support gemini 2.5 pro and add example

Author: Wendong-Fan

README.md

@@ -126,6 +126,7 @@ Our vision is to revolutionize how AI agents collaborate to solve real-world tas
   </p>
 </div>
 
+- **[2025.03.26]**: Supported Gemini 2.5 Pro, added example run code
 - **[2025.03.21]**: Integrated OpenRouter model platform, fix bug with Gemini tool calling
 - **[2025.03.20]**: Accept header in MCP Toolkit, support automatic playwright installation
 - **[2025.03.16]**: Support Bing search, Baidu search
@@ -383,6 +384,9 @@ python examples/run_deepseek_zh.py
 # Run with other OpenAI-compatible models
 python examples/run_openai_compatible_model.py
 
+# Run with Gemini model
+python examples/run_gemini.py
+
 # Run with Azure OpenAI
 python examples/run_azure_openai.py
 

README_zh.md

@@ -126,6 +126,7 @@
   </p>
 </div>
 
+- **[2025.03.26]**: 支持Gemini 2.5 Pro模型，添加示例运行代码
 - **[2025.03.21]**: 集成OpenRouter模型平台，修复Gemini工具调用的bug
 - **[2025.03.20]**: 在MCP工具包中添加Accept头部，支持自动安装playwright
 - **[2025.03.16]**: 支持必应搜索、百度搜索
@@ -379,6 +380,9 @@ python examples/run_qwen_zh.py
 # 使用 Deepseek 模型运行
 python examples/run_deepseek_zh.py
 
+# 使用 Gemini 模型运行
+python examples/run_gemini.py
+
 # 使用其他 OpenAI 兼容模型运行
 python examples/run_openai_compatible_model.py
 

examples/run_gemini.py

@@ -0,0 +1,146 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# 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.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+import sys
+import pathlib
+from dotenv import load_dotenv
+from camel.models import ModelFactory
+from camel.toolkits import (
+    AudioAnalysisToolkit,
+    CodeExecutionToolkit,
+    ExcelToolkit,
+    ImageAnalysisToolkit,
+    SearchToolkit,
+    VideoAnalysisToolkit,
+    BrowserToolkit,
+    FileWriteToolkit,
+)
+from camel.types import ModelPlatformType, ModelType
+from camel.logger import set_log_level
+from camel.societies import RolePlaying
+
+from owl.utils import run_society, DocumentProcessingToolkit
+
+base_dir = pathlib.Path(__file__).parent.parent
+env_path = base_dir / "owl" / ".env"
+load_dotenv(dotenv_path=str(env_path))
+
+set_log_level(level="DEBUG")
+
+
+def construct_society(question: str) -> RolePlaying:
+    r"""Construct a society of agents based on the given question.
+
+    Args:
+        question (str): The task or question to be addressed by the society.
+
+    Returns:
+        RolePlaying: A configured society of agents ready to address the question.
+    """
+
+    # Create models for different components
+    models = {
+        "user": ModelFactory.create(
+            model_platform=ModelPlatformType.GEMINI,
+            model_type=ModelType.GEMINI_2_5_PRO_EXP,
+            model_config_dict={"temperature": 0},
+        ),
+        "assistant": ModelFactory.create(
+            model_platform=ModelPlatformType.GEMINI,
+            model_type=ModelType.GEMINI_2_5_PRO_EXP,
+            model_config_dict={"temperature": 0},
+        ),
+        "browsing": ModelFactory.create(
+            model_platform=ModelPlatformType.GEMINI,
+            model_type=ModelType.GEMINI_2_5_PRO_EXP,
+            model_config_dict={"temperature": 0},
+        ),
+        "planning": ModelFactory.create(
+            model_platform=ModelPlatformType.GEMINI,
+            model_type=ModelType.GEMINI_2_5_PRO_EXP,
+            model_config_dict={"temperature": 0},
+        ),
+        "video": ModelFactory.create(
+            model_platform=ModelPlatformType.GEMINI,
+            model_type=ModelType.GEMINI_2_5_PRO_EXP,
+            model_config_dict={"temperature": 0},
+        ),
+        "image": ModelFactory.create(
+            model_platform=ModelPlatformType.GEMINI,
+            model_type=ModelType.GEMINI_2_5_PRO_EXP,
+            model_config_dict={"temperature": 0},
+        ),
+        "document": ModelFactory.create(
+            model_platform=ModelPlatformType.GEMINI,
+            model_type=ModelType.GEMINI_2_5_PRO_EXP,
+            model_config_dict={"temperature": 0},
+        ),
+    }
+
+    # Configure toolkits
+    tools = [
+        *BrowserToolkit(
+            headless=False,  # Set to True for headless mode (e.g., on remote servers)
+            web_agent_model=models["browsing"],
+            planning_agent_model=models["planning"],
+        ).get_tools(),
+        *CodeExecutionToolkit(sandbox="subprocess", verbose=True).get_tools(),
+        *ImageAnalysisToolkit(model=models["image"]).get_tools(),
+        SearchToolkit().search_duckduckgo,
+        SearchToolkit().search_google,  # Comment this out if you don't have google search
+        SearchToolkit().search_wiki,
+        *ExcelToolkit().get_tools(),
+        *DocumentProcessingToolkit(model=models["document"]).get_tools(),
+        *FileWriteToolkit(output_dir="./").get_tools(),
+    ]
+
+    # Configure agent roles and parameters
+    user_agent_kwargs = {"model": models["user"]}
+    assistant_agent_kwargs = {"model": models["assistant"], "tools": tools}
+
+    # Configure task parameters
+    task_kwargs = {
+        "task_prompt": question,
+        "with_task_specify": False,
+    }
+
+    # Create and return the society
+    society = RolePlaying(
+        **task_kwargs,
+        user_role_name="user",
+        user_agent_kwargs=user_agent_kwargs,
+        assistant_role_name="assistant",
+        assistant_agent_kwargs=assistant_agent_kwargs,
+    )
+
+    return society
+
+
+def main():
+    r"""Main function to run the OWL system with an example question."""
+    # Default research question
+    default_task = "Navigate to Amazon.com and identify one product that is attractive to coders. Please provide me with the product name and price. No need to verify your answer."
+
+    # Override default task if command line argument is provided
+    task = sys.argv[1] if len(sys.argv) > 1 else default_task
+
+    # Construct and run the society
+    society = construct_society(task)
+    answer, chat_history, token_count = run_society(society)
+
+    # Output the result
+    print(f"\033[94mAnswer: {answer}\033[0m")
+
+
+if __name__ == "__main__":
+    main()

