feat(llm): declare reasoning levels per configured route

[furgalep]

What and why

Let callers inspect and select reasoning levels without knowing each provider's request fields. Registry YAML maps each label to complete request settings. Selecting a label replaces those top-level settings before dispatch; there are no provider-name rules, nested merge rules or new dependencies.

This PR targets main, which includes #318 and #319. It does not change retained reasoning, replay, archives, cache policy or the TUI.

Code walkthrough — what changed and why

• unifiedllm/reasoning.py validates declarations and applies selections for both clients. Unknown support, unsupported selection and invalid labels have distinct errors. Reserved fields prevent a level from changing the route or history. Selection repeats declaration validation because nested configuration dictionaries can be edited after construction.
• unifiedllm.py exposes levels and their documented default, with persistent or per-call selection. No selection preserves raw settings; explicit None bypasses a persistent selection. The documented default is metadata, not an instruction to spend more tokens.
• registry.py and config/model_config.py carry declarations from YAML. Changing an alias's route or client type clears inherited levels, default and selection; callers must declare choices for the replacement route. The fake client validates selections too.
• Settings precedence: a selected level replaces constructor defaults, including values inside extra_body, without mutating the original configuration. Conflicting per-call raw settings fail. A supplied SDK client is rejected during managed selection because it can change the endpoint.
• Examples and documentation use placeholder endpoints and illustrative parameter shapes. They are not a supported-model catalog. Private routes, credentials guidance and deployment measurements belong in private repositories.
• skills/nooa-agent-authoring/SKILL.md teaches agents how to configure and select levels. A test executes its actual YAML/Python example through registry loading and mocked dispatch.
• Tests cover configuration ownership, validation, route changes, sync/async dispatch and outgoing HTTP bodies. Optional live probes resolve aliases supplied through NOOA_REASONING_TEST_MODELS, using normal registry credentials rather than hard-coded infrastructure.

Validation

Review follow-up 5a40b67c addresses the three route/precedence findings and removes internal deployment details from this PR's examples, documentation and live test.

• 11 regressions were observed failing before the fixes.
• Reasoning and HTTP tests: 75 passed, including the registry-driven live-probe function exercised with mocked HTTP.
• Affected UnifiedLLM, context and runtime suites: 2,314 passed, 7 skipped, 67 deselected.
• Ruff, formatting and whitespace checks pass. No paid inference was used for these changes.

These tests prove request construction, not that every provider honors every setting. The caller's route needs its own reviewed declaration and validation.

Architecture and usage.

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📝 Walkthrough

Walkthrough

UnifiedLLM now supports declarative reasoning levels. Configuration flows from registry entries into client and per-call selection, validation, provider-specific request settings, sync and async dispatch, and wire-level tests.

Changes

Reasoning level configuration

Layer / File(s)	Summary
Reasoning declarations and registry wiring src/nooa/config/model_config.py, examples/reasoning_levels/llm_config.yaml, src/nooa/unifiedllm/registry.py, docs/reasoning-levels.md	Model configurations define reasoning levels, defaults, and selections. Registry entries pass these fields to clients. Documentation describes validation, routing, provider mappings, and configuration limits.
Reasoning validation and request resolution src/nooa/unifiedllm/reasoning.py, src/nooa/unifiedllm/unifiedllm.py	ReasoningConfig validates declarations. apply_reasoning_level resolves selections and overrides, rejects conflicts and route changes, and returns provider settings. UnifiedLLM exposes configured levels and defaults.
Dispatch and fake-client integration src/nooa/unifiedllm/unifiedllm.py, src/nooa/unifiedllm/fake.py	Completion and Responses sync and async calls prepare reasoning-aware configuration before API parameter construction. Fake clients validate configuration before recording calls. Token calibration includes Responses instructions, and think-tag cleanup preserves parsed structured results.
Behavior and wire validation tests/unifiedllm/test_reasoning_levels.py, tests/unifiedllm/test_reasoning_levels_wire.py, tests/integration/test_reasoning_levels_live.py	Tests cover selection, validation, immutability, conflicts, routing, registry propagation, provider wire fields, and optional live requests across configured models.

Priority: ➖ Normal

Estimated code review effort: 3 (Moderate) | ~30 minutes

Change: Feature

Sequence Diagram(s)

sequenceDiagram
  participant Caller
  participant UnifiedLLM
  participant apply_reasoning_level
  participant ProviderAPI
  Caller->>UnifiedLLM: call with optional reasoning_level
  UnifiedLLM->>apply_reasoning_level: resolve defaults and overrides
  apply_reasoning_level-->>UnifiedLLM: return provider settings
  UnifiedLLM->>ProviderAPI: send completion or Responses request
  ProviderAPI-->>UnifiedLLM: return model response

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Merge Risk: ⚪ Minimal · up to dd111

The reasoning-level configuration is wired through validation, dispatch, and test coverage with no supported unresolved merge risk.

