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https://github.com/hansjone/oclaw.git
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fix(chat): complete streaming for reasoning, tools, and tool calls
- Forward reasoning_content and non-stream reasoning to on_token in OpenAI chat completions; Gemini thought parts to on_token. - Stream bubble: always render session.tool panels; format tool_use_call payloads and title as call · tool_name. - Emit tool_use_call via on_tool_ui before tool execution so WS shows invoke cards before tool_use_result. Co-authored-by: Cursor <cursoragent@cursor.com>
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75a3038f33
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398d85ba06
4 changed files with 68 additions and 17 deletions
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@ -1730,6 +1730,21 @@ function formatToolPanelText(name, payload, options = {}) {
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};
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const n = String(name || "").trim() || "tool";
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const p = payload && typeof payload === "object" ? payload : {};
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if (String(p.phase || "") === "call" && (p.tool_name != null || p.arguments !== undefined)) {
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const tn = String(p.tool_name || "").trim() || "tool";
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let argsStr = "";
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try {
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argsStr =
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p.arguments && typeof p.arguments === "object"
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? JSON.stringify(p.arguments, null, 2)
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: String(p.arguments ?? "");
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} catch (_) {
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argsStr = String(p.arguments ?? "");
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}
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const tid = String(p.tool_call_id || "").trim();
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const head = currentLang === "zh" ? `[调用] ${tn}` : `[call] ${tn}`;
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return normalizeStreamText([head, tid ? `tool_call_id: ${tid}` : "", "", argsStr].filter(Boolean).join("\n"));
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}
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const r = p.result && typeof p.result === "object" ? p.result : p;
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// SQL audit payload: keep line breaks and key fields visible.
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if (r && typeof r === "object" && (r.input_sql || r.executed_sql || r.sql_guard)) {
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@ -4370,16 +4385,15 @@ async function renderChatUi() {
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const title = String(seg.title || "tool");
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const body = String(seg.body || "");
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const images = Array.isArray(seg.images) ? seg.images : [];
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if (!showToolOutput && !images.length) continue;
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// Live stream: always render tool cards from session.tool so过程态完整；showToolOutput 仍用于历史聚合气泡。
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flushTextBuf();
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const audit = seg.sqlAudit && typeof seg.sqlAudit === "object" ? seg.sqlAudit : null;
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if (showToolOutput) {
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if (audit) {
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const guard = audit.guard && typeof audit.guard === "object" ? audit.guard : {};
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const autoLimit = _sqlLimitSuffix(audit.inputSql, audit.executedSql);
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const executedDiffHtml = _renderExecutedSqlWithAddedHighlight(audit.inputSql, audit.executedSql);
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blocks.push(
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`<details class="chat-msg__reasoning"><summary>${escapeHtml(title)} · SQL audit</summary>
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if (audit) {
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const guard = audit.guard && typeof audit.guard === "object" ? audit.guard : {};
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const autoLimit = _sqlLimitSuffix(audit.inputSql, audit.executedSql);
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const executedDiffHtml = _renderExecutedSqlWithAddedHighlight(audit.inputSql, audit.executedSql);
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blocks.push(
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`<details class="chat-msg__reasoning"><summary>${escapeHtml(title)} · SQL audit</summary>
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<div style="display:grid;grid-template-columns:1fr 1fr;gap:8px;margin-top:8px;">
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<div><div class="muted" style="margin-bottom:4px;">input SQL</div><pre class="chat-msg__reasoning-pre">${escapeHtml(String(audit.inputSql || ""))}</pre></div>
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<div><div class="muted" style="margin-bottom:4px;">executed SQL (added highlighted)</div><pre class="chat-msg__reasoning-pre">${executedDiffHtml}</pre></div>
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@ -4394,12 +4408,11 @@ ${autoLimit ? `<div style="margin-top:8px;"><span class="muted">auto-added claus
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rows_returned=${escapeHtml(String(audit.rowsReturned != null ? audit.rowsReturned : ""))}
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</div>
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</details>`,
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);
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} else {
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blocks.push(
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`<details class="chat-msg__reasoning"><summary>${escapeHtml(title)}</summary><pre class="chat-msg__reasoning-pre">${escapeHtml(body)}</pre></details>`,
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);
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}
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);
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} else {
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blocks.push(
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`<details class="chat-msg__reasoning"><summary>${escapeHtml(title)}</summary><pre class="chat-msg__reasoning-pre">${escapeHtml(body)}</pre></details>`,
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);
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}
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for (const im of images) {
