usecase: Research assistant

examples/usecases/multi_agent_research_assistant/.env.template

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+GOOGLE_API_KEY=''
+SEARCH_ENGINE_ID=''
+OPENAI_API_KEY=''
+DEEPSEEK_API_KEY=''
+CHUNKR_API_KEY=''
+FIRECRAWL_API_KEY=''
+ASKNEWS_CLIENT_ID=''
+ASKNEWS_CLIENT_SECRET=''

examples/usecases/multi_agent_research_assistant/README.md

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+# 🧠 CAMEL Multi-Agent Research Assistant
+
+A powerful, AI-driven research assistant built using the [CAMEL-AI](https://www.camel-ai.org/) framework. This application leverages multiple specialized toolkits and a role-playing agent architecture to autonomously generate comprehensive research reports on any given topic.
+
+## 🚀 Features
+
+- **Multi-Agent Collaboration**: Utilizes CAMEL's role-playing framework to simulate interactions between a "Project Manager" and a "Researcher Agent" for dynamic task execution.
+- **Diverse Toolkit Integration**: Incorporates various CAMEL toolkits including:
+  - `GoogleScholarToolkit` and `SemanticScholarToolkit` for academic paper retrieval.
+  - `ArxivToolkit` for accessing preprints.
+  - `AskNewsToolkit` for fetching relevant news articles.
+  - `ThinkingToolkit` for planning and synthesis.
+  - `FileWriteToolkit` for saving reports locally.
+  - `LinkedInToolkit` for potential dissemination.
+  - `DalleToolkit` for generating illustrative images.
+- **Streamlit Interface**: Provides an intuitive web interface for users to input topics and receive generated reports.
+
+## 🛠️ Installation
+
+1. **Clone the Repository**:
+
+   ```bash
+   git clone https://github.com/camel-ai/camel.git
+   cd camel/examples/usecases/multi_agent_research_assistant
+   ```
+
+2. **Create and Activate a Virtual Environment**:
+
+   ```bash
+   python -m venv venv
+   source venv/bin/activate  # On Windows: venv\Scripts\activate
+   ```
+
+3. **Install Dependencies**:
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+4. ## 🛠️ Setup Environment Variables
+
+   Copy the provided `.env.template` to `.env`:
+
+   Open the `.env` file in your preferred editor and add your required API keys. At a minimum, you need to provide your OpenAI API key:
+
+   ```env
+   OPENAI_API_KEY=your_openai_api_key
+   ```
+
+   Replace `your_openai_api_key` with your actual key from OpenAI.
+
+   Additional environment variables may be required depending on the toolkits you enable (e.g., for LinkedIn, DALL·E, news APIs, etc.).
+
+
+## ▶️ Running the Application
+
+Start the Streamlit application:
+
+```bash
+streamlit run app.py
+```
+
+This will open the application in your default web browser. Enter a research topic and click "Generate Report" to initiate the multi-agent research process.
+
+## 📂 Project Structure
+
+```
+camel-research-assistant/
+├── app.py               # Main Streamlit application
+├── requirements.txt     # Python dependencies
+├── .env                 # Environment variables (not included in version control)
+└── README.md            # Project documentation
+```
+
+## 🧪 Example Usage
+
+1. Launch the application.
+2. Input a topic, e.g., "Latest breakthroughs in quantum computing".
+3. Click "Generate Report".
+4. The application will:
+   - Identify top researchers in the field.
+   - Retrieve relevant academic papers and news articles.
+   - Generate a comprehensive report.
+   - Save the report locally.
+   - Optionally, generate illustrative images and prepare content for LinkedIn dissemination.

