Rekipedia

[](https://pypi.org/project/rekipedia/) [](https://www.python.org/) [](LICENSE) [](https://modelcontextprotocol.io)

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rekipedia

> One scan. A living wiki. AI agents that actually know your code. > > Parse any codebase into a structured SQLite knowledge store, auto-generate wiki pages, and expose everything via CLI and an MCP stdio server — with file:line citations and zero hallucinations.

[](https://pypi.org/project/rekipedia/) [](https://www.python.org/) [](LICENSE)

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Why rekipedia?

| Problem | rekipedia | |---|---| | "Where does the auth logic live?" | reki ask "how does auth work?" → src/auth.py:42 | | Onboarding new devs takes days | reki onboard . generates a guided walkthrough in seconds | | AI agents hallucinate about your codebase | reki mcp gives agents a grounded knowledge base with citations | | Refactor anxiety — will this break everything? | reki hotspots surfaces hub and bridge nodes before you touch anything | | Wiki goes stale immediately | reki watch . auto-reindexes on every file save |

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⚡ Quickstart

Install

pip install rekipedia
# or: npx rekipedia

Scan & Ask (with LLM)

export REKIPEDIA_MODEL=gemini/gemini-2.5-flash
export GOOGLE_API_KEY=...
reki scan .
reki ask "how does authentication work?"

Scan without LLM (zero config, no API key)

reki scan . --no-llm
reki ask "what is the entry point?" --no-llm

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🏗️ Architecture & Data Storage

.rekipedia/
│
├── store.db              # 🗃️ Structured SQLite graph
│   └── symbols, relationships, file manifests, scan history
│
├── rag/
│   └── faiss.index       # 🔍 Dense embedding vectors (FAISS / Qdrant / Chroma)
│
├── wiki/
│   └── *.md              # 📄 Auto-generated wiki pages per module
│
├── diagrams/
│   └── *.md              # 🏛️ Architecture diagrams & hotspot reports
│
└── config.yml            # ⚙️ Backend, LLM, and team-sync settings

Storage at a Glance

| Layer | Format | Purpose | Size (typical 50k–200k LOC repo) | |---|---|---|---| | SQLite | store.db | Structured graph — symbols, callers, exact lookup | ~10–80 MB | | Vector | .rekipedia/rag/ | Dense embeddings for semantic / fuzzy search | ~50–500 MB | | Wiki | wiki/*.md | Human-readable pages, git-publishable | ~1–5 MB |

> Gitignore note: store.db and rag/ are gitignored — they contain machine-specific absolute paths. Each developer runs reki scan . locally. Share human-readable output via reki publish ..

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🚀 Core Features

🗂 reki scan — Instant knowledge store

Parses your repo into a SQLite knowledge store with symbols, relationships, and auto-generated wiki pages.

reki scan .            # full scan with LLM summaries
reki scan . --no-llm   # zero config, no API key required
reki scan . --community-sharding   # group related files by import-graph community before summarising

💬 reki ask — Q&A grounded in your code

Answers with file:line citations and real code examples. No hallucinations — every answer is backed by indexed source, with actual function bodies quoted inline.

reki ask "what is the entry point?"
reki ask "which modules handle payments?" --brief

Example output:

Answer: The entry point is src/main.py:12 — App.run() bootstraps the server.

```python
# src/main.py:12
def run(self):
    server = HTTPServer(self.config)
    server.start()

Sources: src/main.py:12, src/server.py:34

**How it works:** rekipedia extracts actual source bodies of the most relevant functions/classes and passes them directly to the LLM — so answers include real, runnable code, not just paraphrases. When a FAISS index exists (`reki embed .`), RAG chunks are used for even higher precision.

### 🤖 `reki mcp` — MCP server for AI agents

Plug rekipedia directly into Claude Code, Cursor, GitHub Copilot, or any MCP-aware agent.

