Creating a Custom Model Provider

Strands Agents SDK provides an extensible interface for implementing custom model providers, allowing organizations to integrate their own LLM services while keeping implementation details private to their codebase.

Model Provider Functionality

Custom model providers in Strands Agents support two primary interaction modes:

Conversational Interaction

The standard conversational mode where agents exchange messages with the model. This is the default interaction pattern that is used when you call an agent directly:

Python
TypeScript

agent = Agent(model=your_custom_model)
response = agent("Hello, how can you help me today?")

const yourCustomModel = new YourCustomModel()

const agent = new Agent({ model: yourCustomModel })
const response = await agent.invoke('Hello, how can you help me today?')

This invokes the underlying model provided to the agent.

Structured Output

A specialized mode that returns type-safe, validated responses using validated data models instead of raw text. This enables reliable data extraction and processing:

Python
TypeScript

from pydantic import BaseModel

class PersonInfo(BaseModel):
    name: str
    age: int
    occupation: str

result = agent.structured_output(
    PersonInfo,
    "Extract info: John Smith is a 30-year-old software engineer"
)
# Returns a validated PersonInfo object

// Structured output is not available for custom model providers in TypeScript

Both modes work through the same underlying model provider interface, with structured output using tool calling capabilities to ensure schema compliance.

Model Provider Architecture

Strands Agents uses an abstract Model class that defines the standard interface all model providers must implement:

flowchart TD
    Base["Model (Base)"] --> Bedrock["Bedrock Model Provider"]
    Base --> Anthropic["Anthropic Model Provider"]
    Base --> LiteLLM["LiteLLM Model Provider"]
    Base --> Ollama["Ollama Model Provider"]
    Base --> Custom["Custom Model Provider"]

Implementation Overview

The process for implementing a custom model provider is similar across both languages:

Python
TypeScript

In Python, you extend the Model class from strands.models and implement the required abstract methods:

stream(): Core method that handles model invocation and returns streaming events
update_config(): Updates the model configuration
get_config(): Returns the current model configuration

The Python implementation uses async generators to yield StreamEvent objects.

In TypeScript, you extend the Model class from @strands-agents/sdk and implement the required abstract methods:

stream(): Core method that handles model invocation and returns streaming events
updateConfig(): Updates the model configuration
getConfig(): Returns the current model configuration

The TypeScript implementation uses async iterables to yield ModelStreamEvent objects.

TypeScript Model Reference: The Model abstract class is available in the TypeScript SDK at src/models/model.ts. You can extend this class to create custom model providers that integrate with your own LLM services.

Implementing a Custom Model Provider

1. Create Your Model Class

Create a new module in your codebase that extends the Strands Agents Model class.

Python
TypeScript

Create a new Python module that extends the Model class. Set up a ModelConfig to hold the configurations for invoking the model.

import logging
import os
from typing import Any, Iterable, Optional, TypedDict
from typing_extensions import Unpack

from custom.model import CustomModelClient

from strands.models import Model
from strands.types.content import Messages
from strands.types.streaming import StreamEvent
from strands.types.tools import ToolSpec

logger = logging.getLogger(__name__)


class CustomModel(Model):
    """Your custom model provider implementation."""

    class ModelConfig(TypedDict):
        """
        Configuration your model.

        Attributes:
            model_id: ID of Custom model.
            params: Model parameters (e.g., max_tokens).
        """
        model_id: str
        params: Optional[dict[str, Any]]
        # Add any additional configuration parameters specific to your model

    def __init__(
        self,
        api_key: str,
        *,
        **model_config: Unpack[ModelConfig]
    ) -> None:
        """Initialize provider instance.

        Args:
            api_key: The API key for connecting to your Custom model.
            **model_config: Configuration options for Custom model.
        """
        self.config = CustomModel.ModelConfig(**model_config)
        logger.debug("config=<%s> | initializing", self.config)

        self.client = CustomModelClient(api_key)

    @override
    def update_config(self, **model_config: Unpack[ModelConfig]) -> None:
        """Update the Custom model configuration with the provided arguments.

        Can be invoked by tools to dynamically alter the model state for subsequent invocations by the agent.

        Args:
            **model_config: Configuration overrides.
        """
        self.config.update(model_config)


    @override
    def get_config(self) -> ModelConfig:
        """Get the Custom model configuration.

        Returns:
            The Custom model configuration.
        """
        return self.config

Create a TypeScript module that extends the Model class. Define an interface for your model configuration to ensure type safety.

