{"id":"context-engine","name":"Context Engine","homepage":"https://context-engine.ai","repo_url":"https://github.com/Context-Engine-AI/Context-Engine","category":"developer-tools","subcategories":["code-search","semantic-retrieval","context-compression"],"tags":["mcp","code-search","semantic-search","rag","embeddings","qdrant","self-improving","agentic"],"what_it_does":"An open-core, self-improving code search platform that indexes codebases into vector embeddings and exposes semantic search via MCP servers. Uses ONNX embeddings, Qdrant vector DB, Redis cache, and an adaptive reranking system that learns from usage patterns. Provides two MCP endpoints: a memory server and an indexer server.","use_cases":["Semantic code search across large codebases for AI coding assistants","Providing contextual code answers with citations to AI agents","Multi-repository code indexing and retrieval","Augmenting Claude Code, Cursor, Gemini, or Augment with codebase awareness"],"not_for":["Simple keyword-based code search (overkill infrastructure)","Small projects where grep/ripgrep suffices","Teams unwilling to run multiple services (Qdrant, Redis, embedding server, etc.)"],"best_when":"You have large codebases and want AI agents to find semantically relevant code with citations, and you are comfortable running a multi-service stack (vector DB, embeddings, cache).","avoid_when":"Your codebase is small enough for simple text search, or you need a lightweight single-binary solution. The BUSL-1.1 license may also restrict commercial use.","alternatives":["Sourcegraph Cody","Greptile","Aider","contextplus"],"af_score":60.5,"security_score":45.0,"reliability_score":null,"package_type":"mcp_server","discovery_source":["github"],"priority":"low","status":"evaluated","version_evaluated":"unknown","last_evaluated":"2026-03-01T09:50:05.436016+00:00","performance":{"latency_p50_ms":null,"latency_p99_ms":null,"uptime_sla_percent":null,"rate_limits":null,"data_source":"llm_estimated","measured_on":null}}