How to Build vLLM Plugins: A comprehensive Developer Guide with tips and best practices

Building plugins for vLLM allows you to tailor the system to your specific requirements and integrate custom functionality into your LLM workflows. Whether you’re looking to integrate custom functionality, optimize performance, or streamline deployment, understanding how vLLM’s plugin system works is essential. In this comprehensive developer guide, I’ll break down the core concepts, walk through practical implementation steps, and share tips and best practices gathered from official documentation and community experts. By the end, you’ll have a clear roadmap for creating robust, efficient, and maintainable vLLM plugins.

Plugin System Overview
Types of Plugins
How vLLM Discovers Plugins
Creating Your First Plugin
Best Practices
Advanced Topics
Troubleshooting

Plugin System Overview

vLLM’s plugin system uses Python’s standard entry_points mechanism to discover and load extensions. This enables:

Clean modifications: Extend vLLM without forking the codebase
Runtime activation: Plugins load automatically when vLLM starts
Distributed compatibility: Plugins load in all processes (main, workers, etc.)
Selective loading: Use environment variables to control which plugins activate

Plugin Lifecycle

Discovery: vLLM reads entry points from installed packages
Loading: load_general_plugins() is called before initialization
Registration: Plugin functions register models, patches, or other extensions
Execution: Registered functionality is available throughout vLLM’s runtime

Important: Plugin registration occurs in every vLLM process, including the main process, worker processes, GPU and CPU workers, and any auxiliary processes. This ensures that the plugin’s behavior remains consistent and predictable across distributed or multi-process deployments.

Types of Plugins

vLLM supports several plugin entry point groups:

Entry Point Group	Purpose	Registration Target
`vllm.general_plugins`	General extensions, custom models, patches	Function that performs registration
`vllm.platform_plugins`	Hardware backend integrations	Function returning platform class if supported
`vllm.stat_logger_plugins`	Custom metrics/logging	Logger class (StatLoggerBase subclass)
`vllm.logits_processors`	Custom decoding strategies	LogitsProcessor subclass
`vllm.io_processor_plugins`	Input/output processing	IO processor implementation

General Plugins (vllm.general_plugins)

The most common plugin type. Use for:

Registering custom model architectures
Applying patches to vLLM classes
Adding custom samplers or processors

[project.entry-points."vllm.general_plugins"]
my_plugin = "my_package.register:register"

1 2	[project.entry-points."vllm.general_plugins"] my_plugin = "my_package.register:register"

Platform Plugins (vllm.platform_plugins)

For hardware backend integrations (NPU, custom accelerators):

[project.entry-points."vllm.platform_plugins"]
my_platform = "my_package.platform:register"

1 2	[project.entry-points."vllm.platform_plugins"] my_platform = "my_package.platform:register"

Requires implementing:

Platform class
WorkerBase
ModelRunnerBase
AttentionBackend
CommunicatorBase

Stat Logger Plugins (vllm.stat_logger_plugins)

For custom metrics collection and export:

[project.entry-points."vllm.stat_logger_plugins"]
my_logger = "my_package.loggers:MyLoggerClass"

1 2	[project.entry-points."vllm.stat_logger_plugins"] my_logger = "my_package.loggers:MyLoggerClass"

Note: Entry point should reference the class directly, not a registration function.

Logits Processor Plugins (vllm.logits_processors)

vLLM v1 Only – For custom decoding strategies that modify logits before sampling.

[project.entry-points."vllm.logits_processors"]
my_decoder = "my_package.processor:MyLogitsProcessor"

1 2	[project.entry-points."vllm.logits_processors"] my_decoder = "my_package.processor:MyLogitsProcessor"

Important Characteristics:

Global Application: Plugins apply to ALL requests when installed
No Per-Request Selection: vLLM v1 does NOT support per-request logits processor selection via the OpenAI API
One Plugin Per Deployment: Install only ONE decoding strategy plugin per vLLM deployment
Must Inherit Base Class: Your processor MUST inherit from LogitsProcessor

See Logits Processor Plugins (vLLM v1) for detailed implementation guide.

