Welcome to our quick, "explain it like I'm 5" guide to Honcho!

We’ll keep it simple, covering what Honcho is, why we built it, how to use it, and where the product is going. But throughout, we’ll link to places you can dive deeper.

What Is Honcho?

Honcho is a personalization platform for large language model (LLM) applications built by Plastic Labs.

It’s software infrastructure that lets AI apps “get to know” their users, resulting in delightful experiences and optimized time to value.

We’ll have direct consumer experiences in the future, but today, the product is for application developers. It allows them to reduce overhead and enhance their machine learning pipeline.

Right now, Honcho is in private beta, that means integrating our hosted version requires permission and onboarding1. You can sign-up here.

In its current form, Honcho has three core components:

  1. Storage - managing each user’s data & inference about each user
  2. Insights - processing user data with our proprietary AI models
  3. Retrieval - surfacing user data to personalize user experience (UX)

If you’ve heard of Retrieval Augmented Generation (RAG), this might sound familiar. But Honcho is doing much more than simple RAG.

Behind the scenes, Honcho learns about users as people—richly modeling identity. It seeks to understand their beliefs, hopes, dreams, history, interests, and preferences.

It then acts as an oracle to each user, allowing apps to ask for any personal context they need to improve UX and giving them access to a social cognition layer.

Why We Built Honcho

Plastic Labs was founded as an edtech company. The original mission was to build an AI tutor that could reason like the best human instructors. We quickly found the key limitation was data not on the subject matter, but on the student. To overcome it, the tutor needed a way to get to know each of its students deeply.

Honcho was born by running up against this challenge, building technology to solve it, and realizing all AI applications are going to need the same solutions. The promise of generative AI isn’t one-size-fits-all products, but bespoke experiences in each moment for each user. The same limitation emerges—how well do you know your user?

So we believe Honcho will be a critical, table-stakes part of the AI app development stack.

Why? Because users will want their AI experiences to be personalized and app developers shouldn’t be redundantly solving that problem.

But it’s not intuitive for a few reasons:

  • AI app builders are still focused on just getting general tasks to work
  • LLMs’ potential to personalize is still under-appreciated
  • Historic examples of personalized apps usually just leverage our activity & engagement data
  • Those examples tend to target only base user desire, lead to addictive behavior, & have poor privacy records

Still, when interacting with an AI app, there’s a sense that it should be getting to know us. In fact, we’re often surprised when we realize it’s not learning about us over time. And probably annoyed at having to start over.

Think about personalization here as more like the experience of close human companionship or white glove services than the attention hacking mechanisms of TikTok. There’s enormous potenial for more positive-sum use of user data and for aligning AI applications more closely with user needs and preferences2.

How to Use Honcho

Honcho is first and foremost a storage framework. Think of it like an open source version of the OpenAI Assistants API. User sessions store both user and AI generated messages as well as any intermediate inferences you might want to store as metamessages:

user_input = "Here's a message!"
ai_response = "I'm a helpful AI assistant!"
 
session.create_message(is_user=True, content=user_input)
session.create_message(is_user=False, content=ai_response)

But what about vectorDBs? Don’t worry, Honcho has you covered there too. You can embed data and store them as documents in per-user vector DBs called collections:

collection.create_document(content="The user is interested in AI")

Using Honcho as a storage mechanism allows you to retrieve rich insights via the user profiles it’s building and managing on the backend. Your application’s LLM can access theory-of-mind inference over those profiles via the dialectic API.

It’s simple: just query in natural language using the session.chat() method:

session.chat("What are the user's interests?")

There are a ton of ways to use Honcho, this primer only scratches the surface3.

What’s Next for Honcho?

Beyond improving our internal AI models so they can get to know users as richly as possible, we see three natural extensions in Honcho’s future:

  1. Monitoring & Evaluation - developer tools to understand & assess the impact of personalization + machine learning tools to build personalized datasets
  2. User-Facing Controls - chat with your Honcho to direct how it manages & shares data + authenticate with Honcho to sign-in to AI apps
  3. Honcho Application Ecosystem - a network of apps contributing to & sharing Honcho data, user-owned & stored in confidential environments

And in just a few weeks, we’ll be launching a demo platform where anyone can interact with (& eventually build) Honcho powered apps.

Join the Beta

Sign-up for the private beta and start building personalized experiences.

Join Discord, introduce yourself, and tell us what you’re working on.

Visit our open-source repo and get your hands dirty.

🫡

Footnotes

  1. There’s also an open source repo for Honcho, so you can self-host a basic version—join our Discord for support.

  2. If you want to go deeper on the philosophical or machine learning side, take some time to explore the rest of the blog.

  3. To get further into the technical weeds, head over to our docs.