Merge branch 'dev' into saveComponent

.env.example

@@ -46,10 +46,20 @@ LANGFLOW_OPEN_BROWSER=
 # Example: LANGFLOW_REMOVE_API_KEYS=false
 LANGFLOW_REMOVE_API_KEYS=
 
+# Whether to use RedisCache or InMemoryCache
+# Values: memory, redis
+# Example: LANGFLOW_CACHE_TYPE=memory
+# If you want to use redis then the following environment variables must be set:
+# LANGFLOW_REDIS_HOST (default: localhost)
+# LANGFLOW_REDIS_PORT (default: 6379)
+# LANGFLOW_REDIS_DB (default: 0)
+# LANGFLOW_REDIS_CACHE_EXPIRE (default: 3600)
+LANGFLOW_CACHE_TYPE=
+
 # Superuser username
 # Example: LANGFLOW_SUPERUSER=admin
 LANGFLOW_SUPERUSER=
 
 # Superuser password
 # Example: LANGFLOW_SUPERUSER_PASSWORD=123456
-LANGFLOW_SUPERUSER_PASSWORD=
+LANGFLOW_SUPERUSER_PASSWORD=

.github/workflows/ci.yml

@@ -0,0 +1,52 @@
+name: "Async API tests"
+
+on:
+  push:
+    branches:
+      - dev
+    paths:
+      - "tests/**"
+      - "src/backend/**"
+      - "pyproject.toml"
+  pull_request:
+    branches:
+      - dev
+      - main
+    paths:
+      - "tests/**"
+      - "src/backend/**"
+      - "pyproject.toml"
+
+jobs:
+  build-and-test:
+    runs-on: ubuntu-latest
+    env:
+      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v2
+
+      - name: Cache Docker layers
+        uses: actions/cache@v2
+        with:
+          path: /tmp/.buildx-cache
+          key: ${{ runner.os }}-buildx-${{ github.sha }}
+          restore-keys: |
+            ${{ runner.os }}-buildx-
+
+      - name: Set up Docker
+        run: docker --version && docker-compose --version
+
+      - name: "Create env file"
+        working-directory: ./deploy
+        run: |
+          echo "${{ secrets.ENV_FILE }}" > .env
+
+      - name: Build and start services
+
+        working-directory: ./deploy
+        run: docker compose up --exit-code-from tests tests result_backend broker celeryworker db --build
+        continue-on-error: true
+
+      - name: Stop services
+        run: docker compose down

.github/workflows/lint.yml

@@ -3,7 +3,16 @@ name: lint
 on:
   push:
     branches: [main]
+    paths:
+      - "tests/**"
+      - "src/backend/**"
+      - "pyproject.toml"
+
   pull_request:
+    paths:
+      - "tests/**"
+      - "src/backend/**"
+      - "pyproject.toml"
 
 env:
   POETRY_VERSION: "1.4.0"

.github/workflows/release.yml

@@ -45,11 +45,3 @@ jobs:
           POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }}
         run: |
           poetry publish
-
-      - name: Trigger build and push on langchain-serve
-        uses: peter-evans/repository-dispatch@v2
-        with:
-          token: ${{ secrets.SERVE_GITHUB_TOKEN }}
-          repository: jina-ai/langchain-serve
-          event-type: langflow-push
-          client-payload: '{"push_token": "${{ secrets.LCSERVE_PUSH_TOKEN }}", "branch": "main"}'

.github/workflows/test-lcserve-push.yml

@@ -1,17 +0,0 @@
-name: Trigger build and push on langchain-serve
-
-on:
-  workflow_dispatch:
-
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Trigger build and push on langchain-serve
-        uses: peter-evans/repository-dispatch@v2
-        with:
-          token: ${{ secrets.SERVE_GITHUB_TOKEN }}
-          repository: jina-ai/langchain-serve
-          event-type: langflow-push
-          client-payload: '{"push_token": "${{ secrets.LCSERVE_PUSH_TOKEN }}", "branch": "dev"}'

