├── .gitignore ├── pyproject.toml ├── cohere-terrarium └── docker-compose.yml ├── README.md ├── demo-terrarium.py ├── code_gen_agent ├── main.py ├── agent.py └── terrarium_tool.py └── poetry.lock /.gitignore: -------------------------------------------------------------------------------- 1 | .env 2 | .venv 3 | __pycache__ 4 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [tool.poetry] 2 | name = "code-gen-agent" 3 | version = "1" 4 | description = "Demo code generation agent that uses Atomic Agents and Terrarium to generate and execute Python code in a sandboxed environment." 5 | authors = ["Kenny Vaneetvelde "] 6 | license = "MIT" 7 | readme = "README.md" 8 | 9 | [tool.poetry.dependencies] 10 | python = "^3.9" 11 | atomic-agents = "^1.0.15" 12 | instructor = "^1.6.4" 13 | rich = "^13.9.4" 14 | openai = "^1.54.4" 15 | pydantic = "^2.9.2" 16 | requests = "^2.32.3" 17 | 18 | 19 | [build-system] 20 | requires = ["poetry-core"] 21 | build-backend = "poetry.core.masonry.api" 22 | -------------------------------------------------------------------------------- /cohere-terrarium/docker-compose.yml: -------------------------------------------------------------------------------- 1 | services: 2 | terrarium: 3 | build: 4 | context: https://github.com/cohere-ai/cohere-terrarium.git#main 5 | ports: 6 | - "8080:8080" 7 | environment: 8 | - NODE_ENV=production 9 | deploy: 10 | resources: 11 | limits: 12 | cpus: "2" 13 | memory: 4G 14 | reservations: 15 | cpus: "1" 16 | memory: 2G 17 | healthcheck: 18 | test: ["CMD", "curl", "-f", "http://localhost:8080/health"] 19 | interval: 30s 20 | timeout: 1s 21 | retries: 3 22 | volumes: 23 | - pyodide_cache:/app/pyodide_cache 24 | 25 | volumes: 26 | pyodide_cache: 27 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Code Generation Agent Demo 2 | 3 | A practical demo accompanying the article ["Create Your Own Sandboxed Code Generation Agent in Minutes"](https://medium.com/ai-advances/create-your-own-sandboxed-code-generation-agent-in-minutes-1603ae695f16?sk=d8ee2dabfbdc69a877caa50ae29ff61e) showing how to build your own code generation agent that actually executes the code it writes. Built with [Atomic Agents](https://github.com/KennyVaneetvelde/atomic-agents) and [Cohere Terrarium](https://github.com/cohere-ai/cohere-terrarium). 4 | 5 | ## What's This? 6 | 7 | This demo creates an AI agent that: 8 | 1. Generates Python code based on your requirements 9 | 2. Executes it in a sandboxed environment 10 | 3. Shows you the results 11 | 12 | The cool part? It uses Terrarium to safely run the generated code in a WebAssembly sandbox - no need to worry about malicious code execution. 13 | 14 | ## Getting Started 15 | 16 | ### Prerequisites 17 | - Python 3.9+ 18 | - Docker & Docker Compose 19 | - Poetry (for dependency management) 20 | 21 | ### Setup 22 | 23 | 1. Clone this repo and install dependencies: 24 | ```bash 25 | git clone 26 | cd code-gen-agent 27 | poetry install 28 | ``` 29 | 30 | 2. Fire up Terrarium: 31 | ```bash 32 | cd cohere-terrarium 33 | docker-compose up -d 34 | ``` 35 | 36 | 3. Set your OpenAI API key: 37 | ```bash 38 | export OPENAI_API_KEY=your-key-here 39 | ``` 40 | 41 | ### Try It Out 42 | 43 | Run the demo: 44 | ```bash 45 | poetry run python code_gen_agent/main.py 46 | ``` 47 | 48 | This will generate a text analyzer that counts words, calculates average word length, and finds the most frequent word. 49 | For demo purposes, the user request for which code to generate is hardcoded inside the `main.py` file so feel free to change it to something else and have a play around! 50 | 51 | ## How It Works 52 | 53 | The magic happens in just two main components: 54 | 55 | 1. **CodeGenerationAgent**: Handles the code generation using GPT-4o-mini 56 | 2. **TerrariumTool**: Manages code execution in the sandboxed environment 57 | 58 | ## License 59 | 60 | MIT - do whatever you want with it. 61 | -------------------------------------------------------------------------------- /demo-terrarium.py: -------------------------------------------------------------------------------- 1 | import requests 2 | from rich.console import Console 3 | from rich.panel import Panel 4 | from rich.syntax import Syntax 5 | 6 | 7 | def send_to_terrarium(code: