Prompt Caching

Prompt caching avoids reprocessing the same prompt content on repeated calls. This reduces costs and latency for long-running tasks or iterative workflows where the context stays mostly the same.

Enabling Prompt Caching

Set use_prompt_caching=True when creating your agent:

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],
    use_prompt_caching=True
)

Provider Behavior

Anthropic

When enabled, Cua adds {"cache_control": "ephemeral"} to messages. This tells Anthropic to cache the prompt for the duration of the session.

# Required for Anthropic models
agent = ComputerAgent(
    model="anthropic/claude-sonnet-4-5-20250929",
    tools=[computer],
    use_prompt_caching=True
)

OpenAI

OpenAI automatically caches prompts over 1000 tokens. You don't need to set use_prompt_caching—it happens automatically.

# Caching is automatic for OpenAI
agent = ComputerAgent(
    model="openai/computer-use-preview",
    tools=[computer]
)

Other Providers

For providers that don't support caching, the parameter is ignored.

When to Use

Iterative tasks - When the agent runs multiple turns with similar context:

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

# Each turn reuses cached context
for task in tasks:
    async for result in agent.run(task):
        process(result)

Long system prompts - When using detailed instructions that repeat across calls:

agent = ComputerAgent(
    model="anthropic/claude-sonnet-4-5-20250929",
    tools=[computer],
    instructions="Very long system prompt with detailed rules...",
    use_prompt_caching=True
)

Multi-turn conversations - When conversation history grows but the beginning stays constant:

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

messages = [{"role": "system", "content": "Long context..."}]
for user_input in inputs:
    messages.append({"role": "user", "content": user_input})
    async for result in agent.run(messages):
        messages.extend(result["output"])

Cost Impact

With Anthropic's prompt caching:

Cached input tokens cost 90% less than regular input tokens
Cache writes have a small additional cost
Cache hits provide significant savings on repeated runs

Check your usage in the response:

async for result in agent.run(messages):
    usage = result["usage"]
    print(f"Tokens: {usage['total_tokens']}, Cost: ${usage['response_cost']:.4f}")

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