fix: clamp the output-token reservation against the context window - #1196
fix: clamp the output-token reservation against the context window#1196anandgupta42 wants to merge 12 commits into
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`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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📝 WalkthroughWalkthroughThe change expands prompt-token estimation to include tools while excluding media payload bytes. It clamps output and reasoning budgets against the effective context window in request preparation and streaming, including header-based context expansion. Tests cover boundary, estimation, reasoning-budget, and request-preparation behavior. ChangesOutput token budget handling
Estimated code review effort: 4 (Complex) | ~45 minutes Merge Risk: 🟡 Moderate · up to The change prevents oversized LLM requests by clamping output budgets before sending them, but it can still reject valid large-context requests when the capability is provided through model headers rather than chat headers. The header sources should be aligned before merge to avoid incorrect budget enforcement. Sequence Diagram(s)sequenceDiagram
participant LLMStream
participant ChatParamsHook
participant ProviderTransform
participant StreamText
LLMStream->>ChatParamsHook: obtain request parameters and headers
ChatParamsHook-->>LLMStream: return requested maxOutputTokens
LLMStream->>ProviderTransform: estimate system, messages, and resolved tools
ProviderTransform-->>LLMStream: return clamped output and reasoning budgets
LLMStream->>StreamText: stream with clamped provider options and maxOutputTokens
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Full details: Description checkExplanation The description is detailed and covers the issue, implementation, rationale, verification results, known limitations, screenshots status, and checklist. It also reports the existing lint error and lack of live-provider verification. Full details: Linked Issues checkExplanation The PR satisfies the main requirements in issue Full details: Out of Scope Changes checkExplanation The changes remain related to the context-window reservation fix. Tool accounting, media exclusion, reasoning-budget adjustment, header-based context expansion, request-path integration, and regression tests directly support the stated objectives. ✨ Finishing Touches 💡 1📝 Generate docstrings 💡
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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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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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| export function estimateInputTokens(system: string[], messages: unknown[]): number { | ||
| return Token.estimate(system.join("\n")) + Token.estimate(JSON.stringify(messages)) |
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Do not apply the existing 3.2–3.7 characters-per-token heuristic to all serialized text. CJK text and several other scripts commonly consume roughly a token per character rather than one token per several characters, so a long Chinese or Japanese prompt can be underestimated by multiples; the clamp then concludes that the full reservation fits and sends a request whose actual input is already near or beyond the context limit. A tokenizer-aware estimate or at least a conservative script-specific ratio is needed for the clamp to protect non-English sessions.
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Valid, and deliberately not fixed here — leaving this thread open to track it.
You are right about the mechanism. Token.estimate picks a ratio from RATIOS (3.0-4.0 chars/token) via detectRatio, and none of those branches detect CJK, so Chinese or Japanese text falls through to the 3.7 default while real tokenizers are closer to one token per character. A long CJK prompt is therefore under-estimated by roughly 3-4x, and an under-estimate makes the clamp too permissive: it concludes the full reservation fits and sends a request that is already at or past the window.
Three reasons it is not in this PR:
- It is a property of
Token.estimateitself (util/token.ts), not of the clamp.Compactionsizes every session off the same function, so a script-aware ratio changes compaction thresholds for every existing session at the same time — that needs its own measurement and its own PR. - The failure mode is under-protection, not a regression. For CJK sessions the clamp is no worse than the status quo before this PR, where nothing compared input against the window at all. Nothing that works today starts failing.
- A conservative script-specific ratio is the cheap version, but picking one without measuring against real tokenizers would just move the error, and a genuinely tokenizer-aware estimate is a dependency decision.
Other estimator gaps you and the other reviewers raised have been fixed in b06207a — tool schemas are now counted, media is stripped before counting, and the 2% margin now applies to the fit check rather than only the clamp. This thread stays open as the remaining known inaccuracy.
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Re-trigger cubic
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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In `@packages/opencode/src/provider/transform.ts`:
- Around line 1383-1387: Update effectiveContext to inspect both the provided
headers and model.headers when detecting the Anthropic 1M-context beta, merging
their values as needed. Match the anthropic-beta header name case-insensitively
while preserving the existing context maximum behavior.
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All reported issues were addressed across 4 files (changes from recent commits).