.github/workflows/test.yml

@@ -3,11 +3,19 @@ name: test
 on:
   push:
     branches: [main]
+    paths:
+      - "tests/**"
+      - "src/backend/**"
+      - "pyproject.toml"
+
   pull_request:
     branches: [dev]
-
+    paths:
+      - "tests/**"
+      - "src/backend/**"
+      - "pyproject.toml"
 env:
-  POETRY_VERSION: "1.4.0"
+  POETRY_VERSION: "1.5.0"
 
 jobs:
   build:
@@ -16,6 +24,8 @@ jobs:
       matrix:
         python-version:
           - "3.10"
+    env:
+      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
     steps:
       - uses: actions/checkout@v3
       - name: Install poetry

.gitignore

@@ -254,3 +254,4 @@ langflow.db
 
 /tmp/*
 src/backend/langflow/frontend/
+.docker

Dockerfile

@@ -12,4 +12,4 @@ WORKDIR $HOME/app
 COPY --chown=user . $HOME/app
 
 RUN pip install langflow>==0.0.86 -U --user
-CMD ["python", "-m", "langflow", "--host", "0.0.0.0", "--port", "7860"]
+CMD ["python", "-m", "langflow", "run", "--host", "0.0.0.0", "--port", "7860"]

Makefile

@@ -20,7 +20,10 @@ coverage:
 
 tests:
 	@make install_backend
-	poetry run pytest tests -n auto
+	poetry run pytest tests
+
+tests_frontend:
+	cd src/frontend && ./run-tests.sh
 
 format:
 	poetry run black .
@@ -53,19 +56,25 @@ setup_devcontainer:
 	poetry run langflow --path src/frontend/build
 
 frontend:
-	make install_frontend
+	@-make install_frontend || (echo "An error occurred while installing frontend dependencies. Attempting to fix." && make install_frontendc)
-	make run_frontend
+	@make run_frontend
 
 frontendc:
 	make install_frontendc
 	make run_frontend
 
 install_backend:
-	poetry install
+	poetry install --extras deploy
 
 backend:
 	make install_backend
-	poetry run uvicorn --factory src.backend.langflow.main:create_app --port 7860 --reload --log-level debug
+ifeq ($(login),1)
+	@echo "Running backend without autologin";
+	poetry run langflow run --backend-only --port 7860 --host 0.0.0.0 --no-open-browser
+else
+	@echo "Running backend with autologin";
+	LANGFLOW_AUTO_LOGIN=True poetry run langflow run --backend-only --port 7860 --host 0.0.0.0 --no-open-browser
+endif
 
 build_and_run:
 	echo 'Removing dist folder'
@@ -87,17 +96,6 @@ build:
 	poetry build --format sdist
 	rm -rf src/backend/langflow/frontend
 
-lcserve_push:
-	make build_frontend
-	@version=$$(poetry version --short); \
-	lc-serve push --app langflow.lcserve:app --app-dir . \
-		--image-name langflow --image-tag $${version} --verbose --public
-
-lcserve_deploy:
-	@:$(if $(uses),,$(error `uses` is not set. Please run `make uses=... lcserve_deploy`))
-	lc-serve deploy jcloud --app langflow.lcserve:app --app-dir . \
-		--uses $(uses) --config src/backend/langflow/jcloud.yml --verbose
-
 dev:
 	make install_frontend
 ifeq ($(build),1)

README.md

@@ -19,7 +19,7 @@
 </p>
 
 <a href="https://github.com/logspace-ai/langflow">
-    <img width="100%" src="https://github.com/logspace-ai/langflow/blob/main/img/langflow-demo.gif?raw=true"></a>
+    <img width="100%" src="https://github.com/logspace-ai/langflow/blob/dev/img/langflow-demo.gif?raw=true"></a>
 
