context

Class info

Classes

Name	Children	Inherits
AgentContext llmling_agent.agent.context Runtime context for agent execution.		NodeContext Generic
ConversionManager llmling_agent.prompts.conversion_manager Manages document conversion using configured providers.		object
NodeContext llmling_agent.messaging.context NodeContext(*, node_name: 'str', pool: 'AgentPool | None' \= None, config: 'NodeConfig', definition: 'AgentsManifest', current_prompt: 'str | None' \= None, in_async_context: 'bool' \= False, input_provider: 'InputProvider | None' \= None)	AgentContext TeamContext	Generic

🛈 DocStrings

Runtime context models for Agents.

AgentContext dataclass

Bases: NodeContext[TDeps]

Runtime context for agent execution.

Generically typed with AgentContext[Type of Dependencies]

Source code in src/llmling_agent/agent/context.py

@dataclass(kw_only=True)
class AgentContext[TDeps](NodeContext[TDeps]):
    """Runtime context for agent execution.

    Generically typed with AgentContext[Type of Dependencies]
    """

    capabilities: Capabilities
    """Current agent's capabilities."""

    config: AgentConfig
    """Current agent's specific configuration."""

    model_settings: dict[str, Any] = field(default_factory=dict)
    """Model-specific settings."""

    data: TDeps | None = None
    """Custom context data."""

    runtime: RuntimeConfig | None = None
    """Reference to the runtime configuration."""

    @classmethod
    def create_default(
        cls,
        name: str,
        capabilities: Capabilities | None = None,
        deps: TDeps | None = None,
        pool: AgentPool | None = None,
        input_provider: InputProvider | None = None,
    ) -> AgentContext[TDeps]:
        """Create a default agent context with minimal privileges.

        Args:
            name: Name of the agent
            capabilities: Optional custom capabilities (defaults to minimal access)
            deps: Optional dependencies for the agent
            pool: Optional pool the agent is part of
            input_provider: Optional input provider for the agent
        """
        from llmling_agent.config.capabilities import Capabilities
        from llmling_agent.models import AgentConfig, AgentsManifest

        caps = capabilities or Capabilities()
        defn = AgentsManifest()
        cfg = AgentConfig(name=name)
        return cls(
            input_provider=input_provider,
            node_name=name,
            capabilities=caps,
            definition=defn,
            config=cfg,
            data=deps,
            pool=pool,
        )

    @cached_property
    def converter(self) -> ConversionManager:
        """Get conversion manager from global config."""
        return ConversionManager(self.definition.conversion)

    # TODO: perhaps add agent directly to context?
    @property
    def agent(self) -> AnyAgent[TDeps, Any]:
        """Get the agent instance from the pool."""
        assert self.pool, "No agent pool available"
        assert self.node_name, "No agent name available"
        return self.pool.agents[self.node_name]

    async def handle_confirmation(
        self,
        tool: ToolInfo,
        args: dict[str, Any],
    ) -> ConfirmationResult:
        """Handle tool execution confirmation.

        Returns True if:
        - No confirmation handler is set
        - Handler confirms the execution
        """
        provider = self.get_input_provider()
        mode = self.config.requires_tool_confirmation
        if (mode == "per_tool" and not tool.requires_confirmation) or mode == "never":
            return "allow"
        history = self.agent.conversation.get_history() if self.pool else []
        return await provider.get_tool_confirmation(self, tool, args, history)

agent property

agent: AnyAgent[TDeps, Any]

Get the agent instance from the pool.

capabilities instance-attribute

capabilities: Capabilities

Current agent's capabilities.

config instance-attribute

config: AgentConfig

Current agent's specific configuration.

converter cached property

converter: ConversionManager

Get conversion manager from global config.

data class-attribute instance-attribute

data: TDeps | None = None

Custom context data.

model_settings class-attribute instance-attribute

model_settings: dict[str, Any] = field(default_factory=dict)

Model-specific settings.

runtime class-attribute instance-attribute

runtime: RuntimeConfig | None = None

Reference to the runtime configuration.

create_default classmethod

create_default(
    name: str,
    capabilities: Capabilities | None = None,
    deps: TDeps | None = None,
    pool: AgentPool | None = None,
    input_provider: InputProvider | None = None,
) -> AgentContext[TDeps]

Create a default agent context with minimal privileges.

Parameters:

Name	Type	Description	Default
name	str	Name of the agent	required
capabilities	Capabilities | None	Optional custom capabilities (defaults to minimal access)	None
deps	TDeps | None	Optional dependencies for the agent	None
pool	AgentPool | None	Optional pool the agent is part of	None
input_provider	InputProvider | None	Optional input provider for the agent	None

Source code in src/llmling_agent/agent/context.py

@classmethod
def create_default(
    cls,
    name: str,
    capabilities: Capabilities | None = None,
    deps: TDeps | None = None,
    pool: AgentPool | None = None,
    input_provider: InputProvider | None = None,
) -> AgentContext[TDeps]:
    """Create a default agent context with minimal privileges.

    Args:
        name: Name of the agent
        capabilities: Optional custom capabilities (defaults to minimal access)
        deps: Optional dependencies for the agent
        pool: Optional pool the agent is part of
        input_provider: Optional input provider for the agent
    """
    from llmling_agent.config.capabilities import Capabilities
    from llmling_agent.models import AgentConfig, AgentsManifest

    caps = capabilities or Capabilities()
    defn = AgentsManifest()
    cfg = AgentConfig(name=name)
    return cls(
        input_provider=input_provider,
        node_name=name,
        capabilities=caps,
        definition=defn,
        config=cfg,
        data=deps,
        pool=pool,
    )

handle_confirmation async

handle_confirmation(tool: ToolInfo, args: dict[str, Any]) -> ConfirmationResult

Handle tool execution confirmation.

Returns True if: - No confirmation handler is set - Handler confirms the execution

Source code in src/llmling_agent/agent/context.py

async def handle_confirmation(
    self,
    tool: ToolInfo,
    args: dict[str, Any],
) -> ConfirmationResult:
    """Handle tool execution confirmation.

    Returns True if:
    - No confirmation handler is set
    - Handler confirms the execution
    """
    provider = self.get_input_provider()
    mode = self.config.requires_tool_confirmation
    if (mode == "per_tool" and not tool.requires_confirmation) or mode == "never":
        return "allow"
    history = self.agent.conversation.get_history() if self.pool else []
    return await provider.get_tool_confirmation(self, tool, args, history)