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feat: re-org, overview, quickstart drafts

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vintro 2025-11-20 09:08:13 -05:00
parent 2945f6bb3c
commit 90cd0b814f
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10 changed files with 9162 additions and 366 deletions
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@ -4,11 +4,11 @@
"": {
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},
},
},
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}
}

116
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View File

@ -26,32 +26,64 @@
"group": "Introduction",
"pages": [
"v2/documentation/introduction/overview",
"v2/documentation/introduction/quickstart",
"v2/documentation/introduction/vibecoding"
]
},
{
"group": "Core Concepts",
"group": "Honcho Context",
"pages": [
"v2/documentation/core-concepts/architecture",
"v2/documentation/core-concepts/features/storing-data",
"v2/documentation/core-concepts/features/dialectic-endpoint",
"v2/documentation/core-concepts/features/get-context",
"v2/documentation/core-concepts/features/search",
"v2/documentation/core-concepts/features/working-rep",
"v2/documentation/core-concepts/features/streaming-response",
"v2/documentation/core-concepts/features/using-filters",
"v2/documentation/core-concepts/features/file-uploads",
"v2/documentation/core-concepts/features/queue-status",
"v2/documentation/core-concepts/features/local-vs-global",
"v2/documentation/core-concepts/configuration",
"v2/documentation/core-concepts/summarizer",
"v2/documentation/core-concepts/glossary"
"v2/documentation/honcho-context/quickstart",
{
"group": "Storage",
"expanded": true,
"pages": [
"v2/documentation/core-concepts/features/storing-data",
"v2/documentation/core-concepts/features/file-uploads"
]
},
{
"group": "Retrieval",
"expanded": true,
"pages": [
"v2/documentation/honcho-context/retrieval/overview",
"v2/documentation/core-concepts/features/using-filters",
"v2/documentation/core-concepts/features/search",
"v2/documentation/core-concepts/features/get-context"
]
}
]
},
{
"group": "Honcho Memory",
"pages": [
"v2/documentation/honcho-memory/quickstart",
{
"group": "Advanced Storage",
"expanded": true,
"pages": [
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"v2/documentation/core-concepts/features/working-rep"
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},
{
"group": "Advanced Retrieval",
"expanded": true,
"pages": [
"v2/documentation/honcho-memory/advanced-retrieval/get-context",
"v2/documentation/core-concepts/summarizer",
"v2/documentation/core-concepts/features/dialectic-endpoint",
"v2/documentation/core-concepts/features/streaming-response"
]
}
]
},
{
"group": "Reference",
"pages": [
"v2/documentation/core-concepts/architecture",
"v2/documentation/core-concepts/configuration",
"v2/documentation/core-concepts/features/local-vs-global",
"v2/documentation/core-concepts/glossary",
"v2/documentation/reference/platform",
"v2/documentation/reference/sdk"
]
@ -59,7 +91,7 @@
]
},
{
"tab": "Spellbooks",
"tab": "Integrations",
"groups": [
{
"group": "Getting Started",
@ -71,7 +103,36 @@
}
]
},
{
"tab": "Cookbooks",
"groups": [
{
"group": "Cookbooks",
"pages": [
"v2/cookbooks/placeholder"
]
}
]
},
{
"tab": "Open Source",
"groups": [
{
"group": "Self-Hosting",
"pages": [
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"v2/contributing/configuration"
]
},
{
"group": "Contributing",
"pages": [
"v2/contributing/guidelines",
"v2/contributing/license"
]
}
]
},
{
"tab": "API Reference",
"groups": [
@ -161,20 +222,6 @@
]
}
]
},
{
"tab": "Contributing",
"groups": [
{
"group": "Contributing",
"pages": [
"v2/contributing/guidelines",
"v2/contributing/self-hosting",
"v2/contributing/configuration",
"v2/contributing/license"
]
}
]
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@ -358,8 +405,9 @@
},
"footer": {
"socials": {
"twitter": "https://twitter.com/plastic_labs",
"github": "https://github.com/plastic-labs",
"twitter": "https://x.com/honchodotdev",
"github": "https://github.com/plastic-labs/honcho",
"discord": "https://discord.gg/honcho",
"linkedin": "https://www.linkedin.com/company/plasticlabs"
}
},

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@ -11,10 +11,10 @@
"author": "",
"license": "ISC",
"dependencies": {
"@mintlify/scraping": "^4.0.284",
"@mintlify/scraping": "^4.0.467",
"honcho-ai": "^0.0.11"
},
"devDependencies": {
"mint": "^4.2.123"
"mint": "^4.2.204"
}
}

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@ -0,0 +1,63 @@
---
title: "Cookbooks"
description: "Practical examples and patterns for using Honcho"
icon: "book"
sidebarTitle: "Overview"
---
# Honcho Cookbooks
Cookbooks provide practical, end-to-end examples of building applications with Honcho. Each cookbook demonstrates real-world patterns and best practices.
