autogen_core.models#

pydantic model AssistantMessage[source]#

Bases: BaseModel

Assistant message are sampled from the language model.

Show JSON schema
{
   "title": "AssistantMessage",
   "description": "Assistant message are sampled from the language model.",
   "type": "object",
   "properties": {
      "content": {
         "anyOf": [
            {
               "type": "string"
            },
            {
               "items": {
                  "$ref": "#/$defs/FunctionCall"
               },
               "type": "array"
            }
         ],
         "title": "Content"
      },
      "thought": {
         "anyOf": [
            {
               "type": "string"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "title": "Thought"
      },
      "source": {
         "title": "Source",
         "type": "string"
      },
      "type": {
         "const": "AssistantMessage",
         "default": "AssistantMessage",
         "title": "Type",
         "type": "string"
      }
   },
   "$defs": {
      "FunctionCall": {
         "properties": {
            "id": {
               "title": "Id",
               "type": "string"
            },
            "arguments": {
               "title": "Arguments",
               "type": "string"
            },
            "name": {
               "title": "Name",
               "type": "string"
            }
         },
         "required": [
            "id",
            "arguments",
            "name"
         ],
         "title": "FunctionCall",
         "type": "object"
      }
   },
   "required": [
      "content",
      "source"
   ]
}

Fields:

  • content (str | List[autogen_core._types.FunctionCall])

  • source (str)

  • thought (str | None)

  • type (Literal['AssistantMessage'])

field content: str | List[FunctionCall] [Required]#

The content of the message.

field source: str [Required]#

The name of the agent that sent this message.

field thought: str | None = None#

The reasoning text for the completion if available. Used for reasoning model and additional text content besides function calls.

field type: Literal['AssistantMessage'] = 'AssistantMessage'#
class ChatCompletionClient[source]#

Bases: ComponentBase[BaseModel], ABC

abstract actual_usage() → RequestUsage[source]#
abstract property capabilities: ModelCapabilities#
abstract count_tokens(messages: Sequence[Annotated[SystemMessage | UserMessage | AssistantMessage | FunctionExecutionResultMessage, FieldInfo(annotation=NoneType, required=True, discriminator='type')]], *, tools: Sequence[Tool | ToolSchema] = []) → int[source]#
abstract async create(messages: Sequence[Annotated[SystemMessage | UserMessage | AssistantMessage | FunctionExecutionResultMessage, FieldInfo(annotation=NoneType, required=True, discriminator='type')]], *, tools: Sequence[Tool | ToolSchema] = [], json_output: bool | None = None, extra_create_args: Mapping[str, Any] = {}, cancellation_token: CancellationToken | None = None) → CreateResult[source]#
abstract create_stream(messages: Sequence[Annotated[SystemMessage | UserMessage | AssistantMessage | FunctionExecutionResultMessage, FieldInfo(annotation=NoneType, required=True, discriminator='type')]], *, tools: Sequence[Tool | ToolSchema] = [], json_output: bool | None = None, extra_create_args: Mapping[str, Any] = {}, cancellation_token: CancellationToken | None = None) → AsyncGenerator[str | CreateResult, None][source]#
abstract property model_info: ModelInfo#
abstract remaining_tokens(messages: Sequence[Annotated[SystemMessage | UserMessage | AssistantMessage | FunctionExecutionResultMessage, FieldInfo(annotation=NoneType, required=True, discriminator='type')]], *, tools: Sequence[Tool | ToolSchema] = []) → int[source]#
abstract total_usage() → RequestUsage[source]#
pydantic model ChatCompletionTokenLogprob[source]#

Bases: BaseModel

Show JSON schema
{
   "title": "ChatCompletionTokenLogprob",
   "type": "object",
   "properties": {
      "token": {
         "title": "Token",
         "type": "string"
      },
      "logprob": {
         "title": "Logprob",
         "type": "number"
      },
      "top_logprobs": {
         "anyOf": [
            {
               "items": {
                  "$ref": "#/$defs/TopLogprob"
               },
               "type": "array"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "title": "Top Logprobs"
      },
      "bytes": {
         "anyOf": [
            {
               "items": {
                  "type": "integer"
               },
               "type": "array"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "title": "Bytes"
      }
   },
   "$defs": {
      "TopLogprob": {
         "properties": {
            "logprob": {
               "title": "Logprob",
               "type": "number"
            },
            "bytes": {
               "anyOf": [
                  {
                     "items": {
                        "type": "integer"
                     },
                     "type": "array"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "title": "Bytes"
            }
         },
         "required": [
            "logprob"
         ],
         "title": "TopLogprob",
         "type": "object"
      }
   },
   "required": [
      "token",
      "logprob"
   ]
}