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Five follow-ups, four of them defects in the code the previous commit added. - charge stripped media a token allowance instead of nothing. Replacing an attachment with a marker fixed the over-count, but assigned it effectively zero, so a near-window multimodal request could still be waved through. Each stripped payload now costs a flat 1,600 tokens, roughly Anthropic's cap for a full-size image. - strip only fields that belong to a real media part. The replacer matched on the key names `data` and `image` alone, so an ordinary tool argument with either name — plausible for MCP tools taking document data — was dropped from the estimate, which is the failure direction that leaves a request unclamped. It now checks the containing part's `type` via the replacer's receiver. - never demand more headroom than the model itself offers. Catalog entries such as `alibaba/qwen-plus-character-ja` (8,192 context, 512 output) cannot reach the 1,024 floor at all, so a viable request was refused on a threshold that model can never satisfy. The floor is capped at the requested reservation, and when honouring the margin would force a hard failure the request now falls back to the largest budget that fits without it rather than throwing. - find the 1M context beta wherever it is actually set. The first version read only the `chat.headers` hook result, which is the one place GitLab does not put it: the loader stores it in `provider.options.aiGatewayHeaders` (`provider/provider.ts:742-755`) and the SDK sends it directly. All three sources are now checked — `model.headers`, the hook result, and the gateway headers — with case-insensitive header names. - stop deep-cloning provider options on every request. `clampReasoningBudget` round-tripped the options through JSON even when nothing needed lowering, and the clone silently dropped `undefined` and non-JSON values and would throw on a BigInt. It now does a cheap read-only check first and rebuilds only the branch it changes. Six new tests, including the tool-argument false-strip, the multi-attachment allowance, the 512-token output model, the gateway-header source, and a case asserting a lowered budget preserves values a JSON round-trip would lose. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01VqnuBDGkh1ZT65Ti7e6DHZ
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# Conflicts: # packages/opencode/src/provider/transform.ts # packages/opencode/src/session/llm.ts # packages/opencode/src/session/llm/request.ts # packages/opencode/test/provider/transform.test.ts
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| return ( | ||
| Token.estimate(system.join("\n")) + | ||
| Token.estimate(stripped.text) + | ||
| stripped.media * MEDIA_TOKEN_ALLOWANCE + |
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The fresh media allowance still charges every stripped attachment exactly 1,600 tokens, but a PDF is represented as one file part regardless of its page count or decoded size (for example, session/prompt.ts constructs one data URL at lines 2284-2286). A multi-page PDF can therefore consume many times this allowance at the provider; near the context boundary the clamp will underestimate the prompt, reserve too much completion space, and reproduce the provider context-limit rejection. Estimate PDFs by page/content size or apply a PDF-specific conservative bound rather than the capped-image allowance.
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| providerOptions?: Record<string, any>, | ||
| ): number { | ||
| const context = model.limit.context | ||
| const sources = [model.headers, headers, providerOptions?.["aiGatewayHeaders"]] |
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When model.headers contains the 1M beta flag but the chat.headers hook overrides the same header with another value, both request paths send the hook value because it is spread last (session/llm.ts:295-296 and session/llm/request.ts:216-217). This function instead scans the sources independently and returns 1M as soon as it sees the superseded model header, so a prompt above the catalog window can bypass clamping even though the actual request no longer enables the larger window. Merge headers with their outgoing precedence before testing the effective beta value.
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4 issues found across 4 files (changes from recent commits).
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Check if these issues are valid — if so, understand the root cause of each and fix them. If appropriate, use sub-agents to investigate and fix each issue separately.
