honcho/docs/v2/documentation/reference/sdk.mdx

---
title: 'SDK Reference'
description: 'Complete SDK documentation and examples for Python and TypeScript'
icon: 'code'
---

The Honcho SDKs provide ergonomic interfaces for building agentic AI applications with Honcho in Python and TypeScript/JavaScript.

## Installation

<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>

## Quickstart

<Info>
Without configuration, the SDK defaults to the demo server. 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>

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

# Initialize client (using the default workspace)
honcho = Honcho()

# Create peers
alice = honcho.peer("alice")
assistant = honcho.peer("assistant")

# Create a session for conversation
session = honcho.session("conversation-1")

# Add messages to conversation
session.add_messages([
    alice.message("What's the weather like today?"),
    assistant.message("It's sunny and 75°F outside!")
])

# Query peer representations in natural language
response = alice.chat("What did the assistant tell this user about the weather?")

# Get conversation context for LLM completions
context = session.get_context()
openai_messages = context.to_openai(assistant=assistant)
```

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

// Initialize client (using the default workspace)
const honcho = new Honcho({});

// Create peers
const alice = await honcho.peer("alice");
const assistant = await honcho.peer("assistant");

// Create a session for conversation
const session = await honcho.session("conversation-1");

// Add messages to conversation
await session.addMessages([
  alice.message("What's the weather like today?"),
  assistant.message("It's sunny and 75°F outside!")
]);

// Query peer representations in natural language
const response = await alice.chat("What did the assistant tell this user about the weather?");

// Get conversation context for LLM completions
const context = await session.getContext();
const openaiMessages = context.toOpenAI(assistant);
```
</CodeGroup>

## Core Concepts

### Peers and Representations

<Info>
**Representations** are how Honcho models what peers know. Each peer has a **global representation** (everything they know across all sessions) and **local representations** (what other specific peers know about them, scoped by session or globally).
</Info>

<CodeGroup>
```python Python
# Query alice's global knowledge
response = alice.chat("What does the user know about weather?")

# Query what alice knows about the assistant (local representation)
response = alice.chat("What does the user know about the assistant?", target=assistant)

# Query scoped to a specific session
response = alice.chat("What happened in our conversation?", session=session.id)
```

```typescript TypeScript
// Query alice's global knowledge
const response = await alice.chat("What does the user know about weather?");

// Query what alice knows about the assistant (local representation)
const targetResponse = await alice.chat("What does the user know about the assistant?", {
  target: assistant
});

// Query scoped to a specific session
const sessionResponse = await alice.chat("What happened in our conversation?", {
  sessionId: session.id
});
```
</CodeGroup>

## Core Classes

### Honcho Client

The main entry point for workspace operations:

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

# Basic initialization (uses environment variables)
honcho = Honcho(workspace_id="my-app-name")

# Full configuration
honcho = Honcho(
    workspace_id="my-app-name",
    api_key="my-api-key",
    environment="production",  # or "local", "demo"
    base_url="https://api.honcho.dev",
    timeout=30.0,
    max_retries=3
)
```

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

// Basic initialization (uses environment variables)
const honcho = new Honcho({
  workspaceId: "my-app-name"
});

// Full configuration
const honcho = new Honcho({
  workspaceId: "my-app-name",
  apiKey: "my-api-key",
  environment: "production",  // or "local", "demo"
  baseURL: "https://api.honcho.dev",
  timeout: 30000,
  maxRetries: 3,
  defaultHeaders: { "X-Custom-Header": "value" },
  defaultQuery: { "param": "value" }
});
```
</CodeGroup>

**Environment Variables:**
- `HONCHO_API_KEY` - API key for authentication
- `HONCHO_BASE_URL` - Base URL for the Honcho API
- `HONCHO_WORKSPACE_ID` - Default workspace ID

