The prompts API from LangChain — 2 operation(s) for prompts.
openapi: 3.1.0
info:
title: LangSmith access_policies prompts API
description: 'The LangSmith API is used to programmatically create and manage LangSmith resources.
## Host
https://api.smith.langchain.com
## Authentication
To authenticate with the LangSmith API, set the `X-Api-Key` header
to a valid [LangSmith API key](https://docs.langchain.com/langsmith/create-account-api-key#create-an-api-key).
'
version: 0.1.0
servers:
- url: /
tags:
- name: prompts
paths:
/api/v1/prompts/invoke_prompt:
post:
tags:
- prompts
summary: Invoke Prompt
operationId: invoke_prompt_api_v1_prompts_invoke_prompt_post
requestBody:
content:
application/json:
schema:
$ref: '#/components/schemas/InvokePromptPayload'
required: true
responses:
'200':
description: Successful Response
content:
application/json:
schema: {}
'422':
description: Validation Error
content:
application/json:
schema:
$ref: '#/components/schemas/HTTPValidationError'
/api/v1/prompts/canvas:
post:
tags:
- prompts
summary: Prompt Canvas
operationId: prompt_canvas_api_v1_prompts_canvas_post
requestBody:
content:
application/json:
schema:
$ref: '#/components/schemas/PlaygroundPromptCanvasPayload'
required: true
responses:
'200':
description: Successful Response
content:
application/json:
schema: {}
'422':
description: Validation Error
content:
application/json:
schema:
$ref: '#/components/schemas/HTTPValidationError'
security:
- API Key: []
- Tenant ID: []
- Bearer Auth: []
components:
schemas:
ChatMessageChunk:
properties:
content:
anyOf:
- type: string
- items:
anyOf:
- type: string
- additionalProperties: true
type: object
type: array
title: Content
additional_kwargs:
additionalProperties: true
type: object
title: Additional Kwargs
response_metadata:
additionalProperties: true
type: object
title: Response Metadata
type:
type: string
const: ChatMessageChunk
title: Type
default: ChatMessageChunk
name:
anyOf:
- type: string
- type: 'null'
title: Name
id:
anyOf:
- type: string
- type: 'null'
title: Id
role:
type: string
title: Role
additionalProperties: true
type: object
required:
- content
- role
title: ChatMessageChunk
description: Chat Message chunk.
ValidationError:
properties:
loc:
items:
anyOf:
- type: string
- type: integer
type: array
title: Location
msg:
type: string
title: Message
type:
type: string
title: Error Type
type: object
required:
- loc
- msg
- type
title: ValidationError
AIMessageChunk:
properties:
content:
anyOf:
- type: string
- items:
anyOf:
- type: string
- additionalProperties: true
type: object
type: array
title: Content
additional_kwargs:
additionalProperties: true
type: object
title: Additional Kwargs
response_metadata:
additionalProperties: true
type: object
title: Response Metadata
type:
type: string
const: AIMessageChunk
title: Type
default: AIMessageChunk
name:
anyOf:
- type: string
- type: 'null'
title: Name
id:
anyOf:
- type: string
- type: 'null'
title: Id
tool_calls:
items:
$ref: '#/components/schemas/ToolCall'
type: array
title: Tool Calls
invalid_tool_calls:
items:
$ref: '#/components/schemas/InvalidToolCall'
type: array
title: Invalid Tool Calls
usage_metadata:
anyOf:
- $ref: '#/components/schemas/UsageMetadata'
- type: 'null'
tool_call_chunks:
items:
$ref: '#/components/schemas/ToolCallChunk'
type: array
title: Tool Call Chunks
chunk_position:
anyOf:
- type: string
const: last
- type: 'null'
title: Chunk Position
additionalProperties: true
type: object
required:
- content
title: AIMessageChunk
description: Message chunk from an AI (yielded when streaming).
FunctionMessage:
properties:
content:
anyOf:
- type: string
- items:
anyOf:
- type: string
- additionalProperties: true
type: object
type: array
title: Content
additional_kwargs:
additionalProperties: true
type: object
title: Additional Kwargs
response_metadata:
additionalProperties: true
type: object
title: Response Metadata
type:
type: string
const: function
title: Type
default: function
name:
type: string
title: Name
id:
anyOf:
- type: string
- type: 'null'
title: Id
additionalProperties: true
type: object
required:
- content
- name
title: FunctionMessage
description: 'Message for passing the result of executing a tool back to a model.
