.png)
7 hours ago
1
DeepResearch Agent with LangGraph, using any LLM models, search engine, RAG retrieval.
A LangGraph-powered research agent that performs comprehensive web research through dynamic query generation, iterative refinement, and citation-supported responses using any LLM model, search engine, or RAG retrieval.
# Node.js
npm install deepsearcher # or yarn add deepsearcher
- 🧠 Deep Research Agent based on LangGraph
- 🔍 Dynamic search query generation using any LLM model
- 🌐 Integrated web research through web search engines or RAG retrieval
- 🤔 Reflective reasoning, identifying knowledge gaps and optimizing searches
- 📄 Generate cited answers based on collected sources
import { DeepResearch, type SearcherFunction } from 'deepsearcher';
// Search engine adapter
const searcher: SearcherFunction = ({ query, id }) => {
// You need to provide a searcher function
// which can be a web search engine or a RAG retrieval function
}
const instance = new DeepResearch({
searcher,
// OpenAI-Like API (Optional)
options: {
apiKey: 'YOUR_OPENAI_OR_LLM_API_KEY',
baseURL: 'https://api.openai.com/v1',
},
});
// langgraph compile
const agent = await instance.compile();
// debug (optional)
agent.debug = true;
// use the stream() method.
const chunks = await agent.stream(
{
messages: [{
role: 'user',
content: 'How to use LangGraph to build intelligent agents?'
}],
},
{
streamMode: 'updates',
// runtime configuration
configurable: {
maxResearchLoops: 3, // default 3.
numberOfInitialQueries: 3, // default 3.
// Required model parameters (can use same model)
queryGeneratorModel: 'Qwen/Qwen3-32B',
reflectionModel: 'Qwen/Qwen3-32B',
answerModel: 'Qwen/Qwen3-32B',
},
}
);
for await (const chunk of chunks) {
console.log('chunk', chunk);
}
// use the streamEvents() method.
const eventStream = agent.streamEvents(
{
messages: [{
role: 'user',
content: 'How to use LangGraph to build intelligent agents?'
}],
},
{
version: 'v2',
configurable: {
maxResearchLoops: 2,
numberOfInitialQueries: 2,
queryGeneratorModel: 'Qwen/Qwen3-32B',
reflectionModel: 'Qwen/Qwen3-32B',
answerModel: 'Qwen/Qwen3-32B',
},
}
);
for await (const { event, tags, data } of eventStream) {
// Stream outputs from the 'FinalizeAnswer' node
if (
event === EventStreamEnum.ChatModelStream &&
tags?.includes(NodeEnum.FinalizeAnswer)
) {
console.log(data.chunk.content);
}
}
Below are the definitions of nodes and commonly used event names:
export enum NodeEnum {
GenerateQuery = 'generate_query',
Research = 'research',
Reflection = 'reflection',
FinalizeAnswer = 'finalize_answer',
}
export enum EventStreamEnum {
ChatModelStart = 'on_chat_model_start',
ChatModelStream = 'on_chat_model_stream',
ChatModelEnd = 'on_chat_model_end',
}
For detailed usage documentation, please refer to: streaming-from-final-node
- Generate Initial Queries: Based on your input, it generates a set of initial search queries using an LLM model.
- Research: For each query, it uses the LLM model with the Search API (SearcherFunction) to find relevant knowledge.
- Reflection & Knowledge Gap Analysis: The agent analyzes the search results to determine if the information is sufficient or if there are knowledge gaps. It uses an LLM model for this reflection process.
- Iterative Refinement: If gaps are found or the information is insufficient, it generates follow-up queries and repeats the research and reflection steps (up to a configured maximum number of loops).
- Finalize Answer: Once the research is deemed sufficient, the agent synthesizes the gathered information into a coherent answer, including citations from the sources, using an LLM model.
This project is licensed under the Apache License 2.0. See the LICENSE file for details.
