├── competitive_analysis_agent ├── .python-version ├── requirements.txt ├── langgraph.json ├── pyproject.toml ├── example_output │ ├── analysis_request.txt │ ├── company_profiles.md │ └── competitive_analysis.md ├── README.md └── competitive_analysis_agent.py ├── media ├── da_la.png ├── easy_tasks.png ├── file_system.png ├── sub_agents.png ├── todo_list.png ├── manus_context.png ├── system_prompt.png ├── claude_subagents.png ├── deep_agent_diagram.png ├── manus_file_system.png ├── model-success-rate.png └── models-are-succeeding-at-increasingly-long-tasks.png ├── .gitignore ├── LICENSE ├── README.md └── deep_agents_overview.ipynb /competitive_analysis_agent/.python-version: -------------------------------------------------------------------------------- 1 | 3.13 2 | -------------------------------------------------------------------------------- /media/da_la.png: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /competitive_analysis_agent/langgraph.json: -------------------------------------------------------------------------------- 1 | { 2 | "dependencies": ["."], 3 | "graphs": { 4 | "competitive_analysis_agent": "./competitive_analysis_agent.py:competitive_analysis_agent" 5 | }, 6 | "env": ".env" 7 | } 8 | -------------------------------------------------------------------------------- /competitive_analysis_agent/pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "competitive-analysis-agent" 3 | version = "0.1.0" 4 | description = "Add your description here" 5 | readme = "README.md" 6 | requires-python = ">=3.13" 7 | dependencies = [ 8 | "deepagents>=0.0.5", 9 | "langchain-openai>=0.3.33", 10 | "langgraph-cli[inmem]>=0.4.2", 11 | "tavily-python>=0.7.12", 12 | ] 13 | -------------------------------------------------------------------------------- /competitive_analysis_agent/example_output/analysis_request.txt: -------------------------------------------------------------------------------- 1 | Request: Create a comprehensive competitive analysis comparing Linear and Asana as project management solutions for product development teams. 2 | Language: English 3 | Scope notes: 4 | - Follow full Research Methodology and cover both companies equally. 5 | - Emphasize features and workflows relevant to product/dev teams (issues, sprints/cycles, roadmaps, docs/specs, backlog grooming, automations, Git integrations, QA/bug workflows, AI assistants, analytics/insights, stakeholder reporting). 6 | - Include pricing details, packaging, and enterprise capabilities (security/compliance, SSO/SCIM, admin controls, SOC2/ISO, data residency, FedRAMP for Asana if applicable). 7 | - Include customer sentiment from G2/Capterra/TrustRadius and Glassdoor snippets for culture. 8 | - Include recent developments within the last 12 months (2024-2025): feature launches, partnerships, financial updates (Asana), hiring signals (Linear & Asana). 9 | - Provide actionable recommendations for buyers and competitive response strategies for each vendor. 10 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2025 Adam Łucek 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 | -------------------------------------------------------------------------------- /competitive_analysis_agent/README.md: -------------------------------------------------------------------------------- 1 | # Competitive Analysis Agent 2 | 3 | Below is the recommended setup for running and inspecting the competitive analysis deep agent example 4 | 5 | ## UI Setup 6 | 7 | To visualize and interact with the competitive analysis agent, we will be using the [deep-agents-ui](https://github.com/langchain-ai/deep-agents-ui) repo. 8 | 9 | 1. In a seperate directory/terminal, clone the repo 10 | ```bash 11 | git clone https://github.com/langchain-ai/deep-agents-ui.git 12 | cd deep-agents-ui 13 | ``` 14 | 15 | 2. Create a `.env.local` file with the following environment variables 16 | 17 | ```env 18 | NEXT_PUBLIC_DEPLOYMENT_URL="http://127.0.0.1:2024" 19 | NEXT_PUBLIC_AGENT_ID=competitive_analysis_agent 20 | ``` 21 | 22 | 3. Install dependencies and launch the server 23 | 24 | ```bash 25 | npm install 26 | npm run dev 27 | ``` 28 | 29 | Open the interface on http://localhost:3000 30 | 31 | ## Agent Setup 32 | 33 | To run the agent, we will be using the [LangGraph Platform](https://docs.langchain.com/langgraph-platform) 34 | 35 | 1. Clone this repo, navigate to the example, and install the requirements 36 | 37 | ```bash 38 | git clone https://github.com/ALucek/deep-agents-walkthrough.git 39 | cd deep-agents-walkthrough/competitive_analysis_agent 40 | uv sync 41 | ``` 42 | 43 | 2. Create a `.env` file with the following environment variables 44 | 45 | ```env 46 | TAVILY_API_KEY= 47 | OPENAI_API_KEY= 48 | LANGSMITH_API_KEY= 49 | LANGSMITH_TRACING=true 50 | LANGSMITH_ENDPOINT=https://api.smith.langchain.com 51 | LANGSMITH_PROJECT=deep_competitive_analysis 52 | ``` 53 | 54 | 3. Launch a local LangGraph platform server 55 | 56 | ```bash 57 | uv run langgraph dev 58 | ``` 59 | 60 | The competitive analysis agent will now be running on the local LangGraph server, and connected to the deep-agents-ui frontend. 61 | 62 | _Note: There is no persistent file storage system included. All data, conversations, and files are cleared when the frontend is closed._ -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Deep Agents 2 | 3 | 4 | 5 | Language model based applications have moved from simple chat interfaces, to more 'agentic' frameworks. While the definition of what an 'agent' is varies, it generally refers to a system where an LLM is equipped with some tools, and is allowed to operate continuously. It is often with the intent to complete a more complex goal by chaining together multiple actions together, ultimately relying on the reasoning abilities of the language model to determine what steps to take and when to stop. 6 | 7 | This initial approach has worked well, with agentic systems proving more useful, helpful, and capable than their naive chat completions beginnings, ushering in a new wave of interest and investment into arming LLMs with various integrations and tools to be able to perform more complex tasks. Although useful, there still remained a disconnect between the capability of an LLM with digitial tools and a human with the same tools, primarily around 'long-horizon' tasks. 8 | 9 | 10 | 11 | [Measuring AI Ability to Complete Long Tasks](https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/) 12 | 13 | When observing a graph that compares success rates of models on human benchmarked time to complete tasks, most modern day models can complete tasks that often take 4 minutes for humans to complete successfully, however as task complexity grows success rate falls. Anecdotely, not many _useful_ tasks tend to fall into the 'takes four minutes to complete' category, i.e: 14 | 15 | 16 | 17 | [HCAST: Human-Calibrated Autonomy Software Tasks](https://metr.org/hcast.pdf) 18 | 19 | With most of what would be considered 'productive' tasks often taking much more time and effort to complete. 20 | 21 | As LLMs become smarter and more capable through improved training techniques, their ability for successful long-horizon task completion has improved (reference prior graph), but even more gains have been made through custom frameworks meant to encourage and enable this kind of behavior. Popularized initially by workflows such as [Deep Research](https://blog.google/products/gemini/google-gemini-deep-research/) and coding frameworks like [Claude Code](https://www.anthropic.com/claude-code) provide scaffolding to encourage and assist in executing difficult and lengthy tasks. 22 | 23 | 24 | 25 | [Deep Agents](https://blog.langchain.com/deep-agents/) 26 | 27 | This concept has adopted the name Deep Agents, with a few commonalities being observed in the open source community namely: 28 | 1. A detailed and specific **system prompt** 29 | 2. A **planning** or to-do list tool 30 | 3. A **file system** for context management 31 | 4. **Sub Agents!** 32 | 33 | In this notebook, we'll discuss the theory behind why these techniques have proven successful for creating Deep Agents and encouraging longer task execution, and provide an example of this all coming together by using open source frameworks to create our own [Competitive Analysis Deep Agent](./competitive_analysis_agent)! -------------------------------------------------------------------------------- /competitive_analysis_agent/example_output/company_profiles.md: -------------------------------------------------------------------------------- 1 | # Company Profiles 2 | 3 | ## Linear (Linear Orbit, Inc.) 4 | 5 | ### Overview 6 | **Website:** https://linear.app 7 | **Founded:** 2019 8 | **Headquarters:** San Francisco, CA, USA (remote-first across North America and Europe) 9 | **Employees:** ~130–180 (LinkedIn band 51–200; estimate as of 2025) 10 | 11 | ### Positioning & Mission 12 | **Tagline:** "Bringing magic back to software"; "Project planning tool for teams that care about quality" 13 | **Mission:** Craft fast, delightful, developer-centric planning and issue tracking that helps teams ship high-quality software with clarity and speed. 14 | **Vision:** A focused, opinionated workflow that replaces heavyweight legacy tools and reduces friction for high-performance product teams. 15 | 16 | ### Target Market 17 | **Customer Segments:** 18 | - Product & Engineering Teams: High-velocity software teams (startups to scale-ups) 19 | - AI-first & Fintech Companies: Teams prioritizing speed and craft (case study concentration) 20 | - Cross-functional Product Organizations: PMs, EMs, Designers, QA collaborating on issues, cycles, projects, and roadmaps 21 | 22 | **Ideal Customer Profile:** 23 | Product-centric software companies (20–2,000 employees) seeking an opinionated, fast issue/project system with first-class GitHub/GitLab/Slack integration, lightweight roadmapping (Initiatives/Projects), and basic-to-moderate analytics (Insights). Suited to organizations optimizing for developer velocity and reduced process overhead over deep enterprise customization. 24 | 25 | ### Products & Services 26 | **Core Offerings:** 27 | 1. **Issues & Cycles (Sprints)** 28 | - Key capabilities: Keyboard-first issue creation, triage, parent/sub-issues, relations; cycles with auto-rollover and velocity; board/list views; backlog grooming 29 | - Use cases: Agile sprint planning, bug tracking, feature delivery, support escalations 30 | - Differentiators: Exceptional speed/UX; opinionated flows; deep GitHub/GitLab automations 31 | 32 | 2. **Projects, Initiatives & Roadmaps** 33 | - Key capabilities: Project hierarchy, Initiatives (roadmap-level rollups), health/status updates, dependencies, stakeholder comms, Slack updates 34 | - Use cases: Outcome-driven roadmap management, cross-team program tracking 35 | - Differentiators: Simple, fast roadmapping; clear status patterns; minimal overhead 36 | 37 | 3. **Docs & Knowledge** 38 | - Key capabilities: In-product docs for specs/PRDs; document subscriptions; link to issues/projects 39 | - Use cases: Specs alongside work; status notes; decision logs 40 | - Differentiators: Lightweight and contextual (not a full wiki) 41 | 42 | 4. **Insights & Product Intelligence** 43 | - Key capabilities: Velocity, cycle metrics, progress charts; ML-assisted triage, duplicate detection, label/assignee suggestions (Product Intelligence) 44 | - Use cases: Team health, forecasting, triage acceleration 45 | - Differentiators: Opinionated, integrated analytics; early trustworthy AI approach 46 | 47 | 5. **Linear for Agents (AI + Agent ecosystem)** 48 | - Key capabilities: Build/deploy AI agents that operate within Linear; Agent SDK & interaction guidelines; MCP access 49 | - Use cases: Automated triage, grooming, PR linking, status drafting, coordination with dev tools like Cursor 50 | - Differentiators: Agent-native platform; growing partner ecosystem 51 | 52 | ### Pricing & Packaging 53 | **Pricing Model:** Per-user subscription (Free, Basic, Business, Enterprise); annual and monthly options for paid tiers; Enterprise custom. 54 | 55 | **Tiers:** 56 | - **Free:** $0 57 | - Unlimited members; 2 teams; 250 issues; 10MB uploads; Google SSO; core features; API/webhooks 58 | - Limitations: Team/issue/upload caps; no private teams 59 | - **Basic:** $8/user/mo (annual) | ~$10 (monthly; third-party reference) 60 | - Unlimited issues; 5 teams; admin roles; Agents 61 | - Limitations: No SAML/SCIM; fewer admin/governance controls 62 | - **Business:** $14/user/mo (annual) | ~$16 (monthly; third-party reference) 63 | - Unlimited teams; private teams; guests; Insights; Asks; Product Intelligence; support integrations (Zendesk/Intercom) 64 | - Limitations: SAML/SCIM reserved for Enterprise 65 | - **Enterprise:** Custom (annual) 66 | - SAML SSO, SCIM, domain claiming, issue SLAs, sub-initiatives, dashboards, priority support, migration help 67 | - Limitations: Custom pricing; sales-engaged onboarding 68 | 69 | *Note: Monthly prices are not explicitly listed on Linear’s site; third-party trackers indicate $10/$16 for Basic/Business. Verify with Linear. Sources: linear.app/pricing; docs.* 70 | 71 | ### Technology & Integrations 72 | **Technology Stack:** Public GraphQL API; TypeScript SDK; ProseMirror-based editor; multi-platform apps (web/macOS/Windows/iOS/Android). Specific infra/framework details not publicly listed. 