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GPT Researcher

external · status: active · focus: literature · discipline: general · started: 2023

Project page: https://github.com/assafelovic/gpt-researcher

Source: projects/landscape/gpt-researcher.yml

Positioning

An autonomous "deep research" agent that produces long-form, cited reports on any topic from web and local sources. Uses a planner + parallel execution-agent architecture inspired by Plan-and-Solve and RAG; outputs reports of 2,000+ words with smart image scraping and inline image generation.

Distinctive contribution

Earliest and most-adopted general-purpose deep-research agent; the planner/executor split with parallelized sub-queries set the reference pattern that several later systems adopted. Distributed as a pip package, Claude Skill, Docker image, and Colab notebook.

Evaluation scores

Dimension Score (0–3) Note
Lifecycle coverage 1 Four pre-writing + drafting stages; no analysis, formal modeling, or review.
Autonomy level 3 Runs end-to-end from a research query; no per-step approval required.
Architectural transparency 3 Open source under Apache-2.0; planner/executor pattern documented; modular configuration.
Inputs supported 2 Query + optional local documents; multiple LLM and retrieval back-ends supported.
Outputs / reproducibility 2 Pip-installable, Docker image, Colab; outputs depend on live web state — not bitwise reproducible.
Internal evaluation 1 Demonstration-quality evaluation; no rigorous benchmark in the README at this scoring date.
Openness 3 Apache-2.0; broad distribution; active community.
Maturity / traction 3 27k+ stars, regular releases, Claude Skill integration, large Discord community.
Cross-family policy 0 Multi-LLM-provider but no cross-family policy.
Runtime assurance 1 Planner/executor split with summary aggregation; no claim-audit harness.
Cross-platform portability 2 Multi-provider, Claude Skill, Docker, Colab, PyPI; deliberately portable.

Scored on 2026-05-18. See the evaluation rubric.

Tags

Pipeline stages: rq-formulation literature-discovery literature-synthesis paper-drafting

Architectural features: multi-agent tool-use rag-knowledge-base iterative-loop

Inputs: research-query local-documents

Outputs: long-form-report citations inline-images

Data sources: web-search user-provided-documents

Knowledge sources: web-search

Limitations

  • Optimized for general-purpose research; not tuned for academic-rigor outputs (no method/identification awareness).
  • Output reproducibility depends on live web state.
  • No formal evaluation against academic-quality benchmarks reported.

Also compared in

  • Agentic AI for Scientific Discovery: A Survey (gridach2025agenticsurvey) — Covered as a planner/executor deep-research agent.