How vLLM Discovers Plugins

vLLM uses Python’s importlib.metadata.entry_points() to discover plugins:

# Simplified discovery logic
from importlib.metadata import entry_points

eps = entry_points(group='vllm.general_plugins')
for ep in eps:
    register_func = ep.load()
    register_func()

# Simplified discovery logic

from importlib.metadata import entry_points

eps = entry_points(group='vllm.general_plugins')

for ep in eps:

register_func = ep.load()

register_func()

Environment Variable Control

VLLM_PLUGINS: Comma-separated list of plugin names to load
- If not set, all discovered plugins are loaded
- Use to selectively enable plugins: VLLM_PLUGINS=my_plugin,other_plugin

Creating Your First Plugin

Building your first vLLM plugin is a straightforward process that involves setting up a clean project structure, defining an entry point for vLLM to discover, and implementing the registration logic that integrates your custom functionality. The steps below walk you through the minimal setup required—from project scaffolding to installation and testing—so you can get a working plugin up and running quickly.

Step 1: Project Structure

my-vllm-plugin/
├── pyproject.toml
├── src/
│   └── my_vllm_plugin/
│       ├── __init__.py
│       └── register.py
└── tests/
    └── test_plugin.py

my-vllm-plugin/

├── pyproject.toml

├── src/

│ └── my_vllm_plugin/

│ ├── __init__.py

│ └── register.py

└── tests/

└── test_plugin.py

Step 2: Define Entry Point

# pyproject.toml
[project]
name = "my-vllm-plugin"
version = "0.1.0"
dependencies = ["vllm>=0.8.0"]

[project.entry-points."vllm.general_plugins"]
my_plugin = "my_vllm_plugin.register:register"

[build-system]
requires = ["setuptools>=61"]
build-backend = "setuptools.build_meta"

# pyproject.toml

[project]

name = "my-vllm-plugin"

version = "0.1.0"

dependencies = ["vllm>=0.8.0"]

[project.entry-points."vllm.general_plugins"]

my_plugin = "my_vllm_plugin.register:register"

[build-system]

requires = ["setuptools>=61"]

build-backend = "setuptools.build_meta"

Step 3: Implement Registration

# src/my_vllm_plugin/register.py
import logging

logger = logging.getLogger(__name__)
_registered = False

def register() -> None:
    """Register plugin with vLLM."""
    global _registered

    # Ensure re-entrancy
    if _registered:
        return

    logger.info("Registering my vLLM plugin")

    # Your registration logic here
    # Example: Register a custom model
    from vllm import ModelRegistry
    if "MyModel" not in ModelRegistry.get_supported_archs():
        ModelRegistry.register_model(
            "MyModel",
            "my_vllm_plugin.models:MyModelForCausalLM"
        )

    _registered = True

# src/my_vllm_plugin/register.py

import logging

logger = logging.getLogger(__name__)

_registered = False

def register() -> None:

"""Register plugin with vLLM."""

global _registered

# Ensure re-entrancy

if _registered:

return

logger.info("Registering my vLLM plugin")

# Your registration logic here

# Example: Register a custom model

from vllm import ModelRegistry

if "MyModel" not in ModelRegistry.get_supported_archs():

ModelRegistry.register_model(

"MyModel",

"my_vllm_plugin.models:MyModelForCausalLM"

)

_registered = True

Step 4: Install and Test

pip install -e .
python -c "import vllm; print('Plugin loaded!')"

1 2	pip install -e . python -c "import vllm; print('Plugin loaded!')"

Best Practices

When building vLLM plugins, following a few core best practices helps ensure your plugin is reliable, maintainable, and compatible across different environments. Registration functions should be fully re-entrant, since vLLM may invoke them multiple times across multiple processes. Version checks are essential for avoiding compatibility issues, and patches should remain minimal—focused only on the behavior you need to extend or override. Configuration should rely on environment variables to keep runtime behavior flexible, and plugins should degrade gracefully when optional dependencies are missing. Clear logging provides visibility into plugin behavior, and thorough testing—covering re-entrancy, version handling, and missing dependencies—helps ensure consistent performance in real-world deployments.

1. Re-Entrant Registration Functions

Your registration function must be safe to call multiple times:

_registered = False

def register():
    global _registered
    if _registered:
        return  # Already registered

    # ... registration logic ...

    _registered = True

_registered = False

def register():

global _registered

if _registered:

return # Already registered

# ... registration logic ...

_registered = True

Why? vLLM may call your function in multiple processes.

