cryxnet/DeepMCPAgent

Model-agnostic plug-n-play LangChain/LangGraph agents powered entirely by MCP tools over HTTP/SSE.

🤖 DeepMCPAgent

Model-agnostic LangChain/LangGraph agents powered entirely by MCP tools over HTTP/SSE.

Discover MCP tools dynamically. Bring your own LangChain model. Build production-ready agents—fast.

📚 Documentation • 🛠 Issues

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✨ Why DeepMCPAgent?

• 🔌 Zero manual tool wiring — tools are discovered dynamically from MCP servers (HTTP/SSE)
• 🌐 External APIs welcome — connect to remote MCP servers (with headers/auth)
• 🧠 Model-agnostic — pass any LangChain chat model instance (OpenAI, Anthropic, Ollama, Groq, local, …)
• ⚡ DeepAgents (optional) — if installed, you get a deep agent loop; otherwise robust LangGraph ReAct fallback
• 🛠️ Typed tool args — JSON-Schema → Pydantic → LangChain BaseTool (typed, validated calls)
• 🧪 Quality bar — mypy (strict), ruff, pytest, GitHub Actions, docs

MCP first. Agents shouldn’t hardcode tools — they should discover and call them. DeepMCPAgent builds that bridge.

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🚀 Installation

Install from PyPI:

pip install "deepmcpagent[deep]"

This installs DeepMCPAgent with DeepAgents support (recommended) for the best agent loop. Other optional extras:

• dev → linting, typing, tests
• docs → MkDocs + Material + mkdocstrings
• examples → dependencies used by bundled examples

# install with deepagents + dev tooling
pip install "deepmcpagent[deep,dev]"

⚠️ If you’re using zsh, remember to quote extras:

pip install "deepmcpagent[deep,dev]"

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🚀 Quickstart

1) Start a sample MCP server (HTTP)

python examples/servers/math_server.py

This serves an MCP endpoint at: http://127.0.0.1:8000/mcp

2) Run the example agent (with fancy console output)

python examples/use_agent.py

What you’ll see:

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🧑‍💻 Bring-Your-Own Model (BYOM)

DeepMCPAgent lets you pass any LangChain chat model instance (or a provider id string if you prefer init_chat_model):

import asyncio
from deepmcpagent import HTTPServerSpec, build_deep_agent

# choose your model:
# from langchain_openai import ChatOpenAI
# model = ChatOpenAI(model="gpt-4.1")

# from langchain_anthropic import ChatAnthropic
# model = ChatAnthropic(model="claude-3-5-sonnet-latest")

# from langchain_community.chat_models import ChatOllama
# model = ChatOllama(model="llama3.1")

async def main():
    servers = {
        "math": HTTPServerSpec(
            url="http://127.0.0.1:8000/mcp",
            transport="http",    # or "sse"
            # headers={"Authorization": "Bearer <token>"},
        ),
    }

    graph, _ = await build_deep_agent(
        servers=servers,
        model=model,
        instructions="Use MCP tools precisely."
    )

    out = await graph.ainvoke({"messages":[{"role":"user","content":"add 21 and 21 with tools"}]})
    print(out)

asyncio.run(main())

Tip: If you pass a string like "openai:gpt-4.1", we’ll call LangChain’s init_chat_model() for you (and it will read env vars like OPENAI_API_KEY). Passing a model instance gives you full control.

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🤝 Cross-Agent Communication

DeepMCPAgent v0.5 introduces Cross-Agent Communication — agents that can talk to each other without extra servers, message queues, or orchestration layers.

You can now attach one agent as a peer inside another, turning it into a callable tool.
Each peer appears automatically as ask_agent_<name> or can be reached via broadcast_to_agents for parallel reasoning across multiple agents.

This means your agents can delegate, collaborate, and critique each other — all through the same MCP tool interface.
It’s lightweight, model-agnostic, and fully transparent: every peer call is traced like any other tool invocation.

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💻 Example

import asyncio
from deepmcpagent import HTTPServerSpec, build_deep_agent
from deepmcpagent.cross_agent import CrossAgent

async def main():
    # 1️⃣ Build a "research" peer agent
    research_graph, _ = await build_deep_agent(
        servers={"web": HTTPServerSpec(url="http://127.0.0.1:8000/mcp")},
        model="openai:gpt-4o-mini",
        instructions="You are a focused research assistant that finds and summarizes sources.",
    )

    # 2️⃣ Build the main agent and attach the peer as a tool
    main_graph, _ = await build_deep_agent(
        servers={"math": HTTPServerSpec(url="http://127.0.0.1:9000/mcp")},
        model="openai:gpt-4.1",
        instructions="You are a lead analyst. Use peers when you need research or summarization.",
        cross_agents={
            "researcher": CrossAgent(agent=research_graph, description="A web research peer.")
        },
        trace_tools=True,  # see all tool calls + peer responses in console
    )

    # 3️⃣ Ask a question — the main agent can now call the researcher
    result = await main_graph.ainvoke({
        "messages": [{"role": "user", "content": "Find recent research on AI ethics and summarize it."}]
    })

    print(result)

asyncio.run(main())

🧩 Result: Your main agent automatically calls ask_agent_researcher(...) when it decides delegation makes sense, and the peer agent returns its best final answer — all transparently handled by the MCP layer.

