pool

Class info¶

Classes¶

Name	Children	Inherits
Agent llmling_agent.agent.agent Agent for AI-powered interaction with LLMling resources and tools.		MessageNode TaskManagerMixin Generic
AgentPool llmling_agent.delegation.pool Pool managing message processing nodes (agents and teams).		BaseRegistry Generic
CallableConnectionConfig llmling_agent.models.forward_targets Forward messages to a callable.		ConnectionConfig
ConnectionRegistry llmling_agent.talk.registry Registry for managing named connections.		BaseRegistry
FileConnectionConfig llmling_agent.models.forward_targets Write messages to a file using a template.		ConnectionConfig
MCPManager llmling_agent.mcp_server.manager Manages MCP server connections and tools.		ResourceProvider
MessageEmitter llmling_agent.messaging.messageemitter Base class for all message processing nodes.	MessageNode	TaskManagerMixin ABC Generic
MessageFlowTracker llmling_agent.delegation.message_flow_tracker		object
NodeConnectionConfig llmling_agent.models.forward_targets Forward messages to another node.		ConnectionConfig
StructuredAgent llmling_agent.agent.structured Wrapper for Agent that enforces a specific result type.		MessageNode Generic
Talk llmling_agent.talk.talk Manages message flow between agents/groups.		Generic
TaskRegistry llmling_agent.tasks.registry Registry for managing tasks.		BaseRegistry
Team llmling_agent.delegation.team Group of agents that can execute together.		BaseTeam Generic
TeamRun llmling_agent.delegation.teamrun Handles team operations with monitoring.		BaseTeam Generic
TeamTalk llmling_agent.talk.talk Group of connections with aggregate operations.	ExtendedTeamTalk	list Generic

🛈 DocStrings¶

Agent pool management for collaboration.

AgentPool ¶

Bases: BaseRegistry[NodeName, MessageEmitter[Any, Any]]

Pool managing message processing nodes (agents and teams).

Acts as a unified registry for all nodes, providing: - Centralized node management and lookup - Shared dependency injection - Connection management - Resource coordination

Nodes can be accessed through: - nodes: All registered nodes (agents and teams) - agents: Only Agent instances - teams: Only Team instances

Source code in src/llmling_agent/delegation/pool.py

class AgentPool[TPoolDeps](BaseRegistry[NodeName, MessageEmitter[Any, Any]]):
    """Pool managing message processing nodes (agents and teams).

    Acts as a unified registry for all nodes, providing:
    - Centralized node management and lookup
    - Shared dependency injection
    - Connection management
    - Resource coordination

    Nodes can be accessed through:
    - nodes: All registered nodes (agents and teams)
    - agents: Only Agent instances
    - teams: Only Team instances
    """

    def __init__(
        self,
        manifest: StrPath | AgentsManifest | None = None,
        *,
        shared_deps: TPoolDeps | None = None,
        connect_nodes: bool = True,
        input_provider: InputProvider | None = None,
        parallel_load: bool = True,
    ):
        """Initialize agent pool with immediate agent creation.

        Args:
            manifest: Agent configuration manifest
            shared_deps: Dependencies to share across all nodes
            connect_nodes: Whether to set up forwarding connections
            input_provider: Input provider for tool / step confirmations / HumanAgents
            parallel_load: Whether to load nodes in parallel (async)

        Raises:
            ValueError: If manifest contains invalid node configurations
            RuntimeError: If node initialization fails
        """
        super().__init__()
        from llmling_agent.models.manifest import AgentsManifest
        from llmling_agent.storage import StorageManager

        match manifest:
            case None:
                self.manifest = AgentsManifest()
            case str():
                self.manifest = AgentsManifest.from_file(manifest)
            case AgentsManifest():
                self.manifest = manifest
            case _:
                msg = f"Invalid config path: {manifest}"
                raise ValueError(msg)
        self.shared_deps = shared_deps
        self._input_provider = input_provider
        self.exit_stack = AsyncExitStack()
        self.parallel_load = parallel_load
        self.storage = StorageManager(self.manifest.storage)
        self.connection_registry = ConnectionRegistry()
        self.mcp = MCPManager(
            name="pool_mcp", servers=self.manifest.get_mcp_servers(), owner="pool"
        )
        self._tasks = TaskRegistry()
        # Register tasks from manifest
        for name, task in self.manifest.jobs.items():
            self._tasks.register(name, task)
        self.pool_talk = TeamTalk[Any].from_nodes(list(self.nodes.values()))
        if self.manifest.pool_server and self.manifest.pool_server.enabled:
            from llmling_agent.resource_providers.pool import PoolResourceProvider
            from llmling_agent_mcp.server import LLMLingServer

            provider = PoolResourceProvider(
                self, zed_mode=self.manifest.pool_server.zed_mode
            )
            self.server: LLMLingServer | None = LLMLingServer(
                provider=provider,
                config=self.manifest.pool_server,
            )
        else:
            self.server = None
        # Create requested agents immediately
        for name in self.manifest.agents:
            agent = self.manifest.get_agent(name, deps=shared_deps)
            self.register(name, agent)

        # Then set up worker relationships
        for agent in self.agents.values():
            self.setup_agent_workers(agent)
        self._create_teams()
        # Set up forwarding connections
        if connect_nodes:
            self._connect_nodes()

    async def __aenter__(self) -> Self:
        """Enter async context and initialize all agents."""
        try:
            # Add MCP tool provider to all agents
            agents = list(self.agents.values())
            teams = list(self.teams.values())
            for agent in agents:
                agent.tools.add_provider(self.mcp)

            # Collect all components to initialize
            components: list[AbstractAsyncContextManager[Any]] = [
                self.mcp,
                *agents,
                *teams,
            ]

            # Add MCP server if configured
            if self.server:
                components.append(self.server)

            # Initialize all components
            if self.parallel_load:
                await asyncio.gather(
                    *(self.exit_stack.enter_async_context(c) for c in components)
                )
            else:
                for component in components:
                    await self.exit_stack.enter_async_context(component)

        except Exception as e:
            await self.cleanup()
            msg = "Failed to initialize agent pool"
            logger.exception(msg, exc_info=e)
            raise RuntimeError(msg) from e
        return self

    async def __aexit__(
        self,
        exc_type: type[BaseException] | None,
        exc_val: BaseException | None,
        exc_tb: TracebackType | None,
    ):
        """Exit async context."""
        # Remove MCP tool provider from all agents
        for agent in self.agents.values():
            if self.mcp in agent.tools.providers:
                agent.tools.remove_provider(self.mcp)
        await self.cleanup()

    async def cleanup(self):
        """Clean up all agents."""
        await self.exit_stack.aclose()
        self.clear()

    @overload
    def create_team_run(
        self,
        agents: Sequence[str],
        validator: MessageNode[Any, TResult] | None = None,
        *,
        name: str | None = None,
        description: str | None = None,
        shared_prompt: str | None = None,
        picker: AnyAgent[Any, Any] | None = None,
        num_picks: int | None = None,
        pick_prompt: str | None = None,
    ) -> TeamRun[TPoolDeps, TResult]: ...

    @overload
    def create_team_run[TDeps, TResult](
        self,
        agents: Sequence[MessageNode[TDeps, Any]],
        validator: MessageNode[Any, TResult] | None = None,
        *,
        name: str | None = None,
        description: str | None = None,
        shared_prompt: str | None = None,
        picker: AnyAgent[Any, Any] | None = None,
        num_picks: int | None = None,
        pick_prompt: str | None = None,
    ) -> TeamRun[TDeps, TResult]: ...

