__start__
]):::first input(input) supervisor(supervisor) + __end__([__end__
]):::last __start__ --> input; + coder\3a__end__ -.-> supervisor; input --> supervisor; - supervisor -.-> researcher___start__; - supervisor -.-> coder___start__; - researcher___end__ -.-> supervisor; - coder___end__ -.-> supervisor; + researcher\3a__end__ -.-> supervisor; + supervisor -.-> coder\3a__start__; + supervisor -.-> researcher\3a__start__; + supervisor -.-> __end__; subgraph researcher - researcher___start__(__start__
) - researcher_agent(agent) - researcher_tools(tools) - researcher___end__(__end__
) - researcher___start__ --> researcher_agent; - researcher_tools --> researcher_agent; - researcher_agent -.-> researcher_tools; - researcher_agent -.-> researcher___end__; + researcher\3a__start__(__start__
) + researcher\3aagent(agent) + researcher\3atools(tools) + researcher\3a__end__(__end__
) + researcher\3a__start__ --> researcher\3aagent; + researcher\3aagent -.-> researcher\3a__end__; + researcher\3aagent -.-> researcher\3atools; + researcher\3atools --> researcher\3aagent; end subgraph coder - coder___start__(__start__
) - coder_agent(agent) - coder_tools(tools) - coder___end__(__end__
) - coder___start__ --> coder_agent; - coder_tools --> coder_agent; - coder_agent -.-> coder_tools; - coder_agent -.-> coder___end__; + coder\3a__start__(__start__
) + coder\3aagent(agent) + coder\3atools(tools) + coder\3a__end__(__end__
) + coder\3a__start__ --> coder\3aagent; + coder\3aagent -.-> coder\3a__end__; + coder\3aagent -.-> coder\3atools; + coder\3atools --> coder\3aagent; end classDef default fill:#f2f0ff,line-height:1.2 classDef first fill-opacity:0 classDef last fill:#bfb6fc - ``` ## How It Works @@ -68,7 +69,7 @@ graph TD; ## Requirements -- Python 3.10+ +- Python 3.11+ - LangGraph - Anthropic API key (for Claude) - Tavily API key (for web search) diff --git a/samples/multi-agent-supervisor-researcher-coder/agent.mermaid b/samples/multi-agent-supervisor-researcher-coder/agent.mermaid index 9adf1bb..4790d99 100644 --- a/samples/multi-agent-supervisor-researcher-coder/agent.mermaid +++ b/samples/multi-agent-supervisor-researcher-coder/agent.mermaid @@ -7,31 +7,33 @@ graph TD; __start__([__start__
]):::first input(input) supervisor(supervisor) + __end__([__end__
]):::last __start__ --> input; + coder\3a__end__ -.-> supervisor; input --> supervisor; - supervisor -.-> researcher___start__; - supervisor -.-> coder___start__; - researcher___end__ -.-> supervisor; - coder___end__ -.-> supervisor; + researcher\3a__end__ -.-> supervisor; + supervisor -.-> coder\3a__start__; + supervisor -.-> researcher\3a__start__; + supervisor -.-> __end__; subgraph researcher - researcher___start__(__start__
) - researcher_agent(agent) - researcher_tools(tools) - researcher___end__(__end__
) - researcher___start__ --> researcher_agent; - researcher_tools --> researcher_agent; - researcher_agent -.-> researcher_tools; - researcher_agent -.-> researcher___end__; + researcher\3a__start__(__start__
) + researcher\3aagent(agent) + researcher\3atools(tools) + researcher\3a__end__(__end__
) + researcher\3a__start__ --> researcher\3aagent; + researcher\3aagent -.-> researcher\3a__end__; + researcher\3aagent -.-> researcher\3atools; + researcher\3atools --> researcher\3aagent; end subgraph coder - coder___start__(__start__
) - coder_agent(agent) - coder_tools(tools) - coder___end__(__end__
) - coder___start__ --> coder_agent; - coder_tools --> coder_agent; - coder_agent -.-> coder_tools; - coder_agent -.-> coder___end__; + coder\3a__start__(__start__
) + coder\3aagent(agent) + coder\3atools(tools) + coder\3a__end__(__end__
) + coder\3a__start__ --> coder\3aagent; + coder\3aagent -.-> coder\3a__end__; + coder\3aagent -.-> coder\3atools; + coder\3atools --> coder\3aagent; end classDef default fill:#f2f0ff,line-height:1.2 classDef first fill-opacity:0 diff --git a/samples/multi-agent-supervisor-researcher-coder/graph.py b/samples/multi-agent-supervisor-researcher-coder/graph.py index ea2fb1d..4e0b8d4 100644 --- a/samples/multi-agent-supervisor-researcher-coder/graph.py +++ b/samples/multi-agent-supervisor-researcher-coder/graph.py @@ -1,8 +1,8 @@ from typing import Annotated, Literal from langchain_anthropic import ChatAnthropic -from langchain_community.tools.tavily_search import TavilySearchResults -from langchain_core.messages import HumanMessage, SystemMessage +from langchain_tavily import TavilySearch +from langchain_core.messages import HumanMessage, SystemMessage, BaseMessage from langchain_core.tools import tool from langchain_experimental.utilities import PythonREPL from langgraph.graph import END, START, MessagesState, StateGraph @@ -11,7 +11,8 @@ from pydantic import BaseModel from typing_extensions import TypedDict -tavily_tool = TavilySearchResults(max_results=5) + +tavily_tool = TavilySearch(max_results=5) # This executes code locally, which can be unsafe repl = PythonREPL() @@ -28,7 +29,7 @@ def python_repl_tool( result = repl.run(code) except BaseException as e: return f"Failed to execute. Error: {repr(e)}" - result_str = f"Successfully executed:\n\`\`\`python\n{code}\n\`\`\`\nStdout: {result}" + result_str = f"Successfully executed:\n```python\n{code}\n```\nStdout: {result}" return result_str members = ["researcher", "coder"] @@ -62,6 +63,16 @@ class GraphOutput(BaseModel): class State(MessagesState): next: str +def get_message_text(msg: BaseMessage) -> str: + """LangChain-style safe message text extractor.""" + if isinstance(msg.content, str): + return msg.content + if isinstance(msg.content, list): + return "".join( + block.get("text", "") for block in msg.content if block.get("type") == "text" + ) + return "" + def input(state: GraphInput): return { "messages": [ @@ -75,7 +86,7 @@ async def supervisor_node(state: State) -> Command[Literal[*members]] | GraphOut response = await llm.with_structured_output(Router).ainvoke(state["messages"]) goto = response["next"] if goto == "FINISH": - return GraphOutput(answer=state["messages"][-1].content) + return GraphOutput(answer=get_message_text(state["messages"][-1])) else: return Command(goto=goto, update={"next": goto}) diff --git a/samples/multi-agent-supervisor-researcher-coder/pyproject.toml b/samples/multi-agent-supervisor-researcher-coder/pyproject.toml index e3e5069..f6f38c2 100644 --- a/samples/multi-agent-supervisor-researcher-coder/pyproject.toml +++ b/samples/multi-agent-supervisor-researcher-coder/pyproject.toml @@ -5,11 +5,12 @@ description = "Multi-agent system where a supervisor coordinates between a resea authors = [{ name = "John Doe", email = "john.doe@myemail.com" }] requires-python = ">=3.10" dependencies = [ - "langgraph>=0.2.55", - "langchain-anthropic>=0.3.8", + "langgraph>=0.6.11", + "langchain-anthropic>=0.3.22", "langchain-experimental>=0.3.4", - "tavily-python>=0.5.0", - "uipath-langchain==0.0.124" + "tavily-python>=0.7.12", + "uipath-langchain==0.0.141", + "langchain-tavily>=0.2.12", ] [project.optional-dependencies] diff --git a/samples/multi-agent-supervisor-researcher-coder/uipath.json b/samples/multi-agent-supervisor-researcher-coder/uipath.json index 72ab393..800719f 100644 --- a/samples/multi-agent-supervisor-researcher-coder/uipath.json +++ b/samples/multi-agent-supervisor-researcher-coder/uipath.json @@ -1,2659 +1,36 @@ { - "entryPoints": [ - { - "filePath": "agent", - "uniqueId": "3a24ec20-0c58-4a02-838c-3076bb8e3fe8", - "type": "agent", - "input": { - "type": "object", - "properties": { - "messages": { - "items": { - "oneOf": [ - { - "additionalProperties": true, - "description": "Message from an AI.\n\nAIMessage is returned from a chat model as a response to a prompt.\n\nThis message represents the output of the model and consists of both\nthe raw output as returned by the model together standardized fields\n(e.g., tool calls, usage metadata) added by the LangChain framework.", - "properties": { - "content": { - "anyOf": [ - { - "type": "string" - }, - { - "items": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "object" - } - ] - }, - "type": "array" - } - ], - "title": "Content" - }, - "additional_kwargs": { - "title": "Additional Kwargs", - "type": "object" - }, - "response_metadata": { - "title": "Response Metadata", - "type": "object" - }, - "type": { - "const": "ai", - "default": "ai", - "title": "Type", - "type": "string" - }, - "name": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Name" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Id" - }, - "example": { - "default": false, - "title": "Example", - "type": "boolean" - }, - "tool_calls": { - "default": [], - "items": { - "description": "Represents a request to call a tool.\n\nExample:\n\n .. code-block:: python\n\n {\n \"name\": \"foo\",\n \"args\": {\"a\": 1},\n \"id\": \"123\"\n }\n\n This represents a request to call the tool named \"foo\" with arguments {\"a\": 1}\n and an identifier of \"123\".", - "properties": { - "name": { - "title": "Name", - "type": "string" - }, - "args": { - "title": "Args", - "type": "object" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "title": "Id" - }, - "type": { - "const": "tool_call", - "title": "Type", - "type": "string" - } - }, - "required": [ - "name", - "args", - "id" - ], - "title": "ToolCall", - "type": "object" - }, - "title": "Tool Calls", - "type": "array" - }, - "invalid_tool_calls": { - "default": [], - "items": { - "description": "Allowance for errors made by LLM.\n\nHere we add an `error` key to surface errors made during generation\n(e.g., invalid JSON arguments.)", - "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" - }, - "error": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "title": "Error" - }, - "type": { - "const": "invalid_tool_call", - "title": "Type", - "type": "string" - } - }, - "required": [ - "name", - "args", - "id", - "error" - ], - "title": "InvalidToolCall", - "type": "object" - }, - "title": "Invalid Tool Calls", - "type": "array" - }, - "usage_metadata": { - "anyOf": [ - { - "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\n .. code-block:: python\n\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.. versionchanged:: 0.3.9\n\n Added ``input_token_details`` and ``output_token_details``.", - "properties": { - "input_tokens": { - "title": "Input Tokens", - "type": "integer" - }, - "output_tokens": { - "title": "Output Tokens", - "type": "integer" - }, - "total_tokens": { - "title": "Total Tokens", - "type": "integer" - }, - "input_token_details": { - "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\n .. code-block:: python\n\n {\n \"audio\": 10,\n \"cache_creation\": 200,\n \"cache_read\": 100,\n }\n\n.. versionadded:: 0.3.9", - "properties": { - "audio": { - "title": "Audio", - "type": "integer" - }, - "cache_creation": { - "title": "Cache Creation", - "type": "integer" - }, - "cache_read": { - "title": "Cache Read", - "type": "integer" - } - }, - "title": "InputTokenDetails", - "type": "object" - }, - "output_token_details": { - "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\n .. code-block:: python\n\n {\n \"audio\": 10,\n \"reasoning\": 200,\n }\n\n.. versionadded:: 0.3.9", - "properties": { - "audio": { - "title": "Audio", - "type": "integer" - }, - "reasoning": { - "title": "Reasoning", - "type": "integer" - } - }, - "title": "OutputTokenDetails", - "type": "object" - } - }, - "required": [ - "input_tokens", - "output_tokens", - "total_tokens" - ], - "title": "UsageMetadata", - "type": "object" - }, - { - "type": "null" - } - ], - "default": null - } - }, - "required": [ - "content" - ], - "title": "AIMessage", - "type": "object" - }, - { - "additionalProperties": true, - "description": "Message from a human.\n\nHumanMessages are messages that are passed in from a human to the model.\n\nExample:\n\n .. code-block:: python\n\n from langchain_core.messages import HumanMessage, SystemMessage\n\n messages = [\n SystemMessage(\n content=\"You are a helpful assistant! Your name is Bob.\"\n ),\n HumanMessage(\n content=\"What is your name?\"\n )\n ]\n\n # Instantiate a chat model and invoke it with the messages\n model = ...