DeepRepo — Local RAG Engine for Codebases

A production-grade Python library for performing RAG (Retrieval Augmented Generation) on local codebases. No heavy frameworks, no external vector DBs, no cloud required.

What It Does

DeepRepo ingests a codebase and builds three things simultaneously:

Layer	What it stores	Used for
Code Knowledge Graph	Classes, functions, imports, call edges (SQLite)	Symbol lookup, blast-radius analysis
Embeddings + FTS index	Semantic vectors + full-text search	Relevant code retrieval
Hierarchical Wiki	Plain-English .md files per module	AI explanations, chat context

A smart query router classifies every question and picks the cheapest context strategy, reducing LLM token usage by 5–50x compared to naive RAG.

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Features

• Zero dependencies on heavy frameworks — pure Python, SQLite-backed
• Multiple AI providers — Ollama (free/local), OpenAI, Anthropic, Gemini, HuggingFace
• CLI-first — deeprepo ingest . / deeprepo serve / deeprepo query "…"
• Wiki viewer — browsable, searchable HTML wiki with in-page chat (deeprepo serve)
• 7 focused MCP tools — drop DeepRepo into Cursor / Claude Desktop as an MCP server
• Branch isolation — per-branch SQLite databases with copy-on-write from base branches
• 3-tier retrieval — Embeddings → FTS → Graph fallback for resilient search
• Incremental ingestion — unchanged files are skipped; only deltas re-processed

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Quick Start

1. Install

cd deeprepo_core
pip install -e .

For MCP server support:

pip install -e ".[mcp]"

2. Install Ollama (free, local — recommended)

# macOS
brew install ollama
ollama serve                          # keep this running

ollama pull nomic-embed-text          # embedding model
ollama pull llama3.1:8b               # LLM

3. Ingest your codebase

cd /path/to/your/project
deeprepo ingest .

4. Browse the wiki

deeprepo serve                        # opens http://localhost:8080

5. Ask questions

deeprepo query "how does authentication work?"
deeprepo query "what breaks if I change auth.py?"

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CLI Reference

deeprepo <command> [options]

Command	What it does
deeprepo init	Detect provider setup, print the ingest command
deeprepo ingest [PATH]	Scan repo → build graph + wiki + embeddings
deeprepo wiki [PATH]	Regenerate wiki pages only (skip re-indexing)
deeprepo serve	Launch wiki viewer + in-page chat at port 8080
deeprepo query "QUESTION"	Ask a question, get an AI answer
deeprepo status	Show branch isolation & cache freshness

Common flags (all commands)

--llm ollama|openai|anthropic|gemini|huggingface   # LLM provider
--embed ollama|openai|huggingface                  # embedding provider (default: same as --llm)
--branch-isolation                                 # enable per-branch databases
--base-branch main                                 # seed feature-branch cache from main
--wiki-dir .deeprepo/wiki                          # override wiki output directory

ingest flags

--chunk-size N      # chars per text chunk (default: 1000)
--overlap N         # overlap between chunks (default: 100)
--workers N         # wiki parallel workers (default: 3)
--no-wiki           # skip wiki generation

serve flags

--port N            # HTTP port (default: 8080)

Examples

# Ollama (free, fully local)
deeprepo ingest .

# OpenAI embeddings + Anthropic LLM
deeprepo ingest . --embed openai --llm anthropic

# Branch isolation for a feature branch
deeprepo ingest . --branch-isolation --base-branch main

# Serve wiki with chat on a custom port
deeprepo serve --llm openai --port 9000

# Query with specific top-k results
deeprepo query "where is AuthService defined?" --top-k 3

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Python API

from deeprepo import DeepRepoClient

# Single provider (backward-compatible shorthand)
client = DeepRepoClient(provider_name="ollama")

# Split providers — Anthropic LLM + OpenAI embeddings
client = DeepRepoClient(
    embedding_provider_name="openai",
    llm_provider_name="anthropic",
)

# Branch isolation (team workflow)
client = DeepRepoClient(
    provider_name="ollama",
    branch_isolation=True,
    base_branches=["main"],
)

# Ingest (incremental — unchanged files are skipped)
result = client.ingest("/path/to/your/code")
print(f"Files: {result['files_scanned']}, Wiki pages: {result['wiki_generated']}")