    @overload
    def create_team_run(
        self,
        agents: Sequence[AgentName | MessageNode[Any, Any]],
        validator: MessageNode[Any, TResult] | None = None,
        *,
        name: str | None = None,
        description: str | None = None,
        shared_prompt: str | None = None,
        picker: AnyAgent[Any, Any] | None = None,
        num_picks: int | None = None,
        pick_prompt: str | None = None,
    ) -> TeamRun[Any, TResult]: ...

    def create_team_run(
        self,
        agents: Sequence[AgentName | MessageNode[Any, Any]] | None = None,
        validator: MessageNode[Any, TResult] | None = None,
        *,
        name: str | None = None,
        description: str | None = None,
        shared_prompt: str | None = None,
        picker: AnyAgent[Any, Any] | None = None,
        num_picks: int | None = None,
        pick_prompt: str | None = None,
    ) -> TeamRun[Any, TResult]:
        """Create a a sequential TeamRun from a list of Agents.

        Args:
            agents: List of agent names or team/agent instances (all if None)
            validator: Node to validate the results of the TeamRun
            name: Optional name for the team
            description: Optional description for the team
            shared_prompt: Optional prompt for all agents
            picker: Agent to use for picking agents
            num_picks: Number of agents to pick
            pick_prompt: Prompt to use for picking agents
        """
        from llmling_agent.delegation.teamrun import TeamRun

        if agents is None:
            agents = list(self.agents.keys())

        # First resolve/configure agents
        resolved_agents: list[MessageNode[Any, Any]] = []
        for agent in agents:
            if isinstance(agent, str):
                agent = self.get_agent(agent)
            resolved_agents.append(agent)
        team = TeamRun(
            resolved_agents,
            name=name,
            description=description,
            validator=validator,
            shared_prompt=shared_prompt,
            picker=picker,
            num_picks=num_picks,
            pick_prompt=pick_prompt,
        )
        if name:
            self[name] = team
        return team

    @overload
    def create_team(self, agents: Sequence[str]) -> Team[TPoolDeps]: ...

    @overload
    def create_team[TDeps](
        self,
        agents: Sequence[MessageNode[TDeps, Any]],
        *,
        name: str | None = None,
        description: str | None = None,
        shared_prompt: str | None = None,
        picker: AnyAgent[Any, Any] | None = None,
        num_picks: int | None = None,
        pick_prompt: str | None = None,
    ) -> Team[TDeps]: ...

    @overload
    def create_team(
        self,
        agents: Sequence[AgentName | MessageNode[Any, Any]],
        *,
        name: str | None = None,
        description: str | None = None,
        shared_prompt: str | None = None,
        picker: AnyAgent[Any, Any] | None = None,
        num_picks: int | None = None,
        pick_prompt: str | None = None,
    ) -> Team[Any]: ...

    def create_team(
        self,
        agents: Sequence[AgentName | MessageNode[Any, Any]] | None = None,
        *,
        name: str | None = None,
        description: str | None = None,
        shared_prompt: str | None = None,
        picker: AnyAgent[Any, Any] | None = None,
        num_picks: int | None = None,
        pick_prompt: str | None = None,
    ) -> Team[Any]:
        """Create a group from agent names or instances.

        Args:
            agents: List of agent names or instances (all if None)
            name: Optional name for the team
            description: Optional description for the team
            shared_prompt: Optional prompt for all agents
            picker: Agent to use for picking agents
            num_picks: Number of agents to pick
            pick_prompt: Prompt to use for picking agents
        """
        from llmling_agent.delegation.team import Team

        if agents is None:
            agents = list(self.agents.keys())

        # First resolve/configure agents
        resolved_agents: list[MessageNode[Any, Any]] = []
        for agent in agents:
            if isinstance(agent, str):
                agent = self.get_agent(agent)
            resolved_agents.append(agent)

        team = Team(
            name=name,
            description=description,
            agents=resolved_agents,
            shared_prompt=shared_prompt,
            picker=picker,
            num_picks=num_picks,
            pick_prompt=pick_prompt,
        )
        if name:
            self[name] = team
        return team

    @asynccontextmanager
    async def track_message_flow(self) -> AsyncIterator[MessageFlowTracker]:
        """Track message flow during a context."""
        tracker = MessageFlowTracker()
        self.connection_registry.message_flow.connect(tracker.track)
        try:
            yield tracker
        finally:
            self.connection_registry.message_flow.disconnect(tracker.track)

    async def run_event_loop(self):
        """Run pool in event-watching mode until interrupted."""
        import sys

        print("Starting event watch mode...")
        print("Active nodes: ", ", ".join(self.list_nodes()))
        print("Press Ctrl+C to stop")

        stop_event = asyncio.Event()

        if sys.platform != "win32":
            # Unix: Use signal handlers
            loop = asyncio.get_running_loop()
            for sig in (signal.SIGINT, signal.SIGTERM):
                loop.add_signal_handler(sig, stop_event.set)
            while True:
                await asyncio.sleep(1)
        else:
            # Windows: Use keyboard interrupt
            with suppress(KeyboardInterrupt):
                while True:
                    await asyncio.sleep(1)

    @property
    def agents(self) -> dict[str, AnyAgent[Any, Any]]:
        """Get agents dict (backward compatibility)."""
        return {
            i.name: i
            for i in self._items.values()
            if isinstance(i, Agent | StructuredAgent)
        }

    @property
    def teams(self) -> dict[str, BaseTeam[Any, Any]]:
        """Get agents dict (backward compatibility)."""
        from llmling_agent.delegation.base_team import BaseTeam

        return {i.name: i for i in self._items.values() if isinstance(i, BaseTeam)}

    @property
    def nodes(self) -> dict[str, MessageNode[Any, Any]]:
        """Get agents dict (backward compatibility)."""
        from llmling_agent.messaging.messagenode import MessageNode

        return {i.name: i for i in self._items.values() if isinstance(i, MessageNode)}

    @property
    def event_nodes(self) -> dict[str, EventNode[Any]]:
        """Get agents dict (backward compatibility)."""
        from llmling_agent.messaging.eventnode import EventNode

        return {i.name: i for i in self._items.values() if isinstance(i, EventNode)}

    @property
    def node_events(self) -> DictEvents:
        """Get node events."""
        return self._items.events

    @property
    def _error_class(self) -> type[LLMLingError]:
        """Error class for agent operations."""
        return LLMLingError

    def _validate_item(
        self, item: MessageEmitter[Any, Any] | Any
    ) -> MessageEmitter[Any, Any]:
        """Validate and convert items before registration.

        Args:
            item: Item to validate

        Returns:
            Validated Node

        Raises:
            LLMlingError: If item is not a valid node
        """
        if not isinstance(item, MessageEmitter):
            msg = f"Item must be Agent or Team, got {type(item)}"
            raise self._error_class(msg)
        item.context.pool = self
        return item

    def _create_teams(self):
        """Create all teams in two phases to allow nesting."""
        # Phase 1: Create empty teams

        empty_teams: dict[str, BaseTeam[Any, Any]] = {}
        for name, config in self.manifest.teams.items():
            if config.mode == "parallel":
                empty_teams[name] = Team(
                    [], name=name, shared_prompt=config.shared_prompt
                )
            else:
                empty_teams[name] = TeamRun(
                    [], name=name, shared_prompt=config.shared_prompt
                )

