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GPT RESEARCHER / FROM A RESEARCH QUESTION TO A TRACEABLE REPORT
GPT Researcher
An open-source research agent that gathers information and produces source-linked reports. GPT Researcher supports web and local-document workflows, configurable language models and deeper research paths for developers who want control over the process.
GPT Researcher official repository demonstration frame showing the research workspace and agent-work area; illustrative interface, not a completed FindGoodAI testExplore the image ↓
What you can do with it
What this tool is for
GPT Researcher organizes research into a workflow that gathers material, processes it and writes a report with sources. It is useful when the task requires more than one quick answer: for example, comparing approaches, assembling a technical overview or reviewing evidence around a clearly defined question.
The open-source project can be self-hosted or integrated through Python. Its value is control over the research process and supporting providers, not a guarantee that the final report is complete or correct. The user still needs to judge source quality, inspect citations and identify missing perspectives.
01
Compare technical approaches
02
Prepare a literature orientation
03
Summarize an internal collection
04
Build a recurring report workflow
A closer look at the product
Official product image. Click to inspect the details.
Product interface and examples
Key features, practical tasks
Capabilities in practice
- Question-driven research: Begin with a specific topic or question rather than an unrestricted request to research everything. A clear scope helps the system gather relevant material and gives the reviewer a concrete standard for deciding whether the report answered the question.
- Web information gathering: The project combines search and content retrieval as part of the research process. Search-provider choice affects coverage, cost and access, so a working model endpoint alone is not enough to define the complete environment.
- Local-document workflows: The project documents research using supplied documents as well as online sources. Keep source identifiers and distinguish information inside the supplied collection from external context added during a web-enabled task.
- Source-linked reports: Reports include references that can be inspected. A citation is a starting point for review, not automatic proof; verify that the source supports the particular claim and that the report has not merged incompatible dates or populations.
- Configurable language models: Official documentation supports multiple model providers and compatible local routes. Model quality, context capacity and tool behavior matter for research, so test the entire workflow rather than assuming every compatible endpoint performs equally well.
- Deeper research paths: The Deep Research documentation describes iterative exploration with configurable breadth and depth. More exploration can consume more calls and time; choose a scope proportional to the decision the report is meant to support.
- Application integration: The Python package and web-oriented project make it possible to embed research in another product. Developers can separate research execution from presentation and add their own review, storage and export steps around the generated result.
Get started, step by step
- 01
Define the question and budget
- 02
Configure the environment
- 03
Run a limited research job
- 04
Review the report and references
A practical first workflow
A practical first workflow
- Define the question and budget. Specify the time period, desired sources, exclusions and report structure. Decide how much model and search usage is acceptable before enabling a broad research task.
- Configure the environment. Follow the current installation guide and choose model and search providers. Store credentials in the intended environment configuration and confirm any local endpoints before using private material.
- Run a limited research job. Start with a manageable topic or document set. Inspect retrieved sources and verify that the task is gathering the right kind of evidence before increasing depth or breadth.
- Review the report and references. Open the key sources, correct unsupported claims and add missing qualifications. Save the final report with its question, source list and configuration so it can be updated or challenged later.
Example prompt or task: Research the tradeoffs between these two technical approaches using primary documentation and published evaluations. Separate measured results from vendor claims, state the comparison conditions, and list unanswered questions with source links.
This is an editorial starting workflow, not a measured performance result.
Find the right plan for you
Free software
Explore official plans ↗Free software Models, hardware and hosting are separate
GPT Researcher is free software under Apache-2.0. The repository does not provide an unlimited pool of model tokens or web-search requests. Default or optional cloud providers can charge independently, while local alternatives use the operator's resources.
What is free and what can cost extra
GPT Researcher is free software under Apache-2.0. The repository does not provide an unlimited pool of model tokens or web-search requests. Default or optional cloud providers can charge independently, while local alternatives use the operator's resources.
The real cost of a research job depends on model choice, source gathering and exploration settings. Begin with a small task, inspect actual provider usage and set sensible limits before running large batches or making the service available to others.
A few things worth knowing
Frequently asked questions
Is GPT Researcher free?
The source is Apache-2.0 licensed; running providers and infrastructure can still cost money.
Does it include free model credits?
No. Model and search services have separate allowances and billing.
Can it use local models?
Compatible local routes are documented, with additional setup and capability considerations.
Can it work with local documents?
The project supports document-oriented research as well as web research.
Are citations automatically trustworthy?
No. Check the linked source against the specific statement it is meant to support.
Is deeper research always better?
More exploration can add cost and irrelevant material; choose settings suited to the question.
Can developers integrate it?
Yes. The project provides a Python package and application-oriented workflows.
The details behind your decision
Explore platforms, inputs and outputs, licensing, and access requirements.
Four useful starting projects
Four useful starting projects
- Compare technical approaches: Ask a narrow engineering question and require evidence for tradeoffs. Distinguish published measurements from general claims, and note when the compared systems were evaluated under different conditions.
- Prepare a literature orientation: Generate an initial map of concepts and source material before reading deeply. Use the report as a navigation aid, then verify important papers directly and record what the source actually studied.
- Summarize an internal collection: Use approved local documents to assemble a topic brief. Keep the collection version and document identifiers so readers can trace a statement back to the material that was available at the time.
- Build a recurring report workflow: Develop a controlled process that gathers sources, drafts a report and sends it to a human reviewer. Compare changes in sources as well as changes in generated prose when updating an earlier report.
Platforms, inputs, outputs and setup
Platforms, inputs, outputs and setup
The current getting-started guide specifies a Python environment and also links to container routes. Follow the current version's requirements rather than copying an older tutorial, and keep the selected model, embeddings and search configuration consistent.
Web research can encounter inaccessible pages, duplicate sources and unsupported documents. Local-document research can inherit gaps in the supplied collection. A useful report should identify these limits instead of presenting the available material as exhaustive coverage.
Data, permissions and licensing
Data, permissions and licensing
Map the entire processing chain before using internal files: model providers, embeddings, search services and document handling can have different data destinations. A locally hosted interface alone does not establish that every step is local.
Keep research credentials private and control who can launch expensive jobs. Reports should retain their source context so an unsupported claim can be corrected without obscuring the original evidence.
Sources, documentation and help
Official sources and review date
Official sources and review date
Reviewed October 3, 2026. Product facts come from the official sources below. Suggested projects, prompts and review methods are FindGoodAI editorial guidance, not measured performance results.
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