**Key Methods:**

<CodeGroup>
```python Python
# Get or create a peer
peer = honcho.peer(id)

# Get or create a session
session = honcho.session(id)

# List all peers in workspace
peers = honcho.get_peers()

# List all sessions in workspace
sessions = honcho.get_sessions()

# Search across all content in workspace
results = honcho.search(query)

# Workspace metadata management
metadata = honcho.get_metadata()
honcho.set_metadata(dict)

# Get list of all workspace IDs
workspaces = honcho.get_workspaces()
```

```typescript TypeScript
// Get or create a peer
const peer = await honcho.peer(id);

// Get or create a session
const session = await honcho.session(id);

// List all peers in workspace (returns Page<Peer>)
const peers = await honcho.getPeers();

// List all sessions in workspace (returns Page<Session>)
const sessions = await honcho.getSessions();

// Search across all content in workspace (returns Page<any>)
const results = await honcho.search(query);

// Workspace metadata management
const metadata = await honcho.getMetadata();
await honcho.setMetadata(metadata);

// Get list of all workspace IDs
const workspaces = await honcho.getWorkspaces();
```
</CodeGroup>

<Info>
Peer and session creation is **lazy** - no API calls are made until you actually use the peer or session.
</Info>

### Peer

Represents an entity that can participate in conversations:

<CodeGroup>
```python Python
# Create peers (lazy creation - no API call yet)
alice = honcho.peer("alice")
assistant = honcho.peer("assistant")

# Create with immediate configuration
# This will make an API call to create the peer with the custom configuration and/or metadata
alice = honcho.peer("bob", config={"role": "user", "active": True}, metadata={"location": "NYC", "role": "developer"})

# Peer properties
print(f"Peer ID: {alice.id}")
print(f"Workspace: {alice.workspace_id}")

# Chat with peer's representations (supports streaming)
response = alice.chat("What did I have for breakfast?")
response = alice.chat("What do I know about Bob?", target="bob")
response = alice.chat("What happened in session-1?", session="session-1")

# Add content to a session with a peer
session = honcho.session("session-1")
session.add_messages([
  alice.message("I love Python programming"),
  alice.message("Today I learned about async programming"),
  alice.message("I prefer functional programming patterns")
])

# Get peer's sessions
sessions = alice.get_sessions()

# Search peer's messages
results = alice.search("programming")

# Metadata management
metadata = alice.get_metadata()
metadata["location"] = "Paris"
alice.set_metadata(metadata)

# Get peer context (representation + peer card in one call)
context = alice.get_context()
context = alice.get_context(target="bob")  # What alice knows about bob

# Get working representation with semantic search
rep = alice.working_rep(search_query="preferences", search_top_k=10)

# Access observations
self_observations = alice.observations.list()  # Self-observations
bob_observations = alice.observations_of("bob").list()  # Observations of bob
```

```typescript TypeScript
// Create peers (returns Promise<Peer>)
const alice = await honcho.peer("alice");
const assistant = await honcho.peer("assistant");

// Peer properties
console.log(`Peer ID: ${alice.id}`);

// Chat with peer's representations (supports streaming)
const response = await alice.chat("What did I have for breakfast?");
const targetResponse = await alice.chat("What do I know about Bob?", { target: "bob" });
const sessionResponse = await alice.chat("What happened in session-1?", {
  sessionId: "session-1"
});

// Chat with streaming support
const streamResponse = await alice.chat("Tell me a story", { stream: true });

// Add content to a session with a peer
const session = await honcho.session("session-1");
await session.addMessages([
  alice.message("I love TypeScript programming"),
  alice.message("Today I learned about async programming"),
  alice.message("I prefer functional programming patterns")
]);

// Get peer's sessions
const sessions = await alice.getSessions();

// Search peer's messages
const results = await alice.search("programming");

// Metadata management
const metadata = await alice.getMetadata();
await alice.setMetadata({
  ...metadata,
  location: "Paris"
});

// Get peer context (representation + peer card in one call)
const context = await alice.getContext();
const targetContext = await alice.getContext("bob");  // What alice knows about bob

// Get working representation with semantic search
const rep = await alice.workingRep(undefined, undefined, {
  searchQuery: "preferences",
  searchTopK: 10
});

// Access observations
const selfObs = await alice.observations.list();  // Self-observations
const bobObs = await alice.observationsOf("bob").list();  // Observations of bob
```
</CodeGroup>

### Peer Context

The `get_context()` method on peers retrieves both the working representation and peer card in a single API call:

<CodeGroup>
```python Python
# Get peer's own context
context = alice.get_context()
print(context.representation)  # Working representation
print(context.peer_card)       # Peer card as list of strings

# Get context about another peer (what alice knows about bob)
bob_context = alice.get_context(target="bob")

# Get context with semantic search
context = alice.get_context(
    target="bob",
    search_query="work preferences",
    search_top_k=10,
    search_max_distance=0.8,
    include_most_derived=True,
    max_observations=50
)
```

```typescript TypeScript
// Get peer's own context
const context = await alice.getContext();
console.log(context.representation);  // Working representation
console.log(context.peerCard);        // Peer card as array of strings

// Get context about another peer (what alice knows about bob)
const bobContext = await alice.getContext("bob");

// Get context with semantic search
const searchedContext = await alice.getContext("bob", {
  searchQuery: "work preferences",
  searchTopK: 10,
  searchMaxDistance: 0.8,
  includeMostDerived: true,
  maxObservations: 50
});
```
</CodeGroup>

### Observations

Peers can access their observations (facts derived from messages) through the `observations` property and `observations_of()` method:

<CodeGroup>
```python Python
# Access self-observations (what honcho knows about alice)
self_obs = alice.observations

# List self-observations
obs_list = self_obs.list()

# Search self-observations semantically
results = self_obs.query("food preferences")

# Delete an observation
self_obs.delete("observation-id")

# Access observations of another peer (what alice knows about bob)
bob_obs = alice.observations_of("bob")
bob_obs_list = bob_obs.list()
bob_search = bob_obs.query("work history")
```

```typescript TypeScript
// Access self-observations (what honcho knows about alice)
const selfObs = alice.observations;

// List self-observations
const obsList = await selfObs.list();

// Search self-observations semantically
const results = await selfObs.query("food preferences");

// Delete an observation
await selfObs.delete("observation-id");

// Access observations of another peer (what alice knows about bob)
const bobObs = alice.observationsOf("bob");
const bobObsList = await bobObs.list();
const bobSearch = await bobObs.query("work history");
```
</CodeGroup>

#### Creating Observations Manually

You can also create observations directly, which is useful for importing data or adding explicit facts:

<CodeGroup>
```python Python
# Create observations for what alice knows about bob
bob_obs = alice.observations_of("bob")

# Create a single observation
created = bob_obs.create([
    {"content": "User prefers dark mode", "session_id": "session-1"}
])

# Create multiple observations in batch
created = bob_obs.create([
    {"content": "User prefers dark mode", "session_id": "session-1"},
    {"content": "User works late at night", "session_id": "session-1"},
    {"content": "User enjoys programming", "session_id": "session-1"},
])

# Returns list of created Observation objects with IDs
for obs in created:
    print(f"Created observation: {obs.id} - {obs.content}")
```

```typescript TypeScript
// Create observations for what alice knows about bob
const bobObs = alice.observationsOf("bob");

// Create a single observation
const created = await bobObs.create([
    { content: "User prefers dark mode", sessionId: "session-1" }
]);

// Create multiple observations in batch
const batchCreated = await bobObs.create([
    { content: "User prefers dark mode", sessionId: "session-1" },
    { content: "User works late at night", sessionId: "session-1" },
    { content: "User enjoys programming", sessionId: "session-1" },
]);

// Returns array of created Observation objects with IDs
for (const obs of batchCreated) {
    console.log(`Created observation: ${obs.id} - ${obs.content}`);
}
```
</CodeGroup>

<Info>
Manually created observations are marked as "explicit" and are treated the same as system-derived observations. Each observation must be tied to a session and the content length is validated against the embedding token limit.
</Info>

### Session

Manages multi-party conversations:

