Chat History

Chat history tracks the conversation between you and the agent across multiple turns. Each run builds on the previous context, enabling complex multi-step workflows and conversational interactions.

Basic Multi-Turn Conversation

Pass a message history to continue from previous context:

from computer import Computer
from agent import ComputerAgent

computer = Computer(os_type="linux", provider_type="docker", image="trycua/cua-xfce:latest")
await computer.run()

agent = ComputerAgent(
    model="anthropic/claude-sonnet-4-5-20250929",
    tools=[computer]
)

# First turn
messages = [{"role": "user", "content": "Open Firefox"}]
async for result in agent.run(messages):
    messages.extend(result["output"])

# Second turn - agent remembers Firefox is open
messages.append({"role": "user", "content": "Now go to github.com"})
async for result in agent.run(messages):
    messages.extend(result["output"])

Interactive Loop

Build a chat interface where each response adds to the history:

history = []

while True:
    user_input = input("> ")
    if user_input.lower() == "quit":
        break

    history.append({"role": "user", "content": user_input})

    async for result in agent.run(history):
        history.extend(result["output"])

        # Print assistant responses
        for item in result["output"]:
            if item["type"] == "message":
                print(item["content"][0]["text"])

Message Structure

A conversation history contains these message types:

messages = [
    # User input
    {"role": "user", "content": "go to trycua on github"},

    # Agent reasoning (internal thought process)
    {
        "type": "reasoning",
        "summary": [{"type": "summary_text", "text": "Searching for Trycua GitHub"}]
    },

    # Computer action
    {
        "type": "computer_call",
        "call_id": "call_abc123",
        "status": "completed",
        "action": {"type": "type", "text": "Trycua GitHub"}
    },

    # Screenshot result
    {
        "type": "computer_call_output",
        "call_id": "call_abc123",
        "output": {"type": "input_image", "image_url": "data:image/png;base64,..."}
    },

    # Final response
    {
        "type": "message",
        "role": "assistant",
        "content": [{"type": "output_text", "text": "Done! The Trycua GitHub page is now open."}]
    }
]

See Message Format for complete type definitions.

Managing Context Size

Screenshots accumulate in the history and consume context window space. Use only_n_most_recent_images to limit how many are kept:

agent = ComputerAgent(
    model="anthropic/claude-sonnet-4-5-20250929",
    tools=[computer],
    only_n_most_recent_images=3  # Keep only last 3 screenshots
)

Older screenshots are automatically removed, preventing context overflow during long sessions.

Clearing History

Start fresh by resetting the messages array:

# Reset to start a new conversation
messages = []

# Or keep system context but clear conversation
messages = [{"role": "system", "content": "You are a helpful assistant."}]

Saving and Loading History

Persist conversations for later use:

import json

# Save
with open("conversation.json", "w") as f:
    # Filter out large image data if needed
    saveable = [
        {**m, "output": {"type": m["output"]["type"]}}
        if m.get("type") == "computer_call_output"
        else m
        for m in messages
    ]
    json.dump(saveable, f)

# Load
with open("conversation.json", "r") as f:
    messages = json.load(f)

For full conversation recording including screenshots, use Trajectories instead.

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