Charles YaoandClaude Opus 5
f665ddcec2
Code review finding: handle_goal_analysis built its own ProgressReporter (build-review-requirements/build-draft-plan) and finish()ed it, then tail-called _discover_and_offer_resources, which built a SECOND reporter under the SAME env.operation_id. ProgressReporter's revision counter starts at 0 per instance, so the second reporter's activate/finish calls restarted revision at 1 -- two progress events with revision 1 (and 2, and 3) under one operation_id. contract.py's ProgressEventData docstring states revision is monotonic within an operation_id; every other handler across the three handler files uses exactly one reporter per step -- this was the sole exception, on the most-travelled build path. Fix: _discover_and_offer_resources now takes an optional `progress` parameter. handle_goal_analysis passes its live (still-open) reporter through instead of finishing it early; the helper adds the discovery steps onto that SAME reporter via add_steps() and is the one that finish()es it. handle_initial_plan (the continue_adjusting/retry_after_revert loop-back, which has no reporter of its own -- a separate CAS commit/operation) still gets progress=None and the helper builds a fresh one, unchanged. Added test_submitting_requirements_progress_is_one_operation_with_monotonic_ revisions: drives the real Runner with submit_requirements, captures every emitted ProgressEventData, and asserts a single operation_id with strictly increasing, non-repeating revisions across all four progress steps. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Dify Cloud · Self-hosting · Documentation · Dify edition overview
Dify is an open-source LLM app development platform. Its intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features (including Opik, Langfuse, and Arize Phoenix) and more, letting you quickly go from prototype to production. Here's a list of the core features:
Quick start
Before installing Dify, make sure your machine meets the following minimum system requirements:
- CPU >= 2 Core
- RAM >= 4 GiB
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 v2.24.0 or later are installed on your machine:
cd dify
cd docker
cp .env.example .env
docker compose up -d
After running, you can access the Dify dashboard in your browser at http://localhost/install and start the initialization process.
Seeking help
Please refer to our FAQ if you encounter problems setting up Dify. Reach out to the community and us if you are still having issues.
If you'd like to contribute to Dify or do additional development, refer to our guide to deploying from source code
Key features
1. Workflow: Build and test powerful AI workflows on a visual canvas, leveraging all the following features and beyond.
2. Comprehensive model support: Seamless integration with hundreds of proprietary / open-source LLMs from dozens of inference providers and self-hosted solutions, covering GPT, Mistral, Llama3, and any OpenAI API-compatible models. A full list of supported model providers can be found here.
3. Prompt IDE: Intuitive interface for crafting prompts, comparing model performance, and adding additional features such as text-to-speech to a chat-based app.
4. RAG Pipeline: Extensive RAG capabilities that cover everything from document ingestion to retrieval, with out-of-box support for text extraction from PDFs, PPTs, and other common document formats.
5. Agent: Autonomous agents with a sandbox of their own: they run commands, install software, and handle files to get open-ended tasks done. Describe the agent you want and it builds itself. Give it skills, connect tools from Dify Marketplace, MCP servers, or your own APIs, and put it to work as a chat app or as a step in any workflow.
6. LLMOps: Monitor and analyze application logs and performance over time. You could continuously improve prompts, datasets, and models based on production data and annotations.
7. Backend-as-a-Service: All of Dify's offerings come with corresponding APIs, so you could effortlessly integrate Dify into your own business logic.
Using Dify
-
Cloud
We host a Dify Cloud service for anyone to try with zero setup. It provides all the capabilities of the self-deployed version, and includes 200 free GPT-4 calls in the sandbox plan. If you run into issues with Dify Cloud, contact our Cloud support team. -
Self-hosting Dify Community Edition
Quickly get Dify running in your environment with this starter guide. Use our documentation for further references and more in-depth instructions. -
Dify Enterprise
For organizations requiring self-hosting, SSO, RBAC Security and Enterprise Support SLAs, please fill out this form to speak to a solution representative.
Staying ahead
Star Dify on GitHub and be instantly notified of new releases.
Advanced Setup
For custom configuration, observability, and deployment options, see Advanced Setup.
Contributing
Dify welcomes contributions of all kinds:
- Code: Read the Contribution Guide, then browse good first issues.
- Ideas and feedback: Start or join a GitHub Discussion.
- Translations: Follow the internationalization guide to add or update a locale.
- Community: Share the apps you build, help other users, and spread the word about Dify.
Contributors
Community & contact
Choose the channel that best fits your question:
- GitHub Discussions: Get help, share feedback, and propose ideas.
- GitHub Issues: Report reproducible bugs and track engineering work. Read the Contribution Guide before opening one.
- Discord: Chat in real time, share your apps, and connect with other Dify users.
- (https://x.com/dify_ai): Follow Dify for release news and project updates.
Star History
Security disclosure
To protect your privacy, please avoid posting security issues on GitHub. Instead, report issues to security@dify.ai, and our team will respond with detailed answer.
License
This repository is licensed under the Dify Open Source License, based on Apache 2.0 with additional conditions.