Deterministic context for codebases. Safer AI code understanding.

LynkMesh converts software projects into graph-backed MeshContext evidence that AI agents can use without guessing the architecture from raw text alone.

Read evidence pack See validation snapshot Explore MeshContext

Repo github.com/ommukhlis-spec/lynkmesh-open Blog lynkmesh.site X x.com/Lynkmesh

Open-core smoke: PASS MeshContext guardrails: PASS LLM inference in contracts: FALSE

The gap

AI reads tokens. Software behaves as a system.

Most coding assistants retrieve nearby snippets, files, or search results. LynkMesh adds the missing layer: deterministic structure, graph evidence, and compact context that agents can reason from more safely.

Deterministic Context Protocol

Facts below. AI reasoning above.

LynkMesh is designed as a protocol layer between static code analysis and AI-assisted engineering workflows. Its semantic contracts avoid LLM inference and keep evidence separate from interpretation.

01 / LynkMesh

Deterministic Evidence

Repeatable graph-backed facts generated from the codebase.

02 / AI Agent

Human-Reviewed Reasoning

Explanations and decisions can be produced above the evidence layer, while the semantic contract remains deterministic.

Validation snapshot

Open-core release candidate smoke-tested.

The refreshed public repository passed semantic contract tests, core unit tests, pipeline tests, import checks, docs inclusion checks, and privacy spot-checks. This is still early validation, not production readiness or benchmark proof.

Public Evidence Pack

How to read the LynkMesh evidence pack.

LynkMesh now includes a public-safe before/after evidence pack that shows how deterministic graph artifacts can ground AI-assisted code understanding workflows. It is an early validation baseline, not benchmark proof.

01 / Baseline

Evaluation method

The baseline explains how to compare AI coding workflows without LynkMesh versus with LynkMesh, while keeping claims conservative and reviewable.

Repeatable before/after structure
Metric schema and wording rules
Public-safety guidelines
No benchmark or performance guarantee

Open baseline →

02 / First run

Fixture-level evidence

The first committed run uses a synthetic PHP mini shop fixture and includes deterministic CLI artifacts, before/after transcripts, screenshots, and a conservative comparison summary.

Expanded AI Context Pack artifact
Readable node and edge evidence labels
Guardrails against LLM inference claims
Human correctness scoring still treated as pending where applicable

View run evidence →

Early validation baseline Not benchmark proof No “AI became smarter” claim Deterministic graph evidence

MeshContext pipeline

From source tree to AI-ready context.

Capabilities

Built for agents that need structure, not just snippets.

The current open-core release focuses on deterministic graph serialization, MeshContext reports, compact AI context packs, calibrated token benchmarks, structural validation, and privacy-aware release safety.

MeshContext Protocol

A compact contract for AI codebase understanding.

MeshContext turns serialized graph payloads into validated, AI-ready context: report facts, compact context packs, token benchmark baselines, and structural checks. The contract keeps LLM inference outside the deterministic layer.

{
  "mesh_context_report": {
    "status": "ok",
    "graph_facts": {
      "node_count": 26,
      "edge_count": 53
    }
  },
  "ai_context_pack": {
    "profile": "compact",
    "guardrails": {
      "contains_llm_inference": false
    }
  },
  "token_benchmark": {
    "benchmark_source_kind": "mesh_context_report",
    "source_baselines": [
      "mesh_context_report",
      "serialized_graph_payload"
    ]
  },
  "structural_validation": {
    "deterministic": true,
    "privacy_safe": true
  }
}

Roadmap

From release preview to practical onboarding.

Principles

No magic. Evidence first.

For AI-native engineering

Give the model a map before asking it to reason.

LynkMesh is being built as a deterministic context layer for AI coding agents, architecture review, impact analysis, and codebase health workflows. The current release is a research preview for early validation.

Contact LynkMesh

Deterministic Context Protocol MeshContext Report AI Context Pack Token Benchmark Structural Validation Open-Core Safety PASS Deterministic Context Protocol MeshContext Report AI Context Pack Token Benchmark Structural Validation Open-Core Safety PASS