PowerMem

Persistent memory for AI agents and applications.

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PowerMem combines vector, full-text, and graph retrieval with LLM-driven memory extraction and Ebbinghaus-style time decay. It supports multi-agent isolation, user profiles, and multimodal signals (text, image, audio).

Use the Python SDK, CLI (pmem), or the HTTP API Server (with Dashboard at /dashboard/). An MCP server is also available. All share one .env. See .env.example and the configuration guide.

OpenClaw can use PowerMem as long-term memory via the memory-powermem plugin.

openclaw plugins install memory-powermem

Requires the OpenClaw CLI to be installed.

Prerequisites: Copy .env.example to .env and set LLM and embedding credentials (the default database is SQLite; OceanBase can use embedded SeekDB — see .env.example). Alternatively, after installing PowerMem, run pmem config init to create .env interactively. See Getting started.

pip install powermem

Run from a directory that contains your configured .env:

from powermem import Memory, auto_config

config = auto_config()
memory = Memory(config=config)

memory.add("User likes coffee", user_id="user123")

results = memory.search("user preferences", user_id="user123")
for result in results.get("results", []):
    print(f"- {result.get('memory')}")

More patterns: Getting Started.

pmem memory add "User prefers dark mode" --user-id user123
pmem memory search "preferences" --user-id user123

Interactive REPL (run separately; exits with exit or Ctrl+D):

pmem shell

Full reference: CLI usage.

Uses the same .env as the SDK. Dashboard is served under /dashboard/.

powermem-server --host 0.0.0.0 --port 8000

Use Docker or Compose as needed — see API Server and Docker & deployment.

uvx powermem-mcp sse

Compared to stuffing full conversation context on LOCOMO:

Interfaces and tooling — Python integration; CLI (pmem); HTTP API / Dashboard; MCP (optional); IDE apps (VS Code / Cursor, Claude Code, and more).

Memory pipeline and retrieval — Smart extraction and updates; Ebbinghaus-style decay; Hybrid retrieval (vector / full-text / graph); Sub stores and routing.

Profiles and multi-agent — User profile; Shared / isolated memory and scopes.

Multimodal — Text, image, audio.

• Getting started — install, .env, and first Memory usage
• Configuration — settings model, storage backends, environment variables
• Architecture — major components, storage layout, and retrieval flow
• API & services — REST, MCP, HTTP server, and Python-facing APIs
• CLI — pmem commands, interactive shell, backup and migration
• Multi-agent — scopes, isolation, and cross-agent sharing
• Integrations — LangChain and other framework wiring
• Docker & deployment — images, Compose, and running the API server
• Development — local setup, tests, and contributing

More topics: Sub stores, guides index.

• Scenarios & notebooks — walkthroughs by use case (basic usage, multimodal, forgetting curve, and more)
• LangChain sample — medical support chatbot (LangChain + PowerMem + OceanBase)
• LangGraph sample — customer service bot (LangGraph + PowerMem + OceanBase)
• IDE apps — VS Code extension and Claude Code plugin (link PowerMem to Cursor, Copilot, etc.)

• GitHub Issues
• GitHub Discussions

Apache License 2.0 — see LICENSE.