InternAgent

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InternAgent is an AI Agents & Automation tool that provides a unified agentic framework for long-horizon autonomous scientific discovery. It supports both algorithm and empirical discovery across various scientific domains.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is InternAgent?

InternAgent 1.5 is a sophisticated autonomous system designed for end-to-end scientific discovery, encompassing both Algorithm Discovery and Empirical Discovery. Building upon InternAgent 1.0, it structures scientific inquiry into three interconnected subsystems: Generation (hypothesis construction via deep research), Verification (methodological evaluation through solution refinement), and Evolution (evidence-driven refinement using long-horizon memory). This framework achieves leading performance on scientific reasoning benchmarks like GAIA, HLE, GPQA, and FrontierScience, demonstrating sustained autonomous optimization over extended discovery cycles. It supports diverse workflows, from agent memory and reinforcement learning to dry-lab simulations and wet-lab experimentation, across Physical, Biological, Earth, and Life Sciences.

Best used for

Ideal for developers and researchers who need to automate complex scientific research, optimize algorithms, and conduct empirical discovery. Especially valuable for those working in Physical, Biological, Earth, and Life Sciences requiring an end-to-end autonomous system for hypothesis generation to experimental validation.

Common actions

automate scientific research

optimize algorithms

generate hypotheses

refine solutions

conduct experiments

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Capabilities

Key features

Autonomous scientific discovery
Hypothesis construction
Solution refinement
Long-horizon memory
Automated experimental protocols
Algorithm optimization
Empirical discovery

Target Audience

developerdata scientistprofessorresearcher

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What types of scientific discovery tasks does InternAgent 1.5 support?

InternAgent 1.5 supports a wide range of tasks including scientific algorithm discovery (e.g., Suzuki–Miyaura Reaction Yield Prediction, Molecular Dynamics Simulation) and empirical discovery (e.g., Automated Climate Diagnostics, Biological Evidence Synthesis) across Physical, Biological, Earth, and Life Sciences.

How can I access InternAgent 1.5 for algorithm discovery tasks?

For algorithm discovery tasks like Reinforcement Learning or Test-time Scaling, you can submit an issue or pull request in the InternAgent GitHub repository. The team will regularly update the algorithm design results based on your optimization task descriptions.

What are the core subsystems of InternAgent 1.5?

InternAgent 1.5 operates through three coordinated subsystems: Generation, which handles hypothesis construction via deep research; Verification, which focuses on methodological evaluation and solution refinement; and Evolution, which manages evidence-driven refinement through long-horizon memory.

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