feat: concurrent tool execution with ThreadPoolExecutor

When the model returns multiple tool calls in a single response, they are
now executed concurrently using a thread pool instead of sequentially.
This significantly reduces wall-clock time when multiple independent tools
are batched (e.g. parallel web_search, read_file, terminal calls).

Architecture:
- _execute_tool_calls() dispatches to sequential or concurrent path
- Single tool calls and batches containing 'clarify' use sequential path
- Multiple non-interactive tools use ThreadPoolExecutor (max 8 workers)
- Results are collected and appended to messages in original order
- _invoke_tool() extracted as shared tool invocation helper

Safety:
- Pre-flight interrupt check skips all tools if interrupted
- Per-tool exception handling: one failure doesn't crash the batch
- Result truncation (100k char limit) applied per tool
- Budget pressure injection after all tools complete
- Checkpoints taken before file-mutating tools
- CLI spinner shows batch progress, then per-tool completion messages

Tests: 10 new tests covering dispatch logic, ordering, error handling,
interrupt behavior, truncation, and _invoke_tool routing.

run_agent.py

@@ -21,6 +21,7 @@ Usage:
 """
 
 import atexit
+import concurrent.futures
 import copy
 import hashlib
 import json
@@ -193,6 +194,14 @@ class IterationBudget:
             return max(0, self.max_total - self._used)
 
 
+# Tools that must never run concurrently (interactive / user-facing).
+# When any of these appear in a batch, we fall back to sequential execution.
+_NEVER_PARALLEL_TOOLS = frozenset({"clarify"})
+
+# Maximum number of concurrent worker threads for parallel tool execution.
+_MAX_TOOL_WORKERS = 8
+
+
 class AIAgent:
     """
     AI Agent with tool calling capabilities.
@@ -3119,7 +3128,260 @@ class AIAgent:
         return compressed, new_system_prompt
 
