fix: docs introduction section
This commit is contained in:
Vineeth Voruganti
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---
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title: 'Configure Peers'
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title: 'Configure Reasoning'
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description: 'Customizing how Honcho handles peers and sessions'
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icon: 'wrench'
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---
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@ -10,9 +10,9 @@ Honcho is an AI-native memory library for building agents with
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long-term memory.
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Agents using Honcho have perfect recall with a wide variety of tools to traverse
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the history of an agent and get the exact context they need when they need it.
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their history and get the exact context they need when they need it.
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It then goes beyond basic memory by reasoning about the stored messages
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It then goes beyond basic memory by reasoning about the stored history
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to expand the latent information available to your agent. Agents using Honcho
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will understand who they are, who they are interacting with, what happened, and
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when it happened — all without you having to think about it.
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@ -26,11 +26,10 @@ Use it to build
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```python
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# Start simple - just add messages
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# Start simple by just adding messages
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session.add_messages([alice.message("I learn best with examples")])
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# Honcho will automatically reason about the message to generate insights about
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# Alice
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# Honcho will automatically reason about the message to generate insights about Alice
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# Get insights by chatting with the agent
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insight = peer.chat("How should I explain this concept?")
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@ -38,22 +37,20 @@ insight = peer.chat("How should I explain this concept?")
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```
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Designed for developers and agents alike:
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- **Natural Language Queries**: Chat with Honcho in natural language via the [Dialectic API](../core-concepts/architecture#dialectic-api) and let agents backchannel
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- **Automatic Context Management**: Smart summarization that respects token limits
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- **Native multi-agent support**: Break out of User/Assistant Paradigms and build complex multi-agent systems
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- **Natural Language Queries**: Chat with Honcho in natural language via the [Dialectic API](../core-concepts/architecture#dialectic-api) to get insights about your users and agents
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- **Automatic Context Management**: Smart conversation summaries to have infinite chates
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- **Native multi-agent support**: Sessions can natively have as many participants as you need
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- **Agent-first interfaces**: MCP connections and APIs designed for agents to consume and use as tools
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- **Provider Agnostic**: Works with any LLM or Agent Framework
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## How It Works
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<Accordion title="High Level Diagram">
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<Frame>
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<img src="/images/overview/honcho-overview.svg" alt="High Level Honcho Diagram" />
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</Frame>
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<Frame>
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<img src="/images/overview/honcho-overview.svg" alt="High Level Honcho Diagram" />
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</Frame>
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</Accordion>
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At a high level Honcho works very simply:
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1. Store messages sent by users and agents in Honcho
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@ -68,7 +65,7 @@ There are several API endpoints to leverage the memory & insights in Honcho.
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This is the easiest way to leverage Honcho. simply call get context and get the
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most relevant information for your conversation. This endpoint is highly
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customizable so you can specify
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customizable so you can specify parameters such as:
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- A number of tokens you want
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- An option to include summaries of the conversation
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@ -76,8 +73,8 @@ customizable so you can specify
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### Search
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This endpoint lets you search across Honcho for relevant messages either using a
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hybrid search strategy that combines text search and cosine similarity.
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This endpoint lets you search across Honcho for relevant messages using a
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hybrid search strategy that combines full-text and semantic search.
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You can optionally scope the endpoint to a specific workspace, peer, or session.
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@ -97,7 +94,7 @@ will leverage what it has remembered and learned about the entity to provide in-
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This comes in handy when you want your agent to back-channel with Honcho to
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change it's behavior at runtime.
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Example Queries
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Example Queries:
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- "What's the best way to explain technical concepts to this user?"
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- "Is this user more task-oriented or relationship-oriented?"
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- "What time of day is this user most engaged?"
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@ -112,7 +109,7 @@ Ready to integrate Honcho into your application?
