Prompt Blueprints

Prompt Blueprints are a core concept in PromptLayer that provides a standardized, model-agnostic representation of prompts. They serve as an abstraction layer that:

Creates a unified format that works across all LLM providers (OpenAI, Anthropic, etc.)
Enables seamless switching between different models without code changes
Standardizes how prompts, responses, and tool calls are structured and stored
Ensures consistent handling of various content types (text, images, function calls)

Think of Prompt Blueprints as a universal language for LLM interactions that shields your application from provider-specific implementation details.

Accessing the Prompt Blueprint

Instead of accessing the raw LLM response via response["raw_response"], it’s recommended to use the standardized response["prompt_blueprint"]. This ensures consistency across different providers.

Provider-Specific Schema NoticeThe raw_response object structure is provider-specific and may change without notice as LLM providers update their APIs. PromptLayer passes through the native format from each provider. For stable, provider-agnostic prompt data, always use prompt_blueprint instead.

response = promptlayer_client.run(
    prompt_name="ai-poet",
    input_variables={'topic': 'food'},
)

print(response["prompt_blueprint"]["prompt_template"]["messages"][-1]["content"][-1]["text"])

With this approach, you can update from one provider to another (e.g., OpenAI to Anthropic) without any code changes.

Streaming Support

PromptLayer now supports streaming responses with prompt_blueprint integration. When streaming is enabled, each chunk includes both the raw streaming response and the progressively built prompt_blueprint, allowing you to track how the response is constructed in real-time.

OpenAI Streaming Example

import promptlayer

# Initialize PromptLayer client
promptlayer_client = promptlayer.PromptLayer()

# Run with streaming enabled
response_stream = promptlayer_client.run(
    prompt_name="ai-poet",
    input_variables={'topic': 'food'},
    stream=True
)

# Process streaming chunks
for chunk in response_stream:
    # Access the raw streaming response
    raw_chunk = chunk["raw_response"]
    
    # Access the progressively built prompt blueprint
    prompt_blueprint = chunk["prompt_blueprint"]
    
    if raw_chunk.choices and raw_chunk.choices[0].delta.content:
        print(f"Streaming content: {raw_chunk.choices[0].delta.content}")
    
    # The prompt_blueprint shows the current state of the response
    if prompt_blueprint and prompt_blueprint["prompt_template"]["messages"]:
        current_response = prompt_blueprint["prompt_template"]["messages"][-1]
        if current_response.get("content"):
            print(f"Current response: {current_response['content']}")

Anthropic Streaming Example

import promptlayer

# Initialize PromptLayer client
promptlayer_client = promptlayer.PromptLayer()

# Run with streaming enabled for Anthropic
response_stream = promptlayer_client.run(
    prompt_name="helpful-assistant",
    input_variables={'task': 'prompt engineering tip'},
    stream=True
)

# Process streaming chunks
for chunk in response_stream:
    # Access the raw streaming response
    raw_chunk = chunk["raw_response"]
    
    # Access the progressively built prompt blueprint
    prompt_blueprint = chunk["prompt_blueprint"]
    
    # Handle different Anthropic streaming event types
    if raw_chunk.get("type") == "content_block_delta":
        delta = raw_chunk.get("delta", {})
        if delta.get("type") == "text_delta":
            print(f"Streaming content: {delta.get('text', '')}")
    
    # The prompt_blueprint shows the current state of the response
    if prompt_blueprint and prompt_blueprint["prompt_template"]["messages"]:
        current_response = prompt_blueprint["prompt_template"]["messages"][-1]
        if current_response.get("content") and len(current_response["content"]) > 0:
            # Get the text content from the current response
            text_content = current_response["content"][0].get("text", "")
            if text_content:
                print(f"Current response: {text_content}")

Key Features of Streaming with Prompt Blueprint

Progressive Building: Each streaming chunk includes the current state of the prompt_blueprint, showing how the response is built incrementally
Real-time Access: You can access both the raw streaming data and the structured prompt blueprint format simultaneously
Consistent Format: The prompt_blueprint maintains the same standardized format across all streaming chunks
Final State: The last chunk contains the complete prompt_blueprint with the full response

