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memU

Personal memory, stored as Wiki

Across Sessions. Across Agents. Across Devices.

PyPI version License: Apache 2.0 Python 3.13+ Discord Twitter

NevaMind-AI%2FmemU | Trendshift

memU is a lightweight, agent-driven memory system that gives users a shared LLM wiki across sessions, agents, and devices. It automatically distills your own reusable skills from your agent history. Its core memory logic is only 500 lines — compact enough to inspect, understand, and adapt.

Quick start

memU works with Codex, Claude Code, Cursor, OpenClaw, Hermes, WorkBuddy, and more. See Host adapters.

Choose Cloud or Local, then send the corresponding message to your agent.

Cloud (coming soon)

Cross-device · Free · Unlimited · View online

Read https://memu.pro/SKILL.md and follow it to install memU.

Local / self-hosted

Private · Single-device · Embedding key required

Read https://raw.githubusercontent.com/NevaMind-AI/MemU/main/SKILL.md and follow it to install memU.

How it works

memU memory system architecture

Automatic skill extraction

Once the scheduled bridging task is installed, memU can turn useful agent history into reusable Markdown skills automatically.

How memU turns agent history into reusable skills

  1. Capture new sessions. The host adapter reads new session history, including messages and tool calls.
  2. Prepare self-evolve jobs. prepare slices each session into a self-contained job with the paths and context the agent needs.
  3. Let the agent decide. The agent reads related existing skills, then chooses to do nothing, patch an existing skill, or create a new one.
  4. Write readable skill Markdown. Each skill has a name, description, and reusable workflow, including useful branches, edge cases, and pitfalls.
  5. Commit and index. commit submits changed skill files through commit_results; memU embeds the skill name and description and stores it under the skill track.
  6. Retrieve it later. On a similar future task, memU returns the relevant skill so any connected agent can use the learned workflow.

The judgment and synthesis stay inside the agent. MemoryService makes no LLM or chat calls; it stores, embeds, and retrieves the skill Markdown the agent prepared.

Host adapters: memory for desktop coding agents

memU runs as a sidecar to a desktop agent (ADR 0008/0009/0010), one binary per host. Each binds two seams:

  • record — a scheduled bridging task slices new session logs into self-contained job files; the agent itself distills them into memory/skill Markdown; commit submits whatever the agent left on disk back through commit_results.
  • inject — a standing instruction in the host's instruction file tells the agent to run <binary> retrieve (→ progressive_retrieve) before answering.
Host Binary Session log it mines Instruction file it patches
Codex memu-codex ~/.codex/sessions/**/*.jsonl ~/.codex/AGENTS.md
Claude Code memu-claude-code ~/.claude/projects/<project>/<session>.jsonl ~/.claude/CLAUDE.md
Cursor (Agent/CLI) memu-cursor ~/.cursor/projects/<project>/agent-transcripts/**.jsonl ./AGENTS.md (per project)
OpenClaw memu-openclaw ~/.openclaw/agents/<agentId>/sessions/*.jsonl ~/.openclaw/workspace/AGENTS.md
Hermes Agent memu-hermes ~/.hermes/state.db (SQLite, read-only) ~/.hermes/SOUL.md
WorkBuddy memu-workbuddy ~/.workbuddy/projects/<project>/<session>.jsonl ~/.workbuddy/MEMORY.md
any other agent memu-agent found by memu-agent detect (JSONL dialect sniffed) found by detect (AGENTS.md / CLAUDE.md / SOUL.md / …)

For agents without a dedicated binary, memu-agent detect probes the machine and reports per agent whether memorization works (a recognizable session log exists) and whether retrieval works (an instruction file exists to patch) — then the same verbs run against what it found.

All hosts share one configured memory backend via ~/.memu/config.env — local or MemU Cloud. What one host's sessions taught memU, another host retrieves.

Installation is the one-message setup at the top of this README. SKILL.md is the routing skill it hands your agent: install the package, identify which host you are (falling back to memu-agent detect for anything without a dedicated adapter), print that host's packaged install guide (<binary> docs install), and follow it — configure the memory backend, register the scheduled bridging task, patch the instruction file, each step behind a verify gate — then report which seams (memorization / retrieval) are now active.

Afterwards <binary> doctor proves the whole loop resolves: config, selected mode, and a live retrieval.

Adding another host means implementing one TranscriptSource (where its session logs live, how its records are shaped) plus a HostSpec-sized CLI — the pipeline, verbs, and instruction text are shared.

CLI

With memU Cloud, sign in at memu.so to view your memory files. With a local installation, memory lives in the shared store configured by MEMU_DB in ~/.memu/config.env — typically ~/.memu/memu.sqlite3 for local SQLite, or a Postgres DSN.

Once installed, your agent retrieves relevant memory automatically before answering. To retrieve manually, run the adapter for your host:

memu-codex retrieve "What should I remember about this project?"
# or: memu-claude-code / memu-cursor / memu-openclaw / memu-hermes / memu-workbuddy / memu-agent

Install or invoke the CLI directly:

pip install memu-cli         # library + memu + memu-codex CLIs
npx memu-cli --help          # CLI via npm launcher (engine: PyPI package memu-cli)
uvx --from memu-cli memu     # CLI via uv, no install

Configuration

Values resolve in order: process env → ~/.memu/config.env → default. memU supports Local and Cloud memory backends, selected by MEMU_MEMORY_MODE; an unset mode remains Local for backward compatibility.

For Local / self-hosted installations, every CLI flag has a matching variable:

Setting Env var Default
Store MEMU_DB ./data/memu.sqlite3 (CLI); required for host adapters
Embedding provider MEMU_EMBED_PROVIDER openai (also: jina, voyage, doubao, openrouter); legacy MEMU_LLM_PROVIDER still read
API key MEMU_API_KEY the provider's env var, e.g. OPENAI_API_KEY
Embedding model MEMU_EMBED_MODEL the provider's default
Base URL MEMU_BASE_URL the provider's default

Run <binary> doctor to display the resolved mode and verify the same retrieval path the host uses.

Storage backends

Provider DSN Vector search Use for
inmemory — brute-force cosine tests, throwaway sessions
sqlite sqlite:///path.sqlite3 brute-force cosine local/default, single writer
postgres postgresql://... pgvector concurrent access, large stores (pip install "memu-cli[postgres]") 
service = MemoryService(
    database_config={"metadata_store": {"provider": "postgres", "dsn": "postgresql://..."}},
    embedding_profiles={"default": {"provider": "jina"}},
)

License

Apache-2.0

Project links

Data verified by PyPI on Jul 24, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Newer release available (0.10.0)

1 maintainer

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Avatar for Sai1202 from gravatar.com Sai1202

Credits

Author: MemU Team

GitHub Statistics

Data verified by PyPI on Jul 24, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

Requires

Python >=3.13

Provides Extra

postgres

Tags

agent agentic agent-harness harness loop-engineering context-engineering context-window memory personal-information workspace retrieval llm

Classifiers

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