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Honcho is an open source memory library with a managed service for building stateful agents. Use it with any model, framework, or architecture. It enables agents to build and maintain state about any entity—users, agents, groups, ideas, and more. And because it’s a continual learning system, it understands entities that change over time. Using Honcho as your memory system will earn your agents higher retention, more trust, and help you build data moats to out-compete incumbents.
Honcho has defined the Pareto Frontier of Agent Memory. Watch the video, check out our evals page, and read the blog post for more detail.

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Why Use Honcho?

Honcho streamlines the agent building process by offering elegant, flexible primitives for managing context. It also reasons over that context to give developers access to far richer insights only accessible through reasoning. Take the following scenario:
  • You find a use case for LLMs and build an agent around it
  • It works well initially but can’t maintain context across sessions
  • You spend weeks engineering a RAG solution that seems to help
  • Then the cycle begins…
    • Users report the agent forgetting things, contradicting itself, or losing context mid-session
    • You build evals to quantify the problem
    • You re-engineer your entire RAG pipeline with better chunking, embeddings, retrieval strategies
    • The problems shift but don’t disappear
    • Repeat
Eventually you realize the issue isn’t engineering—-it’s that you’re not extracting all the latent information from your data. You need to reason exhaustively, handle contradictions, track patterns over time, and maintain coherent state. In other words, you’d need to build Honcho. Break free from this cycle. Honcho is a general solution to context engineering, memory, and statefulness.

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How Honcho Works

Honcho is a memory system that reasons. Read more on the approach here.
Honcho has four storage primitives that work together:
  • Workspaces - Top-level containers that isolate different applications or environments
  • Peers - Any entity that persists but changes over time (users, agents, objects, and more)
  • Sessions - Interaction threads between peers with temporal boundaries
  • Messages - Units of data that trigger reasoning (conversations, events, activity, documents, and more)
When you write messages to Honcho, they’re stored and processed in the background. Custom reasoning models perform formal logical reasoning to generate conclusions about each peer. These conclusions are stored as representations that you can query to provide rich context for your agents.