b79b8a879b
Workflow KnowledgeFS retrieval nodes ran through `retrieval-tests`, which never wrote an AnswerTrace or an overview event, so their queries were missing from the retrieval history and from the space overview; the history also only ever showed each member their own traces. - AnswerTrace gains a `source` (retrieval_test | workflow | service_api | agent | mcp; migration 0051_answer_trace_source) derived from the Capability v2 caller kind. The retrieval-tests route records one trace per run (stages, evidence bundle, profile metadata), returns its id as `answerTraceId`, and the workflow node's failed-retrieval capture attaches to that trace instead of creating a second record. - The quality trace list exposes and filters by `source`; traces from other caller kinds are visible to any current reader of the space, and counts and scores fall back to the evidence embedded in the trace when no bundle row exists. - Overview accounting: retrieval-tests and Research tasks now emit `query.requested`, and the Research job state machine emits `query.completed` / `query.failed` on terminal stages (wired for both the in-process gateway and the durable runtime), so query volume, answer rate and outcomes include every caller. Activity details keep `source` and `taskKind`. - Console and service trace routes accept a `source` filter; the retrieval test page shows a source badge and an all / retrieval tests / workflow filter, with translations for every locale. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_015zw5G5SX3HmVfnZof6YWAc |
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| dify-agent | ||
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| test-results/knowledge-fs/2026-08-11 | ||
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| LICENSE | ||
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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 capabilities: You can define agents based on LLM Function Calling or ReAct, and add pre-built or custom tools for the agent. Dify provides 50+ built-in tools for AI agents, such as Google Search, DALL·E, Stable Diffusion and WolframAlpha.
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 for enterprise / organizations
We provide additional enterprise-centric features. Send us an email to discuss your enterprise needs.
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.