AgentConfig

Base classes

Name	Children	Inherits
NodeConfig llmling_agent.models.nodes Configuration for a Node of the messaging system.	EventNodeConfig AgentConfig TeamConfig	BaseModel

⋔ Inheritance diagram

graph TD
  94004555546032["agents.AgentConfig"]
  94004552660176["nodes.NodeConfig"]
  94004513496160["main.BaseModel"]
  140104485245120["builtins.object"]
  94004552660176 --> 94004555546032
  94004513496160 --> 94004552660176
  140104485245120 --> 94004513496160

🛈 DocStrings

Bases: NodeConfig

Configuration for a single agent in the system.

Defines an agent's complete configuration including its model, environment, capabilities, and behavior settings. Each agent can have its own: - Language model configuration - Environment setup (tools and resources) - Response type definitions - System prompts and default user prompts - Role-based capabilities

The configuration can be loaded from YAML or created programmatically.

Source code in src/llmling_agent/models/agents.py

class AgentConfig(NodeConfig):
    """Configuration for a single agent in the system.

    Defines an agent's complete configuration including its model, environment,
    capabilities, and behavior settings. Each agent can have its own:
    - Language model configuration
    - Environment setup (tools and resources)
    - Response type definitions
    - System prompts and default user prompts
    - Role-based capabilities

    The configuration can be loaded from YAML or created programmatically.
    """

    provider: ProviderConfig | Literal["pydantic_ai", "human", "litellm"] = "pydantic_ai"
    """Provider configuration or shorthand type"""

    inherits: str | None = None
    """Name of agent config to inherit from"""

    model: str | AnyModelConfig | None = None
    """The model to use for this agent. Can be either a simple model name
    string (e.g. 'openai:gpt-4') or a structured model definition."""

    tools: list[ToolConfig | str] = Field(default_factory=list)
    """A list of tools to register with this agent."""

    toolsets: list[ToolsetConfig] = Field(default_factory=list)
    """Toolset configurations for extensible tool collections."""

    environment: str | AgentEnvironment | None = None
    """Environments configuration (path or object)"""

    capabilities: Capabilities = Field(default_factory=Capabilities)
    """Current agent's capabilities."""

    session: str | SessionQuery | MemoryConfig | None = None
    """Session configuration for conversation recovery."""

    result_type: str | ResponseDefinition | None = None
    """Name of the response definition to use"""

    retries: int = 1
    """Number of retries for failed operations (maps to pydantic-ai's retries)"""

    result_tool_name: str = "final_result"
    """Name of the tool used for structured responses"""

    result_tool_description: str | None = None
    """Custom description for the result tool"""

    result_retries: int | None = None
    """Max retries for result validation"""

    end_strategy: EndStrategy = "early"
    """The strategy for handling multiple tool calls when a final result is found"""

    avatar: str | None = None
    """URL or path to agent's avatar image"""

    system_prompts: list[str] = Field(default_factory=list)
    """System prompts for the agent"""

    library_system_prompts: list[str] = Field(default_factory=list)
    """System prompts for the agent from the library"""

    user_prompts: list[str] = Field(default_factory=list)
    """Default user prompts for the agent"""

    # context_sources: list[ContextSource] = Field(default_factory=list)
    # """Initial context sources to load"""

    config_file_path: str | None = None
    """Config file path for resolving environment."""

    knowledge: Knowledge | None = None
    """Knowledge sources for this agent."""

    workers: list[WorkerConfig] = Field(default_factory=list)
    """Worker agents which will be available as tools."""

    requires_tool_confirmation: ToolConfirmationMode = "per_tool"
    """How to handle tool confirmation:
    - "always": Always require confirmation for all tools
    - "never": Never require confirmation (ignore tool settings)
    - "per_tool": Use individual tool settings
    """

    debug: bool = False
    """Enable debug output for this agent."""

    def is_structured(self) -> bool:
        """Check if this config defines a structured agent."""
        return self.result_type is not None

