Simple Sequential Team (YAML)

Two agents run in order: a researcher gathers facts, then a writer summarizes them.

Quick Start

Drop the YAML in place

Save this as agents.yaml in an empty folder. Tasks run in the order they are declared — the researcher’s task first, then the writer’s.

# yaml-language-server: $schema=https://docs.praison.ai/schemas/agents.schema.json
# Minimal sequential team: researcher -> writer
# Run: praisonai agents.yaml

framework: "praisonai"
topic: "renewable energy"

roles:
  researcher:
    role: "Senior Researcher"
    goal: "Find accurate information about {topic}"
    backstory: "Expert at concise, factual research."
    tasks:
      research_task:
        description: "Research {topic} and list 5 key facts."
        expected_output: "A bullet list of 5 facts."

  writer:
    role: "Report Writer"
    goal: "Turn research into readable summaries about {topic}"
    backstory: "Clear technical writer."
    tasks:
      summary_task:
        description: "Write a 3-sentence executive summary from the research."
        expected_output: "3 sentences, plain text."

# Ordering is by task declaration order above (researcher's task runs first,
# then the writer's), which is how the roles-file loader sequences tasks.
# The optional `dependencies:` block below documents intent; the roles-file
# loader does not consume it, so keep tasks in the order you want them run.
dependencies:
  - task: summary_task
    depends_on: research_task

Run it

Set your API key and run the file with the praisonai CLI:

export OPENAI_API_KEY=sk-...
praisonai agents.yaml

Change the subject by editing the topic: line — it is interpolated into every {topic} placeholder.

How It Works

Fact	Detail
Where tasks live	`roles.<role>.tasks.<task_name>` — each role owns its tasks
Ordering	By declaration order, not the optional top-level `dependencies:` block. The roles-file loader (`AgentsGenerator.generate_crew_and_kickoff` in `praisonai/agents_generator.py`) does not consume `dependencies:`.
`{topic}` interpolation	The top-level `topic:` value is substituted into every `{topic}` in role, goal, and task fields.

The single-brace {topic} interpolation here belongs to the roles-file YAML loader. The Python AgentTeam(variables={...}) path uses double-brace {{key}} placeholders instead — don’t mix the two.

Python equivalent

The same team using friendly top-level imports:

from praisonaiagents import Agent, Task, PraisonAIAgents

researcher = Agent(role="Senior Researcher", goal="Find accurate information")
writer = Agent(role="Report Writer", goal="Turn research into summaries")

t1 = Task(description="Research renewable energy and list 5 key facts", agent=researcher)
t2 = Task(description="Write a 3-sentence summary from the research", agent=writer, context=[t1])

PraisonAIAgents(agents=[researcher, writer], tasks=[t1, t2]).start()

When to use this vs a workflow YAML

You need…	Use	Example
2–5 agents, sequential / hierarchical	this recipe (`roles:` + nested `tasks:`)	this page
Routing by classifier output	workflow YAML	Workflow Routing
Parallel branches	workflow YAML	Workflow Parallelization
Loops / planning / memory	workflow YAML	YAML Workflows

YAML Workflows

Loops, planning, memory, and routing in YAML

Prompt Chaining

Chain task outputs into the next step

Multi-Agent Execution

Configure iteration and retry limits

Multi-Agent Hooks

Add lifecycle callbacks between tasks

Agent Recipes

Examples

Simple Sequential Team (YAML)

Quick Start

How It Works

Python equivalent

When to use this vs a workflow YAML

YAML Workflows

Prompt Chaining

Multi-Agent Execution

Multi-Agent Hooks

​Quick Start

​How It Works

​Python equivalent

​When to use this vs a workflow YAML

​Related

YAML Workflows

Prompt Chaining

Multi-Agent Execution

Multi-Agent Hooks

Quick Start

How It Works

Python equivalent

When to use this vs a workflow YAML

Related