<file name="packages/opencode/src/provider/transform.ts">
<violation number="1" location="packages/opencode/src/provider/transform.ts:1398">
P1: When a file part contains a large or multi-page document, this fixed image-sized allowance undercounts the actual prompt tokens. The clamp can therefore reserve an output that still exceeds the provider context window; estimate files from their MIME/size or use a conservative file-specific bound.</violation>
<violation number="2" location="packages/opencode/src/provider/transform.ts:1422">
P1: Apply outgoing header precedence before detecting the 1M beta flag. Otherwise a stale `model.headers` value can make `effectiveContext` use 1,000,000 tokens while `chat.headers` disables that flag on the actual request, allowing an oversized prompt through.</violation>
<violation number="3" location="packages/opencode/src/provider/transform.ts:1526">
P1: When reasoning is configured and a plugin or experimental override causes the reservation to clamp below 2,048, this line lowers the floor to the requested value. `clampReasoningBudget` then leaves the reasoning option unchanged below 2,048, sending a 16,000-token thinking budget with `maxOutputTokens: 1,500`, which Anthropic rejects; keep the reasoning floor whenever `reasoningBudget > 0` or fail the request.</violation>
<violation number="4" location="packages/opencode/src/provider/transform.ts:1534">
P2: When the margin can't be honored, the `withoutMargin` fallback returns `context - inputTokens`, filling the window exactly with no safety margin. The margin is the mechanism this PR uses to absorb estimator/tokenizer drift, so this path re-introduces the provider 400 it exists to prevent just when the prompt is nearest the window. Cap the fallback (e.g. subtract `CLAMP_MARGIN_MIN`) or keep the floor-only refusal instead of discarding the margin entirely.</violation>
</file>
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| return ( | ||
| Token.estimate(system.join("\n")) + | ||
| Token.estimate(stripped.text) + | ||
| stripped.media * MEDIA_TOKEN_ALLOWANCE + |
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P1: When a file part contains a large or multi-page document, this fixed image-sized allowance undercounts the actual prompt tokens. The clamp can therefore reserve an output that still exceeds the provider context window; estimate files from their MIME/size or use a conservative file-specific bound.
Prompt for AI agents
Check if this issue is valid — if so, understand the root cause and fix it. At packages/opencode/src/provider/transform.ts, line 1398:
<comment>When a file part contains a large or multi-page document, this fixed image-sized allowance undercounts the actual prompt tokens. The clamp can therefore reserve an output that still exceeds the provider context window; estimate files from their MIME/size or use a conservative file-specific bound.</comment>
<file context>
@@ -1347,28 +1347,56 @@ export namespace ProviderTransform {
- Token.estimate(stringifyWithoutMedia(messages)) +
- (tools ? Token.estimate(stringifyWithoutMedia(tools)) : 0)
+ Token.estimate(stripped.text) +
+ stripped.media * MEDIA_TOKEN_ALLOWANCE +
+ (tools ? Token.estimate(stringifyWithoutMedia(tools).text) : 0)
)
</file context>
| // Never demand more headroom than the model itself offers. Some catalog entries cap output | ||
| // below the floor (`alibaba/qwen-plus-character-ja` declares 8,192 context / 512 output), and | ||
| // failing those requests on a threshold the model can never reach would be wrong. | ||
| const floor = Math.min(baseFloor, requested) |
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P1: When reasoning is configured and a plugin or experimental override causes the reservation to clamp below 2,048, this line lowers the floor to the requested value. clampReasoningBudget then leaves the reasoning option unchanged below 2,048, sending a 16,000-token thinking budget with maxOutputTokens: 1,500, which Anthropic rejects; keep the reasoning floor whenever reasoningBudget > 0 or fail the request.
Prompt for AI agents
Check if this issue is valid — if so, understand the root cause and fix it. At packages/opencode/src/provider/transform.ts, line 1526:
<comment>When reasoning is configured and a plugin or experimental override causes the reservation to clamp below 2,048, this line lowers the floor to the requested value. `clampReasoningBudget` then leaves the reasoning option unchanged below 2,048, sending a 16,000-token thinking budget with `maxOutputTokens: 1,500`, which Anthropic rejects; keep the reasoning floor whenever `reasoningBudget > 0` or fail the request.</comment>
<file context>
@@ -1467,8 +1520,18 @@ export namespace ProviderTransform {
+ // Never demand more headroom than the model itself offers. Some catalog entries cap output
+ // below the floor (`alibaba/qwen-plus-character-ja` declares 8,192 context / 512 output), and
+ // failing those requests on a threshold the model can never reach would be wrong.
