honcho/docs/v2/documentation/introduction/quickstart.mdx

---
title: 'Quickstart'
description: 'Start building with Honcho in under 5 minutes.'
icon: 'bolt'
---

<Tip>📢 **Announcing Honcho Platform**: We've raised $5.35M pre-seed from Variant, White Star Capital & Betaworks to build the personal identity layer for AI!</Tip>

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.

<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!

<Note>
This quickstart uses the demo server at https://demo.honcho.dev
</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

<CodeGroup>
```python Python
from honcho import Honcho

# Initialize client (using the demo environment)
client = Honcho()
```

```typescript TypeScript
import { Honcho } from '@honcho-ai/sdk';

// Initialize client (using the demo environment)
const client = new Honcho();
```
</CodeGroup>

## 3. Create Peers

Peers represent individual users, AI agents, or any conversational entity in your system:

<CodeGroup>
```python Python
alice = client.peer("alice")
bob = client.peer("bob")
```

```typescript TypeScript
const alice = client.peer("alice")
const bob = client.peer("bob")
```
</CodeGroup>

## 4. Create a Session

Sessions are independent conversations that can include multiple peers:

<CodeGroup>
```python Python
session = client.session("session_1")
session.add_peers([alice, bob])
```

```typescript TypeScript
const session = client.session("session_1")
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
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. Query for Insights

Now ask Honcho what it's learned - this is where the magic happens:

<CodeGroup>
```python Python
# Ask what Bob is like
response = alice.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
// Ask what Bob is like  
const response = await alice.chat("Tell me about Bob's interests and habits");
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>

## What Just Happened?

Honcho automatically built rich psychological profiles from just a few messages:

- **Theory of Mind Processing**: Understanding personality, preferences, and patterns
- **Ambient Learning**: No surveys or explicit training - just natural conversation
- **Rich Context**: Far more detailed than simple conversation history

The response isn't just retrieving stored text - it's synthesizing insights about Bob's personality, habits, and communication style.

## Next Steps

This covers the core concepts: **peers**, **sessions**, **messages**, and **dialectic queries**. 

- For production use, [sign up for the managed platform](https://app.honcho.dev) or get an [overview here](/v2/documentation/platform/overview).
- For detailed API reference, check out our [SDK documentation](/v2/documentation/platform/sdk).
- For more examples, explore our [guides](/v2/guides).

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