# Query — smart routing selects the cheapest context strategy
response = client.query("How does authentication work?")
print(response['answer'])
print(f"Intent: {response['intent']}, Strategy: {response['strategy']}")
print(f"Sources: {response['sources']}")        # list of file paths

# Browse the generated wiki
print(f"Wiki at: {client.get_wiki_dir()}")

query() return shape

{
    "answer":         str,           # LLM-generated answer
    "sources":        list[str],     # file paths used as context
    "intent":         str,           # navigate | impact | explain | debug | review | general
    "strategy":       str,           # e.g. symbol_lookup, blast_radius, wiki_plus_skeleton, …
    "retrieval":      str,           # embeddings | fts | graph
    "token_estimate": int,           # estimated tokens consumed
    "history":        list[dict],    # conversation history (last N exchanges)
}

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Supported AI Providers

Provider	Cost	Setup	Best For
Ollama	FREE, unlimited	Install app + ollama pull	Local dev, privacy, offline
OpenAI	Paid	OPENAI_API_KEY	Production, best quality
Anthropic	Paid	ANTHROPIC_API_KEY	Production, excellent reasoning
Gemini	Free tier	GEMINI_API_KEY	Experimentation
HuggingFace	Free tier	HUGGINGFACE_API_KEY	Cloud embeddings, no GPU needed

Note: Anthropic has no embeddings API. Pair it with another provider:

client = DeepRepoClient(embedding_provider_name="openai", llm_provider_name="anthropic")

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Architecture

deeprepo_core/src/deeprepo/
├── client.py         # Main facade — branch isolation, freshness, provider wiring
├── graph.py          # SQLite store: graph nodes/edges, embeddings, wiki index, state
├── graph_builder.py  # Tree-sitter AST parser → code knowledge graph
├── wiki.py           # Hierarchical wiki engine — bottom-up LLM synthesis
├── router.py         # Intent classifier + 6 context strategy selectors
├── ingestion.py      # File scanner, chunker, language detection
├── interfaces.py     # Abstract base classes (EmbeddingProvider, LLMProvider)
├── registry.py       # @register_embedding / @register_llm decorator system
├── ui.py             # Wiki viewer (HTTP server + mermaid renderer + chat)
├── mcp/
│   └── server.py     # 7 MCP tools for AI assistants (Cursor, Claude Desktop)
└── providers/
    ├── ollama_v.py
    ├── openai_v.py
    ├── anthropic_v.py
    ├── gemini_v.py
    └── huggingface_v.py

.deeprepo/            # Generated (gitignore this)
├── default.db        # SQLite: graph + embeddings + wiki index + state
├── <branch>.db       # Per-branch database when branch_isolation=True
└── wiki/             # Browsable .md wiki files
    ├── overview.md   # Whole-repo narrative overview
    └── *.md          # One page per module

Storage

Everything lives in a single SQLite file per branch — no Redis, no Postgres, no Chroma.

Table	Contents
nodes	Files, classes, functions with metadata
edges	Import / call relationships between nodes
embeddings	Float vectors for semantic search
wiki_pages	Generated wiki markdown (key → content)
wiki_fts	Full-text search index over wiki
state	Per-file SHA-256 hashes for incremental updates

Design Patterns

• Facade — DeepRepoClient is the single entry point; internals are hidden
• Strategy — LLMProvider / EmbeddingProvider abstract interfaces; providers are swappable
• Registry — @register_llm("ollama") decorator auto-registers providers at import time
• Bottom-up synthesis — wiki pages generated leaves-first; parent pages consume child summaries
• 3-tier fallback — Embeddings → FTS → Graph; queries work even when embeddings are cold
• Copy-on-write branching — feature branches start from base-branch cache, then delta-update

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MCP Server (AI Assistant Integration)

Connect DeepRepo as an MCP server so Cursor, Claude Desktop, or any MCP-compatible AI assistant can call it directly — without ever reading raw files.

