Config

any_agent.config.AgentConfig

Bases: BaseModel

Source code in src/any_agent/config.py

class AgentConfig(BaseModel):
    model_config = ConfigDict(extra="forbid")

    model_id: str
    """Select the underlying model used by the agent.

    If you are using the default model_type (LiteLLM), you can refer to [LiteLLM Provider Docs](https://docs.litellm.ai/docs/providers) for the list of providers and how to access them.
    """

    api_base: str | None = None
    api_key: str | None = None

    description: str | None = None
    """Description of the agent."""

    name: str = "any_agent"
    """The name of the agent.

    Defaults to `any_agent`.
    """

    instructions: str | None = None
    """Specify the instructions for the agent (often also referred to as a `system_prompt`)."""

    tools: Sequence[Tool] = Field(default_factory=list)
    """List of tools to be used by the agent.

    See more info at [Tools](../tools.md).
    """

    agent_type: Callable[..., Any] | None = None
    """Control the type of agent class that is used by the framework, and is unique to the framework used.

    Check the individual `Frameworks` pages for more info on the defaults.
    """

    agent_args: MutableMapping[str, Any] | None = None
    """Pass arguments to the instance used by the underlying framework.

    For example, you can pass `output_type` when using the OpenAI Agents SDK:

    ```py
    from pydantic import BaseModel

    class CalendarEvent(BaseModel):
        name: str
        date: str
        participants: list[str]

    agent = AnyAgent.create(
        AgentConfig(
            model_id="gpt-4.1-mini",
            instructions="Extract calendar events from text",
            agent_args={
                "output_type": CalendarEvent
            }
        )
    )
    ```
    """

    model_type: Callable[..., Any] | None = None
    """Control the type of model class that is used by the agent framework, and is unique to the agent framework being used.

    For each framework, we leverage their support for LiteLLM and use it as default model_type, allowing you to use the same model_id syntax across these frameworks.
    """

    model_args: MutableMapping[str, Any] | None = None
    """Pass arguments to the model instance like `temperature`, `top_k`, as well as any other provider-specific parameters.

    Refer to LiteLLM Completion API Docs for more info.
    """

agent_args = None class-attribute instance-attribute

Pass arguments to the instance used by the underlying framework.

For example, you can pass output_type when using the OpenAI Agents SDK:

from pydantic import BaseModel

class CalendarEvent(BaseModel):
    name: str
    date: str
    participants: list[str]

agent = AnyAgent.create(
    AgentConfig(
        model_id="gpt-4.1-mini",
        instructions="Extract calendar events from text",
        agent_args={
            "output_type": CalendarEvent
        }
    )
)

agent_type = None class-attribute instance-attribute

Control the type of agent class that is used by the framework, and is unique to the framework used.

Check the individual Frameworks pages for more info on the defaults.

description = None class-attribute instance-attribute

Description of the agent.

instructions = None class-attribute instance-attribute

Specify the instructions for the agent (often also referred to as a system_prompt).

model_args = None class-attribute instance-attribute

Pass arguments to the model instance like temperature, top_k, as well as any other provider-specific parameters.

Refer to LiteLLM Completion API Docs for more info.

model_id instance-attribute

Select the underlying model used by the agent.

If you are using the default model_type (LiteLLM), you can refer to LiteLLM Provider Docs for the list of providers and how to access them.

model_type = None class-attribute instance-attribute

Control the type of model class that is used by the agent framework, and is unique to the agent framework being used.

For each framework, we leverage their support for LiteLLM and use it as default model_type, allowing you to use the same model_id syntax across these frameworks.

name = 'any_agent' class-attribute instance-attribute

The name of the agent.

Defaults to any_agent.

tools = Field(default_factory=list) class-attribute instance-attribute

List of tools to be used by the agent.

See more info at Tools.

any_agent.config.TracingConfig

Bases: BaseModel

Source code in src/any_agent/config.py

class TracingConfig(BaseModel):
    model_config = ConfigDict(extra="forbid")

    console: bool = True
    """Whether to show traces in the console."""

    llm: str | None = "yellow"
    """LLM color in console logs"""

    tool: str | None = "blue"
    """Tool color in console logs"""

    agent: str | None = None
    """Agent color in console logs"""

    chain: str | None = None
    """Chain color in console logs"""

    cost_info: bool = True
    """Whether traces should include cost information"""

    @model_validator(mode="after")
    def validate_console_flags(self) -> Self:
        if self.console and not any([self.llm, self.tool, self.agent, self.chain]):
            msg = "At least one of `[self.llm, self.tool, self.agent, self.chain]` must be set"
            raise ValueError(msg)
        return self

agent = None class-attribute instance-attribute

Agent color in console logs

chain = None class-attribute instance-attribute

Chain color in console logs

console = True class-attribute instance-attribute

Whether to show traces in the console.

cost_info = True class-attribute instance-attribute

Whether traces should include cost information

llm = 'yellow' class-attribute instance-attribute

LLM color in console logs

tool = 'blue' class-attribute instance-attribute

Tool color in console logs

any_agent.config.MCPStdio

Bases: BaseModel

Source code in src/any_agent/config.py

class MCPStdio(BaseModel):
    command: str
    """The executable to run to start the server.

