Overview

Dify combines a visual workflow builder, agents, model management and RAG knowledge bases in one web console. People design apps in the browser; an agent works with Dify from the terminal by deploying the Docker Compose stack, editing its environment files, calling the Service API of published apps and knowledge bases, moving app definitions (DSL YAML) between instances with difyctl, and handling upgrades and backups.

Instructions

Installation

bash

# Clone the latest release tag (needs git, curl, jq)
git clone --branch "$(curl -s https://api.github.com/repos/langgenius/dify/releases/latest | jq -r .tag_name)" \
  https://github.com/langgenius/dify.git

cd dify/docker
cp .env.example .env
docker compose version        # must report 2.24.0 or later
docker compose up -d
docker compose ps

Every container should be Up or healthy; init_permissions exits after it has set storage permissions, which is expected. If the clone fails with Remote branch null not found, the GitHub API call was rate limited: pass the tag directly, for example --branch 1.17.1.

The first administrator account is created in a browser at http://localhost/install. That step, adding model provider credentials, and creating API keys are done by a person in the console. Ask the user to do them and to hand over keys through environment variables.

Configure the deployment

Essential settings live in docker/.env. Optional, provider-specific settings have templates under docker/envs/; copy one without the .example suffix to activate it. Values in docker/.env override the envs/*.env files.

Variable	Default	Purpose
`EXPOSE_NGINX_PORT` / `EXPOSE_NGINX_SSL_PORT`	`80` / `443`	Host ports of the bundled Nginx
`NEXT_PUBLIC_SOCKET_URL`	`ws://localhost`	Browser WebSocket endpoint; change it together with the host or port
`INIT_PASSWORD`	empty	Password (30 characters at most) required on `/install` before the admin account can be created
`SECRET_KEY`	empty	Signs sessions and file URLs; empty means a persistent key is generated in storage
`DB_PASSWORD`, `REDIS_PASSWORD`	`difyai123456`	Change before the first start; `CELERY_BROKER_URL` embeds the Redis password
`CONSOLE_API_URL`, `CONSOLE_WEB_URL`, `SERVICE_API_URL`, `APP_API_URL`, `APP_WEB_URL`	empty	Public URLs; set them for custom domains, HTTPS or split subdomains
`NGINX_HTTPS_ENABLED`	`false`	Serve TLS with certificates placed in `docker/nginx/ssl/`
`VECTOR_STORE`	`weaviate`	Vector database; also selects the Compose profile that starts it
`UPLOAD_FILE_SIZE_LIMIT`	`15`	Maximum document size in MB
`SSRF_PROXY_ALLOW_PRIVATE_IPS`	empty	CIDR ranges that HTTP nodes and tools may reach inside private networks
`OPENAPI_ENABLED`, `ENABLE_OAUTH_BEARER`	`false`	Both must be `true` before `difyctl` can sign in

bash

cd dify/docker
cp envs/vectorstores/qdrant.env.example envs/vectorstores/qdrant.env   # optional template
docker compose down
docker compose up -d                                                   # apply changes
docker compose logs --tail 100 api worker

Call a published app

Each app has its own API key, which a person creates inside that app in the console. The base URL of a default self-hosted deployment is http://localhost/v1; Dify Cloud uses https://api.dify.ai/v1.

bash

export DIFY_API_URL="http://localhost/v1"
# DIFY_API_KEY is an app key created by the user in the console

curl -s "$DIFY_API_URL/info" -H "Authorization: Bearer $DIFY_API_KEY"
curl -s "$DIFY_API_URL/parameters" -H "Authorization: Bearer $DIFY_API_KEY" | jq .user_input_form

# Chatbot and Chatflow apps (Agent apps use the same endpoint with "streaming")
curl -s -X POST "$DIFY_API_URL/chat-messages" \
  -H "Authorization: Bearer $DIFY_API_KEY" -H "Content-Type: application/json" \
  -d '{"inputs": {}, "query": "How do I reset my password?", "user": "customer-4821", "response_mode": "blocking"}'

# Workflow apps
curl -s -X POST "$DIFY_API_URL/workflows/run" \
  -H "Authorization: Bearer $DIFY_API_KEY" -H "Content-Type: application/json" \
  -d '{"inputs": {"ticket_text": "Card was charged twice for order 88213"}, "user": "support-bot", "response_mode": "blocking"}'

GET /parameters lists the input variable names an app expects. To continue a chat, send the conversation_id from the previous reply. response_mode is blocking (one JSON body) or streaming (Server-Sent Events); Agent apps only stream.

