AG2 with PraisonAI

Use AG2 framework with PraisonAI for multi-agent GroupChat orchestration, tool registration, and AWS Bedrock support

AG2 is the community fork of AutoGen. PraisonAI supports AG2 as a dedicated framework backend with GroupChat orchestration, automatic tool registration, and native AWS Bedrock support.

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

Which AG2 Option to Choose

PraisonAI has two AG2-related framework options:

Option	Install	When to Use
framework: ag2	pip install "praisonai[ag2]"	New projects, Bedrock support, latest AG2
framework: autogen	pip install "praisonai[autogen]"	Existing AutoGen v0.2/v0.4 projects

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Quick Start (AG2)

1. Install

   pip install "praisonai[ag2]"
   
2. Create a YAML file

   framework: ag2
   topic: Research the latest developments in AI agents
   
   roles:
     researcher:
       role: AI Research Specialist
       goal: Find and summarize recent AI agent developments
       backstory: Expert researcher with deep knowledge of AI trends.
       tasks:
         research_task:
           description: Research the latest developments in {topic}
           expected_output: Summary report with key findings
   
3. Run

   export OPENAI_API_KEY=your-key
   praisonai agents.yaml --framework ag2
   

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How AG2 Works

AG2 uses a GroupChat pattern: a UserProxy agent initiates the conversation, and a GroupChatManager routes messages between your defined agents. Agents collaborate until one says “TERMINATE”.

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Multi-Agent Example

framework: ag2
topic: Write a blog post about renewable energy

roles:
  researcher:
    role: Energy Research Analyst
    goal: Gather facts about renewable energy trends
    backstory: Expert in energy research and data analysis.
    tasks:
      research_task:
        description: Research current renewable energy trends for {topic}
        expected_output: Research brief with statistics and trends

  writer:
    role: Content Writer
    goal: Write engaging blog content based on research
    backstory: Skilled writer who turns technical research into readable content.
    tasks:
      writing_task:
        description: Write a blog post based on the research findings
        expected_output: Complete blog post with introduction, body, and conclusion

praisonai agents.yaml --framework ag2

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AWS Bedrock Support

AG2 natively supports AWS Bedrock models. No API key needed - uses your AWS credentials.

framework: ag2

roles:
  cloud_architect:
    role: Cloud Solutions Architect
    goal: Design cloud architectures
    backstory: Expert in AWS cloud infrastructure.
    llm:
      model: bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0
      api_type: bedrock
      aws_region: us-east-1
    tasks:
      design_task:
        description: Design a serverless architecture for a web app
        expected_output: Architecture diagram and component descriptions

Bedrock credentials come from your standard AWS setup (~/.aws/credentials, environment variables, or IAM role).

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Auto Mode

praisonai --framework ag2 --auto "Create a Movie Script About Cat in Mars"

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Legacy AutoGen (v0.2 / v0.4)

For existing projects using the older AutoGen package:

pip install "praisonai[autogen]"

framework: autogen
topic: Create Movie Script About Cat in Mars

roles:
  researcher:
    role: Research Analyst
    goal: Gather information about Mars and cats
    backstory: Skilled in research, with a focus on gathering accurate and relevant information.
    tasks:
      research_task:
        description: Research about Mars environment and cat behavior
        expected_output: Research findings document with key facts

praisonai agents.yaml --framework autogen

AutoGen Version Selection

# Use AutoGen v0.4 (default if available)
export AUTOGEN_VERSION=v0.4

# Use AutoGen v0.2
export AUTOGEN_VERSION=v0.2

# Auto-select (default: prefers v0.4)
export AUTOGEN_VERSION=auto

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Framework Selection Priority

1. CLI flag (--framework ag2) takes precedence
2. YAML file (framework: ag2) is used if no CLI flag
3. Default: praisonai framework

The roles format YAML is required for both ag2 and autogen frameworks. The newer steps + agents workflow format only supports the praisonai framework.

Direct prompts always use the praisonai framework regardless of the --framework flag:

# This will use praisonai, NOT ag2
praisonai "What is 2+2?" --framework ag2

To use AG2, provide a YAML file with the roles format.

Troubleshooting

Unsupported framework error: If you see Unsupported framework: autogen. Registered: ['ag2', 'autogen', 'autogen_v4', 'crewai', 'praisonai'], check spelling — frameworks are registered via the adapter registry.

Missing installation errors:

• AG2 is not available. Install with: pip install ag2 — Install AG2 dependencies
• AutoGen v0.2 is not available. Install with: pip install autogen — Install AutoGen v0.2 dependencies

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Best Practices

Use AG2 for new projects

The dedicated ag2 framework uses the latest AG2 package with LLMConfig support and native Bedrock. Use autogen only for existing codebases.

Keep agent roles focused

Each role should have a clear, distinct responsibility. The GroupChat manager routes messages based on role descriptions.

Use Bedrock for AWS environments

Set api_type: bedrock in the role’s llm config. No API keys needed when running on AWS with proper IAM roles.

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Related

CrewAI

CrewAI framework integration

Agents

PraisonAI native agents