Migrating from PocketFlow

BrainyFlow originated as a fork of PocketFlow, aiming to refine its core concepts, enhance type safety (in both language ports), and improve the developer experience for building agentic systems. If you have an existing PocketFlow application, migrating to BrainyFlow involves several key changes. This guide focuses on migrating from typical PocketFlow patterns to the modern BrainyFlow (v2.0+).

Key Conceptual Differences & Changes

• Async* Classes Removed: BrainyFlow's Node and Flow are inherently async-capable in both Python and TypeScript. All AsyncNode, AsyncFlow, etc., from PocketFlow are removed. Simply make your prep, exec, post methods async and use await where appropriate.

• Memory Object:

  • BrainyFlow's Memory object is more central and refined.

  • PocketFlow's Params concept is absorbed into the Memory object's local store, typically populated via forkingData.

• Triggering Actions: In post, instead of return "action_name", you must use self.trigger("action_name", forking_data={...}) (Python) or this.trigger("action_name", { ... }) (TypeScript).

• Batch Processing (*BatchNode / *BatchFlow Removal): PocketFlow's specialized batch classes are removed. BrainyFlow handles batching via a "fan-out" pattern: a standard Node calls trigger multiple times in its post method, each call typically including item-specific forkingData. This is then orchestrated by a Flow (for sequential batching) or ParallelFlow (for concurrent batching).

• Flow.run() Result: Returns a structured ExecutionTree detailing the execution path, rather than a simple dictionary of results.

Why Async?

The move to async brings several benefits:

• Improved performance: Asynchronous code can handle I/O-bound operations more efficiently

• Better concurrency: Easier to implement parallel processing patterns

• Simplified codebase: No need for separate sync and async implementations

• Modern Python: Aligns with Python's direction for handling concurrent operations

Migration Steps

Step 1: Update Imports and Dependencies

• Replace all from pocketflow import ... with from brainyflow import ... (Python) or import { ... } from 'brainyflow' (TypeScript).

# Before
from pocketflow import Node, Flow, BatchNode # ... etc

# After
import asyncio
from brainyflow import Node, Flow # ... etc

• Update your requirements.txt or package.json to use brainyflow.

Step 2: Convert to Async and Update Method Signatures

• Add async before def for your prep, exec, post, and exec_fallback methods in Nodes and Flows.

• Remove any _async suffix from the method names.

• Add await before any calls to these methods, run() methods, asyncio.sleep(), or other async library functions.

Node Example (Before):

class MyNode(Node):
    def prep(self, shared):
        # Preparation logic
        return some_data

    def exec(self, prep_res):
        # Execution logic
        return result

    def post(self, shared, prep_res, exec_res):
        # Post-processing logic
        return action

    def exec_fallback(self, prep_res, exc):
        # Handle exception
        return fallback_result

Node Example (After):

class MyNode(Node):
    # Prefer using 'memory' parameter name for consistency
    async def prep(self, memory):
        # Preparation logic
        # If you call other async functions here, use await
        return some_data

    async def exec(self, prep_res):
        # Execution logic
        # If you call other async functions here, use await
        result = await some_async_task(prep_res)
        return result

    async def post(self, memory, prep_res, exec_res):
        # Post-processing logic
        # If you call other async functions here, use await
        memory.result = exec_res # Write to memory (global store)
        self.trigger(action) # Use trigger instead of returning action string

    async def exec_fallback(self, prep_res, exc):
        # Handle exception
        # If you call other async functions here, use await
        return fallback_result

Step 3: Use .trigger() for next actions

In all Node subclasses, within the post method:

• Replace any return "action_name" statements with self.trigger("action_name") (Python) or this.trigger("action_name") (TypeScript).

• If you were passing data to the next node's local context (PocketFlow's params), pass this data as the second argument to trigger (the forkingData object). Example: self.trigger("process_item", {"item": current_item})

• If post simply completed without returning an action (implying default), you can either explicitly call self.trigger("default) or rely on the implicit default trigger if no trigger calls are made.

