Live Tweet Reminder):**\n🚨 Live Tweet Alert! 🚨 Join us on November 2nd as we bring you real-time updates from the fight night at Manuel Art Time Theater! Follow along for exclusive behind-the-scenes content and fighter interviews! 🥊📲 #305FightsTV #FightNight\n\n---\n\nFeel free to adjust any details or add links as necessary! Let’s get the buzz going for this exciting event! 🎉",
33 |             "timestamp": "2024-09-06T19:25:59.463407"
34 |         },
35 |         {
36 |             "agent_name": "TikTok-Agent",
37 |             "task": "Create posts to advertise the upcoming fight night event: November 2nd Manuel art time theater Little Havana 6pm  ",
38 |             "result": "Sure! Here are some engaging TikTok post ideas to promote the upcoming fight night event on November 2nd at the Manuel Art Time Theater in Little Havana:\n\n### Post Idea 1: Countdown Challenge\n**Video Concept:** Create a countdown series leading up to the event. Start with a video of you or a fighter doing a quick training montage, then overlay text that says, \"Only [X] days until Fight Night!\" \n\n**Caption:** \"The countdown is on! 🥊 Are you ready for the ultimate showdown? Join us on November 2nd at the Manuel Art Time Theater! #305FightsTV #FightNight #Countdown\"\n\n---\n\n### Post Idea 2: Fighter Spotlight\n**Video Concept:** Feature short clips of the fighters who will be competing. Include their training routines, quick interviews, and highlight their skills. \n\n**Caption:** \"Meet the fighters who will be throwing down on November 2nd! 💥 Who are you rooting for? #305FightsTV #FighterSpotlight #LittleHavana\"\n\n---\n\n### Post Idea 3: Event Teaser\n**Video Concept:** Create a dynamic teaser video with clips from previous fights, audience reactions, and the venue. Use trending music to enhance the excitement.\n\n**Caption:** \"Get ready for an unforgettable night of action! 🎉 Join us on November 2nd at the Manuel Art Time Theater for an epic fight night! Tag your fight buddies! #305FightsTV #FightNight #Miami\"\n\n---\n\n### Post Idea 4: Behind-the-Scenes\n**Video Concept:** Share behind-the-scenes footage of the event setup, fighter weigh-ins, or training sessions. \n\n**Caption:** \"Behind the scenes of Fight Night preparations! 🥋✨ Don’t miss the action on November 2nd! #305FightsTV #BehindTheScenes #FightNight\"\n\n---\n\n### Post Idea 5: Duet Challenge\n**Video Concept:** Start a duet challenge where followers can show off their best fighting moves or techniques. \n\n**Caption:** \"Show us your best moves! 💪 Duet this video and tag us for a chance to be featured at Fight Night! Join us on November 2nd! #305FightsTV #DuetChallenge #FightNight\"\n\n---\n\n### Post Idea 6: Event Details\n**Video Concept:** Create a simple yet eye-catching video with the event details: date, time, location, and ticket info. Use bold text and energetic music.\n\n**Caption:** \"Mark your calendars! 📅 Fight Night is happening on November 2nd at the Manuel Art Time Theater! Get your tickets now! #305FightsTV #EventDetails #MiamiFights\"\n\n---\n\n### Post Idea 7: Fan Engagement\n**Video Concept:** Ask your followers to comment on who they think will win the upcoming fights. Use a poll format with visuals of the fighters.\n\n**Caption:** \"Who do you think will take the win on November 2nd? 🤔 Comment below! #305FightsTV #FightNight #Poll\"\n\n---\n\n### Additional Tips:\n- Use relevant hashtags to increase visibility.\n- Engage with comments and encourage followers to share the posts.\n- Consider running TikTok Ads to boost the reach of these posts leading up to the event.\n\nLet me know if you need more ideas or specific content!",
39 |             "timestamp": "2024-09-06T19:25:59.463407"
40 |         },
41 |         {
42 |             "agent_name": "YouTube-Agent",
43 |             "task": "Create posts to advertise the upcoming fight night event: November 2nd Manuel art time theater Little Havana 6pm  ",
44 |             "result": "### Post 1: Event Announcement\n\n🎉 **Get Ready for Fight Night!** 🎉\n\nJoin us on **November 2nd** at the **Manuel Art Time Theater** in **Little Havana** for an electrifying night of amateur fighting! 🥊🔥\n\n🗓 **Date:** November 2nd  \n🕕 **Time:** 6 PM  \n📍 **Location:** Manuel Art Time Theater, Little Havana\n\nWitness the best local fighters go head-to-head in an unforgettable showdown! Whether you're a die-hard martial arts fan or just looking for an exciting night out, this is an event you won't want to miss!\n\n👉 **Get your tickets now!** [Link to ticket purchase]\n\nStay tuned for more updates and fighter highlights leading up to the event! 💪\n\n#305FightsTV #FightNight #MiamiFights #AmateurFighting #LittleHavana\n\n---\n\n### Post 2: Countdown to Fight Night\n\n⏳ **Countdown to Fight Night!** ⏳\n\nOnly **[X days]** left until the action kicks off at the **Manuel Art Time Theater** in **Little Havana**! 🥋💥\n\n📅 **Date:** November 2nd  \n🕕 **Time:** 6 PM\n\nGet ready to cheer for your favorite fighters as they battle it out for glory! 🏆\n\n🎟 **Tickets are selling fast!** Don’t miss your chance to be part of this thrilling event! [Link to ticket purchase]\n\nWho are you excited to see in the ring? Let us know in the comments! 👇\n\n#305FightsTV #FightNight #MiamiFights #CountdownToFightNight\n\n---\n\n### Post 3: Fighter Spotlight\n\n🌟 **Fighter Spotlight: [Fighter Name]** 🌟\n\nAs we gear up for Fight Night on **November 2nd**, we’re excited to highlight one of our fierce competitors, **[Fighter Name]**! 🥊\n\n📅 **Event:** Fight Night  \n🕕 **Date & Time:** November 2nd, 6 PM  \n📍 **Location:** Manuel Art Time Theater, Little Havana\n\n[Fighter Name] has been training hard and is ready to bring the heat! 🔥\n\n👉 **Get your tickets now!** [Link to ticket purchase]\n\nWho do you think will take home the win? Comment below! 👇\n\n#305FightsTV #FighterSpotlight #FightNight #MiamiFights\n\n---\n\n### Post 4: Live Stream Announcement\n\n📺 **Can’t Make It to Fight Night?** We’ve Got You Covered! 📺\n\nIf you can’t join us in person on **November 2nd**, don’t worry! We’ll be live streaming the event on our YouTube channel! 🎥✨\n\n🗓 **Date:** November 2nd  \n🕕 **Time:** 6 PM  \n📍 **Location:** Manuel Art Time Theater, Little Havana\n\nSubscribe to **305FightsTV** and turn on notifications so you don’t miss a moment of the action! 🔔\n\n👉 **Watch live here:** [Link to YouTube channel]\n\nSpread the word and let’s make this Fight Night unforgettable! 💪\n\n#305FightsTV #LiveStream #FightNight #MiamiFights\n\n---\n\nFeel free to customize the posts with specific fighter names, ticket links, and any additional details you want to include!",
45 |             "timestamp": "2024-09-06T19:25:59.463407"
46 |         },
47 |         {
48 |             "agent_name": "Event-Hosting-Agent",