+ const floor = Math.min(baseFloor, requested)
+
const clamped = context - inputTokens - margin
</file context>
| const floor = Math.min(baseFloor, requested) | |
| const floor = input.reasoningBudget && input.reasoningBudget > 0 ? baseFloor : Math.min(baseFloor, requested) |
| providerOptions?: Record<string, any>, | ||
| ): number { | ||
| const context = model.limit.context | ||
| const sources = [model.headers, headers, providerOptions?.["aiGatewayHeaders"]] |
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P1: Apply outgoing header precedence before detecting the 1M beta flag. Otherwise a stale model.headers value can make effectiveContext use 1,000,000 tokens while chat.headers disables that flag on the actual request, allowing an oversized prompt through.
Prompt for AI agents
Check if this issue is valid — if so, understand the root cause and fix it. At packages/opencode/src/provider/transform.ts, line 1422:
<comment>Apply outgoing header precedence before detecting the 1M beta flag. Otherwise a stale `model.headers` value can make `effectiveContext` use 1,000,000 tokens while `chat.headers` disables that flag on the actual request, allowing an oversized prompt through.</comment>
<file context>
@@ -1379,10 +1407,28 @@ export namespace ProviderTransform {
+ ): number {
const context = model.limit.context
- if (headers?.["anthropic-beta"]?.includes("context-1m-2025-08-07")) return Math.max(context, 1_000_000)
+ const sources = [model.headers, headers, providerOptions?.["aiGatewayHeaders"]]
+ for (const source of sources) {
+ if (!source || typeof source !== "object") continue
</file context>
| // window without it, prefer sending a marginal request over refusing a viable one — the | ||
| // provider stays the authority on its own tokenizer. | ||
| const withoutMargin = context - inputTokens | ||
| if (withoutMargin >= floor) return Math.min(requested, withoutMargin) |
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P2: When the margin can't be honored, the withoutMargin fallback returns context - inputTokens, filling the window exactly with no safety margin. The margin is the mechanism this PR uses to absorb estimator/tokenizer drift, so this path re-introduces the provider 400 it exists to prevent just when the prompt is nearest the window. Cap the fallback (e.g. subtract CLAMP_MARGIN_MIN) or keep the floor-only refusal instead of discarding the margin entirely.
Prompt for AI agents
Check if this issue is valid — if so, understand the root cause and fix it. At packages/opencode/src/provider/transform.ts, line 1534:
<comment>When the margin can't be honored, the `withoutMargin` fallback returns `context - inputTokens`, filling the window exactly with no safety margin. The margin is the mechanism this PR uses to absorb estimator/tokenizer drift, so this path re-introduces the provider 400 it exists to prevent just when the prompt is nearest the window. Cap the fallback (e.g. subtract `CLAMP_MARGIN_MIN`) or keep the floor-only refusal instead of discarding the margin entirely.</comment>
<file context>
@@ -1467,8 +1520,18 @@ export namespace ProviderTransform {
+ // window without it, prefer sending a marginal request over refusing a viable one — the
+ // provider stays the authority on its own tokenizer.
+ const withoutMargin = context - inputTokens
+ if (withoutMargin >= floor) return Math.min(requested, withoutMargin)
throw new OutputTokenBudgetError({
modelID: input.model.id,
</file context>
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👋 This PR was automatically closed by our quality checks. Common reasons:
If you believe this was a mistake, please open an issue explaining your intended contribution and a maintainer will help you. |
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1 issue found across 2 files (changes from recent commits).
Prompt for AI agents (unresolved issues)
Check if these issues are valid — if so, understand the root cause of each and fix them. If appropriate, use sub-agents to investigate and fix each issue separately.
<file name="packages/opencode/src/provider/output-token-budget.ts">
<violation number="1" location="packages/opencode/src/provider/output-token-budget.ts:289">
P2: When the remaining window is between the floor and the floor plus margin, this fallback can return exactly `context - inputTokens`, eliminating all safety headroom. Because `estimateInputTokens` is heuristic, retain a safety margin or fail instead of reintroducing the provider 400 this clamp is intended to prevent.</violation>
</file>
Tip: Review your code locally with the cubic CLI to iterate faster.
Re-trigger cubic
| // If only the estimator margin causes the failure, preserve a reservation that still fits | ||
| // the declared window. The provider remains authoritative for its exact tokenizer. | ||
| const withoutMargin = Math.floor(context - inputTokens) | ||
| if (withoutMargin >= floor) return Math.min(requested, withoutMargin) |
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P2: When the remaining window is between the floor and the floor plus margin, this fallback can return exactly context - inputTokens, eliminating all safety headroom. Because estimateInputTokens is heuristic, retain a safety margin or fail instead of reintroducing the provider 400 this clamp is intended to prevent.
