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Message Properties and Methods

The Messages API provides access to conversation history and methods for modifying messages in xpander agents.

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agent.messages

Array of conversation messages in OpenAI-compatible format.

messages = agent.messages

Returns: Array of message objects with the following structure:

interface Message {
  role: string;         // "system", "user", "assistant", or "tool"
  content: string;      // The message content
  tool_calls?: ToolCall[]; // For assistant messages with tool calls
  tool_call_id?: string;   // For tool messages
}

Messages are automatically populated when agent.add_task() is called. The array is in OpenAI-compatible format and can be used directly with any supported LLM provider without manual conversion.

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add_messages() / addMessages()

Adds one or more messages to the conversation history.

# Add LLM response to messages
agent.add_messages(response.model_dump())

# Add custom message
agent.add_messages({
    "role": "system", 
    "content": "Provide concise answers."
})

# Add multiple messages
agent.add_messages([
    {"role": "user", "content": "Hello"},
    {"role": "assistant", "content": "Hi there!"}
])
ParameterTypeRequiredDescription
messagesLLM response or Message(s)YesMessages to add (accepts raw LLM responses, single messages, or arrays)

Returns: void

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Type Definitions

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Message

Standard message object in OpenAI-compatible format.

interface Message {
  role: string;         // "system", "user", "assistant", or "tool"
  content: string;      // The message content
  tool_calls?: ToolCall[]; // For assistant messages with tool calls
  tool_call_id?: string;   // For tool messages
}

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Usage Examples

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Passing Messages to LLM API

# Pass messages directly to LLM API
response = openai_client.chat.completions.create(
    model="gpt-4o",
    messages=agent.messages,
    tools=agent.get_tools(llm_provider=LLMProvider.OPEN_AI),
    temperature=0.0
)

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Analyzing Messages

# Count tokens
from tiktoken import encoding_for_model
enc = encoding_for_model("gpt-4o")
total_tokens = sum(len(enc.encode(msg.get("content", ""))) for msg in agent.messages)

# Extract user messages
user_messages = [
    msg.get("content") for msg in agent.messages 
    if msg.get("role") == "user"
]

# Find messages containing specific content
relevant_messages = [
    msg for msg in agent.messages
    if msg.get("content") and "specific term" in msg.get("content").lower()
]

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Complete Agent Loop with Messages

# Initialize task and get initial messages
agent.add_task(input="What's the weather in New York?")

while not agent.is_finished():
    # Get LLM response
    response = openai_client.chat.completions.create(
        model="gpt-4o",
        messages=agent.messages,
        tools=agent.get_tools(llm_provider=LLMProvider.OPEN_AI),
        temperature=0
    )

    # Add LLM response to messages
    agent.add_messages(response.model_dump())

    # Extract and run tool calls
    tool_calls = XpanderClient.extract_tool_calls(
        llm_response=response.model_dump(),
        llm_provider=LLMProvider.OPEN_AI
    )

    if tool_calls:
        results = agent.run_tools(tool_calls=tool_calls)
        # Tool results are automatically added to messages

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Integration with Tasks API

The Messages API is tightly integrated with the Tasks API:

  • Messages are initialized when agent.add_task() is called
  • Message history is loaded when a thread_id is provided to add_task()
  • The execution ends when the LLM calls the xpfinish-agent-execution-finished tool

For task management functionality, see the Tasks API documentation.

Agent

Core agent functionality

Agent.Tools

Using agent tools and function calling

Tasks

Managing agent tasks

XpanderClient

Main client for the xpander.ai SDK