feat(llm): mark explicit cache boundaries before dynamic context

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Summary

• mark the start of CachedBlockFormatter's volatile trailing context with one provider-neutral IR bit
• carry that marker outside public JSON through formatting, reasoning replay, and no-op middleware
• translate it only at the UnifiedLLM provider edge:
  • OpenAI Responses: explicit breakpoint plus explicit-only cache mode
  • Anthropic Chat: native cache_control on the last stable block
  • Gemini/undeclared routes: consume the marker without inventing a wire field
• let a registry/client entry declare cache_breakpoint: openai|anthropic
• fail loudly on ambiguous boundaries, malformed cache configuration, or incompatible per-call model overrides
• persist normalized input, output, cached-input, cache-write, reasoning, total-token, and cost telemetry on LLMResponse.usage

Why this layer exists

NOOA deliberately renders live state—timestamps, progress, environment state—as a separate trailing message. That keeps the preceding prompt byte-stable, but ordering alone is not a complete cache policy. OpenAI explicit caching needs a breakpoint at the last reusable input and explicit-only mode. Anthropic expresses the same semantic boundary by marking the last stable content block. Gemini uses implicit caching (or a separately managed CachedContent resource), so it needs the stable layout but no fabricated per-message field.

The renderer is the only layer that reliably knows “dynamic context starts here.” It publishes that one semantic fact in the provider-independent message IR. UnifiedLLM translates it at the final wire edge, where provider syntax belongs. Events, SQLite archives, agents, and ordinary middleware remain unaware of provider cache formats.

A boundary after every message would be noisier and less correct: it consumes provider cache slots, obscures the volatile suffix, and risks marking changing content. One automatically computed boundary immediately before the live suffix expresses the real stability contract.

The boundary must also survive exact reasoning replay. A canonical assistant turn can expand into several provider-native reasoning/message/tool items, so a raw list index would drift. Keeping the marker on the logical message and resolving it after replay preserves both the provider's exact turn and the reusable prefix.

Finally, cache behavior is only operationally useful if NOOA records what happened. Provider usage shapes differ, and LiteLLM reports computed cost outside its usage object. Normalizing all of them once onto durable LLMResponse.usage gives future TUI/observability code one stable source without another provider abstraction.

Code walkthrough: what changed and why

1. Identify the volatile suffix.
   What: CachedBlockFormatter keeps dynamic context as its own trailing message and marks it with cache_boundary_before=True.
   Why: inferring stability later from roles or text is brittle; the context renderer already knows the stable/dynamic partition.

2. Carry intent outside public JSON.
   What: provider formatters transfer the bit as a private attribute on ReplayCarryingMessage. Transient message serialization excludes reasoning, opaque state, and the marker.
   Why: custom serializers and tracing must see an ordinary provider-independent message, never NOOA control fields or provider blobs.

3. Preserve the anchor through replay and middleware.
   What: Chat preparation carries the marker with the logical message. Responses uses an out-of-band sentinel so replay expansion remains entirely on the stable side. No-op Relay and copy-on-write cache decoration preserve private sidecars; a real public mutation drops them.
   Why: one stored turn can expand to multiple provider items, and cache decoration must never duplicate or discard reasoning state.

4. Map OpenAI Responses policy.
   What: UnifiedLLM walks backward from the boundary to the latest eligible stable input/function result, marks it, and enables explicit cache mode. With no eligible stable input it still enables explicit-only mode.
   Why: this prevents implicit write-through of the volatile suffix, including the edge case where the stable prefix contains only assistant output that cannot legally become input_text.

5. Map Anthropic Chat policy.
   What: UnifiedLLM marks the latest eligible stable public content block, including system-only prefixes, after reasoning replay and before LiteLLM dispatch. It scans past tool-call-only or thinking-only turns that have no markable public content.
   Why: Anthropic puts the breakpoint on a content block; LiteLLM can coalesce adjacent messages and drops a message-level marker on a tool-call-only turn. Selecting an earlier eligible block preserves a usable cache boundary without inventing assistant text or patching the HTTP client.

6. Keep unsupported behavior honest.
   What: registry/client configuration declares the mapping. Without it, the neutral marker is consumed and no provider field is emitted; Gemini remains inert. A per-call model override on a mapped client raises before dispatch.
   Why: a routing string is not evidence of cache capability, and a mapping declared for one model must not silently apply to another.

7. Fail on invalid IR/configuration.
   What: multiple boundaries, non-mapping extra_body, malformed prompt_cache_options, and invalid model overrides raise helpful errors. Ordinary empty messages remain; only the tagged sentinel is consumed.
   Why: silently choosing a boundary or repairing bad configuration would hide framework bugs.