`FunctionMessage` are an older version of the `ToolMessage` schema, and
do not contain the `tool_call_id` field.
The `tool_call_id` field is used to associate the tool call request with the
tool call response. Useful in situations where a chat model is able
to request multiple tool calls in parallel.'
ToolMessage:
properties:
content:
anyOf:
- type: string
- items:
anyOf:
- type: string
- additionalProperties: true
type: object
type: array
title: Content
additional_kwargs:
additionalProperties: true
type: object
title: Additional Kwargs
response_metadata:
additionalProperties: true
type: object
title: Response Metadata
type:
type: string
const: tool
title: Type
default: tool
name:
anyOf:
- type: string
- type: 'null'
title: Name
id:
anyOf:
- type: string
- type: 'null'
title: Id
tool_call_id:
type: string
title: Tool Call Id
artifact:
title: Artifact
status:
type: string
enum:
- success
- error
title: Status
default: success
additionalProperties: true
type: object
required:
- content
- tool_call_id
title: ToolMessage
description: "Message for passing the result of executing a tool back to a model.\n\n`ToolMessage` objects contain the result of a tool invocation. Typically, the result\nis encoded inside the `content` field.\n\n`tool_call_id` is used to associate the tool call request with the tool call\nresponse. Useful in situations where a chat model is able to request multiple tool\ncalls in parallel.\n\nExample:\n A `ToolMessage` representing a result of `42` from a tool call with id\n\n ```python\n from langchain_core.messages import ToolMessage\n\n ToolMessage(content=\"42\", tool_call_id=\"call_Jja7J89XsjrOLA5r!MEOW!SL\")\n ```\n\nExample:\n A `ToolMessage` where only part of the tool output is sent to the model\n and the full output is passed in to artifact.\n\n ```python\n from langchain_core.messages import ToolMessage\n\n tool_output = {\n \"stdout\": \"From the graph we can see that the correlation between \"\n \"x and y is ...\",\n \"stderr\": None,\n \"artifacts\": {\"type\": \"image\", \"base64_data\": \"/9j/4gIcSU...\"},\n }\n\n ToolMessage(\n content=tool_output[\"stdout\"],\n artifact=tool_output,\n tool_call_id=\"call_Jja7J89XsjrOLA5r!MEOW!SL\",\n )\n ```"
OutputTokenDetails:
properties:
audio:
type: integer
title: Audio
reasoning:
type: integer
title: Reasoning
type: object
title: OutputTokenDetails
description: "Breakdown of output token counts.\n\nDoes *not* need to sum to full output token count. Does *not* need to have all keys.\n\nExample:\n ```python\n {\n \"audio\": 10,\n \"reasoning\": 200,\n }\n ```\n\nMay also hold extra provider-specific keys.\n\n!!! version-added \"Added in `langchain-core` 0.3.9\""
ChatMessage:
properties:
content:
anyOf:
- type: string
- items:
anyOf:
- type: string
- additionalProperties: true
type: object
type: array
title: Content
additional_kwargs:
additionalProperties: true
type: object
title: Additional Kwargs
response_metadata:
additionalProperties: true
type: object
title: Response Metadata
type:
type: string
const: chat
title: Type
default: chat
name:
anyOf:
- type: string
- type: 'null'
title: Name
id:
anyOf:
- type: string
- type: 'null'
title: Id
role:
type: string
title: Role
additionalProperties: true
type: object
required:
- content
- role
title: ChatMessage
description: Message that can be assigned an arbitrary speaker (i.e. role).
