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Langflow Integration

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Installation

pip install "praisonai[flow]"

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Environment support

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Launch

# Launches Langflow on http://localhost:7860 with PraisonAI components in the sidebar
praisonai flow

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Components

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PraisonAI Agent

• Response - Agent response as Message
• Agent - Agent instance for multi-agent workflows

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PraisonAI Agents

• Name - Team identifier (default AgentTeam)
• Agents - List of PraisonAI Agent components
• Input - Initial input to start the workflow
• Process - sequential, hierarchical, or workflow
• Manager Agent - Explicit manager for hierarchical process (optional)
• Manager LLM - LLM for auto-created manager (default openai/gpt-4o)
• Variables - Global substitution variables for task descriptions
• Shared Memory - Enable shared memory across all agents
• Guardrails - Team-level output validation
• Verbose - Detailed execution logs
• Full Output - Return results from every task
• Planning - Decompose complex tasks
• Reflection - Self-reflection for better results
• Caching - Cache agent responses

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Quick Start

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Single Agent

1. Drag PraisonAI Agent onto the canvas
2. Set instructions: “You are a helpful assistant”
3. Connect Chat Input to the Agent’s Input
4. Connect Agent’s Response to Chat Output
5. Run the flow!

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Multi-Agent Team

1. Create 3 PraisonAI Agent components with different roles (e.g. Researcher, Analyst, Writer)
2. Set each agent’s instructions describing what it should accomplish
3. (Optional) Chain execution order by connecting the Agent output of each predecessor to the next agent’s Previous Agent input
4. Create an Agent Team component
5. Connect all three agents to the Agent Team’s Agents input
6. Set Process to sequential (or hierarchical / workflow)
7. Wire Chat Input → Agent Team Input, Agent Team Response → Chat Output
8. Run the flow — tasks are generated automatically from each agent’s instructions

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Model Format

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Memory Options

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Structured Output

from pydantic import BaseModel
from praisonaiagents import Agent, Task, PraisonAIAgents


class Review(BaseModel):
    title: str
    score: int
    summary: str
    approved: bool


agent = Agent(
    name="Reviewer",
    instructions="Review the submission and return a verdict",
)

task = Task(
    description="Review the submission",
    agent=agent,
    output_pydantic=Review,
)

PraisonAIAgents(agents=[agent], tasks=[task]).start()

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Workflow Branching

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Agent Collaboration

1. Create a second agent (e.g., “Expert Agent”)
2. Connect it to the first agent’s Handoffs input
3. The primary agent can now hand off conversations

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Related

• Agent Concepts
• Multi-Agent Orchestration
• Memory Configuration
• Knowledge/RAG
• Handoffs