🤖 Agents: read .claude/skills/memu/SKILL.md and you can memorize-workspace and retrieve-workspace right away.

memU compiles conversations, documents, code, images, audio, video, URLs, and tool traces into human-readable Markdown files (INDEX.md, MEMORY.md, SKILL.md). Agents traverse the tree and load only what the moment needs — instead of rescanning everything or stuffing long histories into every prompt.

await service.memorize_workspace(folder="./workspace")

context = await service.retrieve_workspace("What should I know about this user's launch preferences?")

Or straight from the terminal — no code:

npx memu-cli memorize-workspace ./workspace
npx memu-cli retrieve-workspace "What should I know about this user's launch preferences?"

That's it. Instead of one giant prompt about a person or their workspace, your agent gets three durable layers it can traverse:

workspace/
├── INDEX.md              ← Index: a map of everything — raw sources and summaries
├── MEMORY.md             ← Memory: an overview that links into memory/
├── SKILL.md              ← Skill: an overview that links into skill/
├── resource/             ← the raw source files, copied verbatim
├── memory/
│   └── <topic>.md        ← one memory file per topic: facts, preferences, goals, events
└── skill/
    └── <name>.md         ← one skill file per learned pattern, workflow, or mistake to avoid

• Index (INDEX.md) — a map of your memories: what exists, where it came from, and where to look first
• Memory (MEMORY.md) — personal facts, preferences, goals, events, and decisions extracted from source data
• Skill (SKILL.md) — auto-extracted from agent traces and refined on every workspace sync so the agent improves at recurring tasks

When you sync a folder with memorize_workspace, the top-level directory decides the treatment: files under chat/ become memory, files under agent/ become skills, and everything else is indexed as workspace context.

Three things make it different from stuffing everything into the prompt:

• Fast retrieval — walk to the right folder and rank the right files instead of scanning everything every time.
• Higher accuracy — scope by user, task, or session, and trace every item back to the exact conversation, document, image, or log it came from.
• Lower cost — retrieve compact, scoped context instead of reinjecting long histories, documents, logs, and media-derived text into every prompt.
• Yours to inspect — a human-readable file tree you can audit, edit, scope, and route through your own storage (inmemory, sqlite, postgres) and LLM providers.

If you find memU useful or interesting, a GitHub Star ⭐️ would be greatly appreciated.

Every use case is the same loop: drop sources into a folder, sync it with memorize_workspace(), then ask with retrieve_workspace(). The sync is incremental (only changed files are reprocessed), and the top-level directory decides the treatment — chat/ → memory topics, agent/ → skills, everything else → indexed context.

Turn chat logs into user preferences, goals, events, decisions, and relationship context.

# workspace/chat/*.json — conversation logs become memory topic files
await service.memorize_workspace(folder="./workspace")

context = await service.retrieve_workspace("What should I remember about this user?")

Convert docs, PR notes, logs, and design decisions into reusable project memory.

# docs, notes, and logs anywhere in the folder are captioned and indexed
await service.memorize_workspace(folder="./workspace")

context = await service.retrieve_workspace("How should I structure this module?")

Extract searchable facts from documents, screenshots, images, videos, and audio notes.

# modality is inferred per file: .pdf/.docx/.pptx/.xlsx/.html (via MarkItDown —
# pip install 'memu-py[document]'), .png/.jpg, .mp3/.wav, .mp4/.mov, ...
await service.memorize_workspace(folder="./workspace")

context = await service.retrieve_workspace("What matters for the next research plan?")

Turn execution traces into skills that tell future agents what worked and what to avoid.

# workspace/agent/*.txt — execution traces are distilled into skill files
await service.memorize_workspace(folder="./workspace")

context = await service.retrieve_workspace("Which tools worked for config editing?")

The compiled workspace is easiest to read as two directions:

• memorize_workspace() writes a folder into durable memory files, skill files, resource records, segments, links, and embeddings.
• retrieve_workspace() reads those layers directly, ranking segments first and rolling results up to the files and resources an agent should load.

Memory is stored in three representation layers:

retrieve_workspace() embeds the query once, ranks segments and resources by similarity, and returns compact context with zero chat-LLM calls.

See docs/architecture.md for the runtime view of MemoryService, workflow pipelines, storage backends, and LLM routing, and docs/adr/ for the decision records behind the layered design.

The repo ships one Agent Skill — .claude/skills/memu/SKILL.md — that gives Claude Code (and any skills-compatible agent) the workspace pair. The agent decides when to use each direction:

• memorize (memu memorize-workspace) — "remember this", "sync this folder into memory", finishing work worth persisting
• retrieve (memu retrieve-workspace) — "what do we know about…", starting a task with likely prior context

It works out of the box inside this repo. To use it in your own project, copy the skill folder into that project's .claude/skills/ (or ~/.claude/skills/ to enable it everywhere):

cp -r .claude/skills/memu /path/to/your-project/.claude/skills/

The skill locates the CLI automatically (memu, uvx --from memu-py memu, or npx memu-cli) and keeps state in the project-local ./data/memu.sqlite3, so what one session memorizes the next can retrieve. For LangGraph agents, see the LangGraph integration instead.

