strands-sglang

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SGLang model provider of Strands Agents for on-policy agentic RL training.

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# Strands-SGLang

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**Agentic RL, done correctly.**

- [Strands Agents SDK](https://github.com/strands-agents/sdk-python): a harness builder whose event-based hooks make the agent loop fully customizable.
- [SGLang](https://docs.sglang.io/): a high-performance serving framework for fast, high-concurrency rollouts that exposes per-token metadata.

**Strands-SGLang** bridges the two so multi-turn rollouts stay on-policy — token-in, token-out, no silent retokenization drift.

## Features

- **Token-In/Token-Out** rollouts: model-generated tokens carried through as-is
    - Only new messages are tokenized each turn with **incremental chat templating**
- **Strict tool-call parsing**: parsed exactly as generated, no heuristic repair
- **Harness customization**: pass tools and hooks into `Agent` to customize your harness
- **Native SGLang `/generate` endpoint**: high-throughput, non-streaming rollouts

> For RL environment integration, please refer to [`strands-env`](https://github.com/strands-rl/strands-env)

## Installation

```bash
pip install strands-sglang strands-agents-tools
```

Or install from source with development dependencies:

```bash
git clone https://github.com/strands-rl/strands-sglang.git
cd strands-sglang
pip install -e ".[dev]"
```

## Quick Start

### 1. Start SGLang Server

```bash
python -m sglang.launch_server --model-path Qwen/Qwen3.5-4B
```

### 2. Run an Agent

```python
import asyncio
from transformers import AutoTokenizer
from strands import Agent
from strands_tools import calculator
from strands_sglang import SGLangClient, SGLangModel
from strands_sglang.tool_parsers import get_tool_parser

async def main():
    model = SGLangModel(
        client=SGLangClient(base_url="http://localhost:30000"),
        tokenizer=AutoTokenizer.from_pretrained("Qwen/Qwen3.5-4B"),
        tool_parser=get_tool_parser("qwen_xml"),
    )
    agent = Agent(model=model, tools=[calculator])
    await agent.invoke_async("What is 25 * 17?")

# SGLangModel captures the full token trajectory for on-policy RL training
    rollout = model.rollout
    print(rollout.token_ids, rollout.loss_mask, rollout.logprobs)

asyncio.run(main())
```

## RL Training

In principle, Strands-SGLang can be seen as a drop-in agentic rollout service and can be integrated with any RL training framework. A concrete example of training a math coding agent ([ReTool](https://arxiv.org/abs/2504.11536)) is available at [slime/examples/strands_sglang](https://github.com/THUDM/slime/tree/main/examples/strands_sglang).

Some key highlights of adapting Strands-SGLang to any RL framework:
- Pass tokens and token metadata from `Rollout` for on-policy rollouts
- Hook your harness with the built-in `LoopLimiter` (or your own) for controlled rollouts
- Use a shared `SGLangClient` and HF tokenizer; don't create one instance per rollout
- Classify the rollout's termination reason properly — it shapes reward and sampling

## Testing

```bash
# Unit tests
pytest tests/unit/ -v

# Integration tests (requires SGLang server)
pytest tests/integration/ -v --sglang-base-url=http://localhost:30000
```

## Contributing

Contributions welcome! Install pre-commit hooks for code style and commit message validation:

```bash
pip install -e ".[dev]"
pre-commit install -t pre-commit -t commit-msg
```

This project uses [Conventional Commits](https://www.conventionalcommits.org/). Commit messages must follow the format:

```
<type>(<scope>): <description>

# Examples:
feat(client): add retry backoff configuration
fix(sglang): handle empty response from server
docs: update usage examples
```

Allowed types: `feat`, `fix`, `docs`, `style`, `refactor`, `perf`, `test`, `build`, `ci`, `chore`, `revert`

## License

Apache License 2.0 - see [LICENSE](LICENSE).

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