Deterministic codebase facts. Safer AI reasoning.

LynkMesh turns a software project into a semantic graph that AI agents can consume through MCP — separating machine-verifiable facts from LLM-generated conclusions.

See validation snapshot Explore MeshContext

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MCP diagnostics: PASS Graph build: PASS AI reasoning smoke test: PASS

The gap

AI reads text. Software behaves as a system.

Most AI coding tools retrieve nearby files and tokens. LynkMesh builds the missing layer: a stable graph of symbols, dependencies, calls, confidence, and architectural risk.

AI Consumption Protocol

Machine facts below. LLM reasoning above.

LynkMesh is designed as a protocol layer between deterministic software analysis and probabilistic AI explanation.

01 / LynkMesh

Graph Facts

Deterministic, repeatable, versioned evidence from the codebase.

02 / LLM

Inferred Conclusions

Human-readable interpretation generated from graph-backed evidence.

Validation snapshot

Verified through Claude Desktop MCP smoke tests.

A real PHP project was analyzed through LynkMesh MCP from Claude Desktop. This is early validation, not benchmark proof.

Runtime pipeline

From source tree to AI-ready context.

Capabilities

Built for agents that need structure, not just snippets.

The current focus is reliability: deterministic graph builds, MCP diagnostics, open-core safety, and AI-context readiness.

Next: MeshContext Report

A stable JSON contract for AI codebase understanding.

The next major step is an architecture_report.json / MeshContext Report that separates graph facts, hotspots, risk scores, limitations, and LLM instructions.

{
  "schema_version": "0.1",
  "graph_facts": {
    "nodes": 1108,
    "edges": 2400,
    "analyzed_files": 282
  },
  "hotspots": [
    { "symbol": "ApprovalService", "risk": "critical" },
    { "symbol": "AccountingService", "risk": "critical" }
  ],
  "llm_instructions": {
    "separate_facts_from_inferences": true
  }
}

Roadmap

From graph engine to codebase health protocol.

Principles

No magic. Evidence first.

For AI-native engineering

Give the model a map before asking it to reason.

LynkMesh is being built as the deterministic context layer for AI coding agents, architecture review, impact analysis, and codebase health monitoring.

Contact LynkMesh

Deterministic Graph Facts MCP Validated Claude Desktop Smoke-Tested Open-Core Safety PASS MeshContext Report Next Deterministic Graph Facts MCP Validated Claude Desktop Smoke-Tested Open-Core Safety PASS MeshContext Report Next