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Dify

Dify puts the parts of an AI application onto a visual canvas where you can build the logic and see what happens at each step. It supports chat assistants, autonomous agents, document processing jobs, internal search tools, content pipelines, customer support bots, and backend automation.

You can start with a template or connect nodes yourself. Available nodes cover model calls, knowledge retrieval, classifiers, conditions, loops, code, HTTP requests, tools, human approval, and structured output.

Dify can call Jev by TypeSafe AI through an HTTP node when a workflow needs a fast typed choice, score, or probability for routing, moderation, or escalation.

Workflows can start from a user message, API request, schedule, webhook, plugin event, or uploaded file. Individual nodes can be tested before running the complete flow. When something fails, Dify records the execution path, inputs, outputs, variables, latency, and error details. Recovery branches can stop the run, return a default value, or send the work down another path.

There are two main workflow types. A standard Workflow runs once from input to output, which suits document processing, batch jobs, and backend automation. Chatflow keeps conversation memory and runs the workflow again for every message. Both use the same node library.

Dify also has a visual Knowledge Pipeline for RAG applications. It can collect material from uploaded files, websites, document platforms, cloud drives, and data source plugins. Processing steps cover parsing, cleaning, enrichment, chunking, embedding, indexing, metadata, and retrieval.

That detail matters. Throwing a pile of PDFs into a chatbot and hoping retrieval behaves itself is not much of a system. Dify lets you inspect how documents were divided, test retrieval before connecting the knowledge base, and choose vector, keyword, or hybrid search. Parent and child chunking can retrieve a precise passage while returning the surrounding context.

Model access is handled through plugins. You can connect commercial providers, open models, local inference services, and OpenAI compatible endpoints. A model can be swapped without rebuilding the rest of the workflow, although different models will still behave differently with tool calls, structured output, context limits, and reasoning.

The plugin system covers more than models. Plugins can add tools, agent strategies, data sources, triggers, and external services. Workflows can also call REST APIs, MCP servers, HTTP endpoints, or sandboxed Python and JavaScript code.

Finished applications can be published as hosted web apps, website embeds, API endpoints, reusable templates, or MCP tools. The same workflow can therefore sit behind a public chatbot, an internal form, a product feature, or a tool used by an MCP compatible coding assistant. Dify provides API credentials, session handling, response streaming, logs, and usage data.

You can use Dify Cloud or run the Community Edition on your own infrastructure. The cloud version removes the server work and includes shared workspaces, model credits, document storage, logs, and managed updates. The free Sandbox plan is enough to test a few apps, but its application, document, storage, and usage limits are fairly small. Paid workspace plans raise those limits and support larger teams. You can connect your own model API keys when the included credits run out.

The Community Edition can be installed with Docker Compose and gives you a single workspace with the public repository’s core features. Self hosting gives you more control over data and model connections, but it also leaves upgrades, backups, databases, queues, vector storage, plugin security, and uptime in your hands. Starting a Docker stack is easy. Keeping it healthy for a busy team is the actual job.

There is an important licence detail. Dify calls the Community Edition open source, but the repository uses a modified Apache 2.0 licence. Operating a multi tenant service requires commercial permission, and the Dify logo and copyright information cannot be removed from its frontend. Companies planning to rebrand Dify or sell access to separate customer workspaces should read the licence before building around it.

Dify suits teams that need more than a chatbot wrapper and want the workflow, retrieval, tools, API, and monitoring in one place. It is probably too much machinery for a single prompt and response form. For an internal knowledge assistant, support agent, document processor, or multi step business workflow, the visual execution history makes debugging considerably less painful.

  • Visual Workflow and Chatflow builder
  • AI agents with tools, memory, and limits
  • Model provider and local model plugins
  • RAG knowledge bases and retrieval testing
  • Visual document processing pipelines
  • Vector, keyword, and hybrid retrieval
  • Parent and child document chunking
  • Conditions, loops, iteration, and code nodes
  • Scheduled, webhook, API, and plugin triggers
  • Human approval steps
  • MCP servers and MCP tool publishing
  • REST API and HTTP integrations
  • Hosted web apps and website embeds
  • Application APIs with streaming responses
  • Workflow version history and restoration
  • Execution logs, variables, latency, and error traces
  • Langfuse, LangSmith, Arize, and other observability connections
  • Plugin marketplace for models, tools, data sources, and triggers
  • Dify Cloud and Docker based self hosting
  • Enterprise deployment, SSO, governance, and support options