8. Normalize durable usage and cost.
   What: common Chat/Responses usage shapes map to LLMUsage, including nested OpenAI cache_write_tokens. A shared extractor overlays finite nonnegative LiteLLM _hidden_params.response_cost for sync/async Chat and Responses; malformed cost warns without losing token data. The canonical LLMResponse persists this usage with the session.
   Why: cache read/write effectiveness and cost otherwise live in inconsistent provider/LiteLLM locations and disappear before the TUI can display them.

9. Handle existing sessions without migration.
   What: the cache boundary is recomputed on every render rather than persisted in old archives.
   Why: stability is a rendering property. Existing sessions adopt the policy on their next request; that first request may warm a new cache entry.

10. Preserve boundaries across edge cases and resume.
    What: an empty incompatible turn retains its boundary marker; system messages after the boundary remain in the suffix; overlapping cache rules copy the content block they modify. An opt-in live test closes/reopens SQLite and constructs a fresh client before replay.
    Why: dropping or moving a boundary can put live state into the cacheable prefix, while mutating old content breaks append-only replay. Testing rebuilt HTTP requests and provider cache hits verifies the actual session-resume path.

Provider behavior

• OpenAI prompt caching: explicit content-block breakpoint plus explicit-only mode
• Anthropic prompt caching: cache_control on the last stable block
• Gemini context caching: implicit caching or a separately managed CachedContent resource

Non-goals

• no TUI changes
• no model capability catalog
• no Gemini CachedContent lifecycle manager
• no additional telemetry event type

Validation

• scrubber/transport follow-up (2026-09-11): 1,056 passed, 5 deselected, across UnifiedLLM, context blocks, tracing, and Relay (uv run --extra nemo-relay pytest ...). Includes 12 actual LiteLLM/HTTP-mocked Responses override cases, nine Unicode/JSON-prefix/fallback cases, and readable-reasoning OTLP coverage. Ruff and targeted Pyright pass; no inference calls.

• compact-binding follow-up (2026-09-11): 1,035 passed, 5 deselected, across UnifiedLLM, context blocks, tracing, and Relay. Cache-boundary replay uses feat(llm): retain compatibility-scoped OpenAI reasoning state #310/feat(llm): retain Anthropic/Gemini state and portable plain-text reasoning #311's version-2 fingerprints and the real capture path; stable-prefix expansion and readable-reasoning OTLP tests pass. Native Anthropic/Gemini SDK → SQLite → serialized-request probes pass with mocked HTTP. Ruff and targeted Pyright pass; no inference calls.

• latest combined-stack copy/OTLP regression round (2026-09-11): 1,003 passed, 5 deselected, across UnifiedLLM, context blocks, tracing, and Relay. Includes 15 copy-ownership regressions and eight readable-reasoning OTLP cases. Native Anthropic/Gemini SDK → SQLite → serialized-request probes also pass with mocked HTTP; no inference tokens used.

• latest regression round: real SDK → JSON/SQLite → serialized HTTP tests cover multi-block text, ordered/phased messages, summary-only reasoning, unsupported whole-turn rejection, and eligible Anthropic cache-marker fallback

• prior combined-tip full suite (before this follow-up): 7,364 passed, 10 skipped, 239 deselected, 3 expected xfails

• focused cache/Responses/replay set after review: 101 passed

• Ruff, targeted Pyright, DCO trailers, and diff checks pass

• real LiteLLM serialized Responses regression verifies nested cache-write tokens and computed response_cost

• live SQLite-resume tests through NVIDIA Inference Hub: obtain a reasoning-bearing tool turn, warm the stable history, close/reopen SQLite, create a fresh client, and change the trailing dynamic context. Persisted events, opaque state, usage, and the stable serialized HTTP request all compare equal.

  Model	Resumed input tokens	Cached input tokens
  GPT-5.6 Sol (Responses)	6,186	6,162
  Claude Sonnet 5 (native Anthropic Messages via hub)	10,877	10,847
  Gemini 3.1 Pro	24,569	24,491

• reproducible opt-in runner: NOOA_RUN_CACHE_RESUME_LIVE=1 uv run --env-file /path/to/.env pytest tests/integration/test_cache_resume_live.py -m integration -s; requires NVIDIA_INFERENCE_API_KEY. Approximately 90k input tokens for all three cases, with endpoint retries disabled.

• Sonnet 5's Chat-compatible gateway path rejected its thinking configuration; the native Anthropic endpoint on the same hub accepted adaptive thinking and exact signed replay. No provider credits outside NVIDIA were used.