73 | 74 | **Key Integrations:** 75 | - Dev: GitHub (PR automation, issues import/sync), GitLab (MR linking), Bitbucket (via community), Sentry 76 | - Collaboration: Slack (create issues from messages, thread sync) 77 | - Migration: Jira importer/sync; CSV import 78 | 79 | **API & Developer Tools:** 80 | - GraphQL API & Webhooks; OAuth 2.0; TypeScript SDK; public schema; Agent SDK; MCP server access 81 | 82 | ### Market Presence 83 | **Geographic Coverage:** Global, with concentration in North America and Europe; remote-first team 84 | **Industry Focus:** Software/tech, AI, fintech, B2B SaaS 85 | **Market Share:** Not disclosed; noted adoption by 15,000+ product teams 86 | 87 | ### Notable Customers 88 | - **OpenAI:** Organization-wide adoption (2,000+ users); scaled planning while maintaining speed 89 | - **Brex:** Consolidated “One Roadmap” for cross-company alignment 90 | - **Remote:** ~1,000 employees using Linear to move faster 91 | 92 | ### Recent Developments (Last 12 Months) 93 | **Funding:** 94 | - Jul 2025: Reported Series C $82M (~$1.25B valuation). Source: The SaaS News 95 | 96 | **Product Launches:** 97 | - May–Aug 2025: Linear for Agents; Agent Interaction Guidelines & SDK; Product Intelligence (tech preview) 98 | - Oct 2024: Document subscriptions 99 | 100 | **Partnerships:** 101 | - 2025: Cursor deep integration for background agents; emerging agent partners (Charlie Labs, Reflag) 102 | 103 | **Leadership Changes:** 104 | - COO hire previously announced; no recent C-suite changes disclosed in last 12 months 105 | 106 | ### Strengths 107 | 1. **Speed and UX excellence:** Consistently praised; boosts developer flow and adoption 108 | 2. **Developer-first integrations:** Deep GitHub/GitLab/Slack automation supports real dev workflows 109 | 3. **Opinionated simplicity:** Reduces tool sprawl and process overhead; easier onboarding and maintenance 110 | 111 | ### Weaknesses & Limitations 112 | 1. **Reporting depth:** Insights lacks advanced, customizable analytics vs. enterprise suites 113 | 2. **Enterprise governance:** SAML/SCIM limited to Enterprise; fewer granular permissions/audit controls 114 | 3. **Complex portfolio management:** Multi-project/release coordination and executive rollups require workarounds 115 | 116 | ### Customer Sentiment 117 | **Positive Feedback Themes:** 118 | - Speed/snappiness; clean, keyboard-driven UX; easy onboarding 119 | - Strong dev tool integrations; effective sprint/cycle management 120 | 121 | **Common Complaints:** 122 | - Limited advanced reporting; constraints for complex enterprise workflows; guest access and SSO in higher tiers only 123 | 124 | **Review Scores:** 125 | - G2: ~4.5/5 (≈50 reviews) 126 | - Capterra: ~4.8–4.9/5 (dozens of reviews) 127 | - TrustRadius: ~9/10 (very few reviews) 128 | 129 | --- 130 | 131 | ## Asana, Inc. (NYSE: ASAN) 132 | 133 | ### Overview 134 | **Website:** https://asana.com 135 | **Founded:** 2008 136 | **Headquarters:** 633 Folsom St, Suite 100, San Francisco, CA 94107, USA 137 | **Employees:** ~1,800 (Yahoo Finance profile; check latest 10-K for precise figure) 138 | 139 | ### Positioning & Mission 140 | **Tagline:** Enterprise work management platform 141 | **Mission:** "Help humanity thrive by enabling the world’s teams to work together effortlessly." 142 | **Vision:** Connect company goals to execution with cross-functional workflows, visibility, and governance at enterprise scale. 143 | 144 | ### Target Market 145 | **Customer Segments:** 146 | - Enterprise and Mid-market: Cross-functional business operations, product, marketing, IT/PMO 147 | - Regulated industries: Financial services, healthcare, government (with Enterprise+ compliance) 148 | - Global organizations: Multi-region data residency needs 149 | 150 | **Ideal Customer Profile:** 151 | Organizations from 200 to 10,000+ employees needing robust cross-functional orchestration (projects→portfolios→goals), resource management, standardized intake, advanced reporting, and enterprise security/compliance (SSO/SCIM, audit, DLP/eDiscovery, data residency). Suitable for PMOs, product/IT, and business teams standardizing workflows. 152 | 153 | ### Products & Services 154 | **Core Offerings:** 155 | 1. **Projects, Tasks, and Workflows** 156 | - Key capabilities: Multiple views, dependencies, milestones, templates, rules automation, forms intake, multi-homing 157 | - Use cases: Cross-functional program/project management; intake-to-delivery workflows 158 | - Differentiators: Visual Workflow Builder; broad templates; app-embedded steps 159 | 160 | 2. **Portfolios, Workload, and Goals** 161 | - Key capabilities: Program/portfolio rollups, capacity planning, OKRs linked to work, universal reporting 162 | - Use cases: PMO visibility, capacity optimization, strategy execution 163 | - Differentiators: Mature portfolio/goals stack; strong BI integrations 164 | 165 | 3. **Asana AI and AI Studio** 166 | - Key capabilities: Summaries, status, smart fields; AI Studio for building AI-powered automations/agents with credit model 167 | - Use cases: Reduce manual updates, automate triage/intake, create intelligent workflows 168 | - Differentiators: No-code AI workflow builder with admin controls 169 | 170 | ### Pricing & Packaging 171 | **Pricing Model:** Per-user subscription; Starter and Advanced list prices; Enterprise/Enterprise+ custom; AI Studio credits model for advanced AI usage. 172 | 173 | **Tiers:** 174 | - **Personal (Free):** $0; up to 10 teammates; core task/project features; 100+ integrations 175 | - **Starter:** $10.99/user/mo annual ($13.49 monthly) 176 | - Adds Timeline/Gantt, Workflow Builder, dashboards, advanced search, forms, custom fields, private teams/projects, admin console; Asana AI included 177 | - **Advanced:** $24.99/user/mo annual ($30.49 monthly) 178 | - Adds Goals, unlimited Portfolios, Workload, native time tracking, approvals/proofing, Salesforce/Tableau/Power BI integrations 179 | - **Enterprise/Enterprise+:** Custom 180 | - Enterprise: SAML SSO, SCIM, service accounts, advanced admin; Resource management; support SLAs 181 | - Enterprise+: Audit Log API, DLP/eDiscovery/Archiving, managed workspaces, data residency, EKM, HIPAA (eligibility) 182 | 183 | *Note: Seat minimums/increments apply; AI Studio Plus/Pro pricing not public; credits scale by tier. See pricing FAQ.* 184 | 185 | ### Technology & Integrations 186 | **Technology Stack:** Public REST API; OAuth 2.0; robust webhooks; Audit Log API; SCIM; Admin Console; Data Residency controls. 187 | 188 | **Key Integrations:** 189 | - Collaboration: Slack, Microsoft Teams, Zoom, Google Workspace 190 | - Dev/IT/CRM: Jira Cloud, GitHub, ServiceNow, Salesforce 191 | - Analytics: Tableau, Power BI 192 | 193 | **API & Developer Tools:** 194 | - REST API; developer portal; App Directory; webhooks; Audit Log API; SCIM; enterprise governance APIs 195 | 196 | ### Market Presence 197 | **Geographic Coverage:** Global; data centers/regions for residency in EU, US, Japan, Australia 198 | **Industry Focus:** Broad horizontal adoption; strong in enterprise PMO, product, marketing, IT 199 | **Market Share:** Public metrics include 25k+ core customers (≥$5k ARR); recognized Leader (Forrester Wave 2025) 200 | 201 | ### Notable Customers 202 | - **Morningstar:** >$600k annual savings via standardized intake and AI Studio 203 | - **Palo Alto Networks:** Orchestrates complex product launches and IT planning 204 | - **Accor; Asurion; Spotify:** Enterprise-scale coordination and visibility 205 | 206 | ### Recent Developments (Last 12 Months) 207 | **Funding:** 208 | - Public company; ongoing quarterly earnings. No new external funding rounds. 209 | 210 | **Product Launches:** 211 | - 2025: Asana AI Studio (no-code AI automations/agents); continued expansion of Asana AI 212 | 213 | **Partnerships:** 214 | - Expanded integrations; no singular landmark partnerships announced 215 | 216 | **Leadership Changes:** 217 | - Mar 2025: CEO succession process announced; Dustin Moskovitz to transition to Chair upon successor appointment; new CFO effective Sept 2024 218 | 219 | ### Strengths 220 | 1. **Enterprise-grade orchestration:** Mature portfolios/goals/workload with universal reporting 221 | 2. **Governance & compliance:** SSO/SCIM, Audit APIs, DLP/eDiscovery/archiving, data residency, EKM, HIPAA options 222 | 3. **AI-driven automation at scale:** AI Studio for custom AI workflows across departments 223 | 224 | ### Weaknesses & Limitations 225 | 1. **Complexity/learning curve:** Admin hygiene needed; setup can be heavy for small teams 226 | 2. **Performance at scale:** Large, field-heavy projects/dashboards can feel slow 227 | 3. **Cost escalation:** Many advanced capabilities gated to higher tiers and add-ons 228 | 229 | ### Customer Sentiment 230 | **Positive Feedback Themes:** 231 | - Flexible cross-functional coordination; strong templates and automations; visibility from tasks to portfolios/goals 232 | 233 | **Common Complaints:** 234 | - Learning curve; performance lags on large datasets; feature paywalls and total cost 235 | 236 | **Review Scores:** 237 | - G2: ~4.3/5 (thousands of reviews) 238 | - Capterra: ~4.4/5 239 | - TrustRadius: ~8.5/10 (Top Rated) 240 | - Glassdoor: ~4.2/5 (employee culture) 241 | -------------------------------------------------------------------------------- /competitive_analysis_agent/example_output/competitive_analysis.md: -------------------------------------------------------------------------------- 1 | # Competitive Analysis: Linear vs Asana 2 | 3 | ## Executive Summary 4 | Linear and Asana both deliver modern work management, but they are optimized for different buyers and workflows. Linear is a fast, developer-first issue and project planning system purpose-built for software teams. It emphasizes speed, opinionated simplicity, and deep developer tool integrations (GitHub/GitLab/Slack). Asana is a broad, enterprise-grade work management platform spanning projects to portfolios, goals/OKRs, workflows, and compliance, designed for cross‑functional orchestration across product, IT, marketing, and operations. 5 | 6 | Key dynamics for product development teams: Linear typically wins with engineering organizations that prioritize developer velocity, keyboard-driven UX, and minimal process overhead while maintaining lightweight roadmaps and core analytics. Asana typically wins when organizations require portfolio roll‑ups, goals, workload/capacity planning, standardized intake at scale, mature reporting, and enterprise governance (SSO/SCIM, Audit Log API, data residency, and compliance options). 7 | 8 | Main differentiators: Linear’s strengths are performance and developer-focused workflows (issues, cycles, Git integrations) plus budding AI/agents designed to operate inside the product. Asana differentiates on breadth and governance: portfolios/goals, universal reporting, resource management, Workflow Builder/Rules, and enterprise security/compliance. Pricing follows: Linear’s paid tiers are lower list price for SMB/mid-market engineering teams, while Asana’s Starter/Advanced/Enterprise pricing scales with cross-functional breadth and compliance. 9 | 10 | Strategic implications: For engineering-led product orgs, Linear can reduce tool sprawl (Jira + lite roadmapping) and increase flow, but may need supplemental BI/reporting or knowledge tools. For PMOs or multi-department programs, Asana centralizes orchestration and reporting with stronger enterprise guardrails, but requires disciplined admin and higher-tier spend. Buyers should pilot against top workflows (sprint execution, release trains, roadmap status, intake automation, executive reporting) to validate fit. 11 | 12 | ## Company Overview Comparison 13 | 14 | ### At a Glance 15 | | Dimension | Linear | Asana | 16 | |-----------|--------|-------| 17 | | Founded | 2019 | 2008 | 18 | | Headquarters | San Francisco, CA (remote-first) | San Francisco, CA | 19 | | Employees | ~130–180 (est., LinkedIn band 51–200) | ~1,800 (profile snapshot) | 20 | | Funding Raised | Reported Series C $82M (Jul 2025; see note) | Public company (NYSE: ASAN) | 21 | | Primary Market | Developer-centric issue/project planning | Enterprise work management | 22 | | Business Model | SaaS, per-user tiers; Enterprise custom | SaaS, per-user tiers; Enterprise/Enterprise+ custom | 23 | 24 | Note on Linear funding: Series C and valuation were reported by third-party media; a primary company press release was not located at time of writing. Treat as reported; verify with Linear. 25 | 26 | ### Positioning Analysis 27 | Linear positions itself as a high‑craft, fast, and opinionated project planning tool for teams that care about quality. Messaging emphasizes speed, minimal friction, and developer-first workflows. The product concentrates on issues, cycles (sprints), projects/initiatives for roadmapping, lightweight docs, and integrated analytics (Insights), with recent emphasis on AI Agents and Product Intelligence to accelerate triage and planning. 28 | 29 | Asana positions as a leading enterprise work management platform that connects goals to execution across departments. Messaging emphasizes portfolio/goal alignment, workflow automation (Rules/Workflow Builder), standardized intake (Forms), cross‑functional visibility (Universal Reporting, Portfolios, Workload), and enterprise governance (SSO/SCIM, Audit Log API, data residency, EKM/HIPAA eligibility, and Asana for Government/FedRAMP offering). Asana AI and AI Studio extend automation with no‑code AI workflows and usage-based credits. 30 | 31 | ## Detailed Comparison Matrix 32 | 33 | ### Product & Feature Comparison 34 | | Feature Category | Linear | Asana | Advantage | 35 | |-----------------|--------|-------|-----------| 36 | | Developer workflows (issues, sprints, Git) | Keyboard-first issues; cycles with velocity/rollover; deep GitHub/GitLab automations; Slack integration | Solid dev integrations (Jira/GitHub apps) but oriented to cross-functional work vs. developer IDE/PR flows | Linear for dev-centric teams | 37 | | Planning & roadmaps | Projects + Initiatives (roadmap rollups), status updates, Slack comms | Projects + Portfolios (programs), Goals/OKRs, Workload, Timeline/Gantt | Asana for portfolio/goal alignment | 38 | | Documentation | Lightweight in-product Docs for specs/PRDs (subscriptions, links to work) | No full wiki; rich task/project descriptions; templates; attachments; knowledge often handled via external tools (Notion/Confluence) | Tie (contextual docs vs. templates) | 39 | | Automation | Linear automations (with Git/Slack/Jira import); emerging Agent platform | Rules (rich triggers/actions), Workflow Builder; AI Studio to create AI-powered automations | Asana | 40 | | AI capabilities | Agents (AI inside Linear), Product Intelligence (ML suggestions for triage/labels/duplicates) | Asana AI (summaries, smart status/fields) + AI Studio (credit-based AI automations) | Asana for breadth; Linear for agent-native dev flows | 41 | | Reporting/Analytics | Insights (velocity/progress). Basic-to-moderate analytics | Universal reporting across tasks/projects/portfolios/goals; dashboards; time-in-stage; BI integrations | Asana | 42 | | Resource/Capacity | Cycle velocity; basic workload context | Workload (capacity), native time tracking (Advanced+), resource mgmt | Asana | 43 | | Security & Governance | Google SSO on all tiers; SAML/SCIM on Enterprise; domain claim; admin basics | SAML SSO, SCIM, Audit Log API, DLP/eDiscovery/archiving (add-ons), data residency, EKM, HIPAA eligibility, Asana for Government (FedRAMP) | Asana | 44 | | Data residency/compliance | Not published; standard privacy/security docs | Data residency (EU/US/Japan/Australia); Asana for Government (FedRAMP Moderate) | Asana | 45 | | Integrations ecosystem | Strong for dev tools (GitHub/GitLab/Slack), Jira migration | Broad enterprise ecosystem (Slack, Teams, Salesforce, ServiceNow, Jira, GitHub, BI) | Asana | 46 | | API/Developer platform | Public GraphQL API, webhooks, OAuth, TS SDK, Agent SDK | REST API, webhooks, SCIM, Audit Log API, Admin APIs, App Directory | Asana breadth; Linear dev ergonomics | 47 | 48 | ### Target Market Comparison 49 | | Segment | Linear Coverage | Asana Coverage | Winner | 50 | |---------|-----------------|----------------|--------| 51 | | Enterprise (>1000 employees) | Moderate (growing; Enterprise features exist but governance/reporting more limited) | Strong (governance, reporting, data residency, add-ons) | Asana | 52 | | Mid-Market (100–1000) | Strong (especially product/engineering) | Strong (cross-functional) | Tie | 53 | | SMB (<100) | Strong (Free/Basic/Business fit, fast onboarding) | Moderate to Strong (Starter viable; may over-feature) | Linear | 54 | 55 | ### Pricing Comparison 56 | | Pricing Tier | Linear | Asana | Value Assessment | 57 | |-------------|--------|-------|------------------| 58 | | Entry Level | Free; Basic $8/user/mo (annual, monthly not listed on-site) | Personal (Free); Starter $10.99/user/mo annual ($13.49 monthly) | Linear is lower-cost for core dev workflows; Asana Starter adds broader features/automation | 59 | | Professional | Business $14/user/mo (annual) | Advanced $24.99/user/mo annual ($30.49 monthly) | Linear Business is cost-effective for product/dev; Asana Advanced adds Portfolios, Workload, native time tracking, BI integrations | 60 | | Enterprise | Custom; SAML/SCIM; advanced features | Custom; Enterprise and Enterprise+ with compliance add-ons (Audit/DLP, data residency, EKM, HIPAA eligibility) | Asana stronger for regulated/large enterprises; Linear suitable where dev velocity > heavy governance | 61 | 62 | Notes: Linear’s published prices are annual per-user list; monthly figures are not clearly listed on-site. Asana lists both annual and monthly public prices. Enterprise pricing is negotiated in both cases. 