Can i use tasks to perform tool chaining of sort ? How do i implement this on livekit ?

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Within a agent, instead of relying on agent to make sequential calls. Could we have a compose_tool that adds tasks and constructs the graph

Thanks — you're asking whether an agent can expose a

compose_tool

that programmatically adds tasks and builds a task graph instead of relying on the agent/LLM to call tools sequentially. Short answer: yes — you can have a tool that composes tasks or a TaskGroup to orchestrate a graph of tasks, then trigger that orchestration from the agent. A few quick clarifying questions so I give the right pattern and sample code: • Do you want the composition to be fully deterministic (agent/tool constructs a fixed graph and executes tasks automatically), or do you want the LLM to still choose which tasks to run at runtime? • Do tasks need to run strictly in sequence, in parallel, or as a directed graph with dependencies and conditional branching? • Which language do you want examples for: Python or Node.js? • Should tasks share mutable state, or should each task return a typed result that the composer aggregates? Reply with those details and I’ll provide a concise implementation pattern and sample code for a

compose_tool

that builds and runs a TaskGroup (or a custom graph executor) in LiveKit. , with interactive elements

determinstic

Thanks — you want a deterministic Python pattern where an agent exposes a

compose_tool

that programmatically constructs and runs a task graph (no LLM-driven ordering). Below is a concise pattern and example showing how to: • expose a tool on an agent that builds a TaskGroup (the deterministic graph), • add tasks programmatically, • run them in sequence, and • collect typed results. Key notes before code: • Each Task should extend

AgentTask[ResultType]

and return a typed result via

self.complete(result)

. • The composer tool builds a

TaskGroup

, adds tasks (in the required order), awaits execution, and returns the aggregated result. • This keeps orchestration deterministic — the agent/tool controls ordering and data flow. Example (Python):

from livekit.agents.logic.tasks import AgentTask, TaskGroup, TaskFactory, task_tool
from typing import NamedTuple

# Define typed results
class TaskAResult(NamedTuple):
    value: str

class TaskBResult(NamedTuple):
    transformed: str

class TaskCResult(NamedTuple):
    final: str

# Implement tasks
class TaskA(AgentTask[TaskAResult]):
    async def on_enter(self):
        # deterministic work
        result = TaskAResult(value="hello")
        self.complete(result)

class TaskB(AgentTask[TaskBResult]):
    async def on_enter(self):
        # expect input via kwargs (composer will pass)
        input_value = self.task_args.get("from_a")
        transformed = input_value.upper()
        self.complete(TaskBResult(transformed=transformed))

class TaskC(AgentTask[TaskCResult]):
    async def on_enter(self):
        part1 = self.task_args.get("from_b")
        final = f"{part1}-world"
        self.complete(TaskCResult(final=final))

# Agent-exposed compose tool
class MyAgent:
    # register as a tool so the agent (or LLM) can call it if desired
    @task_tool()  # or appropriate decorator for tools in your agent framework
    async def compose_tool(self, **kwargs):
        # Build deterministic graph as TaskGroup
        tg = TaskGroup()

        # Add tasks with factories; pass dependencies via task args when starting
        tg.add(TaskFactory(TaskA))
        tg.add(TaskFactory(TaskB))
        tg.add(TaskFactory(TaskC))

        # Run tasks sequentially and wire outputs deterministically
        results = {}
        # Start Task A and wait
        a_res = await tg.start_task(TaskA)  # pseudocode start API
        results["a"] = a_res  # TaskAResult

        # Start Task B with data from A
        b_res = await tg.start_task(TaskB, from_a=a_res.value)
        results["b"] = b_res

        # Start Task C with data from B
        c_res = await tg.start_task(TaskC, from_b=b_res.transformed)
        results["c"] = c_res

        # Optionally await group completion if underlying framework requires
        await tg.wait_complete()

        # Aggregate and return
        return {
            "a": results["a"]._asdict(),
            "b": results["b"]._asdict(),
            "c": results["c"]._asdict(),
        }

Practical details to adapt to your codebase: • Replace

tg.start_task(...)

