Local-first AI memory layer for any LLM. Persistent knowledge graph, entity extraction, semantic retrieval — no cloud required.

Most LLMs forget everything the moment a conversation ends. mnemo fixes that.

mnemo is a sidecar service that watches every conversation you feed it, extracts named entities and relationships using an LLM, builds a persistent knowledge graph in SQLite, and injects relevant context back into future prompts — automatically, in under 50ms. It works with Ollama (fully local, free), OpenAI, Anthropic, or any OpenAI-compatible API. It ships as a single static binary with zero cloud dependency.

  your app
     │
     ▼
  POST /ingest ──► entity extraction (LLM) ──► knowledge graph (SQLite + petgraph)
                                                        │
  POST /retrieve ◄── scoring + ranking ◄── graph traversal + full-text search
     │
     ▼
  context_prompt  ──► inject into your LLM prompt

1. You POST raw text to /ingest (a conversation turn, a document, a note).
2. mnemo sends it to your configured LLM and extracts entities (people, tools, places, concepts) and the relationships between them.
3. Entities are deduplicated by name+type, aliases are merged, and everything is written to SQLite. The in-memory petgraph is updated atomically.
4. On POST /retrieve, mnemo runs a 6-stage pipeline: full-text chunk search → entity name search → graph expansion (BFS over the knowledge graph) → relation filter → score+rank → assemble a context_prompt string.
5. You inject context_prompt into your LLM's system prompt. Done.

git clone https://github.com/zaydmulani09/mnemo
cd mnemo
docker compose up -d

# Pull the llama3 model the first time (~4 GB)
docker exec mnemo-ollama ollama pull llama3

# Verify everything is healthy
curl http://localhost:8080/health

cargo install --path crates/mnemo-api

# With Ollama
export MNEMO_LLM_BASE_URL=http://localhost:11434/v1
mnemo-api

# With OpenAI
export MNEMO_LLM_BASE_URL=https://api.openai.com/v1
export MNEMO_LLM_API_KEY=sk-...
export MNEMO_LLM_MODEL=gpt-4o-mini
export MNEMO_LLM_PROVIDER=openai
mnemo-api

from mnemo import MnemoClient

client = MnemoClient()  # server at http://localhost:8080

# Store a memory
client.ingest("I'm building a Rust vector database called vecdb")

# Get context for injection into your next LLM prompt
print(client.get_context("what am I working on?"))

All endpoints accept and return application/json. Base URL: http://localhost:8080.

Key request/response types:

Full endpoint documentation with curl examples: docs/api.md

Pass --config path/to/config.toml to mnemo-api. See mnemo.example.toml:

db_path = "mnemo.db"
port = 8080

[llm]
provider = "ollama"
base_url = "http://localhost:11434/v1"
model = "llama3"
api_key = "ollama"
timeout_secs = 30
max_retries = 3
max_tokens = 2048
temperature = 0.1

Environment variables take precedence over TOML values. The active config source is reported in GET /health → config_source.

Install:

cargo install --path crates/mnemo-cli

Usage:

# Store a memory
mnemo ingest "I use Neovim and prefer dark mode"

# Retrieve relevant context
mnemo search "what editor do I use?"

# List all extracted entities
mnemo entities

# Show entity detail + graph neighbors
mnemo entity <uuid> --neighbors

# List memory chunks
mnemo chunks

# Server health
mnemo health

# Memory statistics
mnemo stats

# Delete everything (prompts for confirmation)
mnemo wipe

# Skip confirmation prompt
mnemo wipe --yes

# Point at a non-default server
mnemo --server http://192.168.1.10:8080 stats

Install:

See sdk/python/README.md for the full API reference.

Async example:

import asyncio
from mnemo import AsyncMnemoClient

async def main():
    async with AsyncMnemoClient() as client:
        await client.ingest(
            "Alice is a principal engineer at Stripe working on payment infrastructure.",
            session_id="session-001",
        )
        context = await client.get_context(
            "what does Alice work on?",
            session_id="session-001",
        )
        print(context)

asyncio.run(main())

A working standalone example: examples/basic_usage.py

Four Rust crates wired together:

Full architecture documentation: docs/architecture.md

Benchmarked on Apple M2, SQLite WAL mode, in-memory petgraph. Debug build numbers — release build (--release) is 3–5× faster.

Run cargo run -p mnemo-bench to benchmark on your hardware.

cargo test --workspace          # run all 122 tests
make coverage                  # HTML coverage report (requires cargo-llvm-cov)
make coverage-summary          # summary to stdout

cd sdk/python && pytest tests/ -v

cargo run -p mnemo-bench                    # all 12 benchmarks
cargo run -p mnemo-bench -- --filter graph  # graph benchmarks only
cargo run -p mnemo-bench -- --json out.json # save results to JSON

Current test counts: 122 Rust tests · 21 Python tests · 12 benchmarks

PRs welcome. Please run make fmt && make lint before submitting. Open an issue first for large changes.

See CONTRIBUTING.md for full setup instructions, code style guide, and how to add a new LLM provider.

MIT — see LICENSE