# The Claude API multi-agent loop, without the framework

> Source: <https://dev.to/espanhol/the-claude-api-multi-agent-loop-without-the-framework-2nh5>
> Published: 2026-06-15 14:56:59+00:00

Most Claude API tutorials show a single tool call. Most frameworks hide the loop behind abstractions you can't read. This post shows the loop directly — what actually happens between "Claude requests a tool" and "Claude finishes."

When you give Claude tools, a single API call isn't always enough. Claude decides whether to call a tool, you execute it, then you send the result back. Claude might call another tool, or it might answer. That cycle is the agent loop.

```
user message
    ↓
Claude responds
    ↓
stop_reason == "tool_use"?  →  execute tools  →  back to Claude
    ↓
stop_reason == "end_turn"
    ↓
return final text
```

The entire loop is in [ agent.py](https://github.com/espanhol6/claude-multiagent-loop/blob/main/agent.py) — about 80 lines.

``` python
def run_agent(
    system: str,
    user_message: str,
    tools: list[dict],
    tool_handlers: dict[str, Callable],
    max_rounds: int = 10,
) -> str:
    messages = [{"role": "user", "content": user_message}]

    for round_num in range(max_rounds):
        response = client.messages.create(
            model=MODEL,
            max_tokens=4096,
            system=system,
            tools=tools,
            messages=messages,
        )

        messages.append({"role": "assistant", "content": response.content})

        if response.stop_reason == "end_turn":
            return _extract_text(response.content)

        if response.stop_reason == "tool_use":
            tool_results = []
            for block in response.content:
                if block.type == "tool_use":
                    result = _call_tool(block, tool_handlers)
                    tool_results.append({
                        "type": "tool_result",
                        "tool_use_id": block.id,
                        "content": json.dumps(result),
                    })
            messages.append({"role": "user", "content": tool_results})
            continue

        break

    return _extract_text(response.content)
```

That's the core. The rest of the file is `_call_tool`

(dispatch to your Python function) and `_extract_text`

(pull text blocks from the response).

Define tools in Anthropic's schema format:

```
TOOLS = [
    {
        "name": "read_file",
        "description": "Read the contents of a file.",
        "input_schema": {
            "type": "object",
            "properties": {
                "path": {"type": "string", "description": "File path to read"},
            },
            "required": ["path"],
        },
    },
]
```

Define handlers as plain Python functions:

``` php
def read_file(path: str) -> dict:
    return {"content": Path(path).read_text()}

tool_handlers = {"read_file": read_file}
```

Run the agent:

```
result = run_agent(
    system="You are a helpful assistant.",
    user_message="What's in README.md?",
    tools=TOOLS,
    tool_handlers=tool_handlers,
)
```

Frameworks aren't wrong. But when something breaks in production — and it will — you want to know exactly what message went to Claude and exactly what came back. Abstractions make that harder.

This implementation is meant to be read, modified, and owned. The loop is visible. You can add logging, approval gates, retry logic, or conditional execution exactly where you need it.

The repo includes:

`example_research.py`

`search`

and `read_page`

tools (swap in your real implementations)`example_code.py`

`read_file`

, `write_file`

, and `list_files`

toolsBoth run end-to-end with real Claude API calls.

```
pip install anthropic
export ANTHROPIC_API_KEY=sk-ant-...
python example_code.py
```

The repo: [github.com/espanhol6/claude-multiagent-loop](https://github.com/espanhol6/claude-multiagent-loop)

This pattern is what I used as the foundation for [Cluster OS Jarvis](https://github.com/espanhol6) — a production multi-agent framework with SSE streaming, up to 6 tool-calling rounds, and cron-scheduled autonomous agents. The loop here is the simplified, standalone version.

If you're building something with Claude and want to understand what's happening under the hood before adding abstractions, this is a good starting point.

[João Daniel Espanhol Miguel](https://linkedin.com/in/jo%C3%A3o-espanhol-miguel) — AI engineer, Lisbon. Also wrote about [debugging a silent native crash in ctranslate2 + WinRT](https://dev.to/espanhol/debugging-a-silent-native-crash-when-combining-faster-whisper-and-winrt-on-windows-2ik9).