        # Phase 2: Resolve members
        for name, config in self.manifest.teams.items():
            team = empty_teams[name]
            members: list[MessageNode[Any, Any]] = []
            for member in config.members:
                if member in self.agents:
                    members.append(self.agents[member])
                elif member in empty_teams:
                    members.append(empty_teams[member])
                else:
                    msg = f"Unknown team member: {member}"
                    raise ValueError(msg)
            team.agents.extend(members)
            self[name] = team

    def _connect_nodes(self):
        """Set up connections defined in manifest."""
        # Merge agent and team configs into one dict of nodes with connections
        for name, config in self.manifest.nodes.items():
            source = self[name]
            for target in config.connections or []:
                match target:
                    case NodeConnectionConfig():
                        if target.name not in self:
                            msg = f"Forward target {target.name} not found for {name}"
                            raise ValueError(msg)
                        target_node = self[target.name]
                    case FileConnectionConfig() | CallableConnectionConfig():
                        target_node = Agent(provider=target.get_provider())
                    case _:
                        msg = f"Invalid connection config: {target}"
                        raise ValueError(msg)

                source.connect_to(
                    target_node,  # type: ignore  # recognized as "Any | BaseTeam[Any, Any]" by mypy?
                    connection_type=target.connection_type,
                    name=name,
                    priority=target.priority,
                    delay=target.delay,
                    queued=target.queued,
                    queue_strategy=target.queue_strategy,
                    transform=target.transform,
                    filter_condition=target.filter_condition.check
                    if target.filter_condition
                    else None,
                    stop_condition=target.stop_condition.check
                    if target.stop_condition
                    else None,
                    exit_condition=target.exit_condition.check
                    if target.exit_condition
                    else None,
                )
                source.connections.set_wait_state(
                    target_node,
                    wait=target.wait_for_completion,
                )

    @overload
    async def clone_agent[TDeps](
        self,
        agent: AgentName | Agent[TDeps],
        new_name: AgentName | None = None,
        *,
        system_prompts: list[str] | None = None,
        template_context: dict[str, Any] | None = None,
    ) -> Agent[TDeps]: ...

    @overload
    async def clone_agent[TDeps, TResult](
        self,
        agent: StructuredAgent[TDeps, TResult],
        new_name: AgentName | None = None,
        *,
        system_prompts: list[str] | None = None,
        template_context: dict[str, Any] | None = None,
    ) -> StructuredAgent[TDeps, TResult]: ...

    async def clone_agent[TDeps, TAgentResult](
        self,
        agent: AgentName | AnyAgent[TDeps, TAgentResult],
        new_name: AgentName | None = None,
        *,
        system_prompts: list[str] | None = None,
        template_context: dict[str, Any] | None = None,
    ) -> AnyAgent[TDeps, TAgentResult]:
        """Create a copy of an agent.

        Args:
            agent: Agent instance or name to clone
            new_name: Optional name for the clone
            system_prompts: Optional different prompts
            template_context: Variables for template rendering

        Returns:
            The new agent instance
        """
        from llmling_agent.agent import Agent, StructuredAgent

        # Get original config
        if isinstance(agent, str):
            if agent not in self.manifest.agents:
                msg = f"Agent {agent} not found"
                raise KeyError(msg)
            config = self.manifest.agents[agent]
            original_agent: AnyAgent[Any, Any] = self.get_agent(agent)
        else:
            config = agent.context.config  # type: ignore
            original_agent = agent

        # Create new config
        new_config = config.model_copy(deep=True)

        # Apply overrides
        if system_prompts:
            new_config.system_prompts = system_prompts

        # Handle template rendering
        if template_context:
            new_config.system_prompts = new_config.render_system_prompts(template_context)

        # Create new agent with same runtime
        new_agent = Agent[TDeps](
            runtime=original_agent.runtime,
            context=original_agent.context,
            # result_type=original_agent.actual_type,
            provider=new_config.get_provider(),
            system_prompt=new_config.system_prompts,
            name=new_name or f"{config.name}_copy_{len(self.agents)}",
        )
        if isinstance(original_agent, StructuredAgent):
            new_agent = new_agent.to_structured(original_agent.actual_type)

        # Register in pool
        agent_name = new_agent.name
        self.manifest.agents[agent_name] = new_config
        self.agents[agent_name] = new_agent
        return await self.exit_stack.enter_async_context(new_agent)

    @overload
    async def create_agent(
        self,
        name: AgentName,
        *,
        session: SessionIdType | SessionQuery = None,
        name_override: str | None = None,
    ) -> Agent[TPoolDeps]: ...

    @overload
    async def create_agent[TCustomDeps](
        self,
        name: AgentName,
        *,
        deps: TCustomDeps,
        session: SessionIdType | SessionQuery = None,
        name_override: str | None = None,
    ) -> Agent[TCustomDeps]: ...

    @overload
    async def create_agent[TResult](
        self,
        name: AgentName,
        *,
        return_type: type[TResult],
        session: SessionIdType | SessionQuery = None,
        name_override: str | None = None,
    ) -> StructuredAgent[TPoolDeps, TResult]: ...

    @overload
    async def create_agent[TCustomDeps, TResult](
        self,
        name: AgentName,
        *,
        deps: TCustomDeps,
        return_type: type[TResult],
        session: SessionIdType | SessionQuery = None,
        name_override: str | None = None,
    ) -> StructuredAgent[TCustomDeps, TResult]: ...

    async def create_agent(
        self,
        name: AgentName,
        *,
        deps: Any | None = None,
        return_type: Any | None = None,
        session: SessionIdType | SessionQuery = None,
        name_override: str | None = None,
    ) -> AnyAgent[Any, Any]:
        """Create a new agent instance from configuration.

        Args:
            name: Name of the agent configuration to use
            deps: Optional custom dependencies (overrides pool deps)
            return_type: Optional type for structured responses
            session: Optional session ID or query to recover conversation
            name_override: Optional different name for this instance

        Returns:
            New agent instance with the specified configuration

        Raises:
            KeyError: If agent configuration not found
            ValueError: If configuration is invalid
        """
        if name not in self.manifest.agents:
            msg = f"Agent configuration {name!r} not found"
            raise KeyError(msg)

        # Use Manifest.get_agent for proper initialization
        final_deps = deps if deps is not None else self.shared_deps
        agent = self.manifest.get_agent(name, deps=final_deps)
        # Override name if requested
        if name_override:
            agent.name = name_override

        # Set pool reference
        agent.context.pool = self

        # Handle session if provided
        if session:
            agent.conversation.load_history_from_database(session=session)

        # Initialize agent through exit stack
        agent = await self.exit_stack.enter_async_context(agent)

        # Override structured configuration if provided
        if return_type is not None:
            return agent.to_structured(return_type)

        return agent

    def setup_agent_workers(self, agent: AnyAgent[Any, Any]):
        """Set up workers for an agent from configuration."""
        for worker_config in agent.context.config.workers:
            try:
                worker = self.get_agent(worker_config.name)
                agent.register_worker(
                    worker,
                    name=worker_config.name,
                    reset_history_on_run=worker_config.reset_history_on_run,
                    pass_message_history=worker_config.pass_message_history,
                    share_context=worker_config.share_context,
                )
            except KeyError as e:
                msg = f"Worker agent {worker_config.name!r} not found"
                raise ValueError(msg) from e

    @overload
    def get_agent(
        self,
        agent: AgentName | Agent[Any],
        *,
        model_override: str | None = None,
        session: SessionIdType | SessionQuery = None,
    ) -> Agent[TPoolDeps]: ...

    @overload
    def get_agent[TResult](
        self,
        agent: AgentName | Agent[Any],
        *,
        return_type: type[TResult],
        model_override: str | None = None,
        session: SessionIdType | SessionQuery = None,
    ) -> StructuredAgent[TPoolDeps, TResult]: ...

    @overload
    def get_agent[TCustomDeps](
        self,
        agent: AgentName | Agent[Any],
        *,
        deps: TCustomDeps,
        model_override: str | None = None,
        session: SessionIdType | SessionQuery = None,
    ) -> Agent[TCustomDeps]: ...

    @overload
    def get_agent[TCustomDeps, TResult](
        self,
        agent: AgentName | Agent[Any],
        *,
        deps: TCustomDeps,
        return_type: type[TResult],
        model_override: str | None = None,
        session: SessionIdType | SessionQuery = None,
    ) -> StructuredAgent[TCustomDeps, TResult]: ...

    def get_agent(
        self,
        agent: AgentName | Agent[Any],
        *,
        deps: Any | None = None,
        return_type: Any | None = None,
        model_override: str | None = None,
        session: SessionIdType | SessionQuery = None,
    ) -> AnyAgent[Any, Any]:
        """Get or configure an agent from the pool.