Prompt for AI agents
Check if this issue is valid — if so, understand the root cause and fix it. At packages/opencode/src/provider/output-token-budget.ts, line 289:
<comment>When the remaining window is between the floor and the floor plus margin, this fallback can return exactly `context - inputTokens`, eliminating all safety headroom. Because `estimateInputTokens` is heuristic, retain a safety margin or fail instead of reintroducing the provider 400 this clamp is intended to prevent.</comment>
<file context>
@@ -279,15 +279,21 @@ export function clampOutputTokens(input: {
+ // If only the estimator margin causes the failure, preserve a reservation that still fits
+ // the declared window. The provider remains authoritative for its exact tokenizer.
+ const withoutMargin = Math.floor(context - inputTokens)
+ if (withoutMargin >= floor) return Math.min(requested, withoutMargin)
throw new OutputTokenBudgetError({
modelID: input.model.id,
</file context>
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| // If only the estimator margin causes the failure, preserve a reservation that still fits | ||
| // the declared window. The provider remains authoritative for its exact tokenizer. | ||
| const withoutMargin = Math.floor(context - inputTokens) | ||
| if (withoutMargin >= floor) return Math.min(requested, withoutMargin) |
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When a model requests at most 1,024 output tokens and the estimated request fits only without the safety margin, this branch returns the unadjusted reservation; for example, an 8,192-token window with 7,680 estimated input tokens and a 512-token reservation returns 512 with zero margin, so any estimator undercount can still cause the provider context-limit failure. Fresh evidence since the earlier margin thread is this new withoutMargin fallback, which bypasses the margin after the normal fit check; clamp below the requested value where useful or reject rather than sending an exact-fill estimate.
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| inputTokens: () => | ||
| estimateInputTokens({ | ||
| system, | ||
| messages: ProviderTransform.messagesForInputEstimate(input.messages, input.model), |
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When a Mistral/Devstral history contains tool messages followed directly by user messages, this estimate sees only the original messages, but the later ProviderTransform.message call runs normalizeMessages, which inserts an additional Done. assistant message for every such transition (provider/transform.ts:217-228). In a long near-window history, the accumulated synthetic messages can exceed the 2% margin, so the clamp reserves output against a smaller prompt than the one actually sent and can still produce a context-limit rejection; estimate the normalized message sequence or account for these inserted messages.
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| model: input.model, | ||
| headerSources: [input.model.headers, headers, input.provider.options], | ||
| }), | ||
| inputTokens: () => |
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For a non-OpenAI-OAuth request with isWorkflow: true, the prepared messages deliberately omit the generated system entries (request.ts:109-118) and no instructions option sends them, but this estimate still charges the entire generated system prompt. A workflow with a large agent or custom system prompt can therefore have its output unnecessarily clamped or fail with OutputTokenBudgetError based on tokens that are absent from the returned request; base the estimate on the same workflow-specific message selection used by messages.
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| for (const source of input.headerSources ?? []) { | ||
| for (const value of anthropicBetaValues(source)) { | ||
| for (const beta of value.split(/[\s,]+/)) { | ||
| context = Math.max(context, CONTEXT_WINDOW_BETAS.get(beta) ?? 0) |
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WARNING: effectiveContextWindow honors a superseded anthropic-beta value, widening the clamp window the outgoing request does not actually have
Both request builders spread ...input.model.headers, ...headers, so the chat.headers hook wins when it overrides anthropic-beta. This function instead scans every headerSources entry independently and takes Math.max, so a context-1m-2025-08-07 value in model.headers (or provider.options) still widens the window to 1,000,000 even when the hook later sets anthropic-beta to a different value. The clamp then lets an oversized prompt through to the exact provider 400 it exists to prevent. Merge the sources with the same precedence the request builders use (hook headers over model headers) before testing the beta value.