// Mock client for documentation purposes
interface CustomModelClient {
  streamCompletion: (request: any) => AsyncIterable<any>
}

/**
 * Configuration interface for the custom model.
 */
export interface CustomModelConfig extends BaseModelConfig {
  apiKey?: string
  modelId?: string
  maxTokens?: number
  temperature?: number
  topP?: number
  // Add any additional configuration parameters specific to your model
}

/**
 * Custom model provider implementation.
 *
 * Note: In practice, you would extend the Model abstract class from the SDK.
 * This example shows the interface implementation for documentation purposes.
 */
export class CustomModel {
  private client: CustomModelClient
  private config: CustomModelConfig

  constructor(config: CustomModelConfig) {
    this.config = { ...config }
    // Initialize your custom model client
    this.client = {
      streamCompletion: async function* () {
        yield { type: 'message_start', role: 'assistant' }
      },
    }
  }

  updateConfig(config: Partial<CustomModelConfig>): void {
    this.config = { ...this.config, ...config }
  }

  getConfig(): CustomModelConfig {
    return { ...this.config }
  }

  async *stream(
    messages: Message[],
    options?: {
      systemPrompt?: string | string[]
      toolSpecs?: ToolSpec[]
      toolChoice?: any
    }
  ): AsyncIterable<ModelStreamEvent> {
    // Implementation in next section
    // This is a placeholder that yields nothing
    if (false) yield {} as ModelStreamEvent
  }
}

2. Implement the `stream` Method

The core of the model interface is the stream method that serves as the single entry point for all model interactions. This method handles request formatting, model invocation, and response streaming.

Python
TypeScript

The stream method accepts three parameters:

Messages: A list of Strands Agents messages, containing a Role and a list of ContentBlocks.
list[ToolSpec]: List of tool specifications that the model can decide to use.
SystemPrompt: A system prompt string given to the Model to prompt it how to answer the user.

    @override
    async def stream(
        self,
        messages: Messages,
        tool_specs: Optional[list[ToolSpec]] = None,
        system_prompt: Optional[str] = None,
        **kwargs: Any
    ) -> AsyncIterable[StreamEvent]:
        """Stream responses from the Custom model.

        Args:
            messages: List of conversation messages
            tool_specs: Optional list of available tools
            system_prompt: Optional system prompt
            **kwargs: Additional keyword arguments for future extensibility

        Returns:
            Iterator of StreamEvent objects
        """
        logger.debug("messages=<%s> tool_specs=<%s> system_prompt=<%s> | formatting request",
                    messages, tool_specs, system_prompt)

        # Format the request for your model API
        request = {
            "messages": messages,
            "tools": tool_specs,
            "system_prompt": system_prompt,
            **self.config,  # Include model configuration
        }

        logger.debug("request=<%s> | invoking model", request)

        # Invoke your model
        try:
            response = await self.client(**request)
        except OverflowException as e:
            raise ContextWindowOverflowException() from e

        logger.debug("response received | processing stream")

        # Process and yield streaming events
        # If your model doesn't return a MessageStart event, create one
        yield {
            "messageStart": {
                "role": "assistant"
            }
        }

        # Process each chunk from your model's response
        async for chunk in response["stream"]:
            # Convert your model's event format to Strands Agents StreamEvent
            if chunk.get("type") == "text_delta":
                yield {
                    "contentBlockDelta": {
                        "delta": {
                            "text": chunk.get("text", "")
                        }
                    }
                }
            elif chunk.get("type") == "message_stop":
                yield {
                    "messageStop": {
                        "stopReason": "end_turn"
                    }
                }

        logger.debug("stream processing complete")

For more complex implementations, you may want to create helper methods to organize your code:

    def _format_request(
        self,
        messages: Messages,
        tool_specs: Optional[list[ToolSpec]] = None,
        system_prompt: Optional[str] = None
    ) -> dict[str, Any]:
        """Optional helper method to format requests for your model API."""
        return {
            "messages": messages,
            "tools": tool_specs,
            "system_prompt": system_prompt,
            **self.config,
        }

    def _format_chunk(self, event: Any) -> Optional[StreamEvent]:
        """Optional helper method to format your model's response events."""
        if event.get("type") == "text_delta":
            return {
                "contentBlockDelta": {
                    "delta": {
                        "text": event.get("text", "")
                    }
                }
            }
        elif event.get("type") == "message_stop":
            return {
                "messageStop": {
                    "stopReason": "end_turn"
                }
            }
        return None

Note: stream must be implemented async. If your client does not support async invocation, you may consider wrapping the relevant calls in a thread so as not to block the async event loop. For an example on how to achieve this, you can check out the BedrockModel provider implementation.