        This method provides flexible agent configuration with dependency injection:
        - Without deps: Agent uses pool's shared dependencies
        - With deps: Agent uses provided custom dependencies
        - With return_type: Returns a StructuredAgent with type validation

        Args:
            agent: Either agent name or instance
            deps: Optional custom dependencies (overrides shared deps)
            return_type: Optional type for structured responses
            model_override: Optional model override
            session: Optional session ID or query to recover conversation

        Returns:
            Either:
            - Agent[TPoolDeps] when using pool's shared deps
            - Agent[TCustomDeps] when custom deps provided
            - StructuredAgent when return_type provided

        Raises:
            KeyError: If agent name not found
            ValueError: If configuration is invalid
        """
        from llmling_agent.agent import Agent
        from llmling_agent.agent.context import AgentContext

        # Get base agent
        base = agent if isinstance(agent, Agent) else self.agents[agent]

        # Setup context and dependencies
        if base.context is None:
            base.context = AgentContext[Any].create_default(base.name)

        # Use custom deps if provided, otherwise use shared deps
        base.context.data = deps if deps is not None else self.shared_deps
        base.context.pool = self

        # Apply overrides
        if model_override:
            base.set_model(model_override)

        if session:
            base.conversation.load_history_from_database(session=session)

        # Convert to structured if needed
        if return_type is not None:
            return base.to_structured(return_type)

        return base

    def list_nodes(self) -> list[str]:
        """List available agent names."""
        return list(self.list_items())

    def get_job(self, name: str) -> Job[Any, Any]:
        return self._tasks[name]

    def register_task(self, name: str, task: Job[Any, Any]):
        self._tasks.register(name, task)

    @overload
    async def add_agent(
        self,
        name: AgentName,
        *,
        result_type: None = None,
        **kwargs: Unpack[AgentKwargs],
    ) -> Agent[Any]: ...

    @overload
    async def add_agent[TResult](
        self,
        name: AgentName,
        *,
        result_type: type[TResult] | str | ResponseDefinition,
        **kwargs: Unpack[AgentKwargs],
    ) -> StructuredAgent[Any, TResult]: ...

    async def add_agent(
        self,
        name: AgentName,
        *,
        result_type: type[Any] | str | ResponseDefinition | None = None,
        **kwargs: Unpack[AgentKwargs],
    ) -> Agent[Any] | StructuredAgent[Any, Any]:
        """Add a new permanent agent to the pool.

        Args:
            name: Name for the new agent
            result_type: Optional type for structured responses:
                - None: Regular unstructured agent
                - type: Python type for validation
                - str: Name of response definition
                - ResponseDefinition: Complete response definition
            **kwargs: Additional agent configuration

        Returns:
            Either a regular Agent or StructuredAgent depending on result_type
        """
        from llmling_agent.agent import Agent

        agent: AnyAgent[Any, Any] = Agent(name=name, **kwargs)
        agent.tools.add_provider(self.mcp)
        agent = await self.exit_stack.enter_async_context(agent)
        # Convert to structured if needed
        if result_type is not None:
            agent = agent.to_structured(result_type)
        self.register(name, agent)
        return agent

    def get_mermaid_diagram(
        self,
        include_details: bool = True,
    ) -> str:
        """Generate mermaid flowchart of all agents and their connections.

        Args:
            include_details: Whether to show connection details (types, queues, etc)
        """
        lines = ["flowchart LR"]

        # Add all agents as nodes
        for name in self.agents:
            lines.append(f"    {name}[{name}]")  # noqa: PERF401

        # Add all connections as edges
        for agent in self.agents.values():
            connections = agent.connections.get_connections()
            for talk in connections:
                talk = cast(Talk[Any], talk)  # help mypy understand it's a Talk
                source = talk.source.name
                for target in talk.targets:
                    if include_details:
                        details: list[str] = []
                        details.append(talk.connection_type)
                        if talk.queued:
                            details.append(f"queued({talk.queue_strategy})")
                        if fn := talk.filter_condition:  # type: ignore
                            details.append(f"filter:{fn.__name__}")
                        if fn := talk.stop_condition:  # type: ignore
                            details.append(f"stop:{fn.__name__}")
                        if fn := talk.exit_condition:  # type: ignore
                            details.append(f"exit:{fn.__name__}")

                        label = f"|{' '.join(details)}|" if details else ""
                        lines.append(f"    {source}--{label}-->{target.name}")
                    else:
                        lines.append(f"    {source}-->{target.name}")

        return "\n".join(lines)

_error_class `property` ¶

_error_class: type[LLMLingError]

Error class for agent operations.

agents `property` ¶

agents: dict[str, AnyAgent[Any, Any]]

Get agents dict (backward compatibility).

event_nodes `property` ¶

event_nodes: dict[str, EventNode[Any]]

Get agents dict (backward compatibility).

node_events `property` ¶

node_events: DictEvents

Get node events.

nodes `property` ¶

nodes: dict[str, MessageNode[Any, Any]]

Get agents dict (backward compatibility).

teams `property` ¶

teams: dict[str, BaseTeam[Any, Any]]

Get agents dict (backward compatibility).

aenter `async` ¶

__aenter__() -> Self

Enter async context and initialize all agents.

Source code in src/llmling_agent/delegation/pool.py

async def __aenter__(self) -> Self:
    """Enter async context and initialize all agents."""
    try:
        # Add MCP tool provider to all agents
        agents = list(self.agents.values())
        teams = list(self.teams.values())
        for agent in agents:
            agent.tools.add_provider(self.mcp)

        # Collect all components to initialize
        components: list[AbstractAsyncContextManager[Any]] = [
            self.mcp,
            *agents,
            *teams,
        ]

        # Add MCP server if configured
        if self.server:
            components.append(self.server)

        # Initialize all components
        if self.parallel_load:
            await asyncio.gather(
                *(self.exit_stack.enter_async_context(c) for c in components)
            )
        else:
            for component in components:
                await self.exit_stack.enter_async_context(component)

    except Exception as e:
        await self.cleanup()
        msg = "Failed to initialize agent pool"
        logger.exception(msg, exc_info=e)
        raise RuntimeError(msg) from e
    return self

aexit `async` ¶

__aexit__(
    exc_type: type[BaseException] | None,
    exc_val: BaseException | None,
    exc_tb: TracebackType | None,
)

Exit async context.

Source code in src/llmling_agent/delegation/pool.py

async def __aexit__(
    self,
    exc_type: type[BaseException] | None,
    exc_val: BaseException | None,
    exc_tb: TracebackType | None,
):
    """Exit async context."""
    # Remove MCP tool provider from all agents
    for agent in self.agents.values():
        if self.mcp in agent.tools.providers:
            agent.tools.remove_provider(self.mcp)
    await self.cleanup()

init ¶

__init__(
    manifest: StrPath | AgentsManifest | None = None,
    *,
    shared_deps: TPoolDeps | None = None,
    connect_nodes: bool = True,
    input_provider: InputProvider | None = None,
    parallel_load: bool = True,
)

Initialize agent pool with immediate agent creation.

Parameters:

Name	Type	Description	Default
`manifest`	`StrPath \| AgentsManifest \| None`	Agent configuration manifest	`None`
`shared_deps`	`TPoolDeps \| None`	Dependencies to share across all nodes	`None`
`connect_nodes`	`bool`	Whether to set up forwarding connections	`True`
`input_provider`	`InputProvider \| None`	Input provider for tool / step confirmations / HumanAgents	`None`
`parallel_load`	`bool`	Whether to load nodes in parallel (async)	`True`

Raises:

Type	Description
`ValueError`	If manifest contains invalid node configurations
`RuntimeError`	If node initialization fails

Source code in src/llmling_agent/delegation/pool.py

def __init__(
    self,
    manifest: StrPath | AgentsManifest | None = None,
    *,
    shared_deps: TPoolDeps | None = None,
    connect_nodes: bool = True,
    input_provider: InputProvider | None = None,
    parallel_load: bool = True,
):
    """Initialize agent pool with immediate agent creation.

