.__         .__
___________  |  | _____  |__|____
\____ \__  \ |  | \__  \ |  \__  \
|  |_> > __ \|  |__/ __ \|  |/ __ \_
|   __(____  /____(____  /__(____  /
|__|       \/          \/        \/

Your agents forget. palaia doesn't.

AI agents are stateless by default. Every session starts from scratch — no memory of past decisions, no shared knowledge between agents, no context that survives a restart.

palaia gives your agents a persistent, searchable knowledge store. They save what they learn. They find it again by meaning, not keyword. They share it across tools and sessions — automatically.

• Not a chatbot or prompt manager
• Not a cloud service (everything runs locally)
• Not a vector database you manage yourself (it manages itself)
• Not limited to one tool — works across OpenClaw, Claude Code, and any MCP client

Both OpenClaw and Claude Code support fully autonomous setup. Copy the prompt below and paste it directly into your agent's chat — the agent handles everything from there.

OpenClaw — copy this prompt into your OpenClaw agent:

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.

Claude Code — copy this prompt into Claude Code:

Install palaia for persistent memory in this Claude Code environment.
Run: pip install "palaia[mcp,fastembed]" && palaia init && palaia setup claude-code --global
Then tell me to restart Claude Code so the MCP tools become active.
After restart, read the CLAUDE.md and follow its instructions.

See Claude Code Integration for the full guide.

OpenClaw:

pip install "palaia[fastembed]"
palaia init
openclaw plugins install @byte5ai/palaia
palaia doctor --fix

Then activate the memory slot in your OpenClaw config:

// openclaw.json
{
  plugins: {
    slots: { memory: "palaia" }
  }
}

Restart the gateway: openclaw gateway restart

Claude Code:

pip install "palaia[mcp,fastembed]"
palaia init
palaia setup claude-code --global

Restart Claude Code after setup.

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

Add to your MCP config:

• Claude Desktop: ~/.config/claude/claude_desktop_config.json
• Cursor: .cursor/mcp.json

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

Note: These clients require manual MCP configuration. palaia provides the memory tools, but you need to instruct the agent yourself.

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.

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

git clone https://github.com/byte5ai/palaia.git
cd palaia
pip install -e ".[dev]"
pytest

• palaia.ai — Homepage
• PyPI — Package registry
• ClawHub — Install via agent skill
• OpenClaw — The agent platform palaia is built for
• CHANGELOG — Release history

MIT — (c) 2026 byte5 GmbH