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fix: normalize finish_reason to 'tool_calls' when tool calls are present (#379)

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Some upstream providers (e.g. Antigravity) return non-standard finish_reason
values like 'other' instead of the OpenAI-standard 'tool_calls' when the
model invokes tools. This causes downstream consumers (e.g. OpenClaw) to
fail to execute tool calls, breaking agentic sub-agent workflows.

Changes:
- nonStreamingHandler: post-translation guard that normalizes finish_reason
  to 'tool_calls' when message.tool_calls is present
- sseToJsonHandler: accumulate tool_calls from streaming deltas in
  parseSSEToOpenAIResponse; extract function_call items from Responses API
  output in handleForcedSSEToJson
- openai-responses translator: use toolCallIndex to choose between
  'tool_calls' and 'stop' in flush and response.completed events

Tested: 7 scenarios (non-stream text, single/multiple tool calls, stream
text/tool calls, multi-turn tool conversation, tools present but unused)
This commit is contained in:
Liam
2026-03-23 09:35:25 +07:00
committed by GitHub
parent b8918c0c1c
commit 01e4a28f0a
3 changed files with 51 additions and 5 deletions
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open-sse/handlers/chatCore/sseToJsonHandler.js
+35 -2
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@@ -50,6 +50,7 @@ export function parseSSEToOpenAIResponse(rawSSE, fallbackModel) {
const first = chunks[0];
const contentParts = [];
const reasoningParts = [];
const toolCallMap = new Map(); // index -> { id, type, function: { name, arguments } }
let finishReason = "stop";
let usage = null;
@@ -60,10 +61,27 @@ export function parseSSEToOpenAIResponse(rawSSE, fallbackModel) {
if (typeof delta.reasoning_content === "string" && delta.reasoning_content.length > 0) reasoningParts.push(delta.reasoning_content);
if (choice?.finish_reason) finishReason = choice.finish_reason;
if (chunk?.usage && typeof chunk.usage === "object") usage = chunk.usage;
// Accumulate tool_calls from streaming deltas
if (Array.isArray(delta.tool_calls)) {
for (const tc of delta.tool_calls) {
const idx = tc.index ?? 0;
if (!toolCallMap.has(idx)) {
toolCallMap.set(idx, { id: tc.id || "", type: "function", function: { name: "", arguments: "" } });
}
const existing = toolCallMap.get(idx);
if (tc.id) existing.id = tc.id;
if (tc.function?.name) existing.function.name += tc.function.name;
if (tc.function?.arguments) existing.function.arguments += tc.function.arguments;
}
}
}
const message = { role: "assistant", content: contentParts.join("") };
const message = { role: "assistant", content: contentParts.join("") || (toolCallMap.size > 0 ? null : "") };
if (reasoningParts.length > 0) message.reasoning_content = reasoningParts.join("");
if (toolCallMap.size > 0) {
message.tool_calls = [...toolCallMap.entries()].sort((a, b) => a[0] - b[0]).map(([, tc]) => tc);
}
const result = {
id: first.id || `chatcmpl-${Date.now()}`,
@@ -125,6 +143,18 @@ export async function handleForcedSSEToJson({ providerResponse, sourceFormat, pr
const outTokens = usage.output_tokens || 0;
let finalResp;
// Extract tool calls from Responses API output (function_call items)
const funcCallItems = (jsonResponse.output || []).filter(item => item.type === "function_call");
const toolCalls = funcCallItems.map((item, idx) => ({
id: item.call_id || `call_${item.name}_${Date.now()}_${idx}`,
type: "function",
function: {
name: item.name,
arguments: typeof item.arguments === "string" ? item.arguments : JSON.stringify(item.arguments || {})
}
}));
const hasToolCalls = toolCalls.length > 0;
if (sourceFormat === FORMATS.ANTIGRAVITY || sourceFormat === FORMATS.GEMINI || sourceFormat === FORMATS.GEMINI_CLI) {
finalResp = {
response: {
@@ -135,12 +165,15 @@ export async function handleForcedSSEToJson({ providerResponse, sourceFormat, pr
}
};
} else {
const message = { role: "assistant", content: textContent || (hasToolCalls ? null : "") };
if (hasToolCalls) message.tool_calls = toolCalls;
const finishReason = hasToolCalls ? "tool_calls" : (jsonResponse.status === "completed" ? "stop" : (jsonResponse.status || "stop"));
finalResp = {
id: jsonResponse.id || `chatcmpl-${Date.now()}`,
object: "chat.completion",
created: jsonResponse.created_at || Math.floor(Date.now() / 1000),
model: jsonResponse.model || model,
choices: [{ index: 0, message: { role: "assistant", content: textContent || "" }, finish_reason: jsonResponse.status === "completed" ? "stop" : (jsonResponse.status || "stop") }],
choices: [{ index: 0, message, finish_reason: finishReason }],
usage: { prompt_tokens: inTokens, completion_tokens: outTokens, total_tokens: inTokens + outTokens }
};
}