python

import json
import os
import requests

url = f"{os.environ['DIFY_API_URL']}/chat-messages"
headers = {"Authorization": f"Bearer {os.environ['DIFY_API_KEY']}"}
body = {"inputs": {}, "query": "Summarize this week's refund requests",
        "user": "analyst-17", "response_mode": "streaming"}

with requests.post(url, headers=headers, json=body, stream=True, timeout=120) as r:
    for line in r.iter_lines(decode_unicode=True):
        if not line or not line.startswith("data: "):
            continue                      # blank separators and "event: ping"
        event = json.loads(line[6:])
        if event["event"] == "message":   # Agent apps send "agent_message" chunks instead
            print(event["answer"], end="", flush=True)
        elif event["event"] == "error":
            raise RuntimeError(event["message"])

Files go through POST /files/upload (multipart fields file and user). Reference the returned id in an input as {"type": "document", "transfer_method": "local_file", "upload_file_id": "..."}.

Manage knowledge bases

Knowledge endpoints use a separate key, created under Knowledge → Service API. It can be scoped to specific knowledge bases.

bash

# DIFY_DATASET_KEY is a knowledge base key created by the user in the console

DATASET_ID=$(curl -s -X POST "$DIFY_API_URL/datasets" \
  -H "Authorization: Bearer $DIFY_DATASET_KEY" -H "Content-Type: application/json" \
  -d '{"name": "Support KB", "indexing_technique": "high_quality", "permission": "all_team_members"}' | jq -r .id)

BATCH=$(curl -s -X POST "$DIFY_API_URL/datasets/$DATASET_ID/document/create-by-file" \
  -H "Authorization: Bearer $DIFY_DATASET_KEY" \
  -F 'data={"indexing_technique":"high_quality","process_rule":{"mode":"automatic"}}' \
  -F "file=@support-kb.pdf" | jq -r .batch)

# Indexing is asynchronous: waiting, parsing, cleaning, splitting, indexing, completed
curl -s "$DIFY_API_URL/datasets/$DATASET_ID/documents/$BATCH/indexing-status" \
  -H "Authorization: Bearer $DIFY_DATASET_KEY" | jq '.data[] | {indexing_status, completed_segments, total_segments}'

curl -s -X POST "$DIFY_API_URL/datasets/$DATASET_ID/retrieve" \
  -H "Authorization: Bearer $DIFY_DATASET_KEY" -H "Content-Type: application/json" \
  -d '{"query": "refund window for annual plans", "retrieval_model": {"search_method": "semantic_search", "reranking_enable": false, "top_k": 3, "score_threshold_enabled": false}}'

Export, import and run apps with difyctl

difyctl ships with each Dify release. Install the build that matches the server version, and enable OPENAPI_ENABLED=true and ENABLE_OAUTH_BEARER=true in docker/.env on self-hosted servers.

bash

DIFY_VERSION=1.17.1
BASE="https://github.com/langgenius/dify/releases/download/$DIFY_VERSION"
curl -fsSLO "$BASE/difyctl-v$DIFY_VERSION-linux-x64"
curl -fsSLO "$BASE/difyctl-v$DIFY_VERSION-checksums.txt"
sha256sum --check --ignore-missing "difyctl-v$DIFY_VERSION-checksums.txt"
install -m 0755 "difyctl-v$DIFY_VERSION-linux-x64" "$HOME/.local/bin/difyctl"
difyctl version

bash

# Device-flow login: prints a one-time code that the user approves in a browser
difyctl auth login --host http://localhost --insecure --no-browser

difyctl get app --mode workflow                  # table: NAME, ID, MODE, UPDATED
difyctl describe app 7f3e9a2b-1c4d-4e8f-9a0b-2d5c8e1f4a7b -o json | jq '.input_schema'
difyctl run app 7f3e9a2b-1c4d-4e8f-9a0b-2d5c8e1f4a7b --inputs '{"topic":"quarterly report"}' -o json

difyctl export studio-app 7f3e9a2b-1c4d-4e8f-9a0b-2d5c8e1f4a7b --output ./daily-report.yaml
difyctl import studio-app --from-file ./daily-report.yaml --name "Daily Report (staging)"

--insecure is only needed for plain http:// or self-signed hosts. Exit code 4 means the session expired and a person must sign in again; 7 means rate limited.