2. Version Compatibility

Always specify and check vLLM version requirements:

from packaging.version import Version
import vllm

def register():
    current = Version(vllm.__version__)
    required = Version("0.9.0")

    if current < required:
        logger.warning(f"Plugin requires vLLM >= 0.9.0, got {current}")
        return

    # ... registration logic ...

from packaging.version import Version

import vllm

def register():

current = Version(vllm.__version__)

required = Version("0.9.0")

if current < required:

logger.warning(f"Plugin requires vLLM >= 0.9.0, got {current}")

return

# ... registration logic ...

Or use the decorator pattern:

@min_vllm_version("0.9.0")
class MyPatch(VLLMPatch[Scheduler]):
    pass

@min_vllm_version("0.9.0")

class MyPatch(VLLMPatch[Scheduler]):

pass

3. Minimal Patches

When patching vLLM classes:

Do: Add single methods, override specific behavior
Don’t: Duplicate entire classes, make sweeping changes

# Good: Minimal patch
class PriorityPatch(VLLMPatch[Scheduler]):
    def get_priority(self, request):
        return request.metadata.get("priority", 0)

# Bad: Reimplementing entire class
class MyScheduler(Scheduler):
    # ... hundreds of lines ...

# Good: Minimal patch

class PriorityPatch(VLLMPatch[Scheduler]):

def get_priority(self, request):

return request.metadata.get("priority", 0)

# Bad: Reimplementing entire class

class MyScheduler(Scheduler):

# ... hundreds of lines ...

4. Configuration via Environment Variables

Use environment variables for runtime configuration:

import os

def register():
    enabled = os.environ.get("MY_PLUGIN_ENABLED", "true").lower() == "true"
    if not enabled:
        return

    # ... registration logic ...

import os

def register():

enabled = os.environ.get("MY_PLUGIN_ENABLED", "true").lower() == "true"

if not enabled:

return

# ... registration logic ...

5. Graceful Degradation

Handle missing dependencies gracefully:

def register():
    try:
        from vllm import ModelRegistry
    except ImportError:
        logger.warning("vLLM not available, skipping registration")
        return

    # ... registration logic ...

def register():

try:

from vllm import ModelRegistry

except ImportError:

logger.warning("vLLM not available, skipping registration")

return

# ... registration logic ...

6. Logging

Use Python’s logging module for visibility:

import logging

logger = logging.getLogger(__name__)

def register():
    logger.info("Starting plugin registration")
    # ...
    logger.info("Plugin registered successfully")

import logging

logger = logging.getLogger(__name__)

def register():

logger.info("Starting plugin registration")

# ...

logger.info("Plugin registered successfully")

7. Testing

Always test:

Re-entrancy (multiple calls)
Without vLLM installed
With different vLLM versions

def test_register_is_reentrant():
    from my_plugin.register import register
    register()
    register()  # Should not raise

def test_handles_missing_vllm(monkeypatch):
    monkeypatch.setattr('builtins.__import__', mock_import_error)
    # Should not raise, just log warning

def test_register_is_reentrant():

from my_plugin.register import register

def test_handles_missing_vllm(monkeypatch):

monkeypatch.setattr('builtins.__import__', mock_import_error)

# Should not raise, just log warning

Advanced Topics

Once you’ve mastered the basics of plugin development, vLLM provides several powerful mechanisms for extending and customizing its behavior at a deeper level. These advanced techniques—such as surgical patching with VLLMPatch, runtime patch control via environment variables, model-specific patch activation, and containerized deployment strategies—allow you to modify core components without forking the vLLM codebase.

Surgical Patching with VLLMPatch

For modifying existing vLLM classes without forking:

from vllm.core.scheduler import Scheduler

class PrioritySchedulerPatch(VLLMPatch[Scheduler]):
    """Add priority-based scheduling."""

    def get_priority(self, request) -> int:
        """New method added to Scheduler."""
        return request.metadata.get("priority", 0)

    def schedule(self, waiting_queue):
        """Override existing method."""
        sorted_queue = sorted(
            waiting_queue,
            key=lambda r: self.get_priority(r),
            reverse=True
        )
        # Call original via _original_ prefix
        return self._original_schedule(sorted_queue)

# Apply at registration time
PrioritySchedulerPatch.apply()

from vllm.core.scheduler import Scheduler

class PrioritySchedulerPatch(VLLMPatch[Scheduler]):

"""Add priority-based scheduling."""

def get_priority(self, request) -> int:

"""New method added to Scheduler."""

return request.metadata.get("priority", 0)

def schedule(self, waiting_queue):

"""Override existing method."""

sorted_queue = sorted(

waiting_queue,

key=lambda r: self.get_priority(r),

reverse=True

)

# Call original via _original_ prefix

return self._original_schedule(sorted_queue)

