agentmemory-rrs 3.0.1

Persistent memory for AI coding agents. Correct once, remembered forever. Every design decision validated by experiment before shipping to production.

agentmemory

Correct your AI agent once. It remembers forever.

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You tell your AI agent "use uv, not pip." It says "got it." Next session, it reaches for pip. You correct it again. And again. And again.

agentmemory makes the next correction your last. It captures what matters from your conversations -- corrections, decisions, preferences -- stores them locally, and injects them into every future session. Silently. Automatically. You stop repeating yourself.

pip install agentmemory-rrs
agentmemory setup

Restart Claude Code. In any project: /mem:onboard .

That's it. Three commands. Your agent now remembers permanently.

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What It Actually Does

Here's a real example. You type push the release to github. Before the agent sees your message, agentmemory's hook fires and runs a 7-layer search in ~50ms:

Layer 0: Structural analysis    -> task type: deployment, target: github
Layer 1: FTS5 full-text search  -> 4 hits (publish script, CI checks, remote config)
Layer 2: Entity expansion       -> "github" links to 3 beliefs about repo setup
Layer 3: Action-context         -> "push to github" triggers activation condition
Layer 4: Supersession check     -> old remote URL excluded (superseded)
Layer 5: Recent observations    -> correction from 2 days ago about publish script
Layer 6: Cross-project scopes   -> checks shared infra beliefs

The agent receives this context injection alongside your message:

== OPERATIONAL STATE ==
[!] GitHub account renamed (changed 2d ago)

== STANDING CONSTRAINTS ==
- NEVER use git push github directly. Use scripts/publish-to-github.sh
- Pre-push hook scans for PII; direct push bypasses safety checks
- To release with tag: bash scripts/publish-to-github.sh --tag vX.Y.Z

== BACKGROUND ==
- Remote 'github' points to [e-mail]:robot-rocket-science/agentmemory.git

Without agentmemory, the agent runs git push github main and bypasses every safety check. With it, the agent already knows. You said nothing.

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What It Remembers

You say	It stores
"Use uv, not pip"	Permanent rule. Injected every session.
"The endpoint moved to /v2"	Correction. Replaces the old belief.
"I prefer terse commits"	Preference. Shapes behavior silently.

Beliefs accumulate over time. Each one carries a Bayesian confidence score that strengthens when the belief proves useful and fades when it doesn't. After a few weeks:

/mem:stats
Beliefs: 312 (18 locked, 294 learned)
Sessions: 47
Corrections surfaced this session: 3
Last locked: "never commit .env files" (4 weeks ago)

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Your Data Stays Yours

• 100% local. SQLite on your machine. No network calls in the retrieval pipeline.
• No cloud, no accounts. No server, no signup, no telemetry by default.
• No GPU or vector database. Runs on any machine that runs Python.

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What Happens After Install

You don't need to learn commands. agentmemory works in the background:

1. It listens -- picks up decisions, corrections, and preferences from your conversations
2. It retrieves -- injects relevant beliefs before the agent responds to each prompt
3. It learns -- beliefs that help get stronger; beliefs that don't, fade

To explicitly lock a permanent rule:

/mem:lock "always use uv, never poetry"

For deeper exploration:

Command	What it does
/mem:search <query>	Find specific memories
/mem:lock <rule>	Create a permanent rule
/mem:wonder <topic>	Deep parallel research across the memory graph
/mem:reason <question>	Test a hypothesis against stored evidence
/mem:stats	See what's in memory

Full command reference: docs/COMMANDS.md.

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Emergent Behavior

These weren't planned features. They emerged from persistent memory + reasoning tools.

The agent audited its own memory and found a bug

user    Clean up the config file, I think agentmemory handles this now.

agent   [searches memory, compares against config]
        Found 2 redundant items. But also: this locked belief about
        your server setup is stale -- it references infrastructure
        you removed. Every session is getting incorrect instructions.

user    Fix it.

agent   [corrects the belief, supersedes the old one]
        Done. Want to lock the updated version?

The system caught its own error because it had enough accumulated context to notice the inconsistency. More examples and a full session transcript showing wonder + reason in action.

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Under the Hood

Conversations are broken into individual beliefs stored in a local SQLite database. Retrieval uses full-text search, graph traversal, and vocabulary bridging -- no embeddings, no vector database, no external API calls.

98 experiments drove every design decision. 954 tests. 5 academic benchmarks. Architecture details: docs/ARCHITECTURE.md.

The knowledge graph after a few weeks of daily use. Each dot is a belief. Lines are relationships (supports, contradicts, supersedes).

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Compatibility

Currently supports Claude Code via MCP (Model Context Protocol). The architecture is agent-agnostic -- any MCP-compatible client can use agentmemory as a memory backend.

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Documentation

• Getting Started: Installation -- Workflow
• Reference: Commands -- Obsidian Integration -- Privacy
• Technical: Architecture -- Benchmarks -- Case Studies

Development

git clone https://github.com/robot-rocket-science/agentmemory.git
cd agentmemory
uv sync --all-groups
uv run pytest tests/ -x -q

Contributions welcome. See CONTRIBUTING.md.

Citation

@software{agentmemory2026,
  author    = {robotrocketscience},
  title     = {agentmemory: Persistent Memory for AI Coding Agents},
  year      = {2026},
  url       = {https://github.com/robot-rocket-science/agentmemory},
  license   = {MIT}
}

License

MIT