mnemostack 0.1.0a10

Memory stack for AI agents: BM25 + Qdrant + Memgraph + RRF retrieval, Gemini Flash inference layer, 8-stage recall pipeline.

mnemostack

Memory stack for AI agents — durable, structured, semantically searchable.

mnemostack is a hybrid memory system combining BM25, vector search (Qdrant), and knowledge graph (Memgraph) with a unified recall pipeline, reranker, and optional LLM inference layer.

Status: 🚧 alpha — API may change between 0.1.x releases.

Features

• 🧠 Hybrid retrieval — BM25 (exact tokens) + vector (semantic), fused via Reciprocal Rank Fusion
• 🔌 Pluggable embeddings — Gemini, Ollama, or HuggingFace (local GPU), via provider registry
• 🤖 Pluggable LLM — Gemini Flash / Ollama for answer generation and reranking
• 📚 Temporal knowledge graph — facts have valid_from/valid_until, query point-in-time state
• 💬 Answer mode — inference layer synthesizes concise factual answers with source citations and confidence
• 🔁 Reranker — LLM-based reordering of top results
• ⚙ Consolidation runtime — phase orchestrator for nightly memory lifecycle
• 🔌 MCP server — expose memory tools to Claude Desktop, ChatGPT, Cursor, etc.
• 🛡 Graceful degradation — retrieval keeps working if graph is down

Installation

# From PyPI
pip install mnemostack

# Optional extras
pip install 'mnemostack[huggingface]'  # local GPU embeddings
pip install 'mnemostack[mcp]'          # MCP server
pip install 'mnemostack[dev]'          # tests + linters

Run a local Qdrant for the vector store:

docker run -p 6333:6333 qdrant/qdrant:latest

Optionally a Memgraph for the knowledge graph:

docker run -p 7687:7687 memgraph/memgraph:latest

Quick start

CLI

# Health check
mnemostack health --provider ollama

# Index a directory of notes
mnemostack index ./my-notes/ --provider gemini --collection my-memory --recreate

# Hybrid recall
mnemostack search "what did we decide about auth" --provider gemini --collection my-memory

# Synthesize answer
mnemostack answer "what is the capital of France" --provider gemini --collection my-memory

# MCP server (for Claude Desktop, Cursor, etc.)
mnemostack mcp-serve --provider gemini --collection my-memory

Python API

from mnemostack.embeddings import get_provider
from mnemostack.vector import VectorStore
from mnemostack.recall import Recaller, AnswerGenerator
from mnemostack.llm import get_llm

emb = get_provider("gemini")
store = VectorStore(collection="my-memory", dimension=emb.dimension)
store.ensure_collection()

# ... index data here ...

recaller = Recaller(embedding_provider=emb, vector_store=store)
results = recaller.recall("what did we decide", limit=10)

# Optional: synthesize a concise answer
gen = AnswerGenerator(llm=get_llm("gemini"))
answer = gen.generate("what did we decide", results)
print(answer.text, answer.confidence, answer.sources)

Knowledge graph (optional)

from mnemostack.graph import GraphStore

graph = GraphStore(uri="bolt://localhost:7687")
graph.add_triple("alice", "works_on", "project-x", valid_from="2024-01-01")
graph.add_triple("alice", "works_on", "project-y", valid_from="2024-07-01")

# Who was alice working on in March?
march_facts = graph.query_triples(subject="alice", as_of="2024-03-15")

MCP server for Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "mnemostack": {
      "command": "mnemostack",
      "args": ["mcp-serve", "--provider", "gemini", "--collection", "my-memory"],
      "env": {
        "GEMINI_API_KEY": "your-key-here"
      }
    }
  }
}

Claude will then be able to call mnemostack_search, mnemostack_answer, and graph tools.

Custom embedding provider

from mnemostack.embeddings import EmbeddingProvider, register_provider

class MyProvider(EmbeddingProvider):
    @property
    def name(self): return "my-provider"
    @property
    def dimension(self): return 512
    def embed(self, text): ...
    def embed_batch(self, texts): ...

register_provider("my-provider", MyProvider)

Design

See ARCHITECTURE.md for detailed design: pipeline stages, Qdrant schema, Memgraph temporal model, consolidation runtime, MCP tools.

Roadmap

• Embedding provider registry (Gemini / Ollama / HuggingFace)
• LLM provider registry (Gemini Flash / Ollama)
• Qdrant wrapper
• BM25 + RRF recall pipeline
• Answer mode with confidence + citations
• LLM-based reranker
• Memgraph wrapper with temporal validity
• Consolidation runtime (phase orchestrator)
• CLI (mnemostack health/search/answer/index/mcp-serve)
• MCP server (Model Context Protocol)
• Text → graph triple extractor helpers
• Config file support (YAML/JSON)
• Async variants for high-throughput servers
• Docker compose examples

License

Apache 2.0 — see LICENSE.

Contributing

Early days. Issues and PRs welcome once API stabilizes.