langchain-loadout

Per-turn skill selection for LangChain and deepagents agents: the model sees the few skills it needs, not a catalog of hundreds.

What it gives · Limits · How it works · Contributing

An agent with hundreds of skills carries every name and description in its system prompt, on every model call. Loadout decides each turn which skills matter and shows the model only those.

• Each turn stands alone. "Is a skill needed at all" is asked alongside the ranking, so a request that needs no skill costs one cheap answer and nothing is loaded. No state, no checkpointer, nothing to carry between turns.
• The logic is Loadout's, the judge is yours. The questions are simple — "pick one", "yes or no" — and go through a single port. A ready adapter ships for Jev.
• Confidence decides what the agent sees. High: the skill's instructions go straight into the request. Medium: two or three candidates. Low: nothing, and the model can still call find_skill. Every threshold is a setting.
• A failure does not break the agent. Loadout wraps the ordinary skills middleware. If it times out or errors, the agent gets the full list, exactly as it would without Loadout.

What it gives

Measured on a testbed — a bank-statement assistant with a catalog of 236 skills, an agent on deepagents, 50 conversations of 5 turns each:

When the agent has tools and can work the answer out for itself, the skill barely affects whether the answer is right. What Loadout delivers consistently is context and a predictable skill choice. The reasoning behind the design is in docs/design.md.

Limits

• It is not an accuracy feature. Where a skill only restates what the model could work out, the answer is the same either way.
• A decision costs about 3 s on a catalog of 236 skills. Two seconds is reachable on a catalog of about a hundred, or on a turn that continues a topic.
• A turn costs slightly more, not less. The full catalog is identical every message and caches well; the Loadout prompt changes every turn and does not.
• Thresholds have to be fitted on your own data, and the library has no procedure for that yet.
• Everything above was measured on generated data, with one judge and one agent model.

The reasoning behind each of these is in docs/design.md.

Development

uv sync
just test     # the test suite; no network needed
just lint     # formatting, style and import order
just type     # types

See CONTRIBUTING.md for issues, branches, commits and reviews, and docs/development.md for the coding rules.

License

MIT