    @model_validator(mode="before")
    @classmethod
    def normalize_workers(cls, data: dict[str, Any]) -> dict[str, Any]:
        """Convert string workers to WorkerConfig."""
        if workers := data.get("workers"):
            data["workers"] = [
                WorkerConfig.from_str(w)
                if isinstance(w, str)
                else w
                if isinstance(w, WorkerConfig)  # Keep existing WorkerConfig
                else WorkerConfig(**w)  # Convert dict to WorkerConfig
                for w in workers
            ]
        return data

    @model_validator(mode="before")
    @classmethod
    def validate_result_type(cls, data: dict[str, Any]) -> dict[str, Any]:
        """Convert result type and apply its settings."""
        result_type = data.get("result_type")
        if isinstance(result_type, dict):
            # Extract response-specific settings
            tool_name = result_type.pop("result_tool_name", None)
            tool_description = result_type.pop("result_tool_description", None)
            retries = result_type.pop("result_retries", None)

            # Convert remaining dict to ResponseDefinition
            if "type" not in result_type:
                result_type["type"] = "inline"
            data["result_type"] = InlineResponseDefinition(**result_type)

            # Apply extracted settings to agent config
            if tool_name:
                data["result_tool_name"] = tool_name
            if tool_description:
                data["result_tool_description"] = tool_description
            if retries is not None:
                data["result_retries"] = retries

        return data

    @model_validator(mode="before")
    @classmethod
    def handle_model_types(cls, data: dict[str, Any]) -> dict[str, Any]:
        """Convert model inputs to appropriate format."""
        model = data.get("model")
        match model:
            case str():
                data["model"] = {"type": "string", "identifier": model}
        return data

    async def get_toolsets(self) -> list[ResourceProvider]:
        """Get all resource providers for this agent."""
        providers: list[ResourceProvider] = []

        # Add providers from toolsets
        for toolset_config in self.toolsets:
            try:
                provider = toolset_config.get_provider()
                providers.append(provider)
            except Exception as e:
                logger.exception(
                    "Failed to create provider for toolset: %r", toolset_config
                )
                msg = f"Failed to create provider for toolset: {e}"
                raise ValueError(msg) from e

        return providers

    def get_tool_provider(self) -> ResourceProvider | None:
        """Get tool provider for this agent."""
        from llmling_agent.tools.base import ToolInfo

        # Create provider for static tools
        if not self.tools:
            return None
        static_tools: list[ToolInfo] = []
        for tool_config in self.tools:
            try:
                match tool_config:
                    case str():
                        if tool_config.startswith("crewai_tools"):
                            obj = import_class(tool_config)()
                            static_tools.append(ToolInfo.from_crewai_tool(obj))
                        elif tool_config.startswith("langchain"):
                            obj = import_class(tool_config)()
                            static_tools.append(ToolInfo.from_langchain_tool(obj))
                        else:
                            tool = ToolInfo.from_callable(tool_config)
                            static_tools.append(tool)
                    case BaseToolConfig():
                        static_tools.append(tool_config.get_tool())
            except Exception:
                logger.exception("Failed to load tool %r", tool_config)
                continue

        return StaticResourceProvider(name="builtin", tools=static_tools)

    def get_session_config(self) -> MemoryConfig:
        """Get resolved memory configuration."""
        match self.session:
            case str() | UUID():
                return MemoryConfig(session=SessionQuery(name=str(self.session)))
            case SessionQuery():
                return MemoryConfig(session=self.session)
            case MemoryConfig():
                return self.session
            case None:
                return MemoryConfig()

    def get_system_prompts(self) -> list[BasePrompt]:
        """Get all system prompts as BasePrompts."""
        prompts: list[BasePrompt] = []
        for prompt in self.system_prompts:
            match prompt:
                case str():
                    # Convert string to StaticPrompt
                    static_prompt = StaticPrompt(
                        name="system",
                        description="System prompt",
                        messages=[PromptMessage(role="system", content=prompt)],
                    )
                    prompts.append(static_prompt)
                case BasePrompt():
                    prompts.append(prompt)
        return prompts

    def get_provider(self) -> AgentProvider:
        """Get resolved provider instance.

