8 Best GPT Researcher Alternatives in 2026 (Open Source)
GPT Researcher — An autonomous agent that conducts deep research on any data using any LLM providers. Purpose-built autonomous research agent with plan-and-solve + parallel execution — vs generic LLM chat that produces shallow, uncited answers
These 8 open-source tools do the same job. They are ordered by how closely they match GPT Researcher, with live GitHub data so you can see which projects are actively maintained.
| Tool | GitHub stars | Stars / 30d | Last commit |
|---|---|---|---|
| GPT Researcher(original) | 29.8k | +607 | 2026-09-26 |
| STORM | 31.5k | +562 | 2025-09-30 |
| BlockAGI | 325 | +1 | 2023-07-24 |
| DeerFlow | 83.3k | +5,343 | 2026-09-30 |
| XAgent | 8.6k | +5 | 2026-07-31 |
| GPT-Agent | 3.6k | +385 | 2026-09-28 |
| DocsGPT | 18.3k | +80 | 2026-09-30 |
| GPT Newspaper | 1.5k | +3 | 2024-02-25 |
| AutoAct | 239 | +0 | 2025-01-13 |
1. STORM
An LLM-powered knowledge curation system that researches a topic and generates a full-length report with citations.
What sets it apart: vs generic RAG/chatbots: simulates Wikipedia editorial process with perspective-guided expert conversations, producing structured long-form articles with citations — not just Q&A
Best for: Pre-writing research and article drafting for knowledge workers; Exploratory research on complex topics with multi-perspective analysis
2. BlockAGI
Your Self-Hosted, Hackable Research Agent Inspired by AutoGPT
What sets it apart: vs AutoGPT / BabyAGI: focused single-purpose research agent with interactive web UI and narrative report output — works well with GPT-3.5 (cheaper), no Docker/sandbox/vector DB required
Best for: Automated research report generation with real-time progress tracking; Domain-specific research tasks (crypto, market analysis, competitive intelligence); Developers wanting a simpler alternative to AutoGPT for focused research
3. DeerFlow
An open-source long-horizon SuperAgent harness that researches, codes, and creates. With the help of sandboxes, memories, tools, skill, subagents and message gateway, it handles different levels of ta
What sets it apart: vs AutoGPT: purpose-built for deep research with sub-agent orchestration and sandbox; vs LangGraph: higher-level harness with built-in memory, sandbox, and skill system rather than bare graph framework
Best for: Deep research and exploration tasks; Building multi-agent systems with sub-agent orchestration; Teams wanting coding agent integration (Claude Code/Codex)
4. XAgent
An Autonomous LLM Agent for Complex Task Solving
What sets it apart: vs AutoGPT: dual-loop mechanism with human-agent collaboration and active help-seeking — demonstrated superiority over AutoGPT in human preference evaluation across 50+ real-world tasks
Best for: Complex multi-step tasks: data analysis, coding, research, reports; Tasks requiring human-AI collaboration with approval gates; Autonomous problem-solving with tool-use capabilities
5. GPT-Agent
🚀 Introducing 🐪 CAMEL: a game-changing role-playing approach for LLMs and auto-agents like BabyAGI & AutoGPT! Watch two agents 🤝 collaborate and solve tasks together, unlocking endless possibilitie
What sets it apart: CAMEL-based dual AI agent system where two configurable personas collaborate and communicate to solve tasks together
Best for: exploring-multi-agent-collaboration; research-on-agent-communication; prototyping-dual-agent-systems
6. DocsGPT
Private AI platform for agents, assistants and enterprise search. Built-in Agent Builder, Deep research, Document analysis, Multi-model support, and API connectivity for agents.
Best for: Enterprise teams building private document Q&A systems; Organizations needing on-premise AI deployment with data privacy control; Teams requiring multi-format document ingestion including audio workflows
7. GPT Newspaper
GPT based autonomous agent designed to create personalized newspapers tailored to user preferences.
What sets it apart: vs news aggregators: six autonomous agents (search, curate, write, critique, design, edit) create fully personalized newspapers — generates original content rather than simply filtering existing articles
Best for: Personalized news aggregation with AI curation; Research into multi-agent content creation workflows; Custom topic-based news digests
8. AutoAct
[ACL 2024] AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning
What sets it apart: vs ReAct/Reflexion/BOLAA: division-of-labor strategy automatically creates specialized Plan/Tool/Reflect sub-agents from self-synthesized trajectories — zero dependency on closed-source model data or human annotations
Best for: Research on automatic agent learning without GPT-4 dependency; Multi-hop QA requiring complex question decomposition; Teams wanting to train specialized sub-agents from self-generated data