## Coming Soon
We're currently developing comprehensive cookbooks covering:
### Application Patterns
- Building a personalized chatbot
- Multi-agent conversation systems
- Context-aware RAG applications
- Long-term memory for autonomous agents
### Advanced Use Cases
- Psychological profiling for adaptive UX
- Social dynamics in multi-peer systems
- Custom theory-of-mind implementations
- Hybrid memory architectures
### Integration Examples
- Integrating with popular frameworks (LangChain, LlamaIndex)
- Combining Honcho with vector databases
- Using webhooks for real-time updates
- Scaling Honcho for production
## Available Resources
While we develop these cookbooks, check out our existing resources:
<CardGroup cols={2}>
<Card title="Integrations" icon="plug" href="/v2/guides/overview">
Framework-specific integration guides
</Card>
<Card title="Core Concepts" icon="brain" href="/v2/documentation/core-concepts/architecture">
Deep dive into Honcho's architecture
</Card>
<Card title="API Reference" icon="code" href="/v2/api-reference/introduction">
Complete API documentation
</Card>
<Card title="Community" icon="discord" href="https://discord.gg/plasticlabs">
Join our Discord for examples and help
</Card>
</CardGroup>
## Contributing
Have a great Honcho use case or pattern to share? We'd love to feature it in our cookbooks!
- Submit cookbook ideas via [GitHub Issues](https://github.com/plastic-labs/honcho/issues)
- Share your implementations in our [Discord community](https://discord.gg/plasticlabs)
- Contribute directly via [Pull Request](https://github.com/plastic-labs/honcho/pulls)
## Stay Updated
Follow our [changelog](/changelog/introduction) and [blog](https://blog.plasticlabs.ai) for announcements about new cookbooks and examples.

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---
title: 'Honcho Context Quickstart'
icon: 'bolt'
sidebarTitle: 'Quickstart'
---
Implement Honcho in just a few steps. No signup required.
<Note>
By default, the SDK uses the demo server hosted at demo.honcho.dev. The demo server is meant for quick experimentation and the data is cleared on a regular basis. Do not use for production applications.
</Note>
## 1. Install the SDK
<CodeGroup>
```bash Python (uv)
uv add honcho-ai
```
```bash Python (pip)
pip install honcho-ai
```
```bash TypeScript (npm)
npm install @honcho-ai/sdk
```
```bash TypeScript (yarn)
yarn add @honcho-ai/sdk
```
```bash TypeScript (pnpm)
pnpm add @honcho-ai/sdk
```
</CodeGroup>
## 2. Initialize the Client
The Honcho client is the main entry point for interacting with Honcho's API. By default, it uses the demo environment and a default workspace.