# Apply at registration time

PrioritySchedulerPatch.apply()

Runtime Patch Control

Control patches via environment variables:

# Enable specific patches
VLLM_CUSTOM_PATCHES=PrioritySchedulerPatch,CustomSamplerPatch python app.py

# Enable all patches
VLLM_CUSTOM_PATCHES=* python app.py

# Enable specific patches

VLLM_CUSTOM_PATCHES=PrioritySchedulerPatch,CustomSamplerPatch python app.py

# Enable all patches

VLLM_CUSTOM_PATCHES=* python app.py

Multi-Model Support

Different models can enable different patches:

def register():
    model_type = os.environ.get("MODEL_TYPE", "")

    if model_type == "priority":
        PrioritySchedulerPatch.apply()
    elif model_type == "batch":
        BatchOptimizationPatch.apply()

def register():

model_type = os.environ.get("MODEL_TYPE", "")

if model_type == "priority":

PrioritySchedulerPatch.apply()

elif model_type == "batch":

BatchOptimizationPatch.apply()

Docker Configuration

FROM vllm/vllm-openai:latest

# Install plugins
COPY plugins/ /plugins/
RUN pip install /plugins/vllm-custom-models /plugins/vllm-patches

# Configure patches
ENV VLLM_CUSTOM_PATCHES=PrioritySchedulerPatch

FROM vllm/vllm-openai:latest

# Install plugins

COPY plugins/ /plugins/

RUN pip install /plugins/vllm-custom-models /plugins/vllm-patches

# Configure patches

ENV VLLM_CUSTOM_PATCHES=PrioritySchedulerPatch

Logits Processor Plugins (vLLM v1)

Logits processors modify the model’s output logits before sampling. vLLM v1 has a specific interface that must be followed.

Required Interface

Your processor MUST:

Inherit from LogitsProcessor – Not just implement the methods
Use the exact constructor signature
Implement all required methods

from typing import Optional
import torch
from vllm.v1.sample.logits_processor.interface import LogitsProcessor

# TYPE_CHECKING imports to avoid circular dependencies
from typing import TYPE_CHECKING
if TYPE_CHECKING:
    from vllm.config import VllmConfig
    from vllm.sampling_params import SamplingParams
    from vllm.v1.sample.logits_processor.interface import BatchUpdate


class MyLogitsProcessor(LogitsProcessor):
    """Custom logits processor for vLLM v1."""

    def __init__(
        self,
        vllm_config: "VllmConfig",
        device: torch.device,
        is_pin_memory: bool
    ):
        """Initialize the processor.

        Args:
            vllm_config: vLLM configuration object
            device: Target device for tensors (cuda:0, cpu, etc.)
            is_pin_memory: Whether to use pinned memory for CPU tensors
        """
        self.device = device
        self.is_pin_memory = is_pin_memory
        self.batch_size = 0

        # Load configuration from environment variables
        import os
        self.my_param = float(os.environ.get("MY_PROCESSOR_PARAM", "1.0"))

    def is_argmax_invariant(self) -> bool:
        """Return whether this processor preserves the argmax.

        Returns:
            True: Processor never changes which token has highest logit
                  (can be skipped during greedy/beam search)
            False: Processor may change the argmax
                   (must always be applied)
        """
        return False  # Most custom processors should return False

    def update_state(self, batch_update: Optional["BatchUpdate"]) -> None:
        """Update internal state when batch composition changes.

        Called at the start of each engine step BEFORE apply().

        Args:
            batch_update: Contains info about added, removed, moved requests.
                         None if no changes to the batch.

        The BatchUpdate contains:
            - batch_size: Current number of requests
            - added: List of (index, SamplingParams, output_tok_ids, req_id)
            - removed: List of removed request indices
            - moved: List of (from_idx, to_idx, direction) for reordered requests
        """
        if batch_update:
            self.batch_size = batch_update.batch_size

    def apply(self, logits: torch.Tensor) -> torch.Tensor:
        """Apply the logits processing.

        Args:
            logits: Tensor of shape (batch_size, vocab_size)

        Returns:
            Modified logits tensor with same shape
        """
        if logits.size(0) == 0:
            return logits

        # Your processing logic here
        modified_logits = logits / self.my_param
        return modified_logits

    @classmethod
    def validate_params(cls, sampling_params: "SamplingParams") -> None:
        """Validate sampling parameters at request creation time.

