Output Variables

Pass values between tasks with output_variable

An output_variable names a task’s result so later tasks can read it.

graph LR
    A[🔍 Task A] -->|output_variable=research| S[📦 variables]
    S -->|{{research}}| B[✍️ Task B]

    classDef task fill:#8B0000,stroke:#7C90A0,color:#fff
    classDef store fill:#6366F1,stroke:#7C90A0,color:#fff

    class A,B task
    class S store

Quick Start

1. Store one task's output

   Name the result with output_variable.

   from praisonaiagents import Agent, Task
   
   researcher = Agent(name="Researcher", instructions="Research the topic and list key facts.")
   
   research = Task(
       name="research",
       action="Research renewable energy trends",
       agent=researcher,
       output_variable="research_data",
   )

2. Read it in the next task

   Reference the stored value with {{research_data}} in a later task’s action.

   from praisonaiagents import Agent, Task, PraisonAIAgents
   
   writer = Agent(name="Writer", instructions="Write a short report.")
   
   report = Task(
       action="Write a report using {{research_data}}",
       agent=writer,
       context=[research],
   )
   
   PraisonAIAgents(
       agents=[researcher, writer],
       tasks=[research, report],
   ).start()

How It Works

After any task completes — agent or handler — its raw output is stored under the given name in the team’s shared variables.

Reading Variables

Substitute a stored value directly in action or description with {{name}}.

from praisonaiagents import Agent, Task

writer = Agent(name="Writer", instructions="Write a report.")

Task(action="Summarise {{research_data}} in three bullets", agent=writer)

Read the same value inside a handler or should_run with ctx.variables["name"].

from praisonaiagents import Task

def summarise(ctx):
    return {"summary": ctx.variables["research_data"][:200]}

Task(name="summarise", handler=summarise)

Inside parallel blocks

An output_variable set inside a parallel([...]) branch is visible to every step after the block, keyed by that exact name.

from praisonaiagents import AgentFlow, Task, WorkflowContext, StepResult, parallel

def score_a(ctx: WorkflowContext) -> StepResult:
    return StepResult(output="9")

def score_b(ctx: WorkflowContext) -> StepResult:
    return StepResult(output="7")

compare = Task(action="Compare {{branch_a_score}} and {{branch_b_score}}")

workflow = AgentFlow(steps=[
    parallel([
        Task(name="branch_a", handler=score_a, output_variable="branch_a_score"),
        Task(name="branch_b", handler=score_b, output_variable="branch_b_score"),
    ]),
    compare,
])
workflow.start("go")

If two branches use the same output_variable, the later-declared branch wins and a warning is logged — give them distinct names. See Named outputs from branches for the full merge rule.

Best Practices

Related