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<CardGroup cols={2}> <Card title="Quickstart Guide" icon="rocket"
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href="/v2/documentation/introduction/quickstart"> Get up and running with
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Honcho in minutes </Card> <Card title="Core Concepts" icon="brain"
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href="/v2/documentation/core-concepts/glossary"> Understand Honcho's
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href="/v2/documentation/core-concepts/architecture"> Understand Honcho's
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fundamental concepts </Card> </CardGroup>
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## Community & Support
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@ -62,7 +62,7 @@ The Honcho client is the main entry point for interacting with Honcho's API. By
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from honcho import Honcho
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# Initialize client (uses demo environment and default workspace)
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client = Honcho()
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honcho = Honcho()
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```
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@ -70,7 +70,7 @@ client = Honcho()
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import { Honcho } from '@honcho-ai/sdk';
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// Initialize client (uses demo environment and default workspace)
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const client = new Honcho({});
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const honcho = new Honcho({});
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```
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</CodeGroup>
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@ -83,7 +83,7 @@ import os
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from honcho import Honcho
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# Production environment with API key
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client = Honcho(
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honcho = Honcho(
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api_key=os.environ["HONCHO_API_KEY"],
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environment="production",
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# Create a workspace, otherwise set to "default"
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@ -95,7 +95,7 @@ client = Honcho(
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import { Honcho } from '@honcho-ai/sdk';
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// Production environment with API key
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const client = new Honcho({
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const honcho = new Honcho({
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apiKey: process.env.HONCHO_API_KEY!,
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environment: "production",
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// Create a workspace, otherwise set to "default"
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@ -110,13 +110,13 @@ Peers represent individual users, AI agents, or any conversational entity in you
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<CodeGroup>
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```python Python
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alice = client.peer("alice")
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bob = client.peer("bob")
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alice = honcho.peer("alice")
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bob = honcho.peer("bob")
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```
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```typescript TypeScript
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const alice = await client.peer("alice")
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const bob = await client.peer("bob")
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const alice = await honcho.peer("alice")
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const bob = await honcho.peer("bob")
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```
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</CodeGroup>
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@ -126,12 +126,12 @@ Sessions are independent conversations that can include multiple peers:
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<CodeGroup>
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```python Python
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session = client.session("session_1")
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session = honcho.session("session_1")
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session.add_peers([alice, bob])
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```
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```typescript TypeScript
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const session = await client.session("session_1")
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const session = await honcho.session("session_1")
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await session.addPeers([alice, bob])
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```
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</CodeGroup>
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@ -171,7 +171,7 @@ Now ask Honcho what it's learned - this is where the magic happens:
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<CodeGroup>
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```python Python
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# Ask what Bob is like
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response = alice.chat("Tell me about Bob's interests and habits")
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response = bob.chat("Tell me about Bob's interests and habits")
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print(response)
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# Returns rich context like:
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@ -182,36 +182,128 @@ print(response)
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```
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```typescript TypeScript
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(async () => {
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// Ask what Bob is like
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const response = await alice.chat("Tell me about Bob's interests and habits");
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console.log(response);
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bob.chat("Tell me about Bob's interests and habits").then((response) => {
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console.log(response);
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// Returns rich context like:
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// "Bob is health-conscious and has been working on getting back in shape.
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// He regularly goes to the gym, particularly in the evenings, and finds
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// exercise helps him relax. He's encouraging about fitness and willing
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// to share advice about workout routines."
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})
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```
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</CodeGroup>
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// Returns rich context like:
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// "Bob is health-conscious and has been working on getting back in shape.
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// He regularly goes to the gym, particularly in the evenings, and finds
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// exercise helps him relax. He's encouraging about fitness and willing
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// to share advice about workout routines."
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})();
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## 7. Putting it all together
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<CodeGroup>
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```python Python
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import os
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from honcho import Honcho
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# Create your client
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honcho = Honcho(
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api_key=os.environ["HONCHO_API_KEY"],
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environment="production",
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# Create a workspace, otherwise set to "default"
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# workspaceId="your-workspace-id"
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)
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# Get your Peers
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alice = honcho.peer("alice")
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bob = honcho.peer("bob")
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# Make a Session and add your Peers
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session = honcho.session("session_1")
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session.add_peers([alice, bob])
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# Add messages sent by your Peers
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session.add_messages([
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alice.message("Hi Bob, how are you?"),
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bob.message("I'm good, thank you!"),
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alice.message("What are you doing today after work?"),
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bob.message("I'm going to the gym! I've been trying to get back in shape."),
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alice.message("That's great! I should probably start exercising too."),
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bob.message("You should! I find that evening workouts help me relax."),
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])
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# Get insights about your Peers
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response = bob.chat("Tell me about Bob's interests and habits")
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print(response)
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# Returns rich context like:
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# "Bob is health-conscious and has been working on getting back in shape.
