Visual Workflow Builder

Build complex AI workflows visually using a drag-and-drop interface powered by Langflow.

Visual workflow builder allows you to create, connect, and manage agentic processes using a drag-and-drop interface.

The [flow] extra is opt-in and heavy (~500MB) because Langflow pulls many dependencies. Install only when you need the visual builder: pip install "praisonai[flow]".

Quick Start

1. Install Flow Addon

   Install the visual builder components.

   pip install "praisonai[flow]"
   
2. Launch Visual Builder

   Start the local server and open the web interface.

   praisonai flow
   
3. Connect Components

   Open http://localhost:7860 to access the interface. Drag and drop Agent and Agent Team nodes to orchestrate workflows.

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CLI Subcommands

Command	Purpose
praisonai flow	Launch Langflow with PraisonAI components
praisonai flow import <yaml>	Convert PraisonAI YAML → Langflow JSON and upload
praisonai flow export <flow_id>	Download a Langflow flow and convert back to YAML
praisonai flow list	List flows on a running Langflow server
praisonai flow version	Show installed Langflow version

See Flow CLI for the full flag reference.

Which subcommand should I use?

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Start Command Flags

Flag	Type	Default	Description
--port, -p	int	7860	Port to listen on
--host, -H	str	127.0.0.1	Host to bind to (use 0.0.0.0 to expose)
--env-file	str	None	Path to a .env file Langflow should load
--no-open	flag	False	Don’t open the browser on start
--log-level, -l	str	error	debug, info, warning, error, critical
--backend-only	flag	False	Run backend API only (no frontend UI)
--components-path	str	None	Extra custom components directory (appended to PraisonAI’s)

Import flags

Flag	Type	Default	Description
yaml_path	str	—	Path to YAML workflow file (positional)
--url	str	http://localhost:7860	Langflow server URL
--dry-run	flag	False	Preview JSON without uploading
--open	flag	False	Open the imported flow in the browser
--output, -o	str	None	Save JSON to file instead of uploading

Export flags

Flag	Type	Default	Description
flow_id	str	—	Flow ID to export (positional)
--output, -o	str	<flow_name>.<ext>	Output file path
--url	str	http://localhost:7860	Langflow server URL
--format	str	yaml	Output format: yaml or json

List flags

Flag	Type	Default	Description
--url	str	http://localhost:7860	Langflow server URL
--search, -s	str	None	Search flows by name or description

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How It Works

Component	Purpose	Availability
Agent Node	Individual AI entity with tailored instructions	Sidebar > PraisonAI
Agent Team Node	Multi-agent orchestrator connecting multiple nodes	Sidebar > PraisonAI
CLI Command	Backend server runtime wrapper	praisonai CLI

If PRAISONAI_OBSERVE=langfuse is set, the Agent component auto-wires a LangfuseSink with praisonai_source=langflow trace metadata.

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Agent Configuration Options

Options available on the Agent node visual component.

Option	Type	Default	Description
Agent Name	str	"Agent"	Name for identification and logging
Previous Agent	Handle	None	Connect from previous agent to define execution order
Role	str	None	Role defining the agent’s expertise
Goal	str	None	Primary objective the agent aims to achieve
Backstory	str (multiline)	None	Background context shaping personality
Instructions	str	"You are a helpful assistant."	System prompt for the agent
Input	Handle	None	User input to process
Model	str	"openai/gpt-4o-mini"	LLM model (provider/model format)
Language Model	Handle (LanguageModel)	None	External LLM from Langflow model components (overrides dropdown)
Base URL	str	None	Custom LLM endpoint (e.g. http://localhost:11434 for Ollama)
API Key	Secret	None	API key for LLM provider (overrides env var)
Tools	Handle	None	Tools available to the agent
Allow Delegation	bool	False	Allow task delegation to other agents
Allow Code Execution	bool	False	Enable code execution during tasks
Code Execution Mode	str	safe	safe (sandboxed) or unsafe (full system access)
Handoffs	Handle[Agent]	[]	Other agents this agent can hand off conversations to
Memory	bool	False	Enable context retention
Memory Provider	str	""	"", rag, or mem0
Memory Config	dict	None	Full MemoryConfig dictionary
Knowledge Files	File	None	Files to use as knowledge sources (PDF, TXT, etc.)
Knowledge URLs	str (multiline)	None	URLs to use as knowledge sources (one per line)
Guardrails	bool	False	Enable output validation guardrails
Verbose	bool	False	Show detailed execution logs
Markdown Output	bool	True	Format output as markdown
Self Reflect	bool	False	Enable self-reflection
Max Iterations	int	20	Maximum agent loop iterations

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Agent Team Configuration Options

Options available on the Agent Team node visual component for multi-agent workflows.

Option	Type	Default	Description
Name	str	"AgentTeam"	Name for this multi-agent team
Agents	Handle	[]	List of connected PraisonAI agents to orchestrate
Input	Handle	None	Initial input to start the multi-agent workflow
Process	str	"sequential"	Collaboration mode (sequential, hierarchical, workflow)
Manager Agent	Handle[Agent]	None	Explicit manager agent for hierarchical process
Manager LLM	str	"openai/gpt-4o"	LLM used for auto-created managers
Variables	dict	None	Global variables for substitution in task descriptions
Shared Memory	bool	False	Enable shared memory across all agents
Guardrails	bool	False	Enable team-level validation guardrails
Verbose	bool	False	Show detailed execution logs
Full Output	bool	False	Return full output including all task results
Planning	bool	False	Enable planning mode for task decomposition
Reflection	bool	False	Enable self-reflection for improved results
Caching	bool	False	Enable caching of agent responses

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Common Patterns

YAML round-trip

praisonai flow                                   # 1. start the builder
praisonai flow import my_workflow.yaml --open    # 2. push YAML in
# (edit in browser, copy the flow ID from the URL)
praisonai flow export <flow_id> -o my_workflow.yaml   # 3. pull edits back to YAML

Sequential Connections

Connect individual Agent nodes linearly (Agent 1 → Agent 2) by linking the Agent output handle on the first node to the Previous Agent input handle on the second node.

Multi-Agent Orchestration

Connect multiple Agent nodes directly to the Agents input handle of an Agent Team node. The team node evaluates the topology and runs the sub-agents properly.

Headless / LAN deploy

praisonai flow --host 0.0.0.0 --port 8080
praisonai flow --backend-only --no-open

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Best Practices

Configure Knowledge Properly

When using the agent node’s knowledge capabilities, upload compatible document types (PDF, TXT, CSV) or provide valid URLs in the Knowledge inputs.

Use Sequential Handles

Use the Previous Agent connection to establish hard execution boundaries. The orchestrator automatically parses node inputs backwards to map deterministic execution flow.

Enable Advanced Node Features

Click the “Advanced” toggle on nodes inside Langflow to expose robust SDK parameters like custom Base URLs, Code Execution Mode, Verbose logging, and specific Memory Providers.

Keep YAML as source of truth

Export flows back to YAML after visual edits so workflows stay in version control.

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Related

Flow CLI

CLI reference for all subcommands and flags

Langflow Integration

Component reference and model formats

Agent Workflow

Core workflow concepts

Installation Extras

[flow] extra reference