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Migrating from Other Agent Frameworks

If you're coming from other agent frameworks like LangGraph, CrewAI, or AutoGen, this guide will help you translate your existing implementations into idiomatic Playbooks code. You can expect significant reduction in complexity and code size.

Why Migrate to Playbooks?

Benefit Description
less code Eliminate boilerplate and framework complexity
Natural language first Write agent behavior in plain English
Soft + hard logic Seamlessly mix LLM reasoning with deterministic Python
Verifiable execution Compiled to auditable PBAsm for debugging
First principles Built from the ground up for the LLM era (Software 3.0)
No framework lock-in Natural language programs are portable

Traditional Agent Frameworks

Playbooks can express the same agent behaviors as these popular frameworks:

Framework Type Playbooks Advantage
LangGraph State graph-based agents Replace complex state graphs with natural language workflows
CrewAI Multi-agent collaboration Native multi-agent support without role/task boilerplate
AutoGen Multi-agent conversations Simpler agent communication with triggers
LangChain Agents Classic agent patterns Natural language replaces chain composition
Semantic Kernel AI orchestration SDK Direct LLM execution vs orchestration layer
Haystack NLP with agent capabilities Focused on agents, not general NLP
AutoGPT Autonomous agents Structured playbooks vs autonomous loops

Migration Process

Step 1: Understand Your Agent's Behavior

Before migrating code, understand:

  • What does the agent do?
  • What are the key workflows?
  • What tools/functions does it use?
  • How do agents communicate (if multi-agent)?

Focus on behavior, not framework mechanics.

Step 2: If using AI coding assistants

  1. Create a playbooks folder for the converted code
  2. Copy prompt for your AI assistant from here
  3. Ask the AI assistant to convert the source implementation to Playbooks with the following prompt: 
    <prompt copied above>

Read source implementation at <agent>.py carefully. Convert to an equivalent Playbooks program in playbooks/<agent>.pb. Create all new files in the playbooks folder. Create MIGRATION.md file at the end with before/after comparison include code size reduction.
    Use appropriate and file name.

Step 3: If doing manual conversion

Use this mapping to translate concepts:

Source Framework Concept Playbooks Equivalent
LangGraph State graph Agent with variables
Nodes A playbook to represent a sequence of nodes
Edges Control flow in Steps
State Agent variables ($variable)
Tools Python playbooks or MCP server
CrewAI Crew Multi-agent Playbooks program file
Agent roles H1 agent definitions
Tasks H2 playbook definitions
Tools Python playbooks or MCP server
Process (sequential/hierarchical) Triggers and control flow
AutoGen Agents H1 agent definitions
Conversations Conversation loop in a playbook
Function calling Python playbooks
Group chat Multi-party meetings
LangChain Agent H1 agent definition
Tools Python playbooks or MCP server
ReAct agent ReAct-type playbook
Memory Artifacts or variables
Chains Playbook Steps

Step 4: Test and Iterate

Run your Playbooks agent: 

# export ANTHROPIC_API_KEY=<your Anthropic API key here>
# make sure python --version is 3.12+
cd playbooks
playbooks run <agent>.pb

Compare behavior with the original implementation and iterate.

Next steps

Happy migrating! 🚀