49 |             "task": "Create posts to advertise the upcoming fight night event: November 2nd Manuel art time theater Little Havana 6pm  ",
50 |             "result": "### Post 1: Event Announcement\n\n🚨 **Fight Night Alert!** 🚨\n\nGet ready for an electrifying evening of amateur fighting action! Join us on **November 2nd** at the **Manuel Artime Theater** in **Little Havana** for an unforgettable night of skill, passion, and competition! 🥋💥\n\n🗓 **Date:** November 2nd  \n🕕 **Time:** 6 PM  \n📍 **Location:** Manuel Artime Theater, Little Havana\n\nWhether you're a fighter or a fan, this is the event you won't want to miss! Come support your favorite fighters and witness some incredible matchups. \n\n🎟 **Tickets are on sale now!** Grab yours before they sell out! \n\n👉 Follow us for updates and sneak peeks of the fighters!  \n#305FightsTV #FightNight #AmateurFighting #MiamiEvents\n\n---\n\n### Post 2: Fighter Spotlight\n\n🔥 **Fighter Spotlight!** 🔥\n\nAs we gear up for Fight Night on **November 2nd**, we’re excited to introduce some of the incredible fighters who will be stepping into the ring at the **Manuel Artime Theater**! 🥊\n\n💪 **Meet [Fighter Name]** - Known for their explosive style and relentless determination, [Fighter Name] is ready to make their mark! \n\n📅 **Date:** November 2nd  \n🕕 **Time:** 6 PM  \n📍 **Location:** Manuel Artime Theater, Little Havana\n\nWho are you rooting for? Let us know in the comments! \n\n🎟 **Get your tickets now and be part of the action!**  \n#305FightsTV #FighterSpotlight #SupportLocalFighters\n\n---\n\n### Post 3: Countdown to Fight Night\n\n⏳ **Countdown to Fight Night!** ⏳\n\nOnly **[X days]** left until the most anticipated amateur fighting event in Miami! Join us on **November 2nd** at the **Manuel Artime Theater** for an evening filled with adrenaline and excitement! \n\n🗓 **Date:** November 2nd  \n🕕 **Time:** 6 PM  \n📍 **Location:** Manuel Artime Theater, Little Havana\n\nBring your friends and family to cheer on our talented fighters! Let’s make this a night to remember! \n\n🎟 **Tickets available now!** Don’t miss out!  \n#305FightsTV #CountdownToFightNight #MiamiFights\n\n---\n\n### Post 4: Community Engagement\n\n🤼‍♂️ **Calling All Fighting Enthusiasts!** 🤼‍♀️\n\nAre you ready to witness the best amateur fighters in Miami? Join us on **November 2nd** at the **Manuel Artime Theater** for an action-packed Fight Night! \n\n🗓 **Date:** November 2nd  \n🕕 **Time:** 6 PM  \n📍 **Location:** Manuel Artime Theater, Little Havana\n\nWe want to hear from YOU! What are you most excited to see at the event? Comment below and let’s get the conversation going! \n\n🎟 **Secure your tickets today!**  \n#305FightsTV #FightNight #MiamiFightingCommunity\n\n---\n\nFeel free to customize the fighter names and any other details as needed!",
51 |             "timestamp": "2024-09-06T19:25:59.463407"
52 |         },
53 |         {
54 |             "agent_name": "Facebook-Agent",
55 |             "task": "Create posts to advertise the upcoming fight night event: November 2nd Manuel art time theater Little Havana 6pm  ",
56 |             "result": "### Post 1: Event Announcement\n\n🎉 **Get Ready for Fight Night!** 🥊\n\nJoin us on **November 2nd** at the **Manuel Art Time Theater** in **Little Havana** for an unforgettable evening of amateur fights! \n\n📍 **Location:** Manuel Art Time Theater, Little Havana  \n🕕 **Time:** 6 PM  \n\nWitness the passion and skill of Miami's finest fighters as they battle it out in the ring! Whether you're a seasoned fan or new to the fight scene, this is the event for you! \n\n👉 **Tag your fight buddies** and let us know who you’re cheering for in the comments! \n\n🎟️ **Limited tickets available!** Don’t miss out—grab yours now! [Link to tickets]\n\n#305FightsTV #FightNight #MiamiFights #SupportLocalFighters\n\n---\n\n### Post 2: Meet the Fighters\n\n🔥 **Fight Night is Almost Here! Meet the Fighters!** 🔥\n\nAs we gear up for the big night on **November 2nd**, let’s shine a spotlight on some of the incredible fighters who will be showcasing their skills at the **Manuel Art Time Theater**! 🥋\n\n💪 **[Fighter Name 1]** - Known for their explosive energy and powerful strikes!  \n💪 **[Fighter Name 2]** - A tactical genius with a knack for submissions!  \n💪 **[Fighter Name 3]** - The crowd favorite with a reputation for thrilling knockouts!\n\nWho are you most excited to see in action? Drop their name in the comments! 👇\n\n📅 **Don’t forget:** November 2nd, 6 PM, Little Havana!  \n🎟️ **Get your tickets now!** [Link to tickets]\n\n#305FightsTV #MeetTheFighters #FightNight #MiamiFights\n\n---\n\n### Post 3: One Week Countdown\n\n⏳ **One Week Until Fight Night!** ⏳\n\nThe countdown is on! Just **7 days** until we light up the **Manuel Art Time Theater** with an action-packed night of fights! 🥊\n\n🎉 **What to Expect:**  \n- Intense matchups  \n- Local talent showcasing their skills  \n- An electric atmosphere with fellow fight enthusiasts!  \n\n📍 **Location:** Manuel Art Time Theater, Little Havana  \n🕕 **Time:** 6 PM  \n\n👉 **Share this post** and invite your friends to join the fight community! Let’s make this a night to remember! \n\n#305FightsTV #FightNightCountdown #MiamiFights #SupportLocalFighters\n\n---\n\n### Post 4: Live Streaming Announcement\n\n🚨 **Fight Night is Almost Here!** 🚨\n\nJust a few days left until we take over the **Manuel Art Time Theater** in **Little Havana** for an epic night of fights! 🥋🔥\n\n📅 **Date:** November 2nd  \n🕕 **Time:** 6 PM  \n\n🎥 **Can’t make it in person?** No worries! We’ll be live streaming the event on our Facebook page! Stay tuned for all the action! \n\n👉 **Comment below** if you’ll be joining us live or watching from home! \n\n#305FightsTV #FightNight #LiveStreaming #MiamiFights\n\n---\n\nFeel free to adjust fighter names and ticket links as necessary! Let’s create excitement for Fight Night! 🥊🎉",
57 |             "timestamp": "2024-09-06T19:25:59.463407"
58 |         },
59 |         {
60 |             "agent_name": "Instagram-Agent",
61 |             "task": "Create posts to advertise the upcoming fight night event: November 2nd Manuel art time theater Little Havana 6pm  ",