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💡 Use Cases

• Researcher → Writer → Editor pipelines
• Safety or reviewer peers that audit outputs
• Retrieval or reasoning specialists
• Multi-model ensembles combining small and large LLMs

No new infrastructure. No complex orchestration. Just agents helping agents, powered entirely by MCP over HTTP/SSE.

🧠 One framework, many minds — DeepMCPAgent turns individual LLMs into a cooperative system.

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🖥️ CLI (no Python required)

# list tools from one or more HTTP servers
deepmcpagent list-tools \
  --http name=math url=http://127.0.0.1:8000/mcp transport=http \
  --model-id "openai:gpt-4.1"

# interactive agent chat (HTTP/SSE servers only)
deepmcpagent run \
  --http name=math url=http://127.0.0.1:8000/mcp transport=http \
  --model-id "openai:gpt-4.1"

The CLI accepts repeated --http blocks; add header.X=Y pairs for auth:

--http name=ext url=https://api.example.com/mcp transport=http header.Authorization="Bearer TOKEN"

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Full Architecture & Agent Flow

1) High-level Architecture (modules & data flow)

flowchart LR
    %% Groupings
    subgraph User["👤 User / App"]
      Q["Prompt / Task"]
      CLI["CLI (Typer)"]
      PY["Python API"]
    end

    subgraph Agent["🤖 Agent Runtime"]
      DIR["build_deep_agent()"]
      PROMPT["prompt.py\n(DEFAULT_SYSTEM_PROMPT)"]
      subgraph AGRT["Agent Graph"]
        DA["DeepAgents loop\n(if installed)"]
        REACT["LangGraph ReAct\n(fallback)"]
      end
      LLM["LangChain Model\n(instance or init_chat_model(provider-id))"]
      TOOLS["LangChain Tools\n(BaseTool[])"]
    end

    subgraph MCP["🧰 Tooling Layer (MCP)"]
      LOADER["MCPToolLoader\n(JSON-Schema ➜ Pydantic ➜ BaseTool)"]
      TOOLWRAP["_FastMCPTool\n(async _arun → client.call_tool)"]
    end

    subgraph FMCP["🌐 FastMCP Client"]
      CFG["servers_to_mcp_config()\n(mcpServers dict)"]
      MULTI["FastMCPMulti\n(fastmcp.Client)"]
    end

    subgraph SRV["🛠 MCP Servers (HTTP/SSE)"]
      S1["Server A\n(e.g., math)"]
      S2["Server B\n(e.g., search)"]
      S3["Server C\n(e.g., github)"]
    end

    %% Edges
    Q -->|query| CLI
    Q -->|query| PY
    CLI --> DIR
    PY --> DIR

    DIR --> PROMPT
    DIR --> LLM
    DIR --> LOADER
    DIR --> AGRT

    LOADER --> MULTI
    CFG --> MULTI
    MULTI -->|list_tools| SRV
    LOADER --> TOOLS
    TOOLS --> AGRT

    AGRT <-->|messages| LLM
    AGRT -->|tool calls| TOOLWRAP
    TOOLWRAP --> MULTI
    MULTI -->|call_tool| SRV

    SRV -->|tool result| MULTI --> TOOLWRAP --> AGRT -->|final answer| CLI
    AGRT -->|final answer| PY

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2) Runtime Sequence (end-to-end tool call)

sequenceDiagram
    autonumber
    participant U as User
    participant CLI as CLI/Python
    participant Builder as build_deep_agent()
    participant Loader as MCPToolLoader
    participant Graph as Agent Graph (DeepAgents or ReAct)
    participant LLM as LangChain Model
    participant Tool as _FastMCPTool
    participant FMCP as FastMCP Client
    participant S as MCP Server (HTTP/SSE)

    U->>CLI: Enter prompt
    CLI->>Builder: build_deep_agent(servers, model, instructions?)
    Builder->>Loader: get_all_tools()
    Loader->>FMCP: list_tools()
    FMCP->>S: HTTP(S)/SSE list_tools
    S-->>FMCP: tools + JSON-Schema
    FMCP-->>Loader: tool specs
    Loader-->>Builder: BaseTool[]
    Builder-->>CLI: (Graph, Loader)