     def _execute_tool_calls(self, assistant_message, messages: list, effective_task_id: str, api_call_count: int = 0) -> None:
-        """Execute tool calls from the assistant message and append results to messages."""
+        """Execute tool calls from the assistant message and append results to messages.
+
+        Dispatches to concurrent execution when multiple independent tool calls
+        are present, falling back to sequential execution for single calls or
+        when interactive tools (e.g. clarify) are in the batch.
+        """
+        tool_calls = assistant_message.tool_calls
+
+        # Single tool call or interactive tool present → sequential
+        if (len(tool_calls) <= 1
+                or any(tc.function.name in _NEVER_PARALLEL_TOOLS for tc in tool_calls)):
+            return self._execute_tool_calls_sequential(
+                assistant_message, messages, effective_task_id, api_call_count
+            )
+
+        # Multiple non-interactive tools → concurrent
+        return self._execute_tool_calls_concurrent(
+            assistant_message, messages, effective_task_id, api_call_count
+        )
+
+    def _invoke_tool(self, function_name: str, function_args: dict, effective_task_id: str) -> str:
+        """Invoke a single tool and return the result string. No display logic.
+
+        Handles both agent-level tools (todo, memory, etc.) and registry-dispatched
+        tools. Used by the concurrent execution path; the sequential path retains
+        its own inline invocation for backward-compatible display handling.
+        """
+        if function_name == "todo":
+            from tools.todo_tool import todo_tool as _todo_tool
+            return _todo_tool(
+                todos=function_args.get("todos"),
+                merge=function_args.get("merge", False),
+                store=self._todo_store,
+            )
+        elif function_name == "session_search":
+            if not self._session_db:
+                return json.dumps({"success": False, "error": "Session database not available."})
+            from tools.session_search_tool import session_search as _session_search
+            return _session_search(
+                query=function_args.get("query", ""),
+                role_filter=function_args.get("role_filter"),
+                limit=function_args.get("limit", 3),
+                db=self._session_db,
+                current_session_id=self.session_id,
+            )
+        elif function_name == "memory":
+            target = function_args.get("target", "memory")
+            from tools.memory_tool import memory_tool as _memory_tool
+            result = _memory_tool(
+                action=function_args.get("action"),
+                target=target,
+                content=function_args.get("content"),
+                old_text=function_args.get("old_text"),
+                store=self._memory_store,
+            )
+            # Also send user observations to Honcho when active
+            if self._honcho and target == "user" and function_args.get("action") == "add":
+                self._honcho_save_user_observation(function_args.get("content", ""))
+            return result
+        elif function_name == "clarify":
+            from tools.clarify_tool import clarify_tool as _clarify_tool
+            return _clarify_tool(
+                question=function_args.get("question", ""),
+                choices=function_args.get("choices"),
+                callback=self.clarify_callback,
+            )
+        elif function_name == "delegate_task":
+            from tools.delegate_tool import delegate_task as _delegate_task
+            return _delegate_task(
+                goal=function_args.get("goal"),
+                context=function_args.get("context"),
+                toolsets=function_args.get("toolsets"),
+                tasks=function_args.get("tasks"),
+                max_iterations=function_args.get("max_iterations"),
+                parent_agent=self,
+            )
+        else:
+            return handle_function_call(
+                function_name, function_args, effective_task_id,
+                enabled_tools=list(self.valid_tool_names) if self.valid_tool_names else None,
+            )
+
+    def _execute_tool_calls_concurrent(self, assistant_message, messages: list, effective_task_id: str, api_call_count: int = 0) -> None:
+        """Execute multiple tool calls concurrently using a thread pool.
+
+        Results are collected in the original tool-call order and appended to
+        messages so the API sees them in the expected sequence.
+        """
+        tool_calls = assistant_message.tool_calls
+        num_tools = len(tool_calls)
+
+        # ── Pre-flight: interrupt check ──────────────────────────────────
+        if self._interrupt_requested:
+            print(f"{self.log_prefix}⚡ Interrupt: skipping {num_tools} tool call(s)")
+            for tc in tool_calls:
+                messages.append({
+                    "role": "tool",
+                    "content": f"[Tool execution cancelled — {tc.function.name} was skipped due to user interrupt]",
+                    "tool_call_id": tc.id,
+                })
+            return
+
+        # ── Parse args + pre-execution bookkeeping ───────────────────────
+        parsed_calls = []  # list of (tool_call, function_name, function_args)
+        for tool_call in tool_calls:
+            function_name = tool_call.function.name
+
+            # Reset nudge counters
+            if function_name == "memory":
+                self._turns_since_memory = 0
+            elif function_name == "skill_manage":
+                self._iters_since_skill = 0
+
+            try:
+                function_args = json.loads(tool_call.function.arguments)
+            except json.JSONDecodeError:
+                function_args = {}
+            if not isinstance(function_args, dict):
+                function_args = {}
+
+            # Checkpoint for file-mutating tools
+            if function_name in ("write_file", "patch") and self._checkpoint_mgr.enabled:
+                try:
+                    file_path = function_args.get("path", "")
+                    if file_path:
+                        work_dir = self._checkpoint_mgr.get_working_dir_for_path(file_path)
+                        self._checkpoint_mgr.ensure_checkpoint(work_dir, f"before {function_name}")
+                except Exception:
+                    pass
+
+            parsed_calls.append((tool_call, function_name, function_args))
+
+        # ── Logging / callbacks ──────────────────────────────────────────
+        tool_names_str = ", ".join(name for _, name, _ in parsed_calls)
+        if not self.quiet_mode:
+            print(f"  ⚡ Concurrent: {num_tools} tool calls — {tool_names_str}")
+            for i, (tc, name, args) in enumerate(parsed_calls, 1):
+                args_str = json.dumps(args, ensure_ascii=False)
+                args_preview = args_str[:self.log_prefix_chars] + "..." if len(args_str) > self.log_prefix_chars else args_str
+                print(f"  📞 Tool {i}: {name}({list(args.keys())}) - {args_preview}")
+
+        for _, name, args in parsed_calls:
+            if self.tool_progress_callback:
+                try:
+                    preview = _build_tool_preview(name, args)
+                    self.tool_progress_callback(name, preview, args)
+                except Exception as cb_err:
+                    logging.debug(f"Tool progress callback error: {cb_err}")
+
+        # ── Concurrent execution ─────────────────────────────────────────
+        # Each slot holds (function_name, function_args, function_result, duration, error_flag)
+        results = [None] * num_tools
+
+        def _run_tool(index, tool_call, function_name, function_args):
+            """Worker function executed in a thread."""
+            start = time.time()
+            try:
+                result = self._invoke_tool(function_name, function_args, effective_task_id)
+            except Exception as tool_error:
+                result = f"Error executing tool '{function_name}': {tool_error}"
+                logger.error("_invoke_tool raised for %s: %s", function_name, tool_error, exc_info=True)
+            duration = time.time() - start
+            is_error, _ = _detect_tool_failure(function_name, result)
+            results[index] = (function_name, function_args, result, duration, is_error)
+
+        # Start spinner for CLI mode
+        spinner = None
+        if self.quiet_mode:
+            face = random.choice(KawaiiSpinner.KAWAII_WAITING)
+            spinner = KawaiiSpinner(f"{face} ⚡ running {num_tools} tools concurrently", spinner_type='dots')
+            spinner.start()
+
+        try:
+            max_workers = min(num_tools, _MAX_TOOL_WORKERS)
+            with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
+                futures = []
+                for i, (tc, name, args) in enumerate(parsed_calls):
+                    f = executor.submit(_run_tool, i, tc, name, args)
+                    futures.append(f)
+
+                # Wait for all to complete (exceptions are captured inside _run_tool)
+                concurrent.futures.wait(futures)
+        finally:
+            if spinner:
+                # Build a summary message for the spinner stop
+                completed = sum(1 for r in results if r is not None)
+                total_dur = sum(r[3] for r in results if r is not None)
+                spinner.stop(f"⚡ {completed}/{num_tools} tools completed in {total_dur:.1f}s total")
+
+        # ── Post-execution: display per-tool results ─────────────────────
+        for i, (tc, name, args) in enumerate(parsed_calls):
+            r = results[i]
+            if r is None:
+                # Shouldn't happen, but safety fallback
+                function_result = f"Error executing tool '{name}': thread did not return a result"
+                tool_duration = 0.0
+            else:
+                function_name, function_args, function_result, tool_duration, is_error = r
+
+                if is_error:
+                    result_preview = function_result[:200] if len(function_result) > 200 else function_result
+                    logger.warning("Tool %s returned error (%.2fs): %s", function_name, tool_duration, result_preview)
+
+                if self.verbose_logging:
+                    result_preview = function_result[:200] if len(function_result) > 200 else function_result
+                    logging.debug(f"Tool {function_name} completed in {tool_duration:.2f}s")
+                    logging.debug(f"Tool result preview: {result_preview}...")
+
+            # Print cute message per tool
+            if self.quiet_mode:
+                cute_msg = _get_cute_tool_message_impl(name, args, tool_duration, result=function_result)
+                print(f"  {cute_msg}")
+            elif not self.quiet_mode:
+                response_preview = function_result[:self.log_prefix_chars] + "..." if len(function_result) > self.log_prefix_chars else function_result
+                print(f"  ✅ Tool {i+1} completed in {tool_duration:.2f}s - {response_preview}")
+
+            # Truncate oversized results
+            MAX_TOOL_RESULT_CHARS = 100_000
+            if len(function_result) > MAX_TOOL_RESULT_CHARS:
+                original_len = len(function_result)
+                function_result = (
+                    function_result[:MAX_TOOL_RESULT_CHARS]
+                    + f"\n\n[Truncated: tool response was {original_len:,} chars, "
+                    f"exceeding the {MAX_TOOL_RESULT_CHARS:,} char limit]"
+                )
+
+            # Append tool result message in order
+            tool_msg = {
+                "role": "tool",
+                "content": function_result,
+                "tool_call_id": tc.id,
+            }
+            messages.append(tool_msg)
+
+        # ── Budget pressure injection ────────────────────────────────────
+        budget_warning = self._get_budget_warning(api_call_count)
+        if budget_warning and messages and messages[-1].get("role") == "tool":
+            last_content = messages[-1]["content"]
+            try:
+                parsed = json.loads(last_content)
+                if isinstance(parsed, dict):
+                    parsed["_budget_warning"] = budget_warning
+                    messages[-1]["content"] = json.dumps(parsed, ensure_ascii=False)
+                else:
+                    messages[-1]["content"] = last_content + f"\n\n{budget_warning}"
+            except (json.JSONDecodeError, TypeError):
+                messages[-1]["content"] = last_content + f"\n\n{budget_warning}"
+            if not self.quiet_mode:
+                remaining = self.max_iterations - api_call_count
+                tier = "⚠️  WARNING" if remaining <= self.max_iterations * 0.1 else "💡 CAUTION"
+                print(f"{self.log_prefix}{tier}: {remaining} iterations remaining")
+
+    def _execute_tool_calls_sequential(self, assistant_message, messages: list, effective_task_id: str, api_call_count: int = 0) -> None:
+        """Execute tool calls sequentially (original behavior). Used for single calls or interactive tools."""
         for i, tool_call in enumerate(assistant_message.tool_calls, 1):
             # SAFETY: check interrupt BEFORE starting each tool.
             # If the user sent "stop" during a previous tool's execution,