• live Nemotron reasoning survived JSON resume, was replayed as ordinary text to GPT-5.6-sol, and the target accepted it

Stacked on #311.

Summary by CodeRabbit

• New Features
  • Added provider-specific cache boundary support for OpenAI and Anthropic requests.
  • Preserved cache-boundary metadata across rendered messages, replayed conversations, and tool-call batches.
  • Added configuration options for selecting supported cache-breakpoint behavior.
  • Improved usage reporting for Responses API cost metadata.
• Bug Fixes
  • Prevented dynamic context suffixes from being incorrectly included in stable cached content.
  • Ensured unsupported providers fail safely without emitting cache controls.
• Documentation
  • Documented provider-specific cache behavior and dynamic context markers.

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📝 Walkthrough

Walkthrough

The change carries replay metadata and cache-boundary markers through rendered messages, replay preparation, provider formatters, and Chat Completions and Responses clients. Registry settings, documentation, and regression tests cover provider mappings, replay ordering, usage parsing, and unsupported-provider behavior.

Changes

Replay State and Cache Boundaries

Layer / File(s)	Summary
Replay metadata contract and wire formatting src/nooa/_llm_state.py, src/nooa/context_blocks/models.py, src/nooa/context_blocks/renderers/cached.py, src/nooa/context_blocks/formatter.py	Rendered messages and replay carriers retain cache-boundary metadata outside serialized fields. Provider formatters attach the metadata to the first emitted provider item.
Replay preparation and provider state handling src/nooa/unifiedllm/replay_state.py, src/nooa/unifiedllm/unifiedllm.py	Replay preparation preserves cache-boundary markers. UnifiedLLM validates markers, maps them to provider controls, and preserves their position during replay expansion.
Provider client cache mapping and response handling src/nooa/unifiedllm/unifiedllm.py	CompletionClient and ResponsesClient validate provider-specific settings, prepare synchronous and asynchronous requests, enable explicit OpenAI caching, and normalize response cost metadata.
Registry configuration and behavior validation src/nooa/unifiedllm/registry.py, tests/unifiedllm/test_explicit_cache_boundary.py, tests/unifiedllm/test_model_registry.py, tests/test_nemo_relay_middleware.py, skills/nooa-context-and-state/SKILL.md, src/nooa/llm_types.py	Registry settings, tests, documentation, middleware coverage, and formatting-only support changes cover serialization, provider mappings, replay ordering, usage parsing, and unsupported-provider behavior.

Estimated code review effort: 4 (Complex) | ~60 minutes

Sequence Diagram(s)

sequenceDiagram
  participant RenderedMessage
  participant ReplayPreparation
  participant UnifiedLLM
  participant ProviderAPI
  RenderedMessage->>ReplayPreparation: provide replay metadata and cache boundary
  ReplayPreparation->>UnifiedLLM: prepare provider request
  UnifiedLLM->>ProviderAPI: send provider-specific cache controls
  ProviderAPI-->>UnifiedLLM: return response items and usage data

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The cache-boundary implementation is broadly covered, but existing synchronous tool-call, test-resource, and replay-fallback concerns remain unresolved. Address or explicitly accept these before merging.

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tests/unifiedllm/test_explicit_cache_boundary.py (1)

256-277: 🎯 Functional Correctness | 🔵 Trivial | ⚡ Quick win

Skip plain-string assistant items when selecting the Responses cache breakpoint.

ResponsesClient preserves assistant string content through _transform_messages. Before the boundary, _mark_responses_cache_breakpoint rewrites that content as an input_text block and stops, so the breakpoint lands on assistant output instead of the preceding user content. Skip assistant strings like the existing output_text list case, and add this regression test.

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In `@tests/unifiedllm/test_explicit_cache_boundary.py` around lines 256 - 277,
Update ResponsesClient._mark_responses_cache_breakpoint to skip assistant
messages whose content is a plain string, matching the existing output_text-list
handling, so the explicit cache breakpoint is applied to the preceding user
content. Preserve the existing behavior for assistant output_text lists and add
coverage in test_openai_does_not_decorate_prior_output_text for the plain-string
assistant case.

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Inline comments:
In `@src/nooa/unifiedllm/unifiedllm.py`:
- Line 3082: Update the cache handling around _prepare_cache_boundary to apply
the Anthropic cache mapping only when _is_anthropic_model(self.model) is true;
preserve the existing OpenAI mapping for non-Anthropic models, or validate and
reject a mismatched cache_breakpoint during construction.

