feat: add StateBaseAgent

docs/developer_guide.md

@@ -132,7 +132,7 @@ Choose the conversational_agent when beginning with RAI, and consider upgrading
 from myrobot import robot
 
 from rai.agents.conversational_agent import create_conversational_agent
-from rai.agents.state_based import create_state_based_agent
+from rai.agents.langchain import create_state_based_runnable
 from rai import get_llm_model
 
 SYSTEM_PROMPT = "You are a robot with interfaces..."
@@ -144,7 +144,7 @@ tools = [pick_up_object, scan_object, SayTool(robot=robot)]
 conversational_agent = create_conversational_agent(
     llm=llm, tools=tools, system_prompt=SYSTEM_PROMPT
 )
-state_based_agent = create_state_based_agent(
+state_based_agent = create_state_based_runnable(
     llm=llm, state_retriever=state_retriever, tools=tools
 )
 

examples/agents/state_based.py

@@ -0,0 +1,57 @@
+# Copyright (C) 2025 Robotec.AI
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#         http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language goveself.rning permissions and
+# limitations under the License.
+
+from rai.agents import wait_for_shutdown
+from rai.agents.langchain import StateBasedConfig
+from rai.agents.ros2 import ROS2StateBasedAgent
+from rai.aggregators.ros2 import (
+    ROS2ImgVLMDiffAggregator,
+    ROS2LogsAggregator,
+)
+from rai.communication.ros2 import (
+    ROS2Connector,
+    ROS2Context,
+    ROS2HRIConnector,
+)
+from rai.tools.ros2.generic.toolkit import ROS2Toolkit
+
+
+@ROS2Context()
+def main():
+    hri_connector = ROS2HRIConnector()
+    ros2_connector = ROS2Connector()
+
+    config = StateBasedConfig(
+        aggregators={
+            ("/camera/camera/color/image_raw", "sensor_msgs/msg/Image"): [
+                ROS2ImgVLMDiffAggregator()
+            ],
+            "/rosout": [
+                ROS2LogsAggregator()
+            ],  # if msg_type is not provided, topic has to exist
+        }
+    )
+
+    agent = ROS2StateBasedAgent(
+        config=config,
+        target_connectors={"to_human": hri_connector},
+        tools=ROS2Toolkit(connector=ros2_connector).get_tools(),
+    )
+    agent.subscribe_source("/from_human", hri_connector)
+    agent.run()
+    wait_for_shutdown([agent])
+
+
+if __name__ == "__main__":
+    main()

src/rai_core/rai/agents/__init__.py

@@ -16,7 +16,6 @@
 from rai.agents.conversational_agent import create_conversational_agent
 from rai.agents.langchain.react_agent import ReActAgent
 from rai.agents.runner import AgentRunner, wait_for_shutdown
-from rai.agents.state_based import create_state_based_agent
 from rai.agents.tool_runner import ToolRunner
 
 __all__ = [
@@ -25,6 +24,5 @@
     "ReActAgent",
     "ToolRunner",
     "create_conversational_agent",
-    "create_state_based_agent",
     "wait_for_shutdown",
 ]

src/rai_core/rai/agents/langchain/__init__.py

@@ -12,7 +12,25 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from .agent import BaseState, LangChainAgent, newMessageBehaviorType
 from .callback import HRICallbackHandler
-from .runnables import create_react_runnable
+from .react_agent import ReActAgent
+from .runnables import (
+    ReActAgentState,
+    create_react_runnable,
+    create_state_based_runnable,
+)
+from .state_based_agent import BaseStateBasedAgent, StateBasedConfig
 
-__all__ = ["HRICallbackHandler", "create_react_runnable"]
+__all__ = [
+    "BaseState",
+    "BaseStateBasedAgent",
+    "HRICallbackHandler",
+    "LangChainAgent",
+    "ReActAgent",
+    "ReActAgentState",
+    "StateBasedConfig",
+    "create_react_runnable",
+    "create_state_based_runnable",
+    "newMessageBehaviorType",
+]

src/rai_core/rai/agents/langchain/agent.py

@@ -22,12 +22,13 @@
 from langchain_core.messages import BaseMessage
 from langchain_core.runnables import Runnable
 
