> ## Documentation Index
> Fetch the complete documentation index at: https://docs.swarms.world/llms.txt
> Use this file to discover all available pages before exploring further.

# DeepWiki: repo Q&A

> Build an agent that answers questions about any public GitHub repository, using the free DeepWiki MCP server. No API key required.

DeepWiki (by Cognition) exposes an MCP server that reads and answers questions about the documentation of any public GitHub repository. It is free, needs **no authentication**, and is the best first MCP integration to write — the only key you need is the one for your LLM.

|                     |                                                             |
| ------------------- | ----------------------------------------------------------- |
| **Server**          | `https://mcp.deepwiki.com/mcp`                              |
| **Auth**            | none                                                        |
| **Tools**           | `read_wiki_structure`, `read_wiki_contents`, `ask_question` |
| **Model used here** | `claude-sonnet-5`                                           |

## Prerequisites

* Python 3.10+
* One LLM provider key. This tutorial uses Anthropic; any LiteLLM model with tool-calling support works.

## Build it

<Steps>
  <Step title="Install swarms">
    ```bash theme={null}
    pip install -U swarms
    ```
  </Step>

  <Step title="Set your LLM key">
    ```bash theme={null}
    export ANTHROPIC_API_KEY="sk-ant-..."
    ```

    Only your model provider needs a key. DeepWiki itself is open.
  </Step>

  <Step title="Write a system prompt that forces grounding">
    An agent with a research tool will still answer from memory unless you tell it not to. This prompt is the difference between a citation and a guess:

    ```python theme={null}
    DEEPWIKI_SYSTEM_PROMPT = (
        "You are a repository research specialist who uses the DeepWiki MCP "
        "server to answer questions about public GitHub repositories. Inspect the "
        "repository's wiki structure and relevant documentation before responding, "
        "then provide a clear, technically accurate explanation grounded only in "
        "the retrieved material. Cite relevant files, modules, or documentation "
        "sections when available, distinguish verified details from reasonable "
        "inferences, and state clearly when DeepWiki does not provide enough "
        "information to answer a question."
    )
    ```
  </Step>

  <Step title="Point the agent at the server">
    ```python theme={null}
    from swarms import Agent

    agent = Agent(
        agent_name="DeepWiki-Agent",
        agent_description="Answers questions about GitHub repos via DeepWiki MCP.",
        system_prompt=DEEPWIKI_SYSTEM_PROMPT,
        model_name="claude-sonnet-5",
        mcp_url="https://mcp.deepwiki.com/mcp",
        max_loops=1,
    )
    ```

    `mcp_url` is the entire integration. On startup the agent connects, fetches the server's tool list, and converts each tool into a function-calling schema the model can use.
  </Step>

  <Step title="Run a task">
    ```python theme={null}
    result = agent.run(
        "Use your DeepWiki tools to explain what the kyegomez/swarms "
        "repository is for and list its main multi-agent structures."
    )
    print(result)
    ```
  </Step>
</Steps>

## The complete script

```python theme={null}
from swarms import Agent

DEEPWIKI_SYSTEM_PROMPT = (
    "You are a repository research specialist who uses the DeepWiki MCP "
    "server to answer questions about public GitHub repositories. Inspect the "
    "repository's wiki structure and relevant documentation before responding, "
    "then provide a clear, technically accurate explanation grounded only in "
    "the retrieved material. Cite relevant files, modules, or documentation "
    "sections when available, distinguish verified details from reasonable "
    "inferences, and state clearly when DeepWiki does not provide enough "
    "information to answer a question."
)

agent = Agent(
    agent_name="DeepWiki-Agent",
    agent_description="Answers questions about GitHub repos via DeepWiki MCP.",
    system_prompt=DEEPWIKI_SYSTEM_PROMPT,
    model_name="claude-sonnet-5",
    mcp_url="https://mcp.deepwiki.com/mcp",
    max_loops=1,
)

result = agent.run(
    "Use your DeepWiki tools to explain what the kyegomez/swarms "
    "repository is for and list its main multi-agent structures."
)
print(result)
```

## What happens when you call `run()`

1. **Transport is detected from the URL scheme.** An `https://` URL uses streamable HTTP; you never configure this by hand.
2. **Tools are discovered.** The agent asks the server what it exposes and converts each tool to an OpenAI-format function schema.
3. **The model chooses.** It typically calls `read_wiki_structure` to see what exists, then `read_wiki_contents` or `ask_question` for the parts that matter.
4. **Results come back into the conversation**, and the model writes its answer from them.

## The smallest possible version

Strip the system prompt and you still have a working integration — useful for a first smoke test:

```python theme={null}
from swarms import Agent

agent = Agent(
    agent_name="MCP-Agent",
    model_name="claude-sonnet-5",
    mcp_url="https://mcp.deepwiki.com/mcp",
    max_loops=1,
    max_tokens=16_000,
)

print(agent.run("Use your tools to explain what the kyegomez/swarms repository does."))
```

## Troubleshooting

<AccordionGroup>
  <Accordion title="The agent answers without calling a tool">
    Two fixes, in order: say "use your tools" in the task, and put the instruction to retrieve before answering in the system prompt. Weaker models need both.
  </Accordion>

  <Accordion title="Timeouts on large repositories">
    `read_wiki_contents` can return a lot of text. Raise the ceiling with `mcp_timeout=120` on the `Agent`.
  </Accordion>

  <Accordion title="`cannot import name 'streamablehttp_client'`">
    You are on `mcp` 2.x, which renamed it. Pin the 1.x line: `pip install 'mcp>=1.28.1,<2.0.0'`.
  </Accordion>
</AccordionGroup>

<Note>
  Source: [examples/mcp/agents/01\_deepwiki\_repo\_qa.py](https://github.com/kyegomez/swarms/blob/master/examples/mcp/agents/01_deepwiki_repo_qa.py) and [deepwiki\_minimal.py](https://github.com/kyegomez/swarms/blob/master/examples/mcp/agents/deepwiki_minimal.py)
</Note>

## Next

* [GitMCP](/examples/mcp/gitmcp-repo-docs) — scope a server to one repository.
* [Several servers at once](/examples/mcp/multi-server-agent) — combine DeepWiki with Microsoft Learn.