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Honcho is a user context management system for AI-powered applications. The storage concepts are inspired by, but not a 1:1 mapping of, the OpenAI Assistants API. The insights concepts are inspired by cognitive science, philosophy, and machine learning. Honcho is open source. We believe trust and transparency are vital for developing AI technology. We’re also focused on using and supporting existing tools rather than developing from scratch. We focus on flexible, user-centric storage primitives to promote community exploration of novel memory frameworks and the usage of the Dialectic API to support them. Language models are highly capable of modeling human psychology. By building a data management framework that is user-centric, we aim to address not only practical application development issues (like scaling, statefulness, etc.) but also kickstart exploration of the design space of what’s possible given access to rich user models. You can read more about Honcho’s origin, inspiration and philosophy on our blog.

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Core Primitives

Using Honcho has the following flow:
  1. Initialize your Honcho instance and App
  2. Create a User
  3. Create a Session for a User.
  4. Create a Collection for a User
  5. Add Messages to a User’s Session.
  6. Add Metamessages to a User (optional links to Session, Message)
  7. Add Documents to a User’s Collection

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Apps

An App is the highest-level primitive in Honcho. It is the scope that all of your Users are bound to.

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Users

The User object is the main interface for managing a User’s context. With it you can interface with the User’s Sessions and Collectionss directly.

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Sessions

The Session object is useful for organizing your interactions with Users. Different Users can have different sessions enabling you to neatly segment user context. It also accepts a location_id parameter which can specifically denote where users’ sessions are taking place.

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Messages

Sessions are made up of Message objects. You can append them to sessions. This is pretty straightforward.

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Metamessages

Success in LLM applications is dependent on elegant context management, so we provide a Metamessage object for flexible context storage and construction. Each Metamessage is tied to a User object via the required user_id argument. Keeping this separate from the core user-assistant message history ensures the insights service running ambiently is doing so on authentic ground truth We’ve found this particularly useful for storing intermediate inferences, constructing very specific chat histories, and more. Metamessages can optionally be attached to sessions and/or messages, so constructing historical context for inference is as easy as possible.

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Collections

Collections are used to organize information about the User. These can be thought of as stores for more global data about the User that spans sessions while Metamessages are local to a session and the message they are linked to.

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Documents

Documents are the individual facts that are stored in the Collection. They are stored as vector embeddings to allow for a RAG like interface. Using honcho a developer can query a collection of documents using methods like cosine similarity search

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Conclusion

Too often we hear developers enjoying a certain framework for building LLM-powered applications only to see their codebase reach a level of complexity that hits the limits of said framework. It ultimately gets abandoned and developers implement their own solutions that without a doubt increase overhead and maintenance. Our goal with Honcho is to provide a simple and flexible storage framework accompanied by a smooth developer experience to ease pains building the cumbersome parts of LLM applications. We hope this will allow developers more freedom to explore exciting, yet-to-be-discovered areas!