MemPalace is an open-source tool that stores and retrieves verbatim conversation history on your machine using semantic search and a pluggable vector-store backend. It supports ChromaDB, SQLite exact vectors, Qdrant, and pgvector, and ships a CLI and Docker images for easy setup without external APIs. Benchmarks show 96.6% recall at top-5 on LongMemEval with zero cloud or LLM calls.

• 96.6% recall at top-5 on LongMemEval with zero cloud or LLM calls, rising to 98.4% with hybrid keyword/temporal boosting and ≥99% with LLM rerank
• Runs fully local by default (verbatim storage, semantic search, ChromaDB/SQLite/Qdrant/pgvector options) so nothing leaves your disk unless you opt into a remote backend
• Also benchmarked at 60.3% R@10 on LoCoMo (88.9% with hybrid v5) and 80.3% R@5 on MemBench
• Ships as CLI, Docker (CPU/GPU) images, and an MCP server for direct integration with tools like Claude Code

Stash is an open-source service that adds continuous memory to any AI model by storing and organizing session data in PostgreSQL with pgvector. It transforms raw conversations into structured facts, relationships, and patterns, so agents recall preferences, avoid repeated errors, and track long-term goals. Integration via MCP makes it model-agnostic, self-hostable, and free of vendor lock-in.

• Stash gives AI persistent memory by storing conversations in PostgreSQL/pgvector and running a 9-stage pipeline that turns raw episodes into facts, relationships, causal links, patterns, contradictions, goals, and failure detection
• Unlike RAG's static document search, Stash learns automatically from conversations and outcomes without manual tagging, improving over time
• It's model-agnostic and vendor-lock-in-free via MCP integration, working with Claude, GPT, or local models
• Setup requires just three commands (clone repo, configure .env, docker compose up), with self-hosting keeping data local

Stash lets your AI agent keep memory across conversations, so you don’t have to repeat context every time. It’s an open-source, self-hosted layer that uses Postgres, pgvector, and an eight-stage consolidation pipeline to turn raw observations into structured knowledge. It plugs into any MCP-compatible agent with a single Docker command.

• Stash gives AI agents persistent memory across sessions using Postgres + pgvector, run via a self-hosted Docker stack (no cloud dependency).
• A background service runs an eight-stage consolidation pipeline (fact extraction, relationship mapping, causal links, goal tracking, failure patterns, hypothesis checks, confidence decay) that only processes new entries, keeping the knowledge base efficient as it grows.
• It works with any MCP-compatible agent (OpenAI Agents, Claude Desktop, Continue, Cursor, Windsurf, Ollama, OpenRouter) via a single Docker command setup.
• It's open source (Apache 2.0), letting users customize consolidation rules, swap models, or plug in custom retrieval logic.