and

tg.wait_complete()

with the exact TaskGroup/task start APIs in your LiveKit SDK (the pattern is: create TaskGroup, add Task factories, start tasks programmatically with kwargs, then await results). • Pass inputs to tasks via

task_args

or whatever mechanism

AgentTask

supports for constructor/runtime args. • For branching or conditional deterministic graphs, have the composer decide which tasks to add/run before starting them. If you’d like, I can: • convert the above to exact LiveKit SDK calls from your codebase if you share the AgentTask/TaskGroup start APIs you’re using, or • provide a version with parallel execution and joins for DAG-style graphs. Which would you prefer?, with interactive elements

how do you define the inputs and outputs from each task to flow to the next

Great — I'll show a clear, deterministic pattern for defining task inputs/outputs and passing data between tasks in Python. Key ideas • Each Task exposes a typed result (e.g., NamedTuple or dataclass) and reads inputs from

task_args

(or the Task constructor arguments your AgentTask supports). • The composer (your

compose_tool

) is authoritative: it starts tasks in order, passes prior task outputs into the next task’s args, and awaits each result. • Keep tasks pure: return structured results and avoid shared mutable state. The composer aggregates/transform results. Compact example (Python-style, adapt to your LiveKit SDK method names):

from typing import NamedTuple
from livekit.agents.logic.tasks import AgentTask, TaskGroup, TaskFactory, task_tool

# 1) Define typed outputs
class TaskAResult(NamedTuple):
    value: str

class TaskBResult(NamedTuple):
    transformed: str

class TaskCResult(NamedTuple):
    final: str

# 2) Tasks read inputs from task_args and call self.complete(result)
class TaskA(AgentTask[TaskAResult]):
    async def on_enter(self):
        # no input needed
        self.complete(TaskAResult(value="hello"))

class TaskB(AgentTask[TaskBResult]):
    async def on_enter(self):
        # composer passed 'from_a'
        from_a = self.task_args.get("from_a")
        self.complete(TaskBResult(transformed=from_a.upper()))

class TaskC(AgentTask[TaskCResult]):
    async def on_enter(self):
        from_b = self.task_args.get("from_b")
        self.complete(TaskCResult(final=f"{from_b}-world"))

# 3) Deterministic composer tool: start tasks, pass outputs into next
class MyAgent:
    @task_tool()
    async def compose_tool(self, **kwargs):
        tg = TaskGroup()

        # register factories (order here is informational; composer controls run)
        tg.add(TaskFactory(TaskA))
        tg.add(TaskFactory(TaskB))
        tg.add(TaskFactory(TaskC))

        # Run Task A
        a_task = await tg.start_task(TaskA)           # adapt to SDK start API
        a_res: TaskAResult = await a_task.wait_result()

        # Run Task B with input from A
        b_task = await tg.start_task(TaskB, from_a=a_res.value)
        b_res: TaskBResult = await b_task.wait_result()

        # Run Task C with input from B
        c_task = await tg.start_task(TaskC, from_b=b_res.transformed)
        c_res: TaskCResult = await c_task.wait_result()

        # Optionally await overall group completion
        await tg.wait_complete()

        return {
            "a": a_res._asdict(),
            "b": b_res._asdict(),
            "c": c_res._asdict(),
        }

Best practices • Use explicit typed results (NamedTuple/dataclass) so the composer knows what fields to read. • Use explicit task arg names (e.g.,

from_a

,

from_b

) rather than positional or implicit context. • Validate inputs at task start and raise/complete with errors so the composer can decide recovery/branching. • For DAGs, build a dependency map in the composer and start tasks once their inputs are ready; use

asyncio.gather

for parallel runs where independent. • For complex transformations, keep small focused tasks and let the composer handle aggregation. If you want, I can convert the pseudocode to the exact LiveKit SDK call signatures you have (share the `TaskGroup`/`Task` start/wait API you’re using) or show a DAG implementation with parallel starts and joins. Which would you prefer? , with interactive elements

Generic version for doing this

Below is a compact, generic Python pattern you can drop into your agent to deterministically compose tasks, define task inputs/outputs, and wire results from one task to the next. It’s written to be SDK-agnostic—replace the placeholder start/wait APIs with your LiveKit TaskGroup/AgentTask methods if names differ. Key ideas (one line each) • Tasks return typed results (NamedTuple/dataclass). • Tasks read inputs from

task_args

(or equivalent). • A composer tool (deterministic) builds a sequence/DAG, starts tasks, and passes prior outputs into subsequent tasks. Generic example

from typing import NamedTuple, Any, Dict, List, Callable
import asyncio

# --- Typed result alias for clarity ---
TaskResult = Any

# --- Task base placeholder (replace with actual AgentTask) ---
class BaseTask:
    def __init__(self, task_args: Dict[str, Any] = None):
        self.task_args = task_args or {}
    async def run(self) -> TaskResult:
        raise NotImplementedError

