feat: planning task and megamind agent

src/rai_bench/rai_bench/examples/manipulation_o3de.py

@@ -31,7 +31,6 @@
         model_name=args.model_name,
         vendor=args.vendor,
     )
-
     run_benchmark(
         llm=llm,
         out_dir=experiment_dir,

src/rai_bench/rai_bench/examples/tool_calling_custom_agent.py

@@ -0,0 +1,100 @@
+# 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 governing permissions and
+# limitations under the License.
+import logging
+import uuid
+from datetime import datetime
+from pathlib import Path
+
+from rai.agents.langchain.core import (
+    Executor,
+    create_megamind,
+    get_initial_megamind_state,
+)
+
+from rai_bench import (
+    define_benchmark_logger,
+)
+from rai_bench.tool_calling_agent.benchmark import ToolCallingAgentBenchmark
+from rai_bench.tool_calling_agent.interfaces import TaskArgs
+from rai_bench.tool_calling_agent.tasks.warehouse import SortingTask
+from rai_bench.utils import get_llm_for_benchmark
+
+if __name__ == "__main__":
+    now = datetime.now()
+    out_dir = f"src/rai_bench/rai_bench/experiments/tool_calling/{now.strftime('%Y-%m-%d_%H-%M-%S')}"
+    experiment_dir = Path(out_dir)
+    experiment_dir.mkdir(parents=True, exist_ok=True)
+    bench_logger = define_benchmark_logger(out_dir=experiment_dir, level=logging.DEBUG)
+
+    task = SortingTask(task_args=TaskArgs(extra_tool_calls=50))
+    task.set_logger(bench_logger)
+
+    supervisor_name = "gpt-4o"
+
+    executor_name = "gpt-4o-mini"
+    model_name = f"supervisor-{supervisor_name}_executor-{executor_name}"
+    supervisor_llm = get_llm_for_benchmark(model_name=supervisor_name, vendor="openai")
+    executor_llm = get_llm_for_benchmark(
+        model_name=executor_name,
+        vendor="openai",
+    )
+
+    benchmark = ToolCallingAgentBenchmark(
+        tasks=[task],
+        logger=bench_logger,
+        model_name=model_name,
+        results_dir=experiment_dir,
+    )
+    manipulation_system_prompt = """You are a manipulation specialist robot agent.
+Your role is to handle object manipulation tasks including picking up and droping objects using provided tools.
+
+Ask the VLM for objects detection and positions before perfomring any manipulation action.
+If VLM doesn't see objects that are objectives of the task, return this information, without proceeding"""
+
+    navigation_system_prompt = """You are a navigation specialist robot agent.
+Your role is to handle navigation tasks in space using provided tools.
+
+After performing navigation action, always check your current position to ensure success"""
+
+    executors = [
+        Executor(
+            name="manipulation",
+            llm=executor_llm,
+            tools=task.manipulation_tools(),
+            system_prompt=manipulation_system_prompt,
+        ),
+        Executor(
+            name="navigation",
+            llm=executor_llm,
+            tools=task.navigation_tools(),
+            system_prompt=navigation_system_prompt,
+        ),
+    ]
+    agent = create_megamind(
+        megamind_llm=supervisor_llm,
+        megamind_system_prompt=task.get_system_prompt(),
+        executors=executors,
+        task_planning_prompt=task.get_planning_prompt(),
+    )
+
+    experiment_id = uuid.uuid4()
+    benchmark.run_next(
+        agent=agent,
+        initial_state=get_initial_megamind_state(task=task.get_prompt()),
+        experiment_id=experiment_id,
+    )
+
+    bench_logger.info("===============================================================")
+    bench_logger.info("ALL SCENARIOS DONE. BENCHMARK COMPLETED!")
+    bench_logger.info("===============================================================")

src/rai_bench/rai_bench/tool_calling_agent/benchmark.py

@@ -19,14 +19,14 @@
 from typing import Iterator, List, Optional, Sequence, Tuple
 
 from langchain_core.language_models import BaseChatModel
-from langchain_core.messages import BaseMessage
+from langchain_core.messages import AIMessage, BaseMessage
 from langchain_core.runnables.config import RunnableConfig
 from langgraph.errors import GraphRecursionError
 from langgraph.graph.state import CompiledStateGraph
 from rai.agents.langchain.core import (
     create_conversational_agent,
 )
-from rai.messages import HumanMultimodalMessage
+from rai.agents.langchain.core.react_agent import ReActAgentState
 
 from rai_bench.agents import create_multimodal_to_tool_agent
 from rai_bench.base_benchmark import BaseBenchmark, TimeoutException
@@ -38,9 +38,6 @@
     TaskResult,
     ToolCallingAgentRunSummary,
 )
-from rai_bench.tool_calling_agent.tasks.spatial import (
-    SpatialReasoningAgentTask,
-)
 from rai_bench.utils import get_llm_model_name
 