Fields:

  • bytes (List[int] | None)

  • logprob (float)

  • token (str)

  • top_logprobs (List[autogen_core.models._types.TopLogprob] | None)

field bytes: List[int] | None = None#
field logprob: float [Required]#
field token: str [Required]#
field top_logprobs: List[TopLogprob] | None = None#
pydantic model CreateResult[source]#

Bases: BaseModel

Create result contains the output of a model completion.

Show JSON schema
{
   "title": "CreateResult",
   "description": "Create result contains the output of a model completion.",
   "type": "object",
   "properties": {
      "finish_reason": {
         "enum": [
            "stop",
            "length",
            "function_calls",
            "content_filter",
            "unknown"
         ],
         "title": "Finish Reason",
         "type": "string"
      },
      "content": {
         "anyOf": [
            {
               "type": "string"
            },
            {
               "items": {
                  "$ref": "#/$defs/FunctionCall"
               },
               "type": "array"
            }
         ],
         "title": "Content"
      },
      "usage": {
         "$ref": "#/$defs/RequestUsage"
      },
      "cached": {
         "title": "Cached",
         "type": "boolean"
      },
      "logprobs": {
         "anyOf": [
            {
               "items": {
                  "$ref": "#/$defs/ChatCompletionTokenLogprob"
               },
               "type": "array"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "title": "Logprobs"
      },
      "thought": {
         "anyOf": [
            {
               "type": "string"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "title": "Thought"
      }
   },
   "$defs": {
      "ChatCompletionTokenLogprob": {
         "properties": {
            "token": {
               "title": "Token",
               "type": "string"
            },
            "logprob": {
               "title": "Logprob",
               "type": "number"
            },
            "top_logprobs": {
               "anyOf": [
                  {
                     "items": {
                        "$ref": "#/$defs/TopLogprob"
                     },
                     "type": "array"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "title": "Top Logprobs"
            },
            "bytes": {
               "anyOf": [
                  {
                     "items": {
                        "type": "integer"
                     },
                     "type": "array"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "title": "Bytes"
            }
         },
         "required": [
            "token",
            "logprob"
         ],
         "title": "ChatCompletionTokenLogprob",
         "type": "object"
      },
      "FunctionCall": {
         "properties": {
            "id": {
               "title": "Id",
               "type": "string"
            },
            "arguments": {
               "title": "Arguments",
               "type": "string"
            },
            "name": {
               "title": "Name",
               "type": "string"
            }
         },
         "required": [
            "id",
            "arguments",
            "name"
         ],
         "title": "FunctionCall",
         "type": "object"
      },
      "RequestUsage": {
         "properties": {
            "prompt_tokens": {
               "title": "Prompt Tokens",
               "type": "integer"
            },
            "completion_tokens": {
               "title": "Completion Tokens",
               "type": "integer"
            }
         },
         "required": [
            "prompt_tokens",
            "completion_tokens"
         ],
         "title": "RequestUsage",
         "type": "object"
      },
      "TopLogprob": {
         "properties": {
            "logprob": {
               "title": "Logprob",
               "type": "number"
            },
            "bytes": {
               "anyOf": [
                  {
                     "items": {
                        "type": "integer"
                     },
                     "type": "array"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "title": "Bytes"
            }
         },
         "required": [
            "logprob"
         ],
         "title": "TopLogprob",
         "type": "object"
      }
   },
   "required": [
      "finish_reason",
      "content",
      "usage",
      "cached"
   ]
}

Fields:

  • cached (bool)

  • content (str | List[autogen_core._types.FunctionCall])

  • finish_reason (Literal['stop', 'length', 'function_calls', 'content_filter', 'unknown'])

  • logprobs (List[autogen_core.models._types.ChatCompletionTokenLogprob] | None)

  • thought (str | None)

  • usage (autogen_core.models._types.RequestUsage)

field cached: bool [Required]#

Whether the completion was generated from a cached response.

field content: str | List[FunctionCall] [Required]#

The output of the model completion.

field finish_reason: Literal['stop', 'length', 'function_calls', 'content_filter', 'unknown'] [Required]#

The reason the model finished generating the completion.

field logprobs: List[ChatCompletionTokenLogprob] | None = None#

The logprobs of the tokens in the completion.

field thought: str | None = None#

The reasoning text for the completion if available. Used for reasoning models and additional text content besides function calls.

field usage: RequestUsage [Required]#

The usage of tokens in the prompt and completion.

pydantic model FunctionExecutionResult[source]#

Bases: BaseModel

Function execution result contains the output of a function call.