62 |             "result": "### Instagram Post Ideas for Upcoming Fight Night Event\n\n#### Post 1: Event Announcement\n**Image/Video:** A bold graphic featuring the event details with an action-packed background of fighters in the ring.\n\n**Caption:**\n🥊 **Fight Night is Here!** 🥊  \nMark your calendars for **November 2nd** at the **Manuel Art Time Theater** in Little Havana! Get ready for an adrenaline-fueled evening of amateur fighting! 💥  \n🕕 **Time:** 6 PM  \n🎟️ **Tickets:** [Link in Bio]  \n\nBring your friends and let’s support our fighters! Who are you rooting for? 🔥  \n#305FightsTV #FightNight #MiamiFights #AmateurFighting #LittleHavana\n\n---\n\n#### Post 2: Meet the Fighters\n**Image/Video:** A carousel post featuring headshots of the fighters with their names and a brief highlight of their fighting styles.\n\n**Caption:**\n👊 **Meet the Fighters!**  \nGet ready to cheer for these incredible athletes on **November 2nd**!  \n💪 **[Fighter Name 1]** - [Fighting Style]  \n💪 **[Fighter Name 2]** - [Fighting Style]  \n💪 **[Fighter Name 3]** - [Fighting Style]  \n\nWho are you most excited to see in action? Let us know in the comments! 🎉  \n#305FightsTV #MeetTheFighters #FightNight #MiamiFights\n\n---\n\n#### Post 3: Event Countdown\n**Image/Video:** A countdown graphic with a dynamic video montage of previous fights.\n\n**Caption:**\n⏳ **The Countdown Begins!**  \nOnly [X days] until Fight Night at the **Manuel Art Time Theater**!  \nJoin us on **November 2nd** for an unforgettable night of action!  \nTag your fight crew and let’s get hyped! 💥  \n#305FightsTV #CountdownToFightNight #MiamiFights\n\n---\n\n### Instagram Story Ideas\n\n#### Story 1: Event Details\n- **Slide 1:** “Fight Night is Almost Here!” (with a countdown sticker)\n- **Slide 2:** Event details: “November 2nd | Manuel Art Time Theater | 6 PM”\n- **Slide 3:** “Get Your Tickets Now!” (swipe up link or link sticker)\n\n#### Story 2: Fighter Previews\n- **Slide 1:** “Meet Our Fighters!” (show clips of fighters training)\n- **Slide 2:** “Who’s Ready to Rumble?” (short clips of fighters hyping up)\n- **Slide 3:** “Don’t Miss Out!” (reminder to grab tickets)\n\n#### Story 3: Engagement Poll\n- **Slide 1:** “What’s Your Favorite Fighting Style?” (poll: “MMA / Boxing / Kickboxing”)\n- **Slide 2:** “Who Do You Think Will Win?” (question sticker for responses)\n\n### Instagram Ads\n- **Ad Type:** Video Ad showcasing highlights from previous events, fighter introductions, and event details.\n- **Target Audience:** Individuals aged 17-65 interested in martial arts, combat sports, and local Miami events.\n\nBy implementing these posts and stories, you can effectively create buzz around the upcoming fight night event and engage your audience on Instagram!",
63 |             "timestamp": "2024-09-06T19:25:59.463407"
64 |         },
65 |         {
66 |             "agent_name": "Twitter-Agent",
67 |             "task": "Create posts to advertise the upcoming fight night event: November 2nd Manuel art time theater Little Havana 6pm  ",
68 |             "result": "Absolutely! Here are some fresh tweets to promote the upcoming fight night event on November 2nd at the Manuel Art Time Theater in Little Havana:\n\n---\n\n**Tweet 1:**\n🥊💥 Fight fans, are you ready? The ultimate showdown is happening on November 2nd at the Manuel Art Time Theater in Little Havana! Join us at 6 PM for an unforgettable night of amateur fighting! 🎉 #305FightsTV #FightNight\n\n---\n\n**Tweet 2:**\n📣 Calling all fight enthusiasts! 📅 November 2nd is your chance to witness the fiercest amateur fighters in action! Don’t miss out at the Manuel Art Time Theater, doors open at 6 PM! 🥋👊 #305FightsTV #MiamiFights\n\n---\n\n**Tweet 3:**\n🔥 The countdown is on! 🔥 Join us for an electrifying fight night on November 2nd at the Manuel Art Time Theater! Bring your friends and let’s cheer for our favorite fighters! 🙌🥊 #305FightsTV #FightNight #LittleHavana\n\n---\n\n**Tweet 4 (Poll):**\n🤔 Who are you most excited to see fight on November 2nd? \n1️⃣ Fighter A \n2️⃣ Fighter B \n3️⃣ Fighter C \n4️⃣ Just here for the thrill! \nCast your vote! 🥊💪 #305FightsTV #FightNight\n\n---\n\n**Tweet 5 (Thread):**\n🎊 Fight Night is almost here! Here’s what you need to know for November 2nd: \n1️⃣ 📍 Location: Manuel Art Time Theater, Little Havana \n2️⃣ ⏰ Time: 6 PM \n3️⃣ 🎟️ Tickets: [Link to purchase] \nStay tuned for fighter spotlights and event updates! 🥋🔥 #305FightsTV #MiamiFights\n\n---\n\n**Tweet 6 (Live Tweet Reminder):**\n🚨 Get ready for LIVE updates! 🚨 On November 2nd, follow us for real-time action from the fight night at Manuel Art Time Theater! Exclusive content and fighter interviews await! 🥊📲 #305FightsTV #FightNight\n\n---\n\nFeel free to customize these tweets with any additional details or links! Let’s create some excitement for this amazing event! 🎉",
69 |             "timestamp": "2024-09-06T19:25:59.463407"
70 |         },
71 |         {
72 |             "agent_name": "TikTok-Agent",
73 |             "task": "Create posts to advertise the upcoming fight night event: November 2nd Manuel art time theater Little Havana 6pm  ",
74 |             "result": "Absolutely! Here are some tailored TikTok post ideas specifically for the upcoming fight night event on November 2nd at the Manuel Art Time Theater in Little Havana at 6 PM:\n\n### Post Idea 1: Event Announcement\n**Video Concept:** Create a visually striking video with clips of past fights, the venue, and a countdown timer. Use upbeat music to build excitement.\n\n**Caption:** \"🔥 Fight Night is almost here! Join us on November 2nd at the Manuel Art Time Theater in Little Havana at 6 PM! Don’t miss the action! #305FightsTV #FightNight #Miami\"\n\n---\n\n### Post Idea 2: Meet the Fighters\n**Video Concept:** Feature short clips of the fighters who will be competing, showcasing their skills and personalities. Include a quick intro for each fighter.\n\n**Caption:** \"Meet the warriors stepping into the ring on November 2nd! 💪 Who are you cheering for? #305FightsTV #MeetTheFighters #LittleHavana\"\n\n---\n\n### Post Idea 3: Venue Highlight\n**Video Concept:** Showcase the Manuel Art Time Theater with a quick tour of the venue, highlighting its atmosphere and seating arrangements.\n\n**Caption:** \"Check out the venue for Fight Night! 🏟️ Join us at the Manuel Art Time Theater on November 2nd at 6 PM! Get your tickets now! #305FightsTV #VenueHighlight #FightNight\"\n\n---\n\n### Post Idea 4: Training Montage\n**Video Concept:** Compile a montage of fighters training for the event, showing their dedication and hard work.\n\n**Caption:** \"The grind never stops! 💥 Watch our fighters prepare for the ultimate showdown on November 2nd! #305FightsTV #Training #FightNight\"\n\n---\n\n### Post Idea 5: Ticket Reminder\n**Video Concept:** Create a fun and engaging video reminding fans to get their tickets, using eye-catching graphics and animations.\n\n**Caption:** \"Have you got your tickets yet? 🎟️ Don’t miss out on Fight Night on November 2nd! Get yours now! #305FightsTV #TicketReminder #MiamiFights\"\n\n---\n\n### Post Idea 6: Fan Reactions\n**Video Concept:** Share clips of fans from previous events cheering and reacting to the fights. \n\n**Caption:** \"The energy is electric! ⚡️ Be part of the excitement on November 2nd at the Manuel Art Time Theater! #305FightsTV #FanReactions #FightNight\"\n\n---\n\n### Post Idea 7: Challenge Your Followers\n**Video Concept:** Create a challenge for your followers to show their best fight moves or techniques, encouraging them to tag you.\n\n**Caption:** \"Think you’ve got what it takes? 💥 Show us your best moves and tag us! Join us for Fight Night on November 2nd! #305FightsTV #Challenge #FightNight\"\n\n---\n\n### Post Idea 8: Last-Minute Hype\n**Video Concept:** As the event approaches, create a hype video with fast cuts of fighters, the crowd, and the venue, building anticipation.\n\n**Caption:** \"Just days away from the action! Are you ready for Fight Night on November 2nd? Let’s go! 🔥 #305FightsTV #Hype #FightNight\"\n\n---\n\n### Additional Tips:\n- Use trending sounds and effects to enhance your videos.\n- Engage with your audience by responding to comments and encouraging shares.\n- Utilize TikTok Ads to promote these posts for greater reach.\n\nLet me know if you need further assistance or more ideas!",