\n print(model.invoke(messages))", - "properties": { - "content": { - "anyOf": [ - { - "type": "string" - }, - { - "items": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "object" - } - ] - }, - "type": "array" - } - ], - "title": "Content" - }, - "additional_kwargs": { - "title": "Additional Kwargs", - "type": "object" - }, - "response_metadata": { - "title": "Response Metadata", - "type": "object" - }, - "type": { - "const": "human", - "default": "human", - "title": "Type", - "type": "string" - }, - "name": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Name" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Id" - }, - "example": { - "default": false, - "title": "Example", - "type": "boolean" - } - }, - "required": [ - "content" - ], - "title": "HumanMessage", - "type": "object" - }, - { - "additionalProperties": true, - "description": "Message that can be assigned an arbitrary speaker (i.e. role).", - "properties": { - "content": { - "anyOf": [ - { - "type": "string" - }, - { - "items": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "object" - } - ] - }, - "type": "array" - } - ], - "title": "Content" - }, - "additional_kwargs": { - "title": "Additional Kwargs", - "type": "object" - }, - "response_metadata": { - "title": "Response Metadata", - "type": "object" - }, - "type": { - "const": "chat", - "default": "chat", - "title": "Type", - "type": "string" - }, - "name": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Name" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Id" - }, - "role": { - "title": "Role", - "type": "string" - } - }, - "required": [ - "content", - "role" - ], - "title": "ChatMessage", - "type": "object" - }, - { - "additionalProperties": true, - "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\n .. code-block:: python\n\n from langchain_core.messages import HumanMessage, SystemMessage\n\n messages = [\n SystemMessage(\n content=\"You are a helpful assistant! Your name is Bob.\"\n ),\n HumanMessage(\n content=\"What is your name?\"\n )\n ]\n\n # Define a chat model and invoke it with the messages\n print(model.invoke(messages))", - "properties": { - "content": { - "anyOf": [ - { - "type": "string" - }, - { - "items": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "object" - } - ] - }, - "type": "array" - } - ], - "title": "Content" - }, - "additional_kwargs": { - "title": "Additional Kwargs", - "type": "object" - }, - "response_metadata": { - "title": "Response Metadata", - "type": "object" - }, - "type": { - "const": "system", - "default": "system", - "title": "Type", - "type": "string" - }, - "name": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Name" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Id" - } - }, - "required": [ - "content" - ], - "title": "SystemMessage", - "type": "object" - }, - { - "additionalProperties": true, - "description": "Message for passing the result of executing a tool back to a model.\n\nFunctionMessage are an older version of the ToolMessage schema, and\ndo not contain the tool_call_id field.\n\nThe tool_call_id field is used to associate the tool call request with the\ntool call response. This is useful in situations where a chat model is able\nto request multiple tool calls in parallel.", - "properties": { - "content": { - "anyOf": [ - { - "type": "string" - }, - { - "items": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "object" - } - ] - }, - "type": "array" - } - ], - "title": "Content" - }, - "additional_kwargs": { - "title": "Additional Kwargs", - "type": "object" - }, - "response_metadata": { - "title": "Response Metadata", - "type": "object" - }, - "type": { - "const": "function", - "default": "function", - "title": "Type", - "type": "string" - }, - "name": { - "title": "Name", - "type": "string" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Id" - } - }, - "required": [ - "content", - "name" - ], - "title": "FunctionMessage", - "type": "object" - }, - { - "additionalProperties": true, - "description": "Message for passing the result of executing a tool back to a model.\n\nToolMessages contain the result of a tool invocation. Typically, the result\nis encoded inside the `content` field.\n\nExample: A ToolMessage representing a result of 42 from a tool call with id\n\n .. code-block:: python\n\n from langchain_core.messages import ToolMessage\n\n ToolMessage(content='42', tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL')\n\n\nExample: 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 .. versionadded:: 0.2.17\n\n .. code-block:: python\n\n from langchain_core.messages import ToolMessage\n\n tool_output = {\n \"stdout\": \"From the graph we can see that the correlation between 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\nThe tool_call_id field is used to associate the tool call request with the\ntool call response. This is useful in situations where a chat model is able\nto request multiple tool calls in parallel.", - "properties": { - "content": { - "anyOf": [ - { - "type": "string" - }, - { - "items": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "object" - } - ] - }, - "type": "array" - } - ], - "title": "Content" - }, - "additional_kwargs": { - "title": "Additional Kwargs", - "type": "object" - }, - "response_metadata": { - "title": "Response Metadata", - "type": "object" - }, - "type": { - "const": "tool", - "default": "tool", - "title": "Type", - "type": "string" - }, - "name": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Name" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Id" - }, - "tool_call_id": { - "title": "Tool Call Id", - "type": "string" - }, - "artifact": { - "default": null, - "title": "Artifact" - }, - "status": { - "default": "success", - "enum": [ - "success", - "error" - ], - "title": "Status", - "type": "string" - } - }, - "required": [ - "content", - "tool_call_id" - ], - "title": "ToolMessage", - "type": "object" - }, - { - "additionalProperties": true, - "description": "Message chunk from an AI.", - "properties": { - "content": { - "anyOf": [ - { - "type": "string" - }, - { - "items": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "object" - } - ] - }, - "type": "array" - } - ], - "title": "Content" - }, - "additional_kwargs": { - "title": "Additional Kwargs", - "type": "object" - }, - "response_metadata": { - "title": "Response Metadata", - "type": "object" - }, - "type": { - "const": "AIMessageChunk", - "default": "AIMessageChunk", - "title": "Type", - "type": "string" - }, - "name": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Name" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Id" - }, - "example": { - "default": false, - "title": "Example", - "type": "boolean" - }, - "tool_calls": { - "default": [], - "items": { - "description": "Represents a request to call a tool.