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+# vLLM
+
+{{ community_contribution_banner }}
+
+!!! info "Language Support"
+    This provider is only supported in Python.
+
+[strands-vllm](https://github.com/agents-community/strands-vllm) is a [vLLM](https://docs.vllm.ai/) model provider for Strands Agents SDK with Token-In/Token-Out (TITO) support for agentic RL training. It provides integration with vLLM's OpenAI-compatible API, optimized for reinforcement learning workflows with [Agent Lightning](https://blog.vllm.ai/2025/10/22/agent-lightning.html).
+
+**Features:**
+
+- **OpenAI-Compatible API**: Uses vLLM's OpenAI-compatible `/v1/chat/completions` endpoint with streaming
+- **TITO Support**: Captures `prompt_token_ids` and `token_ids` directly from vLLM - no retokenization drift
+- **Tool Call Validation**: Optional hooks for RL-friendly error messages (allowed tools list, schema validation)
+- **Agent Lightning Integration**: Automatically adds token IDs to OpenTelemetry spans for RL training data extraction
+- **Streaming**: Full streaming support with token ID capture via `VLLMTokenRecorder`
+
+!!! tip "Why TITO?"
+    Traditional retokenization can cause drift in RL training—the same text may tokenize differently during inference vs. training (e.g., "HAVING" → `H`+`AVING` vs. `HAV`+`ING`). TITO captures exact tokens from vLLM, eliminating this issue. See [No More Retokenization Drift](https://blog.vllm.ai/2025/10/22/agent-lightning.html) for details.
+
+## Installation
+
+Install strands-vllm along with the Strands Agents SDK:
+
+```bash
+pip install strands-vllm strands-agents-tools
+```
+
+For retokenization drift demos (requires HuggingFace tokenizer):
+
+```bash
+pip install "strands-vllm[drift]" strands-agents-tools
+```
+
+## Requirements
+
+- vLLM server running with your model (v0.10.2+ for `return_token_ids` support)
+- For tool calling: vLLM must be started with tool-calling enabled and appropriate chat template
+
+## Usage
+
+### 1. Start vLLM Server
+
+First, start a vLLM server with your model:
+
+```bash
+vllm serve <MODEL_ID> \
+    --host 0.0.0.0 \
+    --port 8000
+```
+
+For tool calling support, add the appropriate flags for your model:
+
+```bash
+vllm serve <MODEL_ID> \
+    --host 0.0.0.0 \
+    --port 8000 \
+    --enable-auto-tool-choice \
+    --tool-call-parser <PARSER>  # e.g., llama3_json, hermes, etc.
+```
+
+See [vLLM tool calling documentation](https://docs.vllm.ai/en/latest/features/tool_calling.html) for supported parsers and chat templates.
+
+### 2. Basic Agent
+
+```python
+import os
+from strands import Agent
+from strands_vllm import VLLMModel, VLLMTokenRecorder
+
+# Configure via environment variables or directly
+base_url = os.getenv("VLLM_BASE_URL", "http://localhost:8000/v1")
+model_id = os.getenv("VLLM_MODEL_ID", "<YOUR_MODEL_ID>")
+
+model = VLLMModel(
+    base_url=base_url,
+    model_id=model_id,
+    return_token_ids=True,
+)
+
+recorder = VLLMTokenRecorder()
+agent = Agent(model=model, callback_handler=recorder)
+
+result = agent("What is the capital of France?")
