Langflow Integration

Use PraisonAI agents in Langflow visual workflows

Langflow Integration

Langflow is a visual authoring platform for AI agents and workflows. PraisonAI provides native Langflow components for building agent workflows visually.

Installation

pip install "praisonai[flow]"

Launch

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

See Flow CLI and Visual Workflow Builder for subcommands and component options.

Components

PraisonAI provides three components for Langflow:

PraisonAI Agent

Creates a single PraisonAI agent with full tool, memory, and knowledge support.

Key Inputs:

Input	Description
Name	Agent identifier
Instructions	System prompt for the agent
Model	LLM model (e.g., openai/gpt-4o-mini, anthropic/claude-3-5-sonnet-20241022)
Tools	Connected Langflow tools
Memory	Enable context retention
Knowledge	Files/URLs for RAG
Handoffs	Other agents for collaboration
Guardrails	Output validation

Outputs:

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

PraisonAI Agents

Orchestrates multiple agents working together.

Process Types:

Process	Description
Sequential	Agents execute in order
Hierarchical	Manager agent coordinates workers
Workflow	Custom agent routing with decision points

Key Inputs:

• Agents - List of PraisonAI Agent components
• Tasks - List of PraisonAI Task components
• Process - Orchestration mode
• Variables - Global substitution variables
• Guardrails - Team-level validation

PraisonAI Task

Defines a task for multi-agent workflows with structured output support.

Key Inputs:

Input	Description
Description	What the task should accomplish
Expected Output	Desired output format
Output JSON	JSON schema for structured output
Task Type	task, decision, or loop
Condition	Branching conditions for workflow
Guardrail	Task-specific validation

Quick Start

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!

Multi-Agent Team

ChatInput → PraisonAI Agents → ChatOutput
              ↑
    ┌─────────┼─────────┐
    │         │         │
Researcher  Analyst   Writer
  Agent      Agent     Agent

1. Create 3 PraisonAI Agent components with different roles
2. Create a PraisonAI Agents component
3. Connect all agents to the Agents component
4. Set process to “sequential”
5. Connect input/output

Model Format

PraisonAI uses provider/model-name format:

Provider	Examples
OpenAI	openai/gpt-4o-mini, openai/gpt-4o, openai/o1-mini
Anthropic	anthropic/claude-3-5-sonnet-20241022, anthropic/claude-3-opus-20240229
Google	google/gemini-1.5-pro, google/gemini-2.0-flash
DeepSeek	deepseek/deepseek-chat, deepseek/deepseek-reasoner
Groq	groq/llama-3.3-70b-versatile
Ollama	ollama/llama3.2, ollama/mistral

Memory Options

Option	Description
Simple	Toggle memory on/off
Provider	rag or mem0
Config	Full MemoryConfig dictionary (advanced)

Structured Output

Define JSON schemas for structured responses:

{
  "title": "string",
  "score": "int",
  "summary": "string",
  "approved": "bool"
}

The agent will return data matching this schema.

Workflow Branching

Use decision tasks for conditional flows:

1. Set Task Type to decision
2. Define Condition:

{
  "approved": ["process_task"],
  "rejected": ["review_task"]
}

3. The agent’s decision determines which task runs next

Agent Collaboration

Use Handoffs for agent-to-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

Related

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