\n\nExample:\n\n .. code-block:: python\n\n {\n \"name\": \"foo\",\n \"args\": {\"a\": 1},\n \"id\": \"123\"\n }\n\n This represents a request to call the tool named \"foo\" with arguments {\"a\": 1}\n and an identifier of \"123\".", - "properties": { - "name": { - "title": "Name", - "type": "string" - }, - "args": { - "title": "Args", - "type": "object" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "title": "Id" - }, - "type": { - "const": "tool_call", - "title": "Type", - "type": "string" - } - }, - "required": [ - "name", - "args", - "id" - ], - "title": "ToolCall", - "type": "object" - }, - "title": "Tool Calls", - "type": "array" - }, - "invalid_tool_calls": { - "default": [], - "items": { - "description": "Allowance for errors made by LLM.\n\nHere we add an `error` key to surface errors made during generation\n(e.g., invalid JSON arguments.)", - "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" - }, - "error": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "title": "Error" - }, - "type": { - "const": "invalid_tool_call", - "title": "Type", - "type": "string" - } - }, - "required": [ - "name", - "args", - "id", - "error" - ], - "title": "InvalidToolCall", - "type": "object" - }, - "title": "Invalid Tool Calls", - "type": "array" - }, - "usage_metadata": { - "anyOf": [ - { - "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\n .. code-block:: python\n\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.. versionchanged:: 0.3.9\n\n Added ``input_token_details`` and ``output_token_details``.", - "properties": { - "input_tokens": { - "title": "Input Tokens", - "type": "integer" - }, - "output_tokens": { - "title": "Output Tokens", - "type": "integer" - }, - "total_tokens": { - "title": "Total Tokens", - "type": "integer" - }, - "input_token_details": { - "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\n .. code-block:: python\n\n {\n \"audio\": 10,\n \"cache_creation\": 200,\n \"cache_read\": 100,\n }\n\n.. versionadded:: 0.3.9", - "properties": { - "audio": { - "title": "Audio", - "type": "integer" - }, - "cache_creation": { - "title": "Cache Creation", - "type": "integer" - }, - "cache_read": { - "title": "Cache Read", - "type": "integer" - } - }, - "title": "InputTokenDetails", - "type": "object" - }, - "output_token_details": { - "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\n .. code-block:: python\n\n {\n \"audio\": 10,\n \"reasoning\": 200,\n }\n\n.. versionadded:: 0.3.9", - "properties": { - "audio": { - "title": "Audio", - "type": "integer" - }, - "reasoning": { - "title": "Reasoning", - "type": "integer" - } - }, - "title": "OutputTokenDetails", - "type": "object" - } - }, - "required": [ - "input_tokens", - "output_tokens", - "total_tokens" - ], - "title": "UsageMetadata", - "type": "object" - }, - { - "type": "null" - } - ], - "default": null - }, - "tool_call_chunks": { - "default": [], - "items": { - "description": "A chunk of a tool call (e.g., as part of a stream).\n\nWhen merging ToolCallChunks (e.g., via AIMessageChunk.__add__),\nall string attributes are concatenated. Chunks are only merged if their\nvalues of `index` are equal and not None.\n\nExample:\n\n.. code-block:: python\n\n left_chunks = [ToolCallChunk(name=\"foo\", args='{\"a\":', index=0)]\n right_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)]", - "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": { - "const": "tool_call_chunk", - "title": "Type", - "type": "string" - } - }, - "required": [ - "name", - "args", - "id", - "index" - ], - "title": "ToolCallChunk", - "type": "object" - }, - "title": "Tool Call Chunks", - "type": "array" - } - }, - "required": [ - "content" - ], - "title": "AIMessageChunk", - "type": "object" - }, - { - "additionalProperties": true, - "description": "Human Message chunk.", - "properties": { - "content": { - "anyOf": [ - { - "type": "string" - }, - { - "items": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "object" - } - ] - }, - "type": "array" - } - ], - "title": "Content" - }, - "additional_kwargs": { - "title": "Additional Kwargs", - "type": "object" - }, - "response_metadata": { - "title": "Response Metadata", - "type": "object" - }, - "type": { - "const": "HumanMessageChunk", - "default": "HumanMessageChunk", - "title": "Type", - "type": "string" - }, - "name": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Name" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Id" - }, - "example": { - "default": false, - "title": "Example", - "type": "boolean" - } - }, - "required": [ - "content" - ], - "title": "HumanMessageChunk", - "type": "object" - }, - { - "additionalProperties": true, - "description": "Chat Message chunk.", - "properties": { - "content": { - "anyOf": [ - { - "type": "string" - }, - { - "items": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "object" - } - ] - }, - "type": "array" - } - ], - "title": "Content" - }, - "additional_kwargs": { - "title": "Additional Kwargs", - "type": "object" - }, - "response_metadata": { - "title": "Response Metadata", - "type": "object" - }, - "type": { - "const": "ChatMessageChunk", - "default": "ChatMessageChunk", - "title": "Type", - "type": "string" - }, - "name": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Name" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Id" - }, - "role": { - "title": "Role", - "type": "string" - } - }, - "required": [ - "content", - "role" - ], - "title": "ChatMessageChunk", - "type": "object" - }, - { - "additionalProperties": true, - "description": "System Message chunk.", - "properties": { - "content": { - "anyOf": [ - { - "type": "string" - }, - { - "items": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "object" - } - ] - }, - "type": "array" - } - ], - "title": "Content" - }, - "additional_kwargs": { - "title": "Additional