Ask your codebase questions using Ollama and Mnemosyne -- zero-config local code search
Project description
Mnemosyne-Ollama
Ask your codebase questions using local LLMs. Zero config, zero cloud, zero new dependencies.
Bridges Ollama to Mnemosyne via MCP -- the same 6-signal hybrid retrieval that powers Mnemosyne's Claude Code integration, now available with any tool-calling Ollama model.
Install
pip install mnemosyne-ollama
This installs everything: the Ollama bridge, the MCP server, and the Mnemosyne retrieval engine.
Quick Start
cd /your/project
mnemosyne-ollama "how does authentication work"
That's it. Auto-detects your Ollama model, indexes if needed, searches with ranked hybrid retrieval, and returns an answer with file paths and line numbers.
Requirements
- Python 3.11+
- Ollama running locally with a tool-calling model
Supported Models
Any Ollama model with function/tool-calling support:
qwen2.5,qwen3llama3.1,llama3.2,llama3.3,llama4gemma3,gemma4phi4mistral-nemocommand-r
If no --model is specified, the first installed tool-capable model is used automatically.
Usage
Single query
mnemosyne-ollama "how does the rate limiter work"
mnemosyne-ollama "find all database queries" --model qwen2.5 --budget 12000
mnemosyne-ollama "explain the auth flow" -v # verbose: shows tool calls
Interactive mode
mnemosyne-ollama
> how does the auth middleware work?
[searches, responds with code citations]
> what about rate limiting?
[follows up with conversation context]
> ^C
Python library
from mnemosyne_ollama import run
result = await run("how does auth work", model="qwen2.5", budget=8000)
print(result.response)
CLI Reference
mnemosyne-ollama [QUERY] [OPTIONS]
positional:
query Question about the codebase (omit for interactive)
options:
-m, --model MODEL Ollama model (auto-detected if omitted)
-b, --budget INT Token budget for search results (default: 8000)
-r, --project-root PATH Project root directory (default: cwd)
--ollama-url URL Ollama URL (default: OLLAMA_HOST env or localhost:11434)
-v, --verbose Print tool calls to stderr
--version Show version
How It Works
mnemosyne-ollama
|
| 1. Spawns mnemosyne-mcp as subprocess (stdio)
| 2. Discovers tools: search, index, stats
| 3. Sends query + tools to Ollama /api/chat
|
Ollama (local model)
|
| 4. Model calls search tool with your question
|
mnemosyne-mcp
|
| 5. 6-signal hybrid retrieval (BM25 + TF-IDF + symbols + usage + prefetch + RRF)
| 6. AST-aware compression, budget-cut to token limit
|
| 7. Results fed back to model
| 8. Model generates answer with file citations
Everything runs locally. No API keys, no cloud, no data leaves your machine.
Configuration
Mnemosyne search settings are configured via .mnemosyne/config.toml in your project root (created on first index). See the Mnemosyne documentation for details.
The --budget flag overrides the configured default per query.
Trademarks
Ollama, Qwen, Llama, Gemma, Phi, Mistral, and Command-R are trademarks of their respective owners. mnemosyne-ollama is an independent project and is not endorsed by or affiliated with any of these companies.
License
AGPL-3.0 -- commercial licensing available from Cast Net Technology.
Copyright 2026 Cast Rock Innovation L.L.C.
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