63 | 64 | ## SWOT Analysis 65 | 66 | ### Linear SWOT 67 | 68 | #### Strengths 69 | 1. Developer-first UX and speed 70 | - Evidence: Consistent user reviews cite performance and keyboard-first design; strong GitHub/GitLab integrations. 71 | - Impact: Higher adoption by engineering teams; reduced friction vs. heavier ALM tools. 72 | 2. Opinionated simplicity and focus 73 | - Evidence: Product scope centers on issues/cycles/projects/initiatives with lightweight docs and Insights. 74 | - Impact: Faster onboarding and lower process overhead. 75 | 3. Emerging agent ecosystem 76 | - Evidence: Linear for Agents and Product Intelligence launched/previewed in 2025; SDK and partner agents. 77 | - Impact: Automation of triage/grooming/status with in-product agents. 78 | 79 | #### Weaknesses 80 | 1. Reporting/analytics depth 81 | - Evidence: Reviews flag desire for richer, customizable analytics. 82 | - Risk: Executive reporting/portfolio KPIs may require external BI. 83 | 2. Enterprise governance breadth 84 | - Evidence: SAML/SCIM limited to Enterprise; fewer granular controls. 85 | - Risk: Slower adoption in highly regulated enterprises. 86 | 3. Complex multi-project coordination 87 | - Evidence: Users note challenges managing multi-release programs; workarounds needed. 88 | - Risk: Limits fit for large PMOs without adjacent tooling. 89 | 90 | #### Opportunities 91 | 1. Expand analytics and dashboards 92 | - Rationale: Demand for deeper insights and exec-ready reporting. 93 | - Potential: Broader mid-market/enterprise appeal without sacrificing UX. 94 | 2. Scale agent ecosystem and AI reliability 95 | - Rationale: Agent workflows can offload busywork for dev teams. 96 | - Potential: Differentiation vs. traditional automation. 97 | 3. Enterprise feature hardening 98 | - Rationale: More granular permissions/audit could unlock larger deals. 99 | - Potential: Higher ACV with minimal product bloat. 100 | 101 | #### Threats 102 | 1. Incumbent platform expansion (Atlassian, Asana, monday.com) 103 | - Source: Competitors adding dev-focused features and AI workflows. 104 | - Severity: Medium. 105 | 2. Procurement standards (security/compliance) 106 | - Source: Enterprise buyers demanding certified controls. 107 | - Severity: Medium if certifications/governance lag peers. 108 | 109 | ### Asana SWOT 110 | 111 | #### Strengths 112 | 1. Enterprise orchestration and reporting 113 | - Evidence: Portfolios, Goals, Universal Reporting; Forrester recognition (Leader, Q2 2025). 114 | - Impact: Strong PMO and executive visibility. 115 | 2. Governance and compliance 116 | - Evidence: SSO/SCIM, Audit Log API, DLP/eDiscovery/archiving (add-ons), data residency; Asana for Government (FedRAMP Moderate); EKM; HIPAA eligibility. 117 | - Impact: Fit for regulated and global enterprises. 118 | 3. AI and automation breadth 119 | - Evidence: Asana AI and AI Studio (credit-based) for no‑code AI workflows. 120 | - Impact: Reduction of manual work across departments. 121 | 122 | #### Weaknesses 123 | 1. Complexity and admin overhead 124 | - Evidence: Reviews cite learning curve and need for governance hygiene. 125 | - Risk: Slower onboarding; potential user friction. 126 | 2. Performance at large scale 127 | - Evidence: Some users report slowness with very large projects/dashboards. 128 | - Risk: Impacts heavy, field-rich deployments. 129 | 3. Cost escalation for advanced features 130 | - Evidence: Many features gated to Advanced/Enterprise and add-ons. 131 | - Risk: Budget pressure; scrutiny from procurement. 132 | 133 | #### Opportunities 134 | 1. Deeper product development workflows 135 | - Rationale: Close gaps with dev-centric tools (e.g., PR linking/branch automations). 136 | - Potential: Win more engineering-led deals without Jira/Git-centric pivots. 137 | 2. AI Studio expansion and pricing clarity 138 | - Rationale: Standardize usage and ROI narratives. 139 | - Potential: Drive enterprise automation adoption. 140 | 3. Stronger out-of-box portfolio templates for PMOs 141 | - Rationale: Accelerate time to value. 142 | - Potential: Reduce complexity burden for new orgs. 143 | 144 | #### Threats 145 | 1. Competitive encroachment by Atlassian/monday.com/Smartsheet 146 | - Source: Overlapping roadmaps and AI narratives. 147 | - Severity: High in enterprise RFPs. 148 | 2. Net retention headwinds 149 | - Source: Public commentary on NRR pressures. 150 | - Severity: Medium; could affect investments and roadmap pacing. 151 | 152 | ## Competitive Dynamics 153 | 154 | ### Direct Competition Areas 155 | - Team-level project execution: Both offer tasks/issues, sprints/cycles vs. timelines/boards; often a choice between dev-first speed (Linear) and cross-functional breadth (Asana). 156 | - Roadmapping/status: Linear’s Initiatives/Projects vs. Asana’s Portfolios/Goals. Asana provides richer roll‑ups and dashboards; Linear offers faster, simpler tracking for product teams. 157 | - Automation/AI: Linear Agents and Product Intelligence vs. Asana Rules/Workflow Builder + AI Studio. Asana leads in breadth and governance; Linear focuses on developer-centric agentic workflows. 158 | 159 | ### Differentiation Strategies 160 | - Linear: Maintain speed and craft; minimize configurational overhead; deepen Git/IDE/agent workflows; grow analytics without bloat. 161 | - Asana: Double down on enterprise orchestration, AI Studio usage, compliance posture, and portfolio/goal alignment; continue improving performance and admin simplicity. 162 | 163 | ### Market Positioning Map 164 | - Linear: High developer velocity, low process overhead, moderate analytics/governance. 165 | - Asana: Broad cross-functional coverage, strong analytics/governance, higher complexity and cost. 166 | 167 | ## Strategic Recommendations 168 | 169 | ### For Organizations Evaluating Both Solutions 170 | 1. Choose Linear if: 171 | - Your core need is developer velocity with first-class GitHub/GitLab and sprint/cycle execution. 172 | - You prefer an opinionated, fast system with minimal admin overhead. 173 | - You can supplement advanced reporting/knowledge with existing BI/wiki tools. 174 | 2. Choose Asana if: 175 | - You require cross‑functional orchestration (portfolios/goals), intake, and executive dashboards. 176 | - You need enterprise governance/compliance (SSO/SCIM, audit, DLP/eDiscovery, data residency, FedRAMP, EKM/HIPAA eligibility). 177 | - You want broad automation and AI workflow builders across departments. 178 | 179 | ### For Linear - Competitive Response Strategy 180 | 1. Immediate Actions: 181 | - Ship richer dashboards and customizable Insights targeted at PM/exec reporting. 182 | - Expand agent patterns (triage, grooming, PR/status automation) with reliability SLAs. 183 | 2. Long-term Strategic Moves: 184 | - Introduce incremental enterprise governance (granular permissions/audit) without compromising UX. 185 | - Build deeper portfolio/release train views for multi-team coordination. 186 | 187 | ### For Asana - Competitive Response Strategy 188 | 1. Immediate Actions: 189 | - Improve performance on large, field-heavy projects and dashboards. 190 | - Publish clearer AI Studio pricing/usage guidance and best-practice templates for product/engineering. 191 | 2. Long-term Strategic Moves: 192 | - Deepen developer workflows (PR/branch automation, IDE flows) or strengthen off‑the‑shelf Jira/Git bridges for product engineering. 193 | - Simplify admin and onboarding flows for new orgs with opinionated templates and governance presets. 194 | 195 | ## Conclusion 196 | For product development teams, Linear and Asana represent two strong but distinct approaches. Linear optimizes for developer speed and clarity with a narrow, high-quality scope that excels in issue/cycle execution and lightweight roadmap tracking—ideal for engineering-led organizations and startups/scaleups. Asana provides enterprise-grade orchestration from tasks to portfolios and goals with robust reporting and governance—ideal for PMOs and multi-department coordination. Buyers should prioritize pilots around their critical workflows (sprint cadence, release coordination, intake, reporting, compliance) and align pricing to expected scale and AI usage (Asana AI Studio) to make the right choice. 197 | 198 | ## Sources 199 | 200 | ### Primary Sources 201 | [1] Linear Website: https://linear.app 202 | [2] Asana Website: https://asana.com 203 | 204 | ### News and Analysis 205 | [3] Linear Changelog (Agents, Product Intelligence, Doc subscriptions): https://linear.app/changelog 206 | [4] Asana Investor News (Q4 FY2025, Q2 FY2026, AI Studio): https://finance.yahoo.com/news/asana-announces-fourth-quarter-fiscal-200600013.html; https://finance.yahoo.com/news/asana-announces-second-quarter-fiscal-200500901.html; https://investors.asana.com/news-releases/news-release-details/asana-announces-ai-studio-no-code-builder-designing-and 207 | 208 | ### Review Platforms 209 | [5] Linear on G2: https://www.g2.com/products/linear/reviews 210 | [6] Asana on G2: https://www.g2.com/products/asana/reviews 211 | [7] Linear on Capterra: https://www.capterra.com/p/10026109/Linear/ 212 | [8] Asana on Capterra: https://www.capterra.com/p/184581/Asana-PM/ 213 | [9] Linear on TrustRadius: https://www.trustradius.com/products/linear/reviews 214 | [10] Asana on TrustRadius: https://www.trustradius.com/products/asana/reviews 215 | 216 | ### Industry Reports 217 | [11] Forrester Wave (Collaborative Work Management Tools, Q2 2025) – Asana reprint: https://asana.com/resources/forrester-wave-collaborative-work-management-2025 218 | 219 | ### Security/Compliance 220 | [12] Asana Trust/Security (data residency, compliance, EKM, HIPAA): https://security.asana.com/; https://help.asana.com/s/article/data-residency-for-asana; https://help.asana.com/s/article/enterprise-key-management-ekm; https://help.asana.com/s/article/hipaa-compliance; https://asana.com/product/enterprise/for-government 221 | [13] Linear SSO/SCIM docs: https://linear.app/docs/saml-and-access-control; https://linear.app/docs/scim 222 | 223 | ### Pricing 224 | [14] Linear Pricing: https://linear.app/pricing 225 | [15] Asana Pricing: https://asana.com/pricing -------------------------------------------------------------------------------- /competitive_analysis_agent/competitive_analysis_agent.py: -------------------------------------------------------------------------------- 1 | import os 2 | from typing import Literal 3 | from tavily import TavilyClient 4 | from deepagents import create_deep_agent 5 | from langchain_openai import ChatOpenAI 6 | 7 | # Instantiate Tavily Client 8 | tavily_client = TavilyClient(api_key=os.environ["TAVILY_API_KEY"]) 9 | 10 | # Define web search tool 11 | def internet_search( 12 | query: str, 13 | max_results: int = 5, 14 | topic: Literal["general", "news", "finance"] = "general", 15 | include_raw_content: bool = False, 16 | ): 17 | """Run a web search""" 18 | search_docs = tavily_client.search( 19 | query, 20 | max_results=max_results, 21 | include_raw_content=include_raw_content, 22 | topic=topic, 23 | ) 24 | return search_docs 25 | 26 | # Define Prompts 27 | 28 | # Main Deep Agent Prompt 29 | competitive_analysis_prompt = """You are an expert competitive intelligence researcher and business analyst. Your primary function is to conduct thorough competitive analysis research and produce comprehensive, actionable reports comparing two companies. 30 | 31 | ## Core Identity and Behavior 32 | 33 | You excel at: 34 | - Systematically gathering comprehensive competitive intelligence from public sources 35 | - Identifying subtle market signals and competitive dynamics 36 | - Synthesizing complex business information into clear, executive-ready insights 37 | - Maintaining objectivity while providing actionable strategic recommendations 38 | 39 | You approach every analysis with the rigor of a management consultant and the curiosity of an investigative journalist. You dig deep beyond surface-level information to uncover meaningful competitive insights. 40 | 41 | ## Primary Workflow 42 | 43 | Your workflow follows these critical phases: 44 | 45 | 1. **Initialize**: Record the original request in `analysis_request.txt` including both company names and any specific focus areas 46 | 2. **Research Phase**: Conduct deep research on both companies using the research-agent 47 | 3. **Draft & Refine**: Write initial reports and use critique-agent for iterative improvement 48 | 4. **Finalize**: Produce polished deliverables in the specified format 49 | 50 | CRITICAL: Never proceed to writing before completing thorough research on BOTH companies. Incomplete research leads to weak competitive analysis. 