Kwargs", - "type": "object" - }, - "response_metadata": { - "title": "Response Metadata", - "type": "object" - }, - "type": { - "const": "SystemMessageChunk", - "default": "SystemMessageChunk", - "title": "Type", - "type": "string" - }, - "name": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Name" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Id" - } - }, - "required": [ - "content" - ], - "title": "SystemMessageChunk", - "type": "object" - }, - { - "additionalProperties": true, - "description": "Function Message chunk.", - "properties": { - "content": { - "anyOf": [ - { - "type": "string" - }, - { - "items": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "object" - } - ] - }, - "type": "array" - } - ], - "title": "Content" - }, - "additional_kwargs": { - "title": "Additional Kwargs", - "type": "object" - }, - "response_metadata": { - "title": "Response Metadata", - "type": "object" - }, - "type": { - "const": "FunctionMessageChunk", - "default": "FunctionMessageChunk", - "title": "Type", - "type": "string" - }, - "name": { - "title": "Name", - "type": "string" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Id" - } - }, - "required": [ - "content", - "name" - ], - "title": "FunctionMessageChunk", - "type": "object" - }, - { - "additionalProperties": true, - "description": "Tool Message chunk.", - "properties": { - "content": { - "anyOf": [ - { - "type": "string" - }, - { - "items": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "object" - } - ] - }, - "type": "array" - } - ], - "title": "Content" - }, - "additional_kwargs": { - "title": "Additional Kwargs", - "type": "object" - }, - "response_metadata": { - "title": "Response Metadata", - "type": "object" - }, - "type": { - "const": "ToolMessageChunk", - "default": "ToolMessageChunk", - "title": "Type", - "type": "string" - }, - "name": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Name" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Id" - }, - "tool_call_id": { - "title": "Tool Call Id", - "type": "string" - }, - "artifact": { - "default": null, - "title": "Artifact" - }, - "status": { - "default": "success", - "enum": [ - "success", - "error" - ], - "title": "Status", - "type": "string" - } - }, - "required": [ - "content", - "tool_call_id" - ], - "title": "ToolMessageChunk", - "type": "object" - } - ] - }, - "title": "Messages", - "type": "array" - } - }, - "required": [ - "messages" - ] - }, - "output": { - "type": "object", - "properties": { - "messages": { - "items": { - "oneOf": [ - { - "additionalProperties": true, - "description": "Message from an AI.\n\nAIMessage is returned from a chat model as a response to a prompt.\n\nThis message represents the output of the model and consists of both\nthe raw output as returned by the model together standardized fields\n(e.g., tool calls, usage metadata) added by the LangChain framework.", - "properties": { - "content": { - "anyOf": [ - { - "type": "string" - }, - { - "items": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "object" - } - ] - }, - "type": "array" - } - ], - "title": "Content" - }, - "additional_kwargs": { - "title": "Additional Kwargs", - "type": "object" - }, - "response_metadata": { - "title": "Response Metadata", - "type": "object" - }, - "type": { - "const": "ai", - "default": "ai", - "title": "Type", - "type": "string" - }, - "name": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Name" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Id" - }, - "example": { - "default": false, - "title": "Example", - "type": "boolean" - }, - "tool_calls": { - "default": [], - "items": { - "description": "Represents a request to call a tool.\n\nExample:\n\n .. code-block:: python\n\n {\n \"name\": \"foo\",\n \"args\": {\"a\": 1},\n \"id\": \"123\"\n }\n\n This represents a request to call the tool named \"foo\" with arguments {\"a\": 1}\n and an identifier of \"123\".", - "properties": { - "name": { - "title": "Name", - "type": "string" - }, - "args": { - "title": "Args", - "type": "object" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "title": "Id" - }, - "type": { - "const": "tool_call", - "title": "Type", - "type": "string" - } - }, - "required": [ - "name", - "args", - "id" - ], - "title": "ToolCall", - "type": "object" - }, - "title": "Tool Calls", - "type": "array" - }, - "invalid_tool_calls": { - "default": [], - "items": { - "description": "Allowance for errors made by LLM.\n\nHere we add an `error` key to surface errors made during generation\n(e.g., invalid JSON arguments.)", - "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" - }, - "error": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "title": "Error" - }, - "type": { - "const": "invalid_tool_call", - "title": "Type", - "type": "string" - } - }, - "required": [ - "name", - "args", - "id", - "error" - ], - "title": "InvalidToolCall", - "type": "object" - }, - "title": "Invalid Tool Calls", - "type": "array" - }, - "usage_metadata": { - "anyOf": [ - { - "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\n .. code-block:: python\n\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.. versionchanged:: 0.3.9\n\n Added ``input_token_details`` and ``output_token_details``.", - "properties": { - "input_tokens": { - "title": "Input Tokens", - "type": "integer" - }, - "output_tokens": { - "title": "Output Tokens", - "type": "integer" - }, - "total_tokens": { - "title": "Total Tokens", - "type": "integer" - }, - "input_token_details": { - "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\n .. code-block:: python\n\n {\n \"audio\": 10,\n \"cache_creation\": 200,\n \"cache_read\": 100,\n }\n\n.. versionadded:: 0.3.9", - "properties": { - "audio": { - "title": "Audio", - "type": "integer" - }, - "cache_creation": { - "title": "Cache Creation", - "type": "integer" - }, - "cache_read": { - "title": "Cache Read", - "type": "integer" - } - }, - "title": "InputTokenDetails", - "type": "object" - }, - "output_token_details": { - "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\n .. code-block:: python\n\n {\n \"audio\": 10,\n \"reasoning\": 200,\n }\n\n.. versionadded:: 0.3.9", - "properties": { - "audio": { - "title": "Audio", - "type": "integer" - }, - "reasoning": { - "title": "Reasoning", - "type": "integer" - } - }, - "title": "OutputTokenDetails", - "type": "object" - } - }, - "required": [ - "input_tokens", - "output_tokens", - "total_tokens" - ], - "title": "UsageMetadata", - "type": "object" - }, - { - "type": "null" - } - ], - "default": null - } - }, - "required": [ - "content" - ], - "title": "AIMessage", - "type": "object" - }, - { - "additionalProperties": true, - "description": "Message from a human.