Reply with @kilocode-bot fix it to have Kilo Code address this issue.
| // media bytes do not count as literal text. The estimator runs lazily so providers that omit | ||
| // maxOutputTokens pay no serialization cost. Known context beta headers widen the catalog | ||
| // limit before the clamp. Fixed reasoning budgets are reconciled with the final reservation. | ||
| const maxOutputTokens = clampOutputTokens({ |
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SUGGESTION: The clamp composition is duplicated across the two request paths and has already drifted
clampOutputTokens + effectiveContextWindow + estimateInputTokens + clampReasoningBudget are assembled identically in session/llm.ts:229-244 and session/llm/request.ts:181-196. The two have already diverged — request.ts feeds the estimator a sorted sortedTools while llm.ts passes the raw tools object, and the earlier Marker Guard failure came from exactly this twin-path drift. A single shared helper (e.g. OutputTokenBudget.clampRequest(...)) would remove the duplication and make the two paths impossible to drift.
Reply with @kilocode-bot fix it to have Kilo Code address this issue.
1 participant
Issue for this PR
Closes #1194
Type of change
What does this PR do?
The bug.
ProviderTransform.maxOutputTokens(packages/opencode/src/provider/transform.ts:1274) returnsMath.min(model.limit.output, ceiling). It takes no input-size argument and never readsmodel.limit.context.session/llm.ts:144-147passes that value straight tostreamText, andsession/llm/request.ts:129does the same on the Effect path. Nothing anywhere comparesinput + reservationto the window.On a model where the prompt and the completion share one window, a large system prompt therefore produces a request that arithmetically cannot succeed. The measured case: a ~52,180-token prompt plus the shipped 16,384-token reservation against a 65,536-token window. The provider rejects it with a hard 400 before generating anything — zero tool calls. The identical prompt runs fine once the reservation drops, so this is config-dependent, not content-dependent.
Why compaction does not already cover it.
Compaction.isOverflow(session/compaction.ts:80-95) andsession/overflow.ts:16-19do reserve headroom, but they run off the previous assistant message's reported token counts, so on the first request of a session there is nothing to check — and that is when this fires. More fundamentally, compaction only shortens conversation messages. Here the entire 52K lives in the system prompt, which compaction never touches, so no amount of compacting can make the request fit.Why the error was opaque. The provider writes "maximum context length of 65536 tokens". The closest entry in
OVERFLOW_PATTERNS(provider/error.ts:30-47) is/maximum context length is \d+ tokens/i, the status is 400 rather than 413, and the body carries nocontext_length_exceededcode — soparseAPICallErrorclassifies it as a genericAPIError,SessionRetry.retryablereturnsundefinedfor a non-retryable 400, and the session dies showing raw provider text with no hint that one config value fixes it.The fix. A new
ProviderTransform.clampOutputTokensshrinks the reservation soinput + reservationfits the window, plusestimateInputTokensto size the prompt about to be sent. It is applied insession/llm.tsafter thechat.paramshook, so a plugin override is checked too, and in thesession/llm/request.tstwin.When clamping cannot leave a usable budget it throws
OutputTokenBudgetErrorbefore the request, naming the prompt size, the requested reservation, the window, and the three ways out (shrink the prompt, lowerOPENCODE_EXPERIMENTAL_OUTPUT_TOKEN_MAX, use a bigger model).Design choices worth flagging:
OUTPUT_TOKEN_FLOOR(1,024) draws the line and below it the run fails loudly client-side instead. A clear local error beats a provider 400 either way.context - inputwould leave no room for estimator error and still 400. The margin is deliberately larger than the observed drift.test/session/processor-effect.test.tsdeliberately drives acontext: 20model — which is exactly the case the guard should stay out of.limit.inputmodels are skipped. Where a model budgets input separately from output the two are not shared, so clamping would wrongly shrink the completion.The estimate covers exactly what goes on the wire:
session/llm.ts:288-296builds the request payload assystemmapped to system messages followed byinput.messages, which is precisely whatestimateInputTokens(system, input.messages)measures — no double count, nothing omitted. Inrequest.tsthe mergedmessagescan already carrysystemas leading system messages, so the rawinput.messagesis used there for the same reason.Considered and rejected: raising
OutputTokenBudgetErroras aMessageV2.ContextOverflowError. That type setsneedsCompaction = trueinsession/processor.ts:550, which would spin the session through compaction passes that cannot shrink a system prompt. This failure has to be terminal, so it surfaces as a plain error with a full message instead. Traced end to end: the throw lands in thetryaroundLLM.stream(processor.ts:88),MessageV2.fromErrormaps it toNamedError.Unknowncarrying the message,SessionRetry.retryablereturnsundefined, andprocessor.ts:596-615sets the error on the assistant message, publishes it, and goes idle. One error, immediately, no retry storm.How did you verify your code works?
