fix: clamp the output-token reservation against the context window - #1196
Conversation
`ProviderTransform.maxOutputTokens` is a per-model ceiling that never reads
`limit.context`, so on a model where the prompt and the completion share one
window a large system prompt pushed `input + reservation` past that window and
the provider rejected the request with a hard 400 before generating anything.
Measured case: a ~52,180-token prompt plus the 16,384-token reservation on a
65,536-token window.
Compaction cannot cover this. `Compaction.isOverflow` runs off the previous
assistant message's token counts, so there is nothing to check on the first
request of a session, and compaction only shortens conversation messages —
never the system prompt, which is where the whole overflow lives here.
The failure was also not classified as an overflow: the provider's wording
("maximum context length **of** 65536 tokens") matches no pattern in
`OVERFLOW_PATTERNS`, the status is 400 rather than 413, and the body carries no
`context_length_exceeded` code. It surfaced as a generic non-retryable
`APIError` and the session died showing raw provider text.
- add `ProviderTransform.clampOutputTokens`, which shrinks the reservation so
`input + reservation` fits the window, and `estimateInputTokens` to size the
prompt about to be sent
- throw `OutputTokenBudgetError` before the request when clamping cannot leave
`OUTPUT_TOKEN_FLOOR` (1,024) tokens, naming the prompt size, the requested
reservation, the window, and the three ways to fix it
- keep a 2% (minimum 512-token) margin, since the input count is a
character-ratio estimate rather than the provider's tokenizer
- apply the clamp in `session/llm.ts` after the `chat.params` hook so plugin
overrides are checked too, and in the `session/llm/request.ts` twin
- leave untouched: configs that already fit, models that budget input
separately via `limit.input`, models declaring no window, and windows too
small to hold a floor-sized completion (those limits are not credible enough
to fail a request on)
11 tests cover the reported case, the floor, and the unchanged paths.
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Included review availability: Your plan provides up to 4 included reviews per hour; 2 remain after this review. 📝 WalkthroughWalkthroughThe change centralizes input estimation, output-token clamping, reasoning-budget adjustment, and request-header merging. Request preparation and streaming now use finalized messages, tools, instructions, provider options, and headers before sending requests. Tests cover media, context, budget, and header behavior. ChangesOutput token budget handling
Estimated code review effort: 4 (Complex) | ~60 minutes Merge Risk: ⚪ Minimal · up to The PR centralizes local context budgeting and clamps output reservations before requests are sent; no actionable merge-blocking risk remains, so it is merge-ready after normal checks and review. Sequence Diagram(s)sequenceDiagram
participant LLMRequest
participant HeaderMerger
participant BudgetModule
participant ProviderTransport
LLMRequest->>HeaderMerger: merge provider, model, request, and hook headers
HeaderMerger-->>LLMRequest: return canonical request headers
LLMRequest->>BudgetModule: estimate finalized input and resolve context
BudgetModule-->>LLMRequest: return clamped output and reasoning budgets
LLMRequest->>ProviderTransport: send prompt, tools, headers, and clamped options
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✅ Passed checks (3 passed)
Full details: Linked Issues checkExplanation The changes satisfy issue Full details: Out of Scope Changes checkExplanation Most changes support the linked issue, but the OAuth callback regex reformat in the upstream bridge test is unrelated to output-token budgeting and has no semantic effect. The committed research review artifacts are also process documentation rather than implementation work for issue Full details: Docstring CoverageExplanation Docstring coverage is 76.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 25 functions across 11 files. (2 skipped: 2 unsupported.) Full details: Description checkExplanation The description is complete and directly addresses the issue, change scope, implementation, verification results, limitations, screenshots requirement, and checklist. The selected bug-fix type matches the primary change.
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…st builder Marker Guard diffs against `origin/main`, which treats `session/llm/request.ts` as upstream-shared. The changed `params:` line in the returned object sat outside any `altimate_change` block, so the strict check failed. The local run missed it because a stale `main` ref put the file outside the shared set. No behaviour change.
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Inline comments:
In `@packages/opencode/src/provider/transform.ts`:
- Line 1301: Move the new budget API symbols, including OUTPUT_TOKEN_FLOOR, its
related constants, error, and functions, out of the ProviderTransform namespace
into flat module-scope exports. Update all callers to import these symbols
directly, and preserve any existing namespace access only through the module’s
bottom-of-file self-reexport pattern.
- Around line 1338-1340: Update estimateInputTokens in
packages/opencode/src/provider/transform.ts:1338-1340 to account for the final
tool definitions or a conservative serialized tool-token budget. In
packages/opencode/src/session/llm/request.ts:179-183 and
packages/opencode/src/session/llm.ts:158-162, move clamping until after tool
resolution and retrieval filtering, then pass the resulting tools into the
estimate. Add a regression test with sufficiently large tool schemas to verify
the shared context limit is respected.