str) -> dict: 8 | response = requests.post( 9 | "http://localhost:8080", 10 | json={"code": code}, 11 | headers={"Content-Type": "application/json"}, 12 | timeout=30, 13 | ) 14 | return response.json() 15 | 16 | 17 | # Initialize console for rich output 18 | console = Console() 19 | 20 | # Python code to generate Fibonacci sequence 21 | fibonacci_code = """ 22 | def fibonacci(n): 23 | if n <= 0: 24 | return [] 25 | elif n == 1: 26 | return [0] 27 | 28 | sequence = [0, 1] 29 | while len(sequence) < n: 30 | sequence.append(sequence[-1] + sequence[-2]) 31 | return sequence 32 | 33 | # Generate first 10 numbers 34 | result = fibonacci(10) 35 | print(f"Fibonacci sequence: {result}") 36 | """ 37 | 38 | # Display the code we're sending 39 | console.print( 40 | Panel( 41 | Syntax(fibonacci_code, "python", theme="monokai"), 42 | title="📤 Sending Code", 43 | border_style="blue", 44 | ) 45 | ) 46 | 47 | # Send code to Terrarium and handle the response 48 | try: 49 | result = send_to_terrarium(fibonacci_code) 50 | 51 | # Display the output if the execution was successful 52 | if result.get("success"): 53 | console.print( 54 | Panel( 55 | result.get("std_out", "No output"), 56 | title="📥 Result", 57 | border_style="green", 58 | ) 59 | ) 60 | 61 | # Optionally display the final expression if available 62 | if result.get("final_expression"): 63 | console.print( 64 | Panel( 65 | str(result["final_expression"]), 66 | title="🔄 Final Expression", 67 | border_style="yellow", 68 | ) 69 | ) 70 | else: 71 | # Display error message if execution failed 72 | console.print( 73 | Panel( 74 | result.get("std_err", "Unknown error"), 75 | title="❌ Error", 76 | border_style="red", 77 | ) 78 | ) 79 | 80 | # Display execution time 81 | console.print( 82 | f"⏱️ Execution time: {result.get('code_runtime', 0)}ms", style="italic" 83 | ) 84 | 85 | except requests.RequestException as e: 86 | # Handle connection errors 87 | console.print(f"[red]Error connecting to Terrarium service: {e}[/red]") 88 | -------------------------------------------------------------------------------- /code_gen_agent/main.py: -------------------------------------------------------------------------------- 1 | from rich.console import Console 2 | from rich.tree import Tree 3 | from rich.panel import Panel 4 | from rich.syntax import Syntax 5 | 6 | from terrarium_tool import TerrariumTool, TerrariumToolInputSchema 7 | from agent import code_generation_agent, CodeGenerationAgentInputSchema 8 | 9 | console = Console() 10 | 11 | response = code_generation_agent.run( 12 | CodeGenerationAgentInputSchema( 13 | task_description="Create a text analyzer that processes a string of text and returns statistics including: total word count, average word length, and most frequent word.", 14 | requirements=[ 15 | "Must handle empty strings gracefully", 16 | "Should ignore case when counting words", 17 | "Should ignore punctuation", 18 | "Should handle ties for most frequent word by returning the first alphabetically", 19 | ], 20 | ) 21 | ) 22 | 23 | # Print internal thoughts as a list 24 | console.print("\n[bold blue]Development Process:[/bold blue]") 25 | for i, thought in enumerate(response.technical_analysis, 1): 26 | console.print(f"[cyan]{i}. {thought}[/cyan]") 27 | 28 | # Print required imports 29 | console.print("\n[bold blue]Required Imports:[/bold blue]") 30 | for import_statement in response.imports: 31 | syntax = Syntax(import_statement, "python", theme="monokai") 32 | console.print(Panel(syntax)) 33 | 34 | # Print helper functions 35 | console.print("\n[bold blue]Functions:[/bold blue]") 36 | for i, function in enumerate(response.functions, 1): 37 | console.print(f"\n[bold green]Function {i}:[/bold green]") 38 | syntax = Syntax( 39 | function, "python", theme="monokai", line_numbers=True, word_wrap=True 40 | ) 41 | console.print(Panel(syntax)) 42 | 43 | # Print main guard clause 44 | console.print("\n[bold blue]Main Guard Clause:[/bold blue]") 45 | main_syntax = Syntax( 46 | response.main_guard_clause, "python", theme="monokai", line_numbers=True 47 | ) 48 | console.print(Panel(main_syntax)) 49 | 50 | # Execute the code using Terrarium 51 | console.print("\n[bold blue]Code Execution Results:[/bold blue]") 52 | terrarium_tool = TerrariumTool() 53 | 54 | # Combine all code into a single string 55 | full_code = "\n".join( 56 | [ 57 | "\n".join(response.imports), 58 | "\n".join(response.functions), 59 | response.main_guard_clause, 60 | ] 61 | ) 62 | 63 | # Execute the code 64 | result = terrarium_tool.run(TerrariumToolInputSchema(code=full_code)) 65 | 66 | if result.success: 67 | console.print(Panel("[green]✓ Code executed successfully![/green]")) 68 | if result.std_out: 69 | console.print(Panel(result.std_out, title="Output")) 70 | if result.final_expression: 71 | console.print(Panel(result.final_expression, title="Final Expression")) 72 | else: 73 | console.print(Panel(f"[red]✗ Error:[/red]\n{result.std_err}", title="Error")) 74 | 75 | console.print(f"[dim]Execution time: {result.code_runtime}ms[/dim]") 76 | -------------------------------------------------------------------------------- /code_gen_agent/agent.py: -------------------------------------------------------------------------------- 1 | import os 2 | from typing import List 3 | from atomic_agents.agents.base_agent import BaseAgent, BaseAgentConfig 4 | from atomic_agents.lib.components.system_prompt_generator import SystemPromptGenerator 5 | from atomic_agents.lib.base.base_io_schema import BaseIOSchema 6 | import instructor 7 | import openai 8 | from pydantic import Field 9 | 10 | 11 | class CodeGenerationAgentInputSchema(BaseIOSchema): 12 | """Schema for input to the CodeGenAgent""" 13 | 14 | task_description: str = Field( 15 | ..., description="Instructions for the code generation" 16 | ) 17 | requirements: List[str] = Field( 18 | ..., 19 | description="List of requirements for the code generation", 20 | ) 21 | 22 | 23 | class CodeGenerationAgentOutputSchema(BaseIOSchema): 24 | """This schema defines the output schema for the CodeGenerationAgent.""" 25 | 26 | technical_analysis: List[str] = Field( 27 | ..., 28 | description=( 29 | "A step-by-step breakdown of the problem to be solved and how it could be approached, including:\n" 30 | "1. Problem analysis\n" 31 | "2. Design considerations\n" 32 | "3. Potential challenges\n" 33 | "4. Implementation strategy\n" 34 | "5. Testing approach" 35 | ), 36 | min_length=3, 37 | max_length=10, 38 | ) 39 | imports: List[str] = Field( 40 | ..., 41 | description=( 42 | "List of import statements required for the solution. " 43 | "Each item should be a complete import statement. " 44 | "Example: ['import re', 'from collections import Counter']" 45 | ), 46 | ) 47 | functions: List[str] = Field( 48 | ..., 49 | description=( 50 | "List of functions needed for the solution. " 51 | "Each item in the array should be a complete function definition, including google-style docstrings and type hints. " 52 | "Example: " 53 | 'def example_function(a: int, b: int) -> int:\\n """Example function with two integer inputs and an integer output."""\\n ...\\n return a + b\\n' 54 | ), 55 | ) 56 | main_guard_clause: str = Field( 57 | ..., 58 | description=( 59 | "The primary guard clause that uses the helper functions to solve the problem. " 60 | "Must include both the main guard clause definition AND a '__main__' guard clause " 61 | "that demonstrates example usage. Format should be:\n" 62 | "if __name__ == '__main__':\n" 63 | " # example usage" 64 | ), 65 | ) 66 | 67 | 68 | # NOTE: Make sure your API key is set in your environment variables 69 | # This tutorial uses OpenAI with gpt-4o-mini, but you can use any other LLM 70 | # from any other provider. See: https://github.com/instructor-ai/instructor 71 | code_generation_agent = BaseAgent( 72 | BaseAgentConfig( 73 | client=instructor.from_openai( 74 | openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY")) 75 | ), 76 | model="gpt-4o-mini", 77 | input_schema=CodeGenerationAgentInputSchema, 78 | output_schema=CodeGenerationAgentOutputSchema, 79 | system_prompt_generator=SystemPromptGenerator( 80 | background=[ 81 | "You are an AI assistant that is an expert in Python code generation.", 82 | "This AI has access to a sandboxed environment based on Piodide, meaning it has access to popular libraries like:", 83 | "numpy, pandas, matplotlib, scipy, scikit-learn, sympy, seaborn, nltk, networkx, requests, etc.", 84 | ], 85 | steps=[ 86 | "Analyze the user's instructions and determine the necessary functions and main guard clause to solve the problem.", 87 | "Generate the code for all necessary functions except the main guard clause.", 88 | "Finally, generate the main guard clause that uses the helper functions to solve the problem. ", 89 | ], 90 | output_instructions=[ 91 | "Your output should be as clean and professional as possible, featuring proper separation of concerns." 