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. Local-only, hybrid or cloud.

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 "file://$(keep config tool)/docs/library/han_verse.txt" -t type=teaching
keep put "Rate limit is 100 req/min" -t topic=api

# Search by meaning
keep find "what's the rate limit?"

# Track what you're working on
keep now "Debugging auth flow"
keep now -V 1                    # Previous intentions

# Instructions for reflection
keep prompt reflect

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What It Does

Store anything — URLs, files, notes — 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: when you view your current context (keep now) or retrieve any item (keep get), keep automatically surfaces relevant open commitments, past learnings, and breakdowns — ranked by similarity and recency. The right things appear at the right time. That's what makes reflection real.

• 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
• 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
• 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
• 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? I wrote a story: Wisdom, or Prompt-Engineering?

Integration

The skill instructions and hooks install into your agent's configuration automatically on first use (Claude Code, Kiro, OpenAI Codex, OpenClaw). Hooks inject keep now context at session start, on each prompt, and at session end — so the agent always knows its current intentions.

Layer	What it does
Skill prompt	Always in system prompt — guides reflection, breakdown capture, document indexing
Hooks	Inject keep now -n 10 context at session start and prompt submit
MCP	Stdio server with 9 tools — any MCP-compatible agent gets full memory
LangChain	LangGraph BaseStore, retriever, tools, and middleware
Daily cron	Scheduled deep reflection in an isolated session (OpenClaw cron)

The CLI alone is enough to start. The hooks make it automatic.

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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://inguz.substack.com/p/keep -t topic=practice
keep put "file://$(keep config tool)/docs/library/han_verse.txt" -t type=teaching
keep put "Token refresh needs clock sync" -t topic=auth

# Search
keep find "authentication flow" --limit 5
keep find "auth" --deep                # Follow edges to discover related items
keep find "auth" --since P7D           # Last 7 days

# Retrieve
keep get file:///path/to/doc.md
keep get ID -V 1                       # Previous version
keep get "ID@V{1}"                     # Same as -V 1 (version identifier)
keep get ID --history                  # All versions

# Tags
keep list --tag project=myapp          # Find by tag
keep find "auth" -t topic=auth         # Cross-project topic search
keep list --tags=                      # List all tag keys

# 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

• docs/QUICKSTART.md — Setup, configuration, async summarization
• docs/REFERENCE.md — Quick reference index
• docs/TAGGING.md — Tags, speech acts, project/topic organization
• docs/VERSIONING.md — Document versioning and history
• docs/META-TAGS.md — Contextual queries (.meta/*)
• docs/PROMPTS.md — Prompts for summarization, analysis, and agent workflows
• docs/EDGE-TAGS.md — Edge tags and relationship navigation
• docs/ANALYSIS.md — Document decomposition into searchable parts
• docs/AGENT-GUIDE.md — Working session patterns
• docs/LANGCHAIN-INTEGRATION.md — LangChain/LangGraph integration
• docs/KEEP-MCP.md — MCP server for AI agent integration
• 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.