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Local, cloud-free memory for OpenClaw agents.

Project description

Palaia — The Knowledge OS for AI Agent Teams

Crash-safe. Local-first. Zero-cloud. The memory system that makes your agents smarter over time.

CI PyPI Python 3.9+ License: MIT OpenClaw Plugin


Install

Recommended: Tell your agent

Paste this into your OpenClaw agent (works for install AND update):

Install or update the Palaia memory skill from ClawHub to the latest version (even if already present). Read the SKILL.md completely and follow it step by step. Run palaia init, then palaia doctor --fix and resolve all warnings — don't stop until the doctor report is clean. Set up completely.

The agent handles everything: ClawHub skill install, pip setup, plugin config, and verification.

Manual Setup

pip install "palaia[fastembed]"
palaia init
palaia doctor --fix

Optional extras:

pip install "palaia[mcp]"          # MCP server for Claude Desktop, Cursor
pip install "palaia[curate]"       # Knowledge curation
pip install "palaia[postgres]"     # PostgreSQL + pgvector backend

Note: palaia[fastembed] already includes sqlite-vec for native vector search and the embed-server auto-starts on first query. No manual optimization needed.

Upgrading? palaia upgrade — auto-detects install method, preserves extras, runs doctor.

MCP Setup (Claude Desktop, Cursor — no OpenClaw needed)

pip install "palaia[mcp,fastembed]"
palaia init

Add to ~/.config/claude/claude_desktop_config.json (Claude Desktop) or .cursor/mcp.json (Cursor):

{
  "mcpServers": {
    "palaia": {
      "command": "palaia-mcp"
    }
  }
}

Quick Start

palaia write "API rate limit is 100 req/min" \
  --type memory --tags api,limits                   # Save knowledge
palaia query "what's the rate limit"                # Find it by meaning
palaia status                                        # Check health

Why Palaia?

Feature Details
Semantic Search Hybrid BM25 + vector embeddings. 6 providers: fastembed, sentence-transformers, Ollama, OpenAI, Gemini, BM25.
Native Vector Search sqlite-vec (SIMD KNN) or pgvector (ANN/HNSW). Not Python cosine — real database-level acceleration.
MCP Server palaia-mcp — standalone memory for Claude Desktop, Cursor, any MCP host. No OpenClaw required.
Multi-Backend SQLite (default, zero-config) or PostgreSQL + pgvector for distributed teams.
Crash-Safe SQLite WAL mode — survives power loss, kills, OOM.
Auto-Capture OpenClaw plugin captures significant exchanges automatically.
Structured Types memory, process, task — with status, priority, assignee, due date.
Multi-Agent Shared store, scopes (private/team/public), agent aliases, per-agent injection priorities.
Smart Tiering HOT/WARM/COLD rotation based on decay scores and access patterns.
Embed Server Background process holds model in RAM. CLI queries: <500ms (was ~5s).
Zero-Cloud Everything local. No API keys needed for core functionality.

Comparison

Feature Palaia claude-mem Mem0 Stock Memory
Local-first Yes Yes No (cloud) Yes
Cross-tool (MCP) Yes (any MCP client) No (Claude Code only) No No
Native Vector Search sqlite-vec / pgvector ChromaDB (separate) Cloud No
Structured Types memory/process/task decisions/bugfixes No No
Multi-Agent Scopes private/team/public No Per-user No
Smart Tiering HOT/WARM/COLD No No No
Embedding Providers 6 (configurable) 1 (fixed) Cloud None
Open Source MIT AGPL-3.0 Partial N/A
Crash-safe (WAL) Yes Partial N/A No

Documentation

Document Description
Getting Started Installation, first steps, quick tour
Storage & Search SQLite, PostgreSQL, sqlite-vec, pgvector, embedding providers
MCP Server Setup for Claude Desktop, Cursor, tool reference, read-only mode
Embed Server Performance optimization, socket transport, daemon mode
Multi-Agent Scopes, agent identity, team setup, aliases
Configuration All config keys, embedding chain, tuning
CLI Reference All commands with flags and examples
Migration Guide Import from other systems, flat-file migration
Architecture Module map, data flows, design decisions
SKILL.md Agent-facing documentation (what agents read)

Development

git clone https://github.com/iret77/palaia.git
cd palaia
pip install -e ".[dev]"
pytest

Links


MIT — (c) 2026 byte5 GmbH

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