Charles YaoandClaude Opus 5
71cb5688ac
The question-classifier default shipped two `classes` with blank names. That
turns a loud preflight refusal into a node that is written, reported as a
success, and silently classifies into two meaningless categories -- and the
fabricated ids `["1", "2"]` pin `declared_branch_handles`, so a semantic
`connect` is dropped while the create survives. That is the failure shape the
registry exists to eliminate.
Draw the line explicitly and apply it to every entry: a default may supply a
field whose empty value is semantically NEUTRAL -- a container, a mode, a config
or auth block -- but never one that carries the node's PURPOSE, where an empty
value produces a node that RUNS and yields nothing. Dropped, with the engine
behaviour each empty value would have produced:
end.outputs End runs OK, so run_finished_without_output is
False -> "All checks passed" on an empty result
answer.answer streams nothing
template-transform.template renders ""
variable-aggregator.variables SUCCEEDED, outputs={} -- triage cause (d)
question-classifier.classes two blank categories + fabricated handle ids
question-classifier.query_variable_selector classifies `query or ""`
knowledge-retrieval.dataset_ids retrieves from no dataset, succeeds empty
The whole `if-else` entry goes too, though nothing is gained or lost by it: its
one case has an empty `conditions` list that graphon combines with `all([])`, so
it always matches -- but `iter_cases()` synthesizes that identical case when
`cases` is absent, so dropping the entry does not cure it. It only stops us
writing a fabricated always-true condition that makes the node look configured.
An empty value that fails LOUDLY is not in that category: there is no silence to
protect against, and dropping it would pay the full batch-refusal cost for
nothing. Verified loud, so these stay -- `llm.model` (never launches, "model not
configured"), `llm.prompt_template` (empty messages are filtered out, leaving
zero, so graphon raises `NoPromptFoundError`), `code.code` (the sandbox runner
calls `main(**inputs_obj)` with no `main` defined), `code.outputs` (graphon
raises `CodeNodeError("Not all output parameters are validated.")` for any key
the code returns), and `http-request.url` (`Executor._init_url` raises
`InvalidURLError("url is required")` before any request goes out).
`template-transform.variables` stays too -- it is structural, and it is the
exact field the live F4 failure was missing.
The placeholder agent genuinely means "no dataset, no output", so it now says
so itself instead of borrowing a default that would say it for every caller.
The engine sweep is re-pinned in both directions: the fields the engine reports
missing from a node built from a default ALONE must be exactly the purpose
fields the registry refuses to fabricate -- no more (a structural regression)
and no fewer (a silent-success regression).
Also: name the `filter_applicable` test after what it actually asserts, since
that function does not return its working graph; and reword the comment in
`graph_normalizers` that quoted a bare `type` marker, one reflow away from
being read as a type comment and silently killing the type check again.
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.