Show JSON schema
{
   "title": "FunctionExecutionResult",
   "description": "Function execution result contains the output of a function call.",
   "type": "object",
   "properties": {
      "content": {
         "title": "Content",
         "type": "string"
      },
      "name": {
         "title": "Name",
         "type": "string"
      },
      "call_id": {
         "title": "Call Id",
         "type": "string"
      },
      "is_error": {
         "anyOf": [
            {
               "type": "boolean"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "title": "Is Error"
      }
   },
   "required": [
      "content",
      "name",
      "call_id"
   ]
}

Fields:

  • call_id (str)

  • content (str)

  • is_error (bool | None)

  • name (str)

field call_id: str [Required]#

The ID of the function call. Note this ID may be empty for some models.

field content: str [Required]#

The output of the function call.

field is_error: bool | None = None#

Whether the function call resulted in an error.

field name: str [Required]#

The name of the function that was called.

pydantic model FunctionExecutionResultMessage[source]#

Bases: BaseModel

Function execution result message contains the output of multiple function calls.

Show JSON schema
{
   "title": "FunctionExecutionResultMessage",
   "description": "Function execution result message contains the output of multiple function calls.",
   "type": "object",
   "properties": {
      "content": {
         "items": {
            "$ref": "#/$defs/FunctionExecutionResult"
         },
         "title": "Content",
         "type": "array"
      },
      "type": {
         "const": "FunctionExecutionResultMessage",
         "default": "FunctionExecutionResultMessage",
         "title": "Type",
         "type": "string"
      }
   },
   "$defs": {
      "FunctionExecutionResult": {
         "description": "Function execution result contains the output of a function call.",
         "properties": {
            "content": {
               "title": "Content",
               "type": "string"
            },
            "name": {
               "title": "Name",
               "type": "string"
            },
            "call_id": {
               "title": "Call Id",
               "type": "string"
            },
            "is_error": {
               "anyOf": [
                  {
                     "type": "boolean"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "title": "Is Error"
            }
         },
         "required": [
            "content",
            "name",
            "call_id"
         ],
         "title": "FunctionExecutionResult",
         "type": "object"
      }
   },
   "required": [
      "content"
   ]
}

Fields:

  • content (List[autogen_core.models._types.FunctionExecutionResult])

  • type (Literal['FunctionExecutionResultMessage'])

field content: List[FunctionExecutionResult] [Required]#
field type: Literal['FunctionExecutionResultMessage'] = 'FunctionExecutionResultMessage'#
class ModelCapabilities(**kwargs)[source]#

Bases: TypedDict

function_calling: Required[bool]#
json_output: Required[bool]#
vision: Required[bool]#
class ModelFamily(*args: Any, **kwargs: Any)[source]#

Bases: object

A model family is a group of models that share similar characteristics from a capabilities perspective. This is different to discrete supported features such as vision, function calling, and JSON output.

This namespace class holds constants for the model families that AutoGen understands. Other families definitely exist and can be represented by a string, however, AutoGen will treat them as unknown.

ANY#

alias of Literal[‘gpt-4o’, ‘o1’, ‘o3’, ‘gpt-4’, ‘gpt-35’, ‘r1’, ‘gemini-1.5-flash’, ‘gemini-1.5-pro’, ‘gemini-2.0-flash’, ‘claude-3-haiku’, ‘claude-3-sonnet’, ‘claude-3-opus’, ‘claude-3.5-haiku’, ‘claude-3.5-sonnet’, ‘unknown’]