owl/webapp.py

@@ -244,6 +244,7 @@ def process_message(role, content):
 MODULE_DESCRIPTIONS = {
     "run": "Default mode: Using OpenAI model's default agent collaboration mode, suitable for most tasks.",
     "run_mini": "Using OpenAI model with minimal configuration to process tasks",
+    "run_gemini": "Using Gemini model to process tasks",
     "run_deepseek_zh": "Using deepseek model to process Chinese tasks",
     "run_openai_compatible_model": "Using openai compatible model to process tasks",
     "run_ollama": "Using local ollama model to process tasks",

owl/webapp_zh.py

@@ -244,6 +244,7 @@ def process_message(role, content):
 MODULE_DESCRIPTIONS = {
     "run": "默认模式：使用OpenAI模型的默认的智能体协作模式，适合大多数任务。",
     "run_mini": "使用使用OpenAI模型最小化配置处理任务",
+    "run_gemini": "使用 Gemini模型处理任务",
     "run_deepseek_zh": "使用deepseek模型处理中文任务",
     "run_openai_compatible_model": "使用openai兼容模型处理任务",
     "run_ollama": "使用本地ollama模型处理任务",

pyproject.toml

@@ -21,7 +21,7 @@ keywords = [
     "learning-systems"
 ]
 dependencies = [
-    "camel-ai[all]==0.2.36",
+    "camel-ai[all]==0.2.37",
     "chunkr-ai>=0.0.41",
     "docx2markdown>=0.1.1",
     "gradio>=3.50.2",

requirements.txt

@@ -1,4 +1,4 @@
-camel-ai[all]==0.2.36
+camel-ai[all]==0.2.37
 chunkr-ai>=0.0.41
 docx2markdown>=0.1.1
 gradio>=3.50.2