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Langflow Integration

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

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Installation

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Environment support

praisonai[flow] and praisonai[claw] cannot be installed in the same environment. [flow] pins a2a-sdk[http-server]>=1.0.0 (Langflow requirement); [claw] transitively pins a2a-sdk<1.0.0 (Google A2UI SDK requirement). Use two separate virtualenvs.
If praisonai flow appears to hang after Starting Core Services... on Windows, wait — first-boot commonly takes 5 minutes while Langflow runs Alembic migrations and warms the component cache. Look for Uvicorn running on http://127.0.0.1:7860 in the log tail.

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Launch

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

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Components

PraisonAI provides three components for Langflow:

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PraisonAI Agent

Creates a single PraisonAI agent with full tool, memory, and knowledge support. Key Inputs: Outputs:
  • Response - Agent response as Message
  • Agent - Agent instance for multi-agent workflows

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PraisonAI Agents

Orchestrates multiple agents working together. Process Types: 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

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PraisonAI Task

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

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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
  2. Create a PraisonAI Agents component
  3. Connect all agents to the Agents component
  4. Set process to “sequential”
  5. Connect input/output

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Model Format

PraisonAI uses provider/model-name format:

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Memory Options

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Structured Output

Define JSON schemas for structured responses:
The agent will return data matching this schema.

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Workflow Branching

Use decision tasks for conditional flows:
  1. Set Task Type to decision
  2. Define Condition:
  1. The agent’s decision determines which task runs next

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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