CLAUDE_3_5_HAIKU = 'claude-3.5-haiku'#
CLAUDE_3_5_SONNET = 'claude-3.5-sonnet'#
CLAUDE_3_7_SONNET = 'claude-3.7-sonnet'#
CLAUDE_3_HAIKU = 'claude-3-haiku'#
CLAUDE_3_OPUS = 'claude-3-opus'#
CLAUDE_3_SONNET = 'claude-3-sonnet'#
GEMINI_1_5_FLASH = 'gemini-1.5-flash'#
GEMINI_1_5_PRO = 'gemini-1.5-pro'#
GEMINI_2_0_FLASH = 'gemini-2.0-flash'#
GPT_35 = 'gpt-35'#
GPT_4 = 'gpt-4'#
GPT_4O = 'gpt-4o'#
O1 = 'o1'#
O3 = 'o3'#
R1 = 'r1'#
UNKNOWN = 'unknown'#
static is_claude(family: str) → bool[source]#
static is_gemini(family: str) → bool[source]#
static is_openai(family: str) → bool[source]#
class ModelInfo[source]#

Bases: TypedDict

ModelInfo is a dictionary that contains information about a model’s properties. It is expected to be used in the model_info property of a model client.

We are expecting this to grow over time as we add more features.

family: Required[Literal['gpt-4o', 'o1', 'o3', 'gpt-4', 'gpt-35', 'r1', 'gemini-1.5-flash', 'gemini-1.5-pro', 'gemini-2.0-flash', 'claude-3-haiku', 'claude-3-sonnet', 'claude-3-opus', 'claude-3.5-haiku', 'claude-3.5-sonnet', 'unknown'] | str]#

Model family should be one of the constants from ModelFamily or a string representing an unknown model family.

function_calling: Required[bool]#

True if the model supports function calling, otherwise False.

json_output: Required[bool]#

this is different to structured json.

Type:

True if the model supports json output, otherwise False. Note

vision: Required[bool]#

True if the model supports vision, aka image input, otherwise False.

class RequestUsage(prompt_tokens: int, completion_tokens: int)[source]#

Bases: object

completion_tokens: int#
prompt_tokens: int#
pydantic model SystemMessage[source]#

Bases: BaseModel

System message contains instructions for the model coming from the developer.

Note

Open AI is moving away from using ‘system’ role in favor of ‘developer’ role. See Model Spec for more details. However, the ‘system’ role is still allowed in their API and will be automatically converted to ‘developer’ role on the server side. So, you can use SystemMessage for developer messages.

Show JSON schema
{
   "title": "SystemMessage",
   "description": "System message contains instructions for the model coming from the developer.\n\n.. note::\n\n    Open AI is moving away from using 'system' role in favor of 'developer' role.\n    See `Model Spec <https://cdn.openai.com/spec/model-spec-2024-05-08.html#definitions>`_ for more details.\n    However, the 'system' role is still allowed in their API and will be automatically converted to 'developer' role\n    on the server side.\n    So, you can use `SystemMessage` for developer messages.",
   "type": "object",
   "properties": {
      "content": {
         "title": "Content",
         "type": "string"
      },
      "type": {
         "const": "SystemMessage",
         "default": "SystemMessage",
         "title": "Type",
         "type": "string"
      }
   },
   "required": [
      "content"
   ]
}

Fields:

  • content (str)

  • type (Literal['SystemMessage'])

field content: str [Required]#

The content of the message.

field type: Literal['SystemMessage'] = 'SystemMessage'#
class TopLogprob(logprob: float, bytes: List[int] | None = None)[source]#

Bases: object

bytes: List[int] | None = None#
logprob: float#
pydantic model UserMessage[source]#

Bases: BaseModel

User message contains input from end users, or a catch-all for data provided to the model.

Show JSON schema
{
   "title": "UserMessage",
   "description": "User message contains input from end users, or a catch-all for data provided to the model.",
   "type": "object",
   "properties": {
      "content": {
         "anyOf": [
            {
               "type": "string"
            },
            {
               "items": {
                  "anyOf": [
                     {
                        "type": "string"
                     },
                     {}
                  ]
               },
               "type": "array"
            }
         ],
         "title": "Content"
      },
      "source": {
         "title": "Source",
         "type": "string"
      },
      "type": {
         "const": "UserMessage",
         "default": "UserMessage",
         "title": "Type",
         "type": "string"
      }
   },
   "required": [
      "content",
      "source"
   ]
}

Fields:

  • content (str | List[str | autogen_core._image.Image])

  • source (str)

  • type (Literal['UserMessage'])

field content: str | List[str | Image] [Required]#

The content of the message.

field source: str [Required]#

The name of the agent that sent this message.

field type: Literal['UserMessage'] = 'UserMessage'#
validate_model_info(model_info: ModelInfo) → None[source]#

Validates the model info dictionary.

Raises:

ValueError – If the model info dictionary is missing required fields.

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