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How to use Goal Flow: what you can do with it

What is Goal Flow? GoalFlow is a Python framework on LangGraph that transpiles Dify workflow exports into runnable code and runs code-first agent loops behind a FastAPI server.

How do I use it? Run git clone on the goalflow repository and cd goalflow; then run cp .env.example .env and fill in the values you need; run pip install -e . to put the goalflow package on your path. Full steps below.

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What do you want to do?

4 use cases

  • Turn a Dify flow into a version-controlled LangGraph file

    Convert a Dify DSL export into a runnable LangGraph Python workflow you own.

    View prompt
    In the goalflow repo, transpile the Dify DSL export at <path-to-dsl.yml> using the Dify transformer in src/goalflow/tool/dify_transformer/. Write the generated Python workflow class to <output-path>.py. Verify that the file defines a class that extends BaseWorkflow and report any Dify node types the transformer could not map.

  • Serve a workflow as a chat API with Redis and MySQL

    Run the GoalFlow FastAPI server with persistence so clients can call your workflow.

    View prompt
    Start the goalflow server in the goalflow repo and send one POST request to /v1/chat-messages for the workflow <workflow-name> using the Bearer token <token>. Expected output is a streamed SSE response. Verify that the conversation is stored in Redis and MySQL.

  • Build a code-first agent loop with agent_kit

    Use the vendored agent_kit SDK to create a ReAct or custom agent and run it inside a graph node.

    View prompt
    In the goalflow repo, create a ReAct agent with agent_kit under src/agent_kit/ that answers questions about <topic> using the skills in skills/. Save the code to <output-path>.py. Verify that the agent imports cleanly and report which middleware and model routing options you configured.

  • Load documents into Milvus for retrieval in a workflow

    Extract, chunk, and embed your documents into Milvus, then point a workflow at the retriever.

    View prompt
    In the goalflow repo, follow docs/knowledge-retrieval.md to ingest the documents in <document-folder> into Milvus. Expected output is a Milvus collection with embedded chunks. Verify the ingestion count matches the number of input documents and run one retrieval query for <question>.

Quick start with Goal Flow

  1. Check you have what it needs: Python 3.12; Redis (cluster or standalone) and MySQL; A cloned copy of the goalflow repository and a filled-in .env file copied from .env.example.
  2. Open wanmol/goal-flow on GitHub and read the README and license.
  3. Run git clone on the goalflow repository and cd goalflow.
  4. Run cp .env.example .env and fill in the values you need.
  5. Run pip install -e . to put the goalflow package on your path.
  6. Run the Dify transformer at src/goalflow/tool/dify_transformer/wf_code_generator.py against your exported Dify DSL .yml file to emit a workflow .py file. (inferred, check the README)

These steps come from our top use case, Turn a Dify flow into a version-controlled LangGraph file, which also has a copy-paste agent prompt.

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About this repo

  • Who it is forPython developers and AI agents who build LLM apps, especially teams that design flows visually in Dify and want to run them without Dify's runtime.
  • Why it is hot135 stars, launched 81 days ago
  • LanguagePython
  • Sourcegithub.com/wanmol/goal-flow · Back to Hot repos

Requirements

  • Python 3.12
  • Redis (cluster or standalone) and MySQL
  • A cloned copy of the goalflow repository and a filled-in .env file copied from .env.example
  • A Dify DSL export (.yml) if you want to transpile a visual flow

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Questions about Goal Flow

What is Goal Flow?

GoalFlow is a Python framework on LangGraph that transpiles Dify workflow exports into runnable code and runs code-first agent loops behind a FastAPI server. It is an open-source project on GitHub: https://github.com/wanmol/goal-flow.

How do I use Goal Flow?

Run git clone on the goalflow repository and cd goalflow; then run cp .env.example .env and fill in the values you need; run pip install -e . to put the goalflow package on your path. The use cases on this page give the full steps and a copy-paste agent prompt for each job.

Who is Goal Flow for?

Python developers and AI agents who build LLM apps, especially teams that design flows visually in Dify and want to run them without Dify's runtime.

What can I do with Goal Flow?

4 use cases so far, for example: Turn a Dify flow into a version-controlled LangGraph file; Serve a workflow as a chat API with Redis and MySQL; Build a code-first agent loop with agent_kit. Each has steps and a copy-paste agent prompt.

What do I need to run Goal Flow?

Python 3.12; Redis (cluster or standalone) and MySQL; A cloned copy of the goalflow repository and a filled-in .env file copied from .env.example; A Dify DSL export (.yml) if you want to transpile a visual flow.

What are alternatives to Goal Flow?

Other hot AI repos in Software and DevOps: Deep Infer, Harness2, Orca, Opencode, Graphify.

Is Goal Flow safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.