The stream method is the core interface that handles model invocation and returns streaming events. This method must be implemented as an async generator.

// Implementation of the stream method and helper methods

export class CustomModelStreamExample {
  private config: CustomModelConfig
  private client: CustomModelClient

  constructor(config: CustomModelConfig) {
    this.config = config
    this.client = {
      streamCompletion: async function* () {
        yield { type: 'message_start', role: 'assistant' }
      },
    }
  }

  updateConfig(config: Partial<CustomModelConfig>): void {
    this.config = { ...this.config, ...config }
  }

  getConfig(): CustomModelConfig {
    return { ...this.config }
  }

  async *stream(
    messages: Message[],
    options?: {
      systemPrompt?: string | string[]
      toolSpecs?: ToolSpec[]
      toolChoice?: any
    }
  ): AsyncIterable<ModelStreamEvent> {
    // 1. Format messages for your model's API
    const formattedMessages = this.formatMessages(messages)
    const formattedTools = options?.toolSpecs ? this.formatTools(options.toolSpecs) : undefined

    // 2. Prepare the API request
    const request = {
      model: this.config.modelId,
      messages: formattedMessages,
      systemPrompt: options?.systemPrompt,
      tools: formattedTools,
      maxTokens: this.config.maxTokens,
      temperature: this.config.temperature,
      topP: this.config.topP,
      stream: true,
    }

    // 3. Call your model's API and stream responses
    const response = await this.client.streamCompletion(request)

    // 4. Convert API events to Strands ModelStreamEvent format
    for await (const chunk of response) {
      yield this.convertToModelStreamEvent(chunk)
    }
  }

  private formatMessages(messages: Message[]): any[] {
    return messages.map((message) => ({
      role: message.role,
      content: this.formatContent(message.content),
    }))
  }

  private formatContent(content: ContentBlock[]): any {
    // Convert Strands content blocks to your model's format
    return content.map((block) => {
      if (block.type === 'textBlock') {
        return { type: 'text', text: block.text }
      }
      // Handle other content types...
      return block
    })
  }

  private formatTools(toolSpecs: ToolSpec[]): any[] {
    return toolSpecs.map((tool) => ({
      name: tool.name,
      description: tool.description,
      parameters: tool.inputSchema,
    }))
  }

  private convertToModelStreamEvent(chunk: any): ModelStreamEvent {
    // Convert your model's streaming response to ModelStreamEvent

    if (chunk.type === 'message_start') {
      const event: ModelMessageStartEventData = {
        type: 'modelMessageStartEvent',
        role: chunk.role,
      }
      return event
    }

    if (chunk.type === 'content_block_delta') {
      if (chunk.delta.type === 'text_delta') {
        const event: ModelContentBlockDeltaEventData = {
          type: 'modelContentBlockDeltaEvent',
          delta: {
            type: 'textDelta',
            text: chunk.delta.text,
          },
        }
        return event
      }
    }

    if (chunk.type === 'message_stop') {
      const event: ModelMessageStopEventData = {
        type: 'modelMessageStopEvent',
        stopReason: this.mapStopReason(chunk.stopReason),
      }
      return event
    }

    throw new Error(`Unsupported chunk type: ${chunk.type}`)
  }

  private mapStopReason(reason: string): 'endTurn' | 'maxTokens' | 'toolUse' | 'stopSequence' {
    const stopReasonMap: Record<string, 'endTurn' | 'maxTokens' | 'toolUse' | 'stopSequence'> = {
      end_turn: 'endTurn',
      max_tokens: 'maxTokens',
      tool_use: 'toolUse',
      stop_sequence: 'stopSequence',
    }
    return stopReasonMap[reason] || 'endTurn'
  }
}

3. Understanding StreamEvent Types

Your custom model provider needs to convert your model’s response events to Strands Agents streaming event format.

Python
TypeScript

The Python SDK uses dictionary-based StreamEvent format:

messageStart: Event signaling the start of a message in a streaming response. This should have the role: assistant

{
    "messageStart": {
        "role": "assistant"
    }
}

contentBlockStart: Event signaling the start of a content block. If this is the first event of a tool use request, then set the toolUse key to have the value ContentBlockStartToolUse

{
    "contentBlockStart": {
        "start": {
            "name": "someToolName", # Only include name and toolUseId if this is the start of a ToolUseContentBlock
            "toolUseId": "uniqueToolUseId"
        }
    }
}

contentBlockDelta: Event continuing a content block. This event can be sent several times, and each piece of content will be appended to the previously sent content.

