⚡ Bolt: Concurrent LLM Tool Execution using asyncio.gather

src/pipelines/code_retrieval.py

@@ -25,6 +25,7 @@
 
 from __future__ import annotations
 
+import asyncio
 import logging
 from typing import Any, Callable, Dict, List, Optional
 
@@ -37,7 +38,6 @@
 from src.scanner.code_store import CodeStore
 from src.schemas.code import (
     annotations_namespace,
-    directories_namespace,
     files_namespace,
     snippets_namespace,
     symbols_namespace,
@@ -375,17 +375,24 @@ async def run(
             turn_records: List[SourceRecord] = []
             only_read_tools = True
 
-            for tc in ai_response.tool_calls:
-                tool_name = tc["name"]
-                tool_args = tc["args"]
-                tool_id = tc["id"]
-
+            async def _process_tool_call(tc: Dict[str, Any]):
                 t1 = _time.perf_counter()
                 records = await self._execute_tool(
-                    tool_name, tool_args, repo=repo, top_k=top_k,
+                    tc["name"], tc["args"], repo=repo, top_k=top_k,
                     user_id=user_id,
                 )
                 tool_ms = (_time.perf_counter() - t1) * 1000
+                return tc, records, tool_ms
+
+            results = await asyncio.gather(
+                *[_process_tool_call(tc) for tc in ai_response.tool_calls]
+            )
+
+            for tc, records, tool_ms in results:
+                tool_name = tc["name"]
+                tool_args = tc["args"]
+                tool_id = tc["id"]
+
                 logger.info("  Tool: %s(%s) → %d results (%.0fms)", tool_name, tool_args, len(records), tool_ms)
                 turn_records.extend(records)
                 sources.extend(records)
@@ -471,17 +478,24 @@ async def run_stream(
         if ai_response.tool_calls:
             yield json.dumps({"type": "status", "content": f"Running {len(ai_response.tool_calls)} search tool(s)..."}) + "\n"
 
-            for tc in ai_response.tool_calls:
+            async def _process_tool_call_stream(tc: Dict[str, Any]):
+                records = await self._execute_tool(
+                    tc["name"], tc["args"], repo=repo, top_k=top_k,
+                    user_id=user_id,
+                )
+                return tc, records
+
+            results = await asyncio.gather(
+                *[_process_tool_call_stream(tc) for tc in ai_response.tool_calls]
+            )
+
+            for tc, records in results:
                 tool_name = tc["name"]
                 tool_args = tc["args"]
                 tool_id = tc["id"]
 
                 logger.info("  Tool: %s(%s)", tool_name, tool_args)
 
-                records = await self._execute_tool(
-                    tool_name, tool_args, repo=repo, top_k=top_k,
-                    user_id=user_id,
-                )
                 sources.extend(records)
 
                 tool_result_text = self._format_tool_results(records)

src/pipelines/retrieval.py

@@ -20,8 +20,8 @@
 
 from __future__ import annotations
 
+import asyncio
 import logging
-import os
 from typing import Any, Callable, Dict, List, Optional
 
 from dotenv import load_dotenv
@@ -177,16 +177,24 @@ async def run(
 
         if ai_response.tool_calls:
             called_tools = set()
-            for tc in ai_response.tool_calls:
+
+            async def _process_tool_call(tc: Dict[str, Any]):
+                records = await self._execute_tool(
+                    tc["name"], tc["args"], user_id, top_k,
+                )
+                return tc, records
+
+            results = await asyncio.gather(
+                *[_process_tool_call(tc) for tc in ai_response.tool_calls]
+            )
+
+            for tc, records in results:
                 tool_name = tc["name"]
                 tool_args = tc["args"]
                 tool_id = tc["id"]
 
                 logger.info("  Tool call: %s(%s)", tool_name, tool_args)
 
-                records = await self._execute_tool(
-                    tool_name, tool_args, user_id, top_k,
-                )
                 sources.extend(records)
 
                 # Build ToolMessage for the LLM