    Args:
        manifest: Agent configuration manifest
        shared_deps: Dependencies to share across all nodes
        connect_nodes: Whether to set up forwarding connections
        input_provider: Input provider for tool / step confirmations / HumanAgents
        parallel_load: Whether to load nodes in parallel (async)

    Raises:
        ValueError: If manifest contains invalid node configurations
        RuntimeError: If node initialization fails
    """
    super().__init__()
    from llmling_agent.models.manifest import AgentsManifest
    from llmling_agent.storage import StorageManager

    match manifest:
        case None:
            self.manifest = AgentsManifest()
        case str():
            self.manifest = AgentsManifest.from_file(manifest)
        case AgentsManifest():
            self.manifest = manifest
        case _:
            msg = f"Invalid config path: {manifest}"
            raise ValueError(msg)
    self.shared_deps = shared_deps
    self._input_provider = input_provider
    self.exit_stack = AsyncExitStack()
    self.parallel_load = parallel_load
    self.storage = StorageManager(self.manifest.storage)
    self.connection_registry = ConnectionRegistry()
    self.mcp = MCPManager(
        name="pool_mcp", servers=self.manifest.get_mcp_servers(), owner="pool"
    )
    self._tasks = TaskRegistry()
    # Register tasks from manifest
    for name, task in self.manifest.jobs.items():
        self._tasks.register(name, task)
    self.pool_talk = TeamTalk[Any].from_nodes(list(self.nodes.values()))
    if self.manifest.pool_server and self.manifest.pool_server.enabled:
        from llmling_agent.resource_providers.pool import PoolResourceProvider
        from llmling_agent_mcp.server import LLMLingServer

        provider = PoolResourceProvider(
            self, zed_mode=self.manifest.pool_server.zed_mode
        )
        self.server: LLMLingServer | None = LLMLingServer(
            provider=provider,
            config=self.manifest.pool_server,
        )
    else:
        self.server = None
    # Create requested agents immediately
    for name in self.manifest.agents:
        agent = self.manifest.get_agent(name, deps=shared_deps)
        self.register(name, agent)

    # Then set up worker relationships
    for agent in self.agents.values():
        self.setup_agent_workers(agent)
    self._create_teams()
    # Set up forwarding connections
    if connect_nodes:
        self._connect_nodes()

_connect_nodes ¶

_connect_nodes()

Set up connections defined in manifest.

Source code in src/llmling_agent/delegation/pool.py

def _connect_nodes(self):
    """Set up connections defined in manifest."""
    # Merge agent and team configs into one dict of nodes with connections
    for name, config in self.manifest.nodes.items():
        source = self[name]
        for target in config.connections or []:
            match target:
                case NodeConnectionConfig():
                    if target.name not in self:
                        msg = f"Forward target {target.name} not found for {name}"
                        raise ValueError(msg)
                    target_node = self[target.name]
                case FileConnectionConfig() | CallableConnectionConfig():
                    target_node = Agent(provider=target.get_provider())
                case _:
                    msg = f"Invalid connection config: {target}"
                    raise ValueError(msg)

            source.connect_to(
                target_node,  # type: ignore  # recognized as "Any | BaseTeam[Any, Any]" by mypy?
                connection_type=target.connection_type,
                name=name,
                priority=target.priority,
                delay=target.delay,
                queued=target.queued,
                queue_strategy=target.queue_strategy,
                transform=target.transform,
                filter_condition=target.filter_condition.check
                if target.filter_condition
                else None,
                stop_condition=target.stop_condition.check
                if target.stop_condition
                else None,
                exit_condition=target.exit_condition.check
                if target.exit_condition
                else None,
            )
            source.connections.set_wait_state(
                target_node,
                wait=target.wait_for_completion,
            )

_create_teams ¶

_create_teams()

Create all teams in two phases to allow nesting.

Source code in src/llmling_agent/delegation/pool.py

def _create_teams(self):
    """Create all teams in two phases to allow nesting."""
    # Phase 1: Create empty teams

    empty_teams: dict[str, BaseTeam[Any, Any]] = {}
    for name, config in self.manifest.teams.items():
        if config.mode == "parallel":
            empty_teams[name] = Team(
                [], name=name, shared_prompt=config.shared_prompt
            )
        else:
            empty_teams[name] = TeamRun(
                [], name=name, shared_prompt=config.shared_prompt
            )

    # Phase 2: Resolve members
    for name, config in self.manifest.teams.items():
        team = empty_teams[name]
        members: list[MessageNode[Any, Any]] = []
        for member in config.members:
            if member in self.agents:
                members.append(self.agents[member])
            elif member in empty_teams:
                members.append(empty_teams[member])
            else:
                msg = f"Unknown team member: {member}"
                raise ValueError(msg)
        team.agents.extend(members)
        self[name] = team

_validate_item ¶

_validate_item(item: MessageEmitter[Any, Any] | Any) -> MessageEmitter[Any, Any]

Validate and convert items before registration.

Parameters:

Name	Type	Description	Default
`item`	`MessageEmitter[Any, Any] \| Any`	Item to validate	required

Returns:

Type	Description
`MessageEmitter[Any, Any]`	Validated Node

Raises:

Type	Description
`LLMlingError`	If item is not a valid node

Source code in src/llmling_agent/delegation/pool.py

def _validate_item(
    self, item: MessageEmitter[Any, Any] | Any
) -> MessageEmitter[Any, Any]:
    """Validate and convert items before registration.

    Args:
        item: Item to validate

    Returns:
        Validated Node

    Raises:
        LLMlingError: If item is not a valid node
    """
    if not isinstance(item, MessageEmitter):
        msg = f"Item must be Agent or Team, got {type(item)}"
        raise self._error_class(msg)
    item.context.pool = self
    return item

add_agent `async` ¶

add_agent(
    name: AgentName, *, result_type: None = None, **kwargs: Unpack[AgentKwargs]
) -> Agent[Any]

add_agent(
    name: AgentName,
    *,
    result_type: type[TResult] | str | ResponseDefinition,
    **kwargs: Unpack[AgentKwargs],
) -> StructuredAgent[Any, TResult]

add_agent(
    name: AgentName,
    *,
    result_type: type[Any] | str | ResponseDefinition | None = None,
    **kwargs: Unpack[AgentKwargs],
) -> Agent[Any] | StructuredAgent[Any, Any]

Add a new permanent agent to the pool.

Parameters:

Name	Type	Description	Default
`name`	`AgentName`	Name for the new agent	required
`result_type`	`type[Any] \| str \| ResponseDefinition \| None`	Optional type for structured responses: - None: Regular unstructured agent - type: Python type for validation - str: Name of response definition - ResponseDefinition: Complete response definition	`None`
`**kwargs`	`Unpack[AgentKwargs]`	Additional agent configuration	`{}`

Returns:

Type	Description
`Agent[Any] \| StructuredAgent[Any, Any]`	Either a regular Agent or StructuredAgent depending on result_type

Source code in src/llmling_agent/delegation/pool.py

async def add_agent(
    self,
    name: AgentName,
    *,
    result_type: type[Any] | str | ResponseDefinition | None = None,
    **kwargs: Unpack[AgentKwargs],
) -> Agent[Any] | StructuredAgent[Any, Any]:
    """Add a new permanent agent to the pool.

