> ## Documentation Index
> Fetch the complete documentation index at: https://honcho.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# SDK Reference

> Complete SDK documentation and examples for Python and TypeScript

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

## Installation

<CodeGroup>
  ```bash Python (uv) theme={null}
  uv add honcho-ai
  ```

  ```bash Python (pip) theme={null}
  pip install honcho-ai
  ```

  ```bash TypeScript (npm) theme={null}
  npm install @honcho-ai/sdk
  ```

  ```bash TypeScript (yarn) theme={null}
  yarn add @honcho-ai/sdk
  ```

  ```bash TypeScript (pnpm) theme={null}
  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 theme={null}
  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 theme={null}
  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 theme={null}
  # 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 theme={null}
  // 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 theme={null}
  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 theme={null}
  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 theme={null}
  # 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 theme={null}
  // 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 theme={null}
  # 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 theme={null}
  // 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 theme={null}
  # 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 theme={null}
  // 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 theme={null}
  # 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 theme={null}
  // 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 theme={null}
  # 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 theme={null}
  // 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 theme={null}
  # 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 theme={null}
  // 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 theme={null}
  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 theme={null}
  // 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 theme={null}
  # 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 theme={null}
  // 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 theme={null}
{
  "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 theme={null}
  # 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 theme={null}
  // 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 theme={null}
  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 theme={null}
  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 theme={null}
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 theme={null}
  # 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 theme={null}
  // 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 theme={null}
  # 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 theme={null}
  // 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 theme={null}
  # 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 theme={null}
  // 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 theme={null}
  # 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 theme={null}
  // 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>