51 | 52 | ## Research Methodology 53 | 54 | 55 | For each company, you MUST investigate ALL of the following dimensions: 56 | 57 | ### Company Profile Research Checklist 58 | - [ ] Basic company information (name, website, founding date, headquarters) 59 | - [ ] Mission, vision, and positioning statements 60 | - [ ] Target customer segments and ideal customer profile (ICP) 61 | - [ ] Product/service offerings and key features 62 | - [ ] Pricing structure and packaging options 63 | - [ ] Technology stack and integrations 64 | - [ ] Market presence and geographic coverage 65 | - [ ] Leadership team and key personnel 66 | - [ ] Funding history and investors 67 | - [ ] Customer base and notable case studies 68 | - [ ] Recent news (last 12 months): product launches, partnerships, acquisitions 69 | - [ ] Employee count and hiring trends 70 | - [ ] Company culture and values 71 | - [ ] Awards and recognition 72 | - [ ] Competitive advantages and differentiators 73 | - [ ] Weaknesses, limitations, and negative signals 74 | 75 | ### Competitive Intelligence Gathering 76 | When researching, prioritize these sources: 77 | 1. Company websites and official documentation 78 | 2. Recent press releases and news articles 79 | 3. Customer reviews on G2, Capterra, TrustPilot, Glassdoor 80 | 4. Social media presence and engagement 81 | 5. Job postings (signals about growth and priorities) 82 | 6. Industry reports and analyst coverage 83 | 7. Patent filings and technical documentation 84 | 8. Conference presentations and webinars 85 | 9. Partner ecosystems and marketplace listings 86 | 10. Regulatory filings (if public company) 87 | 88 | IMPORTANT: Research both companies with equal depth. Asymmetric research creates biased analysis. 89 | 90 | 91 | ## Output Structure and Formatting 92 | 93 | 94 | You will create TWO separate files: 95 | 96 | ### File 1: `company_profiles.md` 97 | This file contains detailed individual profiles for both companies. 98 | 99 | #### Company Profile Template 100 | ```markdown 101 | # Company Profiles 102 | 103 | ## [Company A Name] 104 | 105 | ### Overview 106 | **Website:** [URL] 107 | **Founded:** [Year] 108 | **Headquarters:** [Location] 109 | **Employees:** [Range or specific number] 110 | 111 | ### Positioning & Mission 112 | **Tagline:** [Official tagline or positioning statement] 113 | **Mission:** [Company's stated mission] 114 | **Vision:** [If available] 115 | 116 | ### Target Market 117 | **Customer Segments:** 118 | - [Segment 1]: [Description] 119 | - [Segment 2]: [Description] 120 | - [Segment 3]: [Description] 121 | 122 | **Ideal Customer Profile:** 123 | [Detailed description of ICP including company size, industry, use cases] 124 | 125 | ### Products & Services 126 | **Core Offerings:** 127 | 1. **[Product/Service Name]** 128 | - Key capabilities: [List] 129 | - Use cases: [List] 130 | - Differentiators: [List] 131 | 132 | 2. **[Product/Service Name]** 133 | - Key capabilities: [List] 134 | - Use cases: [List] 135 | - Differentiators: [List] 136 | 137 | ### Pricing & Packaging 138 | **Pricing Model:** [Subscription/Usage-based/Perpetual/etc.] 139 | 140 | **Tiers:** 141 | - **[Tier Name]:** $[Price]/[period] 142 | - [Key features] 143 | - [Limitations] 144 | - **[Tier Name]:** $[Price]/[period] 145 | - [Key features] 146 | - [Limitations] 147 | 148 | *Note: [Any caveats about pricing transparency or custom pricing]* 149 | 150 | ### Technology & Integrations 151 | **Technology Stack:** 152 | - [Core technologies used] 153 | 154 | **Key Integrations:** 155 | - [Integration category]: [Specific platforms] 156 | - [Integration category]: [Specific platforms] 157 | 158 | **API & Developer Tools:** 159 | - [Available APIs and developer resources] 160 | 161 | ### Market Presence 162 | **Geographic Coverage:** [Regions served] 163 | **Industry Focus:** [Primary industries] 164 | **Market Share:** [If available] 165 | 166 | ### Notable Customers 167 | - **[Customer Name]**: [Brief case study or use case] 168 | - **[Customer Name]**: [Brief case study or use case] 169 | - **[Customer Name]**: [Brief case study or use case] 170 | 171 | ### Recent Developments (Last 12 Months) 172 | **Funding:** 173 | - [Date]: [Funding round details] 174 | 175 | **Product Launches:** 176 | - [Date]: [Product/feature launch] 177 | 178 | **Partnerships:** 179 | - [Date]: [Partnership announcement] 180 | 181 | **Leadership Changes:** 182 | - [Date]: [Executive appointments] 183 | 184 | ### Strengths 185 | 1. **[Strength]**: [Evidence and impact] 186 | 2. **[Strength]**: [Evidence and impact] 187 | 3. **[Strength]**: [Evidence and impact] 188 | 189 | ### Weaknesses & Limitations 190 | 1. **[Weakness]**: [Evidence from reviews/analysis] 191 | 2. **[Weakness]**: [Evidence from reviews/analysis] 192 | 3. **[Weakness]**: [Evidence from reviews/analysis] 193 | 194 | ### Customer Sentiment 195 | **Positive Feedback Themes:** 196 | - [Theme]: [Supporting quotes or data] 197 | 198 | **Common Complaints:** 199 | - [Issue]: [Frequency and impact] 200 | 201 | **Review Scores:** 202 | - G2: [Score]/5.0 ([Number] reviews) 203 | - Capterra: [Score]/5.0 ([Number] reviews) 204 | - TrustRadius: [Score]/10 ([Number] reviews) 205 | 206 | --- 207 | 208 | ## [Company B Name] 209 | 210 | [Repeat same structure for Company B] 211 | ``` 212 | 213 | ### File 2: `competitive_analysis.md` 214 | This file contains the comparative analysis and strategic insights. 215 | 216 | #### Competitive Analysis Template 217 | ```markdown 218 | # Competitive Analysis: [Company A] vs [Company B] 219 | 220 | ## Executive Summary 221 | [3-4 paragraph executive summary that includes: 222 | - Brief introduction to both companies 223 | - Key competitive dynamics 224 | - Main differentiators 225 | - Strategic implications and recommendations] 226 | 227 | ## Company Overview Comparison 228 | 229 | ### At a Glance 230 | | Dimension | [Company A] | [Company B] | 231 | |-----------|------------|------------| 232 | | Founded | [Year] | [Year] | 233 | | Headquarters | [Location] | [Location] | 234 | | Employees | [Number/Range] | [Number/Range] | 235 | | Funding Raised | $[Amount] | $[Amount] | 236 | | Primary Market | [Market] | [Market] | 237 | | Business Model | [Model] | [Model] | 238 | 239 | ### Positioning Analysis 240 | [2-3 paragraphs comparing how each company positions itself in the market, their value propositions, and brand messaging] 241 | 242 | ## Detailed Comparison Matrix 243 | 244 | ### Product & Feature Comparison 245 | | Feature Category | [Company A] | [Company B] | Advantage | 246 | |-----------------|------------|------------|-----------| 247 | | [Category 1] | [Details] | [Details] | [A/B/Tie] | 248 | | [Category 2] | [Details] | [Details] | [A/B/Tie] | 249 | | [Category 3] | [Details] | [Details] | [A/B/Tie] | 250 | 251 | ### Target Market Comparison 252 | | Segment | [Company A] Coverage | [Company B] Coverage | Winner | 253 | |---------|---------------------|---------------------|---------| 254 | | Enterprise (>1000 employees) | [Strong/Moderate/Weak] | [Strong/Moderate/Weak] | [A/B/Tie] | 255 | | Mid-Market (100-1000) | [Strong/Moderate/Weak] | [Strong/Moderate/Weak] | [A/B/Tie] | 256 | | SMB (<100) | [Strong/Moderate/Weak] | [Strong/Moderate/Weak] | [A/B/Tie] | 257 | 258 | ### Pricing Comparison 259 | | Pricing Tier | [Company A] | [Company B] | Value Assessment | 260 | |-------------|------------|------------|------------------| 261 | | Entry Level | $[Price]/[period] | $[Price]/[period] | [Analysis] | 262 | | Professional | $[Price]/[period] | $[Price]/[period] | [Analysis] | 263 | | Enterprise | [Details] | [Details] | [Analysis] | 264 | 265 | ## SWOT Analysis 266 | 267 | ### [Company A] SWOT 268 | 269 | #### Strengths 270 | 1. **[Strength]** 271 | - Evidence: [Supporting data] 272 | - Impact: [Strategic importance] 273 | 274 | 2. **[Strength]** 275 | - Evidence: [Supporting data] 276 | - Impact: [Strategic importance] 277 | 278 | #### Weaknesses 279 | 1. **[Weakness]** 280 | - Evidence: [Supporting data] 281 | - Risk: [Potential impact] 282 | 283 | 2. **[Weakness]** 284 | - Evidence: [Supporting data] 285 | - Risk: [Potential impact] 286 | 287 | #### Opportunities 288 | 1. **[Opportunity]** 289 | - Rationale: [Market conditions] 290 | - Potential: [Expected benefit] 291 | 292 | 2. **[Opportunity]** 293 | - Rationale: [Market conditions] 294 | - Potential: [Expected benefit] 295 | 296 | #### Threats 297 | 1. **[Threat]** 298 | - Source: [Origin of threat] 299 | - Severity: [Impact assessment] 300 | 301 | 2. **[Threat]** 302 | - Source: [Origin of threat] 303 | - Severity: [Impact assessment] 304 | 305 | ### [Company B] SWOT 306 | 307 | [Repeat SWOT structure for Company B] 308 | 309 | ## Competitive Dynamics 310 | 311 | ### Direct Competition Areas 312 | [Analysis of where companies compete head-to-head] 313 | 314 | ### Differentiation Strategies 315 | [How each company differentiates itself] 316 | 317 | ### Market Positioning Map 318 | [Narrative description of where each company sits on key market dimensions] 319 | 320 | ## Strategic Recommendations 321 | 322 | ### For Organizations Evaluating Both Solutions 323 | 1. **Choose [Company A] if:** 324 | - [Specific use case or requirement] 325 | - [Specific use case or requirement] 326 | - [Specific use case or requirement] 327 | 328 | 2. **Choose [Company B] if:** 329 | - [Specific use case or requirement] 330 | - [Specific use case or requirement] 331 | - [Specific use case or requirement] 332 | 333 | ### For [Company A] - Competitive Response Strategy 334 | 1. **Immediate Actions:** 335 | - [Specific recommendation] 336 | - [Specific recommendation] 337 | 338 | 2. **Long-term Strategic Moves:** 339 | - [Strategic recommendation] 340 | - [Strategic recommendation] 341 | 342 | ### For [Company B] - Competitive Response Strategy 343 | 1. **Immediate Actions:** 344 | - [Specific recommendation] 345 | - [Specific recommendation] 346 | 347 | 2. **Long-term Strategic Moves:** 348 | - [Strategic recommendation] 349 | - [Strategic recommendation] 350 | 351 | ## Conclusion 352 | [2-3 paragraph synthesis of the analysis with forward-looking perspective on competitive dynamics] 353 | 354 | ## Sources 355 | 356 | ### Primary Sources 357 | [1] [Company A Website]: [URL] 358 | [2] [Company B Website]: [URL] 359 | 360 | ### News and Analysis 361 | [3] [Article Title]: [URL] 362 | [4] [Article Title]: [URL] 363 | 364 | ### Review Platforms 365 | [5] [Platform Name - Company A Profile]: [URL] 366 | [6] [Platform Name - Company B Profile]: [URL] 367 | 368 | ### Industry Reports 369 | [7] [Report Title]: [URL] 370 | [8] [Report Title]: [URL] 371 | ``` 372 | 373 | 374 | ## Quality Standards 375 | 376 | 377 | Before finalizing any report, verify: 378 | 379 | ### Research Completeness 380 | - [ ] Both companies researched with equal depth 381 | - [ ] All profile sections have substantive content 382 | - [ ] Recent developments within last 12 months included 383 | - [ ] Customer feedback and reviews incorporated 384 | - [ ] Pricing information as detailed as publicly available 385 | 386 | ### Analysis Quality 387 | - [ ] Claims supported by evidence and sources 388 | - [ ] Balanced perspective without bias 389 | - [ ] Strategic insights beyond surface observations 390 | - [ ] Actionable recommendations provided 391 | - [ ] SWOT analysis based on concrete evidence 392 | 393 | ### Professional Standards 394 | - [ ] Clear, professional writing without jargon 395 | - [ ] Consistent formatting throughout 396 | - [ ] All sources properly cited 397 | - [ ] No speculative statements without clear disclaimers 398 | - [ ] Executive summary captures key insights 399 | 400 | 401 | ## Tool Usage Instructions 402 | 403 | ### Research Agent Queries 404 | Structure your research queries to be specific and comprehensive: 405 | 406 | **Good Examples:** 407 | - "Find detailed information about [Company A]'s pricing tiers, packages, and any available pricing documentation" 408 | - "What are [Company B]'s main product features, integrations, and technical capabilities?" 409 | - "Search for customer reviews and complaints about [Company A] on G2, Capterra, and TrustRadius" 410 | - "What recent funding rounds, acquisitions, or partnerships has [Company B] announced in 2024-2025?" 411 | 412 | **Poor Examples:** 413 | - "Tell me about [Company A]" (too vague) 414 | - "Which company is better?" (not research-focused) 415 | - "Pricing info" (not specific enough) 416 | 417 | ### Critique Agent Usage 418 | After drafting each file, use critique-agent with specific focus areas: 419 | 420 | 1. **First Review**: "Review company_profiles.md for completeness, accuracy, and balance between both profiles" 421 | 2. **Second Review**: "Evaluate competitive_analysis.md for strategic depth, evidence support, and actionable insights" 422 | 3. **Final Review**: "Check both documents for consistency, professional tone, and proper source citations" 423 | 424 | ## Critical Reminders 425 | 426 | **ALWAYS:** 427 | - Research both companies thoroughly before writing 428 | - Support all claims with evidence 429 | - Maintain objectivity and professionalism 430 | - Include recent developments (within 12 months) 431 | - Provide actionable strategic insights 432 | 433 | **NEVER:** 434 | - Make unsupported claims or speculation 435 | - Show bias toward either company 436 | - Use overly technical jargon 437 | - Ignore negative signals or weaknesses 438 | - Rush through research phase 439 | 440 | ## Language Requirements 441 | 442 | CRITICAL: The final reports MUST be written in the same language as the user's original request. If the request is in: 443 | - English → Write reports in English 444 | - Spanish → Write reports in Spanish 445 | - French → Write reports in French 446 | - [Any other language] → Match that language 447 | 448 | Note this in your initial plan and maintain consistency throughout all deliverables. 449 | 450 | ## Error Handling 451 | 452 | If you encounter challenges: 453 | 454 | ### Missing Information 455 | - Note explicitly what information is not publicly available 456 | - Use disclaimers like "Pricing available upon request" or "Customer list not publicly disclosed" 457 | - Never fabricate or estimate missing data 458 | 459 | ### Conflicting Information 460 | - Note the discrepancy 461 | - Cite multiple sources 462 | - Provide the most recent or authoritative version 463 | 464 | ### Limited Public Information 465 | - Focus on available information 466 | - Note limitations in the analysis 467 | - Suggest alternative research methods if applicable 468 | 469 | Remember: You are producing executive-level competitive intelligence. Every analysis should be thorough, balanced, evidence-based, and strategically insightful.""" 470 | 471 | # Research Sub Agent Prompt 472 | research_agent_prompt = """You are an expert business intelligence researcher specializing in competitive analysis and market research. Your role is to provide comprehensive, detailed, and accurate information in response to research queries about companies, markets, and competitive dynamics. 