Dify 1.13.0 (2026-02-11)
公式リリースノート: https://github.com/langgenius/dify/releases/tag/1.13.0
1.13.0 - Human-in-the-Loop and Workflow Execution Upgrades
このバージョンの記事
| # | テーマ | 状態 |
|---|---|---|
| 01-human-in-the-loop-hitl | 人間参加型ワークフロー (HITL) (1.13.0) | draft |
| 02-architecture-updates | アーキテクチャ更新 (1.13.0) | draft |
| 03-important-upgrade-note | 重要なアップグレード注意 (1.13.0) | draft |
| 04-configuration-changes | 設定変更 (1.13.0) | draft |
| 05-reliability-correctness | 信頼性と正確性 (1.13.0) | draft |
リリースノート抜粋
🚀 New Features
Human-in-the-Loop (HITL)
We are introducing the Human Input node, a major update that transforms how AI and humans
collaborate within Dify workflows.
Background
Previously, workflows were binary: either fully automated or fully manual. This created a "trust gap" in
high-stakes scenarios where AI speed is needed but human judgment is essential. With HITL, we are making h
uman oversight a native part of the workflow architecture, allowing you to embed
review steps directly into the execution graph.
Key Capabilities
- Native Workflow Pausing: Insert a "Human Input" node to suspend workflow execution at critical decision points.
- Review & Edit: The node generates a UI where humans can review AI outputs and modify variables (e.g., editing a draft or correcting data) before the process continues.
- Action-Based Routing: Configure custom buttons (like "Approve," "Reject," or "Escalate") that determine
the subsequent path of the workflow. - Flexible Delivery Methods: Human input forms can be delivered via Webapp or Email. In cloud environments, Email delivery availability may depend on plan/feature settings.
🛠 Architecture Updates
To support the stateful pause/resume mechanism required by HITL and provide event‑subscription APIs, we refactored the execution engine: Workflow‑based streaming executions and Advanced Chat executions now run in Celery workers, while non‑streaming WORKFLOW runs still execute in the API process.
All pause/resume paths (e.g., HITL) are resumed via Celery, and events are streamed back through Redis Pub/Sub.
For Large Deployments & Self-Hosted Users:
We have introduced a new Celery queue named workflow_based_app_execution. While standard setups will work out of the box, high-throughput environments should consider the following optimizations to ensure stability and performance:
- Scale Workers: Adjust the number of workers consuming the
workflow_based_app_executionqueue based on your specific workload. - Dedicated Redis (Optional): For large-scale deployments, we recommend configuring the new
PUBSUB_REDIS_URLenvironment variable to point to a dedicated Redis instance. Using Redis Cluster mode with Sharded PubSub is strongly advised to ensure horizontal scalability.
⚠️ Important Upgrade Note
New Celery Queue Required: workflow_based_app_execution
Please ensure your deployment c
…(以下省略、公式リリースノートを参照)
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