    U->>Graph: ainvoke({messages:[user prompt]})
    Graph->>LLM: Reason over system + messages + tool descriptions
    LLM-->>Graph: Tool call (e.g., add(a=3,b=5))
    Graph->>Tool: _arun(a=3,b=5)
    Tool->>FMCP: call_tool("add", {a:3,b:5})
    FMCP->>S: POST /mcp tools.call("add", {...})
    S-->>FMCP: result { data: 8 }
    FMCP-->>Tool: result
    Tool-->>Graph: ToolMessage(content=8)

    Graph->>LLM: Continue with observations
    LLM-->>Graph: Final response "(3 + 5) * 7 = 56"
    Graph-->>CLI: messages (incl. final LLM answer)

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3) Agent Control Loop (planning & acting)

stateDiagram-v2
    [*] --> AcquireTools
    AcquireTools: Discover MCP tools via FastMCP\n(JSON-Schema ➜ Pydantic ➜ BaseTool)
    AcquireTools --> Plan

    Plan: LLM plans next step\n(uses system prompt + tool descriptions)
    Plan --> CallTool: if tool needed
    Plan --> Respond: if direct answer sufficient

    CallTool: _FastMCPTool._arun\n→ client.call_tool(name, args)
    CallTool --> Observe: receive tool result
    Observe: Parse result payload (data/text/content)
    Observe --> Decide

    Decide: More tools needed?
    Decide --> Plan: yes
    Decide --> Respond: no

    Respond: LLM crafts final message
    Respond --> [*]

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4) Code Structure (types & relationships)

classDiagram
    class StdioServerSpec {
      +command: str
      +args: List[str]
      +env: Dict[str,str]
      +cwd: Optional[str]
      +keep_alive: bool
    }

    class HTTPServerSpec {
      +url: str
      +transport: Literal["http","streamable-http","sse"]
      +headers: Dict[str,str]
      +auth: Optional[str]
    }

    class FastMCPMulti {
      -_client: fastmcp.Client
      +client(): Client
    }

    class MCPToolLoader {
      -_multi: FastMCPMulti
      +get_all_tools(): List[BaseTool]
      +list_tool_info(): List[ToolInfo]
    }

    class _FastMCPTool {
      +name: str
      +description: str
      +args_schema: Type[BaseModel]
      -_tool_name: str
      -_client: Any
      +_arun(**kwargs) async
    }

    class ToolInfo {
      +server_guess: str
      +name: str
      +description: str
      +input_schema: Dict[str,Any]
    }

    class build_deep_agent {
      +servers: Mapping[str,ServerSpec]
      +model: ModelLike
      +instructions?: str
      +returns: (graph, loader)
    }

    StdioServerSpec <|-- ServerSpec
    HTTPServerSpec <|-- ServerSpec
    FastMCPMulti o--> ServerSpec : uses servers_to_mcp_config()
    MCPToolLoader o--> FastMCPMulti
    MCPToolLoader --> _FastMCPTool : creates
    _FastMCPTool ..> BaseTool
    build_deep_agent --> MCPToolLoader : discovery
    build_deep_agent --> _FastMCPTool : tools for agent

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These diagrams reflect the current implementation:

• Model is required (string provider-id or LangChain model instance).
• MCP tools only, discovered at runtime via FastMCP (HTTP/SSE).
• Agent loop prefers DeepAgents if installed; otherwise LangGraph ReAct.
• Tools are typed via JSON-Schema ➜ Pydantic ➜ LangChain BaseTool.
• Fancy console output shows discovered tools, calls, results, and final answer.

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🧪 Development

# install dev tooling
pip install -e ".[dev]"

# lint & type-check
ruff check .
mypy

# run tests
pytest -q

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🛡️ Security & Privacy

• Your keys, your model — we don’t enforce a provider; pass any LangChain model.
• Use HTTP headers in HTTPServerSpec to deliver bearer/OAuth tokens to servers.

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🧯 Troubleshooting

• PEP 668: externally managed environment (macOS + Homebrew) Use a virtualenv:

  python3 -m venv .venv
  source .venv/bin/activate

• 404 Not Found when connecting Ensure your server uses a path (e.g., /mcp) and your client URL includes it.

• Tool calls failing / attribute errors Ensure you’re on the latest version; our tool wrapper uses PrivateAttr for client state.

• High token counts That’s normal with tool-calling models. Use smaller models for dev.

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📄 License

Apache-2.0 — see LICENSE.

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⭐ Stars

🙏 Acknowledgments

• The MCP community for a clean protocol.
• LangChain and LangGraph for powerful agent runtimes.
• FastMCP for solid client & server implementations.