fix: react agent with custom tool parser n_iters

src/llama_stack_client/lib/agents/agent.py

@@ -3,20 +3,20 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
+import logging
 from typing import Iterator, List, Optional, Tuple, Union
 
 from llama_stack_client import LlamaStackClient
-import logging
 
 from llama_stack_client.types import ToolResponseMessage, ToolResponseParam, UserMessage
 from llama_stack_client.types.agent_create_params import AgentConfig
 from llama_stack_client.types.agents.turn import CompletionMessage, Turn
 from llama_stack_client.types.agents.turn_create_params import Document, Toolgroup
 from llama_stack_client.types.agents.turn_create_response import AgentTurnResponseStreamChunk
 from llama_stack_client.types.shared.tool_call import ToolCall
+from llama_stack_client.types.shared_params.agent_config import ToolConfig
 from llama_stack_client.types.shared_params.response_format import ResponseFormat
 from llama_stack_client.types.shared_params.sampling_params import SamplingParams
-from llama_stack_client.types.shared_params.agent_config import ToolConfig
 
 from .client_tool import ClientTool
 from .tool_parser import ToolParser
@@ -91,10 +91,10 @@ def __init__(
             # Add optional parameters if provided
             if enable_session_persistence is not None:
                 agent_config["enable_session_persistence"] = enable_session_persistence
-            if input_shields is not None:
-                agent_config["input_shields"] = input_shields
             if max_infer_iters is not None:
                 agent_config["max_infer_iters"] = max_infer_iters
+            if input_shields is not None:
+                agent_config["input_shields"] = input_shields
             if output_shields is not None:
                 agent_config["output_shields"] = output_shields
             if response_format is not None:
@@ -254,7 +254,9 @@ def _create_turn_streaming(
                 else:
                     is_turn_complete = False
                     # End of turn is reached, do not resume even if there's a tool call
-                    if chunk.event.payload.turn.output_message.stop_reason in {"end_of_turn"}:
+                    # We only check for this if tool_parser is not set, because otherwise
+                    # tool call will be parsed on client side, and server will always return "end_of_turn"
+                    if not self.tool_parser and chunk.event.payload.turn.output_message.stop_reason in {"end_of_turn"}:
                         yield chunk
                         break
 
@@ -274,3 +276,6 @@ def _create_turn_streaming(
                         stream=True,
                     )
                     n_iter += 1
+
+            if self.tool_parser and n_iter > self.agent_config.get("max_infer_iters", DEFAULT_MAX_ITER):
+                raise Exception("Max inference iterations reached")

src/llama_stack_client/lib/agents/react/tool_parser.py

@@ -4,13 +4,14 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+import uuid
+from typing import List, Optional, Union
+
 from pydantic import BaseModel, ValidationError
-from typing import Optional, List, Union
-from ..tool_parser import ToolParser
+
 from llama_stack_client.types.shared.completion_message import CompletionMessage
 from llama_stack_client.types.shared.tool_call import ToolCall
-
-import uuid
+from ..tool_parser import ToolParser
 
 
 class Param(BaseModel):