    Args:
        name: Name for the new agent
        result_type: Optional type for structured responses:
            - None: Regular unstructured agent
            - type: Python type for validation
            - str: Name of response definition
            - ResponseDefinition: Complete response definition
        **kwargs: Additional agent configuration

    Returns:
        Either a regular Agent or StructuredAgent depending on result_type
    """
    from llmling_agent.agent import Agent

    agent: AnyAgent[Any, Any] = Agent(name=name, **kwargs)
    agent.tools.add_provider(self.mcp)
    agent = await self.exit_stack.enter_async_context(agent)
    # Convert to structured if needed
    if result_type is not None:
        agent = agent.to_structured(result_type)
    self.register(name, agent)
    return agent

cleanup `async` ¶

cleanup()

Clean up all agents.

Source code in src/llmling_agent/delegation/pool.py

async def cleanup(self):
    """Clean up all agents."""
    await self.exit_stack.aclose()
    self.clear()

clone_agent `async` ¶

clone_agent(
    agent: AgentName | Agent[TDeps],
    new_name: AgentName | None = None,
    *,
    system_prompts: list[str] | None = None,
    template_context: dict[str, Any] | None = None,
) -> Agent[TDeps]

clone_agent(
    agent: StructuredAgent[TDeps, TResult],
    new_name: AgentName | None = None,
    *,
    system_prompts: list[str] | None = None,
    template_context: dict[str, Any] | None = None,
) -> StructuredAgent[TDeps, TResult]

clone_agent(
    agent: AgentName | AnyAgent[TDeps, TAgentResult],
    new_name: AgentName | None = None,
    *,
    system_prompts: list[str] | None = None,
    template_context: dict[str, Any] | None = None,
) -> AnyAgent[TDeps, TAgentResult]

Create a copy of an agent.

Parameters:

Name	Type	Description	Default
`agent`	`AgentName \| AnyAgent[TDeps, TAgentResult]`	Agent instance or name to clone	required
`new_name`	`AgentName \| None`	Optional name for the clone	`None`
`system_prompts`	`list[str] \| None`	Optional different prompts	`None`
`template_context`	`dict[str, Any] \| None`	Variables for template rendering	`None`

Returns:

Type	Description
`AnyAgent[TDeps, TAgentResult]`	The new agent instance

Source code in src/llmling_agent/delegation/pool.py

async def clone_agent[TDeps, TAgentResult](
    self,
    agent: AgentName | AnyAgent[TDeps, TAgentResult],
    new_name: AgentName | None = None,
    *,
    system_prompts: list[str] | None = None,
    template_context: dict[str, Any] | None = None,
) -> AnyAgent[TDeps, TAgentResult]:
    """Create a copy of an agent.

    Args:
        agent: Agent instance or name to clone
        new_name: Optional name for the clone
        system_prompts: Optional different prompts
        template_context: Variables for template rendering

    Returns:
        The new agent instance
    """
    from llmling_agent.agent import Agent, StructuredAgent

    # Get original config
    if isinstance(agent, str):
        if agent not in self.manifest.agents:
            msg = f"Agent {agent} not found"
            raise KeyError(msg)
        config = self.manifest.agents[agent]
        original_agent: AnyAgent[Any, Any] = self.get_agent(agent)
    else:
        config = agent.context.config  # type: ignore
        original_agent = agent

    # Create new config
    new_config = config.model_copy(deep=True)

    # Apply overrides
    if system_prompts:
        new_config.system_prompts = system_prompts

    # Handle template rendering
    if template_context:
        new_config.system_prompts = new_config.render_system_prompts(template_context)

    # Create new agent with same runtime
    new_agent = Agent[TDeps](
        runtime=original_agent.runtime,
        context=original_agent.context,
        # result_type=original_agent.actual_type,
        provider=new_config.get_provider(),
        system_prompt=new_config.system_prompts,
        name=new_name or f"{config.name}_copy_{len(self.agents)}",
    )
    if isinstance(original_agent, StructuredAgent):
        new_agent = new_agent.to_structured(original_agent.actual_type)

    # Register in pool
    agent_name = new_agent.name
    self.manifest.agents[agent_name] = new_config
    self.agents[agent_name] = new_agent
    return await self.exit_stack.enter_async_context(new_agent)

create_agent `async` ¶

create_agent(
    name: AgentName,
    *,
    session: SessionIdType | SessionQuery = None,
    name_override: str | None = None,
) -> Agent[TPoolDeps]

create_agent(
    name: AgentName,
    *,
    deps: TCustomDeps,
    session: SessionIdType | SessionQuery = None,
    name_override: str | None = None,
) -> Agent[TCustomDeps]

create_agent(
    name: AgentName,
    *,
    return_type: type[TResult],
    session: SessionIdType | SessionQuery = None,
    name_override: str | None = None,
) -> StructuredAgent[TPoolDeps, TResult]

create_agent(
    name: AgentName,
    *,
    deps: TCustomDeps,
    return_type: type[TResult],
    session: SessionIdType | SessionQuery = None,
    name_override: str | None = None,
) -> StructuredAgent[TCustomDeps, TResult]

create_agent(
    name: AgentName,
    *,
    deps: Any | None = None,
    return_type: Any | None = None,
    session: SessionIdType | SessionQuery = None,
    name_override: str | None = None,
) -> AnyAgent[Any, Any]

Create a new agent instance from configuration.

Parameters:

Name	Type	Description	Default
`name`	`AgentName`	Name of the agent configuration to use	required
`deps`	`Any \| None`	Optional custom dependencies (overrides pool deps)	`None`
`return_type`	`Any \| None`	Optional type for structured responses	`None`
`session`	`SessionIdType \| SessionQuery`	Optional session ID or query to recover conversation	`None`
`name_override`	`str \| None`	Optional different name for this instance	`None`

Returns:

Type	Description
`AnyAgent[Any, Any]`	New agent instance with the specified configuration

Raises:

Type	Description
`KeyError`	If agent configuration not found
`ValueError`	If configuration is invalid

Source code in src/llmling_agent/delegation/pool.py

async def create_agent(
    self,
    name: AgentName,
    *,
    deps: Any | None = None,
    return_type: Any | None = None,
    session: SessionIdType | SessionQuery = None,
    name_override: str | None = None,
) -> AnyAgent[Any, Any]:
    """Create a new agent instance from configuration.

    Args:
        name: Name of the agent configuration to use
        deps: Optional custom dependencies (overrides pool deps)
        return_type: Optional type for structured responses
        session: Optional session ID or query to recover conversation
        name_override: Optional different name for this instance

    Returns:
        New agent instance with the specified configuration

    Raises:
        KeyError: If agent configuration not found
        ValueError: If configuration is invalid
    """
    if name not in self.manifest.agents:
        msg = f"Agent configuration {name!r} not found"
        raise KeyError(msg)

    # Use Manifest.get_agent for proper initialization
    final_deps = deps if deps is not None else self.shared_deps
    agent = self.manifest.get_agent(name, deps=final_deps)
    # Override name if requested
    if name_override:
        agent.name = name_override

    # Set pool reference
    agent.context.pool = self

    # Handle session if provided
    if session:
        agent.conversation.load_history_from_database(session=session)

    # Initialize agent through exit stack
    agent = await self.exit_stack.enter_async_context(agent)

    # Override structured configuration if provided
    if return_type is not None:
        return agent.to_structured(return_type)

    return agent

create_team ¶

create_team(agents: Sequence[str]) -> Team[TPoolDeps]

create_team(
    agents: Sequence[MessageNode[TDeps, Any]],
    *,
    name: str | None = None,
    description: str | None = None,
    shared_prompt: str | None = None,
    picker: AnyAgent[Any, Any] | None = None,
    num_picks: int | None = None,
    pick_prompt: str | None = None,
) -> Team[TDeps]

create_team(
    agents: Sequence[AgentName | MessageNode[Any, Any]],
    *,
    name: str | None = None,
    description: str | None = None,
    shared_prompt: str | None = None,
    picker: AnyAgent[Any, Any] | None = None,
    num_picks: int | None = None,
    pick_prompt: str | None = None,
) -> Team[Any]

create_team(
    agents: Sequence[AgentName | MessageNode[Any, Any]] | None = None,
    *,
    name: str | None = None,
    description: str | None = None,
    shared_prompt: str | None = None,
    picker: AnyAgent[Any, Any] | None = None,
    num_picks: int | None = None,
    pick_prompt: str | None = None,
) -> Team[Any]

Create a group from agent names or instances.