        Args:
            sampling_params: The sampling parameters to validate

        Raises:
            ValueError: If parameters are invalid
        """
        # Validate any custom parameters in sampling_params.extra_args
        pass

from typing import Optional

import torch

from vllm.v1.sample.logits_processor.interface import LogitsProcessor

# TYPE_CHECKING imports to avoid circular dependencies

from typing import TYPE_CHECKING

if TYPE_CHECKING:

from vllm.config import VllmConfig

from vllm.sampling_params import SamplingParams

from vllm.v1.sample.logits_processor.interface import BatchUpdate

class MyLogitsProcessor(LogitsProcessor):

"""Custom logits processor for vLLM v1."""

def __init__(

self,

vllm_config: "VllmConfig",

device: torch.device,

is_pin_memory: bool

"""Initialize the processor.

Args:

vllm_config: vLLM configuration object

device: Target device for tensors (cuda:0, cpu, etc.)

is_pin_memory: Whether to use pinned memory for CPU tensors

"""

self.device = device

self.is_pin_memory = is_pin_memory

self.batch_size = 0

# Load configuration from environment variables

import os

self.my_param = float(os.environ.get("MY_PROCESSOR_PARAM", "1.0"))

def is_argmax_invariant(self) -> bool:

"""Return whether this processor preserves the argmax.

Returns:

True: Processor never changes which token has highest logit

(can be skipped during greedy/beam search)

False: Processor may change the argmax

(must always be applied)

"""

return False # Most custom processors should return False

def update_state(self, batch_update: Optional["BatchUpdate"]) -> None:

"""Update internal state when batch composition changes.

Called at the start of each engine step BEFORE apply().

Args:

batch_update: Contains info about added, removed, moved requests.

None if no changes to the batch.

The BatchUpdate contains:

- batch_size: Current number of requests

- added: List of (index, SamplingParams, output_tok_ids, req_id)

- removed: List of removed request indices

- moved: List of (from_idx, to_idx, direction) for reordered requests

"""

if batch_update:

self.batch_size = batch_update.batch_size

def apply(self, logits: torch.Tensor) -> torch.Tensor:

"""Apply the logits processing.

Args:

logits: Tensor of shape (batch_size, vocab_size)

Returns:

Modified logits tensor with same shape

"""

if logits.size(0) == 0:

return logits

# Your processing logic here

modified_logits = logits / self.my_param

return modified_logits

@classmethod

def validate_params(cls, sampling_params: "SamplingParams") -> None:

"""Validate sampling parameters at request creation time.

Args:

sampling_params: The sampling parameters to validate

Raises:

ValueError: If parameters are invalid

"""

# Validate any custom parameters in sampling_params.extra_args

pass

Common Mistakes

Mistake	Error Message	Fix
Not inheriting from base class	`must be a subclass of LogitsProcessor`	Add `(LogitsProcessor)` to class definition
Missing `is_argmax_invariant()`	`has no attribute 'is_argmax_invariant'`	Add the method, return `False`
Missing `update_state()`	`has no attribute 'update_state'`	Add the method, track batch_size
Wrong constructor signature	Various init errors	Use `(vllm_config, device, is_pin_memory)`
Using `__call__` instead of `apply`	Processor not called	Rename to `apply()`

Configuration via Environment Variables

Since processors are instantiated by vLLM (not by your code), you cannot pass custom constructor parameters. Use environment variables instead:

import os

class MyProcessor(LogitsProcessor):
    def __init__(self, vllm_config, device, is_pin_memory):
        # Configuration from environment
        self.temperature = float(os.environ.get("MY_PROCESSOR_TEMP", "0.8"))
        self.threshold = float(os.environ.get("MY_PROCESSOR_THRESHOLD", "0.5"))

# Configure at runtime
MY_PROCESSOR_TEMP=0.7 MY_PROCESSOR_THRESHOLD=0.3 python -m vllm.entrypoints.openai.api_server ...

import os

class MyProcessor(LogitsProcessor):

def __init__(self, vllm_config, device, is_pin_memory):

# Configuration from environment

self.temperature = float(os.environ.get("MY_PROCESSOR_TEMP", "0.8"))

self.threshold = float(os.environ.get("MY_PROCESSOR_THRESHOLD", "0.5"))

# Configure at runtime

MY_PROCESSOR_TEMP=0.7 MY_PROCESSOR_THRESHOLD=0.3 python -m vllm.entrypoints.openai.api_server ...