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const src = String((im && im.src) || "").trim();
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@ -4508,6 +4521,14 @@ ${autoLimit ? `<div style="margin-top:8px;"><span class="muted">auto-added claus
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const liveTag = currentLang === "zh" ? "[实时全量]" : "[LIVE FULL]";
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const key = `${name}:${++toolSeq}`;
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const rawPayload = p.payload != null ? p.payload : p;
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const titleTool =
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rawPayload && typeof rawPayload === "object" && String(rawPayload.tool_name || "").trim()
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? String(rawPayload.tool_name || "").trim()
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: "";
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const displayName =
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name === "tool_use_call" && titleTool
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? `${currentLang === "zh" ? "调用" : "call"} · ${titleTool}`
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: name;
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const body = formatToolPanelText(name, rawPayload, { streamMode: true });
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const sqlAudit = extractSqlAuditPayload(rawPayload);
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const images = extractToolImageItems(rawPayload);
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@ -4536,7 +4557,14 @@ ${autoLimit ? `<div style="margin-top:8px;"><span class="muted">auto-added claus
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return false;
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};
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if (_containsRefAttachments(rawPayload)) sawStreamToolRefAttachments = true;
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chatStreamSegments.push({ type: "tool", key, title: `${name} ${liveTag}`, body, sqlAudit, images });
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chatStreamSegments.push({
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type: "tool",
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key,
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title: `${displayName} ${liveTag}`.trim(),
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body,
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sqlAudit,
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images,
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});
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};
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const appendFinalAssistant = async (message, fallbackText) => {
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const wsAttachments =
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@ -224,6 +224,8 @@ class GoogleGeminiChatModel(ChatModel):
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txt = str(p.get("text") or "")
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if txt:
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thinking_parts.append(txt)
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if on_token:
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on_token(txt)
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sig = p.get("thoughtSignature")
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if isinstance(sig, str) and sig:
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thinking_signature = sig
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@ -551,8 +551,11 @@ class OpenAIChatModel(ChatModel):
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reasoning_parts = getattr(msg, "reasoning_content", None) or ""
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reasoning_text = str(reasoning_parts).strip() if reasoning_parts else ""
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content = msg.content or ""
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if on_token and content:
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on_token(content)
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if on_token:
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if reasoning_text:
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on_token(reasoning_text)
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if content:
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on_token(content)
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tool_calls: list[LLMToolCall] = []
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if msg.tool_calls:
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@ -620,6 +623,8 @@ class OpenAIChatModel(ChatModel):
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rc = getattr(delta, "reasoning_content", None) or ""
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if rc:
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reasoning_parts.append(rc)
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if on_token:
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on_token(rc)
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if delta.tool_calls:
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for tc in delta.tool_calls:
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idx = int(tc.index)
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@ -1354,6 +1354,22 @@ def run_oclaw_direct_loop(
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# force one no-tool synthesis pass to guarantee a visible assistant body.
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hit_tool_round_limit = True
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# Stream UI: emit one session.tool per pending call before execution (tool_use_result fires after).
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if callable(on_tool_ui):
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for tc in step.llm_tool_calls:
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try:
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on_tool_ui(
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"tool_use_call",
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{
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"phase": "call",
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"tool_name": str(getattr(tc, "name", "") or ""),
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"tool_call_id": str(getattr(tc, "id", "") or ""),
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"arguments": dict(getattr(tc, "arguments", {}) or {}),
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},
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)
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except Exception:
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pass
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elapsed_ms, results_by_id = _execute_tool_step(
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skill_exec=skill_exec,
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store=store,
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