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Designed for AI coding assistants — add one MCP endpoint and your assistant gains deep codebase understanding with call-graph-aware retrieval.\n\n## Install\n\n```bash\nnpx @lebatuananh/context-engine@latest\n```\n\nOr install globally:\n\n```bash\nnpm install -g @lebatuananh/context-engine@latest\ncontext-engine\n```\n\nThis boots the HTTP server on port 6699:\n- Web UI: http://127.0.0.1:6699\n- MCP endpoint: http://127.0.0.1:6699/mcp\n\n## Supported Platforms\n\n| Platform | Package |\n|----------|---------|\n| Windows x64 | `@lebatuananh/context-engine-win32-x64` |\n\nThe correct binary is installed automatically via `optionalDependencies`.\n\n## CLI Options\n\n```bash\ncontext-engine --port 8080 --bind 0.0.0.0 --data-dir /path/to/data\n```\n\n| Flag | Env Variable | Default | Description |\n|------|-------------|---------|-------------|\n| `--port` | `CONTEXT_ENGINE_PORT` | `6699` | Port to listen on |\n| `--bind` | `CONTEXT_ENGINE_BIND` | `127.0.0.1` | Bind address |\n| `--data-dir` | `CONTEXT_ENGINE_DATA_DIR` | `~/.context-engine` | Data directory |\n| `--embeddings-dir` | `CONTEXT_ENGINE_EMBEDDINGS_DIR` | `~/.context-engine/embeddings` | Embedding cache |\n\n## Features\n\n- **Semantic code search** — embedding-based vector search with cosine similarity\n- **Call-graph expansion** — BFS-expands matched symbols through caller/callee edges (2 levels deep)\n- **Multi-language parsing** — Tree-sitter extraction for 22 languages (Python, JS, TS, Rust, Go, Java, C, C++, C#, PHP, Ruby, Objective-C, Swift, Kotlin, Dart, Lua, Luau, Svelte, Pascal, Liquid)\n- **Agentic RAG** — optional tool-calling agent loop that iteratively searches and accumulates context\n- **LLM reranking** — reorders and line-prunes candidate chunks via Google or OpenAI-compatible LLMs\n- **Dual embedding providers** — Voyage AI or any OpenAI-compatible endpoint\n- **Content-addressed embedding cache** — on-disk, shareable across instances\n- **Real-time file watching** — incremental re-index on changes\n- **Knowledge Bases** — global reference folders always included in every search query (0.8 score multiplier)\n- **Linked Workspaces** — per-repo related repositories queried in parallel during search (0.9 score multiplier)\n- **Project Memory** — per-workspace observations (decisions, bugfixes, patterns, learnings) with semantic recall\n- **Import resolution** — resolves real import targets for candidate ranking (barrel chasing, tsconfig aliases, depth cap 8)\n- **Framework detection** — React/Express/Django/Spring/Gin route and DI edges extracted at index time\n- **Generated-file downranking** — auto-detected generated/minified files deprioritised in results\n- **Field-qualified search** — `filter_lang`, `filter_path`, `filter_name` MCP params narrow results post-retrieval\n- **Built-in Chat Agent** — per-repo AI chat with tool-calling (grep/read), per-turn model picker, streaming markdown\n- **Memory-mapped vector shards** — persisted shard files with mmap loading for faster cold starts\n- **Windows Auto-start** — one-click Task Scheduler registration via Web UI\n- **MCP session persistence** — BoundedSessionStore (LRU 8192) restores sessions after idle timeout\n- **Web UI** — multi-language (EN/VI/ZH), dark mode, index explorer, call-graph visualization, built-in chat agent, auto-start toggle\n\n## MCP Tools\n\n### `codebase-retrieval`\n\nSemantic search across indexed codebases. Takes a natural-language query and returns relevant code snippets with file paths and line numbers. Automatically merges results from Knowledge Bases, Linked Workspaces, and Project Memory.\n\n### `file-retrieval`\n\nTargeted search within a specific file. Takes a file path and a description of what you're looking for.\n\n### `save-observation`\n\nPersist a project observation (decision, bugfix, pattern, or learning) to the workspace's memory store. Observations are embedded and automatically included in future `codebase-retrieval` queries with a 0.7 score multiplier.\n\n### `recall-observations`\n\nSemantically search past observations for a workspace. Returns the most relevant observations matching a natural-language query.\n\n## Knowledge Bases\n\nGlobal reference directories (coding rules, templates, standards) automatically included in every `codebase-retrieval` query. Results receive a 0.8 score multiplier so project code always ranks higher.\n\nConfigure via Web UI (Repo tab → Knowledge Bases) or REST API:\n\n```bash\ncurl -X PUT http://127.0.0.1:6699/api/config \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"knowledge_bases\": [\"D:\\\\Rules\\\\coding-standards\"]}'\n```\n\n- Max 10 entries, absolute paths only\n- Indexed using the same pipeline as repos\n- Searched in parallel with the primary repo on every query\n\n## Linked Workspaces\n\nPer-repo related repositories automatically queried alongside the primary workspace during `codebase-retrieval`. Unlike Knowledge Bases (global), linked workspaces are configured per-repo — useful for frontend/backend pairs, monorepo splits, or shared library relationships.\n\nConfigure via Web UI (open repo → MCP tab → Linked Workspaces) or REST API:\n\n```bash\ncurl -X PUT http://127.0.0.1:6699/api/config \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"linked_workspaces\": {\"D:\\\\Projects\\\\frontend\": [\"D:\\\\Projects\\\\backend-api\"]}}'\n```\n\n- Max 5 linked workspaces per repo\n- Results receive a 0.9 score multiplier (primary: 1.0 > linked: 0.9 > KB: 0.8 > observations: 0.7)\n- Linking is one-directional: A→B does not automatically create B→A\n- Unindexed linked workspaces are skipped silently\n\n## Project Memory\n\nPersistent per-workspace memory for AI assistants. Observations (decisions, bugfixes, patterns, learnings) are stored with vector embeddings and automatically surfaced during `codebase-retrieval` queries.\n\n- Stored in a dedicated SurrealDB database (separate from code indexes)\n- Max 200 observations per workspace, auto-prunes oldest\n- Results receive a 0.7 score multiplier\n- Types: `decision`, `bugfix`, `pattern`, `learning`\n- AI assistants call `save-observation` to persist and `recall-observations` to search\n\n## Chat Agent\n\nBuilt-in AI chat accessible per-repo from the Web UI. Uses the same LLM provider configured in Settings, or select a custom endpoint per-turn via the model picker.\n\n- **Tool-calling agent** — grep files and read source code to answer codebase questions\n- **Streaming responses** — SSE-based with full markdown rendering (tables, code blocks)\n- **Multi-conversation** — separate threads per repo, create and delete freely\n- **Custom model picker** — switch model/endpoint per turn without changing global settings\n\n## Connect to AI Assistants\n\n### Claude Code\n\n```bash\nclaude mcp add context-engine --transport http http://127.0.0.1:6699/mcp\n```\n\n### Per-repo endpoint\n\n```bash\nclaude mcp add my-project --transport http http://127.0.0.1:6699/mcp-repo/BASE64URL_ENCODED_PATH\n```\n\n### Any MCP client\n\nPoint any Streamable-HTTP MCP client to `http://127.0.0.1:6699/mcp`.\n\n## Alternative Install Methods\n\n### Docker\n\n```bash\ndocker compose up -d\n```\n\n## Documentation\n\nFull documentation, configuration guide, and architecture details: [GitHub](https://github.com/lebatuananh/context-engine)\n\n## License\n\nMIT\n","readmeFilename":"README.md"}