strands-vllm 0.0.3

Community vLLM provider utilities for Strands Agents (OpenAI-compatible).

strands-vllm

Community vLLM utilities for the Strands Agents SDK.

vLLM serves an OpenAI-compatible API, so most users can simply use OpenAIModel with base_url. This package provides small convenience helpers and (optionally) token-id/TITO-friendly defaults.

Credit / reference

This community package is inspired by the structure and example style of horizon-rl/strands-sglang.

Install

pip install strands-vllm

vLLM server notes (tools + token IDs)

• Tools: if you want tool calling, your vLLM server must be started with tool-calling enabled and an appropriate chat template for your model (e.g., Llama 3.2 tool template).
• Token IDs (TITO): return_token_ids=True requests vLLM token IDs; vLLM will include prompt_token_ids and streamed token_ids when supported.

Usage

Minimal: OpenAIModel pointed at vLLM

from strands import Agent
from strands.models.openai import OpenAIModel

model = OpenAIModel(
    client_args={"api_key": "EMPTY", "base_url": "http://localhost:8000/v1"},
    model_id="AMead10/Llama-3.2-3B-Instruct-AWQ",
)

agent = Agent(model=model)
print(agent("Hi"))

Convenience: VLLMModel

from strands import Agent
from strands_vllm import VLLMModel

model = VLLMModel(
    base_url="http://localhost:8000/v1",
    model_id="AMead10/Llama-3.2-3B-Instruct-AWQ",
    return_token_ids=True,
)

agent = Agent(model=model)
print(agent("Say hello"))

Tip: if you want to print only the final result (without streaming output being printed along the way), pass callback_handler=None:

agent = Agent(model=model, callback_handler=None)
print(agent("Say hello"))

Examples

All examples can be pointed at your server with:

export VLLM_BASE_URL="http://localhost:8000/v1"
export VLLM_MODEL_ID="AMead10/Llama-3.2-3B-Instruct-AWQ"

Tools (strands-agents-tools)

Install the optional tools package and run the example:

pip install strands-agents-tools
python examples/math_agent.py

Tool-call validation example

pip install strands-agents-tools
python examples/tool_validation_agent.py

RL rollout (TITO + loss_mask + retokenization check)

pip install "strands-vllm[drift]" strands-agents-tools
python examples/rl_rollout_tito.py

Tool-call validation (recommended with vLLM tool parsers)

vLLM tool calling can involve server-side post-processing, so it can be useful to guard tool execution:

from strands import Agent
from strands_tools.calculator import calculator
from strands_vllm import VLLMModel, VLLMToolValidationHooks

model = VLLMModel(base_url="http://localhost:8000/v1", model_id="...", return_token_ids=True)
agent = Agent(model=model, tools=[calculator], hooks=[VLLMToolValidationHooks()])
print(agent("Compute 17 * 19 using the calculator tool."))

Retokenization drift (educational)

This demo mirrors the idea from strands-sglang and shows why TITO matters: encode(decode(tokens)) != tokens can happen.

pip install "strands-vllm[drift]" strands-agents-tools
python examples/retokenization_drift.py

Token-in / token-out (TITO)

If your vLLM server includes token IDs in streaming responses, you can capture them using VLLMTokenRecorder (see examples/basic_agent.py).

Development

Install from source:

git clone <your-fork-url>
cd strands-vllm
pip install -e ".[dev]"

License

Apache-2.0