<CodeGroup>
```python Python
from honcho import Honcho
# Initialize client (uses demo environment and default workspace)
honcho = Honcho()
```
```typescript TypeScript
import { Honcho } from '@honcho-ai/sdk';
// Initialize client (uses demo environment and default workspace)
const honcho = new Honcho({});
```
</CodeGroup>
## 3. Create Peers
Peers represent individual users, AI agents, or any conversational entity in your system:
<CodeGroup>
```python Python
alice = honcho.peer("alice")
bob = honcho.peer("bob")
```
```typescript TypeScript
const alice = await honcho.peer("alice")
const bob = await honcho.peer("bob")
```
</CodeGroup>
## 4. Create a Session
Sessions are independent conversations that can include multiple peers:
<CodeGroup>
```python Python
session = honcho.session("session_1")
session.add_peers([alice, bob])
```
```typescript TypeScript
const session = await honcho.session("session_1")
await session.addPeers([alice, bob])
```
</CodeGroup>
## 5. Add Messages
Add some conversation messages. Honcho automatically learns from these interactions:
<CodeGroup>
```python Python
session.add_messages([
alice.message("Hi Bob, how are you?"),
bob.message("I'm good, thank you!"),
alice.message("What are you doing today after work?"),
bob.message("I'm going to the gym! I've been trying to get back in shape."),
alice.message("That's great! I should probably start exercising too."),
bob.message("You should! I find that evening workouts help me relax."),
])
```
```typescript TypeScript
await session.addMessages([
alice.message("Hi Bob, how are you?"),
bob.message("I'm good, thank you!"),
alice.message("What are you doing today after work?"),
bob.message("I'm going to the gym! I've been trying to get back in shape."),
alice.message("That's great! I should probably start exercising too."),
bob.message("You should! I find that evening workouts help me relax."),
])
```
</CodeGroup>
## 6. Get Context
Curating your peer's context window is remarkably simple. The `get_context` method pulls in a mixture of recent messages and messages relevant to the one just sent.
<CodeGroup>
```python Python
context = session.get_context()
```
```typescript TypeScript
const context = await session.getContext();
```
</CodeGroup>
## 7. Putting it all together
<CodeGroup>
```python Python
import os
from honcho import Honcho
# Create your client
honcho = Honcho()
# Get your Peers
alice = honcho.peer("alice")
bob = honcho.peer("bob")
# Make a Session and add your Peers
session = honcho.session("session_1")
session.add_peers([alice, bob])
# Add messages sent by your Peers
session.add_messages([
alice.message("Hi Bob, how are you?"),
bob.message("I'm good, thank you!"),
alice.message("What are you doing today after work?"),
bob.message("I'm going to the gym! I've been trying to get back in shape."),
alice.message("That's great! I should probably start exercising too."),
bob.message("You should! I find that evening workouts help me relax."),
])
# Get insights about your Peers
response = bob.chat("Tell me about Bob's interests and habits")
print(response)
# Returns rich context like:
# "Bob is health-conscious and has been working on getting back in shape.
# He regularly goes to the gym, particularly in the evenings, and finds
# exercise helps him relax. He's encouraging about fitness and willing
# to share advice about workout routines."
```
```typescript TypeScript
import { Honcho } from '@honcho-ai/sdk';
// Create your client
const honcho = new Honcho({});
// Get your Peers
const alice = await honcho.peer("alice")
const bob = await honcho.peer("bob")
// Make a Session and add your peers
const session = await honcho.session("session_1")
await session.addPeers([alice, bob])
// Add messages sent by your Peers
await session.addMessages([
alice.message("Hi Bob, how are you?"),
bob.message("I'm good, thank you!"),
alice.message("What are you doing today after work?"),
bob.message("I'm going to the gym! I've been trying to get back in shape."),
alice.message("That's great! I should probably start exercising too."),
bob.message("You should! I find that evening workouts help me relax."),
])
// Get insights about your peers
bob.chat("Tell me about Bob's interests and habits").then((response) => {
console.log(response);
// Returns rich context like:
// "Bob is health-conscious and has been working on getting back in shape.
// He regularly goes to the gym, particularly in the evenings, and finds
// exercise helps him relax. He's encouraging about fitness and willing
// to share advice about workout routines."
