Action guide
Build a Research Agent in LangChain
The smallest LangChain stack for a ReAct research loop. Chat model. Tools. Scratchpad prompt. Executor. Skip drowning in framework trivia.
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Why subscribe
LangChain’s surface area is huge. Your deadline is not. Grab the smallest spine that still runs ReAct. Model. Tools. Scratchpad. Executor. Then harden what you own.
For: Python engineers who need a research loop they can read top-to-bottom without a framework archaeology degree.
- A compact ReAct map with only the primitives that matter
- Prompt and tool patterns that actually steer behavior
- A baseline you can instrument and retrofit for prod
- Annotated build walkthrough end to end
- A concept checklist so docs stop overwhelming you
- Extension hooks where complexity belongs
- Cuts abstraction fog to what ships
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What you’ll learn
How to ignore most of LangChain’s surface area and still ship a research-style agent: a ChatModel wrapper with a single invoke path, @tool functions whose docstrings become tool specs, a ChatPromptTemplate that reserves {agent_scratchpad} for the thought/action/observation trace, and an AgentExecutor that drives the loop until the model finishes.
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