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Review of the clamp found five ways the guard read the wrong numbers. Two were regressions the clamp itself introduced, so they are fixed here rather than deferred. - count tool definitions in the estimate. Providers bill tool schemas as prompt tokens, so omitting them under-counted exactly the tool-heavy sessions most likely to overflow. In `session/llm.ts` the clamp moves below `resolveTools` and the retrieval filter — still after the `chat.params` hook — so it sees the tools actually sent; `session/llm/request.ts` already had them in scope. - strip media before estimating. Attachments arrive as base64 payloads or byte arrays on the model messages, and `JSON.stringify` counted a 1 MiB screenshot as hundreds of thousands of text tokens, refusing a multimodal request the provider would have accepted. `Compaction.estimate` already does this via `stripMedia`; this follows the same trade-off. - apply the estimator margin to the fit check, not just to the clamp. The boundary was discontinuous: one token over clamped to a ~1K cushion, one token under was sent with no protection at all, even though the input count is a character-ratio estimate. - clamp models that declare `limit.input`. That field is an input ceiling inside the shared window, not a separate budget: `Session.Overflow.usable` subtracts the reserved completion from it, and catalog-shaped fixtures pair it with an equal or larger `context` (context 200K / input 200K / output 32K). The early return left the exact 400 this guard prevents. - clamp the configured reasoning budget alongside `maxOutputTokens`. Anthropic rejects a request whose thinking budget is not below `max_tokens`, and this repository configures fixed 16,000/31,999-token budgets, so a clamp on its own turned one provider 400 into another. The floor also rises when a reasoning variant is configured so thinking and an answer both fit. Also honours a context window widened by a request header — the GitLab AI-gateway loader always sends `anthropic-beta: context-1m-2025-08-07` while its catalog entries still declare 200K — and takes the prompt estimate lazily so the history is not serialized on paths that return before it is needed. 13 new tests; the two that pinned the old exact-fill and `limit.input` behaviour are rewritten to assert the corrected outcome. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01VqnuBDGkh1ZT65Ti7e6DHZ
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…eady # Conflicts: # packages/opencode/test/session/llm.test.ts
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Issue for this PR
Closes #1194
Type of change
What does this PR do?
Fixes requests that reserve more output tokens than remain beside the finalized prompt in a model's context window. The original failure was a roughly 52K-token system prompt plus a 16,384-token output reservation on a 65,536-token window, which guaranteed a provider 400 before any generation or tool call.
The PR centralizes request budgeting in
packages/opencode/src/provider/output-token-budget.tsand applies it at both production boundaries:packages/opencode/src/session/llm.tsfor the AI SDK stream path;packages/opencode/src/session/llm/request.tsfor native request preparation.The branch is reconciled with current
main(7f07b7d3b6); the only textual conflict was resolved by preserving both test import sets.The clamp runs after plugin options, headers, tools, instructions, reasoning options, and model-visible messages are finalized. It:
model.limit.inputwhen present;URLrepresentations; andPDF and document policy
PDFs are intentionally handled without a local parser. The estimate is
max(32,768, decoded inline payload bytes)for parts identifiable as PDFs. Remote references and declared-PDF file IDs receive the fixed allowance; untyped provider file IDs receive the generic 16,384-token file allowance. Raw page markers are never trusted, and the request path does not decompress or traverse PDF structure.This is a best-effort local heuristic. The configured provider remains authoritative for exact tokenization and for unusually compact or dense PDFs. Exact page accounting or an isolated document-ingestion service is outside this PR.
How did you verify the code?
git diff --check origin/main...HEAD: passed.Detailed reports:
research/code-reviews/PR 1196 Consensus Review.mdresearch/security-reviews/PR 1196 Security Review.mdPrettier still reports the same existing whole-file drift in
provider/provider.tsandprovider/transform.tsasorigin/main; every other changed file passes. No unrelated whole-file formatting rewrite was included.Not verified: no live request was made against every provider/context combination. Compact or unusually dense PDFs can still be underestimated locally and rejected by the provider.
Screenshots / recordings
Not a UI change.
Checklist
Summary by CodeRabbit
Note
Medium Risk
Touches every LLM request’s token limits, headers, and reasoning options on both AI SDK and native paths; mistakes could reject valid turns or still under/over-estimate versus providers, though behavior is centralized and heavily tested.
Overview
Fixes provider 400s when a large finalized prompt plus a fixed output reservation exceeds the model’s context window (e.g. ~52K input + 16K output on a 65K window).
Adds
output-token-budget.tsand wires it into both request paths (session/llm.tsandsession/llm/request.ts). After plugins finalize headers, tools, instructions, and messages, the stack estimates prompt size (system entries framed like wire messages, tools, instructions, semantic media—not raw base64 as text), applies dedicated input limits and effective context (including known Anthropic 1M beta headers), then clampsmaxOutputTokensor fails locally with clear errors when no usable completion budget remains. Reasoning/thinking budgets are reconciled with the final output cap.mergeRequestHeadersgives case-insensitive, last-wins header merging across provider setup, native adapters, and stream prep so budgeting and transport agree.messagesForInputEstimatereuses unsupported-media projection (and Mistral tool→user bridge) for estimates without mutating history; image empty-data checks cover URL payloads.Large regression coverage documents the reported case, media/PDF heuristics (parser-free PDF floor), dense ASCII, and end-to-end prep/stream behavior.
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