HumanMessage:
properties:
content:
anyOf:
- type: string
- items:
anyOf:
- type: string
- additionalProperties: true
type: object
type: array
title: Content
additional_kwargs:
additionalProperties: true
type: object
title: Additional Kwargs
response_metadata:
additionalProperties: true
type: object
title: Response Metadata
type:
type: string
const: human
title: Type
default: human
name:
anyOf:
- type: string
- type: 'null'
title: Name
id:
anyOf:
- type: string
- type: 'null'
title: Id
additionalProperties: true
type: object
required:
- content
title: HumanMessage
description: "Message from the user.\n\nA `HumanMessage` is a message that is passed in from a user to the model.\n\nExample:\n ```python\n from langchain_core.messages import HumanMessage, SystemMessage\n\n messages = [\n SystemMessage(content=\"You are a helpful assistant! Your name is Bob.\"),\n HumanMessage(content=\"What is your name?\"),\n ]\n\n # Instantiate a chat model and invoke it with the messages\n model = ...\n print(model.invoke(messages))\n ```"
HTTPValidationError:
properties:
detail:
items:
$ref: '#/components/schemas/ValidationError'
type: array
title: Detail
type: object
title: HTTPValidationError
ToolCallChunk:
properties:
name:
anyOf:
- type: string
- type: 'null'
title: Name
args:
anyOf:
- type: string
- type: 'null'
title: Args
id:
anyOf:
- type: string
- type: 'null'
title: Id
index:
anyOf:
- type: integer
- type: 'null'
title: Index
type:
type: string
const: tool_call_chunk
title: Type
type: object
required:
- name
- args
- id
- index
title: ToolCallChunk
description: "A chunk of a tool call (yielded when streaming).\n\nWhen merging `ToolCallChunk` objects (e.g., via `AIMessageChunk.__add__`), all\nstring attributes are concatenated. Chunks are only merged if their values of\n`index` are equal and not `None`.\n\nExample:\n```python\nleft_chunks = [ToolCallChunk(name=\"foo\", args='{\"a\":', index=0)]\nright_chunks = [ToolCallChunk(name=None, args=\"1}\", index=0)]\n\n(\n AIMessageChunk(content=\"\", tool_call_chunks=left_chunks)\n + AIMessageChunk(content=\"\", tool_call_chunks=right_chunks)\n).tool_call_chunks == [ToolCallChunk(name=\"foo\", args='{\"a\":1}', index=0)]\n```"
SystemMessage:
properties:
content:
anyOf:
- type: string
- items:
anyOf:
- type: string
- additionalProperties: true
type: object
type: array
title: Content
additional_kwargs:
additionalProperties: true
type: object
title: Additional Kwargs
response_metadata:
additionalProperties: true
type: object
title: Response Metadata
type:
type: string
const: system
title: Type
default: system
name:
anyOf:
- type: string
- type: 'null'
title: Name
id:
anyOf:
- type: string
- type: 'null'
title: Id
additionalProperties: true
type: object
required:
- content
title: SystemMessage
description: "Message for priming AI behavior.\n\nThe system message is usually passed in as the first of a sequence\nof input messages.\n\nExample:\n ```python\n from langchain_core.messages import HumanMessage, SystemMessage\n\n messages = [\n SystemMessage(content=\"You are a helpful assistant! Your name is Bob.\"),\n HumanMessage(content=\"What is your name?\"),\n ]\n\n # Define a chat model and invoke it with the messages\n print(model.invoke(messages))\n ```"
UsageMetadata:
properties:
input_tokens:
type: integer
title: Input Tokens
output_tokens:
type: integer
title: Output Tokens
total_tokens:
type: integer
title: Total Tokens
input_token_details:
$ref: '#/components/schemas/InputTokenDetails'
output_token_details:
$ref: '#/components/schemas/OutputTokenDetails'
type: object
required:
- input_tokens
- output_tokens
- total_tokens
title: UsageMetadata
description: "Usage metadata for a message, such as token counts.\n\nThis is a standard representation of token usage that is consistent across models.\n\nExample:\n ```python\n {\n \"input_tokens\": 350,\n \"output_tokens\": 240,\n \"total_tokens\": 590,\n \"input_token_details\": {\n \"audio\": 10,\n \"cache_creation\": 200,\n \"cache_read\": 100,\n },\n \"output_token_details\": {\n \"audio\": 10,\n \"reasoning\": 200,\n },\n }\n ```\n\n!!! warning \"Behavior changed in `langchain-core` 0.3.9\"\n\n Added `input_token_details` and `output_token_details`.\n\n!!! note \"LangSmith SDK\"\n\n The LangSmith SDK also has a `UsageMetadata` class. While the two share fields,\n LangSmith's `UsageMetadata` has additional fields to capture cost information\n used by the LangSmith platform."