+print(result)
+
+# Access TITO data for RL training
+print(f"Prompt tokens: {len(recorder.prompt_token_ids or [])}")
+print(f"Response tokens: {len(recorder.token_ids or [])}")
+```
+
+### 3. Tool Call Validation (Optional, Recommended for RL)
+
+Strands SDK already handles unknown tools and malformed JSON gracefully. `VLLMToolValidationHooks` adds RL-friendly enhancements:
+
+```python
+import os
+from strands import Agent
+from strands_tools.calculator import calculator
+from strands_vllm import VLLMModel, VLLMToolValidationHooks
+
+model = VLLMModel(
+    base_url=os.getenv("VLLM_BASE_URL", "http://localhost:8000/v1"),
+    model_id=os.getenv("VLLM_MODEL_ID", "<YOUR_MODEL_ID>"),
+    return_token_ids=True,
+)
+
+agent = Agent(
+    model=model,
+    tools=[calculator],
+    hooks=[VLLMToolValidationHooks()],
+)
+
+result = agent("Compute 17 * 19 using the calculator tool.")
+print(result)
+```
+
+**What it adds beyond Strands defaults:**
+
+- **Unknown tool errors include allowed tools list** — helps RL training learn valid tool names
+- **Schema validation** — catches missing required args and unknown args before tool execution
+
+Invalid tool calls receive deterministic error messages, providing cleaner RL training signals.
+
+### 4. Agent Lightning Integration
+
+`VLLMTokenRecorder` automatically adds token IDs to OpenTelemetry spans for [Agent Lightning](https://blog.vllm.ai/2025/10/22/agent-lightning.html) compatibility:
+
+```python
+import os
+from strands import Agent
+from strands_vllm import VLLMModel, VLLMTokenRecorder
+
+model = VLLMModel(
+    base_url=os.getenv("VLLM_BASE_URL", "http://localhost:8000/v1"),
+    model_id=os.getenv("VLLM_MODEL_ID", "<YOUR_MODEL_ID>"),
+    return_token_ids=True,
+)
+
+# add_to_span=True (default) adds token IDs to OpenTelemetry spans
+recorder = VLLMTokenRecorder(add_to_span=True)
+agent = Agent(model=model, callback_handler=recorder)
+
+result = agent("Hello!")
+```
+
+The following span attributes are set:
+
+| Attribute | Description |
+| --------- | ----------- |
+| `llm.token_count.prompt` | Token count for the prompt (OpenTelemetry semantic convention) |
+| `llm.token_count.completion` | Token count for the completion (OpenTelemetry semantic convention) |
+| `llm.hosted_vllm.prompt_token_ids` | Token ID array for the prompt |
+| `llm.hosted_vllm.response_token_ids` | Token ID array for the response |
+
+### 5. RL Training with TokenManager
+
+For building RL-ready trajectories with loss masks:
+
+```python
+import asyncio
+import os
+from strands import Agent, tool
+from strands_tools.calculator import calculator as _calculator_impl
+from strands_vllm import TokenManager, VLLMModel, VLLMTokenRecorder, VLLMToolValidationHooks
+
+@tool
+def calculator(expression: str) -> dict:
+    return _calculator_impl(expression=expression)
+
+async def main():
+    model = VLLMModel(
+        base_url=os.getenv("VLLM_BASE_URL", "http://localhost:8000/v1"),
+        model_id=os.getenv("VLLM_MODEL_ID", "<YOUR_MODEL_ID>"),
+        return_token_ids=True,
+    )
+
+    recorder = VLLMTokenRecorder()
+    agent = Agent(
+        model=model,
+        tools=[calculator],
+        hooks=[VLLMToolValidationHooks()],
+        callback_handler=recorder,
+    )
+
+    await agent.invoke_async("What is 25 * 17?")
+
+    # Build RL trajectory with loss mask
+    tm = TokenManager()
+    for entry in recorder.history:
+        if entry.get("prompt_token_ids"):
+            tm.add_prompt(entry["prompt_token_ids"])  # loss_mask=0
+        if entry.get("token_ids"):
+            tm.add_response(entry["token_ids"])       # loss_mask=1
+
+    print(f"Total tokens: {len(tm)}")
+    print(f"Prompt tokens: {sum(1 for m in tm.loss_mask if m == 0)}")
+    print(f"Response tokens: {sum(1 for m in tm.loss_mask if m == 1)}")
+    print(f"Token IDs: {tm.token_ids[:20]}...")  # First 20 tokens
+    print(f"Loss mask: {tm.loss_mask[:20]}...")
