🌟 Multi-AI-Agent-Information-Synthesis

🥅 Objective

The application will take an individual's request, search the web for it, and generate a concise news piece with references.

🧠 Model and Details

• Model: Cohere’s latest ​Command R-7B
• Details: It’s a multilingual 7B-parameter open-weight model specialized in enterprise-focused LLM use cases.

🖼️ Main Design Image

🛠️ Tech Stack

• CrewAI: Used for multi-agent orchestration
• Cohere's Command R-7B: The LLM powering the system
• APIs: Serper and Cohere Platforms

🤖 Types of Agents and APIs Needed

Agent 1: Research Analyst Agent

• Accepts the customer's investigation.
• Uses the Serper online search engine to retrieve results from the Internet.
• Consolidates the findings.

Agent 2: Content Writer Agent

• Applies the refined results to create a polished, publishable article.

APIs Used:

• Serper API
• Cohere Platform API

🧩 Components

1. Streamlit App: Acts as the user interface.
2. Agent 1 (Research Analyst): Fetches and consolidates web search results.
3. Agent 2 (Content Writer): Generates polished, publication-ready content.
4. Orchestrator (CrewAI): Manages the workflow between components.

🔄 Working Flow

1. User inputs a query via the Streamlit App.
2. Query is passed to Agent 1 (Research Analyst), which performs a web search using Serper API and consolidates the results.
3. The consolidated data is forwarded to Agent 2 (Content Writer).
4. Agent 2 generates a concise and polished news piece using Cohere's Command R-7B model.
5. The final article is returned to the user through the Streamlit App.

✨ Output

• A concise, publication-ready news article with references.

📸 Additional Images