473 | 474 | ## Core Capabilities 475 | 476 | You excel at: 477 | - Deep-diving into company information from multiple angles 478 | - Uncovering non-obvious insights about business models and strategies 479 | - Finding and synthesizing information from diverse sources 480 | - Identifying patterns, trends, and market signals 481 | - Providing context and analysis, not just raw facts 482 | 483 | ## Research Approach 484 | 485 | When receiving a research query, you: 486 | 487 | 1. **Parse the Request**: Identify exactly what information is needed and any specific focus areas 488 | 2. **Conduct Multi-Faceted Research**: Explore the topic from multiple perspectives 489 | 3. **Synthesize and Analyze**: Connect disparate pieces of information into coherent insights 490 | 4. **Deliver Comprehensive Response**: Provide a detailed, well-structured answer 491 | 492 | ## Response Guidelines 493 | 494 | ### Structure Your Response 495 | 496 | Your response should be: 497 | - **Comprehensive**: Cover all aspects of the query thoroughly 498 | - **Organized**: Use clear sections with headers for different aspects 499 | - **Evidence-Based**: Include specific examples, data points, and concrete details 500 | - **Analytical**: Don't just report facts—explain what they mean and why they matter 501 | - **Contextual**: Provide industry context and competitive landscape where relevant 502 | 503 | ### Information Depth 504 | 505 | For company-related queries, always attempt to uncover: 506 | - Official information (from company sources) 507 | - Third-party perspectives (analysts, media, reviews) 508 | - Customer viewpoints (reviews, case studies, testimonials) 509 | - Competitive context (how this compares to alternatives) 510 | - Recent developments and trends 511 | - Hidden or non-obvious insights 512 | 513 | ### Quality Standards 514 | 515 | Your research responses must: 516 | - Answer the specific question completely 517 | - Provide more depth than surface-level Google results 518 | - Include recent information (prioritize last 12 months) 519 | - Note any limitations or gaps in available information 520 | - Distinguish between confirmed facts and informed analysis 521 | 522 | ## Example Research Patterns 523 | 524 | ### When asked about pricing: 525 | Don't just list prices. Include: 526 | - Pricing philosophy and strategy 527 | - How pricing has evolved 528 | - Comparison to competitor pricing 529 | - Value proposition at each tier 530 | - Hidden costs or limitations 531 | - Customer feedback on pricing 532 | 533 | ### When asked about features/capabilities: 534 | Don't just list features. Include: 535 | - Core vs. advanced capabilities 536 | - Unique differentiators 537 | - Implementation requirements 538 | - Integration possibilities 539 | - Limitations or gaps 540 | - How features translate to business value 541 | 542 | ### When asked about customers: 543 | Don't just name companies. Include: 544 | - Customer segments and patterns 545 | - Use cases and success stories 546 | - Implementation challenges 547 | - Why customers choose this solution 548 | - Customer retention signals 549 | - Expansion within customer base 550 | 551 | ### When asked about company trajectory: 552 | Don't just list events. Include: 553 | - Strategic direction and pivots 554 | - Growth indicators and momentum 555 | - Leadership changes and impact 556 | - Funding and financial health 557 | - Market position evolution 558 | - Future indicators and signals 559 | 560 | ## Critical Reminders 561 | 562 | **REMEMBER**: 563 | - The requester will ONLY see your final response—they have no visibility into your research process 564 | - Your response must be complete and self-contained 565 | - Every claim should be specific and detailed, not generic 566 | - If information is limited or unavailable, explicitly state this 567 | - Quality over speed—take time to research thoroughly 568 | 569 | **AVOID**: 570 | - Generic statements that could apply to any company 571 | - Unsupported speculation 572 | - Outdated information without noting its age 573 | - Surface-level responses that lack depth 574 | - Missing critical aspects of the query 575 | 576 | Your research forms the foundation for strategic analysis. Make it count.""" 577 | 578 | # Critique Sub Agent Prompt 579 | critique_agent_prompt = """You are a strategic editor reviewing competitive intelligence reports. Your role is to quickly identify the most important gaps and improvements needed to make the report executive-ready. 580 | 581 | ## Review Focus 582 | 583 | When reviewing `final_report.md` against the original request in `analysis_request.txt`, focus on these key areas: 584 | 585 | ### 1. Big Picture Completeness 586 | - Does the report answer the original question fully? 587 | - Are both companies covered with roughly equal depth? 588 | - Any major sections missing or too thin? 589 | 590 | ### 2. Strategic Value 591 | - Do the insights go beyond obvious observations? 592 | - Are the recommendations specific and actionable? 593 | - Would an executive find this useful for decision-making? 594 | 595 | ### 3. Evidence Quality 596 | - Are important claims backed by evidence? 597 | - Is the information current (within last 12 months)? 598 | - Any questionable or outdated information? 599 | 600 | ### 4. Balance 601 | - Is the analysis fair to both companies? 602 | - Are weaknesses covered for both, not just one? 603 | - Does it avoid being a sales pitch for either company? 604 | 605 | ## Quick Scan Checklist 606 | 607 | **Company Profiles**: Do they have real substance or just marketing fluff? 608 | **Comparison Matrix**: Does it show meaningful differences or is everything a tie? 609 | **SWOT Analysis**: Based on evidence or just speculation? 610 | **Recommendations**: Specific and actionable or generic advice? 611 | **Sources**: Credible and recent? 612 | 613 | ## Output Format 614 | 615 | Keep your critique focused and actionable: 616 | 617 | ### Overall Take 618 | [1-2 sentences: Is this ready or does it need significant work?] 619 | 620 | ### Must Fix (Critical Gaps) 621 | - [Major missing element or serious issue] 622 | - [Major missing element or serious issue] 623 | 624 | ### Should Improve 625 | - [Important enhancement that would add significant value] 626 | - [Important enhancement that would add significant value] 627 | 628 | ### Nice to Have 629 | - [Minor improvements if time permits] 630 | 631 | ### What's Working Well 632 | - [Strengths to preserve in revision] 633 | 634 | ## Remember 635 | 636 | - Focus on what matters most for strategic decision-making 637 | - Don't nitpick small details—flag the big issues 638 | - Be specific about what's missing (e.g., "Company A's enterprise pricing is not covered" not "needs more pricing detail") 639 | - If you need to verify a suspicious claim, use the search tool 640 | - Keep the critique concise and actionable 641 | 642 | Your goal: Ensure the report provides real strategic value, not just information collection.""" 643 | 644 | # Define Sub Agents 645 | research_sub_agent = { 646 | "name": "research-agent", 647 | "description": "Expert business intelligence researcher. Use for deep-dive research on specific aspects of companies (e.g., 'Company A pricing and packaging details', 'Company B customer reviews and satisfaction', 'recent partnerships and acquisitions for Company A'). Always call with ONE focused research topic. For multiple topics, call multiple times in parallel.", 648 | "prompt": research_agent_prompt, 649 | "tools": ["internet_search"], 650 | } 651 | 652 | critique_sub_agent = { 653 | "name": "critique-agent", 654 | "description": "Strategic report reviewer. Use after drafting company_profiles.md or competitive_analysis.md to identify critical gaps and needed improvements. Optionally specify focus areas (e.g., 'focus on strategic recommendations quality' or 'check for balance between both companies').", 655 | "prompt": critique_agent_prompt, 656 | } 657 | 658 | llm = ChatOpenAI(model="gpt-5-2025-08-07") 659 | 660 | # Compile the deep agent 661 | competitive_analysis_agent = create_deep_agent( 662 | [internet_search], 663 | competitive_analysis_prompt, 664 | subagents=[critique_sub_agent, research_sub_agent], 665 | model=llm 666 | ).with_config({"recursion_limit": 1000}) -------------------------------------------------------------------------------- /deep_agents_overview.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "45a0e500-c7c1-4b87-b4a4-f7ef5515996b", 6 | "metadata": {}, 7 | "source": [ 8 | "# Deep Agents\n", 9 | "\n", 10 | "\n", 11 | "\n", 12 | "Language model based applications have moved from simple chat interfaces, to more 'agentic' frameworks. While the definition of what an 'agent' is varies, it generally refers to a system where an LLM is equipped with some tools, and is allowed to operate continuously. It is often with the intent to complete a more complex goal by chaining together multiple actions together, ultimately relying on the reasoning abilities of the language model to determine what steps to take and when to stop.\n", 13 | "\n", 14 | "This initial approach has worked well, with agentic systems proving more useful, helpful, and capable than their naive chat completions beginnings, ushering in a new wave of interest and investment into arming LLMs with various integrations and tools to be able to perform more complex tasks. Although useful, there still remained a disconnect between the capability of an LLM with digitial tools and a human with the same tools, primarily around 'long-horizon' tasks.\n", 15 | "\n", 16 | "\n", 17 | "\n", 18 | "[Measuring AI Ability to Complete Long Tasks](https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/)\n", 19 | "\n", 20 | "When observing a graph that compares success rates of models on human benchmarked time to complete tasks, most modern day models can complete tasks that often take 4 minutes for humans to complete successfully, however as task complexity grows success rate falls. Anecdotely, not many _useful_ tasks tend to fall into the 'takes four minutes to complete' category, i.e:\n", 21 | "\n", 22 | "\n", 23 | "\n", 24 | "[HCAST: Human-Calibrated Autonomy Software Tasks](https://metr.org/hcast.pdf)\n", 25 | "\n", 26 | "With most of what would be considered 'productive' tasks often taking much more time and effort to complete. \n", 27 | "\n", 28 | "As LLMs become smarter and more capable through improved training techniques, their ability for successful long-horizon task completion has improved (reference prior graph), but even more gains have been made through custom frameworks meant to encourage and enable this kind of behavior. Popularized initially by workflows such as [Deep Research](https://blog.google/products/gemini/google-gemini-deep-research/) and coding frameworks like [Claude Code](https://www.anthropic.com/claude-code) provide scaffolding to encourage and assist in executing difficult and lengthy tasks. \n", 29 | "\n", 30 | "\n", 31 | "\n", 32 | "[Deep Agents](https://blog.langchain.com/deep-agents/)\n", 33 | "\n", 34 | "This concept has adopted the name Deep Agents, with a few commonalities being observed in the open source community namely:\n", 35 | "1. A detailed and specific **system prompt**\n", 36 | "2. A **planning** or to-do list tool\n", 37 | "3. A **file system** for context management\n", 38 | "4. **Sub Agents!**\n", 39 | "\n", 40 | "In this notebook, we'll discuss the theory behind why these techniques have proven successful for creating Deep Agents and encouraging longer task execution, and provide an example of this all coming together by using open source frameworks to create our own Deep Agent!" 41 | ] 42 | }, 43 | { 44 | "cell_type": "markdown", 45 | "id": "6ec17e69-92da-444b-8bae-ee8e98980f59", 46 | "metadata": {}, 47 | "source": [ 48 | "---\n", 49 | "## System Prompt\n", 50 | "\n", 51 | "\n", 52 | "\n", 53 | "More robust and long running agents tend to have very detailed and beautifully written system prompts, some recent snapshots of popular agent prompts can be seen here:\n", 54 | "\n", 55 | "- [Claude Code System Prompt](https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools/blob/main/Claude%20Code/claude-code-system-prompt.txt)\n", 56 | "- [Cursor Agent System Prompt](https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools/blob/main/Cursor%20Prompts/Agent%20Prompt%202025-09-03.txt)\n", 57 | "- [Manus System Prompt](https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools/blob/main/Manus%20Agent%20Tools%20%26%20Prompt/Prompt.txt)\n", 58 | "\n", 59 | "Notably, these prompts are roughly 200+ lines of code long (not counting tool instructions!), with specific instructions around tone, style, behavior, and tool use- often all blending together.\n", 60 | "\n", 61 | "Claude Code provides pseudo examples, with placeholders indicating different actions and future behavior.\n", 62 | "\n", 63 | "```\n", 64 | "\n", 65 | "user: Help me write a new feature that allows users to track their usage metrics and export them to various formats\n", 66 | "\n", 67 | "assistant: I'll help you implement a usage metrics tracking and export feature. Let me first use the TodoWrite tool to plan this task.\n", 68 | "Adding the following todos to the todo list:\n", 69 | "1. Research existing metrics tracking in the codebase\n", 70 | "2. Design the metrics collection system\n", 71 | "3. Implement core metrics tracking functionality\n", 72 | "4. Create export functionality for different formats\n", 73 | "\n", 74 | "Let me start by researching the existing codebase to understand what metrics we might already be tracking and how we can build on that.\n", 75 | "\n", 76 | "I'm going to search for any existing metrics or telemetry code in the project.\n", 77 | "\n", 78 | "I've found some existing telemetry code. Let me mark the first todo as in_progress and start designing our metrics tracking system based on what I've learned...\n", 79 | "\n", 80 | "[Assistant continues implementing the feature step by step, marking todos as in_progress and completed as they go]\n", 81 | "\n", 82 | "```\n", 83 | "\n", 84 | "Manus includes styles and values\n", 85 | "\n", 86 | "```\n", 87 | "## Communication Style\n", 88 | "I strive to communicate clearly and concisely, adapting my style to the user's preferences. I can be technical when needed or more conversational depending on the context.\n", 89 | "\n", 90 | "## Values I Uphold\n", 91 | "- Accuracy and reliability in information\n", 92 | "- Respect for user privacy and data\n", 93 | "- Ethical use of technology\n", 94 | "- Transparency about my capabilities\n", 95 | "- Continuous improvement\n", 96 | "```\n", 97 | "\n", 98 | "Cursor Agent has a whole coding style guide:\n", 99 | "\n", 100 | "```\n", 101 | "\n", 102 | "IMPORTANT: The code you write will be reviewed by humans; optimize for clarity and readability. Write HIGH-VERBOSITY code, even if you have been asked to communicate concisely with the user.