 <p>
 </p>
@@ -36,8 +36,6 @@
     - [Environment Variables](#environment-variables)
 - [Deployment](#deployment)
   - [Deploy Langflow on Google Cloud Platform](#deploy-langflow-on-google-cloud-platform)
-  - [Deploy Langflow on Jina AI Cloud](#deploy-langflow-on-jina-ai-cloud)
-    - [API Usage](#api-usage)
   - [Deploy on Railway](#deploy-on-railway)
   - [Deploy on Render](#deploy-on-render)
 - [🎨 Creating Flows](#-creating-flows)
@@ -78,7 +76,7 @@ python -m langflow
 or
 
 ```shell
-langflow # or langflow --help
+langflow run # or langflow --help
 ```
 
 ### HuggingFace Spaces
@@ -94,7 +92,7 @@ Langflow provides a command-line interface (CLI) for easy management and configu
 You can run the Langflow using the following command:
 
 ```shell
-langflow [OPTIONS]
+langflow run [OPTIONS]
 ```
 
 Each option is detailed below:
@@ -110,7 +108,6 @@ Each option is detailed below:
 - `--components-path`: Specifies the path to the directory containing custom components. Can be set using the `LANGFLOW_COMPONENTS_PATH` environment variable. The default is `langflow/components`.
 - `--log-file`: Specifies the path to the log file. Can be set using the `LANGFLOW_LOG_FILE` environment variable. The default is `logs/langflow.log`.
 - `--cache`: Selects the type of cache to use. Options are `InMemoryCache` and `SQLiteCache`. Can be set using the `LANGFLOW_LANGCHAIN_CACHE` environment variable. The default is `SQLiteCache`.
-- `--jcloud/--no-jcloud`: Toggles the option to deploy on Jina AI Cloud. The default is `no-jcloud`.
 - `--dev/--no-dev`: Toggles the development mode. The default is `no-dev`.
 - `--path`: Specifies the path to the frontend directory containing build files. This option is for development purposes only. Can be set using the `LANGFLOW_FRONTEND_PATH` environment variable.
 - `--open-browser/--no-open-browser`: Toggles the option to open the browser after starting the server. Can be set using the `LANGFLOW_OPEN_BROWSER` environment variable. The default is `open-browser`.
@@ -134,115 +131,6 @@ Alternatively, click the **"Open in Cloud Shell"** button below to launch Google
 
 [![Open in Cloud Shell](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/logspace-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial_spot.md)
 