})
```
</CodeGroup>
## Recap
Honcho just reasoned about a conversation between two people--Alice
and Bob. We:
1. Set up our connection to Honcho.
2. Setup the participants of our conversation--these are called `Peers`.
3. Made a `Session` and added our `Peers`.
4. Sent messages from our `Peers`.
5. Queried Honcho to get insights about one of the `Peers` in the conversation.
As soon as you save a message in Honcho, it will start to reason about it to
pull out insights and develop a profile of the user. This is the default
behavior and can be toggled off via [the configuration](/v2/documentation/core-concepts/configuration).
## Next Steps
<CardGroup cols={3}>
<Card title="Architecture" icon="rocket"
href="/v2/documentation/core-concepts/architecture">
Learn about the data primitives in Honcho and how they work together
</Card>
<Card title="Start Building" icon="brain" href="https://app.honcho.dev">
Sign up for Managed Honcho and get started building agents now.
</Card>
<Card title="Guides" icon="book" href="/v2/guides/overview">
Check out spellbooks to see different examples apps built with Honcho
</Card>
</CardGroup>
---
# SCRATCH
### Production Environment
<CodeGroup>
```python Python
import os
from honcho import Honcho
# Production environment with API key
honcho = Honcho(
api_key=os.environ["HONCHO_API_KEY"],
environment="production",
# Create a workspace, otherwise set to "default"
# workspaceId="your-workspace-id"
)
```
```typescript TypeScript
import { Honcho } from '@honcho-ai/sdk';
// Production environment with API key
const honcho = new Honcho({
apiKey: process.env.HONCHO_API_KEY!,
environment: "production",
// Create a workspace, otherwise set to "default"
// workspace: "your-workspace-id"
});
```
</CodeGroup>

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---
title: "Retrieval Overview"
description: "Understanding how to retrieve and query data from Honcho"
icon: "search"
sidebarTitle: "Overview"
---
# Retrieval in Honcho
Honcho provides multiple approaches for retrieving stored context, each optimized for different use cases.
## Retrieval Methods
### Basic Retrieval
Direct queries for messages and session data:
- **List Messages**: Retrieve messages from a session with pagination
- **Get Message**: Fetch a specific message by ID
- **List Sessions**: Query sessions with filtering options
### Advanced Retrieval
Context-aware retrieval methods:
- **Search**: Hybrid search across messages using semantic and full-text search
- **Get Context**: Intelligent context retrieval optimized for LLM consumption
- **Filters**: Powerful filtering options to narrow down results
## When to Use Each Method
| Method | Use Case | Best For |
|--------|----------|----------|
| List Messages | Sequential access | Displaying conversation history |
| Search | Find specific information | User searches, knowledge retrieval |
| Get Context | Provide context to LLM | Real-time agent responses |
| Filters | Narrow results | Time-based queries, peer-specific data |
## Retrieval Patterns
### Pattern 1: Conversation History
```python
# Get recent messages for display
messages = session.get_messages(limit=50)
```
### Pattern 2: Semantic Search
```python
# Find relevant past discussions
results = workspace.search(query="user preferences", limit=10)
```
### Pattern 3: Context for LLM
```python
# Get optimized context for agent
context = session.get_context(max_tokens=1000)
```
## Next Steps
<CardGroup cols={3}>
<Card title="Filters" icon="filter" href="/v2/documentation/core-concepts/features/using-filters">
Learn about filtering options
</Card>
<Card title="Search" icon="magnifying-glass" href="/v2/documentation/core-concepts/features/search">
Explore search capabilities
</Card>
<Card title="Get Context" icon="brain" href="/v2/documentation/core-concepts/features/get-context">
Understand intelligent context retrieval
</Card>
</CardGroup>

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@ -0,0 +1,162 @@
---
title: "Get Context (Memory-Enhanced)"
description: "Intelligent context retrieval powered by Honcho Memory"
icon: "brain"
sidebarTitle: "Get Context"
---
# Memory-Enhanced Context Retrieval
The Get Context endpoint provides intelligent, memory-enhanced context retrieval that combines raw conversation history with derived insights and representations.
