GPT Researcher the first open deep research agent designed for both web and local research on any given task.
The agent produces detailed, factual, and unbiased research reports with citations. GPT Researcher provides a full suite of customization options to create tailor made and domain specific research agents. GPT Researcher goes well beyond RAG: planner and execution agents break each task into research questions, keep only the context that is actually useful for answering them, workflows coordinate specialized agents from planning to publication, and explores a topic in both breadth and depth. Research runs in parallel, which keeps it fast, stable and reliable, and reduces misinformation.
Our mission is to empower individuals and organizations with accurate, unbiased, and factual information through AI.
Why GPT Researcher?
Objective conclusions for manual research can take weeks, requiring vast resources and time.
LLMs trained on outdated information can hallucinate, becoming irrelevant for current research tasks.
Current LLMs have token limitations, insufficient for generating long research reports.
Limited web sources in existing services lead to misinformation and shallow results.
Selective web sources can introduce bias into research tasks.
Demo
Install as Claude Skill
Extend Claude's deep research capabilities by installing GPT Researcher as a Claude Skill:
npx skills add assafelovic/gpt-researcher
Once installed, Claude can leverage GPT Researcher's deep research capabilities directly within your conversations.
Architecture
The core idea is to utilize 'planner' and 'execution' agents. The planner generates research questions, while the execution agents gather relevant information. The publisher then aggregates all findings into a comprehensive report.
Steps:
Create a task-specific agent based on a research query.
Generate questions that collectively form an objective opinion on the task.
Use a crawler agent for gathering information for each question.
Summarize and source-track each resource.
Filter and aggregate summaries into a final research report.
For other setups (e.g., Poetry or virtual environments), check the Getting Started page.
Run as PIP package
pip install gpt-researcher
Example Usage:
...
from gpt_researcher import GPTResearcher
query = "why is Nvidia stock going up?"
researcher = GPTResearcher(query=query)
# Conduct research on the given query
research_result = await researcher.conduct_research()
# Write the report
report = await researcher.write_report()
...
For more examples and configurations, please refer to the PIP documentation page.
🔧 MCP Client
GPT Researcher supports MCP integration to connect with specialized data sources like GitHub repositories, databases, and custom APIs. This enables research from data sources alongside web search.
export RETRIEVER=tavily,mcp # Enable hybrid web + MCP research
from gpt_researcher import GPTResearcher
import asyncio
import os
async def mcp_research_example():
# Enable MCP with web search
os.environ["RETRIEVER"] = "tavily,mcp"
researcher = GPTResearcher(
query="What are the top open source web research agents?",
mcp_configs=[
{
"name": "github",
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-github"],
"env": {"GITHUB_TOKEN": os.getenv("GITHUB_TOKEN")}
}
]
)
research_result = await researcher.conduct_research()
report = await researcher.write_report()
return report
For comprehensive MCP documentation and advanced examples, visit the MCP Integration Guide.
🍌 Inline Image Generation
GPT Researcher can automatically generate and embed AI-created illustrations in your research reports using Google's Gemini models (Nano Banana).
# Enable in your .env file
IMAGE_GENERATION_ENABLED=true
GOOGLE_API_KEY=your_google_api_key
IMAGE_GENERATION_MODEL=models/gemini-2.5-flash-image
When enabled, the system will:
Analyze your research context to identify visualization opportunities
Pre-generate 2-3 relevant images during the research phase
Embed them inline as the report is written
Images are generated with dark-mode styling that matches the GPT Researcher UI, featuring professional infographic aesthetics with teal accents.
GPT Researcher now includes Deep Research - an advanced recursive research workflow that explores topics with agentic depth and breadth. This feature employs a tree-like exploration pattern, diving deeper into subtopics while maintaining a comprehensive view of the research subject.
🌳 Tree-like exploration with configurable depth and breadth
⚡️ Concurrent processing for faster results
🤝 Smart context management across research branches
⏱️ Takes ~5 minutes per deep research
💰 Costs ~$0.4 per research (using o3-mini on "high" reasoning effort)
Every research run scrapes dozens of pages, and only some of each page helps answer the question. Before anything reaches the LLM, GPT Researcher decides which passages to keep. By default it uses Jev by TypeSafe, a model that scores how useful each passage is for the question, rather than how similar its words or embedding are.