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# He regularly goes to the gym, particularly in the evenings, and finds
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# exercise helps him relax. He's encouraging about fitness and willing
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# to share advice about workout routines."
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```
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```typescript TypeScript
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import { Honcho } from '@honcho-ai/sdk';
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// Create your client
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const honcho = new Honcho({
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apiKey: process.env.HONCHO_API_KEY!,
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environment: "production",
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// Create a workspace, otherwise set to "default"
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// workspace: "your-workspace-id"
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});
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// Get your Peers
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const alice = await client.peer("alice")
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const bob = await client.peer("bob")
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// Make a Session and add your peers
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const session = await client.session("session_1")
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await session.addPeers([alice, bob])
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// Add messages sent by your Peers
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await session.addMessages([
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alice.message("Hi Bob, how are you?"),
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bob.message("I'm good, thank you!"),
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alice.message("What are you doing today after work?"),
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bob.message("I'm going to the gym! I've been trying to get back in shape."),
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alice.message("That's great! I should probably start exercising too."),
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bob.message("You should! I find that evening workouts help me relax."),
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])
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// Get insights about your peers
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bob.chat("Tell me about Bob's interests and habits").then((response) => {
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console.log(response);
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// Returns rich context like:
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// "Bob is health-conscious and has been working on getting back in shape.
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// He regularly goes to the gym, particularly in the evenings, and finds
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// exercise helps him relax. He's encouraging about fitness and willing
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// to share advice about workout routines."
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})
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```
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</CodeGroup>
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## What Just Happened?
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Honcho automatically built rich psychological profiles from just a few messages:
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You just got through building a simple conversation between two people, Alice
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and Bob. We:
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- **Theory of Mind Processing**: Understanding personality, preferences, and patterns
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- **Ambient Learning**: No surveys or explicit training - just natural conversation
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- **Rich Context**: Far more detailed than simple conversation history
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1. Set up our connection to Honcho.
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2. Setup who the participants of our conversation are, these are called `Peers`.
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3. Made a `Session` and added our `Peers` to it.
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4. Sent messages from our `Peers`
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5. Chat with Honcho to get insights about one of the `Peers` in the conversation
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The response isn't just retrieving stored text - it's synthesizing insights about Bob's personality, habits, and communication style.
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As soon as you save a message in Honcho, it will start to reason about it to
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pull out insights and develop a profile of the user. This is the default
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behavior and can be toggled off via [the configuration](/v2/documentation/core-concepts/configuration).
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## Next Steps
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This covers the core concepts: **peers**, **sessions**, **messages**, and **dialectic queries**.
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- For production use, [sign up for the managed platform](https://app.honcho.dev) or get an [overview here](../reference/platform).
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- For detailed API reference, check out our [SDK documentation](../reference/sdk).
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- For more examples, explore our [guides](../guides/overview).
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---
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<CardGroup cols={3}>
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<Card title="Architecture" icon="rocket"
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href="/v2/documentation/core-concepts/architecture">
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Learn about the data primitives in Honcho and how they work together
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</Card>
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<Card title="Start Building" icon="brain" href="https://app.honcho.dev">
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Sign up for Managed Honcho and get started building agents now.
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</Card>
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<Card title="Guides" icon="book" href="/v2/documentation/guides/overview">
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Check out spellbooks to see different examples apps built with Honcho
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</Card>
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</CardGroup>
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@ -5,7 +5,17 @@ description: "Universal starter prompt for building with Honcho"
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sidebarTitle: 'Vibecoding Setup'
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---
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Copy this prompt into Cursor, Claude, or any AI coding assistant to start building with Honcho.
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These docs are designed to be easily consumable for LLMs. each page has a button
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the lets you copy the page as markdown or put directly into ChatGPT or Claude.
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Additionally, we follow the llms.txt standard. There is both an llms.txt and
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llms-full.txt available.
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- [llms.txt](/llms.txt)
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- [llms-full.txt](/llms-full.txt)
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Additionally, we provide a starter prompt to paste into a coding assistant to
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quickly get started building with Honcho.
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## 🚀 Universal Starter Prompt
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