<CodeGroup>
```python Python
# Create session (like peers, lazy creation)
session = honcho.session("conversation-1")

# Create with immediate configuration
# This will make an API call to create the session with the custom configuration and/or metadata
session = honcho.session("meeting-1", config={"type": "meeting", "max_peers": 10})

# Session properties
print(f"Session ID: {session.id}")
print(f"Workspace: {session.workspace_id}")

# Peer management
session.add_peers([alice, assistant])
session.add_peers([(alice, SessionPeerConfig(observe_others=True))])
session.set_peers([alice, bob, charlie])  # Replace all peers
session.remove_peers([alice])

# Get session peers and their configurations
peers = session.get_peers()
peer_config = session.get_peer_config(alice)
session.set_peer_config(alice, SessionPeerConfig(observe_me=False))

# Message management
session.add_messages([
    alice.message("Hello everyone!"),
    assistant.message("Hi Alice! How can I help today?")
])

# Get messages
messages = session.get_messages()

# Get conversation context
context = session.get_context(summary=True, tokens=2000)

# Get context with peer representation included
context = session.get_context(
    tokens=2000,
    peer_target="user",
    peer_perspective="assistant",
    search_query="What are my preferences?",
    limit_to_session=True,
    search_top_k=10,
    search_max_distance=0.8,
    include_most_derived=True,
    max_observations=25
)

# Search session content
results = session.search("help")

# Working representation queries with semantic search
global_rep = session.working_rep("alice")
targeted_rep = session.working_rep(alice, target=bob)
searched_rep = session.working_rep(
    "alice",
    search_query="preferences",
    search_top_k=10,
    include_most_derived=True
)

# Upload a file to create messages
messages = session.upload_file(
    file=open("document.pdf", "rb"),
    peer="user",
    metadata={"source": "upload"},
    created_at="2024-01-15T10:30:00Z"
)

# Clone a session (creates a copy with all data)
# Copies: messages, metadata, configuration, peers, and peer configurations
cloned = session.clone()

# Clone up to a specific message (inclusive)
# Only messages up to and including the specified message are copied
cloned_partial = session.clone(message_id="msg-123")

# Delete session (async - returns 202)
session.delete()

# Metadata management
session.set_metadata({"topic": "product planning", "status": "active"})
metadata = session.get_metadata()
```

```typescript TypeScript
// Create session (returns Promise<Session>)
const session = await honcho.session("conversation-1");

// Session properties
console.log(`Session ID: ${session.id}`);

// Peer management
await session.addPeers([alice, assistant]);
await session.addPeers("single-peer-id");
await session.setPeers([alice, bob, charlie]);  // Replace all peers
await session.removePeers([alice]);
await session.removePeers("single-peer-id");

// Get session peers
const peers = await session.getPeers();

// Message management
await session.addMessages([
  alice.message("Hello everyone!"),
  assistant.message("Hi Alice! How can I help today?")
]);

// Get messages
const messages = await session.getMessages();

// Get conversation context
const context = await session.getContext({ summary: true, tokens: 2000 });

// Get context with peer representation included
const richContext = await session.getContext({
  tokens: 2000,
  peerTarget: "user",
  peerPerspective: "assistant",
  searchQuery: "What are my preferences?",
  limitToSession: true,
  searchTopK: 10,
  searchMaxDistance: 0.8,
  includeMostDerived: true,
  maxObservations: 25
});

// Search session content
const results = await session.search("help");

// Working representation queries with semantic search
const globalRep = await session.workingRep("alice");
const targetedRep = await session.workingRep(alice, { target: bob });
const searchedRep = await session.workingRep("alice", undefined, {
  searchQuery: "preferences",
  searchTopK: 10,
  includeMostDerived: true
});

// Upload a file to create messages
const messages = await session.uploadFile(
  fileBuffer,
  "user",
  {
    metadata: { source: "upload" },
    createdAt: "2024-01-15T10:30:00Z"
  }
);

// Clone a session (creates a copy with all data)
// Copies: messages, metadata, configuration, peers, and peer configurations
const cloned = await session.clone();

// Clone up to a specific message (inclusive)
// Only messages up to and including the specified message are copied
const clonedPartial = await session.clone("msg-123");

// Delete session (async - returns 202)
await session.delete();

// Metadata management
await session.setMetadata({
  topic: "product planning",
  status: "active"
});
const metadata = await session.getMetadata();
```
</CodeGroup>

**Session-Level Theory of Mind Configuration:**

<Info>
**Theory of Mind** controls whether peers can form models of what other peers think. Use `observe_others=False` to prevent a peer from modeling others within a session, and `observe_me=False` to prevent others from modeling this peer within a session.
</Info>

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

# Configure peer observation settings
config = SessionPeerConfig(
    observe_others=False,  # Form theory-of-mind of other peers -- False by default
    observe_me=True        # Don't let others form theory-of-mind of me -- True by default
)

session.add_peers([(alice, config)])
```

```typescript TypeScript
// Configure peer observation settings
const config = new SessionPeerConfig({
    observeOthers: false,  // Form theory-of-mind of other peers -- False by default
    observeMe: true        // Don't let others form theory-of-mind of me -- True by default
});

await session.addPeers([alice, config]);
```
</CodeGroup>

### SessionContext

Provides formatted conversation context for LLM integration:

<CodeGroup>
```python Python
# Get session context
context = session.get_context(summary=True, tokens=1500)