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❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 40.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 35 functions across 8 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title clearly and concisely describes the main change: adding reasoning-level declarations for configured LLM routes.

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Actionable comments posted: 1

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Inline comments:
In `@src/nooa/unifiedllm/reasoning.py`:
- Around line 31-33: Update ReasoningConfig.validate_declaration to reject
reserved routing and declaration fields in each level’s settings before they can
reach apply_reasoning_level and params.update: model, api_base, base_url,
custom_llm_provider, reasoning_levels, reasoning_default, and reasoning_level.
Preserve the existing validation for non-empty level names and request settings,
and raise ValueError when any reserved field is declared.

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• docs/reasoning-levels.md
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• src/nooa/config/model_config.py
• src/nooa/unifiedllm/fake.py
• src/nooa/unifiedllm/reasoning.py
• src/nooa/unifiedllm/registry.py
• src/nooa/unifiedllm/unifiedllm.py
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• tests/unifiedllm/test_reasoning_levels.py
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Inline comments:
In `@src/nooa/unifiedllm/reasoning.py`:
- Line 36: Update ReasoningConfig.settings() to revalidate the selected level’s
mapping against _RESERVED after retrieving it and before deep-copying or
returning it; reject any reserved keys, including model, so mutations to levels
cannot alter dispatch through UnifiedLLM._effective_model.

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[furgalep]

Updated the agent-facing nooa-agent-authoring skill in debe768. Discovery text now includes model registry/reasoning configuration. The skill explains declarations, unknown vs unsupported, metadata defaults, persistent/per-call selection, whole-block replacement, conflicts, route changes and retention/caching distinctions. Its actual YAML/Python example is tested through NOOA skill loading, registry loading and mocked dispatch; the test failed before the example was added. All 77 selected reasoning/HTTP/skill tests pass, lint and formatting clean. The existing compatibility frontmatter is preserved: NOOA accepts it, although the generic Codex skill validator does not. No inference calls or production-code changes.

[furgalep]

Expressiveness audit: Pi and PydanticAI reasoning controls

Conclusion: high confidence that #320 can express the major static parameter shapes needed to enable/configure reasoning. This is not a claim that every route already supports them through LiteLLM. No provider mappings or dependencies were added for this audit.

Reviewed upstream source at Pi ceea48f5 and PydanticAI cb417b89, against this PR's debe768a. The important comparison is the request fields their adapters produce, not their public option names or the particular budgets they assign to labels.

reasoning_levels[label] is an arbitrary nonempty parameter dictionary (apart from reserved framework/routing fields). Selection assigns its complete top-level values. It can set strings, booleans, numbers, nested objects, arrays, and several related fields together. Labels are not constrained to a shared provider vocabulary.

Coverage by mechanism

The payloads below describe shapes, not ready-to-paste declarations for every NOOA route. Native SDK wrappers and PydanticAI-prefixed settings must be expressed using the configured NOOA transport's parameter names.

Mechanism found upstream	Parameter shape	Fit in #320
OpenAI Chat / compatible effort controls	reasoning_effort: high	Yes: scalar field. PydanticAI also uses this for supported Groq/xAI/Cerebras/Mistral paths.
OpenAI Responses / Codex	reasoning: {effort: high, ...}	Yes: complete nested object; additional mode/summary/context fields are expressible without new NOOA branches.
Older Anthropic budget-based thinking	thinking: {type: enabled, budget_tokens: 4096}	Yes; the same label can set an appropriate max_tokens alongside the budget.
Anthropic adaptive thinking	thinking: {type: adaptive} plus output_config: {effort: high}	Yes: multiple coordinated fields. Required beta headers/settings are also static data where supported by the transport.
Gemini level or token budget	thinking config with thinkingLevel or thinkingBudget, optionally includeThoughts	Yes as nested data; native Google SDK names are not automatically NOOA/LiteLLM argument names. The existing Hub example uses its supported reasoning_effort interface.
DeepSeek / Z.AI / GLM switch plus optional effort	thinking: {type: enabled}, optional clear_thinking and reasoning_effort	Yes. The readable-reasoning retention mechanism is separate from selecting these fields.
Qwen-compatible switches	enable_thinking: true or chat_template_kwargs: {enable_thinking: true, ...}	Yes. Pi's generic template variables can be resolved into literal values per declared label.
Baseten-style template arguments	chat_template_args: {...}, optionally effort/budget fields	Yes: arbitrary nested fields, not a maintained field allowlist.
OpenRouter / Together / Ant-Ling variants	nested reasoning containing effort, enabled, or budget fields, with route-appropriate combinations	Yes as data. Do not copy another library's extra_body wrapper verbatim.
String thinking / Mistral prompt mode	thinking: high or prompt_mode: reasoning	Yes. prompt_mode is a request field, not an instruction inserted into history.
Bedrock family-specific wrappers	additionalModelRequestFields containing thinking/output_config, reasoning_effort, or reasoning_config	Yes as a nested-object shape; the installed adapter must expose the corresponding kwargs.
Reasoning visibility options	reasoning_format: parsed, includeThoughts, display settings	Expressible, but visibility is not equivalent to enabling/disabling computation. Likewise a provider's lowest level does not necessarily turn reasoning off.

