keep-skill 0.130.1

Reflective memory - remember and search documents by meaning

keep

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 reflect

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What It Does

Store anything — notes, files, URLs — and keep summarizes, embeds, and tags each note. You search by meaning, by keyword, and by graph traversal. 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 note, 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
• Search — Semantic similarity, BM25 full-text, and ranked graph traversal; 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 notes across the graph
• Edge tags — Turn tags into navigable relationships with automatic inverse links
• Git changelog — Commits indexed as searchable notes with edges to touched files
• Parts — analyze decomposes 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.

The Practice

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 →

Integration

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.

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Installation

Python 3.11–3.13 required. Use uv (recommended) or pip:

uv tool install keep-skill

Hosted (simplest — no local setup needed):

export KEEPNOTES_API_KEY=...   # Sign up at https://keepnotes.ai

Self-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 services

Local (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.

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Quick Start

# 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 notes
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 intentions

Python API

from 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.

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Documentation

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)

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License

MIT

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Contributing

Published on PyPI as keep-skill.

Issues and PRs welcome:

• Provider implementations
• Performance improvements
• Documentation clarity

See CONTRIBUTING.md for guidelines.