feat(runner): let a target force the reasoning effort of the requests it serves - #666
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WalkthroughThis change adds per-target ChangesReasoning effort override flow
Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: 🟡 Moderate · up to Aliases intended to provide different reasoning tiers can silently send the same effort, defeating target-level routing. This should be corrected before merge. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
Full details: Docstring CoverageExplanation Docstring coverage is 50.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 26 functions across 5 files. (2 skipped: 2 unsupported.)
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In `@crates/switchyard-runner/src/config.rs`:
- Around line 263-268: Update the target validation around build_backend and
build_route_clients to reject duplicate (llm_client, id) targets when their
reasoning_effort values differ, before constructing shared ModelConfig entries;
preserve existing behavior for identical duplicates and distinct client/model
pairs.
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CHANGELOG.mdcrates/libsy-llm-client/src/backend.rscrates/libsy-llm-client/src/client.rscrates/libsy-llm-client/src/run.rscrates/switchyard-runner/src/config.rscrates/switchyard-server/tests/server.rsdocs/reference/toml_schema.md
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The LLM client keeps one backend per model id, so two targets naming the same model on the same llm client share a backend. That was tolerated with a warning, which is fine for an alias with identical settings but silently wrong once targets can carry reasoning_effort or extra_body: the second target's settings never reach the wire. Loading now fails when two such targets differ in reasoning_effort or extra_body, and still only warns for an identical alias. Found by review on #666; covered by a runner test. Signed-off-by: Lin Jia <linj@nvidia.com>
… it serves A target's extra_body only fills keys the caller left out, so it cannot change the reasoning effort of a request from a client that always sets one. Codex, for example, sends reasoning.effort on every Responses request (high by default for gpt-5.6), which meant a route could not run its strong tier at max behind a client configured for high, and a per-target extra_body.reasoning_effort was silently ignored on the Responses wire. Every routed GPT-5.6 run in the DeepSWE program ran at high for that reason. This adds an optional reasoning_effort on [targets.<name>]. When set, the client applies it after the extra_body merge and it replaces the caller's value: reasoning.effort on the Responses wire, reasoning_effort on Chat Completions. Anthropic has no equivalent, so the runner rejects the key on anthropic_messages clients at load time, along with blank values. Tests cover the override on both OpenAI wire formats against a mock upstream, TOML parsing, the blank rejection, and the Anthropic rejection. The TOML schema reference documents the key. Signed-off-by: Lin Jia <linj@nvidia.com>
The runner keeps one target per model id and llm client and drops a duplicate with a warning, so an effort-tier pair for one model must put each tier on its own llm_clients entry pointing at the same endpoint. Say so next to the reasoning_effort key, where the question arises. Signed-off-by: Lin Jia <linj@nvidia.com>
…ffort override Signed-off-by: Lin Jia <linj@nvidia.com>
The LLM client keeps one backend per model id, so two targets naming the same model on the same llm client share a backend. That was tolerated with a warning, which is fine for an alias with identical settings but silently wrong once targets can carry reasoning_effort or extra_body: the second target's settings never reach the wire. Loading now fails when two such targets differ in reasoning_effort or extra_body, and still only warns for an identical alias. Found by review on #666; covered by a runner test. Signed-off-by: Lin Jia <linj@nvidia.com>
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Summary
A target's
extra_bodyonly fills keys the caller left out, so it cannot change the reasoning effort of a request from a client that always sets one. Codex sendsreasoning.efforton every Responses request (highby default for gpt-5.6). A route therefore could not run its strong tier atmaxbehind a client configured forhigh, and a per-targetextra_body.reasoning_effort, which several benchmark profiles used, was silently ignored on the Responses wire while every request returned 200. Every routed GPT-5.6 run in the DeepSWE program ran athighfor that reason until the effort was moved to the client side.Change
An optional
reasoning_effortkey on[targets.<name>]. When set, the LLM client applies it after theextra_bodymerge and it replaces the caller's value:reasoning.efforton the Responses wire (creating thereasoningobject when absent, preserving its other fields such assummary),reasoning_efforton Chat Completions. Anthropic has no equivalent, so the runner rejects the key onanthropic_messagesclients when the deployment loads, and rejects blank values. Targets without the key are unchanged.The immediate use is effort-tier routing with one model: a cheap tier at
highand a strong tier atmax, selected by any router, where the client's own setting would otherwise pin both tiers to the same effort.Tests
A client test drives a Chat Completions request carrying
reasoning_effort: highand a Responses request carryingreasoning: {effort: high, summary: auto}through targets configured withmax, and asserts the mock upstream receivesmaxwith the other reasoning fields intact. Runner tests parse the key from TOML and check both rejections. Client and runner suites pass (126 tests), the server integration suite passes, and workspace clippy is clean with-D warnings.Docs
The TOML schema reference gains the key in the target table. Changelog entry under Unreleased.
Summary by CodeRabbit
reasoning_effortconfiguration for OpenAI Chat Completions and Responses requests.