Per-Request State (Advanced)

If you need per-request configuration, use the BatchUpdate in update_state():

def update_state(self, batch_update: Optional["BatchUpdate"]) -> None:
    if not batch_update:
        return

    # Track per-request state
    for index, params, output_tokens, req_id in batch_update.added:
        # params.extra_args contains custom per-request parameters
        threshold = params.extra_args.get("my_threshold", 0.5) if params.extra_args else 0.5
        self.req_state[index] = {"threshold": threshold}

    for index in batch_update.removed:
        self.req_state.pop(index, None)

def update_state(self, batch_update: Optional["BatchUpdate"]) -> None:

if not batch_update:

return

# Track per-request state

for index, params, output_tokens, req_id in batch_update.added:

# params.extra_args contains custom per-request parameters

threshold = params.extra_args.get("my_threshold", 0.5) if params.extra_args else 0.5

self.req_state[index] = {"threshold": threshold}

for index in batch_update.removed:

self.req_state.pop(index, None)

Entry Point Registration

# pyproject.toml
[project.entry-points."vllm.logits_processors"]
my_decoder = "my_package.processor:MyLogitsProcessor"

# pyproject.toml

[project.entry-points."vllm.logits_processors"]

my_decoder = "my_package.processor:MyLogitsProcessor"

The entry point name (my_decoder) is used for identification but cannot be selected per-request in vLLM v1.

Troubleshooting

Even with a well-structured plugin, issues can arise during installation, registration, or distributed execution. This section provides practical checks and diagnostics to help you quickly identify and resolve common problems—from plugins not loading, patches not applying, or version mismatches, to distributed behavior inconsistencies. By verifying installation paths, environment variables, entry points, patch configuration, and version requirements, you can pinpoint root causes efficiently and ensure your plugin behaves reliably across all vLLM environments.

Plugin Not Loading

Check installation: pip list | grep your-plugin
Check entry points: python -c "from importlib.metadata import entry_points; print(list(entry_points(group='vllm.general_plugins')))"
Check VLLM_PLUGINS env var: May be filtering your plugin

Registration Errors

Check logs: Look for registration messages
Test import: python -c "from your_plugin.register import register; register()"
Check vLLM version: Ensure compatibility

Patch Not Applied

Check VLLM_CUSTOM_PATCHES: Must include your patch name
Check version decorator: May be blocking due to version mismatch
Check PatchManager: PatchManager.is_applied("YourPatch")

Distributed Issues

If plugin works locally but not in distributed mode:

Ensure re-entrancy
Check all workers have plugin installed
Verify environment variables propagate to workers

Logits Processor Issues (vLLM v1)

“must be a subclass of LogitsProcessor”

Your class must inherit from the base class:

# Wrong
class MyProcessor:
    ...

# Correct
from vllm.v1.sample.logits_processor.interface import LogitsProcessor

class MyProcessor(LogitsProcessor):
    ...

# Wrong

class MyProcessor:

...

# Correct

from vllm.v1.sample.logits_processor.interface import LogitsProcessor

class MyProcessor(LogitsProcessor):

...

“has no attribute ‘is_argmax_invariant'”

Add the required method:

def is_argmax_invariant(self) -> bool:
    return False

1 2	def is_argmax_invariant(self) -> bool: return False

“has no attribute ‘update_state'”

Add the required method:

def update_state(self, batch_update):
    if batch_update:
        self.batch_size = batch_update.batch_size

def update_state(self, batch_update):

if batch_update:

self.batch_size = batch_update.batch_size

Processor not being called

Ensure you’re using apply() not __call__:

# Wrong
def __call__(self, logits):
    ...

# Correct
def apply(self, logits: torch.Tensor) -> torch.Tensor:
    ...

# Wrong

def __call__(self, logits):

...

# Correct

def apply(self, logits: torch.Tensor) -> torch.Tensor:

...

Per-request selection not working

vLLM v1 does NOT support per-request logits processor selection via the OpenAI API. Processors apply globally to all requests. To use different strategies:

Deploy separate vLLM instances with different plugins
Use different Docker images per strategy

Developing plugins for vLLM gives you a powerful way to extend, customize, and optimize the system without modifying its core codebase. By understanding how plugins are discovered, registered, and executed—and by following best practices for version compatibility, patching, configuration, and testing—you can build robust extensions that behave reliably across both local and distributed deployments. Whether you’re implementing new models, injecting runtime patches, or designing advanced logits processors, the tooling and patterns outlined in this guide provide a solid foundation for creating maintainable and production-ready vLLM plugins.