Highlight:
properties:
prompt_chunk_start_index:
type: integer
title: Prompt Chunk Start Index
prompt_chunk_end_index:
type: integer
title: Prompt Chunk End Index
prompt_chunk:
type: string
title: Prompt Chunk
highlight_text:
type: string
title: Highlight Text
type: object
required:
- prompt_chunk_start_index
- prompt_chunk_end_index
- prompt_chunk
- highlight_text
title: Highlight
SystemMessageChunk:
properties:
content:
anyOf:
- type: string
- items:
anyOf:
- type: string
- additionalProperties: true
type: object
type: array
title: Content
additional_kwargs:
additionalProperties: true
type: object
title: Additional Kwargs
response_metadata:
additionalProperties: true
type: object
title: Response Metadata
type:
type: string
const: SystemMessageChunk
title: Type
default: SystemMessageChunk
name:
anyOf:
- type: string
- type: 'null'
title: Name
id:
anyOf:
- type: string
- type: 'null'
title: Id
additionalProperties: true
type: object
required:
- content
title: SystemMessageChunk
description: System Message chunk.
Artifact:
properties:
id:
type: string
title: Id
contents:
items:
$ref: '#/components/schemas/ArtifactContent'
type: array
title: Contents
current_content_index:
type: integer
title: Current Content Index
type: object
required:
- id
- contents
- current_content_index
title: Artifact
HumanMessageChunk:
properties:
content:
anyOf:
- type: string
- items:
anyOf:
- type: string
- additionalProperties: true
type: object
type: array
title: Content
additional_kwargs:
additionalProperties: true
type: object
title: Additional Kwargs
response_metadata:
additionalProperties: true
type: object
title: Response Metadata
type:
type: string
const: HumanMessageChunk
title: Type
default: HumanMessageChunk
name:
anyOf:
- type: string
- type: 'null'
title: Name
id:
anyOf:
- type: string
- type: 'null'
title: Id
additionalProperties: true
type: object
required:
- content
title: HumanMessageChunk
description: Human Message chunk.
InputTokenDetails:
properties:
audio:
type: integer
title: Audio
cache_creation:
type: integer
title: Cache Creation
cache_read:
type: integer
title: Cache Read
type: object
title: InputTokenDetails
description: "Breakdown of input token counts.\n\nDoes *not* need to sum to full input token count. Does *not* need to have all keys.\n\nExample:\n ```python\n {\n \"audio\": 10,\n \"cache_creation\": 200,\n \"cache_read\": 100,\n }\n ```\n\nMay also hold extra provider-specific keys.\n\n!!! version-added \"Added in `langchain-core` 0.3.9\""
ToolMessageChunk:
properties:
content:
anyOf:
- type: string
- items:
anyOf:
- type: string
- additionalProperties: true
type: object
type: array
title: Content
additional_kwargs:
additionalProperties: true
type: object
title: Additional Kwargs
response_metadata:
additionalProperties: true
type: object
title: Response Metadata
type:
type: string
const: ToolMessageChunk
title: Type
default: ToolMessageChunk
name:
anyOf:
- type: string
- type: 'null'
title: Name
id:
anyOf:
- type: string
- type: 'null'
title: Id
tool_call_id:
type: string
title: Tool Call Id
artifact:
title: Artifact
status:
type: string
enum:
- success
- error
title: Status
default: success
additionalProperties: true
type: object
required:
- content
- tool_call_id
title: ToolMessageChunk
description: Tool Message chunk.
InvokePromptPayload:
properties:
messages:
items:
prefixItems:
- type: string
- type: string
type: array
maxItems: 2
minItems: 2
type: array
title: Messages
template_format:
type: string
title: Template Format
inputs:
additionalProperties: true
type: object
title: Inputs
type: object
required:
- messages
- template_format
- inputs
title: InvokePromptPayload
PlaygroundPromptCanvasPayload:
properties:
messages:
items:
oneOf:
- $ref: '#/components/schemas/AIMessage'
- $ref: '#/components/schemas/HumanMessage'
- $ref: '#/components/schemas/ChatMessage'
- $ref: '#/components/schemas/SystemMessage'
- $ref: '#/components/schemas/FunctionMessage'
- $ref: '#/components/schemas/ToolMessage'
- $ref: '#/components/schemas/AIMessageChunk'
- $ref: '#/components/schemas/HumanMessageChunk'
- $ref: '#/components/schemas/ChatMessageChunk'
- $ref: '#/components/schemas/SystemMessageChunk'
- $ref: '#/components/schemas/FunctionMessageChunk'
- $ref: '#/components/schemas/ToolMessageChunk'
type: array
title: Messages
highlighted:
anyOf:
- $ref: '#/components/schemas/Highlight'
