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How to Build a Research Agent with Multi-Step Reasoning in Langflow
A research agent goes beyond simple responses. Instead of answering directly, it plans, searches, and synthesizes information before generating an output. This workflow shows how to structure a multi-step research process using tools and reasoning.
What this flow solves
Single-step agents are limited. They often generate shallow responses and fail to explore external information properly.
With this approach, you can:
• Plan how to answer a question
• Search external sources
• Structure retrieved information
• Generate more complete and reliable outputs
Step-by-step Setup
Chat Input
Receives the initial research question.
Prompt Template (Planning)
Defines how the model should create a research plan.
Language Model
Generates a structured plan based on the user input.
Prompt Template (Plan Structuring)
Formats the previous output into a clearer research plan.
Tavily AI Search
Provides access to external information through search.
Agent
Uses the search tool to retrieve relevant information.
Prompt Template (Context Structuring)
Organizes the retrieved search results into structured context.
Prompt Template (Final Synthesis)
Defines how the final response should be generated using the query and results.
Language Model
Generates the final synthesized answer.
Chat Output
Returns the final response.
How It Works
Instead of answering immediately, the system follows multiple steps. It first plans what to search, retrieves external data using tools, organizes that information, and then generates the final answer.
This structured approach improves both reasoning and output quality.
How to get started
This template is already available inside Langflow.
Simply click New Flow, select the Research Agent template, and follow the same structure shown above.
Learn more about Langflow:
langflow.org/?utm_source=x&…
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