agent-loops

# Agent Loops

Enable LLMs to reason, plan, and take autonomous actions.

## ReAct Pattern (Reasoning + Acting)

```python
REACT_PROMPT = """You are an agent that reasons step by step.

For each step, respond with:
Thought: [your reasoning about what to do next]
Action: [tool_name(arg1, arg2)]
Observation: [you'll see the result here]

When you have the final answer:
Thought: I now have enough information
Final Answer: [your response]

Available tools: {tools}

Question: {question}
"""

async def react_loop(question: str, tools: dict, max_steps: int = 10) -> str:
    """Execute ReAct reasoning loop."""
    history = REACT_PROMPT.format(tools=list(tools.keys()), question=question)

    for step in range(max_steps):
        response = await llm.chat([{"role": "user", "content": history}])
        history += response.content

        # Check for final answer
        if "Final Answer:" in response.content:
            return response.content.split("Final Answer:")[-1].strip()

        # Extract and execute action
        if "Action:" in response.content:
            action = parse_action(response.content)
            result = await tools[action.name](*action.args)
            history += f"\nObservation: {result}\n"

    return "Max steps reached without answer"
```

## Plan-and-Execute Pattern

```python
async def plan_and_execute(goal: str) -> str:
    """Create plan first, then execute steps."""
    # 1. Generate plan
    plan = await llm.chat([{
        "role": "user",
        "content": f"Create a step-by-step plan to: {goal}\n\nFormat as numbered list."
    }])

    steps = parse_plan(plan.content)
    results = []

    # 2. Execute each step
    for i, step in enumerate(steps):
        result = await execute_step(step, context=results)
        results.append({"step": step, "result": result})

        # 3. Check if replanning needed
        if should_replan(results):
            return await plan_and_execute(
                f"{goal}\n\nProgress so far: {results}"