TIGER-AI-Lab

TIGER-AI-Lab / OpenResearcher

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OpenResearcher: A Fully Open Pipeline for Long-Horizon Deep Research Trajectory Synthesis

github.comTIGER-AI-LabOpenResearcher deep-research llm retrieval
415
45
100% credibility
Found Feb 10, 2026 at 185 stars 2x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

OpenResearcher is a fully open-source AI agent and evaluation framework for long-horizon deep research tasks, featuring a 30B model trained on trajectories with browser tools.

How It Works

1
🔍 Discover OpenResearcher

You stumble upon this open AI helper designed for tackling complex, multi-step research questions better than big commercial AIs.

2
💻 Prepare your setup

You follow simple instructions to get your powerful computer ready with the needed research tools and data collections.

3
🧠 Wake up the AI researcher

With one click, you launch the smart 30-billion-parameter brain trained specifically for deep, long-thinking research tasks.

4
Pick your info finder
📚
Private collection

Keeps everything local and private using your own organized documents.

🌐
Live web search

Pulls in real-time info from the internet easily.

5
❓ Ask deep questions

You give it challenging research problems from tough benchmarks that require many steps of thinking and digging.

6
👀 Watch it work magic

You see the AI search, browse pages, reason step-by-step, and build deep insights over dozens of turns.

🏆 Impressive results

You get top accuracy scores on hard research tests, ready to share or build upon for your own discoveries.

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Star Growth

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AI-Generated Review

What is OpenResearcher?

OpenResearcher is a fully open Python pipeline for long-horizon deep research trajectory synthesis, powering an AI agent that tackles complex scientific queries via web search, browsing, and multi-turn reasoning. It delivers a 30B parameter model trained on 96K high-quality trajectories, beating GPT-4o and Claude-3.5-Sonnet on BrowseComp-Plus with 54.8% accuracy. Users get scripts to deploy vLLM servers, run agents on benchmarks like GAIA or xbench-DeepResearch, and evaluate outputs—no external APIs needed thanks to a local 11B-token retriever.

Why is it gaining traction?

This open researcher AI stands out by open-sourcing everything: dataset, model, distillation recipe, and eval framework, letting devs replicate and improve deep research agents without black-box dependencies. The HF demo, Slack community, and quick-start commands hook experimenters, while scalable local search slashes costs versus API-heavy alternatives. Python simplicity plus benchmark-beating results make it a go-to for openresearcher GitHub forks chasing accelerated scientific research.

Who should use this?

AI researchers benchmarking long-horizon agents against closed models like DeepSeek-R1. Teams at labs or startups fine-tuning open models for research synthesis pipelines. Devs prototyping browser-based research tools who want a battle-tested eval suite out of the box.

Verdict

Grab it if you're into open researcher GitHub experiments—97 stars signal early promise, but 1.0% credibility score and thin tests mean treat as research prototype. Solid docs and HF resources make it worth forking for custom deep research trajectories.

(198 words)

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