RAGBox Core

Live repository context for coding agents.

RAGBox indexes a codebase, keeps the index in sync, and gives Claude Code, Cursor, and other MCP clients hybrid search, symbol-graph context, and exact file:line-line citations. It runs as a local sidecar, with no hosted search service to deploy or maintain.

Install it in a project

Set one LLM provider key for answer generation and prose-document graph extraction. Groq and Anthropic use local CPU embeddings; OpenAI uses its embedding API:

pip install -U ragbox-core
export GROQ_API_KEY="gsk_..."  # or OPENAI_API_KEY / ANTHROPIC_API_KEY

For Claude Code, create a project-scoped MCP configuration:

ragbox-mcp --dir . --write-mcp-json
claude

Or register it through the Claude Code CLI:

claude mcp add --scope project --transport stdio ragbox -- ragbox-mcp --dir .

Cursor reads project MCP servers from .cursor/mcp.json:

mkdir -p .cursor
ragbox-mcp --dir . --print-mcp-config > .cursor/mcp.json

The first start downloads the local embedding model and builds the index. The full retrieval mode downloads its reranking model on first use. After that, ask the agent questions such as:

Find every caller of authorize_request and cite the exact lines.
What connects the payment retry path to idempotency handling?
Show the implementation and tests that define session expiry.

What the agent gets

RAGBox exposes three MCP tools:

• ragbox_search returns ranked passages with revision IDs and exact spans such as src/auth.py:41-78.
• ragbox_graph_context returns matched symbols, entities, relationships, and graph-community context.
• ragbox_status reports index readiness, active documents, graph size, and file-watcher health.

The retrieval path combines:

• local BGE-small embeddings through FastEmbed and ONNX Runtime;
• SQLite FTS5 lexical search over active document revisions;
• a symbol/entity graph ranked with C-backed Personalized PageRank;
• Python AST extraction for classes, functions, methods, imports, inheritance, and intra-file calls without an extraction LLM;
• weighted reciprocal-rank fusion and optional CPU cross-encoder reranking;
• a file watcher that updates changed files and removes deleted revisions.

Generated artifacts, dependency directories, caches, lockfiles, and matching .gitignore entries are excluded from indexing.

Retrieval modes

• fast — dense + FTS5 retrieval. Use it for low-latency lookups.
• tri_modal_fast — dense + FTS5 + graph retrieval. This is the MCP default.
• full — tri-modal retrieval followed by CPU cross-encoder reranking.

Example MCP result:

{
  "query": "where is request authorization enforced?",
  "mode": "tri_modal_fast",
  "citations": [
    {
      "source_path": "/work/api/auth.py",
      "revision_id": "9e34...",
      "start_line": 41,
      "end_line": 78,
      "span": "/work/api/auth.py:41-78",
      "score": 0.0317,
      "text": "..."
    }
  ]
}

Python API

Use the Python API when you want RAGBox to retrieve context and generate the answer itself:

from ragbox import RAGBox

ragbox = RAGBox("./docs")
try:
    answer = ragbox.query("What is the incident escalation policy?")
    print(answer)
finally:
    ragbox.close()

Async applications can call await ragbox.aquery(...) or stream with ragbox.astream(...). Do not call the synchronous methods from an active event loop.

RAGBox selects the first configured provider in this order: OpenAI, Anthropic, then Groq. OpenAI-compatible endpoints can be configured with OPENAI_BASE_URL and OPENAI_MODEL. A local GGUF model is available through the local extra and LOCAL_MODEL_PATH.

Supported input

The base install handles Python, JavaScript, TypeScript, TSX, Go, Rust, Java, C, C++, Markdown, text, CSV, JSON, HTML, XML, PDF, and PowerPoint files. Image OCR is optional:

pip install "ragbox-core[ocr]"

Exact line citations are most useful for code and text files. PDF and slide content is normalized during extraction, so their reported lines refer to the normalized text rather than the original visual page coordinates.

HTTP server

Install the server extra and point it at a document directory:

pip install "ragbox-core[server]"
export RAGBOX_DOCUMENT_DIR=/absolute/path/to/docs
uvicorn ragbox.server:app --host 127.0.0.1 --port 8000

The server provides /health, /query, /query/stream, and /stats.

How state stays correct

RAGBox stores active file revisions, sparse chunks, graph snapshots, and graph community summaries in .ragbox_state/cas_state.db. A changed file is indexed as a new content-addressed revision before the active pointer advances. Deleted files are reconciled at startup, and stale revisions are filtered from search.

files -> parse -> chunk -> dense index ----\
                    |        FTS5 index ----+-> rank fusion -> citations
                    +-> symbol/entity graph-/        |
                                                   optional reranker

Measured retrieval result

The checked-in CPU ablation uses 12 fixture documents and 18 exact, semantic, and relationship queries on an Intel i5-1135G7. In that run, fast retrieval reached 0.972 Recall@3 at 9.89 ms p50; full kept the same Recall@3, improved MRR@5 from 0.880 to 0.917, and increased p50 latency to 463.17 ms because of reranking.

These are local retrieval measurements, not an end-to-end answer-quality or competitor benchmark. See BENCHMARKS.md for the environment, method, raw output, and reproduction command.

Current boundaries

• An LLM provider key or a configured local GGUF model is required for generated answers and prose-document entity extraction.
• Python gets AST-level relationships. Other supported languages currently use deterministic regex symbol extraction.
• The default embedding and reranking models are downloaded on first use.
• This project does not claim that graph retrieval or reranking improves every query; choose the retrieval mode based on the workload.

Development

poetry install
poetry run pytest -q
poetry run ruff check .
poetry run mypy ragbox
poetry run black --check .
poetry run python benchmarks/ablation_cpu.py

See CONTRIBUTING.md for the contribution workflow.

License

MIT