+
+asyncio.run(main())
+```
+
+## Configuration
+
+### Model Configuration
+
+The `VLLMModel` accepts the following parameters:
+
+| Parameter | Description | Example | Required |
+| --------- | ----------- | ------- | -------- |
+| `base_url` | vLLM server URL | `"http://localhost:8000/v1"` | Yes |
+| `model_id` | Model identifier | `"<YOUR_MODEL_ID>"` | Yes |
+| `api_key` | API key (usually "EMPTY" for local vLLM) | `"EMPTY"` | No (default: "EMPTY") |
+| `return_token_ids` | Request token IDs from vLLM | `True` | No (default: False) |
+| `disable_tools` | Remove tools/tool_choice from requests | `True` | No (default: False) |
+| `params` | Additional generation parameters | `{"temperature": 0, "max_tokens": 256}` | No |
+
+### VLLMTokenRecorder Configuration
+
+| Parameter | Description | Default |
+| --------- | ----------- | ------- |
+| `inner` | Inner callback handler to chain | `None` |
+| `add_to_span` | Add token IDs to OpenTelemetry spans | `True` |
+
+### VLLMToolValidationHooks Configuration
+
+| Parameter | Description | Default |
+| --------- | ----------- | ------- |
+| `include_allowed_tools_in_errors` | Include list of allowed tools in error messages | `True` |
+| `max_allowed_tools_in_error` | Maximum tool names to show in error messages | `25` |
+| `validate_input_shape` | Validate required/unknown args against schema | `True` |
+
+**Example error messages** (more informative than Strands defaults):
+
+- Unknown tool: `Error: unknown tool: fake_tool | allowed_tools=[calculator, search, ...]`
+- Missing argument: `Error: tool_name=<calculator> | missing required argument(s): expression`
+- Unknown argument: `Error: tool_name=<calculator> | unknown argument(s): invalid_param`
+
+## Troubleshooting
+
+### Connection errors to vLLM server
+
+Ensure your vLLM server is running and accessible:
+
+```bash
+# Check if server is responding
+curl http://localhost:8000/health
+```
+
+### No token IDs captured
+
+Ensure:
+
+1. vLLM version is 0.10.2 or later
+2. `return_token_ids=True` is set on `VLLMModel`
+3. Your vLLM server supports `return_token_ids` in streaming mode
+
+### RL training needs cleaner error signals
+
+Strands handles unknown tools gracefully, but for RL training you may want more informative errors. Add `VLLMToolValidationHooks` to get errors that include the list of allowed tools and validate argument schemas.
+
+### Model only supports single tool calls
+
+Some models/chat templates only support one tool call per message. If you see `"This model only supports single tool-calls at once!"`, adjust your prompts to request one tool at a time.
+
+## References
+
+* [strands-vllm Repository](https://github.com/agents-community/strands-vllm)
+* [vLLM Documentation](https://docs.vllm.ai/)
+* [Agent Lightning GitHub](https://github.com/microsoft/agent-lightning) - The absolute trainer to light up AI agents
+* [Agent Lightning Blog Post](https://blog.vllm.ai/2025/10/22/agent-lightning.html) - No More Retokenization Drift
+* [Strands Agents API](../../api-reference/python/models/model.md)
diff --git a/mkdocs.yml b/mkdocs.yml
index 7f725987..232898f4 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -228,6 +228,7 @@ nav:
         - Nebius Token Factory: community/model-providers/nebius-token-factory.md
         - NVIDIA NIM: community/model-providers/nvidia-nim.md
         - SGLang: community/model-providers/sglang.md
+        - vLLM: community/model-providers/vllm.md
         - MLX: community/model-providers/mlx.md
       - Session Managers:
         - Amazon AgentCore Memory: community/session-managers/agentcore-memory.md