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Code Action Reuse

Any code action can be saved as a discoverable tool, enabling tool libraries to evolve as agents work. Composite code actions that chain multiple tools are a common example.

Freeact provides the saving-codeacts skill for saving code actions as reusable tools. It separates interface (api.py) from implementation (impl.py). The interface contains the function signature, Pydantic models, and docstrings. The implementation contains the actual logic.

This separation enables efficient tool discovery: agents inspect signatures and docstrings without loading implementation details. The implementation stays hidden, saving tokens and reducing distraction by keeping non-essential details out of context.

The following example shows how to compose and save a code action as a parameterized tool, then discover and reuse it in a new session.

Compose and Save

Recorded session

A recorded session of this example is appended below.

This example continues from the enhancing tools example, where search_repositories was augmented with a run_parsed() function returning typed Repository objects. In the same workspace, start a new CLI tool session with:

uvx freeact

With a query like

get the latest 5 commits of the 3 github repos of torvalds with the most stars. For each repo, output name, stars and the first line of commit messages, and the link to the commit

the agent discovers search_repositories and list_commits as appropriate tools and inspects their APIs. Because the enhanced search_repositories tool now returns typed output via run_parsed(), the agent can compose these tools in a single code action, passing repository names from search results as input to list_commits1.

After code action execution, we instruct the agent to save it as a reusable tool:

save this as tool under category github, with username, top_n_repos, top_n_commits as parameter

The agent:

  1. Loads the saving-codeacts skill
  2. Creates a gentools/github/commits_of_top_repos/ package
  3. Saves the tool API with a parameterized run() function to api.py
  4. Saves the implementation to impl.py, lazily imported by run()
  5. Tests the saved tool to verify it works

The structure of the saved tool is:

gentools/
└── github/
    └── commits_of_top_repos/
        ├── __init__.py
        ├── api.py       # Public interface
        └── impl.py      # Implementation

Interactive mode

Discover and Reuse

Recorded session

A recorded session of this example is appended below.

In a new session, the saved tool is discovered like any other Python tool. During discovery, only the API is inspected, not the implementation. When asked to

get the latest 3 commits of the 2 github repos of torvalds with the most stars. For each repo, output name, stars and the first line of commit messages, and the link to the commit

the agent discovers the previously saved tool, inspects its API, and calls it with different parameters (top_n_repos=2, top_n_commits=3).

Interactive mode

  1. Since list_commits doesn't return typed output, the agent guesses output structure from training data, which may or may not work on first attempt. This can be made more reliable by also generating an output parser for list_commits. ↩