11 new tests in
packages/opencode/test/provider/transform.test.ts, in two layers.Pure-function tests on
clampOutputTokenspin the exact reported numbers:52180 + 16384against a65536window clamps to12312, which is below the requested 16,384, fits the window, and stays above the floor. Others cover the floor throw (asserting the message names 65000, 16384 and 65536), the unchanged-when-it-already-fits path,limit.inputmodels, a zero window, an omitted reservation, and the small-window escape.Three tests drive the real request builder,
LLMRequestPrep.prepare, rather than a provider mock — the same entry point the existing azure test in that file uses. A system prompt sized to ~52K tokens must not come back with the unclamped 16,384; a small prompt must still come back with exactly 16,384; and a prompt that leaves no usable budget must reject before the request is built.Gates run locally on this branch:
bun run typecheck— 13/13 tasks successful.bun run script/upstream/analyze.ts --markers --base origin/main --strict— ok, all custom code marked. This initially passed locally and still failed CI: a stale localmainref putsession/llm/request.tsoutside the upstream-shared set, so the unmarkedparams:line in its return object went unnoticed until Marker Guard diffed againstorigin/main. Fixed in the second commit; also re-ran--require-markers --strict(35/35) and--branding(all blocks closed).bun run lint— 5879 warnings, 1 error. The single error is the pre-existingconsistent-returninpackages/http-recorder/test/record-replay.test.ts. Baseline on the unmodified tree is 5869 warnings and the same 1 error; linting only the changed source files gives 16 warnings before and after, so zero new warnings in source. The 10 added warnings are allno-unsafe-type-assertionfromas anytest fixtures, matching the convention already used throughout that test file.packages/opencode:bun test test/session/ test/provider/— 1336 pass, 21 skip, 45 todo, 0 fail.packages/core:bun test test/session-compaction.test.ts— 1 pass, 0 fail.One test failure was introduced and fixed during development rather than hidden:
session.processor effect tests continue when guarded token fixture does not request compactiondrives acontext: 20model, which the first version of the clamp turned into a hard throw. That is what motivated the small-window escape above; it now passes.Not verified: no live provider request was made against a real 65,536-window endpoint from this branch. The reproduction numbers come from the reported failure and are reproduced here as unit and request-builder assertions, not as a live 400.
One related gap found while tracing this and deliberately not fixed here: a turn that finishes with
finishReason === "length"is recorded inassistantMessage.finishbut raises nothing (MessageV2.OutputLengthErroris defined and never constructed anywhere insrc/). Clamping makes a length-truncated turn slightly more likely, so surfacing that finish reason is worth a follow-up, but changing it affects every provider and belongs in its own PR.Screenshots / recordings
Not a UI change.
Checklist
Summary by CodeRabbit
Note
Medium Risk
Changes every outbound LLM request’s maxOutputTokens and reasoning options based on heuristics; mis-estimation could still 400 at the provider or truncate responses, but the goal is fewer hard failures on large system prompts.
Overview
Fixes provider 400s when a large prompt plus the default maxOutputTokens reservation exceeds the model’s shared context window (e.g. ~52K input + 16K output on a 65K window).
Adds
output-token-budgetto estimate wire payload size (system, messages, tools, instructions; fixed allowance for media; optional Anthropic 1M beta), clampmaxOutputTokenswith a safety margin, and fail early with actionable errors when input or output budgets cannot fit. Reasoning/thinking budgets in provider options are lowered to stay within the clamped output reservation.Wires clamping into
LLM.streamandLLMRequestPrep.prepareafter plugins and tools are finalized;messagesForInputEstimatereuses unsupported-media projection so estimates match what models without image support will send.unsupportedPartsimage handling is tightened (non-string payloads, case-insensitive data URLs, image modality vs file MIME).Reviewed by Cursor Bugbot for commit 9039a17. Bugbot is set up for automated code reviews on this repo. Configure here.