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Model Features and History Saving

Models in fast-agent are specified with a model string, that takes the format provider.model_name.<reasoning_effort>

Precedence

Model specifications in fast-agent follow this precedence order (highest to lowest):

  1. Explicitly set in agent decorators
  2. Command line arguments with --model flag
  3. Default model in fastagent.config.yaml

Format

Model strings follow this format: provider.model_name.reasoning_effort

  • provider: The LLM provider (e.g., anthropic, openai, azure, deepseek, generic,openrouter, tensorzero)
  • model_name: The specific model to use in API calls (for Azure, this is your deployment name)
  • reasoning_effort (optional): Controls the reasoning effort for supported models

Examples:

  • anthropic.claude-3-7-sonnet-latest
  • openai.gpt-4o
  • openai.o3-mini.high
  • azure.my-deployment
  • generic.llama3.2:latest
  • openrouter.google/gemini-2.5-pro-exp-03-25:free
  • tensorzero.my_tensorzero_function

Reasoning Effort

For models that support it (o1, o1-preview and o3-mini), you can specify a reasoning effort of high, medium or low - for example openai.o3-mini.high. medium is the default if not specified.

gpt-5 additionally supports a minimal reasoning effort.

Aliases

For convenience, popular models have an alias set such as gpt-4o or sonnet. These are documented on the LLM Providers page.

Default Configuration

You can set a default model for your application in your fastagent.config.yaml:

default_model: "openai.gpt-4o" # Default model for all agents

History Saving

You can save the conversation history to a file by sending a ***SAVE_HISTORY <filename> message. This can then be reviewed, edited, loaded, or served with the prompt-server or replayed with the playback model.

File Format / MCP Serialization

If the filetype is json, then messages are serialized/deserialized using the MCP Prompt schema. The load_prompt, load_prompt_multipart and prompt-server will load either the text or JSON format directly.

This can be helpful when developing applications to:

  • Save a conversation for editing
  • Set up in-context learning
  • Produce realistic test scenarios to exercise edge conditions etc. with the Playback model