{
    "contentBlockDelta": {
        "delta": { # Only include one of the following keys in each event
            "text": "Some text", # String response from a model
            "reasoningContent": { # Dictionary representing the reasoning of a model.
                "redactedContent": b"Some encrypted bytes",
                "signature": "verification token",
                "text": "Some reasoning text"
            },
            "toolUse": { # Dictionary representing a toolUse request. This is a partial json string.
                "input": "Partial json serialized response"
            }
        }
    }
}

contentBlockStop: Event marking the end of a content block. Once this event is sent, all previous events between the previous ContentBlockStartEvent and this one can be combined to create a ContentBlock

{
    "contentBlockStop": {}
}

messageStop: Event marking the end of a streamed response, and the StopReason. No more content block events are expected after this event is returned.

{
    "messageStop": {
        "stopReason": "end_turn"
    }
}

metadata: Event representing the metadata of the response. This contains the input, output, and total token count, along with the latency of the request.

{
    "metrics": {
        "latencyMs": 123 # Latency of the model request in milliseconds.
    },
    "usage": {
        "inputTokens": 234, # Number of tokens sent in the request to the model.
        "outputTokens": 234, # Number of tokens that the model generated for the request.
        "totalTokens": 468 # Total number of tokens (input + output).
    }
}

redactContent: Event that is used to redact the users input message, or the generated response of a model. This is useful for redacting content if a guardrail gets triggered.

{
    "redactContent": {
        "redactUserContentMessage": "User input Redacted",
        "redactAssistantContentMessage": "Assistant output Redacted"
    }
}

The TypeScript SDK uses data interface types for ModelStreamEvent. Create events as plain objects matching these interfaces:

ModelMessageStartEvent: Signals the start of a message response

const messageStart: ModelMessageStartEventData = {
  type: 'modelMessageStartEvent',
  role: 'assistant',
}

ModelContentBlockStartEvent: Signals the start of a content block

// For text blocks
const textBlockStart: ModelContentBlockStartEventData = {
  type: 'modelContentBlockStartEvent',
}

// For tool use blocks
const toolUseStart: ModelContentBlockStartEventData = {
  type: 'modelContentBlockStartEvent',
  start: {
    type: 'toolUseStart',
    toolUseId: 'tool_123',
    name: 'calculator',
  },
}

ModelContentBlockDeltaEvent: Provides incremental content

// For text
const textDelta: ModelContentBlockDeltaEventData = {
  type: 'modelContentBlockDeltaEvent',
  delta: { type: 'textDelta', text: 'Hello' },
}

// For tool input
const toolInputDelta: ModelContentBlockDeltaEventData = {
  type: 'modelContentBlockDeltaEvent',
  delta: { type: 'toolUseInputDelta', input: '{"x": 1' },
}

// For reasoning content
const reasoningDelta: ModelContentBlockDeltaEventData = {
  type: 'modelContentBlockDeltaEvent',
  delta: {
    type: 'reasoningContentDelta',
    text: 'thinking...',
    signature: 'sig',
    redactedContent: new Uint8Array([]),
  },
}

ModelContentBlockStopEvent: Signals the end of a content block

const blockStop: ModelStreamEvent = {
  type: 'modelContentBlockStopEvent',
}

ModelMessageStopEvent: Signals the end of the message with stop reason

const messageStop: ModelMessageStopEventData = {
  type: 'modelMessageStopEvent',
  stopReason: 'endTurn', // Or 'maxTokens', 'toolUse', 'stopSequence'
}

ModelMetadataEvent: Provides usage and metrics information

const metadata: ModelMetadataEventData = {
  type: 'modelMetadataEvent',
  usage: {
    inputTokens: 234,
    outputTokens: 234,
    totalTokens: 468,
  },
  metrics: {
    latencyMs: 123,
  },
}

To support structured output in your custom model provider, you need to implement a structured_output() method that invokes your model and yields a JSON output. This method leverages the unified stream interface with tool specifications.

T = TypeVar('T', bound=BaseModel)

@override
async def structured_output(
    self,
    output_model: Type[T],
    prompt: Messages,
    system_prompt: Optional[str] = None,
    **kwargs: Any
) -> Generator[dict[str, Union[T, Any]], None, None]:
    """Get structured output using tool calling.