Setup

pip install deeprepo[mcp]

Cursor — create ~/.cursor/mcp.json:

{
  "mcpServers": {
    "deeprepo": {
      "command": "python",
      "args": ["-m", "deeprepo.mcp.server"],
      "env": {
        "LLM_PROVIDER": "ollama"
      }
    }
  }
}

Claude Desktop — add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "deeprepo": {
      "command": "deeprepo-mcp",
      "env": {
        "EMBEDDING_PROVIDER": "openai",
        "LLM_PROVIDER": "anthropic",
        "OPENAI_API_KEY": "sk-...",
        "ANTHROPIC_API_KEY": "sk-ant-..."
      }
    }
  }
}

Available MCP Tools (7 tools)

Tool	When to use	Token cost
ingest_codebase	One-time setup — index a repo directory	—
find_symbol	"Where is X defined / what line is X on"	~50 tokens
get_file_structure	"Show me the API / functions in X"	~150 tokens
explain_file	"How does X work / explain X / what does X do"	~300 tokens
find_change_impact	"What breaks if I change X"	~300 tokens
ask_codebase	Any open-ended question about the code	~600–2000 tokens
get_project_overview	"Give me an overview / what does this project do"	~600 tokens

Token Reduction vs Naive RAG

Query type	Naive RAG	DeepRepo	Reduction
"where is X defined"	~4 000 tokens	~80 tokens	50x
"what breaks if I change X"	~4 000 tokens	~300 tokens	13x
"how does X work"	~4 000 tokens	~600 tokens	7x
"fix the bug in X"	~4 000 tokens	~900 tokens	4x

CLAUDE.md tip

Add this to your project's CLAUDE.md so Claude automatically uses DeepRepo:

## Code navigation
Before reading any source file directly, use these MCP tools:
- `find_symbol(name)` to locate a class or function
- `get_file_structure(filepath)` to see a file's API without reading it
- `explain_file(filepath)` to understand what a file does
- `find_change_impact(filepath)` before editing any file
- `ask_codebase(question)` for open-ended questions
- `get_project_overview()` at the start of a new session

Only call Read/Grep on a file after the above tools have been tried.

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Configuration

Environment Variables

Variable	Default	Description
LLM_PROVIDER	openai	LLM provider name
EMBEDDING_PROVIDER	same as LLM_PROVIDER	Embedding provider name
OPENAI_API_KEY	—	Required for OpenAI
ANTHROPIC_API_KEY	—	Required for Anthropic
GEMINI_API_KEY	—	Required for Gemini
HUGGINGFACE_API_KEY / HF_TOKEN	—	Required for HuggingFace
OLLAMA_MODEL	llama3.1:8b	Ollama LLM model name
OLLAMA_EMBED_MODEL	nomic-embed-text	Ollama embedding model
OLLAMA_BASE_URL	http://localhost:11434	Ollama server URL
OLLAMA_TIMEOUT	300	LLM response timeout (seconds)

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Testing

# Full end-to-end test suite (runs ingest + all checks)
python3 test_deeprepo_flow.py

# Skip ingest, use cached index (faster iteration)
python3 test_deeprepo_flow.py --skip-ingest

# pytest unit tests
pytest tests/unit/ -v

# pytest with coverage
pytest tests/unit/ --cov=deeprepo --cov-report=html

The test_deeprepo_flow.py script tests all 7 sections end-to-end:

1. Client initialisation & branch flags
2. Ingest (graph + embeddings + wiki)
3. WikiEngine — page generation, caching, repo overview
4. Graph API — skeleton, blast-radius, symbol lookup
5. RAG / Router — intent classification, query execution
6. CLI commands — all subcommands + help
7. Branch isolation flag combinations

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Adding a New Provider

1. Create src/deeprepo/providers/myprovider.py
2. Implement EmbeddingProvider and/or LLMProvider interfaces
3. Decorate with @register_embedding("myprovider") / @register_llm("myprovider")
4. Auto-discovered at import time — no other changes needed

from deeprepo.interfaces import EmbeddingProvider, LLMProvider
from deeprepo.registry import register_embedding, register_llm

@register_embedding("myprovider")
class MyEmbeddingProvider(EmbeddingProvider):
    def embed(self, text: str) -> list[float]:
        ...  # return a list of floats

@register_llm("myprovider")
class MyLLMProvider(LLMProvider):
    def generate(self, prompt: str, context: str | None = None) -> str:
        ...  # return generated text

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Documentation

• DEVELOPER_WORKFLOW_GUIDE.md — daily dev workflows and automation recipes
• deeprepo_core/README.md — package README (PyPI)
• docs/high-level-design.excalidraw — process flow diagram
• docs/class-interaction-design.excalidraw — class diagram

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License

MIT License — see LICENSE file for details.

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Built for developers who want full control over their RAG pipelines.