75 |             "timestamp": "2024-09-06T19:25:59.463407"
76 |         },
77 |         {
78 |             "agent_name": "YouTube-Agent",
79 |             "task": "Create posts to advertise the upcoming fight night event: November 2nd Manuel art time theater Little Havana 6pm  ",
80 |             "result": "### Post 1: Event Announcement\n\n🎉 **Fight Night is Coming to Little Havana!** 🎉\n\nMark your calendars for **November 2nd** at the **Manuel Art Time Theater**! Get ready for an adrenaline-pumping evening of amateur fighting starting at **6 PM**! 🥊🔥\n\n📅 **Date:** November 2nd  \n🕕 **Time:** 6 PM  \n📍 **Location:** Manuel Art Time Theater, Little Havana\n\nJoin us as local fighters showcase their skills in an unforgettable showdown! Whether you're a martial arts enthusiast or just looking for an exciting night out, this is the event for you!\n\n👉 **Grab your tickets now!** [Link to ticket purchase]\n\nStay tuned for fighter highlights and event updates leading up to the big night! 💪\n\n#305FightsTV #FightNight #MiamiFights #LittleHavana\n\n---\n\n### Post 2: Countdown to Fight Night\n\n⏳ **The Countdown is On!** ⏳\n\nOnly **[X days]** left until the action begins at the **Manuel Art Time Theater** in **Little Havana**! 🥋💥\n\n📅 **Date:** November 2nd  \n🕕 **Time:** 6 PM\n\nGet ready to support your favorite fighters as they battle it out for victory! 🏆\n\n🎟 **Tickets are going fast!** Don’t miss your chance to be part of this thrilling event! [Link to ticket purchase]\n\nWho are you most excited to see in the ring? Let us know in the comments! 👇\n\n#305FightsTV #FightNight #MiamiFights #CountdownToFightNight\n\n---\n\n### Post 3: Fighter Spotlight\n\n🌟 **Fighter Spotlight: [Fighter Name]** 🌟\n\nAs we gear up for Fight Night on **November 2nd**, we’re thrilled to introduce one of our fierce competitors, **[Fighter Name]**! 🥊\n\n📅 **Event:** Fight Night  \n🕕 **Date & Time:** November 2nd, 6 PM  \n📍 **Location:** Manuel Art Time Theater, Little Havana\n\n[Fighter Name] has been training hard and is ready to bring the heat! 🔥\n\n👉 **Get your tickets now!** [Link to ticket purchase]\n\nWho do you think will take home the win? Share your thoughts below! 👇\n\n#305FightsTV #FighterSpotlight #FightNight #MiamiFights\n\n---\n\n### Post 4: Live Stream Announcement\n\n📺 **Can’t Make It to Fight Night? We’ve Got You Covered!** 📺\n\nIf you can’t join us in person on **November 2nd**, don’t worry! We’ll be live streaming the event on our YouTube channel! 🎥✨\n\n🗓 **Date:** November 2nd  \n🕕 **Time:** 6 PM  \n📍 **Location:** Manuel Art Time Theater, Little Havana\n\nSubscribe to **305FightsTV** and turn on notifications so you don’t miss a moment of the action! 🔔\n\n👉 **Watch live here:** [Link to YouTube channel]\n\nSpread the word and let’s make this Fight Night unforgettable! 💪\n\n#305FightsTV #LiveStream #FightNight #MiamiFights\n\n---\n\nFeel free to fill in the specific fighter names, ticket links, and any additional details you want to include!",
81 |             "timestamp": "2024-09-06T19:25:59.463407"
82 |         },
83 |         {
84 |             "agent_name": "Event-Hosting-Agent",
85 |             "task": "Create posts to advertise the upcoming fight night event: November 2nd Manuel art time theater Little Havana 6pm  ",
86 |             "result": "### Post 1: Event Announcement\n\n🚨 **Fight Night is Coming!** 🚨\n\nMark your calendars for an action-packed evening! Join us on **November 2nd** at the **Manuel Artime Theater** in **Little Havana** for an unforgettable night of amateur fighting! 🥋💥\n\n🗓 **Date:** November 2nd  \n🕕 **Time:** 6 PM  \n📍 **Location:** Manuel Artime Theater, Little Havana\n\nWhether you're a fighter or a fan, this is the event you can't afford to miss! Come out to support your favorite fighters and witness some thrilling matchups!\n\n🎟 **Tickets are on sale now!** Don’t wait—grab yours before they’re gone!\n\n👉 Follow us for updates and exclusive fighter previews!  \n#305FightsTV #FightNight #AmateurFighting #MiamiEvents\n\n---\n\n### Post 2: Meet the Fighters\n\n🔥 **Get Ready to Meet the Fighters!** 🔥\n\nAs we gear up for Fight Night on **November 2nd**, we’re thrilled to introduce some of the talented fighters who will be stepping into the ring at the **Manuel Artime Theater**! 🥊\n\n💪 **Fighter Spotlight: [Fighter Name]** - Known for their incredible technique and heart, [Fighter Name] is ready to showcase their skills!\n\n📅 **Date:** November 2nd  \n🕕 **Time:** 6 PM  \n📍 **Location:** Manuel Artime Theater, Little Havana\n\nWho are you excited to see? Let us know in the comments! \n\n🎟 **Get your tickets now and be part of the action!**  \n#305FightsTV #FighterSpotlight #SupportLocalFighters\n\n---\n\n### Post 3: Countdown to Fight Night\n\n⏳ **The Countdown is On!** ⏳\n\nOnly **[X days]** left until the most thrilling amateur fighting event in Miami! Join us on **November 2nd** at the **Manuel Artime Theater** for an evening of adrenaline and excitement!\n\n🗓 **Date:** November 2nd  \n🕕 **Time:** 6 PM  \n📍 **Location:** Manuel Artime Theater, Little Havana\n\nBring your friends and family to cheer on our amazing fighters! Let’s make this a night to remember!\n\n🎟 **Tickets available now!** Don’t miss out!  \n#305FightsTV #CountdownToFightNight #MiamiFights\n\n---\n\n### Post 4: Community Engagement\n\n🤼‍♂️ **Attention Fighting Fans!** 🤼‍♀️\n\nAre you ready for an epic night of amateur fighting? Join us on **November 2nd** at the **Manuel Artime Theater** for an action-packed Fight Night!\n\n🗓 **Date:** November 2nd  \n🕕 **Time:** 6 PM  \n📍 **Location:** Manuel Artime Theater, Little Havana\n\nWe want to hear from YOU! What matchups are you most excited about? Share your thoughts in the comments below!\n\n🎟 **Secure your tickets today!**  \n#305FightsTV #FightNight #MiamiFightingCommunity\n\n---\n\nFeel free to adjust any details or add specific fighter names as needed!",
87 |             "timestamp": "2024-09-06T19:25:59.463407"
88 |         }
89 |     ]
90 | }