Step 4: Update Batch Processing Implementation (*BatchNode / *BatchFlow Removal)

BrainyFlow removes all specialized BatchNode and BatchFlow classes. Batch functionality is achieved using standard Nodes and Flows combined with the "fan-out/fan-in" trigger pattern.

The batch functionality is now achieved using standard Nodes and Flows combined with a specific pattern:

1. Fan-Out (Map) Phase:

   All BatchNode need to be replaced by a fan-out flow. You can either implement a MapReduce pattern for it, or split the node into two, a Trigger Node and a Processor Node:

   The Prepare/Trigger Node (replaces the prep part of a BatchNode):

   • Use the prep method to fetch the list of items to process, as usual.

   • Use the post method to iterate through these items. For each item, calls self.trigger(action, forkingData={"item": current_item, "index": i, ...}). The forkingData dictionary passes item-specific data into the local memory of the triggered successor. (the action name can be any of your choice as long as you connect the nodes in the flow; e.g. process_one, default)

   • This node might also initialize an aggregate result structure in the global memory (e.g., memory.batch_results = {}).

   The Item Processor Node (replaces the exec_one part of a BatchNode):

   • Its prep method reads the specific item data (e.g., memory.item, memory.index) from its local memory (which was populated by forkingData from the trigger node).

   • The logic previously in the exec_one method of the BatchNode should now be in this node's exec method.

   • Its post method typically writes the individual item's result back to the global memory, often using an index or unique key (e.g., memory.batch_results[prep_res.index] = exec_res.item_result).

   Aggregation (Optional Fan-In Node): If you need to aggregate results after all items are processed (you probably should implement mapreduce), you might have the Item Processor Node also trigger an "aggregation_pending" action, and another (final) node conditionalized on all items being done (e.g., via a counter in global memory or by checking the length of results). Or, the Prepare/Trigger node itself might have a separate trigger for an aggregation step after it has fanned out all items.

2. Choose the Right Flow:

   • Wrap the TriggerNode and ProcessorNode in a standard brainyflow.Flow if you need items processed sequentially.

   • Wrap them in a ParallelFlow if you need items processed concurrently.

3. Rename All Classes:

   • Replace AsyncParallelBatchFlow with ParallelFlow.

   • Replace AsyncParallelBatchNode, ParallelBatchNode, AsyncBatchNode, BatchNode with the standard Node.

   • Replace AsyncBatchFlow, BatchFlow with brainyflow.Flow.

   • Remember to make prep, exec, post methods async as per Step 2.

Step 6: Python NodeError Protocol

If you were catching NodeError exceptions, note that in Python it's now a typing.Protocol. This means you'd typically catch the underlying error (e.g., ValueError) and then check if it conforms to NodeError via isinstance(error, NodeError) if you need to access error.retry_count. For TypeScript, it remains an Error subtype.

Step 6: Run with asyncio:

Ensure your main application entry point uses asyncio.run() (Python) or Promise.all()/async functions (TypeScript) to execute your flows.

import asyncio

async def main():
    # ... setup your BrainyFlow nodes/flows ...
    memory = {}
    result = await my_flow.run(memory) # Use await and pass memory object
    print(result)
    print(memory)

if __name__ == "__main__":
    asyncio.run(main())

Summary of Key Migration Points:

1. Updating imports to brainyflow and adding import asyncio.

2. Adding async to your Node/Flow method definitions (prep, exec, post, exec_fallback) and removing any _async suffix from the method names.

3. Replacing any return action in post() with self.trigger(action, forking_data={...}) (Python) or this.trigger(action, { ... }) (TypeScript).

4. Using await when calling run() methods and any other asynchronous operations within your methods.

5. Refactoring BatchNode/BatchFlow usage to the fan-out pattern using standard Nodes orchestrated by Flow or ParallelFlow.

6. Running your main execution logic within an async def main() function called by asyncio.run().

This transition enables you to leverage the performance and concurrency benefits of asynchronous programming in your workflows.