Visual Workflow Builder

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

Install Flow Addon

Install the visual builder components.

pip install "praisonai[flow]"

Launch Visual Builder

Start the local server and open the web interface.

praisonai flow

Connect Components

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

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?

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

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.

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

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

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

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.

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

​Quick Start

​CLI Subcommands

​Which subcommand should I use?

​Start Command Flags

​Import flags

​Export flags

​List flags

​How It Works

​Agent Configuration Options

​Agent Team Configuration Options

​Common Patterns

​YAML round-trip

​Sequential Connections

​Multi-Agent Orchestration

​Headless / LAN deploy

​Best Practices

​Related

Flow CLI

Langflow Integration

Agent Workflow

Installation Extras

Quick Start

CLI Subcommands

Which subcommand should I use?

Start Command Flags

Import flags

Export flags

List flags

How It Works

Agent Configuration Options

Agent Team Configuration Options

Common Patterns

YAML round-trip

Sequential Connections

Multi-Agent Orchestration

Headless / LAN deploy

Best Practices

Related