\n\nHumanMessages are messages that are passed in from a human to the model.\n\nExample:\n\n .. code-block:: python\n\n from langchain_core.messages import HumanMessage, SystemMessage\n\n messages = [\n SystemMessage(\n content=\"You are a helpful assistant! Your name is Bob.\"\n ),\n HumanMessage(\n content=\"What is your name?\"\n )\n ]\n\n # Instantiate a chat model and invoke it with the messages\n model = ...\n print(model.invoke(messages))", - "properties": { - "content": { - "anyOf": [ - { - "type": "string" - }, - { - "items": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "object" - } - ] - }, - "type": "array" - } - ], - "title": "Content" - }, - "additional_kwargs": { - "title": "Additional Kwargs", - "type": "object" - }, - "response_metadata": { - "title": "Response Metadata", - "type": "object" - }, - "type": { - "const": "human", - "default": "human", - "title": "Type", - "type": "string" - }, - "name": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Name" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Id" - }, - "example": { - "default": false, - "title": "Example", - "type": "boolean" - } - }, - "required": [ - "content" - ], - "title": "HumanMessage", - "type": "object" - }, - { - "additionalProperties": true, - "description": "Message that can be assigned an arbitrary speaker (i.e. role).", - "properties": { - "content": { - "anyOf": [ - { - "type": "string" - }, - { - "items": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "object" - } - ] - }, - "type": "array" - } - ], - "title": "Content" - }, - "additional_kwargs": { - "title": "Additional Kwargs", - "type": "object" - }, - "response_metadata": { - "title": "Response Metadata", - "type": "object" - }, - "type": { - "const": "chat", - "default": "chat", - "title": "Type", - "type": "string" - }, - "name": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Name" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Id" - }, - "role": { - "title": "Role", - "type": "string" - } - }, - "required": [ - "content", - "role" - ], - "title": "ChatMessage", - "type": "object" - }, - { - "additionalProperties": true, - "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\n .. code-block:: python\n\n from langchain_core.messages import HumanMessage, SystemMessage\n\n messages = [\n SystemMessage(\n content=\"You are a helpful assistant! Your name is Bob.\"\n ),\n HumanMessage(\n content=\"What is your name?\"\n )\n ]\n\n # Define a chat model and invoke it with the messages\n print(model.invoke(messages))", - "properties": { - "content": { - "anyOf": [ - { - "type": "string" - }, - { - "items": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "object" - } - ] - }, - "type": "array" - } - ], - "title": "Content" - }, - "additional_kwargs": { - "title": "Additional Kwargs", - "type": "object" - }, - "response_metadata": { - "title": "Response Metadata", - "type": "object" - }, - "type": { - "const": "system", - "default": "system", - "title": "Type", - "type": "string" - }, - "name": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Name" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Id" - } - }, - "required": [ - "content" - ], - "title": "SystemMessage", - "type": "object" - }, - { - "additionalProperties": true, - "description": "Message for passing the result of executing a tool back to a model.\n\nFunctionMessage are an older version of the ToolMessage schema, and\ndo not contain the tool_call_id field.\n\nThe tool_call_id field is used to associate the tool call request with the\ntool call response. This is useful in situations where a chat model is able\nto request multiple tool calls in parallel.", - "properties": { - "content": { - "anyOf": [ - { - "type": "string" - }, - { - "items": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "object" - } - ] - }, - "type": "array" - } - ], - "title": "Content" - }, - "additional_kwargs": { - "title": "Additional Kwargs", - "type": "object" - }, - "response_metadata": { - "title": "Response Metadata", - "type": "object" - }, - "type": { - "const": "function", - "default": "function", - "title": "Type", - "type": "string" - }, - "name": { - "title": "Name", - "type": "string" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Id" - } - }, - "required": [ - "content", - "name" - ], - "title": "FunctionMessage", - "type": "object" - }, - { - "additionalProperties": true, - "description": "Message for passing the result of executing a tool back to a model.\n\nToolMessages contain the result of a tool invocation. Typically, the result\nis encoded inside the `content` field.\n\nExample: A ToolMessage representing a result of 42 from a tool call with id\n\n .. code-block:: python\n\n from langchain_core.messages import ToolMessage\n\n ToolMessage(content='42', tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL')\n\n\nExample: 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 .. versionadded:: 0.2.17\n\n .. code-block:: python\n\n from langchain_core.messages import ToolMessage\n\n tool_output = {\n \"stdout\": \"From the graph we can see that the correlation between 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\nThe tool_call_id field is used to associate the tool call request with the\ntool call response. This is useful in situations where a chat model is able\nto request multiple tool calls in parallel.", - "properties": { - "content": { - "anyOf": [ - { - "type": "string" - }, - { - "items": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "object" - } - ] - }, - "type": "array" - } - ], - "title": "Content" - }, - "additional_kwargs": { - "title": "Additional