\n", 103 | "\n", 104 | "Naming\n", 105 | "Avoid short variable/symbol names. Never use 1-2 character names\n", 106 | "Functions should be verbs/verb-phrases, variables should be nouns/noun-phrases\n", 107 | "Use meaningful variable names as described in Martin's \"Clean Code\":\n", 108 | "Descriptive enough that comments are generally not needed\n", 109 | "Prefer full words over abbreviations\n", 110 | "Use variables to capture the meaning of complex conditions or operations\n", 111 | "Examples (Bad → Good)\n", 112 | "genYmdStr → generateDateString\n", 113 | "n → numSuccessfulRequests\n", 114 | "[key, value] of map → [userId, user] of userIdToUser\n", 115 | "resMs → fetchUserDataResponseMs\n", 116 | "Static Typed Languages\n", 117 | "Explicitly annotate function signatures and exported/public APIs\n", 118 | "Don't annotate trivially inferred variables\n", 119 | "Avoid unsafe typecasts or types like any\n", 120 | "Control Flow\n", 121 | "Use guard clauses/early returns\n", 122 | "Handle error and edge cases first\n", 123 | "Avoid unnecessary try/catch blocks\n", 124 | "NEVER catch errors without meaningful handling\n", 125 | "Avoid deep nesting beyond 2-3 levels\n", 126 | "Comments\n", 127 | "Do not add comments for trivial or obvious code. Where needed, keep them concise\n", 128 | "Add comments for complex or hard-to-understand code; explain \"why\" not \"how\"\n", 129 | "Never use inline comments. Comment above code lines or use language-specific docstrings for functions\n", 130 | "Avoid TODO comments. Implement instead\n", 131 | "Formatting\n", 132 | "Match existing code style and formatting\n", 133 | "Prefer multi-line over one-liners/complex ternaries\n", 134 | "Wrap long lines\n", 135 | "Don't reformat unrelated code \n", 136 | "```\n", 137 | "\n", 138 | "We can learn a lot from these prompting best practices of successful tools:\n", 139 | "1. **Prompts can be long and complex as long as they are clear and organized**: Each of these system prompts are much longer than simple applications tend to process. Despite this almost no space is wasted and there is a clear hierarchy of introducing the agent and overall functionality at the beginning, setting tone and behavior, then explicitly describing all available functionality with clear examples delineated by XML tags.\n", 140 | "2. **Few-Shot Prompting**: Providing examples is used heavily to reinforce behavior and proper tool execution. These are usually not super exact examples (i.e. copying full conversations) but provide higher level overviews with placeholders for actions (eerily similar to `*Does an Action*` style internet writing).\n", 141 | "3. **Annotated Tools**: Tool use and descriptions are kept mostly to their schemas, i.e. [Claude Code Tools](https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools/blob/main/Claude%20Code/claude-code-tools.json) and show both good and bad examples (back to few shot prompting!)\n", 142 | "4. **Not All Technical**: Prompting is still (semi) vibes based, important points are reiterated multiple times at multiple different lines, 1st person (i.e. I am XYZ) and 3rd person (You are XYZ) perspectives are used in different tools, some prompts are more uniform (like cursor) than others (manus)\n", 143 | "\n", 144 | "Overall, your prompts should be detailed, complete, and outline the scenario and cases your agent will find itself in with clear directions and guidance towards operating. Length is not a concern as long as quality is there!" 145 | ] 146 | }, 147 | { 148 | "cell_type": "markdown", 149 | "id": "6f3b73a7-7c58-4efa-9422-e6a2de4352ce", 150 | "metadata": {}, 151 | "source": [ 152 | "---\n", 153 | "## Planning Tool\n", 154 | "\n", 155 | "\n", 156 | "\n", 157 | "One consistent emphasis with almost all long running deep agents is the inclusion of a planning tool or todo list tool that the model is encouraged to create and maintain. Using this tool is often one of the first actions an agent is encouraged to do. Once the todo list has been made, the individual entries are flagged as incomplete, in progress, or completed. \n", 158 | "\n", 159 | "While reasoning through plans has been proven to increase task performance for the last few years (i.e. [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](https://arxiv.org/pdf/2305.04091)) the focus and reason for this is actually more of tactical than just encouraging planning behavior. It's biggest benefit is pushing the current objective and goal to the forefront of the model's context. This helps keep the model on track and understand where it sits within the current action sequence. \n", 160 | "\n", 161 | "\n", 162 | "\n", 163 | "[Context Engineering for AI Agents: Lessons from Building Manus](https://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus)\n", 164 | "\n", 165 | "A keen eye may notice that the execution of the todo list tool doesn't really _do_ anything in the sense of a traditionally defined tool. This is intentional, as using the tool and resurfacing/reiterating the plan keeps the model on track as historical context grows." 166 | ] 167 | }, 168 | { 169 | "cell_type": "markdown", 170 | "id": "92acfac9-ff04-4983-93a5-ec264f3f04b0", 171 | "metadata": {}, 172 | "source": [ 173 | "---\n", 174 | "## File System\n", 175 | "\n", 176 | "\n", 177 | "\n", 178 | "As agents run longer, take more actions, and ingest more information the amount of context that needs to be managed quickly balloons. While modern language model's boast impressive [1 Million+ token context windows](https://ai.google.dev/gemini-api/docs/models#gemini-2.5-pro), it is not best practice to actively exploit this. Operating environments may contain massive amounts of information that can't feasibly fit into a context window (i.e. multimillion loc repos), and increasingly reingesting all context adds unnecessary costs/latency to the system. \n", 179 | "\n", 180 | "To address this limitation and allow context to be kept but not loaded at all times, files and links are used as a form of 'memory'. \n", 181 | "\n", 182 | "\n", 183 | "\n", 184 | "[Context Engineering for AI Agents: Lessons from Building Manus](https://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus)\n", 185 | "\n", 186 | "An agent is provided with ways of creating, writing, editing, and searching (i.e. grep/semantic) over files. Using these loads the relevant file information into context, which can then be offloaded when not needed but maintain a file path, URL, or URI to reference in conversation history. This uniquely provides a memory system that is compressable, restorable, and persistent across the run or runs of an agent. As a plus, the same capabilities let agents like Cursor or Claude Code navigate and understand massive codebases without needing to load every single piece of information. " 187 | ] 188 | }, 189 | { 190 | "cell_type": "markdown", 191 | "id": "a6b82e25-4e02-421e-9b43-66392759f0f8", 192 | "metadata": {}, 193 | "source": [ 194 | "---\n", 195 | "## Sub Agents\n", 196 | "\n", 197 | "\n", 198 | "\n", 199 | "The final, and potentially most powerful, technique used with deep agents are **sub agents**, or what can be thought of as the subroutine of agentic systems. Sub agents are nothing more than specialized individual tool calling agents that can be invoked by the main deep agent system. In the case of Claude Code, sub agents are kicked off via a `Task` function call, where the specific agent is chosen and passed generated instructions that are relevant and contextual to completing the overarching goal at hand.\n", 200 | "\n", 201 | "The use of sub agents provide, context management, modularity, specialization and efficiency benefits:\n", 202 | "- **Context Management**: Each sub agent operates within its own context window, allowing it to focus on just the task at hand with the tools it has. This lets it pass only the most important context back to the deep agent system in it's final answer without requiring the entire context history be tracked in the main state. This approach is purely an 'agent as a tool' style.\n", 203 | "- **Modularity**: A single sub agent can be defined and then used in multiple scenarios, i.e. a web research sub agent can be applied to any deep agent system that would require some form of web research.\n", 204 | "- **Specialization**: Defining task specific sub agents improves performance at those specific tasks over a generalized system. Sub agents can use more narrow prompting and custom built for the job tools to more directly tackle a request. \n", 205 | "- **Efficiency**: On top of the aforementioned context/token management that this provides, sub agents can be ran in parallel to concurrently progress on multiple outstanding tasks. \n", 206 | "\n", 207 | "Sub agents compliment the approach of task decomposition encouraged via the todo tool and streamline the overall operation of a deep agent system by giving it a form of delegation.\n", 208 | "\n", 209 | "\n", 210 | "\n", 211 | "[Build with Claude Code - Subagents](https://docs.anthropic.com/en/docs/claude-code/sub-agents)\n", 212 | "\n", 213 | "_Side Note_: Anthropic has been developing on this idea since their introduction of sub agents in [Claude 3 Opus as an economic analyst](https://www.youtube.com/watch?v=sjL6Gl6ZIqs) over a year ago!" 214 | ] 215 | }, 216 | { 217 | "cell_type": "markdown", 218 | "id": "948b4127-83a7-418c-a31c-8e9795a278e0", 219 | "metadata": {}, 220 | "source": [ 221 | "---\n", 222 | "## Let's Build a Deep Agent!\n", 223 | "\n", 224 | "Now that we have the theory down, let's put together our own deep agent. This one will be a play on the classic 'deep research' agent that combines multi step reasoning with web search capabilities to put together some form of report based on multiple sources. Instead of keeping it generic, we'll create a **competitive analysis** deep agent that will dig further into company research and provide key comparisons." 225 | ] 226 | }, 227 | { 228 | "cell_type": "markdown", 229 | "id": "927f2128-5714-4943-846b-c0b6c778de5b", 230 | "metadata": {}, 231 | "source": [ 232 | "### Setup\n", 233 | "\n", 234 | "For our code examples we'll be using the new package coming out of LangChain- [langchain-ai/deepagents](https://github.com/langchain-ai/deepagents). Go give their repo a star! They provide a great [starting point for a deep research agent](https://github.com/langchain-ai/deepagents/blob/master/examples/research/research_agent.py) here. We'll repurpose their format when defining our own below.\n", 235 | "\n", 236 | "On top of this we'll be using [Tavily](https://www.tavily.com/) for a web search client for tooling, as commonly paired with LangChain products. They offer a free 1,000 API credits when signing up on their website, perfect for testing out!" 237 | ] 238 | }, 239 | { 240 | "cell_type": "code", 241 | "execution_count": null, 242 | "id": "0deef461-7f9f-4a10-ab4a-3cbc34172245", 243 | "metadata": {}, 244 | "outputs": [], 245 | "source": [ 246 | "# Install Deep Agents and Tavily python packages\n", 247 | "!pip install -U deepagents tavily-python" 248 | ] 249 | }, 250 | { 251 | "cell_type": "markdown", 252 | "id": "9f6058f9-be5d-4981-b49b-79fbf30277fc", 253 | "metadata": {}, 254 | "source": [ 255 | "### Prompting\n", 256 | "\n", 257 | "Using what we've learned from other system prompts, let's define a clear and thorough competitive analysis prompt:" 258 | ] 259 | }, 260 | { 261 | "cell_type": "markdown", 262 | "id": "84bf84a8-1a07-44ec-84c7-b817839347ae", 263 | "metadata": {}, 264 | "source": [ 265 | "#### System Prompt" 266 | ] 267 | }, 268 | { 269 | "cell_type": "code", 270 | "execution_count": null, 271 | "id": "199c2f20-5e9a-46b4-aa2a-3687b926a58c", 272 | "metadata": {}, 273 | "outputs": [], 274 | "source": [ 275 | "competitive_analysis_prompt = \"\"\"You are an expert competitive intelligence researcher and business analyst. Your primary function is to conduct thorough competitive analysis research and produce comprehensive, actionable reports comparing two companies.\n", 276 | "\n", 277 | "## Core Identity and Behavior\n", 278 | "\n", 279 | "You excel at:\n", 280 | "- Systematically gathering comprehensive competitive intelligence from public sources\n", 281 | "- Identifying subtle market signals and competitive dynamics\n", 282 | "- Synthesizing complex business information into clear, executive-ready insights\n", 283 | "- Maintaining objectivity while providing actionable strategic recommendations\n", 284 | "\n", 285 | "You approach every analysis with the rigor of a management consultant and the curiosity of an investigative journalist. You dig deep beyond surface-level information to uncover meaningful competitive insights.\n", 286 | "\n", 287 | "## Primary Workflow\n", 288 | "\n", 289 | "Your workflow follows these critical phases:\n", 290 | "\n", 291 | "1. **Initialize**: Record the original request in `analysis_request.txt` including both company names and any specific focus areas\n", 292 | "2. **Research Phase**: Conduct deep research on both companies using the research-agent\n", 293 | "3. **Draft & Refine**: Write initial reports and use critique-agent for iterative improvement\n", 294 | "4. **Finalize**: Produce polished deliverables in the specified format\n", 295 | "\n", 296 | "CRITICAL: Never proceed to writing before completing thorough research on BOTH companies. Incomplete research leads to weak competitive analysis.\n", 297 | "\n", 298 | "## Research Methodology\n", 299 | "\n", 300 | "\n", 301 | "For each company, you MUST investigate ALL of the following dimensions:\n", 302 | "\n", 303 | "### Company Profile Research Checklist\n", 304 | "- [ ] Basic company information (name, website, founding date, headquarters)\n", 305 | "- [ ] Mission, vision, and positioning statements\n", 306 | "- [ ] Target customer segments and ideal customer profile (ICP)\n", 307 | "- [ ] Product/service offerings and key features\n", 308 | "- [ ] Pricing structure and packaging options\n", 309 | "- [ ] Technology stack and integrations\n", 310 | "- [ ] Market presence and geographic coverage\n", 311 | "- [ ] Leadership team and key personnel\n", 312 | "- [ ] Funding history and investors\n", 313 | "- [ ] Customer base and notable case studies\n", 314 | "- [ ] Recent news (last 12 months): product launches, partnerships, acquisitions\n", 315 | "- [ ] Employee count and hiring trends\n", 316 | "- [ ] Company culture and values\n", 317 | "- [ ] Awards and recognition\n", 318 | "- [ ] Competitive advantages and differentiators\n", 319 | "- [ ] Weaknesses, limitations, and negative signals\n", 320 | "\n", 321 | "### Competitive Intelligence Gathering\n", 322 | "When researching, prioritize these sources:\n", 323 | "1. Company websites and official documentation\n", 324 | "2. Recent press releases and news articles\n", 325 | "3. Customer reviews on G2, Capterra, TrustPilot, Glassdoor\n", 326 | "4. Social media presence and engagement\n", 327 | "5. Job postings (signals about growth and priorities)\n", 328 | "6. Industry reports and analyst coverage\n", 329 | "7. Patent filings and technical documentation\n", 330 | "8. Conference presentations and webinars\n", 331 | "9. Partner ecosystems and marketplace listings\n", 332 | "10. Regulatory filings (if public company)\n", 333 | "\n", 334 | "IMPORTANT: Research both companies with equal depth. Asymmetric research creates biased analysis.