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/cookbook/workshops/sep_6_workshop/agent_workspace/error.txt:
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https://raw.githubusercontent.com/The-Swarm-Corporation/Cookbook/cae46162fe4bb0455bd6c29bba8a0fd2acd23207/cookbook/workshops/sep_6_workshop/agent_workspace/error.txt


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/cookbook/workshops/sep_6_workshop/art_therapy_agent.py:
--------------------------------------------------------------------------------
 1 | import os
 2 | from swarms import OpenAIChat, Agent
 3 | from dotenv import load_dotenv
 4 | 
 5 | load_dotenv()
 6 | 
 7 | api_key = os.getenv("OPENAI_API_KEY")
 8 | # .env OPENAI_API_KEY="sk-"
 9 | 
10 | 
11 | ART_THERAPY_AGENT_SYS_PROMPT = """
12 | ### **System Prompt: Warm Outreach for Art Therapy**
13 | 
14 | **Instructions:**
15 | 
16 | Your task is to craft warm and inviting messages for Instagram outreach, asking people if they know anyone who could benefit from art therapy. Always maintain a gentle, kind, and empathetic tone. Make the recipient feel valued, and avoid being pushy or sales-driven. The goal is to build trust and evoke a feeling of care and support.
17 | The call to action is to book a call via through calendly
18 | 
19 | ### **Rules for Crafting the Message:**
20 | 1. **Be Kind and Warm:** Always use warm, friendly language. Avoid any terms that could sound clinical or impersonal.
21 | 2. **Respect Boundaries:** Avoid being intrusive. Be subtle in your request, focusing more on offering help rather than pushing for immediate responses.
22 | 3. **Gratitude and Respect:** Always thank the recipient for their time and consideration, regardless of their response.
23 | 4. **Create Connection:** Where possible, start with something personable or friendly to establish rapport, such as acknowledging a common interest or complimenting something from their profile.
24 | 5. **Empathy First:** Show understanding that not everyone may need art therapy, but offer it as a resource that could help others they know.
25 | 6. **Be Short and Concise:** Keep the message brief but meaningful. Don’t overwhelm the recipient with too much information upfront.
26 | 
27 | ### **Examples of Warm Instagram Outreach Messages:**
28 | 
29 | **Example 1:**
30 | Hi [Name],  
31 | I hope you're doing well! I noticed you have a beautiful and creative spirit, and I wanted to ask if you know anyone who could benefit from art therapy. It’s a wonderful way for people to express emotions and find peace, especially in challenging times. If you think it could help someone you know, I’d love to chat more.  
32 | Thank you so much for taking the time to read this, and have a lovely day! 🌸
33 | 
34 | **Example 2:**
35 | Hey [Name],  
36 | I came across your profile and just wanted to say how inspiring your content is! I’m reaching out because I work with art therapy, and it’s been an amazing tool for helping people manage stress and emotions through creativity. If you know anyone who might find it helpful, I’d be more than happy to share more information.  
37 | Wishing you a wonderful day, and thank you for your time!
38 | 
39 | **Example 3:**
40 | Hello [Name],  
41 | I hope this message finds you well! I wanted to reach out because I work with art therapy, which can be a powerful way to help people explore their emotions and heal through creative expression. I was wondering if you might know someone who could benefit from this?  
42 | No pressure at all, but I’d be happy to provide more details if you think it could help someone in your circle. Wishing you all the best, and thank you for considering! 😊
43 | 
44 | **Example 4:**
45 | Hi [Name],  
46 | I hope you're having a fantastic day! I’m reaching out to see if you know anyone who could use the healing power of art therapy. It’s been such a meaningful resource for people going through difficult times, and I believe it can make a real difference.  
47 | Please let me know if anyone comes to mind, and thank you for taking a moment to read this! 💖
48 | 
49 | **Example 5:**
50 | Hello [Name],  
51 | I was really moved by your creativity and positive energy, so I wanted to take a moment to ask if you know anyone who might be interested in art therapy. It’s an incredible way for people to process emotions and find peace through creative expression.  
52 | No worries if not, but if you do, feel free to reach out anytime! I truly appreciate your time, and hope you have a wonderful day. 😊
53 | 
54 | """
55 | 
56 | model = OpenAIChat(
57 |     model_name="gpt-4o-mini", openai_api_key=api_key, max_tokens=4000, temperature=0.1
58 | )
59 | 
60 | agent = Agent(
61 |     agent_name="Art Therapy Agent",
62 |     system_prompt=ART_THERAPY_AGENT_SYS_PROMPT,
63 |     llm=model,
64 |     max_loops=1,
65 |     dashboard=False,
66 |     stopping_token="<DONE>",
67 |     # tools = [send_instagram_dm],
68 | )
69 | 
70 | 
71 | # Run the agent
72 | out = agent.run(
73 |     "Jasmine: told me that you're offering art therapy, I wanted to try it out:"
74 | )
75 | print(out)
76 | 


--------------------------------------------------------------------------------
/cookbook/workshops/sep_6_workshop/data.csv:
--------------------------------------------------------------------------------
 1 | Vendor,Service,Expense ($),Month
 2 | Amazon Web Services,Payments,4632.83,June
 3 | Google Cloud,Collaboration,1581.25,August
 4 | Microsoft Azure,Cloud Services,460.21,February
 5 | HubSpot,Accounting,1804.32,January
 6 | Slack,Collaboration,3137.03,July
 7 | Stripe,Ads,2592.29,May
 8 | Twilio,Marketing,818.5,March
 9 | Zoom,Marketing,3105.0,April
10 | Trello,Marketing,2986.18,January
11 | GitHub,Collaboration,1958.86,May
12 | LinkedIn Ads,Collaboration,797.92,September
13 | Google Ads,Cloud Services,2862.14,April
14 | Facebook Ads,Accounting,2326.0,August
15 | Mailchimp,Cloud Services,3735.74,April
16 | Salesforce,Cloud Services,2828.26,April
17 | Notion,Accounting,4481.65,September
18 | QuickBooks,Accounting,464.77,May
19 | Zapier,Marketing,1478.0,June
20 | DocuSign,Ads,3267.59,October
21 | PayPal,CRM,3294.24,February
22 | 


--------------------------------------------------------------------------------
/cookbook/workshops/sep_6_workshop/discord_tool.py:
--------------------------------------------------------------------------------
 1 | import os
 2 | import requests
 3 | from dotenv import load_dotenv
 4 | 
 5 | # Load environment variables from .env file
 6 | load_dotenv()
 7 | 
 8 | # Fetch bot token and channel ID from environment variables
 9 | TOKEN = os.getenv("DISCORD_BOT_TOKEN")
10 | CHANNEL_ID = os.getenv("DISCORD_CHANNEL_ID")
11 | 
12 | DISCORD_API_URL = "https://discord.com/api/v10"
13 | 
14 | 
15 | def send_discord_message(message: str) -> None:
16 |     """
17 |     Sends a message to a Discord channel using Discord's HTTP API.
