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    Agent Framework Migration Skill

    1

    Migrate AI agents between frameworks while preserving tool contracts, memory state, and execution logic.

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    Agent Framework Migration Skill

    Agent Framework Migration Skill

    Example session with this skill installed

    Migrate my LangChain Python agent to LangGraph. It uses a SQL tool, a search tool, and needs to persist conversation history in PostgreSQL. Preserve the human-in-the-loop approval step.

    • Read your context and instructions
    • Compiled the agent framework migration

    I have completed the source audit and designed the migration path from LangChain (BaseAgent) to LangGraph. The plan preserves the SQL and Search tool contracts and implements the human-in-the-loop approval gate using LangGraph's breakpoint system.

    Migration Plan: LangChain to LangGraph

    FeatureSource (LangChain)Target (LangGraph)Notes
    OrchestrationAgentExecutorStateGraphMove from linear execution to cyclic graph.
    StateConversationBufferMemoryTypedDict StateExplicit state schema for SQL results and history.
    PersistencePostgresChatMessageHistoryPostgresSaverTransition to checkpointer for thread-level recovery.
    ToolsStructuredTool@tool decoratorPreserve existing Pydantic input schemas.
    Human-in-LoopManual callback checkInterrupt/BreakpointUse interrupt_before=["sql_tool"] for safety.

    Tool Contract Adaptation

    
# target/tools.py
from langchain_core.tools import tool
from pydantic import BaseModel, Field

class SQLQuery(BaseModel):
    query: str = Field(description="The SQL query to execute")

@tool("execute_sql", args_schema=SQLQuery)
def execute_sql(query: str):
    """Executes a SQL query against the production database."""
    # Logic preserved from source/tools/sql.py
    pass

    Graph Lifecycle & State

    from langgraph.graph import StateGraph, END
from langgraph.checkpoint.postgres import PostgresSaver

# Initialize StateGraph with the preserved state schema
workflow = StateGraph(AgentState)

# Define nodes and edges
workflow.add_node("agent", call_model)
workflow.add_node("action", call_tool)

# Set entry point and conditional edges
workflow.set_entry_point("agent")
workflow.add_conditional_edges("agent", should_continue)
workflow.add_edge("action", "agent")

# Compile with human-in-the-loop breakpoint for SQL tool
checkpointer = PostgresSaver(conn)
app = workflow.compile(checkpointer=checkpointer, interrupt_before=["action"])

    Next steps

    1. Initialize the PostgreSQL schema for PostgresSaver to enable state persistence.
    2. Port the call_model logic to handle the new TypedDict state transitions.
    3. Run the validation matrix to verify the interrupt_before gate triggers correctly.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Porting AI agents between frameworks is rarely a direct syntax swap. Differences in memory persistence, tool execution, and state management often lead to silent failures or degraded performance after a move. This skill provides a systematic engineering approach to migrating agent applications while preserving exact behavioral and operational contracts.

    What it does

    • Source auditing identifies agents, graph structures, prompts, and lifecycle hooks in the current project.
    • Behavior mapping creates a capability matrix matching source framework concepts to target equivalents.
    • Contract preservation translates tool schemas, validation rules, and error handling without breaking downstream consumers.
    • State migration maps conversation history, durable memory, and workflow checkpoints to new serialization formats.
    • Validation testing generates a matrix covering golden trace comparisons, idempotency, and security boundaries.

    How it works

    1. Discovery scans the repository to inventory existing nodes, edges, tools, and memory models.
    2. Architecture design defines adapter boundaries to separate business logic from framework-specific APIs.
    3. Interface adaptation normalizes tool inputs and return values for the target framework runtime.
    4. Validation executes contract tests and compares execution traces to verify behavior parity.

    Frameworks & tools

    Works with Python and TypeScript agent frameworks including LangChain, LangGraph, CrewAI, AutoGen, and Haystack, as well as custom orchestration layers.

    Why this beats prompting it yourself

    Generic prompts often miss the subtle semantic shifts in how different frameworks handle async streaming, checkpoint recovery, or tool retry logic. This skill enforces a structured migration protocol that prioritizes state integrity and side-effect safety over simple code translation.

    Use cases

    • Porting a prototype from a high-level framework to a production-grade orchestration layer.
    • Moving legacy LangChain implementations to graph-based architectures like LangGraph.
    • Consolidating disparate agent microservices into a unified framework.
    • Upgrading breaking version changes within the same framework ecosystem.

    Known limitations

    Does not perform production data cutovers or destructive migrations without manual authorization. Requires access to source code and target framework documentation.

    Does not perform production data cutovers. Requires access to source code and target framework documentation. Does not support automated secret rotation.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    What's inside

    Frequently Asked Questions

    This skill automates the complex process of porting agent logic, memory states, and tool contracts between frameworks like LangChain, LangGraph, and CrewAI without losing operational integrity. It solves the problem of 'silent failures' that occur when simple code translation misses framework-specific nuances in state management and async execution.

    The skill supports major Python and TypeScript frameworks including LangChain, LangGraph, CrewAI, AutoGen, and Haystack. It is also designed to work with custom orchestration layers by mapping core agentic behaviors to universal adapter patterns.

    Upon purchase, you receive the full migration engine, documentation for the structured migration protocol, and a suite of validation templates for parity testing. You also get access to the interface adapters used to normalize tool inputs across different runtimes.

    No, the skill prioritizes data integrity by mapping conversation history and checkpoints to the new framework's serialization format. It uses a non-destructive auditing process to ensure that your agent's 'personality' and long-term memory remain intact after the move.

    While the skill automates the mapping and translation of logic, you will need to provide source code access and verify the final validation traces. It does not perform live production data cutovers without manual authorization to ensure you maintain complete control over the deployment.

    New in Agents & Orchestration