toolcontract 0.1.0

Contract testing for LLM agent tool-calls — catch tool-calling regressions when a provider ships a new model version.

toolcontract

Contract testing for LLM agent tool-calls.

When a provider ships a new model version, an agent's tool-calling behavior can silently change — invented or dropped arguments, a different tool chosen for the same input, values that no longer match a schema your downstream code depends on. Nothing in a typical CI pipeline catches this before it reaches production.

toolcontract is built around one trigger event: a model version changed — did your agent's tool calls still do what you expect? Pin a golden set of expected tool-call trajectories, run them against a live model, get a pass/fail/inconclusive verdict and a diff. Think Pact for microservice contracts, or Percy for visual regressions, but for tool calls.

Status

v0.1, in active development. Contract model, comparators/matching engine, verification ledger, OpenAI/Anthropic/LiteLLM adapters, the CLI (run/accept/check-version), a real pytest plugin, and the LLM-judge semantic tier are all built and tested. Not yet published to PyPI.

Quick start

from toolcontract import ArgKind, ArgSpec, Contract, ExpectedCall
from toolcontract.adapters.base import ToolSchema
from toolcontract.adapters.openai_adapter import OpenAIAdapter
from toolcontract.runner.engine import run_contract

log_water = ToolSchema(
    name="log_water",
    description="Log that the user drank water",
    parameters={
        "type": "object",
        "properties": {"amount_ml": {"type": "number"}},
        "required": ["amount_ml"],
        "additionalProperties": False,
    },
    strict=True,
)

contract = Contract(
    id="log-water-basic",
    description="User mentions drinking water, agent logs it",
    input_messages=({"role": "user", "content": "I just drank a glass of water"},),
    expected_calls=(
        ExpectedCall(tool_name="log_water", args={"amount_ml": ArgSpec.exact(250, kind=ArgKind.NUMBER, tolerance=20)}),
    ),
)

result = run_contract(contract, OpenAIAdapter(), [log_water], "gpt-4o")
print(result.verdict)  # Verdict.PASS / FAIL / INCONCLUSIVE

Or from the CLI, against a directory of compiled contracts:

toolcontract run contracts/ --provider openai --model gpt-4o --tools tools.json
toolcontract accept contracts/one.json --provider openai --model gpt-4o   # promote a new baseline
toolcontract check-version contracts/ --provider openai --model gpt-4o-2027 --ledger .toolcontract_ledger.json

See examples/tap_health_agent_get_meal_suggestions.py for a real contract authored against a production tool schema, including a custom comparator and an optional argument.

Providers

• OpenAI, Anthropic — native adapters, pip install "toolcontract[openai]" / [anthropic].
• Any OpenAI-compatible endpoint — a self-hosted LiteLLM proxy, vLLM, Ollama, OpenRouter, Azure OpenAI — already works today with zero new code:

  import openai
  from toolcontract.adapters.openai_adapter import OpenAIAdapter
  
  adapter = OpenAIAdapter(client=openai.OpenAI(base_url="http://localhost:4000", api_key="sk-..."))
  

• LiteLLM (direct SDK routing) — pip install "toolcontract[litellm]", then LiteLLMAdapter() with a provider-prefixed model string ("cerebras/zai-glm-4.7", "gemini/gemini-2.0-flash", and 100+ others LiteLLM supports). Capability (whether the underlying provider actually honors strict/grammar-constrained tool calling) is queried per-model at call time, not assumed statically — see adapters/litellm_adapter.py's module docstring for why, and for the honest caveat about how precise that signal currently is.
• Missing your provider? See CONTRIBUTING.md — adding a native adapter is the single most valuable contribution this project can take right now.

Non-goals

This is deliberately not a general agent evaluation framework. If a request is really about one of the following, a different tool is the right fit — agentevals, DeepEval, Ragas, PydanticAI Evals, LangSmith, or Phoenix all already cover this territory well:

• RAG retrieval or answer-quality evaluation
• Hallucination detection or general output-quality scoring
• Multi-turn simulated-user agent evaluation
• General-purpose "score my agent" metrics unrelated to tool-call structure

toolcontract owns exactly one job: did this model version's tool-calling behavior change against a pinned contract. Everything in scope should trace back to that job — anything that doesn't belongs in a different package.

Design principles (from the architecture review)

• Core engine is framework-agnostic. The pytest plugin is a thin consumer of a plain Python/CLI-callable core — not the other way around.
• Three-valued verdicts. PASS / FAIL / INCONCLUSIVE. An argument that can't be resolved structurally (e.g. free-form natural language) reports INCONCLUSIVE, never a false FAIL.
• Type-aware comparison, not naive equality. Dates, numbers, and strings are normalized before comparison so formatting variance doesn't produce false regressions.
• Contracts compile to portable, versioned JSON. Python is the authoring ergonomics; the JSON file is the diffable artifact checked into a repo and hashed for the verification ledger.
• Custom comparators are registered, never dynamically imported. A contract file's comparator_ref may not share the trust level of the code running it — a PR from an external contributor, or a contract pulled from a shared source. @toolcontract.register_comparator requires the trusted test suite to explicitly opt a function in; anything else in a contract file fails closed instead of being imported and called.
• Capability is per-model, not per-adapter. A single adapter instance (especially a gateway-style one like LiteLLMAdapter) can front providers with genuinely different tool-calling guarantees — capability_for(model) is queried at call time, never assumed as a static fact.

Contributing

See CONTRIBUTING.md — provider adapters especially welcome.

Development

python -m venv .venv
.venv/bin/pip install -e ".[dev]"
.venv/bin/python -m pytest