18 | 
19 |     Args:
20 |         message (str): The message to send to the Discord channel.
21 | 
22 |     Raises:
23 |         ValueError: If the channel ID or message is invalid.
24 |         requests.exceptions.RequestException: If an issue occurs while making the HTTP request.
25 |     """
26 |     if not TOKEN:
27 |         raise ValueError("Discord bot token is not set in the .env file.")
28 |     if not CHANNEL_ID:
29 |         raise ValueError("Discord channel ID is not set in the .env file.")
30 |     if not message:
31 |         raise ValueError("Message cannot be empty.")
32 | 
33 |     url = f"{DISCORD_API_URL}/channels/{CHANNEL_ID}/messages"
34 |     headers = {"Authorization": f"Bot {TOKEN}", "Content-Type": "application/json"}
35 |     payload = {"content": message}
36 | 
37 |     try:
38 |         response = requests.post(url, headers=headers, json=payload)
39 |         response.raise_for_status()  # Raise an exception for non-2xx status codes
40 |         print(f"Message sent: {message}")
41 |     except requests.exceptions.RequestException as e:
42 |         print(f"RequestException: {e}")
43 |         raise
44 | 
45 | 
46 | # Example usage:
47 | if __name__ == "__main__":
48 |     try:
49 |         send_discord_message("Hello, this is a synchronous message from my bot!")
50 |     except Exception as e:
51 |         print(f"Error: {e}")
52 | 


--------------------------------------------------------------------------------
/cookbook/workshops/sep_6_workshop/gptuesday_swarm.py:
--------------------------------------------------------------------------------
  1 | import os
  2 | from swarms import OpenAIChat, Agent, AgentRearrange
  3 | from dotenv import load_dotenv
  4 | 
  5 | load_dotenv()
  6 | 
  7 | # Load API key from environment
  8 | api_key = os.getenv("OPENAI_API_KEY")
  9 | 
 10 | # Define the model to be used
 11 | model = OpenAIChat(
 12 |     model_name="gpt-4o-mini", openai_api_key=api_key, max_tokens=4000, temperature=0.1
 13 | )
 14 | 
 15 | # Generator Agent - generates the core post
 16 | post_generator_agent = Agent(
 17 |     agent_name="Post-Generator-Agent",
 18 |     description="Generates core social media posts to promote GPTuesday's weekly AI events and workshops.",
 19 |     system_prompt="""
 20 |     Your task is to generate an engaging, clear, and informative post to promote GPTuesday's weekly AI educational events and community. Include the following platforms and event links:
 21 |     
 22 |     • Telegram: https://t.me/+w7NqSJA2WmgxMzdh
 23 |     • Discord: https://discord.gg/F8sSH4Gh
 24 |     • Twitter: https://twitter.com/GPTuesdays
 25 |     • Instagram: https://www.instagram.com/gptuesdays
 26 |     • LinkedIn: https://www.linkedin.com/company/gptuesdays
 27 |     • YouTube: https://www.youtube.com/@GPTuesdays
 28 |     • Website: https://gptuesdays.com/
 29 |     • Luma: https://lu.ma/GPTuesdays
 30 |     
 31 |     The tone should be professional yet friendly, encouraging people to join the events and connect on social platforms.
 32 |     """,
 33 |     llm=model,
 34 |     max_loops=1,
 35 |     dashboard=False,
 36 |     stopping_token="<DONE>",
 37 | )
 38 | 
 39 | # Telegram Optimizer Agent
 40 | telegram_optimizer_agent = Agent(
 41 |     agent_name="Telegram-Optimizer-Agent",
 42 |     description="Optimizes posts for the casual and conversational tone of Telegram.",
 43 |     system_prompt="""
 44 |     Your task is to optimize the given post for Telegram. Telegram posts should be informal and community-oriented. Use conversational language and encourage users to join the community.
 45 |     
 46 |     Example:
 47 |     "🚀 Join us for GPTuesday's weekly AI events and workshops! Explore the latest in AI, meet other enthusiasts, and learn something new. Join the chat: [Telegram link]."
 48 |     """,
 49 |     llm=model,
 50 |     max_loops=1,
 51 |     dashboard=False,
 52 |     stopping_token="<DONE>",
 53 | )
 54 | 
 55 | # Discord Optimizer Agent
 56 | discord_optimizer_agent = Agent(
 57 |     agent_name="Discord-Optimizer-Agent",
 58 |     description="Optimizes posts for the vibrant and community-driven nature of Discord.",
 59 |     system_prompt="""
 60 |     Your task is to optimize the post for Discord. Keep it short, fun, and engaging, and be sure to encourage conversation and participation in the Discord server.
 61 |     
 62 |     Example:
 63 |     "🎉 Get ready for GPTuesday's AI events! We're hosting weekly workshops and discussions. Connect with fellow AI enthusiasts and expand your knowledge. Join the server: [Discord link]."
 64 |     """,
 65 |     llm=model,
 66 |     max_loops=1,
 67 |     dashboard=False,
 68 |     stopping_token="<DONE>",
 69 | )
 70 | 
 71 | # Twitter Optimizer Agent
 72 | twitter_optimizer_agent = Agent(
 73 |     agent_name="Twitter-Optimizer-Agent",
 74 |     description="Optimizes posts for Twitter, ensuring concise and engaging messaging.",
 75 |     system_prompt="""
 76 |     Your task is to optimize the post for Twitter. Ensure that it is brief, uses clear language, and includes a call to action. Limit to 280 characters.
 77 |     
 78 |     Example:
 79 |     "🚀 Join GPTuesday for weekly AI workshops & events in Miami! Connect with the AI community and learn the latest trends. Follow us: [Twitter link] #AI #GPTuesday"
 80 |     """,
 81 |     llm=model,
 82 |     max_loops=1,
 83 |     dashboard=False,
 84 |     stopping_token="<DONE>",
 85 | )
 86 | 
 87 | # Instagram Optimizer Agent
 88 | instagram_optimizer_agent = Agent(
 89 |     agent_name="Instagram-Optimizer-Agent",
 90 |     description="Optimizes posts for Instagram, with a focus on visual engagement and concise messaging.",
 91 |     system_prompt="""
 92 |     Your task is to optimize the post for Instagram. Use engaging language, encourage visual engagement, and suggest using event photos or graphics. Ensure a clear call to action.
 93 |     
 94 |     Example:
 95 |     "📸 Join us every week for AI events in Miami with GPTuesday! Expand your skills, network with others, and explore the future of AI. Check out our upcoming events: [Instagram link]."
