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wh/dify

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Apache-2.0

![cover-v5-optimized](./images/GitHub_README_if.png)

📌 <a href="https://dify.ai/blog/introducing-dify-workflow-file-upload-a-demo-on-ai-podcast">Introducing Dify Workflow File Upload: Recreate Google NotebookLM Podcast</a>

<a href="https://cloud.dify.ai">Dify Cloud</a> ·
 <a href="https://docs.dify.ai/getting-started/install-self-hosted">Self-hosting</a> ·
 <a href="https://docs.dify.ai">Documentation</a> ·
 <a href="https://dify.ai/pricing">Dify edition overview</a>

Dify is an open-source platform for developing LLM applications. Its intuitive interface combines agentic AI workflows, RAG pipelines, agent capabilities, model management, observability features, and more—allowing you to quickly move from prototype to production.

## 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](docker/docker-compose.yaml). Before running Dify with the following commands, make sure that [Docker](https://docs.docker.com/get-docker/) and [Docker Compose](https://docs.docker.com/compose/install/) are installed on your machine:

```bash
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](http://localhost/install) and start the initialization process.

#### Seeking help

Please refer to our [FAQ](https://docs.dify.ai/getting-started/install-self-hosted/faqs) if you encounter problems setting up Dify. Reach out to [the community and us](#community--contact) 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](https://docs.dify.ai/getting-started/install-self-hosted/local-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](https://docs.dify.ai/getting-started/readme/model-providers).

![providers-v5](https://github.com/langgenius/dify/assets/13230914/5a17bdbe-097a-4100-8363-40255b70f6e3)

**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](https://dify.ai) 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.

- **Self-hosting Dify Community Edition **
 Quickly get Dify running in your environment with this [starter guide](#quick-start).
 Use our [documentation](https://docs.dify.ai) for further references and more in-depth instructions.

- **Dify for enterprise / organizations **
 We provide additional enterprise-centric features. [Send us an email](mailto:business@dify.ai?subject=%5BGitHub%5DBusiness%20License%20Inquiry) to discuss your enterprise needs.

> For startups and small businesses using AWS, check out [Dify Premium on AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6) and deploy it to your own AWS VPC with one click. It's an affordable AMI offering with the option to create apps with custom logo and branding.

## Staying ahead

Star Dify on GitHub and be instantly notified of new releases.

![star-us](https://github.com/langgenius/dify/assets/13230914/b823edc1-6388-4e25-ad45-2f6b187adbb4)

## Advanced Setup

### Custom configurations

If you need to customize the configuration, please refer to the comments in our [.env.example](docker/.env.example) file and update the corresponding values in your `.env` file. Additionally, you might need to make adjustments to the `docker-compose.yaml` file itself, such as changing image versions, port mappings, or volume mounts, based on your specific deployment environment and requirements. After making any changes, please re-run `docker-compose up -d`. You can find the full list of available environment variables [here](https://docs.dify.ai/getting-started/install-self-hosted/environments).

#### Customizing Suggested Questions

You can now customize the "Suggested Questions After Answer" feature to better fit your use case. For example, to generate longer, more technical questions:

```bash
# In your .env file
SUGGESTED_QUESTIONS_PROMPT='Please help me predict the five most likely technical follow-up questions a developer would ask. Focus on implementation details, best practices, and architecture considerations. Keep each question between 40-60 characters. Output must be JSON array: ["question1","question2","question3","question4","question5"]'
SUGGESTED_QUESTIONS_MAX_TOKENS=512
SUGGESTED_QUESTIONS_TEMPERATURE=0.3
```

See the [Suggested Questions Configuration Guide](docs/suggested-questions-configuration.md) for detailed examples and usage instructions.

### Metrics Monitoring with Grafana

Import the dashboard to Grafana, using Dify's PostgreSQL database as data source, to monitor metrics in granularity of apps, tenants, messages, and more.

- [Grafana Dashboard by @bowenliang123](https://github.com/bowenliang123/dify-grafana-dashboard)

### Deployment with Kubernetes

If you'd like to configure a highly-available setup, there are community-contributed [Helm Charts](https://helm.sh/) and YAML files which allow Dify to be deployed on Kubernetes.

- [Helm Chart by @LeoQuote](https://github.com/douban/charts/tree/master/charts/dify)
- [Helm Chart by @BorisPolonsky](https://github.com/BorisPolonsky/dify-helm)
- [Helm Chart by @magicsong](https://github.com/magicsong/ai-charts)
- [YAML file by @Winson-030](https://github.com/Winson-030/dify-kubernetes)
- [YAML file by @wyy-holding](https://github.com/wyy-holding/dify-k8s)
- [🚀 NEW! YAML files (Supports Dify v1.6.0) by @Zhoneym](https://github.com/Zhoneym/DifyAI-Kubernetes)

#### Using Terraform for Deployment

Deploy Dify to Cloud Platform with a single click using [terraform](https://www.terraform.io/)

##### Azure Global

- [Azure Terraform by @nikawang](https://github.com/nikawang/dify-azure-terraform)

##### Google Cloud

- [Google Cloud Terraform by @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)

#### Using AWS CDK for Deployment

Deploy Dify to AWS with [CDK](https://aws.amazon.com/cdk/)

##### AWS

- [AWS CDK by @KevinZhao (EKS based)](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
- [AWS CDK by @tmokmss (ECS based)](https://github.com/aws-samples/dify-self-hosted-on-aws)

#### Using Alibaba Cloud Computing Nest

Quickly deploy Dify to Alibaba cloud with [Alibaba Cloud Computing Nest](https://computenest.console.aliyun.com/service/instance/create/default?type=user&ServiceName=Dify%E7%A4%BE%E5%8C%BA%E7%89%88)

#### Using Alibaba Cloud Data Management

One-Click deploy Dify to Alibaba Cloud with [Alibaba Cloud Data Management](https://www.alibabacloud.com/help/en/dms/dify-in-invitational-preview/)

#### Deploy to AKS with Azure Devops Pipeline

One-Click deploy Dify to AKS with [Azure Devops Pipeline Helm Chart by @LeoZhang](https://github.com/Ruiruiz30/Dify-helm-chart-AKS)

## Contributing

For those who'd like to contribute code, see our [Contribution Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
At the same time, please consider supporting Dify by sharing it on social media and at events and conferences.

> We are looking for contributors to help translate Dify into languages other than Mandarin or English. If you are interested in helping, please see the [i18n README](https://github.com/langgenius/dify/blob/main/web/i18n-config/README.md) for more information, and leave us a comment in the `global-users` channel of our [Discord Community Server](https://discord.gg/8Tpq4AcN9c).

## Community & contact

- [GitHub Discussion](https://github.com/langgenius/dify/discussions). Best for: sharing feedback and asking questions.
- [GitHub Issues](https://github.com/langgenius/dify/issues). Best for: bugs you encounter using Dify.AI, and feature proposals. See our [Contribution Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
- [Discord](https://discord.gg/FngNHpbcY7). Best for: sharing your applications and hanging out with the community.
- [X(Twitter)](https://twitter.com/dify_ai). Best for: sharing your applications and hanging out with the community.

**Contributors**

## Star history

[![Star History Chart](https://api.star-history.com/svg?repos=langgenius/dify&type=Date)](https://star-history.com/#langgenius/dify&Date)

## 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](LICENSE), based on Apache 2.0 with additional conditions.

# Open Source License Dify is licensed under a modified version of the Apache License 2.0, with the following additional conditions: 1. Dify may be utilized commercially, including as a backend service for other applications or as an application development platform for enterprises. Should the conditions below be met, a commercial license must be obtained from the producer: a. Multi-tenant service: Unless explicitly authorized by Dify in writing, you may not use the Dify source code to operate a multi-tenant environment. - Tenant Definition: Within the context of Dify, one tenant corresponds to one workspace. The workspace provides a separated area for each tenant's data and configurations. b. LOGO and copyright information: In the process of using Dify's frontend, you may not remove or modify the LOGO or copyright information in the Dify console or applications. This restriction is inapplicable to uses of Dify that do not involve its frontend. - Frontend Definition: For the purposes of this license, the "frontend" of Dify includes all components located in the `web/` directory when running Dify from the raw source code, or the "web" image when running Dify with Docker. 2. As a contributor, you should agree that: a. The producer can adjust the open-source agreement to be more strict or relaxed as deemed necessary. b. Your contributed code may be used for commercial purposes, including but not limited to its cloud business operations. Apart from the specific conditions mentioned above, all other rights and restrictions follow the Apache License 2.0. Detailed information about the Apache License 2.0 can be found at http://www.apache.org/licenses/LICENSE-2.0. The interactive design of this product is protected by appearance patent. © 2025 LangGenius, Inc.