        Creates provider instance based on configuration:
        - Full provider config: Use as-is
        - Shorthand type: Create default provider config
        """
        # If string shorthand is used, convert to default provider config
        from llmling_agent.models.providers import (
            CallbackProviderConfig,
            HumanProviderConfig,
            LiteLLMProviderConfig,
            PydanticAIProviderConfig,
        )

        provider_config = self.provider
        if isinstance(provider_config, str):
            match provider_config:
                case "pydantic_ai":
                    provider_config = PydanticAIProviderConfig(model=self.model)
                case "human":
                    provider_config = HumanProviderConfig()
                case "litellm":
                    provider_config = LiteLLMProviderConfig(
                        model=self.model if isinstance(self.model, str) else None
                    )
                case _:
                    try:
                        fn = import_callable(provider_config)
                        provider_config = CallbackProviderConfig(fn=fn)
                    except Exception:  # noqa: BLE001
                        msg = f"Invalid provider type: {provider_config}"
                        raise ValueError(msg)  # noqa: B904

        # Create provider instance from config
        return provider_config.get_provider()

    def render_system_prompts(self, context: dict[str, Any] | None = None) -> list[str]:
        """Render system prompts with context."""
        if not context:
            # Default context
            context = {"name": self.name, "id": 1, "model": self.model}
        return [render_prompt(p, {"agent": context}) for p in self.system_prompts]

    def get_config(self) -> Config:
        """Get configuration for this agent."""
        match self.environment:
            case None:
                # Create minimal config
                caps = LLMCapabilitiesConfig()
                global_settings = GlobalSettings(llm_capabilities=caps)
                return Config(global_settings=global_settings)
            case str() as path:
                # Backward compatibility: treat as file path
                resolved = self._resolve_environment_path(path, self.config_file_path)
                return Config.from_file(resolved)
            case FileEnvironment(uri=uri) as env:
                # Handle FileEnvironment instance
                resolved = env.get_file_path()
                return Config.from_file(resolved)
            case {"type": "file", "uri": uri}:
                # Handle raw dict matching file environment structure
                return Config.from_file(uri)
            case {"type": "inline", "config": config}:
                return config
            case InlineEnvironment() as config:
                return config
            case _:
                msg = f"Invalid environment configuration: {self.environment}"
                raise ValueError(msg)

    def get_environment_path(self) -> str | None:
        """Get environment file path if available."""
        match self.environment:
            case str() as path:
                return self._resolve_environment_path(path, self.config_file_path)
            case {"type": "file", "uri": uri} | FileEnvironment(uri=uri):
                return uri
            case _:
                return None

    @staticmethod
    def _resolve_environment_path(env: str, config_file_path: str | None = None) -> str:
        """Resolve environment path from config store or relative path."""
        from upath import UPath

        try:
            config_store = ConfigStore()
            return config_store.get_config(env)
        except KeyError:
            if config_file_path:
                base_dir = UPath(config_file_path).parent
                return str(base_dir / env)
            return env

avatar class-attribute instance-attribute

avatar: str | None = None

URL or path to agent's avatar image

capabilities class-attribute instance-attribute

capabilities: Capabilities = Field(default_factory=Capabilities)

Current agent's capabilities.

config_file_path class-attribute instance-attribute

config_file_path: str | None = None

Config file path for resolving environment.

debug class-attribute instance-attribute

debug: bool = False

Enable debug output for this agent.

end_strategy class-attribute instance-attribute

end_strategy: EndStrategy = 'early'

