Honcho
Plastic Labs · Coding
Honcho is an AI memory platform from Plastic Labs that gives stateful agents persistent context across sessions. Instead of storing facts and retrieving them later, it reasons over everything a user says to build a changing model of that person over time. Developers reach it through a hosted API or a self-hosted stack, and it plugs into tools like Claude Code, Codex, and OpenClaw.
About Honcho
What Is Honcho
Honcho is a memory and context layer for AI agents. The company behind it, Plastic Labs, describes it as an open source memory library with a managed service, so you can run it yourself or pay for the hosted version. The pitch is simple: most agents forget, contradict themselves, or lose the thread between sessions, and Honcho is meant to fix that.
The core idea is that memory is reasoning, not storage. A plain fact store remembers that a user mentioned a side project once. Honcho keeps track of what that project is, how often it comes up, and how much it seems to matter to them. Every incoming message triggers that reasoning, which is how the system claims to save tokens downstream while giving agents richer context.
There are real limits to know about. Honcho is developer infrastructure, not a consumer app, so you need some coding ability to use it well. It also reasons for you, which means you hand over judgment about what matters and trust the output. Teams that need total control over every retrieval step may find that trade-off uncomfortable. So who is this actually for?
Getting Started
- Create an account at app.honcho.dev and grab an API key from the dashboard.
- Install the SDK for your stack:
pip install honcho-aifor Python,npm install @honcho-ai/sdkfor TypeScript. - Set up a workspace and define the peers your agent will track, such as a user and an assistant.
- Add messages to sessions so Honcho can start building state and reasoning about each peer.
- Query the synthesized insights and feed them back into your agent's prompts.
Product Information
A quick look at Honcho's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Developers building chat agents
- Teams shipping companion or coaching apps
- Solo builders on Claude Code or Codex
Tasks
- Maintaining user profiles across sessions
- Reducing prompt tokens
- Handling contradictions and drift
Scenarios
- Long-running assistants that must recall earlier conversations
- Multi-agent setups where several agents share state about the same entities.
- Prototyping a memory layer before committing to a custom build.
Key features
Reasoning-Based Memory
Honcho doesn't just store and retrieve. It runs a reasoning pass over every message to pull out what matters, which is the core difference from a standard vector store or RAG pipeline. That pass is what lets the system track a user's changing preferences rather than freezing them at first mention. Big difference.
Four Storage Primitives
The system is built on workspaces, peers, sessions, and messages. Workspaces isolate separate apps or environments, peers represent any entity that persists and changes, sessions group conversations, and messages are the raw input. Peers connect to sessions in a many-to-many relationship, so one person can span many threads. That structure is simple on purpose.
Hosted API and Self-Hosting
You can sign up for the managed service and skip infrastructure work, or install the CLI and run a local stack with Docker. The self-hosted path needs your own LLM provider key, but it keeps your data on your own machines. Both routes expose the same core API. Pick whichever fits.
Drop-In Integrations
Honcho ships plugins for Claude Code, OpenAI Codex, OpenClaw, and Hermes Agent. Most install in a single command and register hooks that quietly feed conversations into memory. For developers already living in one of those tools, this is the fastest way to try it.
Neuromancer Reasoning Models
Plastic Labs built its own reasoning models, called Neuromancer, to power the learning loop. The company says these models hit state-of-the-art accuracy at lower cost and latency than frontier models on memory benchmarks. Treat that as a vendor claim until you run your own numbers. Yours may differ.
Python and TypeScript SDKs
Official SDKs cover the two most common agent stacks. You initialize a client, create peers, and add messages without touching raw HTTP. The SDKs are the recommended path, and the underlying REST API is open to any language.
Pros and cons
Pros
- Reasons about context instead of only storing it, which fits agents that need to adapt to users over time.
- Open source with a managed option, so you can self-host or pay to skip the ops work.
- New accounts get $100 in free credits, and a quickstart run costs only a few cents.
- Works with any model or framework and ships ready-made plugins for popular coding agents.
- Handles contradictions and long-term patterns that basic RAG setups tend to miss.
Cons
- It's developer infrastructure, so non-coders won't get far without help.
- The reasoning step adds latency and cost per message, which matters at high volume.
- Pricing beyond the free credits isn't published clearly, so budgeting takes guesswork.
- Handing judgment to the system means less control over exactly what gets remembered.
Frequently asked questions
Honcho gives AI agents persistent memory. It ingests messages, reasons over them, and produces context about each user or entity so your agent stays coherent across sessions instead of starting fresh every time.
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