ChainWeaver MCP server

ChainWeaver

<!-- mcp-name: io.github.dgenio/chainweaver -->

Find where your agent no longer needs to reason. Review the evidence. Turn the accepted path into a governed deterministic capability.

Product thesis under validation — observe → prove → review → compile. ChainWeaver can inspect repeated tool behavior, surface candidates, and execute reviewed deterministic paths with typed contracts. Deterministic execution by itself is not the moat: if you already know the exact workflow, a normal Python function, LangGraph node, or provider-native tool may be simpler. The hypothesis being tested is that trace-derived evidence, useful rejection, governed promotion, security-boundary preservation, and drift detection make ChainWeaver worth adopting. See Product validation & adoption gates and #553.

Remove reasoning boundaries, never security boundaries. Compiling several tool calls into one capability must not silently aggregate privileges or erase child approval requirements. That invariant is tracked explicitly in #554.

Governance for deterministic tool paths. Typed I/O at every step, file-serializable flows, schema-drift detection, determinism attestation, property fuzzing, and structured audit traces provide a disciplined execution substrate for paths that have actually earned deterministic promotion.

Benchmarks are evidence about the executor, not proof of product-market fit.

The repo's benchmark report is reproducible —

regenerate it yourself with python benchmarks/report.py — and shows the

deterministic core avoiding model-mediated transitions in its synthetic

comparison. It does not establish that every repeated path should

be compiled, or that ChainWeaver beats the obvious plain-Python implementation.

The independent validation program requires that manual baseline explicitly.

from chainweaver import Tool, Flow, FlowStep, FlowRegistry, FlowExecutor
# (NumberInput, ValueOutput, double_fn defined in full example below)

# 1. Wrap any function as a schema-validated Tool
double = Tool(name="double", description="Doubles a number.",
              input_schema=NumberInput, output_schema=ValueOutput, fn=double_fn)
# 2. Wire tools into a Flow
flow = Flow(name="calc", description="Double a number.",
            steps=[FlowStep(tool_name="double", input_mapping={"number": "number"})])
# 3. Register and execute — zero LLM calls
registry = FlowRegistry()
registry.register_flow(flow)
executor = FlowExecutor(registry=registry)
executor.register_tool(double)
result = executor.execute_flow("calc", {"number": 5})
# result.final_output → {"number": 5, "value": 10}

See the full example below or run python examples/simplelinearflow.py

Shortened. The full README is on GitHub.