Source anchors: Pi Chat variants, Pi Anthropic, Pi Google, Pi Mistral; PydanticAI OpenAI, Anthropic, Google, Bedrock, Groq, Z.AI, OpenRouter.

What this deliberately does not reproduce

1. Transport translation and capability inference. PydanticAI's google_thinking_config, for example, is its own interface, not a universal wire key. Some adapters put fields in extra_body; feat(llm): declare reasoning levels per configured route #320 reserves that wrapper, so a declaration must use the equivalent supported top-level transport kwargs. If the current adapter exposes no equivalent, that route has a transport gap, not automatic coverage. The existing Sonnet alias rejection/drop behavior demonstrates why inspecting the outgoing HTTP body remains necessary.
2. Dynamic algorithms. Pi adjusts thinking and answer budgets using model limits and remaining context (budget helper, Anthropic call path). feat(llm): declare reasoning levels per configured route #320 can declare the resulting fixed budget and output cap, but does not recompute them from each request. Provider restrictions involving sampling, forced tools, and output mode are not automatically discovered or repaired either.
3. Message-based controls. Pi inserts historical/current effort messages on some Anthropic routes (implementation); OpenAI documents cache-preserving configuration_update input items (official guidance). Neither is a parameter-block update. Prompt-only switches and switching to a separate reasoning model are also outside feat(llm): declare reasoning levels per configured route #320: messages/input/model are deliberately reserved. Ordinary request-level reasoning can still be selected; cache-preserving mid-thread changes are a separate feature.
4. Omission/deletion semantics. Selection sets/replaces fields; it cannot delete an inherited field. None is a value, not a generic remove-key operation. A named empty/no-op patch is rejected. Always-on/default reasoning can use the unselected base configuration (reasoning_level=None), and explicit off/disabled/zero controls fit where the provider actually supports them. No universal 'off' semantics are promised.

Verification

Ran an offline, in-memory check of ReasoningConfig + apply_reasoning_level with 18 representative static shapes: scalar effort; nested Responses options; budget plus output cap; adaptive thinking plus headers; Google level/budget; GLM switch; boolean switch; both template-object forms; nested OpenRouter/Together options; string thinking; Mistral prompt mode; Groq format; Bedrock wrapper; false/zero; and nullable values. All 18 were preserved exactly, with unrelated defaults intact. Also confirmed rejection of the extra_body wrapper, message/model edits, and empty named patches.

These were mechanism-only checks, not live calls or proof of provider acceptance; no mappings were committed. The PR's existing mocked HTTP tests provide transport evidence for its three declared example routes. Recommendation: keep the mechanism unchanged and small; populate future declarations during model onboarding/route validation rather than copying Pi or PydanticAI's runtime decision trees into NOOA.

[furgalep]

Rebased onto main 3083ed0 after #319 merged; published head 26658c2. Resolved overlapping UnifiedLLM setup by preserving cache validation and preparing reasoning settings before cache/provider dispatch in both sync and async clients. Preserved both cache and reasoning sections in the agent-authoring skill. No reasoning-level behavior changes intended.

Validation: 2,298 passed, 6 skipped in UnifiedLLM/context/runtime suites; final combined reasoning/cache tests 161 passed; lint and formatting clean. Wren accepted the earlier rebase and reviewed the skill update; the final post-#319 resolution is sent for recheck. Fresh GitHub checks will run on this head.

[alessiodevoto]

Reviewed this head and ran the UnifiedLLM suite: 951 passed, 5 deselected. The three inline findings were reproduced with additional mocked HTTP probes through the installed SDK; no live inference was used.

[alessiodevoto]

LGTM! Thanks!

[furgalep]

Comment audit: all human and CodeRabbit review threads are addressed and resolved at 5a40b67. I rechecked the reasoning selection, outbound request and registry tests; no additional code change was needed in this pass.

On CodeRabbit’s remaining docstring-coverage warning: the public configuration and selection APIs document their behavior, including replacement rather than nested merging, route isolation and defaults as metadata. I am retaining concise, self-describing test names instead of adding repetitive docstrings solely to meet a percentage. This is an intentional documentation-style exception, not a claim that the automated coverage warning is green. Automated reviews are currently paused; this audit read both the review threads and ordinary PR comments.