Workflow

Create and execute reusable multi-step workflows

The workflow command manages reusable multi-step workflows stored in .praison/workflows/.

Quick Start

praisonai workflow list

Template-Based Workflow

# List available workflows
praisonai workflow list

# Execute a workflow
praisonai workflow run "Research Blog" --tools tavily --save

# Quick workflow without creating a file
praisonai "What is AI?" --workflow "Research,Summarize" --save

# With step actions
praisonai "GPT-5" --workflow "Research:Search for info,Analyze:Analyze findings,Write:Write blog"

Two Ways to Run Workflows

Method	Command	Use Case
Template-based	praisonai workflow run "name"	Reusable, complex workflows with per-step agents
Inline	praisonai "prompt" --workflow "step1,step2"	Quick, ad-hoc workflows

Template-Based Workflows

List Workflows

praisonai workflow list

Expected Output:

╭─ Available Workflows ────────────────────────────────────────────────────────╮
│  📋 deploy - Deploy application to production                               │
│  📋 Research Blog - Research and write blog posts                           │
╰──────────────────────────────────────────────────────────────────────────────╯

Execute Workflow

praisonai workflow run "Research Blog"

Execute with Options

# With variables
praisonai workflow run deploy --workflow-var environment=staging --workflow-var branch=main

# With tools and save output
praisonai workflow run "Research Blog" --tools tavily --save

# With planning mode (AI creates sub-steps for each workflow step)
praisonai workflow run "Research Blog" --planning --verbose

# With memory
praisonai workflow run "Research Blog" --memory

Show Workflow Details

praisonai workflow show deploy

Create Workflow Template

praisonai workflow create my_workflow

Inline Workflows

Run workflows directly from the command line without creating a template file:

# Simple format: step names only
praisonai "What is Python?" --workflow "Research,Analyze,Summarize"

# Detailed format: step_name:action
praisonai "AI trends" --workflow "Research:Search for AI trends,Write:Write a blog post"

# With all options
praisonai "GPT-5 features" --workflow "Research,Write" --tools tavily --save --verbose

Inline Workflow Format

Format	Example	Description
Simple	"Research,Summarize"	Step name = action
Detailed	"Research:Search for info,Write:Write blog"	Custom action per step

CLI Options

Workflows use global flags (same as other commands):

Flag	Description
--workflow-var key=value	Set workflow variable (can be repeated)
--llm <model>	LLM model (e.g., openai/gpt-4o-mini)
--tools <tools>	Tools (comma-separated, e.g., tavily)
--planning	Enable planning mode (AI creates sub-steps)
--memory	Enable memory
--verbose	Enable verbose output
--save	Save output to file

Workflow YAML Schema

framework: key

YAML workflow files accept a top-level framework: key. Only praisonai (case-insensitive) is supported by the native execution engine.

Value	Behaviour
Omitted or empty	Defaults to praisonai — no change
praisonai (any case)	Runs normally
Any other value (crewai, autogen, …)	Raises ValueError at Workflow.run() time

# my_workflow.yaml
framework: praisonai   # ✅ OK — or simply omit this key
roles:
  researcher:
    backstory: "You are a researcher"
    goal: "Research the topic"

If you set a different framework name, PraisonAI raises immediately rather than silently running the native engine:

ValueError: Workflow YAML framework='crewai' is not supported by the native
PraisonAI execution engine. Only 'praisonai' is supported. Either remove the
'framework:' key or set it to 'praisonai'.

The framework: validation only applies to the native YAML workflow executor (Workflow.run()). To run CrewAI or AutoGen, use the framework adapters path via praisonai --framework crewai agents.yaml — see Framework Availability for adapter details.

Workflow File Format

Workflows are stored in .praison/workflows/ as Markdown files with YAML frontmatter:

---
name: Research Blog
description: Research and write blog posts
default_llm: gpt-4o-mini
variables:
  topic: AI trends
---

## Step 1: Research
Research the topic thoroughly.

\`\`\`agent
role: Researcher
goal: Find comprehensive information
\`\`\`

\`\`\`tools
tavily_search
\`\`\`

\`\`\`action
Search for information about {{topic}}
\`\`\`

## Step 2: Write Blog
Write a blog post based on research.

\`\`\`agent
role: Writer
goal: Write engaging content
\`\`\`

context_from: [Research]

\`\`\`action
Write a blog post about {{topic}} using the research data.
\`\`\`

How It Works

1. Load: Workflow file is loaded from .praison/workflows/
2. Variables: Variables are substituted into step prompts
3. Execution: Each step is executed sequentially with its configured agent
4. Context: Results from each step are passed to the next

Examples

Research Workflow

# Execute with tools
praisonai workflow run "Research Blog" --tools tavily --save

# With planning mode (AI creates sub-steps for each step)
praisonai workflow run "Research Blog" --planning --verbose

Deployment Workflow

# With variables
praisonai workflow run deploy --workflow-var environment=staging --workflow-var branch=main

Release Workflow

praisonai workflow run release --workflow-var version=1.2.0

Programmatic Usage

from praisonaiagents import AgentFlowManager

manager = WorkflowManager()

# Execute a workflow
result = manager.execute(
    "deploy",
    executor=lambda prompt: agent.chat(prompt),
    variables={"environment": "production"}
)

Best Practices

Use variables for environment-specific values to make workflows reusable.

Workflows execute steps sequentially. Ensure each step can complete independently.

Do	Don’t
Use variables for environment values	Hardcode environment names
Keep steps focused and atomic	Create monolithic steps
Add descriptions to workflows	Skip documentation
Test workflows in staging first	Deploy directly to production

Auto-Generate Workflows

Fixed in PR #2147: praisonai workflow auto was non-functional in all previous releases — every call raised a NameError on _models_cache and surfaced as Generation failed: in the CLI. The command now works as documented below.

Generate workflow YAML files automatically from a topic description:

# Basic auto-generation
praisonai workflow auto "Research and write a blog post"

# With specific pattern
praisonai workflow auto "Analyze market trends" --pattern orchestrator-workers

# With output file
praisonai workflow auto "Customer support routing" --pattern routing --output support_workflow.yaml

Available Patterns

Pattern	Description	Use Case
sequential	Agents work one after another	Default, step-by-step tasks
parallel	Agents work concurrently	Independent subtasks
routing	Classifier routes to specialists	Different input types
loop	Repeat steps until condition met	Iterative processing
orchestrator-workers	Central orchestrator delegates dynamically	Complex decomposition
evaluator-optimizer	Generate-evaluate loop until quality met	Content refinement

Pattern Examples

Orchestrator-Workers

Central orchestrator analyzes the task and delegates to specialized workers:

praisonai workflow auto "Comprehensive market analysis and report" --pattern orchestrator-workers

Generated workflow includes:

• Orchestrator: Analyzes task, determines required workers
• Workers: Researcher, Analyst, Writer (run in parallel)
• Synthesizer: Combines all worker outputs

Evaluator-Optimizer

Iterative refinement with feedback loops:

praisonai workflow auto "Write and polish a high-quality article" --pattern evaluator-optimizer

Generated workflow includes:

• Generator: Creates initial content
• Evaluator: Scores content (1-10), provides feedback
• Loop: Continues until score >= 7 or max iterations

Parallel

Multiple agents work concurrently:

praisonai workflow auto "Research from news, social media, and academic sources" --pattern parallel

Routing

Classifier routes to specialized agents:

praisonai workflow auto "Handle technical, billing, and general customer requests" --pattern routing

Related

• Workflows Feature
• AutoAgents Feature
• Auto Generation Mode
• Planning CLI
• Hooks CLI