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InternAgent-1.5: A Unified Agentic Framework for Long-Horizon Autonomous Scientific Discovery
| Date | Stars |
|---|---|
| 2026-07-24 | 1385 |
| 2026-07-25 | 1386 |
| 2026-07-28 | 1386 |
| 2026-07-30 | 1386 |
| 2026-07-31 | 1387 |
| 2026-08-06 | 1387 |
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growth rate 0.07%/day
# InternAgent-1.5: A Unified Agentic Framework for Long-Horizon Autonomous Scientific Discovery
> *Autonomous Discovery Across All Sciences*
- **Papers**: [InternAgent 1.0](https://arxiv.org/abs/2505.16938) | [InternAgent 1.5](https://huggingface.co/papers/2602.08990)
- **Links**: [Website](https://discovery.intern-ai.org.cn) | [HuggingFace](https://huggingface.co/collections/InternScience/internagent)
## 🔥 News
- **2026.5.07**: 🔥🔥 We have open-sourced InternAgent-1.5 with advanced features, including enhanced algorithm discovery tasks, autonomous scientific paper reproduction, memory module, and deep research for tackling complex research challenges.
- **2026.3.17**: 🚀🚀 We provide public [access](https://scphub.intern-ai.org.cn/detail/28) to InternAgent's Deep Research capabilities, enabling developers and researchers to seamlessly integrate its advanced deep research functionality into their own workflows.
- **2026.2.14**: ❤️🔥❤️🔥 We open-source **[MLEvolve](https://github.com/InternScience/MLEvolve)**, the core implementation of InternAgent's solution optimization subsystem for algorithm design tasks. As the **open-source method** to achieve **#1 on MLEBench**, MLEvolve demonstrates powerful capabilities in solution optimization within bounded hypothesis spaces.
- **2026.2.10**: 🔥 Official release of the [InternAgent 1.5 Technical Report](https://huggingface.co/papers/2602.08990). InternAgent 1.5 achieves leading performance on scientific reasoning benchmarks including **GAIA, HLE, GPQA, and FrontierScience**, and supports end-to-end autonomous scientific discovery tasks across **Physical, Biology, Earth, and Life Science domains**, enabling both algorithm discovery and empirical discovery (dry/wet-lab experiments).
- **2025.10.13**: InternAgent-1.0 code has been fully open-sourced, supporting end-to-end automation and autonomous evolution across 12 scientific research tasks.
<details>
<summary>more...</summary>
- **2025.07.17**: The source code of InternAgent has been partially open-sourced. The complete version of InternAgent (covering 12 types of tasks for autonomous scientific research) will be open-sourced soon. This code repository can be used for full-cycle autonomous scientific research, ranging from hypothesis generation to automated experimental execution.
- **2025.07.10**: *NovelSeek* has been renamed to **InternAgent**. This change embodies our hopeful vision for autonomous scientific research framework, and we hope it will empower all researchers to achieve great scientific discoveries.
</details>
---
## 🚀 Getting Started
### Installation
```bash
conda create -n InternAgent python=3.11
conda activate InternAgent
pip install -r requirements.txt
```
### Configure API Keys
Rename `.env.example` to `.env` and fill in your API keys:
```bash
mv .env.example .env
```
Key fields in `.env`:
```
OPENAI_API_KEY= # OpenAI or compatible API key (used for embeddings and memory)
OPENAI_API_BASE_URL= # Base URL for OpenAI-compatible endpoints
OPENROUTER_API_KEY= # OpenRouter API key (when using the openrouter provider)
ANTHROPIC_API_KEY= # Anthropic API key (for Claude-based experiment backends)
```
To use OpenRouter as the model gateway, set `OPENROUTER_API_KEY` and run with
`config/openrouter_config.yaml`. See [docs/openrouter.md](docs/openrouter.md)
for setup details.
### Run a Discovery Experiment
`AutoDebug` is a self-contained toy task (no dataset or model downloads required) and is the recommended first run to verify your setup.
```bash
python launch_discovery.py \
--config ./config/default_config.yaml \
--task AutoDebug \
--exp_backend claudecode
```
### Run a QA Query
QA mode uses InternAgent's deep research pipeline to answer a research question directly — no experiment loop, just a synthesized answer grounded in literature.
```bash
python launch_qa.py --question "What are recent advances in memory-augmented LLMs?"
# Optionally save the answer to a file
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