 96 |     """,
 97 |     llm=model,
 98 |     max_loops=1,
 99 |     dashboard=False,
100 |     stopping_token="<DONE>",
101 | )
102 | 
103 | # LinkedIn Optimizer Agent
104 | linkedin_optimizer_agent = Agent(
105 |     agent_name="LinkedIn-Optimizer-Agent",
106 |     description="Optimizes posts for the professional tone of LinkedIn.",
107 |     system_prompt="""
108 |     Your task is to optimize the post for LinkedIn. Focus on a professional tone, highlighting networking opportunities and skill development. Encourage sign-ups and participation.
109 |     
110 |     Example:
111 |     "🌟 Expand your AI knowledge with GPTuesday's weekly workshops and events. Engage with AI professionals, network, and enhance your skills. Sign up for upcoming events here: [LinkedIn link]."
112 |     """,
113 |     llm=model,
114 |     max_loops=1,
115 |     dashboard=False,
116 |     stopping_token="<DONE>",
117 | )
118 | 
119 | # YouTube Optimizer Agent
120 | youtube_optimizer_agent = Agent(
121 |     agent_name="YouTube-Optimizer-Agent",
122 |     description="Optimizes posts for YouTube, ensuring a focus on video content and event highlights.",
123 |     system_prompt="""
124 |     Your task is to optimize the post for YouTube. Emphasize the value of video content, such as event highlights or tutorials, and encourage subscriptions.
125 |     
126 |     Example:
127 |     "🎥 Want to dive deeper into AI? Check out GPTuesday's weekly workshops and event highlights on our YouTube channel. Subscribe for more insights and tutorials: [YouTube link]."
128 |     """,
129 |     llm=model,
130 |     max_loops=1,
131 |     dashboard=False,
132 |     stopping_token="<DONE>",
133 | )
134 | 
135 | # Website Optimizer Agent
136 | website_optimizer_agent = Agent(
137 |     agent_name="Website-Optimizer-Agent",
138 |     description="Optimizes posts for the GPTuesday website, ensuring clarity and engagement.",
139 |     system_prompt="""
140 |     Your task is to optimize the post for the website. The content should be clear, informative, and engaging, ensuring users can easily find event details and sign-up links.
141 |     
142 |     Example:
143 |     "🚀 GPTuesday hosts weekly AI workshops and events in Miami. Join us to explore the latest in AI technology, network with others, and build your skills. Check out our upcoming events here: [Website link]."
144 |     """,
145 |     llm=model,
146 |     max_loops=1,
147 |     dashboard=False,
148 |     stopping_token="<DONE>",
149 | )
150 | 
151 | # List of agents
152 | agents = [
153 |     post_generator_agent,
154 |     telegram_optimizer_agent,
155 |     discord_optimizer_agent,
156 |     twitter_optimizer_agent,
157 |     instagram_optimizer_agent,
158 |     linkedin_optimizer_agent,
159 |     youtube_optimizer_agent,
160 |     website_optimizer_agent,
161 | ]
162 | 
163 | swarm = AgentRearrange(
164 |     agents=agents,
165 |     flow="Post-Generator-Agent -> Telegram-Optimizer-Agent, Discord-Optimizer-Agent, Twitter-Optimizer-Agent, Instagram-Optimizer-Agent, LinkedIn-Optimizer-Agent, YouTube-Optimizer-Agent, Website-Optimizer-Agent",
166 |     # return_json=True,
167 | )
168 | 
169 | 
170 | swarm.run(
171 |     "Create posts to advertise the upcoming GPTuesday event on November 2nd at 6pm in Little Havana."
172 | )
173 | 


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/cookbook/workshops/sep_6_workshop/groq_agent.py:
--------------------------------------------------------------------------------
 1 | import os
 2 | 
 3 | from swarms.models.popular_llms import OpenAIChat
 4 | from swarms.prompts.finance_agent_sys_prompt import (
 5 |     FINANCIAL_AGENT_SYS_PROMPT,
 6 | )
 7 | from swarms.structs.agent import Agent
 8 | from dotenv import load_dotenv
 9 | 
10 | load_dotenv()
11 | 
12 | # Example usage:
13 | api_key = os.getenv("GROQ_API_KEY")
14 | 
15 | # Model
16 | model = OpenAIChat(
17 |     openai_api_base="https://api.groq.com/openai/v1",
18 |     openai_api_key=api_key,
19 |     model_name="llama-3.1-70b-versatile",
20 |     temperature=0.1,
21 | )
22 | 
23 | # Initialize the agent
24 | agent = Agent(
25 |     agent_name="Financial-Analysis-Agent_sas_chicken_eej",
26 |     system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
27 |     llm=model,
28 |     max_loops=2,
29 |     autosave=True,
30 |     dashboard=False,
31 |     verbose=True,
32 |     dynamic_temperature_enabled=True,
33 |     saved_state_path="finance_agent.json",
34 |     user_name="swarms_corp",
35 |     retry_attempts=1,
36 |     context_length=200000,
37 | )
38 | 
39 | 
40 | out = agent.run(
41 |     "How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria"
42 | )
43 | print(out)
44 | 


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/cookbook/workshops/sep_6_workshop/ig_tool.py:
--------------------------------------------------------------------------------
 1 | from instagrapi import Client
 2 | from typing import List
 3 | import os
 4 | 
 5 | 
 6 | def send_instagram_dm(message: str, media_urls: List[str] = None) -> bool:
 7 |     """
 8 |     Sends a direct message to a user on Instagram.
 9 | 
10 |     Args:
11 |         username (str): The username of the recipient.
12 |         message (str): The message to send in the DM.
13 |         media_urls (List[str], optional): A list of URLs to media (images or videos) to send along with the message.
14 | 
15 |     Returns:
16 |         bool: True if the DM was sent successfully, False otherwise.
17 | 
18 |     Raises:
19 |         ValueError: If the username is invalid or the message is empty.
20 |         Exception: If there is an issue with the Instagram API request.
21 |     """
22 |     client = Client()
23 | 
24 |     try:
25 |         username = os.getenv("INSTAGRAM_USERNAME")
26 |         password = os.getenv("INSTAGRAM_PASSWORD")
27 |         # Log in to Instagram
28 |         client.login(username, password)
29 | 
30 |         # Ensure the username and message are valid
31 |         if not username:
32 |             raise ValueError("The username cannot be empty.")
33 |         if not message:
34 |             raise ValueError("The message cannot be empty.")