---

Nitpick comments:
In `@tests/unifiedllm/test_explicit_cache_boundary.py`:
- Around line 256-277: Update ResponsesClient._mark_responses_cache_breakpoint
to skip assistant messages whose content is a plain string, matching the
existing output_text-list handling, so the explicit cache breakpoint is applied
to the preceding user content. Preserve the existing behavior for assistant
output_text lists and add coverage in
test_openai_does_not_decorate_prior_output_text for the plain-string assistant
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Inline comments:
In `@src/nooa/unifiedllm/unifiedllm.py`:
- Around line 796-800: Update the content-marking logic around _mark_text_block
to select output_text for assistant messages and input_text for system,
developer, and user messages before adding prompt_cache_breakpoint. Preserve
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Completed another sequential review of #310 → #311 → #313, with verified findings fixed in the owning PR and the stack restacked.

• feat(llm): retain compatibility-scoped OpenAI reasoning state #310 (baffb0fb): honor per-call endpoint/credential changes and redact private replay envelopes in the shared journal scrubber.
• feat(llm): retain Anthropic/Gemini state and portable plain-text reasoning #311 (4d342390): keep structurally cleaned call/result IDs paired; mark replay-error turns final; remove duplicate HTTP redaction logic.
• feat(llm): mark explicit cache boundaries before dynamic context #313 (7853ce8d): preserve boundaries when empty incompatible turns disappear, keep dynamic system suffixes out of stable instructions, and avoid caller-content mutation from overlapping cache rules. Added an opt-in live SQLite-resume regression.

Full combined suite: 7,330 passed, 10 skipped, 239 deselected, 3 expected xfails. Focused regressions, Ruff, targeted Pyright, and diff checks pass.

Live NVIDIA Inference Hub tests used a reasoning-bearing tool turn, then warmed the history, closed/reopened SQLite, constructed a fresh client, and changed the trailing dynamic context. Every provider preserved event data, usage, opaque state, and the stable serialized HTTP request; matching opaque state was present on the resumed wire request and accepted.

Model	Resumed input	Cache read	Cache write
GPT-5.6 Sol, Responses	6,186	6,162	21
Claude Sonnet 5, native Anthropic Messages through hub	10,877	10,847	0
Gemini 3.1 Pro	24,569	24,491	0

Sonnet 5 required the hub's native Anthropic endpoint: the Chat-compatible gateway path rejected the thinking configuration. This result does not claim that gateway path supports adaptive thinking. OpenAI's initial trivial seed emitted no reasoning even at medium effort; a small reasoning task produced encrypted state and exercised retention successfully.

Reproduce with NVIDIA_INFERENCE_API_KEY in your environment:

NOOA_RUN_CACHE_RESUME_LIVE=1 uv run pytest tests/integration/test_cache_resume_live.py -m integration -s

The cases make three calls each, disable endpoint retries, and use roughly 90k input tokens total. Raw opaque payloads are not printed. All PR descriptions now include the additional what/why walkthrough and current validation results.

[furgalep]

Completed the review → fix → independent re-review round.

PR	Reviewed head	Main corrections
#310	aca3c9ae	SDK-consistent text aggregation; ordered message/phase replay in the private envelope; whole-turn projectability check; captured reasoning-index order validation
#311	9824e1c0	Summary-only reasoning is ordinary portable text; mixed encrypted/summary-only sequences never replay partially; malformed supplied ciphertext and unsupported retained-turn shapes fail clearly; ordinary tool IDs remain intact
#313	b8a18bb6	Anthropic boundaries fall back to eligible earlier public content when the last stable assistant has only thinking/tool calls

Independent reviewers completed simplicity/elegance, file-by-file correctness, and test-coverage passes. No remaining blockers were found after the fixes. No public IR expansion or provider catalog was added.

Validation on the final combined tip b8a18bb6:

uv run pytest -m 'not integration and not stress' -q --tb=short
7364 passed, 10 skipped, 239 deselected, 3 xfailed

Ruff, selected changed-file Pyright, and whitespace checks are clean. New regressions use actual SDK response types and inspect serialized HTTP requests after JSON/SQLite resume; relevant regressions were also verified to fail against the old implementations.

The earlier NVIDIA Hub live cache/resume results remain recorded above. This round used deterministic SDK/HTTP tests and spent no additional inference tokens. All PR descriptions now explain both what changed and why.

[furgalep]

Superseded by #319 (cache policy), stacked on #318 (ordered responses and reasoning replay). The replacement uses a metadata-role boundary and removes the old positional cache policy. Review order: #318, then #319. Closing this implementation to keep review in one place; its branch and review history are retained. Fresh live-provider validation of the replacement remains pending.