Streaming Response Structure

Each streaming chunk contains:

{
    "request_id": None,  # Present only in the final chunk
    "raw_response": ChatCompletionChunk(...),  # Raw streaming response from provider
    "prompt_blueprint": {
        "prompt_template": {
            "type": "chat",
            "messages": [
                {
                    "role": "assistant",
                    "content": [
                        {"type": "text", "text": "Current response text..."}
                    ],
                    "input_variables": [],
                    "template_format": "f-string"
                }
            ],
            "input_variables": []
        },
        "metadata": {
            "model": {
                "provider": "openai",
                "name": "gpt-4o",
                "parameters": {
                    "temperature": 1,
                    "stream": True,
                    # ... other parameters
                }
            }
        }
    }
}

The request_id is only included in the final chunk, indicating the completion of the streaming response.

Placeholder Messages

Placeholder Messages are a powerful feature that allows you to inject messages into a prompt template at runtime. By using the placeholder role, you can define placeholders within your prompt template that can be replaced with full messages when the prompt is executed. For more detailed information on Placeholder Messages, including how to create and use them, please refer to our dedicated Placeholder Messages Documentation page.

Running a Template with Placeholders

When running a prompt that includes placeholders, you need to supply the messages that will replace the placeholders in the input variables.

response = promptlayer_client.run(
    prompt_name="template-name",
    input_variables={
        "fill_in_message": [
            {
                "role": "user",
                "content": [{"type": "text", "text": "My age is 29"}],
            },
            {
                "role": "assistant",
                "content": [{"type": "text", "text": "What a wonderful age!"}],
            }
        ]
    },
)

Note: The messages provided must conform to the Prompt Blueprint format.

Prompt Blueprint Message Format

Each message in a Prompt Blueprint should be a dictionary with the following structure:

role: The role of the message sender (user, assistant, system, tool, function, placeholder, developer).
content: A list of content items, where each item has a type field that determines the content structure.

Supported Content Types

Type	Description
`text`	Text content with a `text` field
`thinking`	Model reasoning with `thinking` and optional `signature` fields
`code`	Code block with a `code` field (from code execution tools)
`image_url`	Image content with an `image_url` object containing a `url`
`media`	Media content (images, PDFs) with a `media` object
`media_variable`	Dynamic media variable with a `name` field
`output_media`	LLM-generated media (e.g. images) with a `url` and `mime_type`
`server_tool_use`	Server-side tool invocation with `id`, `name`, and `input`
`web_search_tool_result`	Web search results with citations
`code_execution_result`	Code execution output with `output` and `outcome` fields
`mcp_list_tools`	MCP server tool listing with `server_label` and `tools`
`mcp_call`	MCP tool call with `name`, `server_label`, and `arguments`
`mcp_approval_request`	MCP tool approval request
`mcp_approval_response`	MCP tool approval response
`bash_code_execution_tool_result`	Bash tool execution result with `tool_use_id` and `content`
`text_editor_code_execution_tool_result`	Text editor tool result with `tool_use_id` and `content`
`shell_call`	Shell tool call with `action` containing commands
`shell_call_output`	Shell tool output with execution results
`apply_patch_call`	Apply patch tool call with `operation` (create, update, delete files)
`apply_patch_call_output`	Apply patch tool output

Example Message

{
    "role": "user",
    "content": [{"type": "text", "text": "Hello, how are you?"}],
}

Example Message with Thinking support

{
    "role": "user",
    "content": [
        {"signature": "xxxxxx-xxxxx-xxxx-xxxx", "type": "thinking", "thinking": "User is greeting and asking for my wellbeing."},
        {"type": "text", "text": "Hello, how are you?"},
    ]
}

You’re absolutely right - let me update that part of the documentation to show a more realistic tool response format that uses structured JSON data. Here’s the revised section:

Tools and Function Calling

The Prompt Blueprint supports tool and function calling capabilities. This section demonstrates how to define available tools, handle assistant tool calls, and provide tool responses.

