LangChain OSS: multi‑agent research stack
LangChain OSS spotlighted an open‑source multi‑agent system for producing company research reports using a LangGraph node architecture and dual‑model setup (Gemini 2.5 Flash + GPT‑4) — repo linked in the post shown here. It’s a ready‑made project you can fork to demonstrate agent orchestration, model chaining, and pipeline observability in a portfolio.
The google-gemini gemini-fullstack-langgraph-quickstart repository on GitHub demonstrates a React (Vite) frontend paired with a LangGraph/FastAPI backend as a full‑stack research agent reference application. (github.com) The backend models iterative web research as LangGraph state machines where nodes represent role‑specific agents and edges carry messages for reflection and follow‑up queries, per the LangGraph architecture write‑ups. (blog.langchain.com) The quickstart wires Gemini 2.5 for dynamic query generation and web reasoning and also includes a Google Search API integration to fetch and cite sources during multi‑step research runs. (github.com) Multiple community forks and variants add multi‑LLM support (OpenAI, Claude) or different backends (Go, Python), for example nodew/fullstack‑langgraph‑quickstart and qianyuliu’s multi‑LLM fork which include guides for swapping providers and running locally. (github.com) The repo ships standard dev tooling in the tree—Dockerfile, docker‑compose.yml, Makefile, and hot‑reloadable frontend code—and many walkthroughs show one‑command local starts after cloning. (github.com) Google published companion examples and notebooks (Colab + Gemini API docs) and major tutorials (DataCamp, MarkTechPost) that reproduce the research‑pipeline examples and explain reflective agent patterns used in the project. (colab.research.google.com) The project is released under an Apache‑2.0 style license and is mirrored/forked widely across GitHub and community mirrors, making it immediately forkable for portfolio projects and experiments with multi‑agent orchestration. (explore.market.dev)