Parameters:

Name	Type	Description	Default
`agents`	`Sequence[AgentName \| MessageNode[Any, Any]] \| None`	List of agent names or instances (all if None)	`None`
`name`	`str \| None`	Optional name for the team	`None`
`description`	`str \| None`	Optional description for the team	`None`
`shared_prompt`	`str \| None`	Optional prompt for all agents	`None`
`picker`	`AnyAgent[Any, Any] \| None`	Agent to use for picking agents	`None`
`num_picks`	`int \| None`	Number of agents to pick	`None`
`pick_prompt`	`str \| None`	Prompt to use for picking agents	`None`

Source code in src/llmling_agent/delegation/pool.py

def create_team(
    self,
    agents: Sequence[AgentName | MessageNode[Any, Any]] | None = None,
    *,
    name: str | None = None,
    description: str | None = None,
    shared_prompt: str | None = None,
    picker: AnyAgent[Any, Any] | None = None,
    num_picks: int | None = None,
    pick_prompt: str | None = None,
) -> Team[Any]:
    """Create a group from agent names or instances.

    Args:
        agents: List of agent names or instances (all if None)
        name: Optional name for the team
        description: Optional description for the team
        shared_prompt: Optional prompt for all agents
        picker: Agent to use for picking agents
        num_picks: Number of agents to pick
        pick_prompt: Prompt to use for picking agents
    """
    from llmling_agent.delegation.team import Team

    if agents is None:
        agents = list(self.agents.keys())

    # First resolve/configure agents
    resolved_agents: list[MessageNode[Any, Any]] = []
    for agent in agents:
        if isinstance(agent, str):
            agent = self.get_agent(agent)
        resolved_agents.append(agent)

    team = Team(
        name=name,
        description=description,
        agents=resolved_agents,
        shared_prompt=shared_prompt,
        picker=picker,
        num_picks=num_picks,
        pick_prompt=pick_prompt,
    )
    if name:
        self[name] = team
    return team

create_team_run ¶

create_team_run(
    agents: Sequence[str],
    validator: MessageNode[Any, TResult] | None = None,
    *,
    name: str | None = None,
    description: str | None = None,
    shared_prompt: str | None = None,
    picker: AnyAgent[Any, Any] | None = None,
    num_picks: int | None = None,
    pick_prompt: str | None = None,
) -> TeamRun[TPoolDeps, TResult]

create_team_run(
    agents: Sequence[MessageNode[TDeps, Any]],
    validator: MessageNode[Any, TResult] | None = None,
    *,
    name: str | None = None,
    description: str | None = None,
    shared_prompt: str | None = None,
    picker: AnyAgent[Any, Any] | None = None,
    num_picks: int | None = None,
    pick_prompt: str | None = None,
) -> TeamRun[TDeps, TResult]

create_team_run(
    agents: Sequence[AgentName | MessageNode[Any, Any]],
    validator: MessageNode[Any, TResult] | None = None,
    *,
    name: str | None = None,
    description: str | None = None,
    shared_prompt: str | None = None,
    picker: AnyAgent[Any, Any] | None = None,
    num_picks: int | None = None,
    pick_prompt: str | None = None,
) -> TeamRun[Any, TResult]

create_team_run(
    agents: Sequence[AgentName | MessageNode[Any, Any]] | None = None,
    validator: MessageNode[Any, TResult] | None = None,
    *,
    name: str | None = None,
    description: str | None = None,
    shared_prompt: str | None = None,
    picker: AnyAgent[Any, Any] | None = None,
    num_picks: int | None = None,
    pick_prompt: str | None = None,
) -> TeamRun[Any, TResult]

Create a a sequential TeamRun from a list of Agents.

Parameters:

Name	Type	Description	Default
`agents`	`Sequence[AgentName \| MessageNode[Any, Any]] \| None`	List of agent names or team/agent instances (all if None)	`None`
`validator`	`MessageNode[Any, TResult] \| None`	Node to validate the results of the TeamRun	`None`
`name`	`str \| None`	Optional name for the team	`None`
`description`	`str \| None`	Optional description for the team	`None`
`shared_prompt`	`str \| None`	Optional prompt for all agents	`None`
`picker`	`AnyAgent[Any, Any] \| None`	Agent to use for picking agents	`None`
`num_picks`	`int \| None`	Number of agents to pick	`None`
`pick_prompt`	`str \| None`	Prompt to use for picking agents	`None`

Source code in src/llmling_agent/delegation/pool.py

def create_team_run(
    self,
    agents: Sequence[AgentName | MessageNode[Any, Any]] | None = None,
    validator: MessageNode[Any, TResult] | None = None,
    *,
    name: str | None = None,
    description: str | None = None,
    shared_prompt: str | None = None,
    picker: AnyAgent[Any, Any] | None = None,
    num_picks: int | None = None,
    pick_prompt: str | None = None,
) -> TeamRun[Any, TResult]:
    """Create a a sequential TeamRun from a list of Agents.

    Args:
        agents: List of agent names or team/agent instances (all if None)
        validator: Node to validate the results of the TeamRun
        name: Optional name for the team
        description: Optional description for the team
        shared_prompt: Optional prompt for all agents
        picker: Agent to use for picking agents
        num_picks: Number of agents to pick
        pick_prompt: Prompt to use for picking agents
    """
    from llmling_agent.delegation.teamrun import TeamRun

    if agents is None:
        agents = list(self.agents.keys())

    # First resolve/configure agents
    resolved_agents: list[MessageNode[Any, Any]] = []
    for agent in agents:
        if isinstance(agent, str):
            agent = self.get_agent(agent)
        resolved_agents.append(agent)
    team = TeamRun(
        resolved_agents,
        name=name,
        description=description,
        validator=validator,
        shared_prompt=shared_prompt,
        picker=picker,
        num_picks=num_picks,
        pick_prompt=pick_prompt,
    )
    if name:
        self[name] = team
    return team

get_agent ¶

get_agent(
    agent: AgentName | Agent[Any],
    *,
    model_override: str | None = None,
    session: SessionIdType | SessionQuery = None,
) -> Agent[TPoolDeps]

get_agent(
    agent: AgentName | Agent[Any],
    *,
    return_type: type[TResult],
    model_override: str | None = None,
    session: SessionIdType | SessionQuery = None,
) -> StructuredAgent[TPoolDeps, TResult]

get_agent(
    agent: AgentName | Agent[Any],
    *,
    deps: TCustomDeps,
    model_override: str | None = None,
    session: SessionIdType | SessionQuery = None,
) -> Agent[TCustomDeps]

get_agent(
    agent: AgentName | Agent[Any],
    *,
    deps: TCustomDeps,
    return_type: type[TResult],
    model_override: str | None = None,
    session: SessionIdType | SessionQuery = None,
) -> StructuredAgent[TCustomDeps, TResult]

get_agent(
    agent: AgentName | Agent[Any],
    *,
    deps: Any | None = None,
    return_type: Any | None = None,
    model_override: str | None = None,
    session: SessionIdType | SessionQuery = None,
) -> AnyAgent[Any, Any]

Get or configure an agent from the pool.