92 | ], 93 | ), 94 | ) 95 | ) 96 | -------------------------------------------------------------------------------- /code_gen_agent/terrarium_tool.py: -------------------------------------------------------------------------------- 1 | import requests 2 | from typing import Optional 3 | from pydantic import Field 4 | 5 | from atomic_agents.agents.base_agent import BaseIOSchema 6 | from atomic_agents.lib.base.base_tool import BaseTool, BaseToolConfig 7 | 8 | 9 | ################ 10 | # INPUT SCHEMA # 11 | ################ 12 | class TerrariumToolInputSchema(BaseIOSchema): 13 | """ 14 | Schema for input to execute Python code in a sandboxed Terrarium environment. 15 | Returns the execution results including stdout, stderr, and runtime metrics. 16 | """ 17 | 18 | code: str = Field( 19 | ..., description="Python code to execute in the Terrarium environment" 20 | ) 21 | 22 | 23 | #################### 24 | # OUTPUT SCHEMA(S) # 25 | #################### 26 | class TerrariumToolOutputSchema(BaseIOSchema): 27 | """Schema representing the output from Terrarium code execution""" 28 | 29 | success: bool = Field(..., description="Whether the code execution was successful") 30 | std_out: Optional[str] = Field( 31 | None, description="Standard output from code execution" 32 | ) 33 | std_err: Optional[str] = Field( 34 | None, description="Standard error from code execution" 35 | ) 36 | final_expression: Optional[str] = Field( 37 | None, description="Result of the final expression evaluated" 38 | ) 39 | code_runtime: float = Field(..., description="Execution time in milliseconds") 40 | 41 | 42 | ################# 43 | # CONFIGURATION # 44 | ################# 45 | class TerrariumToolConfig(BaseToolConfig): 46 | """Configuration for the TerrariumTool""" 47 | 48 | base_url: str = Field( 49 | default="http://localhost:8080", 50 | description="Base URL for the Terrarium service", 51 | ) 52 | timeout: int = Field( 53 | default=30, description="Timeout in seconds for code execution" 54 | ) 55 | 56 | 57 | ##################### 58 | # MAIN TOOL & LOGIC # 59 | ##################### 60 | class TerrariumTool(BaseTool): 61 | """ 62 | Tool for executing Python code in a sandboxed Terrarium environment. 63 | 64 | Attributes: 65 | input_schema (TerrariumToolInputSchema): The schema for the input data 66 | output_schema (TerrariumToolOutputSchema): The schema for the output data 67 | """ 68 | 69 | input_schema = TerrariumToolInputSchema 70 | output_schema = TerrariumToolOutputSchema 71 | 72 | def __init__(self, config: TerrariumToolConfig = TerrariumToolConfig()): 73 | """ 74 | Initializes the TerrariumTool. 75 | 76 | Args: 77 | config (TerrariumToolConfig): Configuration for the tool 78 | """ 79 | super().__init__(config) 80 | self.base_url = config.base_url 81 | self.timeout = config.timeout 82 | 83 | def run(self, params: TerrariumToolInputSchema) -> TerrariumToolOutputSchema: 84 | """ 85 | Executes Python code in the Terrarium environment. 86 | 87 | Args: 88 | params (TerrariumToolInputSchema): The input parameters containing the code to execute 89 | 90 | Returns: 91 | TerrariumToolOutputSchema: The execution results 92 | 93 | Raises: 94 | requests.RequestException: If the request to Terrarium service fails 95 | """ 96 | try: 97 | response = requests.post( 98 | self.base_url, 99 | json={"code": params.code}, 100 | headers={"Content-Type": "application/json"}, 101 | timeout=self.timeout, 102 | ) 103 | 104 | result = response.json() 105 | 106 | return TerrariumToolOutputSchema( 107 | success=result.get("success", False), 108 | std_out=result.get("std_out"), 109 | std_err=result.get("std_err"), 110 | final_expression=result.get("final_expression"), 111 | code_runtime=result.get("code_runtime", 0), 112 | ) 113 | 114 | except requests.RequestException as e: 115 | return TerrariumToolOutputSchema( 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