Internal Models

fast-agent comes with two internal models to aid development and testing: passthrough and playback.

Passthrough

By default, the passthrough model echos messages sent to it.

Fixed Responses

By sending a ***FIXED_RESPONSE <message> message, the model will return <message> to any request.

Tool Calling

By sending a ***CALL_TOOL <tool_name> [<json>] message, the model will call the specified MCP Tool, and return a string containing the results.

Playback

The playback model replays the first conversation sent to it. A typical usage may look like this:

playback.txt

---USER
Good morning!
---ASSISTANT
Hello
---USER
Generate some JSON
---ASSISTANT
{
   "city": "London",
   "temperature": 72
}

This can then be used with the prompt-server you can apply the MCP Prompt to the agent, either programatically with apply_prompt or with the /prompts command in the interactive shell.

Alternatively, you can load the file with load_message_multipart.

JSON contents can be converted to structured outputs:

@fast.agent(name="playback",model="playback")

...

playback_messages: List[PromptMessageMultipart] = load_message_multipart(Path("playback.txt"))
# Set up the Conversation
assert ("HISTORY LOADED") == agent.playback.generate(playback_messages)

response: str = agent.playback.send("Good morning!") # Returns Hello
temperature, _ = agent.playback.structured("Generate some JSON")

When the playback runs out of messages, it returns MESSAGES EXHAUSTED (list size [a]) ([b] overage).

List size is the total number of messages originally loaded, overage is the number of requests made after exhaustion.