Public API · Early access developer preview

LynkMesh

Give your AI coding agent a map of your codebase.

LynkMesh analyzes your repository and generates structured, deterministic context so AI coding agents can understand dependencies, architecture, and relevant code before they start working.

Try LynkMesh View API Docs

Built for developers building AI coding agents.

API api.lynkmesh.it.com Repo github.com/ommukhlis-spec/lynkmesh-open Blog lynkmesh.site

Deterministic analysis — no LLM inference Structured Context Pack — mesh_context_ai_pack.v0.1 Live public API — api.lynkmesh.it.com

The problem

Your AI agent can write code. But does it understand your repository?

Large codebases are more than files and tokens. Dependencies, call relationships, structure, and boundaries matter.

The solution

LynkMesh turns your repository into AI-ready context.

LynkMesh extracts deterministic structural facts from your repository and packages them into a compact Context Pack that an AI agent can consume.

Live API Result

The API is live. This is what it returns.

One repository analysis produces a structured Context Pack containing repository facts that can be consumed by an AI coding system. No LLM inference runs inside the analysis.

Real responses from api.lynkmesh.it.com

The health endpoint is open and needs no API key — open it in your browser. The Context Pack below is one real analysis response from the compact profile.

One real run on a small PHP repository. Not a benchmark — pack size and content vary with repository structure.

{
  "status": "ok",
  "service": "lynkmesh-api",
  "api_version": "v1"
}

{
  "job_id": "d5b3d3751fd34375b4bcc57b69201f33",
  "status": "done",
  "generated_at": "2026-09-14T14:30:28Z",
  "profile": "compact",
  "content_hash": "62bb5ad881ce…",
  "pack": {
    "schema_version": "mesh_context_ai_pack.v0.1",
    "executive_context": { "node_count": 7, "edge_count": 7 },
    "context_budget": {
      "estimated_input_tokens": 575,
      "estimator": "deterministic_char_heuristic_v0.1"
    },
    "guardrails": {
      "deterministic_facts_only": true,
      "contains_llm_inference": false,
      "must_cite_evidence_ids": true
    }
  }
}

What you get

A repository intelligence layer for your agent.

Four properties make the Context Pack useful as machine input for AI coding systems.

Built for AI agent builders

Don't build repository intelligence from scratch.

Building an AI coding agent means solving more than generation. Your agent also needs to navigate repositories, discover dependencies, and establish structural context.

LynkMesh provides a repository intelligence layer that your agent can call through an API.

Developer API

One API call. Structured repository context.

Submit a GitHub repository URL. Poll the job. Retrieve the Context Pack. Authentication is a single X-API-Key header.

# POST https://api.lynkmesh.it.com/v1/analyze
# header: X-API-Key: <your-key>
{
  "repo_url": "https://github.com/your-org/your-repo",
  "branch": "main",
  "profile": "compact"
}

# → 202 Accepted
{
  "job_id": "1f3c9a…",
  "status": "queued"
}

# → poll, then retrieve
# GET /v1/jobs/{job_id}
# GET /v1/jobs/{job_id}/context  → Context Pack

Evidence & trust

Don't take our word for it.

LynkMesh is open about how it works. Explore the source code, generated Context Packs, and validation artifacts.

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

Under the hood: MeshContext

The structured format behind the Context Pack.

MeshContext is the structured format used to package repository intelligence for downstream AI systems. It keeps deterministic graph facts separate from interpretation, runs no LLM inference inside the analysis, and stays diff-ready. The sample below shows values from the public evidence fixture.

{
  "schema_version": "mesh_context_ai_pack.v0.1",
  "profile": "compact",
  "purpose": "ai_consumption",
  "executive_context": {
    "node_count": 61,
    "edge_count": 99
  },
  "context_budget": {
    "estimated_input_tokens": 691,
    "estimator": "deterministic_char_heuristic_v0.1"
  },
  "guardrails": {
    "deterministic_facts_only": true,
    "contains_llm_inference": false,
    "must_cite_evidence_ids": true
  }
}

Local

Coming soon

Working directly with your local codebase?

LynkMesh is also being developed as a local MCP integration for AI coding workflows, allowing developers to use repository intelligence locally without sending their source code to a remote service.

Roadmap

What's shipping now, next, and later.

Principles

No magic. Evidence first.

Get started

Give your agent a better context.

Start with a repository. Get structured context your AI coding system can consume.

Try LynkMesh Explore GitHub

Deterministic Code Intelligence AI-Ready Context Pack Structured Repository Context Submit → Poll → Retrieve No LLM Inference in Analysis Open-Core Engine Deterministic Code Intelligence AI-Ready Context Pack Structured Repository Context Submit → Poll → Retrieve No LLM Inference in Analysis Open-Core Engine