An agent-skill: memory that pays attention.
It includes skill instructions for reflective practice, and a powerful semantic memory system with command-line and MCP interfaces. Fully local, or use API keys for model providers, or cloud-hosted for multi-agent use.
uv tool install keep-skill # or: pip install keep-skill
export OPENAI_API_KEY=... # Or GEMINI_API_KEY (both do embeddings + summarization)
# Index content (store auto-initializes on first use)
keep put https://inguz.substack.com/p/keep -t topic=practice
keep put "Rate limit is 100 req/min" -t topic=api
# Index a codebase — recursive, with daemon-driven watch for changes
keep put ./my-project/ -r --watch
# Search by meaning
keep find "what's the rate limit?"
# Track what you're working on
keep now "Debugging auth flow"
# Instructions for reflection
keep prompt reflectStore anything — notes, files, URLs — and keep summarizes, embeds, and tags each item. You search by meaning, not keywords. Content goes in as text, PDF, HTML, Office documents, audio, or images; what comes back is a summary with tags and semantic neighbors. Audio and image files auto-extract metadata tags (artist, album, camera, date, etc.).
What makes this more than a vector store: tags become edges. Define a tag like author or git_commit and keep creates bidirectional links — a user-defined graph model where every tag can be a navigable relationship. When you retrieve any item, keep follows these edges and fires standing queries — surfacing open commitments, past learnings, referenced files, commit history. The right things appear at the right time, without manual graph construction.
- Summarize, embed, tag — URLs, files, and text are summarized and indexed on ingest
- Contextual feedback — Open commitments and past learnings surface automatically
- Semantic search — Find by meaning, not keywords; scope to a folder or project
- Tag organization — Speech acts, status, project, topic, type — structured and queryable
- Deep search — Follow edges and tags from results to discover related items across the graph
- Edge tags — Turn tags into navigable relationships with automatic inverse links
- Git changelog — Commits indexed as searchable items with edges to touched files
- Parts —
analyzedecomposes documents into searchable sections, each with its own embedding and tags - Strings — Every note is a string of versions; reorganize history by meaning with
keep move - Watches — Daemon-driven directory and file monitoring; re-indexes on change
- Works offline — Local models (MLX, Ollama), or API providers (Voyage, OpenAI, Gemini, Anthropic, Mistral)
Backed by ChromaDB for vectors, SQLite for metadata and versions.
keepnotes.ai — Hosted service. No local setup, no API keys to manage. Same SDK, managed infrastructure.
keep is designed as a skill for AI agents — a practice, not just a tool. The skill instructions teach agents to reflect before, during, and after action: check intentions, recognize commitments, capture learnings, notice breakdowns. keep prompt reflect guides a structured reflection (details); keep now tracks current intentions and surfaces what's relevant.
This works because the tool and the skill reinforce each other. The tool stores and retrieves; the skill says when and why. An agent that uses both develops skillful action across sessions — not just recall, but looking before acting, and a deep review of outcomes afterwards.
Why build memory for AI agents? What does "reflective practice" mean here? Read our blog for the back-story →
The skill instructions and hooks install into your agent's configuration automatically on first use. The CLI alone is enough to start; the hooks make it automatic.
| Tool | Integration |
|---|---|
| OpenClaw | Context engine plugin — full memory assembly, session archival, reflection triggers |
| Claude Desktop | keep config mcpb (details) |
| Claude Code | Plugin: /plugin install keep@keepnotes-ai |
| VS Code Copilot | MCP: code --add-mcp '{"name":"keep","command":"keep","args":["mcp"]}' |
| Kiro | MCP + practice prompt: kiro-cli mcp add --name keep --scope global -- keep mcp |
| OpenAI Codex | MCP: codex mcp add keep -- keep mcp |
| LangChain | LangGraph BaseStore, retriever, tools, and middleware |
| Any MCP client | Stdio server with 3 tools (keep_flow, keep_prompt, keep_help) |
After install, just tell your agent: Please read all the keep_help documentation, and then use keep_prompt(name="reflect") to save some notes about what you learn.
Python 3.11–3.13 required. Use uv (recommended) or pip:
uv tool install keep-skillHosted (simplest — no local setup needed):
export KEEPNOTES_API_KEY=... # Sign up at https://keepnotes.aiSelf-hosted with API providers:
export OPENAI_API_KEY=... # Simplest (handles both embeddings + summarization)
# Or: GEMINI_API_KEY=... # Also does both
# Or: VOYAGE_API_KEY=... and ANTHROPIC_API_KEY=... # Separate servicesLocal (offline, no API keys): If Ollama is running, keep auto-detects it. Or on macOS Apple Silicon: uv tool install 'keep-skill[local]'
LangChain/LangGraph integration: pip install keep-skill[langchain] or pip install langchain-keep
See docs/QUICKSTART.md for all provider options.
# Index URLs, files, and notes (store auto-initializes on first use)
keep put https://example.com/api-docs -t topic=api
keep put "Token refresh needs clock sync" -t topic=auth
# Index a codebase — recursive, with auto-watch for changes
keep put ./my-project/ -r --watch
# git: 2 repo(s) queued for changelog ingest
# Search
keep find "authentication flow" --limit 5
keep find "auth" --deep # Follow edges to discover related items
keep find "auth" --scope 'file:///Users/me/project/*' # Scoped to a folder
# Retrieve
keep get file:///path/to/doc.md
keep get ID --history # All versions
keep get ID --parts # Analyzed sections
# Tags
keep list --tag project=myapp # Find by tag
keep list 'git://github.com/org/repo@*' # All git tags/releases
# Current intentions
keep now # Show what you're working on
keep now "Fixing login bug" # Update intentionsfrom keep import Keeper
kp = Keeper()
# Index
kp.put(uri="file:///path/to/doc.md", tags={"project": "myapp"})
kp.put("Rate limit is 100 req/min", tags={"topic": "api"})
# Search
results = kp.find("rate limit", limit=5)
for r in results:
print(f"[{r.score:.2f}] {r.summary}")
# Version history
prev = kp.get_version("doc:1", offset=1)
versions = kp.list_versions("doc:1")See docs/QUICKSTART.md for configuration and more examples.
Full docs at docs.keepnotes.ai — or browse locally:
- docs/QUICKSTART.md — Setup, configuration, first steps
- docs/REFERENCE.md — Quick reference index
- docs/KEEP-PUT.md — Indexing: files, directories, URLs, git changelog, watches
- docs/KEEP-FIND.md — Semantic search, deep search, scoped search
- docs/TAGGING.md — Tags, speech acts, project/topic organization
- docs/PROMPTS.md — Prompts for summarization, analysis, and agent workflows
- docs/OPENCLAW-INTEGRATION.md — OpenClaw context engine plugin
- docs/KEEP-MCP.md — MCP server for AI agent integration
- docs/AGENT-GUIDE.md — Working session patterns
- docs/ARCHITECTURE.md — How it works under the hood
- SKILL.md — The reflective practice (for AI agents)
MIT
Published on PyPI as keep-skill.
Issues and PRs welcome:
- Provider implementations
- Performance improvements
- Documentation clarity
See CONTRIBUTING.md for guidelines.
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