ncp-langgraph 0.1.0

LangGraph adapter for NCP: invoke NCP graphs as LangGraph nodes via ncp-mcp-server subprocess.

ncp-langgraph

LangGraph adapter for the Neural Computation Protocol (NCP).

ncp-langgraph lets you wrap any NCP graph as a LangGraph node: NCPNode.from_subprocess(...) returns a callable instance that spawns ncp-mcp-server over stdio, performs the locked MCP dialog, and returns a partial state-update dict ready for LangGraph to merge.

One NCP graph = one LangGraph node. No glue code, no protocol-buffer plumbing, no PyO3 (in v0.1.0).

Quick start

from typing import Any, TypedDict

from langgraph.graph import END, START, StateGraph

from ncp_langgraph import NCPNode


class State(TypedDict, total=False):
    company_url: str
    qualification: dict[str, Any]
    ncp_trace: dict[str, Any]


qualify_lead = NCPNode.from_subprocess(
    graph="/abs/path/to/lead-qualification.yaml",
    brick_dir="/abs/path/to/bricks",
    output_key="qualification",
    timeout=30.0,
)

builder = StateGraph(State)
builder.add_node("qualify_lead", qualify_lead)
builder.add_edge(START, "qualify_lead")
builder.add_edge("qualify_lead", END)
compiled = builder.compile()

result = compiled.invoke({"company_url": "https://example.com"})

# result["qualification"]  -- the NCP graph's output_json
# result["ncp_trace"]      -- {"result_type", "trace_id", "trace_path"}

For a runnable end-to-end example using the bundled echo-pipeline graph (stub until issue #29 ships the real lead-qualification graph), see examples/langgraph/.

Install

1. The NCP MCP adapter binary

cargo install ncp-mcp-server --version 0.1.0 --locked

The version is pinned to keep ncp-langgraph v0.1.x reproducible against a known ncp-mcp-server release.

2. The Python adapter (this package)

python -m pip install ncp-langgraph

Or pin to a specific version for reproducibility:

python -m pip install ncp-langgraph==0.1.0

ncp-langgraph does NOT bundle the ncp-mcp-server binary in v0.1.0. The binary is distributed separately as the Rust crate ncp-mcp-server; install it independently and keep it on PATH, or pass its absolute path via NCPNode.from_subprocess(binary=...).

Requirements

• Python 3.10+
• ncp-mcp-server v0.1.x on PATH (or pass an absolute path via NCPNode.from_subprocess(binary=...))
• LangGraph 1.x

v0.1.0 scope and limitations (honest)

• Sync only. NCPNode exposes a synchronous __call__. Native async support (NCPAsyncNode) is a v0.2.0+ addition.
• One subprocess per call. Each invocation spawns a fresh ncp-mcp-server process. No persistent pool. Negligible cost for typical agent workflows; significant for hot-loop / per-token use. Persistent pool is a v0.2.0+ perf optimization.
• One graph per NCPNode instance. Multi-graph adapter instances are a v0.2.0+ addition.
• Subprocess invocation only. PyO3 direct binding and Streamable HTTP transport are deferred (see docs/LANGGRAPH_ADAPTER.md §11 and future-work issue #34 for Streamable HTTP).

License

Apache-2.0. See LICENSE and NOTICE.

Links

• NCP project
• Design doc
• Examples
• MCP adapter (ncp-mcp-server)
• Changelog
• Issues