Kwargs", - "type": "object" - }, - "response_metadata": { - "title": "Response Metadata", - "type": "object" - }, - "type": { - "const": "tool", - "default": "tool", - "title": "Type", - "type": "string" - }, - "name": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Name" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Id" - }, - "tool_call_id": { - "title": "Tool Call Id", - "type": "string" - }, - "artifact": { - "default": null, - "title": "Artifact" - }, - "status": { - "default": "success", - "enum": [ - "success", - "error" - ], - "title": "Status", - "type": "string" - } - }, - "required": [ - "content", - "tool_call_id" - ], - "title": "ToolMessage", - "type": "object" - }, - { - "additionalProperties": true, - "description": "Message chunk from an AI.", - "properties": { - "content": { - "anyOf": [ - { - "type": "string" - }, - { - "items": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "object" - } - ] - }, - "type": "array" - } - ], - "title": "Content" - }, - "additional_kwargs": { - "title": "Additional Kwargs", - "type": "object" - }, - "response_metadata": { - "title": "Response Metadata", - "type": "object" - }, - "type": { - "const": "AIMessageChunk", - "default": "AIMessageChunk", - "title": "Type", - "type": "string" - }, - "name": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Name" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Id" - }, - "example": { - "default": false, - "title": "Example", - "type": "boolean" - }, - "tool_calls": { - "default": [], - "items": { - "description": "Represents a request to call a tool.\n\nExample:\n\n .. code-block:: python\n\n {\n \"name\": \"foo\",\n \"args\": {\"a\": 1},\n \"id\": \"123\"\n }\n\n This represents a request to call the tool named \"foo\" with arguments {\"a\": 1}\n and an identifier of \"123\".", - "properties": { - "name": { - "title": "Name", - "type": "string" - }, - "args": { - "title": "Args", - "type": "object" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "title": "Id" - }, - "type": { - "const": "tool_call", - "title": "Type", - "type": "string" - } - }, - "required": [ - "name", - "args", - "id" - ], - "title": "ToolCall", - "type": "object" - }, - "title": "Tool Calls", - "type": "array" - }, - "invalid_tool_calls": { - "default": [], - "items": { - "description": "Allowance for errors made by LLM.\n\nHere we add an `error` key to surface errors made during generation\n(e.g., invalid JSON arguments.)", - "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" - }, - "error": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "title": "Error" - }, - "type": { - "const": "invalid_tool_call", - "title": "Type", - "type": "string" - } - }, - "required": [ - "name", - "args", - "id", - "error" - ], - "title": "InvalidToolCall", - "type": "object" - }, - "title": "Invalid Tool Calls", - "type": "array" - }, - "usage_metadata": { - "anyOf": [ - { - "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\n .. code-block:: python\n\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.. versionchanged:: 0.3.9\n\n Added ``input_token_details`` and ``output_token_details``.", - "properties": { - "input_tokens": { - "title": "Input Tokens", - "type": "integer" - }, - "output_tokens": { - "title": "Output Tokens", - "type": "integer" - }, - "total_tokens": { - "title": "Total Tokens", - "type": "integer" - }, - "input_token_details": { - "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\n .. code-block:: python\n\n {\n \"audio\": 10,\n \"cache_creation\": 200,\n \"cache_read\": 100,\n }\n\n.. versionadded:: 0.3.9", - "properties": { - "audio": { - "title": "Audio", - "type": "integer" - }, - "cache_creation": { - "title": "Cache Creation", - "type": "integer" - }, - "cache_read": { - "title": "Cache Read", - "type": "integer" - } - }, - "title": "InputTokenDetails", - "type": "object" - }, - "output_token_details": { - "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\n .. code-block:: python\n\n {\n \"audio\": 10,\n \"reasoning\": 200,\n }\n\n.. versionadded:: 0.3.9", - "properties": { - "audio": { - "title": "Audio", - "type": "integer" - }, - "reasoning": { - "title": "Reasoning", - "type": "integer" - } - }, - "title": "OutputTokenDetails", - "type": "object" - } - }, - "required": [ - "input_tokens", - "output_tokens", - "total_tokens" - ], - "title": "UsageMetadata", - "type": "object" - }, - { - "type": "null" - } - ], - "default": null - }, - "tool_call_chunks": { - "default": [], - "items": { - "description": "A chunk of a tool call (e.g., as part of a stream).\n\nWhen merging ToolCallChunks (e.g., via AIMessageChunk.__add__),\nall string attributes are concatenated. Chunks are only merged if their\nvalues of `index` are equal and not None.\n\nExample:\n\n.. code-block:: python\n\n left_chunks = [ToolCallChunk(name=\"foo\", args='{\"a\":', index=0)]\n right_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)]", - "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": { - "const": "tool_call_chunk", - "title": "Type", - "type": "string" - } - }, - "required": [ - "name", - "args", - "id", - "index" - ], - "title": "ToolCallChunk", - "type": "object" - }, - "title": "Tool Call Chunks", - "type": "array" - } - }, - "required": [ - "content" - ], - "title": "AIMessageChunk", - "type": "object" - }, - { - "additionalProperties": true, - "description": "Human Message chunk.", - "properties": { - "content": { - "anyOf": [ - { - "type": "string" - }, - { - "items": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "object" - } - ] - }, - "type": "array" - } - ], - "title": "Content" - }, - "additional_kwargs": { - "title": "Additional Kwargs", - "type": "object" - }, - "response_metadata": { - "title": "Response Metadata", - "type": "object" - }, - "type": { - "const": "HumanMessageChunk", - "default": "HumanMessageChunk", - "title": "Type", - "type": "string" - }, - "name": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Name" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Id" - }, - "example": { - "default": false, - "title": "Example", - "type": "boolean" - } - }, - "required": [ - "content" - ], - "title": "HumanMessageChunk", - "type": "object" - }, - { - "additionalProperties": true, - "description": "Chat Message chunk.", - "properties": { - "content": { - "anyOf": [ - { - "type": "string" - }, - { - "items": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "object" - } - ] - }, - "type": "array" - } - ], - "title": "Content" - }, - "additional_kwargs": { - "title": "Additional Kwargs", - "type": "object" - }, - "response_metadata": { - "title": "Response Metadata", - "type": "object" - }, - "type": { - "const": "ChatMessageChunk", - "default": "ChatMessageChunk", - "title": "Type", - "type": "string" - }, - "name": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Name" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Id" - }, - "role": { - "title": "Role", - "type": "string" - } - }, - "required": [ - "content", - "role" - ], - "title": "ChatMessageChunk", - "type": "object" - }, - { - "additionalProperties": true, - "description": "System Message chunk.", - "properties": { - "content": { - "anyOf": [ - { - "type": "string" - }, - { - "items": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "object" - } - ] - }, - "type": "array" - } - ], - "title": "Content" - }, - "additional_kwargs": { - "title": "Additional Kwargs", - "type": "object" - }, - "response_metadata": { - "title": "Response Metadata", - "type": "object" - }, - "type": { - "const": "SystemMessageChunk", - "default": "SystemMessageChunk", - "title": "Type", - "type": "string" - }, - "name": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Name" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Id" - } - }, - "required": [ - "content" - ], - "title": "SystemMessageChunk", - "type": "object" - }, - { - "additionalProperties": true, - "description": "Function Message chunk.", - "properties": { - "content": { - "anyOf": [ - { - "type": "string" - }, - { - "items": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "object" - } - ] - }, - "type": "array" - } - ], - "title": "Content" - }, - "additional_kwargs": { - "title": "Additional Kwargs", - "type": "object" - }, - "response_metadata": { - "title": "Response Metadata", - "type": "object" - }, - "type": { - "const": "FunctionMessageChunk", - "default": "FunctionMessageChunk", - "title": "Type", - "type": "string" - }, - "name": { - "title": "Name", - "type": "string" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Id" - } - }, - "required": [ - "content", - "name" - ], - "title": "FunctionMessageChunk", - "type": "object" - }, - { - "additionalProperties": true, - "description": "Tool Message chunk.", - "properties": { - "content": { - "anyOf": [ - { - "type": "string" - }, - { - "items": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "object" - } - ] - }, - "type": "array" - } - ], - "title": "Content" - }, - "additional_kwargs": { - "title": "Additional Kwargs", - "type": "object" - }, - "response_metadata": { - "title": "Response Metadata", - "type": "object" - }, - "type": { - "const": "ToolMessageChunk", - "default": "ToolMessageChunk", - "title": "Type", - "type": "string" - }, - "name": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Name" - }, - "id": { - "anyOf": [ - { - "type": "string" - }, - { - "type": "null" - } - ], - "default": null, - "title": "Id" - }, - "tool_call_id": { - "title": "Tool Call Id", - "type": "string" - }, - "artifact": { - "default": null, - "title": "Artifact" - }, - "status": { - "default": "success", - "enum": [ - "success", - "error" - ], - "title": "Status", - "type": "string" - } - }, - "required": [ - "content", - "tool_call_id" - ], - "title": "ToolMessageChunk", - "type": "object" - } - ] + "entryPoints": [ + { + "filePath": "agent", + "uniqueId": "4e7d1db4-7679-4889-9074-abbff0e52737", + "type": "agent", + "input": { + "type": "object", + "properties": { + "question": { + "title": "Question", + "type": "string" + } + }, + "required": [ + "question" + ] }, - "title": "Messages", - "type": "array" - } - }, - "required": [ - "messages" - ] - } + "output": { + "type": "object", + "properties": { + "answer": { + "default": null, + "title": "Answer", + "type": "string" + } + }, + "required": [] + } + } + ], + "bindings": { + "version": "2.0", + "resources": [] } - ], - "bindings": { - "version": "2.0", - "resources": [] - } } \ No newline at end of file diff --git a/samples/multi-agent-supervisor-researcher-coder/uv.lock b/samples/multi-agent-supervisor-researcher-coder/uv.lock index 5cd45b5..e3f7f4b 100644 --- a/samples/multi-agent-supervisor-researcher-coder/uv.lock +++ b/samples/multi-agent-supervisor-researcher-coder/uv.lock @@ -138,22 +138,29 @@ wheels = [ [[package]] name = "anthropic" -version = "0.65.0" +version = "0.71.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "anyio" }, { name = "distro" }, + { name = "docstring-parser" }, { name = "httpx" }, { name = "jiter" }, { name = "pydantic" }, { name = "sniffio" }, { name = "typing-extensions" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/98/14/492482f0144674ef39096de6e075cdd71712401082b43cfafa06ef33ada3/anthropic-0.65.0.tar.gz", hash = "sha256:6b6b6942574e54342050dfd42b8d856a8366b171daec147df3b80be4722733b9", size = 436875, upload-time = "2025-09-02T16:12:27.711Z" } +sdist = { url = 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{ url = "https://files.pythonhosted.org/packages/3e/38/7859ff46355f76f8d19459005ca000b6e7012f2f1ca597746cbcd1fbfe5e/antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b", size = 117034, upload-time = "2021-11-06T17:52:23.524Z" } + [[package]] name = "anyio" version = "4.10.0" @@ -503,12 +510,21 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/12/b3/231ffd4ab1fc9d679809f356cebee130ac7daa00d6d6f3206dd4fd137e9e/distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2", size = 20277, upload-time = "2023-12-24T09:54:30.421Z" }, ] +[[package]] +name = "docstring-parser" +version = "0.17.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/b2/9d/c3b43da9515bd270df0f80548d9944e389870713cc1fe2b8fb35fe2bcefd/docstring_parser-0.17.0.tar.gz", hash = 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