traceix 0.1.1

Trajectory-based CI testing for AI agents

traceix

Trajectory-based CI testing for AI agents.

traceix lets you declare which tools your agent should call — and in what order — as plain YAML, then run those assertions in CI the same way you'd run unit tests. No LLM-as-judge, no flaky eval pipelines: if the trajectory doesn't match, the build fails.

pip install traceix

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Why traceix?

LLM-powered agents are non-deterministic. The same prompt might call search_flights then confirm_booking today, but skip straight to confirm_booking tomorrow. traceix makes that observable and enforceable:

• Declare the expected trajectory in YAML — which tools, in what order, with what args.
• Run it in CI — the agent still calls the real LLM; only the tool responses are mocked.
• Get a clear pass/fail — no prompt-engineering an evaluator, no statistical thresholds.

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Quick start

pip install traceix
traceix init          # detects your framework, scaffolds traceix.yaml + tests/example.yaml

Write a test (tests/book_flight.yaml):

name: book-flight-basic
input: "Book me the cheapest flight from NYC to SFO"

mocks:
  search_flights:
    return: { flights: [{ id: F1, price: 390 }, { id: F2, price: 420 }] }
  confirm_booking:
    return: { booking_id: BK-001, status: confirmed }

expected:
  trajectory:
    mode: contains        # these steps must appear in order (others allowed)
    steps:
      - tool: search_flights
        args: { origin: NYC, destination: SFO }
        arg_mode: partial  # only check the keys listed above
      - tool: confirm_booking
        arg_mode: ignore
  forbidden_tools: [cancel_booking]

Run it:

traceix run tests/ --handler mypackage.agent:run

  ✓  book-flight-basic   1/1   2 steps   142ms
  ──────────────────────────────────────────────
  1 passed · 0 failed

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Integration

tools= parameter (any framework)

Your agent handler accepts a tools list injected by traceix:

# mypackage/agent.py
def run(input: str, tools: list) -> str:
    graph = build_graph(tools)   # rebuild with injected (mocked) tools
    result = graph.invoke({"messages": [HumanMessage(content=input)]})
    return result["messages"][-1].content

@traceix_tool decorator (LangChain / LangGraph)

No handler signature changes needed — just decorate your tools:

from langchain_core.tools import tool
from traceix import traceix_tool

@traceix_tool
@tool
def search_flights(origin: str, destination: str) -> dict:
    """Search available flights."""
    ...  # real implementation

traceix patches the mock in during test runs automatically.

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CLI commands

Command	What it does
traceix init	Detect framework, scaffold traceix.yaml + example test
traceix run tests/	Run tests, exit 0 on pass / 1 on fail
traceix run tests/ --fixture record	Save real tool responses to .traceix/fixtures/
traceix run tests/ --fixture replay	Replay recorded responses in CI
traceix snapshot tests/	Save golden trajectory baselines
traceix check tests/	Compare live run against saved baselines
traceix compare tests/ --a "model=X" --b "model=Y"	A/B test two model configs side by side

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Trajectory modes

Mode	Meaning
strict	Exact tool order and count
contains	Listed steps must appear in order (extra steps allowed)
unordered	All steps present, any order
within	Steps appear as a contiguous block

Arg modes

Mode	Meaning
exact	Args must match exactly
partial	Listed keys must be present with matching values
ignore	Args not checked

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Framework support

Framework	Integration
LangGraph	@traceix_tool decorator or tools= injection
CrewAI	tools= injection
Anthropic SDK	tools= injection
OpenAI SDK	tools= injection
Any other	tools= injection

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Configuration

Set defaults in traceix.yaml (or [tool.traceix] in pyproject.toml):

handler: mypackage.agent:run
runs: 3          # runs per test case (increase in CI for confidence)
tolerance: 0.67  # fraction of runs that must pass
fixture_mode: replay

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License

MIT