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| """Coding agent implementation with iterative LLM loop. | |
| Main agent loop that orchestrates LLM calls, tool execution, | |
| and conversation memory management. | |
| """ | |
| import json | |
| from openai import OpenAI | |
| from e2b_code_interpreter import Sandbox | |
| from .tools import execute_tool | |
| def coding_agent( | |
| client: OpenAI, | |
| query: str, | |
| system: str, | |
| tools: dict, | |
| tools_schemas: list, | |
| sbx: Sandbox, | |
| messages: list = None, | |
| max_steps: int = 5 | |
| ): | |
| """Run coding agent with iterative tool calling loop. | |
| Args: | |
| client: OpenAI client instance | |
| query: User query/prompt | |
| system: System prompt defining agent behavior | |
| tools: Dict mapping tool names to functions | |
| tools_schemas: List of OpenAI function schemas | |
| sbx: E2B Sandbox instance for code execution | |
| messages: Optional existing message history | |
| max_steps: Maximum iteration steps (default 5) | |
| Returns: | |
| Tuple of (messages list, metadata dict) | |
| - messages: Full conversation history | |
| - metadata: Accumulated metadata (especially images) | |
| """ | |
| if messages is None: | |
| messages = [] | |
| messages.append({"role": "user", "content": query}) | |
| metadata = {} | |
| steps = 0 | |
| while steps < max_steps: | |
| # Call LLM with current conversation state | |
| response = client.responses.create( | |
| model="gpt-4.1-mini", | |
| input=[ | |
| {"role": "developer", "content": system}, | |
| *messages | |
| ], | |
| tools=tools_schemas | |
| ) | |
| has_function_call = False | |
| # Process all parts of the response | |
| for part in response.output: | |
| messages.append(part.to_dict()) | |
| if part.type == "message": | |
| print(part.content) | |
| elif part.type == "function_call": | |
| has_function_call = True | |
| name = part.name | |
| args = part.arguments | |
| # Execute the tool and get results | |
| result, tool_metadata = execute_tool(name, args, tools, sbx=sbx) | |
| # Accumulate metadata (especially images) | |
| if "images" in tool_metadata: | |
| metadata.setdefault("images", []).extend(tool_metadata["images"]) | |
| if "error" in tool_metadata: | |
| metadata["error"] = tool_metadata["error"] | |
| # Append function result to conversation | |
| messages.append({ | |
| "type": "function_call_output", | |
| "call_id": part.call_id, | |
| "output": json.dumps(result) | |
| }) | |
| # Stop if no more function calls | |
| if not has_function_call: | |
| break | |
| steps += 1 | |
| return messages, metadata | |