update

app.py

@@ -64,9 +64,11 @@ class BasicAgent:
             model_id="meta-llama/Llama-3.2-11B-Vision-Instruct",
         )
 
-        self.agent = CodeAgent(
+        self.multimodal_agent = CodeAgent(
             tools=[VisitWebpageTool(), DuckDuckGoSearchTool(), get_image_content],
-            model=model,
+            model=InferenceClientModel(
+                model_id="meta-llama/Llama-3.2-11B-Vision-Instruct",
+            ),
             additional_authorized_imports=[
                 "requests",
                 "bs4",
@@ -75,7 +77,33 @@ class BasicAgent:
                 "pandas",
                 "io",
                 "PIL",
+                "chess",
             ],
+            name="multimodal_agent",
+            description="I can understand natural language and answer questions about images.",
+            verbosity_level=0,
+            max_steps=10,
+        )
+
+        self.manager_agent = CodeAgent(
+            model=InferenceClientModel(
+                "deepseek-ai/DeepSeek-R1", provider="together", max_tokens=8096
+            ),
+            tools=[get_image_content],
+            managed_agents=[self.multimodal_agent],
+            additional_authorized_imports=[
+                "requests",
+                "bs4",
+                "markdownify",
+                "wikipedia",
+                "pandas",
+                "io",
+                "PIL",
+                "chess",
+            ],
+            planning_interval=5,
+            verbosity_level=2,
+            max_steps=15,
         )
 
     def __call__(self, question: str, file_name: str) -> str:
@@ -89,7 +117,7 @@ class BasicAgent:
                 2. `pandas` Python package is provided. Please use it when there is need to extract structured data (such as tables) from HTML content.
                 3. Please take the question literally! Do not add any additional information or assumptions.
         """
-        result = self.agent.run(prompt)
+        result = self.manager_agent.run(prompt)
         print(f"Agent responded with: {result}")
         return result
 

requirements.txt

@@ -7,3 +7,4 @@ markdownify
 wikipedia
 pandas
 openai
+chess