GoAtlas는 LLM 및 개발자가 다 언어 AST 패싱, Neo4j 지식 그래프, pgvector semantic search 및 Pluggable LLM 백엔드를 결합하는 데 도움이되는 AI 기반 코드 인텔리전스 플랫폼입니다. ** Model Context Protocol (MCP)**를 통해 모든 노출됩니다.

그것은 다른 것 만드는 것

-Multi-Language — Go, TypeScript/JSX, Python, 그리고 Java는 AST(tree-sitter); 기호, 엔드포인트, 언어당 추출된 가져오기

• Zero-Config Auto-Discovery — go.mod, package.json, requirements.txt, pom.xml] 을 자동으로 GRPC, Kafka, HTTP 클라이언트에 대한 연결 감지 패턴을 활성화
• Cross-Service Connection Detection - Go, TS, Python 및 Java를 통해 인터서비스 연결(gRPC, Kafka, HTTP)을 탐지합니다.
• **Knowledge Graph ** - 패키지의 Neo4j 그래프, 파일, 기능, 가져 오기 / 통화 / 중재 가장자리가있는 유형 -Hybrid BM25 + Semantic Search — Reciprocal Rank Fusion는 키워드와 벡터 결과를 병합합니다. Gemini 또는 Ollama embeddings를 통해 semantic 수색 -Process & Community Detection — BFS from Entry Point 추적 실행 흐름; Louvain 클러스터링 그룹 코드 커뮤니티

GoAtlas

GoAtlas is an AI-powered code intelligence platform that helps LLMs and developers deeply understand large codebases — combining multi-language AST parsing, a Neo4j knowledge graph, pgvector semantic search, and a pluggable LLM backend, all exposed via the Model Context Protocol (MCP).

What Makes It Different

• Multi-Language — Go, TypeScript/JSX, Python, and Java parsed via AST (tree-sitter); symbols, endpoints, imports extracted per language
• Zero-Config Auto-Discovery — Reads go.mod, package.json, requirements.txt, pom.xml to automatically activate connection detection patterns for gRPC, Kafka, HTTP clients, and more
• Cross-Service Connection Detection — Detects inter-service connections (gRPC, Kafka, HTTP) across Go, TS, Python, and Java with no config required
• Knowledge Graph — Neo4j graph of packages, files, functions, types with import/call/implementation edges
• Hybrid BM25 + Semantic Search — Reciprocal Rank Fusion merges keyword and vector results; semantic search via Gemini or Ollama embeddings
• Process & Community Detection — BFS from entry points traces execution flows; Louvain clustering groups code communities
• Pluggable LLM — Supports Gemini (gemini-2.0-flash), Ollama (any local model), and any OpenAI-compatible API (vLLM, LiteLLM, etc.) for both embeddings and agentic Q&A
• MCP Server — 22 tools, 5 resources, 3 prompts via stdio for Cursor, Claude Desktop, and any MCP client
• Claude Code Hooks — SessionStart/PreToolUse/PostToolUse integration for session context injection, semantic enrichment, and incremental re-indexing
• AI-Generated Docs — SKILL.md per community cluster and full Markdown wiki from the knowledge graph

Architecture

graph TB
    subgraph CLI["GoAtlas CLI"]
        direction LR
        IDX["index"]
        EMB["embed"]
        GR["build-graph"]
        DET["detect"]
        ASK["ask"]
        CHT["chat"]
        SRV["serve"]
        SKL["skills"]
        WK["wiki"]
    end

    subgraph Core["Core Engine"]
        direction TB
        PARSER["AST Parsers\nGo · TS/JSX · Python · Java"]
        INDEXER["Code Indexer\nsymbols · endpoints · connections"]