## Overview
Unlike basic message retrieval, memory-enhanced context:
- Includes relevant facts about peers from long-term memory
- Incorporates session summaries for efficient context
- Provides working representations of peer psychology
- Optimizes content for LLM token limits
## Features
### Token-Aware Retrieval
Automatically manages context to fit within your specified token budget:
```python
context = session.get_context(max_tokens=2000)
```
### Multi-Layered Context
Combines multiple information sources:
1. **Recent Messages**: Latest conversation turns
2. **Session Summaries**: Compressed historical context
3. **Peer Representations**: Psychological insights
4. **Peer Cards**: Identity and role information
### Configurable Options
Fine-tune what context is included:
```python
context = session.get_context(
max_tokens=2000,
include_summaries=True,
include_representation=True,
peer_id="peer_123" # Get representation for specific peer
)
```
## Use Cases
### Agent Response Generation
Provide your agent with rich context for personalized responses:
```python
# Get optimized context
context = session.get_context(max_tokens=1500)
# Use in your LLM prompt
response = llm.generate(
messages=[
{"role": "system", "content": context},
{"role": "user", "content": user_message}
]
)
```
### Multi-Peer Conversations
Get context tailored to specific participants:
```python
# Get Alice's perspective
alice_context = session.get_context(peer_id=alice.id)
# Get Bob's perspective
bob_context = session.get_context(peer_id=bob.id)
```
### Dynamic Context Windows
Adjust context size based on task complexity:
```python
# More context for complex tasks
detailed_context = session.get_context(max_tokens=4000)
# Minimal context for simple queries
quick_context = session.get_context(max_tokens=500)
```
## How It Works
The Get Context endpoint uses a sophisticated algorithm to:
1. Estimate token counts for all available context
2. Prioritize recent messages and relevant insights
3. Include summaries when full history exceeds token limit
4. Add peer representations when requested
5. Return optimally structured context
## Best Practices
### Token Budgeting
Leave room in your model's context window:
```python
# For a 8K context model
context = session.get_context(max_tokens=2000) # Leaves room for prompt + response
```
### Representation Updates
Ensure representations are current:
```python
# Check if representation is being generated
status = workspace.get_deriver_status(session_id=session.id)
# Wait for processing if needed
if status.pending > 0:
time.sleep(1) # Or implement proper polling
```
### Caching Strategies
Context can be cached for repeated queries:
```python
# Cache context for multiple agent calls
cached_context = session.get_context(max_tokens=2000)
# Reuse for multiple related queries
for query in user_queries:
response = agent.query(context=cached_context, query=query)
```
## Performance Considerations
- **First Call**: May be slower as representations are generated
- **Subsequent Calls**: Fast retrieval from vector storage
- **Token Counting**: Uses tiktoken for accurate estimation
- **Caching**: Consider caching context for high-frequency scenarios
## Related Features
<CardGroup cols={3}>
<Card title="Basic Get Context" icon="database" href="/v2/documentation/core-concepts/features/get-context">
Learn about basic context retrieval
</Card>
<Card title="Summaries" icon="align-left" href="/v2/documentation/core-concepts/summarizer">
Understand session summarization
</Card>
<Card title="Dialectic API" icon="comments" href="/v2/documentation/core-concepts/features/dialectic-endpoint">
Chat with Honcho for insights
</Card>
</CardGroup>

118
docs/v2/documentation/introduction/quickstart.mdx → docs/v2/documentation/honcho-memory/quickstart.mdx
View File

@ -1,31 +1,13 @@
---
title: 'Quickstart'
description: 'Start building with Honcho in under 5 minutes.'
icon: 'bolt'
---
For production-level use, Honcho offers two powerful ways to leverage ambient personalization: our managed platform and our open source solution. Read further if you want to explore the quickstart demo.
Implement Honcho in just a few steps. No signup required.