-## Deploy Langflow on [Jina AI Cloud](https://github.com/jina-ai/langchain-serve)
-
-Langflow integrates with langchain-serve to provide a one-command deployment to Jina AI Cloud.
-
-Start by installing `langchain-serve` with
-
-```bash
-pip install langflow[deploy]
-# or
-pip install -U langchain-serve
-```
-
-Then, run:
-
-```bash
-langflow --jcloud
-```
-
-```text
-🎉 Langflow server successfully deployed on Jina AI Cloud 🎉
-🔗 Click on the link to open the server (please allow ~1-2 minutes for the server to startup): https://<your-app>.wolf.jina.ai/
-📖 Read more about managing the server: https://github.com/jina-ai/langchain-serve
-```
-
-  <details>
-  <summary>Show complete (example) output</summary>
-
-```text
-  🚀 Deploying Langflow server on Jina AI Cloud
-  ╭───────────────────────── 🎉 Flow is available! ──────────────────────────╮
-  │                                                                          │
-  │   ID                    langflow-e3dd8820ec                              │
-  │   Gateway (Websocket)   wss://langflow-e3dd8820ec.wolf.jina.ai           │
-  │   Dashboard             https://dashboard.wolf.jina.ai/flow/e3dd8820ec   │
-  │                                                                          │
-  ╰──────────────────────────────────────────────────────────────────────────╯
-  ╭──────────────┬──────────────────────────────────────────────────────────────────────────────╮
-  │ App ID       │                     langflow-e3dd8820ec                                      │
-  ├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
-  │ Phase        │                            Serving                                           │
-  ├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
-  │ Endpoint     │          wss://langflow-e3dd8820ec.wolf.jina.ai                              │
-  ├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
-  │ App logs     │                  dashboards.wolf.jina.ai                                     │
-  ├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
-  │ Swagger UI   │          https://langflow-e3dd8820ec.wolf.jina.ai/docs                       │
-  ├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
-  │ OpenAPI JSON │        https://langflow-e3dd8820ec.wolf.jina.ai/openapi.json                 │
-  ╰──────────────┴──────────────────────────────────────────────────────────────────────────────╯
-
-  🎉 Langflow server successfully deployed on Jina AI Cloud 🎉
-  🔗 Click on the link to open the server (please allow ~1-2 minutes for the server to startup): https://langflow-e3dd8820ec.wolf.jina.ai/
-  📖 Read more about managing the server: https://github.com/jina-ai/langchain-serve
-```
-
-  </details>
-
-#### API Usage
-
-You can use Langflow directly on your browser, or use the API endpoints on Jina AI Cloud to interact with the server.
-
-  <details>
-  <summary>Show API usage (with python)</summary>
-
-```python
-import requests
-
-BASE_API_URL = "https://langflow-e3dd8820ec.wolf.jina.ai/api/v1/predict"
-FLOW_ID = "864c4f98-2e59-468b-8e13-79cd8da07468"
-# You can tweak the flow by adding a tweaks dictionary
-# e.g {"OpenAI-XXXXX": {"model_name": "gpt-4"}}
-TWEAKS = {
-"ChatOpenAI-g4jEr": {},
-"ConversationChain-UidfJ": {}
-}
-
-def run_flow(message: str, flow_id: str, tweaks: dict = None) -> dict:
-  """
-  Run a flow with a given message and optional tweaks.
-
-  :param message: The message to send to the flow
-  :param flow_id: The ID of the flow to run
-  :param tweaks: Optional tweaks to customize the flow
-  :return: The JSON response from the flow
-  """
-  api_url = f"{BASE_API_URL}/{flow_id}"
-
-  payload = {"message": message}
-
-  if tweaks:
-      payload["tweaks"] = tweaks
-
-  response = requests.post(api_url, json=payload)
-  return response.json()
-
-# Setup any tweaks you want to apply to the flow
-print(run_flow("Your message", flow_id=FLOW_ID, tweaks=TWEAKS))
-```
-
-```json
-{
-  "result": "Great choice! Bangalore in the 1920s was a vibrant city with a rich cultural and political scene. Here are some suggestions for things to see and do:\n\n1. Visit the Bangalore Palace - built in 1887, this stunning palace is a perfect example of Tudor-style architecture. It was home to the Maharaja of Mysore and is now open to the public.\n\n2. Attend a performance at the Ravindra Kalakshetra - this cultural center was built in the 1920s and is still a popular venue for music and dance performances.\n\n3. Explore the neighborhoods of Basavanagudi and Malleswaram - both of these areas have retained much of their old-world charm and are great places to walk around and soak up the atmosphere.\n\n4. Check out the Bangalore Club - founded in 1868, this exclusive social club was a favorite haunt of the British expat community in the 1920s.\n\n5. Attend a meeting of the Indian National Congress - founded in 1885, the INC was a major force in the Indian independence movement and held many meetings and rallies in Bangalore in the 1920s.\n\nHope you enjoy your trip to 1920s Bangalore!"
-}
-```
-
-  </details>
-
-> Read more about resource customization, cost, and management of Langflow apps on Jina AI Cloud in the **[langchain-serve](https://github.com/jina-ai/langchain-serve)** repository.
-
 ## Deploy on Railway
 
 [![Deploy on Railway](https://railway.app/button.svg)](https://railway.app/template/JMXEWp?referralCode=MnPSdg)
@@ -255,7 +143,7 @@ print(run_flow("Your message", flow_id=FLOW_ID, tweaks=TWEAKS))
 
 # 🎨 Creating Flows
 
-Creating flows with Langflow is easy. Simply drag sidebar components onto the canvas and connect them together to create your pipeline. Langflow provides a range of [LangChain components](https://langchain.readthedocs.io/en/latest/reference.html) to choose from, including LLMs, prompt serializers, agents, and chains.
+Creating flows with Langflow is easy. Simply drag sidebar components onto the canvas and connect them together to create your pipeline. Langflow provides a range of [LangChain components](https://docs.langchain.com/docs/category/components) to choose from, including LLMs, prompt serializers, agents, and chains.
 
