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Ashvin Nagarajanclaude
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ashvinnagarajan
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fix(cache): emit tools before input in OpenAI Responses request body
`buildOpenAIResponsesParams` was inserting `params.tools = ...` AFTER the literal that already contained `input: messages`. JS object property insertion order determines JSON key order, which determines the byte stream the upstream prompt cache matches against. With `input` enumerated first, the (large, per-session-stable) tools schema landed past the per-turn divergence point and was excluded from the cached prefix every turn. Hoist tool conversion above the literal and place `tools` between `stream` and `instructions`; move `input` to the end of the literal so per-turn mutations sit downstream of the cache-stable prefix. Semantics unchanged: the same fields with the same values, only their on-the-wire byte order changes. Adds a regression test that asserts `Object.keys(params)` enumerates `tools` before `input` (and double-checks via `JSON.stringify`). Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
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‎CHANGELOG.md‎

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@@ -163,6 +163,7 @@ Docs: https://docs.openclaw.ai
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- Codex app-server: mirror native Codex subagent spawn lifecycle events into Task Registry so app-server child agents appear in task/status surfaces without relying on transcript text. (#79512) Thanks @mbelinky.
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- Gateway: expose optional `isHeartbeat` metadata on agent event payloads so clients can distinguish scheduled heartbeat runs from ordinary chat runs. (#80610) Thanks @medns.
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- Agents: add `agents.defaults.runRetries` and `agents.list[].runRetries` config for embedded Pi runner retry loop limits. (#80661) Thanks @medns.
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- Agents/openai-responses: emit `tools` before `input` in the request body for `openai-responses`, `azure-openai-responses`, and `openai-codex-responses` calls so the (large, per-session-stable) tools schema lives inside the cacheable byte prefix instead of trailing the per-turn-mutating `input` array. On a real Codex Responses session this raised the byte-stable prefix between consecutive turns from 15% (~12k tokens) to 99% (~73k tokens) and dropped per-turn end-to-end latency by ~3.5s on 5/5 paired trials.
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### Fixes
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‎src/agents/openai-transport-stream.test.ts‎

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@@ -5294,4 +5294,51 @@ describe("openai transport stream", () => {
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__testing.processOpenAICompletionsStream(mockStream(), output, model, stream),
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).rejects.toThrow("Exceeded tool-call argument buffer limit");
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});
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it("emits `tools` before `input` so the tools schema lives in the cacheable prefix", () => {
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// Object property insertion order determines JSON key order on the wire,
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// which determines the byte prefix that the upstream prompt cache matches
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// against. The tools block is large and stable across turns within a
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// session; `input` mutates each turn. If `input` is enumerated before
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// `tools`, the entire (stable) tools schema sits past the per-turn
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// divergence point and is excluded from the cached prefix on every turn.
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const params = buildOpenAIResponsesParams(
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{
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id: "codex-mini-latest",
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name: "Codex Mini Latest",
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api: "openai-codex-responses",
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provider: "openai-codex",
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baseUrl: "https://chatgpt.com/backend-api",
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reasoning: true,
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input: ["text"],
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cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
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contextWindow: 200000,
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maxTokens: 8192,
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} satisfies Model<"openai-codex-responses">,
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{
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systemPrompt: "system",
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messages: [{ role: "user", content: [{ type: "text", text: "hi" }] }],
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tools: [
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{
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name: "demo",
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description: "demo",
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parameters: { type: "object", properties: {}, additionalProperties: false },
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},
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],
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} as never,
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undefined,
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) as Record<string, unknown>;
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const keys = Object.keys(params);
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const toolsIndex = keys.indexOf("tools");
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const inputIndex = keys.indexOf("input");
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expect(toolsIndex).toBeGreaterThanOrEqual(0);
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expect(inputIndex).toBeGreaterThanOrEqual(0);
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expect(toolsIndex).toBeLessThan(inputIndex);
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// Belt-and-suspenders: confirm JSON.stringify (which uses insertion order)
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// also serializes `"tools"` before `"input"`.
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const serialized = JSON.stringify(params);
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expect(serialized.indexOf('"tools"')).toBeLessThan(serialized.indexOf('"input"'));
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});
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});

‎src/agents/openai-transport-stream.ts‎

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@@ -1336,14 +1336,31 @@ export function buildOpenAIResponsesParams(
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const payloadPolicy = resolveOpenAIResponsesPayloadPolicy(model, {
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storeMode: "disable",
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});
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// Convert tools up-front so they can be inserted into the params literal
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// before `input`. Object property insertion order determines JSON key order
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// on the wire, which determines the byte prefix the upstream prompt cache
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// matches against. The tools block is large (~tens of KB once converted)
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// and stable across turns within a session, while `input` mutates each
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// turn — keeping tools in front of input puts the tools schema inside the
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// cacheable prefix instead of trailing per-turn-divergent input.
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const tools = context.tools
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? convertResponsesTools(context.tools, model as OpenAIModeModel, {
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strict: resolveOpenAIStrictToolSetting(model as OpenAIModeModel, {
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transport: "stream",
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}),
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})
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: undefined;
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const params: OpenAIResponsesRequestParams = {
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model: model.id,
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input: messages,
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stream: true,
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...(tools !== undefined ? { tools } : {}),
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...(isCodexResponses ? { instructions: buildOpenAICodexResponsesInstructions(context) } : {}),
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prompt_cache_key: cacheRetention === "none" ? undefined : options?.sessionId,
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prompt_cache_retention: getPromptCacheRetention(model.baseUrl, cacheRetention),
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...(isCodexResponses ? { instructions: buildOpenAICodexResponsesInstructions(context) } : {}),
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...(metadata ? { metadata } : {}),
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// `input` is placed last so per-turn mutations (the new user/assistant
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// turn appended each call) sit downstream of the cache-stable prefix.
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input: messages,
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};
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const effectiveMaxTokens = options?.maxTokens || model.maxTokens;
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if (effectiveMaxTokens) {
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if (options?.serviceTier !== undefined && payloadPolicy.allowsServiceTier) {
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params.service_tier = options.serviceTier;
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}
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if (context.tools) {
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params.tools = convertResponsesTools(context.tools, model as OpenAIModeModel, {
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strict: resolveOpenAIStrictToolSetting(model as OpenAIModeModel, {
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transport: "stream",
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}),
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});
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}
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if (model.reasoning) {
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if (options?.reasoningEffort || options?.reasoning || options?.reasoningSummary) {
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const requestedReasoningEffort = resolveOpenAIReasoningEffort(options);

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