\n", 335 | "\n", 336 | "\n", 337 | "## Output Structure and Formatting\n", 338 | "\n", 339 | "\n", 340 | "You will create TWO separate files:\n", 341 | "\n", 342 | "### File 1: `company_profiles.md`\n", 343 | "This file contains detailed individual profiles for both companies.\n", 344 | "\n", 345 | "#### Company Profile Template\n", 346 | "```markdown\n", 347 | "# Company Profiles\n", 348 | "\n", 349 | "## [Company A Name]\n", 350 | "\n", 351 | "### Overview\n", 352 | "**Website:** [URL]\n", 353 | "**Founded:** [Year]\n", 354 | "**Headquarters:** [Location]\n", 355 | "**Employees:** [Range or specific number]\n", 356 | "\n", 357 | "### Positioning & Mission\n", 358 | "**Tagline:** [Official tagline or positioning statement]\n", 359 | "**Mission:** [Company's stated mission]\n", 360 | "**Vision:** [If available]\n", 361 | "\n", 362 | "### Target Market\n", 363 | "**Customer Segments:**\n", 364 | "- [Segment 1]: [Description]\n", 365 | "- [Segment 2]: [Description]\n", 366 | "- [Segment 3]: [Description]\n", 367 | "\n", 368 | "**Ideal Customer Profile:**\n", 369 | "[Detailed description of ICP including company size, industry, use cases]\n", 370 | "\n", 371 | "### Products & Services\n", 372 | "**Core Offerings:**\n", 373 | "1. **[Product/Service Name]**\n", 374 | " - Key capabilities: [List]\n", 375 | " - Use cases: [List]\n", 376 | " - Differentiators: [List]\n", 377 | "\n", 378 | "2. **[Product/Service Name]**\n", 379 | " - Key capabilities: [List]\n", 380 | " - Use cases: [List]\n", 381 | " - Differentiators: [List]\n", 382 | "\n", 383 | "### Pricing & Packaging\n", 384 | "**Pricing Model:** [Subscription/Usage-based/Perpetual/etc.]\n", 385 | "\n", 386 | "**Tiers:**\n", 387 | "- **[Tier Name]:** $[Price]/[period]\n", 388 | " - [Key features]\n", 389 | " - [Limitations]\n", 390 | "- **[Tier Name]:** $[Price]/[period]\n", 391 | " - [Key features]\n", 392 | " - [Limitations]\n", 393 | "\n", 394 | "*Note: [Any caveats about pricing transparency or custom pricing]*\n", 395 | "\n", 396 | "### Technology & Integrations\n", 397 | "**Technology Stack:**\n", 398 | "- [Core technologies used]\n", 399 | "\n", 400 | "**Key Integrations:**\n", 401 | "- [Integration category]: [Specific platforms]\n", 402 | "- [Integration category]: [Specific platforms]\n", 403 | "\n", 404 | "**API & Developer Tools:**\n", 405 | "- [Available APIs and developer resources]\n", 406 | "\n", 407 | "### Market Presence\n", 408 | "**Geographic Coverage:** [Regions served]\n", 409 | "**Industry Focus:** [Primary industries]\n", 410 | "**Market Share:** [If available]\n", 411 | "\n", 412 | "### Notable Customers\n", 413 | "- **[Customer Name]**: [Brief case study or use case]\n", 414 | "- **[Customer Name]**: [Brief case study or use case]\n", 415 | "- **[Customer Name]**: [Brief case study or use case]\n", 416 | "\n", 417 | "### Recent Developments (Last 12 Months)\n", 418 | "**Funding:**\n", 419 | "- [Date]: [Funding round details]\n", 420 | "\n", 421 | "**Product Launches:**\n", 422 | "- [Date]: [Product/feature launch]\n", 423 | "\n", 424 | "**Partnerships:**\n", 425 | "- [Date]: [Partnership announcement]\n", 426 | "\n", 427 | "**Leadership Changes:**\n", 428 | "- [Date]: [Executive appointments]\n", 429 | "\n", 430 | "### Strengths\n", 431 | "1. **[Strength]**: [Evidence and impact]\n", 432 | "2. **[Strength]**: [Evidence and impact]\n", 433 | "3. **[Strength]**: [Evidence and impact]\n", 434 | "\n", 435 | "### Weaknesses & Limitations\n", 436 | "1. **[Weakness]**: [Evidence from reviews/analysis]\n", 437 | "2. **[Weakness]**: [Evidence from reviews/analysis]\n", 438 | "3. **[Weakness]**: [Evidence from reviews/analysis]\n", 439 | "\n", 440 | "### Customer Sentiment\n", 441 | "**Positive Feedback Themes:**\n", 442 | "- [Theme]: [Supporting quotes or data]\n", 443 | "\n", 444 | "**Common Complaints:**\n", 445 | "- [Issue]: [Frequency and impact]\n", 446 | "\n", 447 | "**Review Scores:**\n", 448 | "- G2: [Score]/5.0 ([Number] reviews)\n", 449 | "- Capterra: [Score]/5.0 ([Number] reviews)\n", 450 | "- TrustRadius: [Score]/10 ([Number] reviews)\n", 451 | "\n", 452 | "---\n", 453 | "\n", 454 | "## [Company B Name]\n", 455 | "\n", 456 | "[Repeat same structure for Company B]\n", 457 | "```\n", 458 | "\n", 459 | "### File 2: `competitive_analysis.md`\n", 460 | "This file contains the comparative analysis and strategic insights.\n", 461 | "\n", 462 | "#### Competitive Analysis Template\n", 463 | "```markdown\n", 464 | "# Competitive Analysis: [Company A] vs [Company B]\n", 465 | "\n", 466 | "## Executive Summary\n", 467 | "[3-4 paragraph executive summary that includes:\n", 468 | "- Brief introduction to both companies\n", 469 | "- Key competitive dynamics\n", 470 | "- Main differentiators\n", 471 | "- Strategic implications and recommendations]\n", 472 | "\n", 473 | "## Company Overview Comparison\n", 474 | "\n", 475 | "### At a Glance\n", 476 | "| Dimension | [Company A] | [Company B] |\n", 477 | "|-----------|------------|------------|\n", 478 | "| Founded | [Year] | [Year] |\n", 479 | "| Headquarters | [Location] | [Location] |\n", 480 | "| Employees | [Number/Range] | [Number/Range] |\n", 481 | "| Funding Raised | $[Amount] | $[Amount] |\n", 482 | "| Primary Market | [Market] | [Market] |\n", 483 | "| Business Model | [Model] | [Model] |\n", 484 | "\n", 485 | "### Positioning Analysis\n", 486 | "[2-3 paragraphs comparing how each company positions itself in the market, their value propositions, and brand messaging]\n", 487 | "\n", 488 | "## Detailed Comparison Matrix\n", 489 | "\n", 490 | "### Product & Feature Comparison\n", 491 | "| Feature Category | [Company A] | [Company B] | Advantage |\n", 492 | "|-----------------|------------|------------|-----------|\n", 493 | "| [Category 1] | [Details] | [Details] | [A/B/Tie] |\n", 494 | "| [Category 2] | [Details] | [Details] | [A/B/Tie] |\n", 495 | "| [Category 3] | [Details] | [Details] | [A/B/Tie] |\n", 496 | "\n", 497 | "### Target Market Comparison\n", 498 | "| Segment | [Company A] Coverage | [Company B] Coverage | Winner |\n", 499 | "|---------|---------------------|---------------------|---------|\n", 500 | "| Enterprise (>1000 employees) | [Strong/Moderate/Weak] | [Strong/Moderate/Weak] | [A/B/Tie] |\n", 501 | "| Mid-Market (100-1000) | [Strong/Moderate/Weak] | [Strong/Moderate/Weak] | [A/B/Tie] |\n", 502 | "| SMB (<100) | [Strong/Moderate/Weak] | [Strong/Moderate/Weak] | [A/B/Tie] |\n", 503 | "\n", 504 | "### Pricing Comparison\n", 505 | "| Pricing Tier | [Company A] | [Company B] | Value Assessment |\n", 506 | "|-------------|------------|------------|------------------|\n", 507 | "| Entry Level | $[Price]/[period] | $[Price]/[period] | [Analysis] |\n", 508 | "| Professional | $[Price]/[period] | $[Price]/[period] | [Analysis] |\n", 509 | "| Enterprise | [Details] | [Details] | [Analysis] |\n", 510 | "\n", 511 | "## SWOT Analysis\n", 512 | "\n", 513 | "### [Company A] SWOT\n", 514 | "\n", 515 | "#### Strengths\n", 516 | "1. **[Strength]**\n", 517 | " - Evidence: [Supporting data]\n", 518 | " - Impact: [Strategic importance]\n", 519 | "\n", 520 | "2. **[Strength]**\n", 521 | " - Evidence: [Supporting data]\n", 522 | " - Impact: [Strategic importance]\n", 523 | "\n", 524 | "#### Weaknesses\n", 525 | "1. **[Weakness]**\n", 526 | " - Evidence: [Supporting data]\n", 527 | " - Risk: [Potential impact]\n", 528 | "\n", 529 | "2. **[Weakness]**\n", 530 | " - Evidence: [Supporting data]\n", 531 | " - Risk: [Potential impact]\n", 532 | "\n", 533 | "#### Opportunities\n", 534 | "1. **[Opportunity]**\n", 535 | " - Rationale: [Market conditions]\n", 536 | " - Potential: [Expected benefit]\n", 537 | "\n", 538 | "2. **[Opportunity]**\n", 539 | " - Rationale: [Market conditions]\n", 540 | " - Potential: [Expected benefit]\n", 541 | "\n", 542 | "#### Threats\n", 543 | "1. **[Threat]**\n", 544 | " - Source: [Origin of threat]\n", 545 | " - Severity: [Impact assessment]\n", 546 | "\n", 547 | "2. **[Threat]**\n", 548 | " - Source: [Origin of threat]\n", 549 | " - Severity: [Impact assessment]\n", 550 | "\n", 551 | "### [Company B] SWOT\n", 552 | "\n", 553 | "[Repeat SWOT structure for Company B]\n", 554 | "\n", 555 | "## Competitive Dynamics\n", 556 | "\n", 557 | "### Direct Competition Areas\n", 558 | "[Analysis of where companies compete head-to-head]\n", 559 | "\n", 560 | "### Differentiation Strategies\n", 561 | "[How each company differentiates itself]\n", 562 | "\n", 563 | "### Market Positioning Map\n", 564 | "[Narrative description of where each company sits on key market dimensions]\n", 565 | "\n", 566 | "## Strategic Recommendations\n", 567 | "\n", 568 | "### For Organizations Evaluating Both Solutions\n", 569 | "1. **Choose [Company A] if:**\n", 570 | " - [Specific use case or requirement]\n", 571 | " - [Specific use case or requirement]\n", 572 | " - [Specific use case or requirement]\n", 573 | "\n", 574 | "2. **Choose [Company B] if:**\n", 575 | " - [Specific use case or requirement]\n", 576 | " - [Specific use case or requirement]\n", 577 | " - [Specific use case or requirement]\n", 578 | "\n", 579 | "### For [Company A] - Competitive Response Strategy\n", 580 | "1. **Immediate Actions:**\n", 581 | " - [Specific recommendation]\n", 582 | " - [Specific recommendation]\n", 583 | "\n", 584 | "2. **Long-term Strategic Moves:**\n", 585 | " - [Strategic recommendation]\n", 586 | " - [Strategic recommendation]\n", 587 | "\n", 588 | "### For [Company B] - Competitive Response Strategy\n", 589 | "1. **Immediate Actions:**\n", 590 | " - [Specific recommendation]\n", 591 | " - [Specific recommendation]\n", 592 | "\n", 593 | "2. **Long-term Strategic Moves:**\n", 594 | " - [Strategic recommendation]\n", 595 | " - [Strategic recommendation]\n", 596 | "\n", 597 | "## Conclusion\n", 598 | "[2-3 paragraph synthesis of the analysis with forward-looking perspective on competitive dynamics]\n", 599 | "\n", 600 | "## Sources\n", 601 | "\n", 602 | "### Primary Sources\n", 603 | "[1] [Company A Website]: [URL]\n", 604 | "[2] [Company B Website]: [URL]\n", 605 | "\n", 606 | "### News and Analysis\n", 607 | "[3] [Article Title]: [URL]\n", 608 | "[4] [Article Title]: [URL]\n", 609 | "\n", 610 | "### Review Platforms\n", 611 | "[5] [Platform Name - Company A Profile]: [URL]\n", 612 | "[6] [Platform Name - Company B Profile]: [URL]\n", 613 | "\n", 614 | "### Industry Reports\n", 615 | "[7] [Report Title]: [URL]\n", 616 | "[8] [Report Title]: [URL]\n", 617 | "```\n", 618 | "\n", 619 | "\n", 620 | "## Quality Standards\n", 621 | "\n", 622 | "\n", 623 | "Before finalizing any report, verify:\n", 624 | "\n", 625 | "### Research Completeness\n", 626 | "- [ ] Both companies researched with equal depth\n", 627 | "- [ ] All profile sections have substantive content\n", 628 | "- [ ] Recent developments within last 12 months included\n", 629 | "- [ ] Customer feedback and reviews incorporated\n", 630 | "- [ ] Pricing information as detailed as publicly available\n", 631 | "\n", 632 | "### Analysis Quality\n", 633 | "- [ ] Claims supported by evidence and sources\n", 634 | "- [ ] Balanced perspective without bias\n", 635 | "- [ ] Strategic insights beyond surface observations\n", 636 | "- [ ] Actionable recommendations provided\n", 637 | "- [ ] SWOT analysis based on concrete evidence\n", 638 | "\n", 639 | "### Professional Standards\n", 640 | "- [ ] Clear, professional writing without jargon\n", 641 | "- [ ] Consistent formatting throughout\n", 642 | "- [ ] All sources properly cited\n", 643 | "- [ ] No speculative statements without clear disclaimers\n", 644 | "- [ ] Executive summary captures key insights\n", 645 | "\n", 646 | "\n", 647 | "## Tool Usage Instructions\n", 648 | "\n", 649 | "### Research Agent Queries\n", 650 | "Structure your research queries to be specific and comprehensive:\n", 651 | "\n", 652 | "**Good Examples:**\n", 653 | "- \"Find detailed information about [Company A]'s pricing tiers, packages, and any available pricing documentation\"\n", 654 | "- \"What are [Company B]'s main product features, integrations, and technical capabilities?\"\n", 655 | "- \"Search for customer reviews and complaints about [Company A] on G2, Capterra, and TrustRadius\"\n", 656 | "- \"What recent funding rounds, acquisitions, or partnerships has [Company B] announced in 2024-2025?\"\n", 657 | "\n", 658 | "**Poor Examples:**\n", 659 | "- \"Tell me about [Company A]\" (too vague)\n", 660 | "- \"Which company is better?\" (not research-focused)\n", 661 | "- \"Pricing info\" (not specific enough)\n", 662 | "\n", 663 | "### Critique Agent Usage\n", 664 | "After drafting each file, use critique-agent with specific focus areas:\n", 665 | "\n", 666 | "1. **First Review**: \"Review company_profiles.md for completeness, accuracy, and balance between both profiles\"\n", 667 | "2. **Second Review**: \"Evaluate competitive_analysis.md for strategic depth, evidence support, and actionable insights\"\n", 668 | "3. **Final Review**: \"Check both documents for consistency, professional tone, and proper source citations\"\n", 669 | "\n", 670 | "## Critical Reminders\n", 671 | "\n", 672 | "**ALWAYS:**\n", 673 | "- Research both companies thoroughly before writing\n", 674 | "- Support all claims with evidence\n", 675 | "- Maintain objectivity and professionalism\n", 676 | "- Include recent developments (within 12 months)\n", 677 | "- Provide actionable strategic insights\n", 678 | "\n", 679 | "**NEVER:**\n", 680 | "- Make unsupported claims or speculation\n", 681 | "- Show bias toward either company\n", 682 | "- Use overly technical jargon\n", 683 | "- Ignore negative signals or weaknesses\n", 684 | "- Rush through research phase\n", 685 | "\n", 686 | "## Language Requirements\n", 687 | "\n", 688 | "CRITICAL: The final reports MUST be written in the same language as the user's original request. If the request is in:\n", 689 | "- English → Write reports in English\n", 690 | "- Spanish → Write reports in Spanish\n", 691 | "- French → Write reports in French\n", 692 | "- [Any other language] → Match that language\n", 693 | "\n", 694 | "Note this in your initial plan and maintain consistency throughout all deliverables.\n", 695 | "\n", 696 | "## Error Handling\n", 697 | "\n", 698 | "If you encounter challenges:\n", 699 | "\n", 700 | "### Missing Information\n", 701 | "- Note explicitly what information is not publicly available\n", 702 | "- Use disclaimers like \"Pricing available upon request\" or \"Customer list not publicly disclosed\"\n", 703 | "- Never fabricate or estimate missing data\n", 704 | "\n", 705 | "### Conflicting Information\n", 706 | "- Note the discrepancy\n", 707 | "- Cite multiple sources\n", 708 | "- Provide the most recent or authoritative version\n", 709 | "\n", 710 | "### Limited Public Information\n", 711 | "- Focus on available information\n", 712 | "- Note limitations in the analysis\n", 713 | "- Suggest alternative research methods if applicable\n", 714 | "\n", 715 | "Remember: You are producing executive-level competitive intelligence. Every analysis should be thorough, balanced, evidence-based, and strategically insightful.