 Explore by editing prompt parameters, link chains and agents, track an agent's thought process, and export your flow.
 

base.Dockerfile

@@ -0,0 +1,97 @@
+
+
+# syntax=docker/dockerfile:1
+# Keep this syntax directive! It's used to enable Docker BuildKit
+
+# Based on https://github.com/python-poetry/poetry/discussions/1879?sort=top#discussioncomment-216865
+# but I try to keep it updated (see history)
+
+################################
+# PYTHON-BASE
+# Sets up all our shared environment variables
+################################
+FROM python:3.10-slim as python-base
+
+# python
+ENV PYTHONUNBUFFERED=1 \
+    # prevents python creating .pyc files
+    PYTHONDONTWRITEBYTECODE=1 \
+    \
+    # pip
+    PIP_DISABLE_PIP_VERSION_CHECK=on \
+    PIP_DEFAULT_TIMEOUT=100 \
+    \
+    # poetry
+    # https://python-poetry.org/docs/configuration/#using-environment-variables
+    POETRY_VERSION=1.5.1 \
+    # make poetry install to this location
+    POETRY_HOME="/opt/poetry" \
+    # make poetry create the virtual environment in the project's root
+    # it gets named `.venv`
+    POETRY_VIRTUALENVS_IN_PROJECT=true \
+    # do not ask any interactive question
+    POETRY_NO_INTERACTION=1 \
+    \
+    # paths
+    # this is where our requirements + virtual environment will live
+    PYSETUP_PATH="/opt/pysetup" \
+    VENV_PATH="/opt/pysetup/.venv"
+
+
+# prepend poetry and venv to path
+ENV PATH="$POETRY_HOME/bin:$VENV_PATH/bin:$PATH"
+
+
+################################
+# BUILDER-BASE
+# Used to build deps + create our virtual environment
+################################
+FROM python-base as builder-base
+RUN apt-get update \
+    && apt-get install --no-install-recommends -y \
+    # deps for installing poetry
+    curl \
+    # deps for building python deps
+    build-essential
+
+
+# install poetry - respects $POETRY_VERSION & $POETRY_HOME
+# The --mount will mount the buildx cache directory to where
+# Poetry and Pip store their cache so that they can re-use it
+RUN --mount=type=cache,target=/root/.cache \
+    curl -sSL https://install.python-poetry.org | python3 -
+
+# copy project requirement files here to ensure they will be cached.
+WORKDIR $PYSETUP_PATH
+COPY poetry.lock pyproject.toml ./
+COPY ./src/backend/langflow/main.py ./src/backend/langflow/main.py
+# Copy README.md to the build context
+COPY README.md .
+# install runtime deps - uses $POETRY_VIRTUALENVS_IN_PROJECT internally
+RUN --mount=type=cache,target=/root/.cache \
+    poetry install --without dev --extras deploy
+
+
+################################
+# DEVELOPMENT
+# Image used during development / testing
+################################
+FROM python-base as development
+WORKDIR $PYSETUP_PATH
+
+# copy in our built poetry + venv
+COPY --from=builder-base $POETRY_HOME $POETRY_HOME
+COPY --from=builder-base $PYSETUP_PATH $PYSETUP_PATH
+
+# Copy just one file to avoid rebuilding the whole image
+COPY ./src/backend/langflow/__init__.py ./src/backend/langflow/__init__.py
+# quicker install as runtime deps are already installed
+RUN --mount=type=cache,target=/root/.cache \
+    poetry install --with=dev --extras deploy
+
+# copy in our app code
+COPY ./src/backend ./src/backend
+RUN --mount=type=cache,target=/root/.cache \
+    poetry install --with=dev --extras deploy
+COPY ./tests ./tests=
+