Jev's context is 59% more relevant than embeddings, at the same cost (73% of kept passages relevant vs 46%). We benchmarked every option on 28 research tasks, replaying the same scraped sources so that only the filter changed:
Context filter
Relevant passages kept
Head-to-head vs embeddings
Filter time
Cost per report
Needs
Jev (default)
73%
15 wins · 10 ties · 3 losses
1.7s
$0.115
TYPESAFE_API_KEY
Keyword (fallback)
51%
14 wins · 8 ties · 6 losses
0.02s
$0.116
nothing
Embeddings
46%
—
1.0s
$0.117
an embedding provider
No filter
—
13 wins · 11 ties · 4 losses
—
$0.192
nothing
Writing the report takes ~45s whichever filter you use, so a run's total time moves only by the filter step. Passing every page unfiltered writes the broadest reports on open-ended questions, but costs 65% more.
Nothing is required. With a TYPESAFE_API_KEY, Jev is used. Without one, or if a Jev call fails, GPT Researcher falls back to keyword (BM25) ranking, which runs locally with no API key, model or embeddings. Embeddings remain available as an option.
export TYPESAFE_API_KEY=... # use Jev (the default when a key is set)
export CONTEXT_FILTER=auto # auto | jev | keyword | embeddings | none
Step 2 - Clone the '.env.example' file, add your API Keys to the cloned file and save the file as '.env'
Step 3 - Within the docker-compose file comment out services that you don't want to run with Docker.
docker-compose up --build
If that doesn't work, try running it without the dash:
docker compose up --build
Step 4 - By default, if you haven't uncommented anything in your docker-compose file, this flow will start 2 processes:
the Python server running on localhost:8000
the React app running on localhost:3000
Visit localhost:3000 on any browser and enjoy researching!
📄 Research on Local Documents
You can instruct the GPT Researcher to run research tasks based on your local documents. Currently supported file formats are: PDF, plain text, CSV, Excel, Markdown, PowerPoint, and Word documents.
Step 1: Add the env variable DOC_PATH pointing to the folder where your documents are located.
export DOC_PATH="./my-docs"
Step 2:
If you're running the frontend app on localhost:8000, simply select "My Documents" from the "Report Source" Dropdown Options.
If you're running GPT Researcher with the PIP package, pass the report_source argument as "local" when you instantiate the GPTResearcher class code sample here.
🤖 MCP Server
We've moved our MCP server to a dedicated repository: gptr-mcp.
The GPT Researcher MCP Server enables AI applications like Claude to conduct deep research. While LLM apps can access web search tools with MCP, GPT Researcher MCP delivers deeper, more reliable research results.
Features:
Deep research capabilities for AI assistants
Higher quality information with optimized context usage
Comprehensive results with better reasoning for LLMs
Claude Desktop integration
For detailed installation and usage instructions, please visit the official repository.
👪 Multi-Agent Assistant
As AI evolves from prompt engineering and RAG to multi-agent systems, we're excited to introduce multi-agent assistants built with LangGraph and AG2.
By using multi-agent frameworks, the research process can be significantly improved in depth and quality by leveraging multiple agents with specialized skills. This project showcases how a team of AI agents can work together to conduct research on a given topic, from planning to publication.
An average run generates a 5-6 page research report in multiple formats such as PDF, Docx and Markdown.
Check it out here or head over to our documentation for LangGraph and AG2 for more information.
🔍 Observability
GPT Researcher supports LangSmith for enhanced tracing and observability, making it easier to debug and optimize complex multi-agent workflows.
This project, GPT Researcher, is an experimental application and is provided "as-is" without any warranty, express or implied. We are sharing codes for academic purposes under the Apache 2 license. Nothing herein is academic advice, and NOT a recommendation to use in academic or research papers.
Our view on unbiased research claims:
The main goal of GPT Researcher is to reduce incorrect and biased facts. How? We assume that the more sites we scrape the less chances of incorrect data. By scraping multiple sites per research, and choosing the most frequent information, the chances that they are all wrong is extremely low.
We do not aim to eliminate biases; we aim to reduce it as much as possible. We are here as a community to figure out the most effective human/llm interactions.
In research, people also tend towards biases as most have already opinions on the topics they research about. This tool scrapes many opinions and will evenly explain diverse views that a biased person would never have read.