The strategy for handling multiple tool calls when a final result is found

environment class-attribute instance-attribute

environment: str | AgentEnvironment | None = None

Environments configuration (path or object)

inherits class-attribute instance-attribute

inherits: str | None = None

Name of agent config to inherit from

knowledge class-attribute instance-attribute

knowledge: Knowledge | None = None

Knowledge sources for this agent.

library_system_prompts class-attribute instance-attribute

library_system_prompts: list[str] = Field(default_factory=list)

System prompts for the agent from the library

model class-attribute instance-attribute

model: str | AnyModelConfig | None = None

The model to use for this agent. Can be either a simple model name string (e.g. 'openai:gpt-4') or a structured model definition.

provider class-attribute instance-attribute

provider: ProviderConfig | Literal['pydantic_ai', 'human', 'litellm'] = 'pydantic_ai'

Provider configuration or shorthand type

requires_tool_confirmation class-attribute instance-attribute

requires_tool_confirmation: ToolConfirmationMode = 'per_tool'

How to handle tool confirmation: - "always": Always require confirmation for all tools - "never": Never require confirmation (ignore tool settings) - "per_tool": Use individual tool settings

result_retries class-attribute instance-attribute

result_retries: int | None = None

Max retries for result validation

result_tool_description class-attribute instance-attribute

result_tool_description: str | None = None

Custom description for the result tool

result_tool_name class-attribute instance-attribute

result_tool_name: str = 'final_result'

Name of the tool used for structured responses

result_type class-attribute instance-attribute

result_type: str | ResponseDefinition | None = None

Name of the response definition to use

retries class-attribute instance-attribute

retries: int = 1

Number of retries for failed operations (maps to pydantic-ai's retries)

session class-attribute instance-attribute

session: str | SessionQuery | MemoryConfig | None = None

Session configuration for conversation recovery.

system_prompts class-attribute instance-attribute

system_prompts: list[str] = Field(default_factory=list)

System prompts for the agent

tools class-attribute instance-attribute

tools: list[ToolConfig | str] = Field(default_factory=list)

A list of tools to register with this agent.

toolsets class-attribute instance-attribute

toolsets: list[ToolsetConfig] = Field(default_factory=list)

Toolset configurations for extensible tool collections.

user_prompts class-attribute instance-attribute

user_prompts: list[str] = Field(default_factory=list)

Default user prompts for the agent

workers class-attribute instance-attribute

workers: list[WorkerConfig] = Field(default_factory=list)

Worker agents which will be available as tools.

_resolve_environment_path staticmethod

_resolve_environment_path(env: str, config_file_path: str | None = None) -> str

Resolve environment path from config store or relative path.

Source code in src/llmling_agent/models/agents.py

@staticmethod
def _resolve_environment_path(env: str, config_file_path: str | None = None) -> str:
    """Resolve environment path from config store or relative path."""
    from upath import UPath

    try:
        config_store = ConfigStore()
        return config_store.get_config(env)
    except KeyError:
        if config_file_path:
            base_dir = UPath(config_file_path).parent
            return str(base_dir / env)
        return env

get_config

get_config() -> Config

Get configuration for this agent.

Source code in src/llmling_agent/models/agents.py

def get_config(self) -> Config:
    """Get configuration for this agent."""
    match self.environment:
        case None:
            # Create minimal config
            caps = LLMCapabilitiesConfig()
            global_settings = GlobalSettings(llm_capabilities=caps)
            return Config(global_settings=global_settings)
        case str() as path:
            # Backward compatibility: treat as file path
            resolved = self._resolve_environment_path(path, self.config_file_path)
            return Config.from_file(resolved)
        case FileEnvironment(uri=uri) as env:
            # Handle FileEnvironment instance
            resolved = env.get_file_path()
            return Config.from_file(resolved)
        case {"type": "file", "uri": uri}:
            # Handle raw dict matching file environment structure
            return Config.from_file(uri)
        case {"type": "inline", "config": config}:
            return config
        case InlineEnvironment() as config:
            return config
        case _:
            msg = f"Invalid environment configuration: {self.environment}"
            raise ValueError(msg)

get_environment_path

get_environment_path() -> str | None

Get environment file path if available.

