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<\/p>\n LangChain Blog explains the deployment of a multi-agent flow on LangGraph Cloud, enhancing GPT Researcher with a complex AI workflow.<\/p>\n
LangChain has announced the successful deployment of its multi-agent flow on LangGraph Cloud, according to a guest blog post by Elisha Kramer, Tech Lead at Fiverr. This development aims to enhance the capabilities of the open-source GPT Researcher project by Assaf Elovic, which is designed for comprehensive online research.<\/p>\n GPT Researcher is an autonomous agent for online research tasks, boasting over 13,000 stars on GitHub and a community of over 4,000 developers. Initially a successful RAG implementation, it now leverages multi-agents with the LangGraph framework. Despite its capabilities, it lacked a top-tier front-end application, which has now been addressed with a new client built using NextJS.<\/p>\n LangGraph is a framework that enables the creation of complex multi-agent flows, where AI agents coordinate and review each other’s work. LangChain found it to be a perfect match for their needs, especially for integrating a cloud-based version of GPT Researcher.<\/p>\n LangGraph Cloud Host is similar to a GraphQL API Server, abstracting access to a LangGraph and leveraging any pip package used within it. Essentially, it allows the deployment of a Python server with LangGraph baked into it. The cloud host automatically exposes API endpoints for easy job-triggering and graph edits.<\/p>\n The multi-agent workflow, initially built by Assaf Elovic, was made easily deployable by Harrison, CEO of LangChain, through a pull request. This allowed GPT Researcher\u2019s LangGraph to be deployed, edited, and triggered with custom parameters via an API call, transforming it into a scalable production-ready service.<\/p>\n The deployment process was streamlined into a few simple steps:<\/p>\n The process involves a task object and a getHost function to trigger a run on the LangGraph server, which is observable on the LangSmith User Interface.<\/p>\n This blog post demonstrates how LangChain deployed its LangGraph multi-agent flows via React and LangGraph Cloud. The API’s elegance simplifies the complex process, making it accessible and efficient for developers.<\/p>\n For more details, visit the LangChain Blog<\/a>.<\/p>\n
\n Tony Kim<\/a>
\n Jul 15, 2024 17:33<\/span>
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\n ![\"LangChain](\"https:\/\/image.blockchain.news:443\/features\/3F55B869665B3A2EF7ECB63E8F4C818C06A0FC3821726049851CEE6FD9A8FE13.jpg\")
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\n <\/figure>\nWhat is GPT Researcher?<\/h2>\n
How does LangGraph fit in?<\/h2>\n
What is LangGraph Cloud?<\/h2>\n
Deployment Details<\/h2>\n
Querying the LangGraph API Server<\/h2>\n
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Summary<\/h2>\n