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layout default
title Chapter 4: API and Webhook Integrations
nav_order 4
parent Refly Tutorial

Chapter 4: API and Webhook Integrations

Welcome to Chapter 4: API and Webhook Integrations. In this part of Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.

This chapter covers the two primary operational integration surfaces for Refly workflows.

Learning Goals

  • authenticate and call workflow APIs correctly
  • track execution state and retrieve outputs reliably
  • enable webhook-driven triggers with variable payloads
  • choose API vs webhook based on control requirements

API Integration Pattern

Step Endpoint Family Outcome
trigger run POST /openapi/workflow/{canvasId}/run receive execution ID
check status GET /openapi/workflow/{executionId}/status monitor state transitions
fetch output GET /openapi/workflow/{executionId}/output collect artifacts/results
abort if needed POST /openapi/workflow/{executionId}/abort controlled interruption

Webhook Usage Pattern

  • enable webhook from workflow integration settings
  • send variables payloads as JSON body
  • use file upload API first when passing file variables
  • monitor run history for runtime validation

Source References

Summary

You now have a production-style pattern for calling and monitoring Refly workflows programmatically.

Next: Chapter 5: Refly CLI and Claude Code Skill Export

Depth Expansion Playbook

Source Code Walkthrough

config/provider-catalog.json

The for interface in config/provider-catalog.json handles a key part of this chapter's functionality:

      "baseUrl": "https://api.siliconflow.cn/v1",
      "description": {
        "en": "SiliconFlow provides a one-stop cloud service platform with high-performance inference for top-tier large language and embedding models.",
        "zh-CN": "SiliconFlow 提供一站式云服务平台，为顶级大语言模型和嵌入模型提供高性能推理服务。"
      },
      "categories": ["llm", "embedding"],
      "documentation": "https://docs.siliconflow.cn/",
      "icon": "https://static.refly.ai/icons/providers/siliconflow.png"
    },
    {
      "name": "litellm",
      "providerKey": "openai",
      "baseUrl": "https://litellm.powerformer.net/v1",
      "description": {
        "en": "LiteLLM is a lightweight library to simplify LLM completion and embedding calls, providing a consistent interface for over 100 LLMs.",
        "zh-CN": "LiteLLM 是一个轻量级库，用于简化 LLM 的补全和嵌入调用，为 100 多个 LLM 提供一致的接口。"
      },
      "categories": ["llm", "embedding"],
      "documentation": "https://docs.litellm.ai/",
      "icon": "https://static.refly.ai/icons/providers/litellm.png"
    },
    {
      "name": "七牛云AI",
      "providerKey": "openai",
      "baseUrl": "https://api.qnaigc.com/v1",
      "description": {
        "en": "Qiniu AI provides efficient, stable, and secure model inference services, supporting mainstream open-source large models.",
        "zh-CN": "七牛云AI 提供高效、稳定、安全的模型推理服务，支持主流开源大模型。"
      },
      "categories": ["llm"],
      "documentation": "https://developer.qiniu.com/aitokenapi",
      "icon": "https://static.refly.ai/icons/providers/qiniu.png"

This interface is important because it defines how Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code implements the patterns covered in this chapter.

config/provider-catalog.json

The for interface in config/provider-catalog.json handles a key part of this chapter's functionality:

      "baseUrl": "https://api.siliconflow.cn/v1",
      "description": {
        "en": "SiliconFlow provides a one-stop cloud service platform with high-performance inference for top-tier large language and embedding models.",
        "zh-CN": "SiliconFlow 提供一站式云服务平台，为顶级大语言模型和嵌入模型提供高性能推理服务。"
      },
      "categories": ["llm", "embedding"],
      "documentation": "https://docs.siliconflow.cn/",
      "icon": "https://static.refly.ai/icons/providers/siliconflow.png"
    },
    {
      "name": "litellm",
      "providerKey": "openai",
      "baseUrl": "https://litellm.powerformer.net/v1",
      "description": {
        "en": "LiteLLM is a lightweight library to simplify LLM completion and embedding calls, providing a consistent interface for over 100 LLMs.",
        "zh-CN": "LiteLLM 是一个轻量级库，用于简化 LLM 的补全和嵌入调用，为 100 多个 LLM 提供一致的接口。"
      },
      "categories": ["llm", "embedding"],
      "documentation": "https://docs.litellm.ai/",
      "icon": "https://static.refly.ai/icons/providers/litellm.png"
    },
    {
      "name": "七牛云AI",
      "providerKey": "openai",
      "baseUrl": "https://api.qnaigc.com/v1",
      "description": {
        "en": "Qiniu AI provides efficient, stable, and secure model inference services, supporting mainstream open-source large models.",
        "zh-CN": "七牛云AI 提供高效、稳定、安全的模型推理服务，支持主流开源大模型。"
      },
      "categories": ["llm"],
      "documentation": "https://developer.qiniu.com/aitokenapi",
      "icon": "https://static.refly.ai/icons/providers/qiniu.png"

This interface is important because it defines how Refly Tutorial: Build Deterministic Agent Skills and Ship Them Across APIs and Claude Code implements the patterns covered in this chapter.

How These Components Connect

flowchart TD
    A[for]
    B[for]
    A --> B
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