-from rai.agents.base import BaseAgent
-from rai.agents.langchain import HRICallbackHandler
-from rai.agents.langchain.runnables import ReActAgentState
 from rai.communication.hri_connector import HRIConnector, HRIMessage
 from rai.initialization import get_tracing_callbacks
 
+from ..base import BaseAgent
+from .callback import HRICallbackHandler
+from .runnables import ReActAgentState
+
 
 class BaseState(TypedDict):
     messages: List[BaseMessage]
@@ -208,6 +209,7 @@ def _run_loop(self):
                 self._run_agent()
 
     def stop(self):
+        """Stop the agent's execution loop."""
         self._stop_event.set()
         self._interrupt_event.set()
         self._agent_ready_event.wait()
@@ -216,6 +218,7 @@ def stop(self):
             self._thread.join()
             self._thread = None
             self.logger.info("Agent stopped")
+        self._stop_event.clear()
 
     @staticmethod
     def _apply_reduction_behavior(

src/rai_core/rai/agents/langchain/react_agent.py

@@ -17,11 +17,11 @@
 from langchain_core.language_models import BaseChatModel
 from langchain_core.tools import BaseTool
 
-from rai.agents.langchain import create_react_runnable
-from rai.agents.langchain.agent import LangChainAgent
-from rai.agents.langchain.runnables import ReActAgentState
 from rai.communication.hri_connector import HRIConnector, HRIMessage
 
+from .agent import LangChainAgent
+from .runnables import ReActAgentState, create_react_runnable
+
 
 class ReActAgent(LangChainAgent):
     def __init__(

src/rai_core/rai/agents/langchain/runnables.py

@@ -12,18 +12,29 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import logging
 from functools import partial
-from typing import List, Optional, TypedDict, cast
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    Optional,
+    TypedDict,
+    cast,
+)
 
 from langchain_core.language_models import BaseChatModel
-from langchain_core.messages import BaseMessage, SystemMessage
+from langchain_core.messages import BaseMessage, HumanMessage, SystemMessage
 from langchain_core.runnables import Runnable
 from langchain_core.tools import BaseTool
 from langgraph.graph import START, StateGraph
 from langgraph.prebuilt.tool_node import tools_condition
 
-from rai.agents.tool_runner import ToolRunner
 from rai.initialization import get_llm_model
+from rai.messages import HumanMultimodalMessage
+
+from ..tool_runner import ToolRunner
 
 
 class ReActAgentState(TypedDict):
@@ -112,3 +123,50 @@ def create_react_runnable(
 
     # Compile the graph
     return graph.compile()
+
+
+def retriever_wrapper(
+    state_retriever: Callable[[], Dict[str, HumanMessage | HumanMultimodalMessage]],
+    state: ReActAgentState,
+):
+    """This wrapper is used to put state messages into LLM context"""
+    for source, message in state_retriever().items():
+        message.content = f"{source}: {message.content}"
+        logging.getLogger("state_retriever").debug(
+            f"Adding state message:\n{message.pretty_repr()}"
+        )
+        state["messages"].append(message)
+    return state
+
+
+def create_state_based_runnable(
+    llm: Optional[BaseChatModel] = None,
+    tools: Optional[List[BaseTool]] = None,
+    system_prompt: Optional[str] = None,
+    state_retriever: Optional[Callable[[], Dict[str, Any]]] = None,
+) -> Runnable[ReActAgentState, ReActAgentState]:
+    if llm is None:
+        llm = get_llm_model("complex_model", streaming=True)
+    graph = StateGraph(ReActAgentState)
+    graph.add_edge(START, "state_retriever")
+    graph.add_edge("state_retriever", "llm")
+    graph.add_conditional_edges(
+        "llm",
+        tools_condition,
+    )
+    graph.add_edge("tools", "state_retriever")
+
+    if state_retriever is None:
+        state_retriever = lambda: {}
+
+    graph.add_node("state_retriever", partial(retriever_wrapper, state_retriever))
+
+    if tools is None:
+        tools = []
+    bound_llm = cast(BaseChatModel, llm.bind_tools(tools))
+    graph.add_node("llm", partial(llm_node, bound_llm, system_prompt))
+
+    tool_runner = ToolRunner(tools)
+    graph.add_node("tools", tool_runner)
+
+    return graph.compile()