Defining Available Tools

When creating a prompt template, you can specify available tools under the tools field. Each tool definition follows this structure:

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string"}
                }
            }
        }
    }
]


prompt_template = {
    "type": "chat",
    "messages": messages,
    "tools": tools
}

The parameters field is of interest because it specifies the expected input parameters for the function. The LLM provider will use this information to generate the appropriate tool call. You can define the parameters using JSON Schema format. You can read moe about how OpenAI uses JSON Schema for defining parameters here. And you can read more about how Anthropic uses JSON Schema for defining parameters here.

Assistant Tool Calls

When the assistant decides to use a tool, the response will include a tool_calls field in the message. The format is:

{
    "role": "assistant",
    "tool_calls": [
        {
            "id": "call_abc123",
            "type": "function",
            "function": {
                "name": "get_weather",
                "arguments": "{\"location\": \"Paris\"}"
            }
        }
    ]
}

id is used by the assistant to track the tool call.
type is always function.
function contains the function details
- name tells us which function to call
- arguments is a JSON string containing the function’s input parameters.

For more information about how PromptLayer structures tool calls, please refer to schema definition towards end of this page.

Providing Tool Responses

After executing the requested function, you can provide the result back to the assistant using a “tool” role message. The response should be structured JSON data:

{
    "role": "tool",
    "content": [
        {
            "type": "text",
            "text": "{\"temperature\": 72, \"conditions\": \"sunny\", \"humidity\": 45}"
        }
    ],
    "tool_call_id": "call_abc123"
}

Here is an example of how to log a request with tool calls and responses using OpenAI:

from openai import OpenAI
client = OpenAI()
model = "gpt-4o"
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "parameters": {
                "type": "object",
                "properties": {"location": {"type": "string"}},
            },
        },
    }
]
messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "What's the weather like in Paris today?"}
        ],
    }
]
prompt_template = {
    "type": "chat",
    "messages": messages,
    "tools": tools,
}

request_start_time = time.time()
completion = client.chat.completions.create(
    model=model,
    messages=prompt_template["messages"],
    tools=prompt_template["tools"],
)
request_end_time = time.time()
print(completion.choices[0].message.tool_calls)

promptlayer.log_request(
    provider="openai",
    model=model,
    input=prompt_template,
    output={
        "type": "chat",
        "messages": [
            {
                "role": "assistant",
                "tool_calls": [
                    tool_call.model_dump()
                    for tool_call in completion.choices[0].message.tool_calls
                ],
            }
        ],
    },
    input_tokens=completion.usage.prompt_tokens,
    output_tokens=completion.usage.completion_tokens,
    request_start_time=request_start_time,
    request_end_time=request_end_time,
)

PromptLayer supports any number of modalities in a single prompt. You can include text, images, videos, and other media types in your prompt templates. The media_variable content allows you to dynamically insert a list of medias into prompt template messages. The media_variable is nested within the message content. The type and name are required fields specifying the type of content and the name of the variable, respectively. The name is the name of the list of medias to be dynamically inserted.

{
    "role": "user",
    "content": [
        {
            "type": "media_variable",
            "name": "media"
        }
    ]
}

When defining a prompt template, you can specify an media_variable to dynamically include medias in your messages.

Running with Media Variables

response = pl_client.run(
    prompt_name="image-prompt",
    input_variables={
        "media": [
            "https://example.com/image1.jpg",
            "https://example.com/image2.jpg"
        ]
    },
)

print(response)

Notice that the media is a list of strings, they can either be public URLs or base64 strings.

Structured Outputs

Prompt Blueprints can be configured to produce structured outputs that follow a specific format defined by JSON Schema. This ensures consistent response formats that are easier to parse and integrate with your applications. For detailed information on creating and using structured outputs with your prompt templates, see our Structured Outputs documentation.