<CardGroup cols={2}>
<Card title="Honcho Platform" icon="cloud" href="https://app.honcho.dev">
Fully managed, hassle-free solution with one-click deployment
</Card>
<Card title="Honcho Open Source" icon="github" href="https://github.com/plastic-labs/honcho">
Self-hosted, fully customizable, and open source
</Card>
</CardGroup>
# Getting Started
Have your project use Honcho's ambient personalization capabilities in just a few steps. No signup required!
<Info>
<Note>
By default, the SDK uses the demo server hosted at demo.honcho.dev. The demo server is meant for quick experimentation and the data is cleared on a regular basis. Do not use for production applications.
For production use:
1. Get your API key at [app.honcho.dev/api-keys](https://app.honcho.dev/api-keys)
2. Set `environment="production"` and provide your `api_key`
</Info>
</Note>
## 1. Install the SDK
@ -55,8 +37,6 @@ pnpm add @honcho-ai/sdk
The Honcho client is the main entry point for interacting with Honcho's API. By default, it uses the demo environment and a default workspace.
### Demo Environment (Default)
<CodeGroup>
```python Python
from honcho import Honcho
@ -75,35 +55,6 @@ const honcho = new Honcho({});
```
</CodeGroup>
### Production Environment
<CodeGroup>
```python Python
import os
from honcho import Honcho
# Production environment with API key
honcho = Honcho(
api_key=os.environ["HONCHO_API_KEY"],
environment="production",
# Create a workspace, otherwise set to "default"
# workspaceId="your-workspace-id"
)
```
```typescript TypeScript
import { Honcho } from '@honcho-ai/sdk';
// Production environment with API key
const honcho = new Honcho({
apiKey: process.env.HONCHO_API_KEY!,
environment: "production",
// Create a workspace, otherwise set to "default"
// workspace: "your-workspace-id"
});
```
</CodeGroup>
## 3. Create Peers
Peers represent individual users, AI agents, or any conversational entity in your system:
@ -193,6 +144,10 @@ bob.chat("Tell me about Bob's interests and habits").then((response) => {
```
</CodeGroup>
<Note>
Writing messages to Honcho triggers reasoning by default...
</Note>
## 7. Putting it all together
<CodeGroup>
@ -201,12 +156,7 @@ import os
from honcho import Honcho
# Create your client
honcho = Honcho(
api_key=os.environ["HONCHO_API_KEY"],
environment="production",
# Create a workspace, otherwise set to "default"
# workspaceId="your-workspace-id"
)
honcho = Honcho()
# Get your Peers
alice = honcho.peer("alice")
@ -241,12 +191,7 @@ print(response)
import { Honcho } from '@honcho-ai/sdk';
// Create your client
const honcho = new Honcho({
apiKey: process.env.HONCHO_API_KEY!,
environment: "production",
// Create a workspace, otherwise set to "default"
// workspace: "your-workspace-id"
});
const honcho = new Honcho({});
// Get your Peers
const alice = await honcho.peer("alice")
@ -278,16 +223,16 @@ bob.chat("Tell me about Bob's interests and habits").then((response) => {
```
</CodeGroup>
## What Just Happened?
## Recap
You just got through building a simple conversation between two people, Alice
Honcho just reasoned about a conversation between two people--Alice
and Bob. We:
