LangGraph with PraisonAI

LangGraph integration lets you run LangGraph state graphs through PraisonAI’s YAML configuration and CLI — no adapter code required.

Need a framework that isn’t listed here? See Framework Adapter Plugins to register your own via Python entry points.

Quick Start

Install

Both extras are required for end-to-end use:

pip install "praisonai[langgraph]"
pip install "praisonai-frameworks[langgraph]"

Create agents.yaml

framework: langgraph
topic: Simple Question Answer

roles:
  researcher:
    role: Helper
    goal: Answer simple questions accurately
    backstory: I am a helpful assistant
    tasks:
      answer:
        description: What is the capital of France? Reply with just the city name.
        expected_output: Paris

Run

export OPENAI_API_KEY=<your-key>
praisonai agents.yaml --framework langgraph

You must either pass --framework langgraph on the CLI or set framework: langgraph in your YAML. If neither is set, PraisonAI uses its default framework.

How LangGraph Works

YAML Format for LangGraph

LangGraph requires the roles format (not the steps workflow format):

framework: langgraph
topic: Planet facts

roles:
  researcher:
    role: Research_Analyst
    goal: Gather concise facts
    backstory: Expert researcher
    tasks:
      research:
        description: Name one planet in our solar system. Reply with only the planet name.
        expected_output: A planet name
      summarise:
        description: Write one short sentence describing that planet.
        expected_output: One sentence
        context:
          - research

context: [research] creates a LangGraph edge from the research task node to the summarise task node — same semantic as in CrewAI. The output of research is injected into the summarise task’s prompt automatically.

Direct Adapter Use

Advanced — most users should use the CLI / YAML flow above. Call the adapter directly without the CLI or YAML loader:

from praisonai_frameworks.langgraph.adapter import LangGraphAdapter

config = {
    "framework": "langgraph",
    "topic": "Quick test",
    "roles": {
        "helper": {
            "role": "Assistant",
            "goal": "Answer briefly",
            "backstory": "Helpful assistant",
            "tasks": {
                "answer": {
                    "description": "Reply with exactly the word OK.",
                    "expected_output": "OK",
                }
            },
        }
    },
}
llm_config = [{"model": "gpt-4o-mini", "api_key": "<OPENAI_API_KEY>"}]
result = LangGraphAdapter().run(config, llm_config, "Quick test", tools_dict={})
# result starts with "### LangGraph Output ###"

Verify Installation

Use praisonai doctor to confirm LangGraph is detected:

$ praisonai doctor
✓ Runtime 'langgraph' available
  capabilities: agent_creation, tool_execution, sequential_execution
  supports_handoff: False
  supports_tool_loop: True

Probe programmatically via _framework_availability:

from praisonai._framework_availability import is_available

if is_available("langgraph"):
    print("LangGraph is installed and importable")

Pip Extras Reference

Extra	Installs	Required for
`praisonai[langgraph]`	`langgraph>=0.2.0,<2`, `langgraph-prebuilt>=0.2.0,<2`, `langchain-core>=0.3.0,<2`, `langchain-openai>=0.2.0,<2`, `praisonai-tools>=0.1.0`	Probe + doctor recognition for LangGraph
`praisonai-frameworks[langgraph]`	LangGraph adapter implementation registered via entry-point group	Actually executing `framework: langgraph`

Both extras are needed. Installing only praisonai[langgraph] makes is_available("langgraph") return True and the doctor pass, but --framework langgraph will fail at adapter dispatch because no adapter is registered under that name yet. This mirrors the AutoGen v0.4 “package vs adapter” distinction described in Framework Availability.

Troubleshooting

framework='langgraph' is not a valid choice — Pre-#2415 PraisonAI versions hardcode choices=["praisonai","crewai","autogen"]. Upgrade to a version that includes dynamic registry-driven CLI choices, or omit --framework and rely on framework: langgraph in your YAML instead. Framework 'langgraph' was requested but is not installed — Install the extras listed above. The CLI emits an install hint from get_install_hint("langgraph") pointing to praisonai-frameworks[langgraph]. tool_retry_policy validation error on praisonai.run() — Fixed in PraisonAI #2495 by normalising the RetryPolicy object into a dict before agent-config merge. If you are on a version before #2495, pass tool_retry_policy as a plain dict:

tool_retry_policy = {"max_attempts": 3, "delay": 1.0, "backoff_factor": 2.0, "max_delay": 10.0}

Best Practices

When to choose LangGraph over other frameworks

Choose LangGraph when you need explicit state graph control, conditional branching between tasks, or when your team already uses LangChain/LangGraph tooling. For simpler sequential workflows, PraisonAI native or CrewAI have lower overhead. For conversational multi-agent setups, AutoGen v0.2 is the more mature option today.

Use context: to express a graph

The context: field in task definitions maps directly to LangGraph edges. A task with context: [task_a, task_b] receives both task outputs as input context. Model your data dependencies as context lists rather than hard-coding them in task descriptions.

Keep the roles format, not steps

LangGraph via PraisonAI requires the roles: YAML format. The newer steps: + agents: workflow format is only supported by the native praisonai framework. If you migrate an existing steps: workflow to LangGraph, convert each step into a task under a role.

Parse the LangGraph Output sentinel

The adapter wraps its result in ### LangGraph Output ###\n.... Downstream parsers and log scrapers can split on this sentinel to extract the LangGraph result separately from PraisonAI’s own output lines.

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Getting Started (No Code)

LangGraph with PraisonAI

Quick Start

How LangGraph Works

YAML Format for LangGraph

Direct Adapter Use

Verify Installation

Pip Extras Reference

Troubleshooting

Best Practices

CrewAI

AutoGen

PraisonAI Agents

Framework Availability

Framework Adapter Plugins

​Quick Start

​How LangGraph Works

​YAML Format for LangGraph

​Direct Adapter Use

​Verify Installation

​Pip Extras Reference

​Troubleshooting

​Best Practices

​Related

CrewAI

AutoGen

PraisonAI Agents

Framework Availability

Framework Adapter Plugins

Quick Start

How LangGraph Works

YAML Format for LangGraph

Direct Adapter Use

Verify Installation

Pip Extras Reference

Troubleshooting

Best Practices

Related