Add trace-based behavioral tests with Monocle Test Tools#288
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Mohammed Ansari (imohammedansari) wants to merge 1 commit into
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Add trace-based behavioral tests with Monocle Test Tools#288Mohammed Ansari (imohammedansari) wants to merge 1 commit into
Mohammed Ansari (imohammedansari) wants to merge 1 commit into
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Trace-based tests under monocle-test/ asserting against the agent's Monocle execution traces (agent, tools, output, token/duration budget).
Mohammed Ansari (imohammedansari)
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Summary
Adds a behavioral test suite that asserts against Open Deep Research's Monocle execution traces: which agent ran, what it was asked, what it returned, and its token and duration cost. Additive only, under
tests/monocle/, with no app-code changes.Why
The repo's existing tests are evaluation harnesses that score final reports over a benchmark with LangSmith and LLM judges. They exercise output quality, not what a real agent run actually did: which agent handled the request, what it returned, and what it cost. This adds that behavioral coverage, so if a later prompt, model, or route change alters routing, output, or token cost, a test catches it.
How it works
It uses Monocle Test Tools. The offline tests load a recorded trace from file with
with_trace_source("file", trace_path=...), which is fast, needs no keys, and is deterministic. They then assert with the fluent API:called_agent,contains_output/contains_any_output,under_token_limit,under_duration. The live test drives the real agent end to end and asserts on structure and budget only.The point of loading from file is that the same assertions run in both modes. You pin a check against a recorded trace while iterating (fast, no keys, deterministic), then run the identical check against a live run for real regression coverage. The offline tests and the live test here share the same fluent assertions for exactly this reason.
Open Deep Research runs its search inside the model call (OpenAI-native web search), so its traces carry no
agentic.toolspans; behavior is validated through the agent invocation, the output, and the workflow budget.Changes (all under
tests/monocle/)test_opendeepresearch.py: 4 offline file-loaded tests, one per curated question (Earth's seasons, renewable vs nonrenewable energy, ocean tides, TCP vs UDP), plus 1 live test (TCP vs UDP, guarded byOPENAI_API_KEY).conftest.py: Monocle setup,.envloading,run_opendeepresearch().traces/: 4 recorded traces, one per question.requirements.txt,README.md.PS: if Monocle looks useful, a ⭐ helps the project (https://github.com/monocle2ai/monocle). The instrumentation that emits these traces is the companion PR (#287).