 
@@ -67,7 +64,12 @@ def __init__(
         self.tasks_results: List[TaskResult] = []
         self.csv_initialize(self.results_filename, TaskResult)
 
-    def run_next(self, agent: CompiledStateGraph, experiment_id: uuid.UUID) -> None:
+    def run_next(
+        self,
+        agent: CompiledStateGraph,
+        initial_state: ReActAgentState,
+        experiment_id: uuid.UUID,
+    ) -> None:
         """Runs the next task of the benchmark.
 
         Parameters
@@ -87,14 +89,16 @@ def run_next(self, agent: CompiledStateGraph, experiment_id: uuid.UUID) -> None:
         )
         callbacks = self.score_tracing_handler.get_callbacks()
         run_id = uuid.uuid4()
-        # NOTE (jmatejcz) recursion limit calculated as all_nodes_num -> one pass though whole node
-        # plus (task.max_tool_calls_number-1 because the first pass is already added in)
-        # times number of nodes - 2 because we dont cout start and end node
-        # this can be to much for larger graphs that dont use all nodes on extra calls
-        # in such ase adjust this value
-        recurssion_limit = len(agent.get_graph().nodes) + (
-            task.max_tool_calls_number - 1
-        ) * (len(agent.get_graph().nodes) - 2)
+        # NOTE (jmatejcz) recursion limit calculated as (all_nodes_num - 2) * required tool calls
+        # -2 because we don't want to include START and END node
+        # then we add numer of additional calls that can be made
+        # and +2 as we have to pass once though START and END
+
+        recurssion_limit = (
+            (len(agent.get_graph().nodes) - 2) * task.required_calls
+            + task.additional_calls
+            + 2
+        )
         config: RunnableConfig = {
             "run_id": run_id,
             "callbacks": callbacks,
@@ -113,40 +117,27 @@ def run_next(self, agent: CompiledStateGraph, experiment_id: uuid.UUID) -> None:
         messages: List[BaseMessage] = []
         prev_count: int = 0
         try:
-            with self.time_limit(20 * task.max_tool_calls_number):
-                if isinstance(task, SpatialReasoningAgentTask):
-                    for state in agent.stream(
-                        {
-                            "messages": [
-                                HumanMultimodalMessage(
-                                    content=task.get_prompt(), images=task.get_images()
-                                )
-                            ]
-                        },
-                        config=config,
-                    ):
-                        node = next(iter(state))
-                        all_messages = state[node]["messages"]
-                        for new_msg in all_messages[prev_count:]:
-                            messages.append(new_msg)
-                        prev_count = len(messages)
-                else:
-                    for state in agent.stream(
-                        {
-                            "messages": [
-                                HumanMultimodalMessage(content=task.get_prompt())
-                            ]
-                        },
-                        config=config,
-                    ):
-                        node = next(iter(state))
+            with self.time_limit(200 * task.max_tool_calls_number):
+                for state in agent.stream(
+                    initial_state,
+                    config=config,
+                ):
+                    node = next(iter(state))
+                    if "messages" in state[node]:
                         all_messages = state[node]["messages"]
                         for new_msg in all_messages[prev_count:]:
                             messages.append(new_msg)
+                            if isinstance(new_msg, AIMessage):
+                                self.logger.debug(
+                                    f"Message from node '{node}': {new_msg.content}, tool_calls: {new_msg.tool_calls}"
+                                )
                         prev_count = len(messages)
         except TimeoutException as e:
             self.logger.error(msg=f"Task timeout: {e}")
         except GraphRecursionError as e:
+            tool_calls = task.get_tool_calls_from_messages(messages=messages)
+            score = task.validate(tool_calls=tool_calls)
+            score = 0.0
             self.logger.error(msg=f"Reached recursion limit {e}")
 
         tool_calls = task.get_tool_calls_from_messages(messages=messages)

src/rai_bench/rai_bench/tool_calling_agent/interfaces.py

@@ -585,6 +585,14 @@ def max_tool_calls_number(self) -> int:
             + self.extra_tool_calls
         )
 
+    @property
+    def additional_calls(self) -> int:
+        """number of additional calls that can be done to still pass task.
+        Includes extra tool calls params.
+        and optional tool calls number which depends on task.
+        """
+        return self.optional_tool_calls_number + self.extra_tool_calls
+
     @property
     def required_calls(self) -> int:
         """Minimal number of calls required to complete task"""