Source code in src/llmling_agent/models/agents.py

def get_environment_path(self) -> str | None:
    """Get environment file path if available."""
    match self.environment:
        case str() as path:
            return self._resolve_environment_path(path, self.config_file_path)
        case {"type": "file", "uri": uri} | FileEnvironment(uri=uri):
            return uri
        case _:
            return None

get_provider

get_provider() -> AgentProvider

Get resolved provider instance.

Creates provider instance based on configuration: - Full provider config: Use as-is - Shorthand type: Create default provider config

Source code in src/llmling_agent/models/agents.py

def get_provider(self) -> AgentProvider:
    """Get resolved provider instance.

    Creates provider instance based on configuration:
    - Full provider config: Use as-is
    - Shorthand type: Create default provider config
    """
    # If string shorthand is used, convert to default provider config
    from llmling_agent.models.providers import (
        CallbackProviderConfig,
        HumanProviderConfig,
        LiteLLMProviderConfig,
        PydanticAIProviderConfig,
    )

    provider_config = self.provider
    if isinstance(provider_config, str):
        match provider_config:
            case "pydantic_ai":
                provider_config = PydanticAIProviderConfig(model=self.model)
            case "human":
                provider_config = HumanProviderConfig()
            case "litellm":
                provider_config = LiteLLMProviderConfig(
                    model=self.model if isinstance(self.model, str) else None
                )
            case _:
                try:
                    fn = import_callable(provider_config)
                    provider_config = CallbackProviderConfig(fn=fn)
                except Exception:  # noqa: BLE001
                    msg = f"Invalid provider type: {provider_config}"
                    raise ValueError(msg)  # noqa: B904

    # Create provider instance from config
    return provider_config.get_provider()

get_session_config

get_session_config() -> MemoryConfig

Get resolved memory configuration.

Source code in src/llmling_agent/models/agents.py

def get_session_config(self) -> MemoryConfig:
    """Get resolved memory configuration."""
    match self.session:
        case str() | UUID():
            return MemoryConfig(session=SessionQuery(name=str(self.session)))
        case SessionQuery():
            return MemoryConfig(session=self.session)
        case MemoryConfig():
            return self.session
        case None:
            return MemoryConfig()

get_system_prompts

get_system_prompts() -> list[BasePrompt]

Get all system prompts as BasePrompts.

Source code in src/llmling_agent/models/agents.py

def get_system_prompts(self) -> list[BasePrompt]:
    """Get all system prompts as BasePrompts."""
    prompts: list[BasePrompt] = []
    for prompt in self.system_prompts:
        match prompt:
            case str():
                # Convert string to StaticPrompt
                static_prompt = StaticPrompt(
                    name="system",
                    description="System prompt",
                    messages=[PromptMessage(role="system", content=prompt)],
                )
                prompts.append(static_prompt)
            case BasePrompt():
                prompts.append(prompt)
    return prompts

get_tool_provider

get_tool_provider() -> ResourceProvider | None

Get tool provider for this agent.

Source code in src/llmling_agent/models/agents.py

def get_tool_provider(self) -> ResourceProvider | None:
    """Get tool provider for this agent."""
    from llmling_agent.tools.base import ToolInfo

    # Create provider for static tools
    if not self.tools:
        return None
    static_tools: list[ToolInfo] = []
    for tool_config in self.tools:
        try:
            match tool_config:
                case str():
                    if tool_config.startswith("crewai_tools"):
                        obj = import_class(tool_config)()
                        static_tools.append(ToolInfo.from_crewai_tool(obj))
                    elif tool_config.startswith("langchain"):
                        obj = import_class(tool_config)()
                        static_tools.append(ToolInfo.from_langchain_tool(obj))
                    else:
                        tool = ToolInfo.from_callable(tool_config)
                        static_tools.append(tool)
                case BaseToolConfig():
                    static_tools.append(tool_config.get_tool())
        except Exception:
            logger.exception("Failed to load tool %r", tool_config)
            continue

    return StaticResourceProvider(name="builtin", tools=static_tools)

get_toolsets async

get_toolsets() -> list[ResourceProvider]

Get all resource providers for this agent.

