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Chat2Workflow

Awesome License arXiv Hugging Face Clawhub

Table of Contents

🔔 News

👀Overview

Demostration

demo

Benchmark

main_picture

📁Directory

Directory Description
.agents/skills/chat2workflow/ The skills used for agentic workflow generation
case_files/ All files required for the test cases
dataset/ Workflow generation instructions and evaluation checks
experiment_run_example/ An example of the results from a single experiment run
assets/ The images used in README.md
nodes/ The functional logic of each node
prompts/ System prompt and evaluation prompts
yaml/ The generated Dify workflow YAML files, you can obtain them from
https://huggingface.co/datasets/zjunlp/Chat2Workflow-Evaluation

🔧Installation

Conda Environment Configuration

Conda virtual environments offer a light and flexible setup. For different projects, we recommend using separate conda environments for management.

conda create -n chat2workflow python=3.10
conda activate chat2workflow
pip install -r requirements.txt

Deploy Dify with Docker Compose

Before installing Dify, make sure your machine meets the following minimum system requirements:

  • CPU >= 2 Core
  • RAM >= 4 GiB

Obtain the specified version of dify:

git clone https://github.com/langgenius/dify.git --branch 1.9.2 --depth 1

The easiest way to start the Dify server is through Docker Compose. Before running Dify with the following commands, make sure that Docker and Docker Compose are installed on your machine:

cd dify
cd docker
cp .env.example .env

# [Optional]: The default port is 80. You can modify it (MY_PORT) here.
# If modified, it needs to be synchronized to `config.yaml`.
perl -pi -e 's/^EXPOSE_NGINX_PORT=.*/EXPOSE_NGINX_PORT={MY_PORT}/' .env

docker compose up -d

After running, you can access the Dify dashboard in your browser at http://localhost:{MY_PORT}/install and start the initialization process.

Dify Initialization

  1. Setting up an admin account. Also fill the following information into config.yaml.
  • email_address
  • user_name
  • password

  1. Install the following specified version plugins in [Plugins]-[MarketPlace]:
  • langgenius/tongyi:0.1.13 —— API Key Configuration in [Settings]-[WORKSPACE]-[Model Provider]
  • langgenius/openai:0.2.7 —— API Key Configuration in [Settings]-[WORKSPACE]-[Model Provider]
  • sawyer-shi/tongyi_aigc:0.0.1 —— API Key Configuration in [Plugins]
  • langgenius/google:0.0.9 —— API Key Configuration in [Plugins]
  • bowenliang123/md_exporter:2.2.0
  • hjlarry/mermaid_converter:0.0.1
  • langgenius/echarts:0.0.1

plugin_version

In this setup, the LLM defaults to tongyi:qwen3-vl-plus, TTS (Text-to-Speech) to openai:gpt-4o-mini-tts, image generation to tongyi_aigc:z-image-turbo, search engines to google:SerpApi, Question Classifier and Parameter Extractor to tongyi:qwen3-max. After the workflow is generated, you can modify the above nodes as needed.

Opencode Initialization (Optional)

The OpenCode framework is only required for the agentic generation mode.

Obtain the specified version of opencode:

curl -fsSL https://opencode.ai/install | VERSION=1.3.17 bash

Please ensure that the APIs for the necessary models are configured in advance.

🧐Evaluation

1. Fill in the information in the config.yaml.

# Github REST API for higher rate limits.
# Used for the GithubSummary task in the resolve stage.
github_rest_token: "github_xxx" # null or "github_xxx"

# Your admin account
user_name: "xxx"
email_address: "xxx@yyy.com"
password: "xxxxx"

# LLM API for workflow generation and evaluation
llm_api_key: "sk-xxxxxx"
base_url: "xxxxx"
evaluation_model: deepseek-chat

# (Optional) —— Required for agentic generation
# OpenCode binary path, supports ~ in path. You can get this path by running `which opencode` in your terminal.
opencode_bin: "~/.opencode/bin/opencode"

2. Generate LLM response.

  • Zero-Shot Mode: Modify the model_name and then execute the script.
# The result will be stored in `output/llm_response`.
bash bash_generation.sh
  • Agentic Mode: Modify the model and then execute the script.

Note:

  • Model Format: The model parameter must follow the provider/name format (e.g., deepseek/deepseek-chat).
  • Available Models: You can view the list of supported models by running the opencode models command.
  • Prerequisite: Ensure that the corresponding API key for your selected model is configured in OpenCode prior to execution.
# The result will be stored in `output/llm_response`.
bash bash_opencode_generation.sh

3. Evaluate the LLM response.

# Step 1: The pass stage of the evaluation.
# The result will be stored in `output/pass_eval` and `output/yaml`.
bash bash_pass_stage.sh

# Step 2: The resolve stage of the evaluation.
# The result will be stored in `output/resolve_eval`.
bash bash_resolve_stage.sh

4. Obtain the evaluation results.

python statistics.py

💻Generation

We provide two interactive approaches for generating workflows.

Launching the Interactive Demo (Zero-Shot Mode + Autofix)

  1. Fill in the information in the config.yaml.
# LLM API for workflow generation and evaluation
llm_api_key: "sk-xxxxxx"
base_url: "xxxxx"
  1. Run the Python script to start the workflow generation program.
chainlit run chat2workflow.py -w

Click on the returned link to start the interactive conversation. The result will be stored in output/generated_workflows. Finally import the generated YAML file into the Dify or Coze platform for execution.

Generating via OpenCode CLI (Agentic Mode + Autofix)

Note:

  • The default SKILL (.agents/skills/chat2workflow) is optimized for automated benchmark evaluation.
  • For real-world interactive applications, please use skill_chat2workflow_interactive. To enable it, replace the default folder in .agents/skills/ with this interactive version, ensuring the directory remains named chat2workflow. We have published this SKILL on Clawhub.
  1. Fill in the information in the config.yaml.
# OpenCode binary path, supports ~ in path. You can get this path by running `which opencode` in your terminal.
opencode_bin: "~/.opencode/bin/opencode"
  1. Launch the OpenCode interactive CLI.
opencode

Once the model generates the target response, it can be prompted to automatically perform self-correction and synthesize the corresponding configuration file.

Optionally, users can manually execute bash_converter.sh to convert the generated responses into the target configuration file.

⚙️Conversion

bash_converter.sh

python converter.py \
    --json_path test.json \ # The JSON file path
    --name test \ # The name of the workflow
    --output_path output/converter \ # The output path
    --type dify # dify or coze

In most cases, the JSON format can be seamlessly converted for both Dify and Coze. In rare instances where the conversion fails, platform-specific system prompts should be used for generation (refer to prompts/builder_prompt.txt and prompts/builder_prompt_coze.txt).