    Args:
        output_model: The output model to use for the agent.
        prompt: The prompt messages to use for the agent.
        system_prompt: The system prompt to use for the agent.
        **kwargs: Additional keyword arguments for future extensibility.
    """

    # Convert Pydantic model to tool specification
    tool_spec = convert_pydantic_to_tool_spec(output_model)

    # Use the stream method with tool specification
    response = await self.stream(messages=prompt, tool_specs=[tool_spec], system_prompt=system_prompt, **kwargs)

    # Process streaming response
    async for event in process_stream(response, prompt):
        yield event  # Passed to callback handler configured in Agent instance

    stop_reason, messages, _, _ = event["stop"]

    # Validate tool use response
    if stop_reason != "tool_use":
        raise ValueError("No valid tool use found in the model response.")

    # Extract tool use output
    content = messages["content"]
    for block in content:
        if block.get("toolUse") and block["toolUse"]["name"] == tool_spec["name"]:
            yield {"output": output_model(**block["toolUse"]["input"])}
            return

    raise ValueError("No valid tool use input found in the response.")

Implementation Suggestions:

Tool Integration: Use the stream() method with tool specifications to invoke your model
Response Validation: Use output_model(**data) to validate the response
Error Handling: Provide clear error messages for parsing and validation failures

For detailed structured output usage patterns, see the Structured Output documentation.

Note, similar to the stream method, structured_output must be implemented async. If your client does not support async invocation, you may consider wrapping the relevant calls in a thread so as not to block the async event loop. Again, for an example on how to achieve this, you can check out the BedrockModel provider implementation.

// Structured output is not available for custom model providers in TypeScript

5. Use Your Custom Model Provider

Once implemented, you can use your custom model provider in your applications for regular agent invocation:

Python
TypeScript

from strands import Agent
from your_org.models.custom_model import CustomModel

# Initialize your custom model provider
custom_model = CustomModel(
    api_key="your-api-key",
    model_id="your-model-id",
    params={
        "max_tokens": 2000,
        "temperature": 0.7,
    },
)

# Create a Strands agent using your model
agent = Agent(model=custom_model)

# Use the agent as usual
response = agent("Hello, how are you today?")

async function usageExample() {
  // Initialize your custom model provider
  const customModel = new YourCustomModel({
    maxTokens: 2000,
    temperature: 0.7,
  })

  // Create a Strands agent using your model
  const agent = new Agent({ model: customModel })

  // Use the agent as usual
  const response = await agent.invoke('Hello, how are you today?')
}

Or you can use the structured_output feature to generate structured output:

Python
TypeScript

from strands import Agent
from your_org.models.custom_model import CustomModel
from pydantic import BaseModel, Field

class PersonInfo(BaseModel):
    name: str = Field(description="Full name")
    age: int = Field(description="Age in years")
    occupation: str = Field(description="Job title")

model = CustomModel(api_key="key", model_id="model")

agent = Agent(model=model)

result = agent.structured_output(PersonInfo, "John Smith is a 30-year-old engineer.")

print(f"Name: {result.name}")
print(f"Age: {result.age}")
print(f"Occupation: {result.occupation}")

// Structured output is not available for custom model providers in TypeScript

Key Implementation Considerations

1. Stream Interface

The model interface centers around a single stream method that:

Accepts messages, tool_specs, and system_prompt directly as parameters
Handles request formatting, model invocation, and response processing internally
Provides debug logging for better observability

2. Message Formatting

Strands Agents’ internal Message, ToolSpec, and SystemPrompt types must be converted to your model API’s expected format:

Strands Agents uses a structured message format with role and content fields
Your model API might expect a different structure
Handle the message content conversion in your stream() method

3. Streaming Response Handling

Strands Agents expects streaming responses to be formatted according to its StreamEvent protocol:

messageStart: Indicates the start of a response message
contentBlockStart: Indicates the start of a content block
contentBlockDelta: Contains incremental content updates
contentBlockStop: Indicates the end of a content block
messageStop: Indicates the end of the response message with a stop reason
metadata: Indicates information about the response like input_token count, output_token count, and latency
redactContent: Used to redact either the user’s input, or the model’s response

Convert your API’s streaming format to match these expectations in your stream() method.

4. Tool Support

If your model API supports tools or function calling:

Format tool specifications appropriately in stream()
Handle tool-related events in response processing
Ensure proper message formatting for tool calls and results

5. Error Handling

Implement robust error handling for API communication:

Context window overflows
Connection errors
Authentication failures
Rate limits and quotas
Malformed responses

6. Configuration Management

The built-in get_config and update_config methods allow for the model’s configuration to be changed at runtime:

get_config exposes the current model config
update_config allows for at-runtime updates to the model config
- For example, changing model_id with a tool call