Back up and upgrade

bash

cd dify/docker
docker compose exec -T db_postgres pg_dump -U postgres dify > "dify-db-$(date +%Y%m%d).sql"

docker compose down
cp .env "env-backup-$(date +%Y%m%d)"
docker run --rm -v "$PWD/volumes:/data:ro" -v "$PWD:/backup" alpine \
  tar czf "/backup/dify-volumes-$(date +%Y%m%d).tar.gz" -C /data .

git fetch --tags
git checkout 1.17.1              # tag of the target release
docker compose pull
docker compose up -d

Database migrations run automatically on start. After an upgrade, compare .env.example with .env for new variables, or run ./dify-env-sync.sh, which adds missing keys and keeps existing values. Read the upgrade guide of the target release first, because some releases need manual steps.

Examples

Example 1: Run a published workflow from a script

Request: "Send this ticket through our Support Triage workflow and give me the category it returns."

bash

curl -s "$DIFY_API_URL/parameters" -H "Authorization: Bearer $DIFY_API_KEY" | jq -c '.user_input_form'
curl -s -X POST "$DIFY_API_URL/workflows/run" \
  -H "Authorization: Bearer $DIFY_API_KEY" -H "Content-Type: application/json" \
  -d '{"inputs": {"ticket_text": "I was charged twice for order 88213 and need one payment refunded."},
       "user": "support-bot", "response_mode": "blocking"}' | jq '.data | {status, outputs, elapsed_time, total_tokens}'

Result:

json

{
  "status": "succeeded",
  "outputs": {"category": "billing", "priority": "high"},
  "elapsed_time": 1.84,
  "total_tokens": 412
}

Example 2: Load a PDF into a new knowledge base

Request: "Create a knowledge base from support-kb.pdf and confirm that retrieval finds the refund policy."

Run the four commands from "Manage knowledge bases", repeating the status call until it reports completed:

json

{"indexing_status": "completed", "completed_segments": 42, "total_segments": 42}

The retrieval call then returns ranked chunks:

json

{
  "query": {"content": "refund window for annual plans"},
  "records": [
    {"score": 0.87, "segment": {"content": "Annual plans can be refunded within 30 days of purchase...",
                                "document": {"name": "support-kb.pdf"}}}
  ]
}

Guidelines

Two kinds of keys: app keys call app endpoints, knowledge base keys call /datasets. Using the wrong one returns 401 unauthorized; a scoped knowledge key returns 403 forbidden for other knowledge bases. Keep both on the server side.
Keep user stable: conversations, uploaded files and resumable workflow streams are tied to the user value. Reuse the same value for upload, send, stop and resume.
Prefer streaming for long runs: proxies cut long blocking requests. With curl, add -N so events print as they arrive.
Do not retry configuration errors: provider_not_initialize, provider_quota_exceeded and model_currently_not_support mean the model setup in Dify is wrong. Retry only too_many_requests, HTTP 500 and network failures, with backoff.
Secrets at first start: replace the default database and Redis passwords before the first docker compose up, and never change SECRET_KEY on a running deployment. Changing it signs out every user and makes stored OAuth credentials unreadable.
Public servers: set INIT_PASSWORD before exposing /install, enable HTTPS, and update the URL variables to https://.
Containers cannot reach 127.0.0.1 on the host: point model providers at the host's LAN address, and allow private ranges with SSRF_PROXY_ALLOW_PRIVATE_IPS when HTTP nodes must reach internal services.
DSL files: an export contains the app definition but no knowledge base content and no third-party tool keys. --include-secret adds encrypted secret values, so treat that file as sensitive. Workflow exports and imports use the draft; publish in the console before API calls see the change.
Stop the vector store gracefully: use docker compose down or docker compose stop, never docker kill, on the bundled Weaviate. A hard stop can damage the vector index.
License: Dify uses the Dify Open Source License, which is Apache 2.0 with additional conditions. Read it before offering Dify as a hosted service.
When not to use: for LLM calls embedded in application code, a library such as LangChain is lighter. Dify fits when non-developers need to edit flows in a browser and you want an API on top.

Dify

Validation

Prove Dify on your task

Getting Started

Example Prompts

Documentation

Overview

Instructions

Installation

Configure the deployment

Call a published app

Manage knowledge bases

Export, import and run apps with difyctl

Back up and upgrade

Examples

Example 1: Run a published workflow from a script

Example 2: Load a PDF into a new knowledge base

Guidelines

Information

Use Cases

Related Skills