examples/usecases/multi_agent_research_assistant/app.py

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+# ========= 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 streamlit as st
+from dotenv import load_dotenv
+from camel.societies import RolePlaying
+from camel.models import ModelFactory
+from camel.types import ModelPlatformType, ModelType
+from camel.toolkits import (
+    GoogleScholarToolkit, SemanticScholarToolkit, ArxivToolkit,
+    AskNewsToolkit, ThinkingToolkit,
+    FileWriteToolkit, LinkedInToolkit
+)
+from camel.logger import set_log_level
+import logging
+
+logging.basicConfig(level=logging.DEBUG)
+load_dotenv()
+set_log_level(level="DEBUG")
+
+# Model Setup
+model = ModelFactory.create(
+    model_platform=ModelPlatformType.OPENAI, 
+    model_type=ModelType.GPT_4O, 
+    model_config_dict={"temperature": 0.0}
+)
+
+class DynamicResearchAgent:
+    """
+    Research agent that can dynamically add GoogleScholar tools based on discovered authors
+    """
+    def __init__(self, base_tools):
+        self.base_tools = base_tools
+        self.google_scholar_tools = {}
+
+    def add_google_scholar_for_author(self, author_identifier: str, author_name: str = None):
+        """Add GoogleScholar tools for a specific author"""
+        if author_identifier not in self.google_scholar_tools:
+            try:
+                toolkit = GoogleScholarToolkit(author_identifier=author_identifier)
+                self.google_scholar_tools[author_identifier] = toolkit.get_tools()
+                return True
+            except Exception as e:
+                st.warning(f"Could not create GoogleScholar toolkit for {author_name or author_identifier}: {e}")
+                return False
+        return True
+
+    def get_all_tools(self):
+        """Get all available tools including dynamically added ones"""
+        all_tools = list(self.base_tools)
+        for tools in self.google_scholar_tools.values():
+            all_tools.extend(tools)
+        return all_tools
+
+# Streamlit UI
+st.title("CAMEL Dynamic Research Assistant")
+topic = st.text_input("Enter a research topic:", value="latest breakthroughs in quantum computing")
+
+if st.button("Generate Report") and topic:
+    st.info("🤖 Starting research agent...")
+
+    # Base tools that don't require author identifiers
+    base_tools = [
+        *SemanticScholarToolkit().get_tools(),
+        *ArxivToolkit().get_tools(),
+        *AskNewsToolkit().get_tools(),
+        *ThinkingToolkit().get_tools(),
+        *FileWriteToolkit().get_tools(),
+        *LinkedInToolkit().get_tools(),
+    ]
+
+    # Create dynamic research agent
+    research_agent = DynamicResearchAgent(base_tools)
+
+    # Enhanced task prompt that explains available capabilities
+    task_prompt = f"""
+    Create a comprehensive research report on: {topic}
+
+    Your complete task includes:
+    1. Search for recent and relevant papers using SemanticScholar and ArXiv
+    2. Identify key researchers and their contributions in this field
+    3. If you find important authors, mention their IDs for potential GoogleScholar integration
+    4. Analyze the latest trends, breakthroughs, and developments
+    5. Get recent news using AskNews if relevant
+    6. Synthesize ALL findings into a well-structured comprehensive report
+    7. Save the final report as a local file using FileWrite tools
+    8. When the report is complete and saved, respond with "CAMEL_TASK_DONE"
+
+    Available tools:
+    - SemanticScholar: Search academic papers, get author information
+    - ArXiv: Search preprints and recent papers
+    - AskNews: Get recent news and developments
+    - Thinking: Plan and reflect on your research strategy
+    - FileWrite: Save your findings and reports
+    - LinkedIn: Research author profiles if needed
+
+    IMPORTANT: Don't just list papers - create a comprehensive analysis report that synthesizes 
+    the information, identifies trends, and provides insights. Save this report to a file.
+    """
+
+    # Initialize RolePlaying session
+    role_play = RolePlaying(
+        assistant_role_name="Senior Research Analyst",
+        user_role_name="Research Director",
+        assistant_agent_kwargs=dict(
+            model=model,
+            tools=research_agent.get_all_tools()
+        ),
+        user_agent_kwargs=dict(model=model),
+        task_prompt=task_prompt,
+        with_task_specify=False
+    )
+
+    # Start conversation
+    next_msg = role_play.init_chat()
+
+    # Conversation loop with dynamic tool addition
+    conversation_container = st.container()
+    step_count = 0
+
+    with conversation_container:
+        while True:
+            step_count += 1
+
+            with st.expander(f"Step {step_count}: Agent Interaction", expanded=True):
+                assistant_resp, user_resp = role_play.step(next_msg)
+
+                if assistant_resp.terminated or user_resp.terminated:
+                    st.info("🏁 Conversation terminated by agent")
+                    break
+
+                # Check if agent mentions needing GoogleScholar for specific authors
+                # This is a simple pattern - you could make this more sophisticated
+                content = assistant_resp.msg.content.lower()
+                if "google scholar" in content and "author" in content:
+                    st.info("🔍 Agent requested GoogleScholar tools - this could be implemented with author discovery")
+
+                # Display conversation
+                st.markdown("**🤖 Research Analyst:**")
+                st.write(assistant_resp.msg.content)
+
+                st.markdown("**👤 Research Director:**")
+                st.write(user_resp.msg.content)
+
+                # Check for completion in both agent responses
+                if ("CAMEL_TASK_DONE" in user_resp.msg.content or 
+                    "CAMEL_TASK_DONE" in assistant_resp.msg.content or
+                    "report is complete" in assistant_resp.msg.content.lower() or
+                    "task completed" in assistant_resp.msg.content.lower()):
+                    st.success("✅ Task completed successfully!")
+                    break
+
+                next_msg = assistant_resp.msg
+
+                # Safety break to prevent infinite loops
+                if step_count > 20:
+                    st.warning("⚠️ Maximum steps reached. Stopping conversation.")
+                    break
+
+    st.success("🎉 Report generation completed!")
+    st.info("📄 The research report has been saved locally by the agent.")

examples/usecases/multi_agent_research_assistant/requirements.txt

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+chunkr-ai==0.1.3
+python-dotenv==1.1.0
+streamlit==1.5.1
+camel-ai[all]==0.2.62
+docx2markdown==0.1.0
+asknews==0.11.10