# Convert to LLM-friendly formats
openai_messages = context.to_openai(assistant=assistant)
anthropic_messages = context.to_anthropic(assistant=assistant)
```

```typescript TypeScript
// Get session context
const context = await session.getContext({ summary: true, tokens: 1500 });

// Convert to LLM-friendly formats
const openaiMessages = context.toOpenAI(assistant);
const anthropicMessages = context.toAnthropic(assistant);
```
</CodeGroup>

The SessionContext object has the following structure:

```json
{
  "id": "string",
  "messages": [
    {
      "id": "string",
      "content": "string",
      "peer_id": "string",
      "session_id": "string",
      "workspace_id": "string",
      "metadata": {},
      "created_at": "2024-01-15T10:30:00Z",
      "token_count": 42
    }
  ],
  "summary": {
    "content": "string",
    "message_id": 123,
    "summary_type": "short|long",
    "created_at": "2024-01-15T10:30:00Z"
  },
  "peer_representation": "string (optional)",
  "peer_card": ["string"] // optional, included when peer_target is provided
}
```

**Session Context Parameters:**

| Parameter | Type | Description |
|-----------|------|-------------|
| `summary` | `bool` | Whether to include summary (default: true) |
| `tokens` | `int` | Maximum tokens to include |
| `peer_target` | `str` | Peer ID to get representation for |
| `peer_perspective` | `str` | Peer ID for perspective (requires peer_target) |
| `search_query` | `str` | Query string for semantic search |
| `limit_to_session` | `bool` | Limit representation to session only |
| `search_top_k` | `int` | Number of semantic search results (1-100) |
| `search_max_distance` | `float` | Max semantic distance (0.0-1.0) |
| `include_most_derived` | `bool` | Include most derived observations |
| `max_observations` | `int` | Max observations to include (1-100) |

## Advanced Usage

### Multi-Party Conversations

<CodeGroup>
```python Python
# Create multiple peers
users = [honcho.peer(f"user-{i}") for i in range(5)]
moderator = honcho.peer("moderator")

# Create group session
group_chat = honcho.session("group-discussion")
group_chat.add_peers(users + [moderator])

# Add messages from different peers
group_chat.add_messages([
    users[0].message("What's our agenda for today?"),
    moderator.message("We'll discuss the new feature roadmap"),
    users[1].message("I have some concerns about the timeline")
])

# Query different perspectives
user_perspective = users[0].chat("What are people's concerns?")
moderator_view = moderator.chat("What feedback am I getting?", session=group_chat.id)
```

```typescript TypeScript
// Create multiple peers
const users = await Promise.all(
  Array.from({ length: 5 }, (_, i) => honcho.peer(`user-${i}`))
);
const moderator = await honcho.peer("moderator");

// Create group session
const groupChat = await honcho.session("group-discussion");
await groupChat.addPeers([...users, moderator]);

// Add messages from different peers
await groupChat.addMessages([
  users[0].message("What's our agenda for today?"),
  moderator.message("We'll discuss the new feature roadmap"),
  users[1].message("I have some concerns about the timeline")
]);

// Query different perspectives
const userPerspective = await users[0].chat("What are people's concerns?");
const moderatorView = await moderator.chat("What feedback am I getting?", {
  sessionId: groupChat.id
});
```
</CodeGroup>

### LLM Integration

<CodeGroup>
```python Python
import openai

# Get conversation context
context = session.get_context(tokens=3000)
messages = context.to_openai(assistant=assistant)

# Call OpenAI API
response = openai.chat.completions.create(
    model="gpt-4",
    messages=messages + [
        {"role": "user", "content": "Summarize the key discussion points."}
    ]
)
```

```typescript TypeScript
import OpenAI from 'openai';

const openai = new OpenAI();

// Get conversation context
const context = await session.getContext({ tokens: 3000 });
const messages = context.toOpenAI(assistant);

// Call OpenAI API
const response = await openai.chat.completions.create({
  model: "gpt-4",
  messages: [
    ...messages,
    { role: "user", content: "Summarize the key discussion points." }
  ]
});
```
</CodeGroup>

### Custom Message Timestamps

When creating messages, you can optionally specify a custom `created_at` timestamp instead of using the server's current time:

```bash
curl -X POST "https://api.honcho.dev/v2/workspaces/{workspace_id}/sessions/{session_id}/messages" \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {
        "peer_id": "user123",
        "content": "This message happened yesterday",
        "created_at": "2024-01-01T12:00:00Z",
        "metadata": {"source": "historical_data"}
      }
    ]
  }'
```

This is useful for:
- Importing historical conversation data
- Backfilling messages from other systems
- Maintaining accurate timeline ordering when processing batch data

If `created_at` is not provided, messages will use the server's current timestamp.

### Metadata and Filtering

See [Using Filters](/v2/guides/using-filters) for more examples on how to use filters.

<CodeGroup>
```python Python
# Add messages with metadata
session.add_messages([
    alice.message("Let's discuss the budget", metadata={
        "topic": "finance",
        "priority": "high"
    }),
    assistant.message("I'll prepare the financial report", metadata={
        "action_item": True,
        "due_date": "2024-01-15"
    })
])

# Filter messages by metadata
finance_messages = session.get_messages(filters={"metadata": {"topic": "finance"}})
action_items = session.get_messages(filters={"metadata": {"action_item": True}})
```

```typescript TypeScript
// Add messages with metadata
await session.addMessages([
  alice.message("Let's discuss the budget", {
    metadata: {
      topic: "finance",
      priority: "high"
    }
  }),
  assistant.message("I'll prepare the financial report", {
    metadata: {
      action_item: true,
      due_date: "2024-01-15"
    }
  })
]);

// Filter messages by metadata
const financeMessages = await session.getMessages({
  filters: { metadata: { topic: "finance" } }
});
const actionItems = await session.getMessages({
  filters: { metadata: { action_item: true } }
});
```
</CodeGroup>

### Pagination

<CodeGroup>
```python Python
# Iterate through all sessions
for session in honcho.get_sessions():
    print(f"Session: {session.id}")

    # Iterate through session messages
    for message in session.get_messages():
        print(f"  {message.peer_id}: {message.content}")
```

```typescript TypeScript
// Get paginated results
const peersPage = await honcho.getPeers();

// Iterate through all items
for await (const peer of peersPage) {
  console.log(`Peer: ${peer.id}`);
}

// Manual pagination
let currentPage = peersPage;
while (currentPage) {
  const data = await currentPage.data();
  console.log(`Processing ${data.length} items`);
  currentPage = await currentPage.nextPage();
}
```
</CodeGroup>

## Best Practices

### Resource Management

<CodeGroup>
```python Python
# Peers and sessions are lightweight - create as needed
alice = honcho.peer("alice")
session = honcho.session("chat-1")

# Use descriptive IDs for better debugging
user_session = honcho.session(f"user-{user_id}-support-{ticket_id}")
support_agent = honcho.peer(f"agent-{agent_id}")
```

```typescript TypeScript
// Peers and sessions are lightweight - create as needed
const alice = await honcho.peer("alice");
const session = await honcho.session("chat-1");

// Use descriptive IDs for better debugging
const userSession = await honcho.session(`user-${userId}-support-${ticketId}`);
const supportAgent = await honcho.peer(`agent-${agentId}`);
```
</CodeGroup>

### Performance Optimization

<CodeGroup>
```python Python
# Lazy creation - no API calls until needed
peers = [honcho.peer(f"user-{i}") for i in range(100)]  # Fast

# Batch operations when possible
session.add_messages([peer.message(f"Message {i}") for i, peer in enumerate(peers)])

# Use context limits to control token usage
context = session.get_context(tokens=1500)  # Limit context size
```

```typescript TypeScript
// Lazy creation - no API calls until needed
const peers = await Promise.all(
  Array.from({ length: 100 }, (_, i) => honcho.peer(`user-${i}`))
);

// Batch operations when possible
await session.addMessages(
  peers.map((peer, i) => peer.message(`Message ${i}`))
);

// Use context limits to control token usage
const context = await session.getContext({ tokens: 1500 }); // Limit context size

// Iterate efficiently with async iteration
for await (const peer of await honcho.getPeers()) {
  // Process one peer at a time without loading all into memory
}
```
</CodeGroup>