Source code in src/llmling_agent/models/agents.py

async def get_toolsets(self) -> list[ResourceProvider]:
    """Get all resource providers for this agent."""
    providers: list[ResourceProvider] = []

    # Add providers from toolsets
    for toolset_config in self.toolsets:
        try:
            provider = toolset_config.get_provider()
            providers.append(provider)
        except Exception as e:
            logger.exception(
                "Failed to create provider for toolset: %r", toolset_config
            )
            msg = f"Failed to create provider for toolset: {e}"
            raise ValueError(msg) from e

    return providers

handle_model_types classmethod

handle_model_types(data: dict[str, Any]) -> dict[str, Any]

Convert model inputs to appropriate format.

Source code in src/llmling_agent/models/agents.py

@model_validator(mode="before")
@classmethod
def handle_model_types(cls, data: dict[str, Any]) -> dict[str, Any]:
    """Convert model inputs to appropriate format."""
    model = data.get("model")
    match model:
        case str():
            data["model"] = {"type": "string", "identifier": model}
    return data

is_structured

is_structured() -> bool

Check if this config defines a structured agent.

Source code in src/llmling_agent/models/agents.py

def is_structured(self) -> bool:
    """Check if this config defines a structured agent."""
    return self.result_type is not None

normalize_workers classmethod

normalize_workers(data: dict[str, Any]) -> dict[str, Any]

Convert string workers to WorkerConfig.

Source code in src/llmling_agent/models/agents.py

@model_validator(mode="before")
@classmethod
def normalize_workers(cls, data: dict[str, Any]) -> dict[str, Any]:
    """Convert string workers to WorkerConfig."""
    if workers := data.get("workers"):
        data["workers"] = [
            WorkerConfig.from_str(w)
            if isinstance(w, str)
            else w
            if isinstance(w, WorkerConfig)  # Keep existing WorkerConfig
            else WorkerConfig(**w)  # Convert dict to WorkerConfig
            for w in workers
        ]
    return data

render_system_prompts

render_system_prompts(context: dict[str, Any] | None = None) -> list[str]

Render system prompts with context.

Source code in src/llmling_agent/models/agents.py

def render_system_prompts(self, context: dict[str, Any] | None = None) -> list[str]:
    """Render system prompts with context."""
    if not context:
        # Default context
        context = {"name": self.name, "id": 1, "model": self.model}
    return [render_prompt(p, {"agent": context}) for p in self.system_prompts]

validate_result_type classmethod

validate_result_type(data: dict[str, Any]) -> dict[str, Any]

Convert result type and apply its settings.

Source code in src/llmling_agent/models/agents.py

@model_validator(mode="before")
@classmethod
def validate_result_type(cls, data: dict[str, Any]) -> dict[str, Any]:
    """Convert result type and apply its settings."""
    result_type = data.get("result_type")
    if isinstance(result_type, dict):
        # Extract response-specific settings
        tool_name = result_type.pop("result_tool_name", None)
        tool_description = result_type.pop("result_tool_description", None)
        retries = result_type.pop("result_retries", None)

        # Convert remaining dict to ResponseDefinition
        if "type" not in result_type:
            result_type["type"] = "inline"
        data["result_type"] = InlineResponseDefinition(**result_type)

        # Apply extracted settings to agent config
        if tool_name:
            data["result_tool_name"] = tool_name
        if tool_description:
            data["result_tool_description"] = tool_description
        if retries is not None:
            data["result_retries"] = retries

    return data

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