deploy/.env.example

@@ -0,0 +1,57 @@
+DOMAIN=localhost
+STACK_NAME=langflow-stack
+ENVIRONMENT=development
+
+TRAEFIK_PUBLIC_NETWORK=traefik-public
+TRAEFIK_TAG=langflow-traefik
+TRAEFIK_PUBLIC_TAG=traefik-public
+
+# RabbitMQ configuration
+RABBITMQ_DEFAULT_USER=langflow
+RABBITMQ_DEFAULT_PASS=langflow
+
+# Database configuration
+DB_USER=langflow
+DB_PASSWORD=langflow
+DB_HOST=db
+DB_PORT=5432
+DB_NAME=langflow
+
+# Logging configuration
+LOG_LEVEL=debug
+
+# DB configuration
+POSTGRES_USER=langflow
+POSTGRES_PASSWORD=langflow
+POSTGRES_DB=langflow
+POSTGRES_PORT=5432
+
+# Flower configuration
+LANGFLOW_CACHE_TYPE=redis
+LANGFLOW_REDIS_HOST=result_backend
+LANGFLOW_REDIS_PORT=6379
+LANGFLOW_REDIS_DB=0
+LANGFLOW_REDIS_EXPIRE=3600
+LANGFLOW_REDIS_PASSWORD=
+FLOWER_UNAUTHENTICATED_API=True
+BROKER_URL=amqp://langflow:langflow@broker:5672
+RESULT_BACKEND=redis://result_backend:6379/0
+C_FORCE_ROOT="true"
+
+# Frontend configuration
+VITE_PROXY_TARGET=http://backend:7860/api/
+BACKEND_URL=http://backend:7860
+
+# PGAdmin configuration
+PGADMIN_DEFAULT_EMAIL=admin@admin.com
+PGADMIN_DEFAULT_PASSWORD=admin
+
+# OpenAI configuration (for testing purposes)
+OPENAI_API_KEY=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
+
+# Superuser configuration
+LANGFLOW_SUPERUSER=superuser
+LANGFLOW_SUPERUSER_PASSWORD=superuser
+
+# New user configuration
+LANGFLOW_NEW_USER_IS_ACTIVE=False