35 | 
36 |         # Get the user ID for the recipient
37 |         user_id = client.user_id_from_username(username)
38 | 
39 |         if media_urls:
40 |             # Send a DM with media (images/videos)
41 |             media_ids = [client.media_pk_from_url(url) for url in media_urls]
42 |             client.direct_send(message, [user_id], media_pk=media_ids)
43 |         else:
44 |             # Send a simple text DM
45 |             client.direct_send(message, [user_id])
46 | 
47 |         return True
48 | 
49 |     except ValueError as e:
50 |         print(f"ValueError: {e}")
51 |         raise
52 |     except Exception as e:
53 |         print(f"Error: {e}")
54 |         raise
55 |     finally:
56 |         client.logout()
57 | 


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/cookbook/workshops/sep_6_workshop/model.py:
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 1 | import os
 2 | from swarms import OpenAIChat
 3 | from dotenv import load_dotenv
 4 | 
 5 | load_dotenv()
 6 | 
 7 | api_key = os.getenv("OPENAI_API_KEY")
 8 | # .env OPENAI_API_KEY="sk-"
 9 | 
10 | model = OpenAIChat(
11 |     model_name="gpt-4o-mini", openai_api_key=api_key, max_tokens=4000, temperature=0.1
12 | )
13 | 
14 | out = model(
15 |     "How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria"
16 | )
17 | print(out)
18 | 


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/cookbook/workshops/sep_6_workshop/test_rearrange.py:
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  1 | import os
  2 | from dotenv import load_dotenv
  3 | from swarms import Agent, AgentRearrange, OpenAIChat
  4 | from swarms.utils import data_to_text
  5 | 
  6 | load_dotenv()
  7 | 
  8 | # Get the OpenAI API key from the environment variable
  9 | api_key = os.getenv("OPENAI_API_KEY")
 10 | 
 11 | # Create an instance of the OpenAIChat class
 12 | model = OpenAIChat(
 13 |     api_key=api_key, model_name="gpt-4o-mini", temperature=0.1, max_tokens=4000
 14 | )
 15 | 
 16 | 
 17 | # Initialize the boss agent (Director)
 18 | boss_agent = Agent(
 19 |     agent_name="BossAgent",
 20 |     system_prompt="""
 21 |     You are the BossAgent responsible for managing and overseeing a swarm of agents analyzing company expenses. 
 22 |     Your job is to dynamically assign tasks, prioritize their execution, and ensure that all agents collaborate efficiently. 
 23 |     After receiving a report on the company's expenses, you will break down the work into smaller tasks, 
 24 |     assigning specific tasks to each agent, such as detecting recurring high costs, categorizing expenditures, 
 25 |     and identifying unnecessary transactions. Ensure the results are communicated back in a structured way 
 26 |     so the finance team can take actionable steps to cut off unproductive spending. You also monitor and 
 27 |     dynamically adapt the swarm to optimize their performance. Finally, you summarize their findings 
 28 |     into a coherent report.
 29 |     """,
 30 |     llm=model,
 31 |     max_loops=1,
 32 |     dashboard=False,
 33 |     streaming_on=True,
 34 |     verbose=True,
 35 |     stopping_token="<DONE>",
 36 |     state_save_file_type="json",
 37 |     saved_state_path="boss_agent.json",
 38 | )
 39 | 
 40 | # Initialize worker 1: Expense Analyzer
 41 | worker1 = Agent(
 42 |     agent_name="ExpenseAnalyzer",
 43 |     system_prompt="""
 44 |     Your task is to carefully analyze the company's expense data provided to you. 
 45 |     You will focus on identifying high-cost recurring transactions, categorizing expenditures 
 46 |     (e.g., marketing, operations, utilities, etc.), and flagging areas where there seems to be excessive spending. 
 47 |     You will provide a detailed breakdown of each category, along with specific recommendations for cost-cutting. 
 48 |     Pay close attention to monthly recurring subscriptions, office supplies, and non-essential expenditures.
 49 |     """,
 50 |     llm=model,
 51 |     max_loops=1,
 52 |     dashboard=False,
 53 |     streaming_on=True,
 54 |     verbose=True,
 55 |     stopping_token="<DONE>",
 56 |     state_save_file_type="json",
 57 |     saved_state_path="worker1.json",
 58 | )
 59 | 
 60 | # Initialize worker 2: Summary Generator
 61 | worker2 = Agent(
 62 |     agent_name="SummaryGenerator",
 63 |     system_prompt="""
 64 |     After receiving the detailed breakdown from the ExpenseAnalyzer, 
 65 |     your task is to create a concise summary of the findings. You will focus on the most actionable insights, 
 66 |     such as highlighting the specific transactions that can be immediately cut off and summarizing the areas 
 67 |     where the company is overspending. Your summary will be used by the BossAgent to generate the final report.
 68 |     Be clear and to the point, emphasizing the urgency of cutting unnecessary expenses.
 69 |     """,
 70 |     llm=model,
 71 |     max_loops=1,
 72 |     dashboard=False,
 73 |     streaming_on=True,
 74 |     verbose=True,
 75 |     stopping_token="<DONE>",
 76 |     state_save_file_type="json",
 77 |     saved_state_path="worker2.json",
 78 | )
 79 | 
 80 | # Swarm-Level Prompt (Collaboration Prompt)
 81 | swarm_prompt = """
 82 |     As a swarm, your collective goal is to analyze the company's expenses and identify transactions that should be cut off. 
 83 |     You will work collaboratively to break down the entire process of expense analysis into manageable steps. 
 84 |     The BossAgent will direct the flow and assign tasks dynamically to the agents. The ExpenseAnalyzer will first 
 85 |     focus on breaking down the expense report, identifying high-cost recurring transactions, categorizing them, 
 86 |     and providing recommendations for potential cost reduction. After the analysis, the SummaryGenerator will then 
 87 |     consolidate all the findings into an actionable summary that the finance team can use to immediately cut off unnecessary expenses. 
 88 |     Together, your collaboration is essential to streamlining and improving the company’s financial health.
 89 | """
 90 | 
 91 | # Create a list of agents
 92 | agents = [boss_agent, worker1, worker2]
 93 | 
 94 | # Define the flow pattern for the swarm
 95 | flow = "BossAgent -> ExpenseAnalyzer -> SummaryGenerator"
 96 | 
 97 | # Using AgentRearrange class to manage the swarm
 98 | agent_system = AgentRearrange(agents=agents, flow=flow, return_json=True)
 99 | 
100 | data = data_to_text("data.csv")
101 | 
102 | # Input task for the swarm
103 | task = """
104 | 
105 |     The company has been facing a rising number of unnecessary expenses, and the finance team needs a detailed 
106 |     analysis of recent transactions to identify which expenses can be cut off to improve profitability. 
107 |     Analyze the provided transaction data and create a detailed report on cost-cutting opportunities, 
108 |     focusing on recurring transactions and non-essential expenditures. 
109 |     
110 |     Transactions: 
111 |         
112 |     Vendor,Service,Expense ($),Month
113 |     Amazon Web Services,Payments,4632.83,June
114 |     Google Cloud,Collaboration,1581.25,August
115 |     Microsoft Azure,Cloud Services,460.21,February
116 |     HubSpot,Accounting,1804.32,January
117 |     Slack,Collaboration,3137.03,July
118 |     Stripe,Ads,2592.29,May
119 |     Twilio,Marketing,818.5,March
120 |     Zoom,Marketing,3105.0,April
121 |     Trello,Marketing,2986.18,January
122 |     GitHub,Collaboration,1958.86,May
123 |     LinkedIn Ads,Collaboration,797.92,September
124 |     Google Ads,Cloud Services,2862.14,April
125 |     Facebook Ads,Accounting,2326.0,August
126 |     Mailchimp,Cloud Services,3735.74,April
127 |     Salesforce,Cloud Services,2828.26,April
128 |     Notion,Accounting,4481.65,September
129 |     QuickBooks,Accounting,464.77,May
130 |     Zapier,Marketing,1478.0,June
131 |     DocuSign,Ads,3267.59,October
132 |     PayPal,CRM,3294.24,February
133 | 
134 | """
135 | 
136 | # Run the swarm system with the task
137 | output = agent_system.run(task)
138 | print(output)
139 | 


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/swarms_logo.png:
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https://raw.githubusercontent.com/The-Swarm-Corporation/Cookbook/cae46162fe4bb0455bd6c29bba8a0fd2acd23207/swarms_logo.png


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