MCP Servers · assafelovic

gpt-researcher

GPT Researcher is an autonomous agent that orchestrates multi-source research using any LLM provider, generating detailed reports with citations. It addresses hallucination and stale-data problems by parallelizing agent work across web and local sources, aggregating findings into structured outputs.

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Source: GitHub — github.com/assafelovic/gpt-researcher

28.1k

GitHub stars

3.8k

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	assafelovic/gpt-researcher
Owner	assafelovic
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	28.1k
Forks	3.8k
Open issues	210
Latest release	v3.5.1 (2026-06-23)
Last updated	2026-07-05
Source	https://github.com/assafelovic/gpt-researcher

What gpt-researcher is

Python-based agentic framework using planner-executor-publisher architecture with web scraping (including JS-enabled), document retrieval, and LLM providers (OpenAI-compatible). Supports MCP integration for custom data sources, PDF/Word export, and inline image generation via Google Gemini.

Quickstart

Get the gpt-researcher source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/assafelovic/gpt-researcher.git2cd gpt-researcher3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Deep research automation for competitive intelligence and market analysis

Systematically gather and synthesize multi-source insights on market trends, competitor positioning, or emerging technologies with minimal manual effort and built-in citation tracking.

Internal knowledge synthesis and documentation generation

Quickly aggregate internal documents, APIs, and databases via MCP to produce comprehensive reference materials, technical overviews, or onboarding documentation for teams.

AI-augmented content creation and report generation

Reduce time-to-publication for newsletters, blog posts, and white papers by automating fact-gathering and outline generation, with customizable report length (2000+ words) and formatting (PDF, Word).

Implementation considerations

Requires API keys for at least one LLM provider (OpenAI, Anthropic, etc.) and a web search provider (Tavily); budget for usage costs and potential rate limits.
MCP configuration is optional but adds complexity if integrating custom data sources (GitHub, databases); test connectivity and auth in staging first.
Python 3.11+ required; dependencies (uvicorn, langchain, etc.) must be pinned for reproducibility; consider containerization for consistency across environments.
Web scraping reliability depends on target site structure and JavaScript rendering; sites with aggressive bot detection or paywalls may fail; no built-in retries for transient failures.

When to avoid it — and what to weigh

Real-time trading or time-critical decision systems — Research latency and potential web-scraping delays make this unsuitable for millisecond-sensitive applications; no guarantees on freshness or update frequency.
Regulated data handling without extended validation — If subject to HIPAA, PCI-DSS, or similar compliance, review API keys, local data handling, and third-party LLM provider terms before deployment; data residency is configuration-dependent.
Closed or air-gapped environments — Requires web access (Tavily) or configured MCP servers; not suitable for fully isolated deployments without significant architectural modification.
Mission-critical systems without guardrails — LLM outputs can still hallucinate despite source citation; requires human review, secondary validation, or embedding in a supervised workflow for high-stakes decisions.

License & commercial use

Apache License 2.0 (permissive OSI license). Permits commercial use, modification, and distribution with standard indemnification and no warranty. Requires retention of license text and copyright notices.

Apache 2.0 permits commercial use freely. However, review dependencies for conflicting licenses (e.g., GPL), and verify that third-party LLM providers and web-scraping services comply with your commercial use case (e.g., Tavily terms, OpenAI commercial policies).

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Low
DEV.co fit	Strong
Assessment confidence	High

Security considerations

No claims of security audit provided. Relevant considerations: API keys stored in environment variables or .env files (standard practice but not encrypted at rest); web scraping may expose your IP or session to target sites; third-party LLM providers receive data (review privacy policies); local document handling depends on file permissions and environment isolation. No mention of input validation or output sanitization for injection attacks; review before exposing to untrusted user queries.

Alternatives to consider

OpenAI Swarm / AutoGPT

Similar agentic frameworks but more generic; lack built-in research workflows, source aggregation, and report formatting; may require more custom orchestration.

Perplexity API / Metaphor Search API

Closed-source SaaS alternatives with real-time web search; eliminate operational overhead but impose rate limits, vendor lock-in, and higher per-query costs for high-volume use.

LlamaIndex / LangChain retrieval chains

Lower-level frameworks for RAG and document retrieval; more flexible but require manual orchestration of agent loops, web scraping, and report generation.

Software development agency

Build on gpt-researcher with DEV.co software developers

GPT Researcher can streamline competitive analysis, documentation, and content generation. Let's discuss integration into your workflow—whether as a standalone service, embedded API, or custom orchestration.

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gpt-researcher FAQ

Can I run this offline or on a local LLM?

Yes, via OPENAI_BASE_URL environment variable for local/custom LLM endpoints. However, web research (Tavily) still requires internet; use MCP for offline data sources or pre-load documents locally.

How do I avoid hallucinated citations or broken references?

The tool tracks sources per summary, but LLMs can still misattribute. Always human-review critical findings, use a secondary fact-checking step, or restrict to trusted data sources via MCP.

What are typical costs to run this?

Costs depend on LLM choice (OpenAI ~$0.01–0.10 per report), web search provider (Tavily pricing unknown from data), and image generation (Google Gemini). Budget $0.10–1.00 per research task for moderate usage.

Does this work with Claude or other non-OpenAI providers?

Yes. Supports any LLM via LangChain; configure OPENAI_BASE_URL or provider-specific settings. Claude Skill integration also available for direct Claude.ai usage.

Work with a software development agency

DEV.co helps companies turn open-source tools like gpt-researcher into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your mcp servers stack.

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