deploy/.gitignore

@@ -0,0 +1 @@
+pgadmin

deploy/base.Dockerfile

@@ -0,0 +1,92 @@
+
+
+# syntax=docker/dockerfile:1
+# Keep this syntax directive! It's used to enable Docker BuildKit
+
+# Based on https://github.com/python-poetry/poetry/discussions/1879?sort=top#discussioncomment-216865
+# but I try to keep it updated (see history)
+
+################################
+# PYTHON-BASE
+# Sets up all our shared environment variables
+################################
+FROM python:3.10-slim as python-base
+
+# python
+ENV PYTHONUNBUFFERED=1 \
+    # prevents python creating .pyc files
+    PYTHONDONTWRITEBYTECODE=1 \
+    \
+    # pip
+    PIP_DISABLE_PIP_VERSION_CHECK=on \
+    PIP_DEFAULT_TIMEOUT=100 \
+    \
+    # poetry
+    # https://python-poetry.org/docs/configuration/#using-environment-variables
+    POETRY_VERSION=1.5.1 \
+    # make poetry install to this location
+    POETRY_HOME="/opt/poetry" \
+    # make poetry create the virtual environment in the project's root
+    # it gets named `.venv`
+    POETRY_VIRTUALENVS_IN_PROJECT=true \
+    # do not ask any interactive question
+    POETRY_NO_INTERACTION=1 \
+    \
+    # paths
+    # this is where our requirements + virtual environment will live
+    PYSETUP_PATH="/opt/pysetup" \
+    VENV_PATH="/opt/pysetup/.venv"
+
+
+# prepend poetry and venv to path
+ENV PATH="$POETRY_HOME/bin:$VENV_PATH/bin:$PATH"
+
+
+################################
+# BUILDER-BASE
+# Used to build deps + create our virtual environment
+################################
+FROM python-base as builder-base
+RUN apt-get update \
+    && apt-get install --no-install-recommends -y \
+    # deps for installing poetry
+    curl \
+    # deps for building python deps
+    build-essential
+
+# install poetry - respects $POETRY_VERSION & $POETRY_HOME
+# The --mount will mount the buildx cache directory to where
+# Poetry and Pip store their cache so that they can re-use it
+RUN --mount=type=cache,target=/root/.cache \
+    curl -sSL https://install.python-poetry.org | python3 -
+
+# copy project requirement files here to ensure they will be cached.
+WORKDIR $PYSETUP_PATH
+COPY ./poetry.lock ./pyproject.toml ./
+# Copy README.md to the build context
+COPY ./README.md ./
+# install runtime deps - uses $POETRY_VIRTUALENVS_IN_PROJECT internally
+RUN --mount=type=cache,target=/root/.cache \
+    poetry install --without dev --extras deploy
+
+
+################################
+# DEVELOPMENT
+# Image used during development / testing
+################################
+FROM python-base as development
+WORKDIR $PYSETUP_PATH
+
+# copy in our built poetry + venv
+COPY --from=builder-base $POETRY_HOME $POETRY_HOME
+COPY --from=builder-base $PYSETUP_PATH $PYSETUP_PATH
+
+# Copy just one file to avoid rebuilding the whole image
+COPY ./src/backend/langflow/__init__.py ./src/backend/langflow/__init__.py
+# quicker install as runtime deps are already installed
+RUN --mount=type=cache,target=/root/.cache \
+    poetry install --with=dev --extras deploy
+
+# copy in our app code
+COPY ./src/backend ./src/backend
+COPY ./tests ./tests

deploy/docker-compose.override.yml

@@ -0,0 +1,67 @@
+version: "3.8"
+
+services:
+  proxy:
+    ports:
+      - "80:80"
+      - "8090:8080"
+    command:
+      # Enable Docker in Traefik, so that it reads labels from Docker services
+      - --providers.docker
+      # Add a constraint to only use services with the label for this stack
+      # from the env var TRAEFIK_TAG
+      - --providers.docker.constraints=Label(`traefik.constraint-label-stack`, `${TRAEFIK_TAG?Variable not set}`)
+      # Do not expose all Docker services, only the ones explicitly exposed
+      - --providers.docker.exposedbydefault=false
+      # Disable Docker Swarm mode for local development
+      # - --providers.docker.swarmmode
+      # Enable the access log, with HTTP requests
+      - --accesslog
+      # Enable the Traefik log, for configurations and errors
+      - --log
+      # Enable the Dashboard and API
+      - --api
+      # Enable the Dashboard and API in insecure mode for local development
+      - --api.insecure=true
+    labels:
+      - traefik.enable=true
+      - traefik.http.routers.${STACK_NAME?Variable not set}-traefik-public-http.rule=Host(`${DOMAIN?Variable not set}`)
+      - traefik.http.services.${STACK_NAME?Variable not set}-traefik-public.loadbalancer.server.port=80
+
+  result_backend:
+    ports:
+      - "6379:6379"
+
+  pgadmin:
+    ports:
+      - "5050:5050"
+
+  flower:
+    ports:
+      - "5555:5555"
+
+  backend:
+    labels:
+      - traefik.enable=true
+      - traefik.constraint-label-stack=${TRAEFIK_TAG?Variable not set}
+      - traefik.http.routers.${STACK_NAME?Variable not set}-backend-http.rule=PathPrefix(`/api/v1`) || PathPrefix(`/docs`) || PathPrefix(`/health`)
+      - traefik.http.services.${STACK_NAME?Variable not set}-backend.loadbalancer.server.port=7860
+
+  frontend:
+    labels:
+      - traefik.enable=true
+      - traefik.constraint-label-stack=${TRAEFIK_TAG?Variable not set}
+      - traefik.http.routers.${STACK_NAME?Variable not set}-frontend-http.rule=PathPrefix(`/`)
+      - traefik.http.services.${STACK_NAME?Variable not set}-frontend.loadbalancer.server.port=80
+
+  celeryworker:
+    labels:
+      - traefik.enable=true
+      - traefik.constraint-label-stack=${TRAEFIK_TAG?Variable not set}
+      - traefik.http.routers.${STACK_NAME?Variable not set}-celeryworker-http.rule=PathPrefix(`/api/v1`) || PathPrefix(`/docs`) || PathPrefix(`/health`)
+      - traefik.http.services.${STACK_NAME?Variable not set}-celeryworker.loadbalancer.server.port=7860
+
+networks:
+  traefik-public:
+    # For local dev, don't expect an external Traefik network
+    external: false