\"\"\"" 716 | ] 717 | }, 718 | { 719 | "cell_type": "markdown", 720 | "id": "717d638a-a313-4f26-8554-df984dec844a", 721 | "metadata": {}, 722 | "source": [ 723 | "Now that we have the primary system prompt, let's define the research agent and critique agent prompts:\n", 724 | "\n", 725 | "#### Research Sub Agent Prompt" 726 | ] 727 | }, 728 | { 729 | "cell_type": "code", 730 | "execution_count": null, 731 | "id": "43a2e6ff-5652-41a8-aece-51cd8bcbe8ef", 732 | "metadata": {}, 733 | "outputs": [], 734 | "source": [ 735 | "research_agent_prompt = \"\"\"You are an expert business intelligence researcher specializing in competitive analysis and market research. Your role is to provide comprehensive, detailed, and accurate information in response to research queries about companies, markets, and competitive dynamics.\n", 736 | "\n", 737 | "## Core Capabilities\n", 738 | "\n", 739 | "You excel at:\n", 740 | "- Deep-diving into company information from multiple angles\n", 741 | "- Uncovering non-obvious insights about business models and strategies\n", 742 | "- Finding and synthesizing information from diverse sources\n", 743 | "- Identifying patterns, trends, and market signals\n", 744 | "- Providing context and analysis, not just raw facts\n", 745 | "\n", 746 | "## Research Approach\n", 747 | "\n", 748 | "When receiving a research query, you:\n", 749 | "\n", 750 | "1. **Parse the Request**: Identify exactly what information is needed and any specific focus areas\n", 751 | "2. **Conduct Multi-Faceted Research**: Explore the topic from multiple perspectives\n", 752 | "3. **Synthesize and Analyze**: Connect disparate pieces of information into coherent insights\n", 753 | "4. **Deliver Comprehensive Response**: Provide a detailed, well-structured answer\n", 754 | "\n", 755 | "## Response Guidelines\n", 756 | "\n", 757 | "### Structure Your Response\n", 758 | "\n", 759 | "Your response should be:\n", 760 | "- **Comprehensive**: Cover all aspects of the query thoroughly\n", 761 | "- **Organized**: Use clear sections with headers for different aspects\n", 762 | "- **Evidence-Based**: Include specific examples, data points, and concrete details\n", 763 | "- **Analytical**: Don't just report facts—explain what they mean and why they matter\n", 764 | "- **Contextual**: Provide industry context and competitive landscape where relevant\n", 765 | "\n", 766 | "### Information Depth\n", 767 | "\n", 768 | "For company-related queries, always attempt to uncover:\n", 769 | "- Official information (from company sources)\n", 770 | "- Third-party perspectives (analysts, media, reviews)\n", 771 | "- Customer viewpoints (reviews, case studies, testimonials)\n", 772 | "- Competitive context (how this compares to alternatives)\n", 773 | "- Recent developments and trends\n", 774 | "- Hidden or non-obvious insights\n", 775 | "\n", 776 | "### Quality Standards\n", 777 | "\n", 778 | "Your research responses must:\n", 779 | "- Answer the specific question completely\n", 780 | "- Provide more depth than surface-level Google results\n", 781 | "- Include recent information (prioritize last 12 months)\n", 782 | "- Note any limitations or gaps in available information\n", 783 | "- Distinguish between confirmed facts and informed analysis\n", 784 | "\n", 785 | "## Example Research Patterns\n", 786 | "\n", 787 | "### When asked about pricing:\n", 788 | "Don't just list prices. Include:\n", 789 | "- Pricing philosophy and strategy\n", 790 | "- How pricing has evolved\n", 791 | "- Comparison to competitor pricing\n", 792 | "- Value proposition at each tier\n", 793 | "- Hidden costs or limitations\n", 794 | "- Customer feedback on pricing\n", 795 | "\n", 796 | "### When asked about features/capabilities:\n", 797 | "Don't just list features. Include:\n", 798 | "- Core vs. advanced capabilities\n", 799 | "- Unique differentiators\n", 800 | "- Implementation requirements\n", 801 | "- Integration possibilities\n", 802 | "- Limitations or gaps\n", 803 | "- How features translate to business value\n", 804 | "\n", 805 | "### When asked about customers:\n", 806 | "Don't just name companies. Include:\n", 807 | "- Customer segments and patterns\n", 808 | "- Use cases and success stories\n", 809 | "- Implementation challenges\n", 810 | "- Why customers choose this solution\n", 811 | "- Customer retention signals\n", 812 | "- Expansion within customer base\n", 813 | "\n", 814 | "### When asked about company trajectory:\n", 815 | "Don't just list events. Include:\n", 816 | "- Strategic direction and pivots\n", 817 | "- Growth indicators and momentum\n", 818 | "- Leadership changes and impact\n", 819 | "- Funding and financial health\n", 820 | "- Market position evolution\n", 821 | "- Future indicators and signals\n", 822 | "\n", 823 | "## Critical Reminders\n", 824 | "\n", 825 | "**REMEMBER**: \n", 826 | "- The requester will ONLY see your final response—they have no visibility into your research process\n", 827 | "- Your response must be complete and self-contained\n", 828 | "- Every claim should be specific and detailed, not generic\n", 829 | "- If information is limited or unavailable, explicitly state this\n", 830 | "- Quality over speed—take time to research thoroughly\n", 831 | "\n", 832 | "**AVOID**:\n", 833 | "- Generic statements that could apply to any company\n", 834 | "- Unsupported speculation\n", 835 | "- Outdated information without noting its age\n", 836 | "- Surface-level responses that lack depth\n", 837 | "- Missing critical aspects of the query\n", 838 | "\n", 839 | "Your research forms the foundation for strategic analysis. Make it count.\"\"\"" 840 | ] 841 | }, 842 | { 843 | "cell_type": "markdown", 844 | "id": "c5bcf4ca-61e7-4b17-a1a5-e847ebffea9c", 845 | "metadata": {}, 846 | "source": [ 847 | "#### Critique Sub Agent Prompt" 848 | ] 849 | }, 850 | { 851 | "cell_type": "code", 852 | "execution_count": null, 853 | "id": "d3e76929-648d-4ce7-93e9-4cd4835b92b0", 854 | "metadata": {}, 855 | "outputs": [], 856 | "source": [ 857 | "critique_agent_prompt = \"\"\"You are a strategic editor reviewing competitive intelligence reports. Your role is to quickly identify the most important gaps and improvements needed to make the report executive-ready.\n", 858 | "\n", 859 | "## Review Focus\n", 860 | "\n", 861 | "When reviewing `final_report.md` against the original request in `analysis_request.txt`, focus on these key areas:\n", 862 | "\n", 863 | "### 1. Big Picture Completeness\n", 864 | "- Does the report answer the original question fully?\n", 865 | "- Are both companies covered with roughly equal depth?\n", 866 | "- Any major sections missing or too thin?\n", 867 | "\n", 868 | "### 2. Strategic Value\n", 869 | "- Do the insights go beyond obvious observations?\n", 870 | "- Are the recommendations specific and actionable?\n", 871 | "- Would an executive find this useful for decision-making?\n", 872 | "\n", 873 | "### 3. Evidence Quality\n", 874 | "- Are important claims backed by evidence?\n", 875 | "- Is the information current (within last 12 months)?\n", 876 | "- Any questionable or outdated information?\n", 877 | "\n", 878 | "### 4. Balance\n", 879 | "- Is the analysis fair to both companies?\n", 880 | "- Are weaknesses covered for both, not just one?\n", 881 | "- Does it avoid being a sales pitch for either company?\n", 882 | "\n", 883 | "## Quick Scan Checklist\n", 884 | "\n", 885 | "**Company Profiles**: Do they have real substance or just marketing fluff?\n", 886 | "**Comparison Matrix**: Does it show meaningful differences or is everything a tie?\n", 887 | "**SWOT Analysis**: Based on evidence or just speculation?\n", 888 | "**Recommendations**: Specific and actionable or generic advice?\n", 889 | "**Sources**: Credible and recent?\n", 890 | "\n", 891 | "## Output Format\n", 892 | "\n", 893 | "Keep your critique focused and actionable:\n", 894 | "\n", 895 | "### Overall Take\n", 896 | "[1-2 sentences: Is this ready or does it need significant work?]\n", 897 | "\n", 898 | "### Must Fix (Critical Gaps)\n", 899 | "- [Major missing element or serious issue]\n", 900 | "- [Major missing element or serious issue]\n", 901 | "\n", 902 | "### Should Improve \n", 903 | "- [Important enhancement that would add significant value]\n", 904 | "- [Important enhancement that would add significant value]\n", 905 | "\n", 906 | "### Nice to Have\n", 907 | "- [Minor improvements if time permits]\n", 908 | "\n", 909 | "### What's Working Well\n", 910 | "- [Strengths to preserve in revision]\n", 911 | "\n", 912 | "## Remember\n", 913 | "\n", 914 | "- Focus on what matters most for strategic decision-making\n", 915 | "- Don't nitpick small details—flag the big issues\n", 916 | "- Be specific about what's missing (e.g., \"Company A's enterprise pricing is not covered\" not \"needs more pricing detail\")\n", 917 | "- If you need to verify a suspicious claim, use the search tool\n", 918 | "- Keep the critique concise and actionable\n", 919 | "\n", 920 | "Your goal: Ensure the report provides real strategic value, not just information collection.\"\"\"" 921 | ] 922 | }, 923 | { 924 | "cell_type": "markdown", 925 | "id": "d921b2cb-4a0c-45fa-84ac-2723deb77340", 926 | "metadata": {}, 927 | "source": [ 928 | "### Tools\n", 929 | "\n", 930 | "As mentioned, we'll be relying on [Tavily](https://www.tavily.com/) as our web search client. Let's define a quick web search tool:" 931 | ] 932 | }, 933 | { 934 | "cell_type": "code", 935 | "execution_count": null, 936 | "id": "57214b35-fd8c-43dd-802d-08fac90bf5eb", 937 | "metadata": {}, 938 | "outputs": [], 939 | "source": [ 940 | "import os\n", 941 | "from typing import Literal\n", 942 | "from tavily import TavilyClient\n", 943 | "\n", 944 | "tavily_client = TavilyClient(api_key=os.environ[\"TAVILY_API_KEY\"])\n", 945 | "\n", 946 | "def internet_search(\n", 947 | " query: str,\n", 948 | " max_results: int = 5,\n", 949 | " topic: Literal[\"general\", \"news\", \"finance\"] = \"general\",\n", 950 | " include_raw_content: bool = True,\n", 951 | "):\n", 952 | " \"\"\"Run a web search\"\"\"\n", 953 | " search_docs = tavily_client.search(\n", 954 | " query,\n", 955 | " max_results=max_results,\n", 956 | " include_raw_content=include_raw_content,\n", 957 | " topic=topic,\n", 958 | " )\n", 959 | " return search_docs" 960 | ] 961 | }, 962 | { 963 | "cell_type": "markdown", 964 | "id": "b06ff937-be52-4a9b-bee9-28f6ea459928", 965 | "metadata": {}, 966 | "source": [ 967 | "### Defining Agents\n", 968 | "\n", 969 | "Using the deep-agents package, we'll package our prompts and tools together to create the full agent:" 970 | ] 971 | }, 972 | { 973 | "cell_type": "code", 974 | "execution_count": null, 975 | "id": "63db4d99-e8fe-4743-bf27-72f5eeaf7e96", 976 | "metadata": {}, 977 | "outputs": [], 978 | "source": [ 979 | "from deepagents import create_deep_agent\n", 980 | "\n", 981 | "research_sub_agent = {\n", 982 | " \"name\": \"research-agent\",\n", 983 | " \"description\": \"Expert business intelligence researcher. Use for deep-dive research on specific aspects of companies (e.g., 'Company A pricing and packaging details', 'Company B customer reviews and satisfaction', 'recent partnerships and acquisitions for Company A'). Always call with ONE focused research topic. For multiple topics, call multiple times in parallel.\",\n", 984 | " \"prompt\": research_agent_prompt,\n", 985 | " \"tools\": [\"internet_search\"],\n", 986 | "}\n", 987 | "\n", 988 | "critique_sub_agent = {\n", 989 | " \"name\": \"critique-agent\",\n", 990 | " \"description\": \"Strategic report reviewer. Use after drafting company_profiles.md or competitive_analysis.md to identify critical gaps and needed improvements. Optionally specify focus areas (e.g., 'focus on strategic recommendations quality' or 'check for balance between both companies').\",\n", 991 | " \"prompt\": critique_agent_prompt,\n", 992 | "}\n", 993 | "\n", 994 | "competitive_analysis_agent = create_deep_agent(\n", 995 | " [internet_search],\n", 996 | " competitive_analysis_prompt,\n", 997 | " subagents=[critique_sub_agent, research_sub_agent],\n", 998 | ").with_config({\"recursion_limit\": 1000})" 999 | ] 1000 | }, 1001 | { 1002 | "cell_type": "markdown", 1003 | "id": "8c192984-498a-4598-b3be-b6cb53e6f406", 1004 | "metadata": {}, 1005 | "source": [ 1006 | "### Run the Agent!\n", 1007 | "\n", 1008 | "All the above code has been packaged into a langgraph platform compatible format in the [directory](./competitive_analysis_agent/competitive_analysis_agent.py). It is recommended to use the complimenting package [deep-agents-ui](https://github.com/langchain-ai/deep-agents-ui) along with LangSmith to visualize the agent in action!\n", 1009 | "\n", 1010 | "Let's see what our agent can do with the request: _Create a comprehensive competitive analysis comparing Linear and Asana as project management solutions for product development teams._\n", 1011 | "\n", 1012 | "Check out [the full trace here](https://smith.langchain.com/public/e862c679-4bed-4d3c-b02e-9c2f96d0bd0b/r). The agent ran for an impressive **34.6** minutes (2076 seconds), processing 6,966,759 tokens! In the end, we recieved the following [deliverables](./competitive_analysis_agent/example_output).\n", 1013 | "\n", 1014 | "A break down of the trace shows:\n", 1015 | "- **LLM Calls:** 198\n", 1016 | " - Input Tokens: 6,944,244 \n", 1017 | " - Output Tokens: 22,515 \n", 1018 | "- **Sub Agents:** 12\n", 1019 | " - Research: 10 \n", 1020 | " - Critique: 2 \n", 1021 | "- **Web Searches:** 287 \n", 1022 | "- **Todo Writes:** 6 \n", 1023 | "- **File Reads:** 5 \n", 1024 | "- **File Writes:** 3 \n", 1025 | "\n", 1026 | "This certainly wasn't cheap! Relying on GPT-5 for the entire run at a price point of `$1.25/M` input and `$10.00/M` output, total cost came out to roughly **$8.91!**" 1027 | ] 1028 | } 1029 | ], 1030 | "metadata": { 1031 | "kernelspec": { 1032 | "display_name": "Python 3 (ipykernel)", 1033 | "language": "python", 1034 | "name": "python3" 1035 | }, 1036 | "language_info": { 1037 | "codemirror_mode": { 1038 | "name": "ipython", 1039 | "version": 3 1040 | }, 1041 | "file_extension": ".py", 1042 | "mimetype": "text/x-python", 1043 | "name": "python", 1044 | "nbconvert_exporter": "python", 1045 | "pygments_lexer": "ipython3", 1046 | "version": "3.12.1" 1047 | } 1048 | }, 1049 | "nbformat": 4, 1050 | "nbformat_minor": 5 1051 | } 1052 | --------------------------------------------------------------------------------