Which integrations are available for this server?

Allows using PostgreSQL with pgvector as the storage backend for scalable, distributed knowledge.

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@palaia save the meeting notes from today's standup" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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The Knowledge System for AI Agent Teams

Your agents forget. palaia doesn't.

PyPI Python 3.9+ License: MIT OpenClaw Plugin

What palaia Does

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.

Related MCP server: AgentBase

What palaia Is Not

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

What You Get

Capability	What it means
Agents remember across sessions	Knowledge survives restarts, tool switches, and team handoffs
Find anything by meaning	Hybrid BM25 + vector search across 6 embedding providers
Zero-config local setup	SQLite with native SIMD vector search — no separate database process
Works everywhere via MCP	OpenClaw and Claude Code: paste a prompt, done. Claude Desktop, Cursor, any MCP host: manual config.
Multi-agent ready	Private, team, and public scopes — agents see what they should
Agent isolation	`--isolated` mode for strict per-agent memory boundaries
Crash-safe by default	SQLite WAL mode survives power loss, kills, OOM
Fast	Embed server keeps model in RAM — CLI queries ~1.5s, MCP/Plugin <500ms
WebUI memory explorer	`palaia ui` — browse, search, create entries in the browser. Localhost only.
Scales when needed	Swap to PostgreSQL + pgvector for distributed teams, no code changes

Comparison

Feature	palaia	claude-mem	Mem0	OpenClaw Built-in
Local-first	Yes	Yes	Yes (optional cloud)	Yes
Cross-tool (MCP)	Yes (any MCP client)	No (Claude Code only)	No	No (OpenClaw only)
Native Vector Search	sqlite-vec / pgvector	ChromaDB (separate)	FAISS (embedded)	No
Structured Types	memory/process/task	Yes (6 categories)	No	No
Multi-Agent Scopes	private/team/public	Partial (session isolation)	Yes (user/agent/run)	No
Smart Tiering	HOT/WARM/COLD	No	No	No
Embedding Providers	6 (configurable)	1 (fixed)	Cloud	External (5+)
Open Source	MIT	AGPL-3.0	Apache 2.0	MIT
Crash-safe (WAL)	Yes	Yes (WAL)	Partial (SQLite, not primary)	No

Install

Recommended: Paste into your agent

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.

Manual Setup

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.

Other MCP Clients (Claude Desktop, Cursor)

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.

Optional Extras

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.

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

Documentation

Document	Description
Getting Started	Installation, first steps, quick tour
Storage & Search	SQLite, PostgreSQL, sqlite-vec, pgvector, embedding providers
Claude Code	Claude Code integration, setup command, paste-this prompt
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)
Contributing	Versioning, release process, development setup
Changelog	Release history

Development

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

Links

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

This server cannot be installed

A

license - permissive license

-

quality - not tested

A

maintenance

How are these scores calculated?

Maintenance

–Maintainers

2dResponse time

1dRelease cycle

46Releases (12mo)

Commit activity

Issues opened vs closed

Resources

GitHub Repository

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