README.md

Firebase Genkit <> Graph Workflows Plugin

Graph Workflows Community Plugin for Google Firebase Genkit

genkitx-graph is a community plugin for creating graph-based workflows with Firebase Genkit. Built by The Fire Company. 🔥

Installation

Install the plugin in your project with your favorite package manager:

• npm install genkitx-graph
• yarn add genkitx-graph
• pnpm add genkitx-graph

Introduction

genkitx-graph is a TypeScript plugin for Firebase Genkit that enables developers to easily build graph-based workflows for AI agents. This plugin provides a powerful and flexible way to create complex, multi-step processes with branching logic and state management.

Key Concepts

• Graph: A collection of nodes connected by edges, representing a workflow.
• Node: A single step in the workflow, which can process input, modify state, and determine the next step.
• State: Current state of the workflow. Data that persists between nodes in the graph.
• Input: The initial data provided to start the graph execution.
• Output: The final result produced by the graph.
• Stream: Intermediate data that can be sent during graph execution.

Usage

Import the necessary functions

import { defineGraph } from 'genkitx-graph';
import { defineFlow, streamFlow, runFlow } from '@genkit-ai/flow';
import { z } from 'zod';

Define your graph

Use the defineGraph function to create a new graph:

const graph = defineGraph(
  {
    name: 'MyGraph',
    stateSchema: z.object({
      // Define your state schema here
    }),
    inputSchema: z.object({
      // Define your input schema here
    }),
    outputSchema: z.object({
      // Define your output schema here
    }),
    streamSchema: z.object({
      // Define your stream schema here (optional)
    }),
  },
  async (input) => {
    // Define your entrypoint logic here
    return {
      state: {
        /* initial state */
      },
      nextNode: 'firstNode',
    };
  }
);

defineGraph has a simillar signature to defineFlow (because it builds an executor flow under the hood) with 2 important changes:

1. stateSchema: stateSchema defines the Schema for the state object which will be passed to every node.

2. entrypoint: entrypoint, as the name suggests, is the entrypoint of your graph. The endpoint must take in the input and provide the initial state along with the name of the nextNode.

Adding nodes to your graph

Use the addNode function to add nodes to your graph:

graph.addNode(
  defineFlow(
    {
      name: 'firstNode',
      // Define input/output schemas if needed
    },
    async (state) => {
      // Node logic here
      return {
        state: {
          /* updated state */
        },
        nextNode: 'secondNode',
      };
    }
  )
);

graph.addNode(
  defineFlow(
    {
      name: 'secondNode',
    },
    async (state) => {
      // Node logic here
      return {
        /* final output */
      };
    }
  )
);

Nodes are the core construct of genkitx-graph.

Each Node is a Flow with a specific signature. Each Node must have the inputSchema of stateSchema defined in the graph and must return either an object with two properties: state which is the modified state and nextNode which is the name of the next node , or an object with the same schema as the Graph's outputSchema. If you are using typecript you don't need to add them to each Node seperately.

Each Node takes the current state of the workflow as an input, uses it, modifies it and returns either the updated state with next node or a final output.

This approach rather than the traditional approach of defined nodes and edges provides a high degree of flexibility to build complex Agentic workflows. We can use LLMs and traditional logic to decide which node should be next and return an output from any node.

Executing the graph

To execute the graph, use the runFlow function with your flow and input:

const result = await runFlow(flow, {
  /* input data */
});

If any node returns an object conforming to the Graph's outputSchema then that value is returned as the Graph's output and the execution finishes

Cleanup before finish

We can execute any arbitrary function before the graph exution finishes using the beforeFinish callback in defineGraph:

const graph = defineGraph(
  {
    name: 'MyGraph',
    stateSchema: z.object({
      // Define your state schema here
    }),
    inputSchema: z.object({
      // Define your input schema here
    }),
    outputSchema: z.object({
      // Define your output schema here
    }),
    streamSchema: z.object({
      // Define your stream schema here (optional)
    }),
  },
  async (input) => {
    // Define your entrypoint logic here
    return {
      state: {
        /* initial state */
      },
      nextNode: 'firstNode',
    };
  }
  async (state, output) => {
    // Do anything with graph state and output
  }
);

The most common usage of beforeFinish is storing the graph state and output in a database.

Together with entrypoint callback this enables graphs to have memory like a chat history.

Basic example

// ...configure Genkit (as shown above)...

const graph = defineGraph(
  {
    name: 'MultiStepGraph',
    inputSchema: z.object({ text: z.string(), iterations: z.number() }),
    outputSchema: z.string(),
  },
  async (input) => {
    return {
      state: { text: input.text, iterations: input.iterations, count: 0 },
      nextNode: 'processText',
    };
  }
);

graph.addNode(
  defineFlow(
    {
      name: 'processText',
    },
    async (state) => {
      state.text = state.text.toUpperCase();
      state.count++;

      return {
        state,
        nextNode:
          state.count < state.iterations ? 'processText' : 'finalizeOutput',
      };
    }
  )
);

graph.addNode(
  defineFlow(
    {
      name: 'finalizeOutput',
    },
    async (state) => {
      return `Processed ${state.count} times: ${state.text}`;
    }
  )
);

// Run the graph
const result = await runFlow(graph.executor, {
  text: 'hello world',
  iterations: 3,
});
console.log(result); // Outputs: "Processed 3 times: HELLO WORLD"

Advanced Features

Streaming

You can use the streamingCallback function to handle streaming data from nodes:

const graph = defineGraph(
  {
    name: 'StreamingGraph',
    inputSchema: z.string(),
    streamingSchema: z.string(),
  },
  async (input) => {
    return {
      state: input,
      nextNode: 'streamingNode',
    };
  }
);

graph.addNode(
  defineFlow(
    {
      name: 'streamingNode',
    },
    async (state, streamingCallback) => {
      // ...
      const result = await generate({
        model: //any model
        prompt: `tell me a joke about ${input}`,
        streamingCallback
      });

      // ...
    }
  )
);

Use streaming for long-running processes or when you need to provide real-time updates.

Note

Steaming does not stop the execution of the graph

Contributing

Want to contribute to the project? That's awesome! Head over to our Contribution Guidelines.

Need support?

Note

This repository depends on Google's Firebase Genkit. For issues and questions related to Genkit, please refer to instructions available in Genkit's repository.

Reach out by opening a discussion on Github Discussions.

Credits

This plugin is proudly maintained by the team at The Fire Company. 🔥

License

This project is licensed under the Apache 2.0 License.