👉 memu.so — Hosted API for managed ingestion, structured memory, and retrieval

For enterprise deployment: [email protected]

📚 Full API Documentation

From a clone of this repository:

uv sync
# or, for the full development setup:
make install

To install the published package instead:

pip install memu-py        # library + `memu` CLI
# or from the JS ecosystem (thin launcher over memu-py, uses uvx/pipx automatically):
npx memu-cli --help

Requirements: Python 3.13+. The default examples use OpenAI, so set OPENAI_API_KEY or pass another provider through llm_profiles.

The memu command wraps the same service the library exposes. State persists in a local SQLite database (./data/memu.sqlite3 by default), so memorize in one invocation and retrieve in the next:

export OPENAI_API_KEY=your_key

memu memorize-workspace ./workspace             # diff-sync a folder (alias: memu sync)
memu retrieve-workspace "deploy checklist"      # LLM-free embedding retrieval (alias: memu search)
memu export                                     # rebuild the INDEX.md/MEMORY.md/SKILL.md tree

Every flag has a MEMU_* environment variable (--provider/MEMU_LLM_PROVIDER, --model/MEMU_CHAT_MODEL, --db/MEMU_DB, ...) — run memu <command> --help for the full list. --db accepts a SQLite path, a postgres:// DSN, or :memory:.

Run an in-memory smoke script:

export OPENAI_API_KEY=your_key
cd tests
uv run python test_inmemory.py

Run with PostgreSQL + pgvector:

uv sync --extra postgres
docker run -d --name memu-postgres \
  -e POSTGRES_USER=postgres \
  -e POSTGRES_PASSWORD=postgres \
  -e POSTGRES_DB=memu \
  -p 5432:5432 \
  pgvector/pgvector:pg16

export OPENAI_API_KEY=your_key
export POSTGRES_DSN=postgresql+psycopg://postgres:[email protected]:5432/memu
cd tests
uv run python test_postgres.py

from memu import MemoryService

service = MemoryService(
    llm_profiles={
        "default": {
            "base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
            "api_key": "your_key",
            "chat_model": "qwen3-max",
            "client_backend": "sdk"
        },
        "embedding": {
            "base_url": "https://api.voyageai.com/v1",
            "api_key": "your_key",
            "embed_model": "voyage-3.5-lite"
        }
    },
)

from memu import MemoryService

service = MemoryService(
    llm_profiles={
        "default": {
            "provider": "openrouter",
            "client_backend": "httpx",
            "base_url": "https://openrouter.ai",
            "api_key": "your_key",
            "chat_model": "anthropic/claude-3.5-sonnet",
            "embed_model": "openai/text-embedding-3-small",
        },
    },
    database_config={"metadata_store": {"provider": "inmemory"}},
)

The primary API pair is memorize_workspace() / retrieve_workspace() — folder in, ranked context out.

result = await service.memorize_workspace(
    folder="./workspace",              # scanned recursively; modality inferred per file
    user={"user_id": "123"},           # optional scope
)
# Returns the diff plus what changed:
# { "added": [...], "modified": [...], "deleted": [...],
#   "resources": [...], "entries": [...], "files": [...] }

• Diffs the folder against a sidecar .memu_manifest.json — only added/modified files are processed, memory from deleted files is cascade-removed
• Routes by top-level directory: chat/ → memory files, agent/ → skill files, everything else → indexed workspace context
• Rebuilds the markdown memory tree (INDEX.md / MEMORY.md / SKILL.md) when memory_files_config.enabled=True

result = await service.retrieve_workspace(
    "deploy checklist",
    where={"user_id": "123"},
)
# Returns:
# { "segments": [...],    # embedded slices ranked by similarity
#   "files": [...],       # the memory/skill files those segments roll up to
#   "resources": [...] }  # workspace resources ranked by similarity

The query is embedded once and ranked by vector similarity — no intention routing, no query rewriting, no sufficiency checks, zero LLM calls. Use it for high-frequency lookups where latency and cost matter more than deep reasoning.

export OPENAI_API_KEY=your_key
uv run python examples/example_1_conversation_memory.py

Automatically extracts preferences, builds relationship models, and surfaces relevant context in future conversations.

uv run python examples/example_2_skill_extraction.py

Monitors agent actions, identifies patterns in successes and failures, auto-generates skill guides from experience.

uv run python examples/example_3_multimodal_memory.py

Cross-references text, images, and documents automatically into a unified memory layer.

memU achieves 92.09% average accuracy on the Locomo benchmark across all reasoning tasks.

View detailed results: memU-experiment

Quick Links:

• 🚀 Try MemU Cloud
• 📚 API Documentation
• 💬 Discord Community

# Fork and clone
git clone https://github.com/YOUR_USERNAME/memU.git
cd memU

# Install dev dependencies
make install

# Run quality checks before submitting
make check

See CONTRIBUTING.md for full guidelines.

Prerequisites: Python 3.13+, uv, Git

Apache License 2.0

• GitHub Issues: Report bugs & request features
• Discord: Join the community
• X (Twitter): Follow @memU_ai
• Contact: [email protected]