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We welcome developers and enterprises to integrate and try Agents-A1 and share their feedback.
We evaluate Agents-A1 in real-world agentic and research-oriented workflows across six directions — long-horizon search, engineering tasks, scientific research, instruction following, general agentic tasks, and scientific agentic tasks. Despite operating in the ~35B model class, Agents-A1 delivers highly competitive performance against frontier-scale systems such as GPT-5.5, DeepSeek-V4-pro, and Kimi-K2.6. It achieves overall SOTA results on several challenging benchmarks, including Seal-0 (56.4), HiPhO (46.4), FrontierScience-Olympiad (79.0), FrontierScience-Research (40.00), IFBench (80.6), and IFEval (94.8), while also ranking as the best among comparable models on a broad range of tasks such as BrowseComp (75.5), XBench-DS-2510 (86.0), GAIA (96.0), SciCode (44.3), HLE with tools (47.6), and MolBench-bind (56.8). These results show that Agents-A1 combines strong long-horizon search ability, robust scientific reasoning, and reliable instruction following, establishing it as a highly capable and efficient agentic model that narrows the gap with much larger frontier models.
SGLang is a fast serving framework for large language models and vision language models.
Install SGLang with uv:
shell
uv venv --python 3.12 --seed --managed-pythonsource .venv/bin/activateuv pip install sglang
See its documentation for more details.
The following commands create API endpoints at http://localhost:8000/v1:
Standard Version (1 GPUs, 262K context):
shell
python -m sglang.launch_server \--model-path InternScience/Agents-A1 \--port 8000 \--tp-size 1 \--mem-fraction-static 0.8 \--context-length 262144 \--reasoning-parser qwen3
Tool Use:
shell
python -m sglang.launch_server \--model-path InternScience/Agents-A1 \--port 8000 \--tp-size 1 \--mem-fraction-static 0.8 \--context-length 262144 \--reasoning-parser qwen3 \--tool-call-parser qwen3_coder
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
Install vLLM from the main branch via uv:
shell
uv venv --python 3.12 --seed --managed-pythonsource .venv/bin/activateuv pip install vllm --torch-backend=auto
See its documentation for more details.
The following commands create API endpoints at http://localhost:8000/v1:
Standard Version (1 GPUs, 262K context):
shell
vllm serve InternScience/Agents-A1 \--port 8000 \--tensor-parallel-size 1 \--max-model-len 262144 \--reasoning-parser qwen3
Tool Call:
shell
vllm serve InternScience/Agents-A1 \--port 8000 \--tensor-parallel-size 1 \--max-model-len 262144 \--reasoning-parser qwen3 \--enable-auto-tool-choice \--tool-call-parser qwen3_coder
Text-Only (skips vision encoder to free KV cache memory):
shell
vllm serve InternScience/Agents-A1 \--port 8000 \--tensor-parallel-size 1 \--max-model-len 262144 \--reasoning-parser qwen3 \--language-model-only
For the best generation quality, we recommend the following sampling parameters:
temperature: 0.85top_p: 0.95top_k: 20min_p: 0.0presence_penalty: 1.1repetition_penalty: 1.0To provide the community with a unified agent evaluation codebase for fair comparison, we have also open-sourced an evaluation framework for assessing agentic models across core capabilities, including tool use and multi-step reasoning. The evaluation code is included in the Agents-A1/evaluation of this repository.
We use this framework to evaluate the released model under a standardized and reproducible setting. Specifically, the model is tested on a set of agent-oriented tasks that require it to understand user goals, decompose complex instructions, interact with tools or environments when necessary, and produce final results. The evaluation results reported in Model Card are generated using the open-source framework above, so that users can reproduce the experiments, compare other models under the same protocol, and further extend the benchmark for new agent scenarios. (Note that: To ensure a fair comparison, we report the benchmark results from their original technical reports. If a model does not report the corresponding benchmark results, we evaluate it using the same evaluation protocol as our model.)
For detailed evaluation scripts, task definitions, metrics, and reproduction instructions, please refer to the evaluation codebase.
If you find our work helpful, feel free to give us a cite.
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