1. Set up our connection to Honcho.
2. Setup who the participants of our conversation are, these are called `Peers`.
3. Made a `Session` and added our `Peers` to it.
4. Sent messages from our `Peers`
5. Chat with Honcho to get insights about one of the `Peers` in the conversation
2. Setup the participants of our conversation--these are called `Peers`.
3. Made a `Session` and added our `Peers`.
4. Sent messages from our `Peers`.
5. Queried Honcho to get insights about one of the `Peers` in the conversation.
As soon as you save a message in Honcho, it will start to reason about it to
pull out insights and develop a profile of the user. This is the default
@ -307,3 +252,36 @@ behavior and can be toggled off via [the configuration](/v2/documentation/core-c
Check out spellbooks to see different examples apps built with Honcho
</Card>
</CardGroup>
---
# SCRATCH
### Production Environment
<CodeGroup>
```python Python
import os
from honcho import Honcho
# Production environment with API key
honcho = Honcho(
api_key=os.environ["HONCHO_API_KEY"],
environment="production",
# Create a workspace, otherwise set to "default"
# workspaceId="your-workspace-id"
)
```
```typescript TypeScript
import { Honcho } from '@honcho-ai/sdk';
// Production environment with API key
const honcho = new Honcho({
apiKey: process.env.HONCHO_API_KEY!,
environment: "production",
// Create a workspace, otherwise set to "default"
// workspace: "your-workspace-id"
});
```
</CodeGroup>

149
docs/v2/documentation/introduction/overview.mdx
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@ -1,119 +1,86 @@
---
title: "Honcho"
description: "Go beyond memory to agents with actual social intelligence"
icon: "brain"
sidebarTitle: "Overview"
---
Honcho is an AI-native memory library for building agents with
[state-of-the-art](https://blog.plasticlabs.ai/research/Introducing-Neuromancer-XR)
long-term memory.
Honcho gives agents state-of-the-art memory.
Agents using Honcho have perfect recall with a wide variety of tools to traverse
their history and get the exact context they need when they need it.
It's is a flexible memory library available through a managed platform and an open source package for self-hosting.
It then goes beyond basic memory by reasoning about the stored history
to expand the latent information available to your agent. Agents using Honcho
will understand who they are, who they are interacting with, what happened, and
when it happened — all without you having to think about it.
Use it to build
- Highly personalized experiences
- Agents with social cognition
- Agents with rich identity that evolve over time
- Multi-agent systems with complex social dynamics
Honcho can be thought of in two ways: Honcho Context and Honcho Memory.
```python
# Start simple by just adding messages
session.add_messages([alice.message("I learn best with examples")])
## Honcho Context
# Honcho will automatically reason about the message to generate insights about Alice
Building LLM-powered systems is still a massive orchestration problem. Just getting a multi-user application off the ground requires extensive database and infrastructure knowledge. Managing context windows for each respective user and scaling that system is far from trivial.
# Get insights by chatting with the agent
insight = peer.chat("How should I explain this concept?")
# > "This user learns best through concrete examples..."
```
Honcho offers elegant, flexible primitives for initializing, storing, retrieving, and scaling all the LLM interaction points in your AI app or agent. Easy orchestration, plus unlimited storage and unlimited retrieval, all out-of-the-box.
Designed for developers and agents alike:
- **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
- **Automatic Context Management**: Smart conversation summaries to have infinite chats
- **Native multi-agent support**: Sessions can natively have as many participants as you need
- **Agent-first interfaces**: MCP connections and APIs designed for agents to consume and use as tools
- **Provider Agnostic**: Works with any LLM or Agent Framework
Don't waste time redundantly building complex systems. Focus on what makes your product unique.
## How It Works
---
<Accordion title="High Level Diagram" defaultOpen="true">
<Frame>
<img src="/images/overview/honcho-overview.svg" alt="High Level Honcho Diagram" />
</Frame>
</Accordion>
(scratch)
At a high level Honcho works very simply:
Honcho solves context engineering so you can spend more time on your product.
1. Store messages sent by users and agents in Honcho
2. Honcho reasons about the messages to generate insights about each entity in
the system
3. At runtime your agents can leverage insights from Honcho to get the exact
context they need
Don't waste time redundantly building complex systems. Get back to what makes your agent unique.
There are several API endpoints to leverage the memory & insights in Honcho.
---
### Get Context
## Honcho Memory
This is the easiest way to leverage Honcho. simply call get context and get the
most relevant information for your conversation. This endpoint is highly
customizable so you can specify parameters such as:
AI users want tasks completed in line with their evolving preferences. They want agents who can learn about them continuously over time. So, agents need as complete a picture of each user as possible on-demand.
- A number of tokens you want
- An option to include summaries of the conversation
- An option to get a profile of a specific user (Peer Card & Representation)
Honcho is built around proprietary reasoning models that ensure the right context is always available. They create modular reasoning traces and compose with them to uncover new insights. Scaffolded reasoning is uniquely traversable, enabling fast context assembly, complete with citation, on-the-fly.