# --- Example typed results ---
class AResult(NamedTuple):
    value: str

class BResult(NamedTuple):
    transformed: str

class CResult(NamedTuple):
    final: str

# --- Example tasks: read from self.task_args, return typed result ---
class TaskA(BaseTask):
    async def run(self) -> AResult:
        # deterministic work
        return AResult(value="hello")

class TaskB(BaseTask):
    async def run(self) -> BResult:
        inp = self.task_args.get("from_a", "")
        return BResult(transformed=inp.upper())

class TaskC(BaseTask):
    async def run(self) -> CResult:
        inp = self.task_args.get("from_b", "")
        return CResult(final=f"{inp}-world")

# --- Composer: deterministic sequence or DAG executor ---
class Composer:
    def __init__(self):
        # map name -> task factory
        self.task_factories: Dict[str, Callable[[Dict], BaseTask]] = {}
        # DAG dependencies: name -> list of names it depends on
        self.deps: Dict[str, List[str]] = {}

    def register(self, name: str, factory: Callable[[Dict], BaseTask], deps: List[str] = None):
        self.task_factories[name] = factory
        self.deps[name] = deps or []

    async def run_sequential(self, order: List[str]) -> Dict[str, TaskResult]:
        results: Dict[str, TaskResult] = {}
        for name in order:
            # build task args by reading required inputs from results
            # convention: a task reads inputs by keys like 'from_<dep>'
            task_args = {}
            for d in self.deps.get(name, []):
                task_args[f"from_{d}"] = results[d]
            task = self.task_factories[name](task_args)
            res = await task.run()
            results[name] = res
        return results

    async def run_dag(self) -> Dict[str, TaskResult]:
        # simple DAG runner: start tasks when deps ready, allow parallelism
        results: Dict[str, TaskResult] = {}
        pending = set(self.task_factories.keys())
        in_progress = set()
        while pending:
            ready = [n for n in pending if all(d in results for d in self.deps[n])]
            if not ready and not in_progress:
                raise RuntimeError("Cyclic or unsatisfiable dependencies")
            # run ready tasks in parallel
            tasks = []
            for name in ready:
                task_args = {f"from_{d}": results[d] for d in self.deps[name]}
                task = self.task_factories[name](task_args)
                tasks.append((name, asyncio.create_task(task.run())))
                pending.remove(name)
                in_progress.add(name)
            for name, t in tasks:
                res = await t
                results[name] = res
                in_progress.remove(name)
        return results

# --- Usage example: register tasks and run sequentially ---
async def compose_tool_example():
    composer = Composer()
    composer.register("a", lambda args: TaskA(args), deps=[])
    composer.register("b", lambda args: TaskB(args), deps=["a"])
    composer.register("c", lambda args: TaskC(args), deps=["b"])

    # Deterministic sequential run
    seq_results = await composer.run_sequential(["a", "b", "c"])
    # Or run as DAG (allows parallel where deps permit)
    dag_results = await composer.run_dag()

    # Convert NamedTuples to dicts for easy aggregation
    def to_dict(x):
        return x._asdict() if hasattr(x, "_asdict") else x

    return {k: to_dict(v) for k, v in seq_results.items()}

How inputs/outputs flow here • Composer registers tasks with a dependency list (deps). • Composer constructs

task_args

for each task by taking results of its dependencies and putting them under keys like

from_<dep>

. • Each Task reads inputs from

self.task_args["from_<dep>"]

and returns a typed result. • Composer aggregates typed results and can map/transform them as needed before passing into next tasks. Adaptation notes • Replace BaseTask/Composer start/wait with LiveKit’s

AgentTask

,

TaskFactory

, and

TaskGroup

APIs; the wiring concept remains identical. • For stricter typing, use dataclasses/NamedTuples for inputs and outputs and validate at task start. • For error handling, have tasks return error objects or raise and let composer decide retry/compensation logic. If you want, I can convert this generic runner to match exact LiveKit APIs (e.g.,

TaskGroup.add

,

start_task

,

wait_result

) if you share those method names in your codebase. Which would you like next? , with interactive elements