- type: 'null'
artifact:
anyOf:
- $ref: '#/components/schemas/Artifact'
- type: 'null'
artifact_length:
anyOf:
- type: string
enum:
- shortest
- short
- long
- longest
- type: 'null'
title: Artifact Length
reading_level:
anyOf:
- type: string
enum:
- child
- teenager
- college
- phd
- type: 'null'
title: Reading Level
custom_action:
anyOf:
- type: string
- type: 'null'
title: Custom Action
template_format:
type: string
enum:
- f-string
- mustache
title: Template Format
secrets:
additionalProperties:
type: string
type: object
title: Secrets
type: object
required:
- messages
- template_format
- secrets
title: PlaygroundPromptCanvasPayload
AIMessage:
properties:
content:
anyOf:
- type: string
- items:
anyOf:
- type: string
- additionalProperties: true
type: object
type: array
title: Content
additional_kwargs:
additionalProperties: true
type: object
title: Additional Kwargs
response_metadata:
additionalProperties: true
type: object
title: Response Metadata
type:
type: string
const: ai
title: Type
default: ai
name:
anyOf:
- type: string
- type: 'null'
title: Name
id:
anyOf:
- type: string
- type: 'null'
title: Id
tool_calls:
items:
$ref: '#/components/schemas/ToolCall'
type: array
title: Tool Calls
invalid_tool_calls:
items:
$ref: '#/components/schemas/InvalidToolCall'
type: array
title: Invalid Tool Calls
usage_metadata:
anyOf:
- $ref: '#/components/schemas/UsageMetadata'
- type: 'null'
additionalProperties: true
type: object
required:
- content
title: AIMessage
description: 'Message from an AI.
An `AIMessage` is returned from a chat model as a response to a prompt.
This message represents the output of the model and consists of both
the raw output as returned by the model and standardized fields
(e.g., tool calls, usage metadata) added by the LangChain framework.'
ArtifactContent:
properties:
index:
type: integer
title: Index
content:
type: string
title: Content
type: object
required:
- index
- content
title: ArtifactContent
FunctionMessageChunk:
properties:
content:
anyOf:
- type: string
- items:
anyOf:
- type: string
- additionalProperties: true
type: object
type: array
title: Content
additional_kwargs:
additionalProperties: true
type: object
title: Additional Kwargs
response_metadata:
additionalProperties: true
type: object
title: Response Metadata
type:
type: string
const: FunctionMessageChunk
title: Type
default: FunctionMessageChunk
name:
type: string
title: Name
id:
anyOf:
- type: string
- type: 'null'
title: Id
additionalProperties: true
type: object
required:
- content
- name
title: FunctionMessageChunk
description: Function Message chunk.
InvalidToolCall:
properties:
type:
type: string
const: invalid_tool_call
title: Type
id:
anyOf:
- type: string
- type: 'null'
title: Id
name:
anyOf:
- type: string
- type: 'null'
title: Name
args:
anyOf:
- type: string
- type: 'null'
title: Args
error:
anyOf:
- type: string
- type: 'null'
title: Error
index:
anyOf:
- type: integer
- type: string
title: Index
extras:
additionalProperties: true
type: object
title: Extras
type: object
required:
- type
- id
- name
- args
- error
title: InvalidToolCall
description: 'Allowance for errors made by LLM.
Here we add an `error` key to surface errors made during generation
(e.g., invalid JSON arguments.)'
ToolCall:
properties:
name:
type: string
title: Name
args:
additionalProperties: true
type: object
title: Args
id:
anyOf:
- type: string
- type: 'null'
title: Id
type:
type: string
const: tool_call
title: Type
type: object
required:
- name
- args
- id
title: ToolCall
description: "Represents an AI's request to call a tool.\n\nExample:\n ```python\n {\"name\": \"foo\", \"args\": {\"a\": 1}, \"id\": \"123\"}\n ```\n\n This represents a request to call the tool named `'foo'` with arguments\n `{\"a\": 1}` and an identifier of `'123'`.\n\n!!! note \"Factory function\"\n\n `tool_call` may also be used as a factory to create a `ToolCall`. Benefits\n include:\n\n * Required arguments strictly validated at creation time"
securitySchemes:
API Key:
type: apiKey
in: header
name: X-API-Key
Tenant ID:
type: apiKey
in: header
name: X-Tenant-Id
Bearer Auth:
type: http
description: Bearer tokens are used to authenticate from the UI. Must also specify x-tenant-id or x-organization-id (for org scoped apis).
scheme: bearer
Organization ID:
type: apiKey
in: header
name: X-Organization-Id