    For example, `docker`, `uvx`, `npx`.
    """

    args: Sequence[str]
    """Command line args to pass to the command executable.

    For example, `["run", "-i", "--rm", "mcp/fetch"]`.
    """

    tools: Sequence[str] | None = None
    """List of tool names to use from the MCP Server.

    Use it to limit the tools accessible by the agent.
    For example, if you use [`mcp/filesystem`](https://hub.docker.com/r/mcp/filesystem),
    you can pass `tools=["read_file", "list_directory"]` to limit the agent to read-only operations.
    """

    client_session_timeout_seconds: float | None = 5
    """the read timeout passed to the MCP ClientSession."""

    model_config = ConfigDict(frozen=True, extra="forbid")

args instance-attribute

Command line args to pass to the command executable.

For example, ["run", "-i", "--rm", "mcp/fetch"].

client_session_timeout_seconds = 5 class-attribute instance-attribute

the read timeout passed to the MCP ClientSession.

command instance-attribute

The executable to run to start the server.

For example, docker, uvx, npx.

tools = None class-attribute instance-attribute

List of tool names to use from the MCP Server.

Use it to limit the tools accessible by the agent. For example, if you use mcp/filesystem, you can pass tools=["read_file", "list_directory"] to limit the agent to read-only operations.

any_agent.config.MCPSse

Bases: BaseModel

Source code in src/any_agent/config.py

class MCPSse(BaseModel):
    url: str
    """The URL of the server."""

    headers: Mapping[str, str] | None = None
    """The headers to send to the server."""

    tools: Sequence[str] | None = None
    """List of tool names to use from the MCP Server.

    Use it to limit the tools accessible by the agent.
    For example, if you use [`mcp/filesystem`](https://hub.docker.com/r/mcp/filesystem),
    you can pass `tools=["read_file", "list_directory"]` to limit the agent to read-only operations.
    """

    client_session_timeout_seconds: float | None = 5
    """the read timeout passed to the MCP ClientSession."""

    model_config = ConfigDict(frozen=True)

client_session_timeout_seconds = 5 class-attribute instance-attribute

the read timeout passed to the MCP ClientSession.

headers = None class-attribute instance-attribute

The headers to send to the server.

tools = None class-attribute instance-attribute

List of tool names to use from the MCP Server.

Use it to limit the tools accessible by the agent. For example, if you use mcp/filesystem, you can pass tools=["read_file", "list_directory"] to limit the agent to read-only operations.

url instance-attribute

The URL of the server.

any_agent.config.ServingConfig

Bases: BaseModel

Configuration for serving an agent using the Agent2Agent Protocol (A2A).

We use the example A2ASever from https://github.com/google/A2A/tree/main/samples/python.

Source code in src/any_agent/config.py

class ServingConfig(BaseModel):
    """Configuration for serving an agent using the Agent2Agent Protocol (A2A).

    We use the example `A2ASever` from https://github.com/google/A2A/tree/main/samples/python.
    """

    model_config = ConfigDict(extra="forbid")

    host: str = "localhost"
    """Will be passed as argument to `uvicorn.run`."""

    port: int = 5000
    """Will be passed as argument to `uvicorn.run`."""

    endpoint: str = "/"
    """Will be pass as argument to `Starlette().add_route`"""

    version: str = "0.1.0"

endpoint = '/' class-attribute instance-attribute

Will be pass as argument to Starlette().add_route

host = 'localhost' class-attribute instance-attribute

Will be passed as argument to uvicorn.run.

port = 5000 class-attribute instance-attribute

Will be passed as argument to uvicorn.run.

any_agent.config.AgentFramework

Bases: str, Enum

Source code in src/any_agent/config.py

class AgentFramework(str, Enum):
    GOOGLE = auto()
    LANGCHAIN = auto()
    LLAMA_INDEX = auto()
    OPENAI = auto()
    AGNO = auto()
    SMOLAGENTS = auto()
    TINYAGENT = auto()

    @classmethod
    def from_string(cls, value: str | Self) -> Self:
        if isinstance(value, cls):
            return value

        formatted_value = value.strip().upper()
        if formatted_value not in cls.__members__:
            error_message = (
                f"Unsupported agent framework: '{value}'. "
                f"Valid frameworks are: {list(cls.__members__.keys())}"
            )
            raise ValueError(error_message)

        return cls[formatted_value]