```bash
reki mcp

Available MCP tools:

| Tool | Purpose | |---|---| | ask | Natural-language Q&A grounded in the scanned wiki | | search_nodes | Fast symbol/file lookup by name | | get_context | Symbols and relationships for a file | | get_relationships | Callers and callees for a symbol | | get_hub_nodes | Architectural chokepoints | | get_god_nodes | Top N symbols by combined in+out degree — find architectural bottlenecks instantly | | shortest_path | BFS shortest directed call-path between any two symbols (e.g. "how does A reach B?") | | get_community | Which import-graph community a symbol belongs to, plus all community members | | get_impact | Blast-radius for a changed file | | get_knowledge_gaps | Untested high-call-count symbols | | list_wiki_pages / get_wiki_page | Wiki browsing |

🔥 reki hotspots — Architectural hotspot detection

Finds hub nodes (files many depend on) and bridge nodes (files connecting clusters).

reki hotspots

Hub nodes:    src/core/engine.py (42 dependents)
Bridge nodes: src/adapters/db.py (connects 3 clusters)

🔄 reki update — Incremental updates

Only regenerates wiki pages affected by your changes.

reki update . --impact-only

🌐 reki serve — Local web UI

Launch a browsable wiki at http://127.0.0.1:7070.

reki serve .

📤 reki publish — Team sharing

Copy generated wiki into a git-tracked directory for team browsing.

reki publish . [--output-dir PATH]

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🛠️ Commands Cheat Sheet

| Command | Description | |---|---| | reki scan . | Full scan — index symbols, generate wiki | | reki scan . --no-llm | Scan without LLM, zero config | | reki ask "question" | Ask anything about the codebase | | reki ask "question" --brief | Short answer mode | | reki update . --impact-only | Incremental update, affected pages only | | reki serve . | Local web UI at http://127.0.0.1:7070 | | reki embed . | Build FAISS semantic index | | reki publish . [--output-dir PATH] | Publish wiki to a git-tracked directory | | reki export . --format bundle | Export a content-addressed wiki bundle | | reki merge [--base BASE] | Three-way conflict-free wiki merge | | reki pull [URL] | Fetch and merge a remote wiki bundle | | reki watch . --publish | Auto-index + auto-publish on every file save | | reki export . --format md\|zip\|json\|html\|bundle | Export the wiki | | reki diff | Show impact of uncommitted changes | | reki hotspots | Hub & bridge node detection | | reki refactor . --dry-run | Preview refactor suggestions | | reki refactor . --apply | Apply refactor suggestions | | reki mcp | Start MCP stdio server | | reki review | LLM-powered PR review | | reki hook install | Install git post-commit hook | | reki init --with-all-ai | Configure MCP for Copilot + Codex + Cursor | | reki init --with-ci | Scaffold GitHub Actions workflow for auto-wiki |

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🤖 AI CLI Tool Integration

reki init --with-all-ai    # configure Copilot + Codex + Cursor in one step

# or pick individually:
reki init --with-copilot   # VS Code — writes .vscode/mcp.json
reki init --with-codex     # Codex CLI — writes .codex/instructions.md
reki init --with-cursor    # Cursor — writes .cursor/mcp.json + rules

Once configured, each tool automatically gets access to the [MCP tools listed above](#-reki-mcp--mcp-server-for-ai-agents).

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⚙️ LLM Setup

rekipedia works entirely without an LLM (--no-llm). To enable richer summaries and Q&A:

export REKIPEDIA_MODEL=gemini/gemini-2.5-pro
export GOOGLE_API_KEY=...

Or use any OpenAI-compatible endpoint:

export REKIPEDIA_MODEL=openai/gpt-4o
export REKIPEDIA_API_KEY=sk-...

# Local / offline
export REKIPEDIA_API_KEY=ollama
export REKIPEDIA_MODEL=ollama/llama3

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❓ FAQ

Q: Why is store.db gitignored? How do teammates use rekipedia?

store.db contains machine-specific absolute paths — committing it would cause path mismatches and noisy binary diffs. Each developer runs reki scan . locally. To share human-readable output, use reki publish ., which copies generated wiki pages to docs/wiki/ so they can be committed and browsed on GitHub or your docs site.