This method provides flexible agent configuration with dependency injection: - Without deps: Agent uses pool's shared dependencies - With deps: Agent uses provided custom dependencies - With return_type: Returns a StructuredAgent with type validation

Parameters:

Name	Type	Description	Default
`agent`	`AgentName \| Agent[Any]`	Either agent name or instance	required
`deps`	`Any \| None`	Optional custom dependencies (overrides shared deps)	`None`
`return_type`	`Any \| None`	Optional type for structured responses	`None`
`model_override`	`str \| None`	Optional model override	`None`
`session`	`SessionIdType \| SessionQuery`	Optional session ID or query to recover conversation	`None`

Returns:

Name	Type	Description
`Either`	`AnyAgent[Any, Any]`
	`AnyAgent[Any, Any]`	Agent[TPoolDeps] when using pool's shared deps
	`AnyAgent[Any, Any]`	Agent[TCustomDeps] when custom deps provided
	`AnyAgent[Any, Any]`	StructuredAgent when return_type provided

Raises:

Type	Description
`KeyError`	If agent name not found
`ValueError`	If configuration is invalid

Source code in src/llmling_agent/delegation/pool.py

def get_agent(
    self,
    agent: AgentName | Agent[Any],
    *,
    deps: Any | None = None,
    return_type: Any | None = None,
    model_override: str | None = None,
    session: SessionIdType | SessionQuery = None,
) -> AnyAgent[Any, Any]:
    """Get or configure an agent from the pool.

    This method provides flexible agent configuration with dependency injection:
    - Without deps: Agent uses pool's shared dependencies
    - With deps: Agent uses provided custom dependencies
    - With return_type: Returns a StructuredAgent with type validation

    Args:
        agent: Either agent name or instance
        deps: Optional custom dependencies (overrides shared deps)
        return_type: Optional type for structured responses
        model_override: Optional model override
        session: Optional session ID or query to recover conversation

    Returns:
        Either:
        - Agent[TPoolDeps] when using pool's shared deps
        - Agent[TCustomDeps] when custom deps provided
        - StructuredAgent when return_type provided

    Raises:
        KeyError: If agent name not found
        ValueError: If configuration is invalid
    """
    from llmling_agent.agent import Agent
    from llmling_agent.agent.context import AgentContext

    # Get base agent
    base = agent if isinstance(agent, Agent) else self.agents[agent]

    # Setup context and dependencies
    if base.context is None:
        base.context = AgentContext[Any].create_default(base.name)

    # Use custom deps if provided, otherwise use shared deps
    base.context.data = deps if deps is not None else self.shared_deps
    base.context.pool = self

    # Apply overrides
    if model_override:
        base.set_model(model_override)

    if session:
        base.conversation.load_history_from_database(session=session)

    # Convert to structured if needed
    if return_type is not None:
        return base.to_structured(return_type)

    return base

get_mermaid_diagram ¶

get_mermaid_diagram(include_details: bool = True) -> str

Generate mermaid flowchart of all agents and their connections.

Parameters:

Name	Type	Description	Default
`include_details`	`bool`	Whether to show connection details (types, queues, etc)	`True`

Source code in src/llmling_agent/delegation/pool.py

def get_mermaid_diagram(
    self,
    include_details: bool = True,
) -> str:
    """Generate mermaid flowchart of all agents and their connections.

    Args:
        include_details: Whether to show connection details (types, queues, etc)
    """
    lines = ["flowchart LR"]

    # Add all agents as nodes
    for name in self.agents:
        lines.append(f"    {name}[{name}]")  # noqa: PERF401

    # Add all connections as edges
    for agent in self.agents.values():
        connections = agent.connections.get_connections()
        for talk in connections:
            talk = cast(Talk[Any], talk)  # help mypy understand it's a Talk
            source = talk.source.name
            for target in talk.targets:
                if include_details:
                    details: list[str] = []
                    details.append(talk.connection_type)
                    if talk.queued:
                        details.append(f"queued({talk.queue_strategy})")
                    if fn := talk.filter_condition:  # type: ignore
                        details.append(f"filter:{fn.__name__}")
                    if fn := talk.stop_condition:  # type: ignore
                        details.append(f"stop:{fn.__name__}")
                    if fn := talk.exit_condition:  # type: ignore
                        details.append(f"exit:{fn.__name__}")

                    label = f"|{' '.join(details)}|" if details else ""
                    lines.append(f"    {source}--{label}-->{target.name}")
                else:
                    lines.append(f"    {source}-->{target.name}")

    return "\n".join(lines)

list_nodes ¶

list_nodes() -> list[str]

List available agent names.

Source code in src/llmling_agent/delegation/pool.py

def list_nodes(self) -> list[str]:
    """List available agent names."""
    return list(self.list_items())

run_event_loop `async` ¶

run_event_loop()

Run pool in event-watching mode until interrupted.

Source code in src/llmling_agent/delegation/pool.py

async def run_event_loop(self):
    """Run pool in event-watching mode until interrupted."""
    import sys

    print("Starting event watch mode...")
    print("Active nodes: ", ", ".join(self.list_nodes()))
    print("Press Ctrl+C to stop")

    stop_event = asyncio.Event()

    if sys.platform != "win32":
        # Unix: Use signal handlers
        loop = asyncio.get_running_loop()
        for sig in (signal.SIGINT, signal.SIGTERM):
            loop.add_signal_handler(sig, stop_event.set)
        while True:
            await asyncio.sleep(1)
    else:
        # Windows: Use keyboard interrupt
        with suppress(KeyboardInterrupt):
            while True:
                await asyncio.sleep(1)

setup_agent_workers ¶

setup_agent_workers(agent: AnyAgent[Any, Any])

Set up workers for an agent from configuration.

Source code in src/llmling_agent/delegation/pool.py

def setup_agent_workers(self, agent: AnyAgent[Any, Any]):
    """Set up workers for an agent from configuration."""
    for worker_config in agent.context.config.workers:
        try:
            worker = self.get_agent(worker_config.name)
            agent.register_worker(
                worker,
                name=worker_config.name,
                reset_history_on_run=worker_config.reset_history_on_run,
                pass_message_history=worker_config.pass_message_history,
                share_context=worker_config.share_context,
            )
        except KeyError as e:
            msg = f"Worker agent {worker_config.name!r} not found"
            raise ValueError(msg) from e

track_message_flow `async` ¶

track_message_flow() -> AsyncIterator[MessageFlowTracker]

Track message flow during a context.

Source code in src/llmling_agent/delegation/pool.py

@asynccontextmanager
async def track_message_flow(self) -> AsyncIterator[MessageFlowTracker]:
    """Track message flow during a context."""
    tracker = MessageFlowTracker()
    self.connection_registry.message_flow.connect(tracker.track)
    try:
        yield tracker
    finally:
        self.connection_registry.message_flow.disconnect(tracker.track)

pool

Class info¶

Classes¶

🛈 DocStrings¶

AgentPool ¶

_error_class property ¶

agents property ¶

event_nodes property ¶

node_events property ¶

nodes property ¶

teams property ¶

__aenter__ async ¶

__aexit__ async ¶

__init__ ¶

_connect_nodes ¶

_create_teams ¶

_validate_item ¶

add_agent async ¶

cleanup async ¶

clone_agent async ¶

create_agent async ¶

create_team ¶

create_team_run ¶

get_agent ¶

get_mermaid_diagram ¶

list_nodes ¶

run_event_loop async ¶

setup_agent_workers ¶

track_message_flow async ¶

_error_class `property` ¶

agents `property` ¶

event_nodes `property` ¶

node_events `property` ¶

nodes `property` ¶

teams `property` ¶

aenter `async` ¶

aexit `async` ¶

init ¶

add_agent `async` ¶

cleanup `async` ¶

clone_agent `async` ¶

create_agent `async` ¶

run_event_loop `async` ¶

track_message_flow `async` ¶