LangGraph outperforms AutoGen: What’s the Future of Internet Search?
Lang graph is the game-changer, beating AutoGen in the future of internet search. By creating language agents and assigning them tasks, Lang chain sets the stage for a new era in search. The process is like having internet search analysts and insight researchers working together, creating a cutting-edge search experience. Lang chain’s customizability and integration with various tools make it the future of internet search. 🔍🚀
Key Takeaways:
- LangGraph introduces language agents as GRS, providing a significant update from LangChain.
- The future of internet search will likely involve creating different agents to complete tasks within the LangChain ecosystem.
- LangGraph enables the integration of tools and assignment to agents, showcasing a use case for the future of internet search.
- LangGraph allows for the creation of a user interface, demonstrating its potential impact on internet search.
Creating Agents and Tools 💻
Use Case: Internet Search Analyst and Insight Researcher 🌐
As a part of the LangGraph demonstration, we will create two agents: the internet search analyst and the insight researcher. The internet search analyst will navigate the internet based on a provided question, summarizing the content. The insight researcher will then dive deeper into the summarized content to provide detailed insights on specific topics.
Tools Installation Process
| Tools | Installation |
|-----------------------------|-------------------------------------------------------------------------------|
| LangChain | `pip install langchain` |
| LangGraph | `pip install langgraph` |
| Open AI | `pip install openai` |
| LSmith | `pip install lsmith` |
| LangChain Hub | `pip install langchainhub` |
| DugDug | `pip install dugdug` |
| GoSearch | `pip install gosearch` |
| Beautiful Soup | `pip install beautifulsoup` |
| Gradio | `pip install gradio` |
Agents and Tools Integration 🤖
Once the tools are installed, we proceed to initialize the model and define the required agents and tools. This includes creating the internet search analyst and insight researcher agents, as well as defining tools such as the search and content processing tools.
Making Agents Work Together 🔄
Creating Workflows and Graphs 📊
To make the internet search analyst and insight researcher work together effectively, we need to create a workflow and define agent states and edges. Additionally, we add nodes to the workflow and set an entry point to kickstart the process.
Workflow Edge Definition
| Workflow Edges | Description |
|-----------------------------|------------------------------------------------------------|
| Agent State | Conversation history between agents |
| Edges | Connections between agents |
| Workflow | Graph, pipeline, or workflow |
Monitoring the Process 📈
We monitor the interaction between agents using LangSmith, which provides a visual layout of how the agents are communicating and working on the assigned tasks.
Running the Code 🚀
Execution and Results 📄
We run the created workflow to demonstrate the functioning of the internet search analyst and insight researcher agents. The process involves sending tasks, gathering and summarizing information, and providing detailed insights.
After executing the code, we receive the output in a mock down format, showcasing the complete process from the initial query to the detailed insights.
Conclusion 🎉
In conclusion, LangGraph’s capability to create and integrate agents and tools within the LangChain ecosystem offers a glimpse into the future of internet search. Its extendability and customization potential make it a strong contender in comparison to Autogen and Crew AI.
With further developments and extensions, LangGraph has the potential to revolutionize the landscape of internet search. Stay tuned for more updates and advancements in this space!