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Q: What is store.db for vs the FAISS index? Why do I need both?

| | store.db | FAISS / Qdrant / Chroma | |---|---|---| | Format | Structured SQLite graph | Dense embedding vectors | | Purpose | Precise filters — "find all callers of function X" | Fuzzy semantic search — "find code that handles authentication" | | Used by | search_nodes, get_relationships, hotspots | reki ask RAG pipeline |

reki ask uses both: BM25 keyword search over SQLite plus vector similarity search, then merges and re-ranks results.

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Q: Can I use Postgres or MySQL instead of SQLite?

Not currently. SQLite is intentional — zero-config, portable, no running server. reki export . produces symbols.json and relationships.json for loading into any database. Postgres/MySQL support is not on the roadmap, but the JSON exports make integration straightforward.

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Q: Does rekipedia support Qdrant or Chroma instead of FAISS?

Yes. FAISS is the default, but Qdrant and Chroma are supported as optional backends — useful for shared, persistent vector stores across a team. Install extras:

pip install rekipedia[qdrant]    # or rekipedia[chroma]

Then configure the backend in .rekipedia/config.yml.

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Q: Do I need an OpenAI API key?

No. reki scan . --no-llm performs static analysis only — fully offline and air-gap safe. When LLM features are enabled, only retrieved chunks (not your full source) are sent as context. Use Ollama for fully local inference — no internet required.

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Q: How do I keep the wiki up to date automatically?

Run reki init --with-ci to scaffold a GitHub Actions workflow that runs reki scan + reki publish on every push to main. The workflow commits changes to docs/wiki/ back to the repo automatically. Set REKIPEDIA_API_KEY as a repository secret for LLM-enriched pages; omit it and the workflow falls back to --no-llm at zero cost.

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Q: How does team sync work for distributed teams?

rekipedia uses a multi-layer, conflict-free collaboration system:

1. Bundle — reki export --format bundle creates a deterministic, content-addressed snapshot.
2. Merge — reki merge bundle-A bundle-B --base bundle-base performs a three-way merge with automatic resolution for non-conflicting changes.
3. Git merge driver — reki init --with-merge-driver registers a custom driver so git merge and git pull use rekipedia's logic — no `` fetches a bundle from HTTPS, S3, or GCS and merges it locally.

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Q: How does reki ask work under the hood?

A hybrid retrieval pipeline:

1. BM25 keyword search against the SQLite store for exact and near-exact matches.
2. Vector similarity search against FAISS/Qdrant/Chroma for semantic matches.
3. Merge & re-rank the two result sets by relevance score.
4. LLM synthesis — top chunks passed to your configured LLM with file:line citations.

With --no-llm, retrieval results are returned directly without synthesis.

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Q: How large does store.db / the FAISS index get on a large repo?

For a typical mid-size repo (50k–200k LOC):

| Storage | Typical Size | |---|---| | store.db | 10–80 MB | | FAISS index | 50–500 MB |

On very large monorepos (1M+ LOC), the FAISS index can exceed 1 GB; switching to a server-backed Qdrant instance is recommended.

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Q: Can rekipedia scan private or fully offline repos?

Yes, fully. reki scan is pure static analysis — it never sends your source code anywhere. With --no-llm, the entire pipeline is offline and air-gap safe. There are no telemetry calls, no license checks against a remote server, and no internet requirement beyond your chosen LLM endpoint.

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🔮 Coming Soon

• Hosted wiki — share your knowledge base with a link, no self-hosting required
• VS Code extension — inline reki ask from your editor

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🤝 Contributing

git clone https://github.com/unrealandychan/rekipedia.git
pip install -e ".[dev]"

Open an issue or PR — the bar is low and the maintainer is responsive.

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Current version: 0.23.0 · PyPI · [MIT License](LICENSE)

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: unrealandychan
• Source: unrealandychan/rekipedia
• License: MIT
• Homepage: https://unrealandychan.github.io/rekipedia/

Install and usage instructions live in the source repository linked above.