218 lines
6.1 KiB
Plaintext
218 lines
6.1 KiB
Plaintext
---
|
|
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.
|
|
|
|
<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>
|
|
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>
|
|
|
|
## 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.
|
|
|
|
### Demo Environment (Default)
|
|
|
|
<CodeGroup>
|
|
```python Python
|
|
from honcho import Honcho
|
|
|
|
# Initialize client (uses demo environment and default workspace)
|
|
client = Honcho()
|
|
|
|
```
|
|
|
|
```typescript TypeScript
|
|
import { Honcho } from '@honcho-ai/sdk';
|
|
|
|
// Initialize client (uses demo environment and default workspace)
|
|
const client = new Honcho({});
|
|
|
|
```
|
|
</CodeGroup>
|
|
|
|
### Production Environment
|
|
|
|
<CodeGroup>
|
|
```python Python
|
|
import os
|
|
from honcho import Honcho
|
|
|
|
# Production environment with API key
|
|
client = 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 client = 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:
|
|
|
|
<CodeGroup>
|
|
```python Python
|
|
alice = client.peer("alice")
|
|
bob = client.peer("bob")
|
|
```
|
|
|
|
```typescript TypeScript
|
|
const alice = await client.peer("alice")
|
|
const bob = await 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 = await client.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. 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
|
|
(async () => {
|
|
// 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](../reference/platform).
|
|
- For detailed API reference, check out our [SDK documentation](../reference/sdk).
|
|
- For more examples, explore our [guides](../guides/overview).
|
|
|
|
---
|