### Search
This endpoint lets you search across Honcho for relevant messages using a
hybrid search strategy that combines full-text and semantic search.
You can optionally scope the endpoint to a specific workspace, peer, or session.
### Working Representation
This endpoint gives you a snapshot of a user or what we call a
**Representation**. Essentially, a list of explicit and deductive facts about
the user that are relevant to the current conversation.
Plug this into your prompt to get a quick overview of the user.
### Dialectic API
This endpoint lets you chat with Honcho about any entity in your system. Honcho
will leverage what it has remembered and learned about the entity to provide in-context actionable insights.
This is especially helpful when you want your agent to back-channel with Honcho to
change its behavior at runtime.
Example Queries:
- "What's the best way to explain technical concepts to this user?"
- "Is this user more task-oriented or relationship-oriented?"
- "What time of day is this user most engaged?"
- "How does this user prefer to receive feedback?"
- "What are this user's core values based on our conversations?"
All this happens ambiently—Honcho stores and reasons over everything you write to it so it can recall and synthesize anything later.
## Getting Started
## Key Features
Ready to integrate Honcho into your application?
<CardGroup cols={3}>
<Card title="Flexible Primitives" icon="puzzle-piece">
Supports any user model: user-assistant, multi-agent workflows, group chats, agents and sub-agents.
</Card>
<Card title="Modular Architecture" icon="layer-group">
Easy to scale vertically and horizontally.
</Card>
<Card title="No Data Structure Management" icon="wand-magic-sparkles">
Throw data into Honcho as messages—it figures out the structure and adapts to changes.
</Card>
<Card title="Directional Relationships" icon="arrows-left-right">
Track what Alice thinks of Bob, not just Alice's preferences or Bob's preferences.
</Card>
<Card title="Self-Healing" icon="heart-pulse">
If Honcho gets something wrong, it learns and corrects through continued use.
</Card>
<Card title="Unopinionated Data" icon="scale-balanced">
Rule-based reasoning toward certain conclusions.
</Card>
<Card title="Traversable Storage" icon="magnifying-glass">
Query the exact relevant context you need.
</Card>
<Card title="Any Stack" icon="cubes">
Model-agnostic, framework-agnostic, composable with any stack—never forces you into proprietary tools.
</Card>
<Card title="Any Deployment" icon="cloud">
Whether you've built a full platform or run agents somewhere else—Honcho works with your architecture.
</Card>
</CardGroup>
<CardGroup cols={2}> <Card title="Quickstart Guide" icon="rocket"
href="/v2/documentation/introduction/quickstart"> Get up and running with
Honcho in minutes </Card> <Card title="Core Concepts" icon="brain"
href="/v2/documentation/core-concepts/architecture"> Understand Honcho's
fundamental concepts </Card> </CardGroup>
## Community & Support
## Solve Memory
- **GitHub**: [plastic-labs/honcho](https://github.com/plastic-labs/honcho)
- **Discord**: [Join our community](http://discord.gg/plasticlabs)
- **Issues**: Report bugs and request features on GitHub
In Honcho, memory is a reasoning task. Beyond static storage and retrieval or naive fact extraction, Honcho arrives at conclusions only accessible via rigorous reasoning.
When your agent needs user context, Honcho returns rich results, drawing on its self-improving body of composable reasoning and synthesizing exactly what's needed.
Memory is table stakes--it's the system that constructs context to make decisions about how to continue. Honcho is a state-of-the-art memory solution built in an AI-native way. Build your agents with a reasoning system that uniquely leverages LLMs to solve memory.
<CardGroup cols={2}> <Card title="Honcho Context Quickstart" icon="rocket"
href="/v2/documentation/honcho-context/quickstart"> Get up and running with
Honcho in minutes </Card> <Card title="Honcho Memory Quickstart" icon="brain"
href="/v2/documentation/honcho-memory/architecture"> Leverage Honcho's Memory in minutes. </Card> </CardGroup>