deploy/docker-compose.with_tests.yml

@@ -0,0 +1,277 @@
+version: "3.8"
+
+services:
+  proxy:
+    image: traefik:v3.0
+    env_file:
+      - .env
+    networks:
+      - ${TRAEFIK_PUBLIC_NETWORK?Variable not set}
+      - default
+    volumes:
+      - /var/run/docker.sock:/var/run/docker.sock
+    command:
+      # Enable Docker in Traefik, so that it reads labels from Docker services
+      - --providers.docker
+      # Add a constraint to only use services with the label for this stack
+      # from the env var TRAEFIK_TAG
+      - --providers.docker.constraints=Label(`traefik.constraint-label-stack`, `${TRAEFIK_TAG?Variable not set}`)
+      # Do not expose all Docker services, only the ones explicitly exposed
+      - --providers.docker.exposedbydefault=false
+      # Enable the access log, with HTTP requests
+      - --accesslog
+      # Enable the Traefik log, for configurations and errors
+      - --log
+      # Enable the Dashboard and API
+      - --api
+    deploy:
+      placement:
+        constraints:
+          - node.role == manager
+      labels:
+        # Enable Traefik for this service, to make it available in the public network
+        - traefik.enable=true
+        # Use the traefik-public network (declared below)
+        - traefik.docker.network=${TRAEFIK_PUBLIC_NETWORK?Variable not set}
+        # Use the custom label "traefik.constraint-label=traefik-public"
+        # This public Traefik will only use services with this label
+        - traefik.constraint-label=${TRAEFIK_PUBLIC_TAG?Variable not set}
+        # traefik-http set up only to use the middleware to redirect to https
+        - traefik.http.middlewares.${STACK_NAME?Variable not set}-https-redirect.redirectscheme.scheme=https
+        - traefik.http.middlewares.${STACK_NAME?Variable not set}-https-redirect.redirectscheme.permanent=true
+        # Handle host with and without "www" to redirect to only one of them
+        # Uses environment variable DOMAIN
+        # To disable www redirection remove the Host() you want to discard, here and
+        # below for HTTPS
+        - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-http.rule=Host(`${DOMAIN?Variable not set}`) || Host(`www.${DOMAIN?Variable not set}`)
+        - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-http.entrypoints=http
+        # traefik-https the actual router using HTTPS
+        - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-https.rule=Host(`${DOMAIN?Variable not set}`) || Host(`www.${DOMAIN?Variable not set}`)
+        - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-https.entrypoints=https
+        - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-https.tls=true
+        # Use the "le" (Let's Encrypt) resolver created below
+        - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-https.tls.certresolver=le
+        # Define the port inside of the Docker service to use
+        - traefik.http.services.${STACK_NAME?Variable not set}-proxy.loadbalancer.server.port=80
+        # Handle domain with and without "www" to redirect to only one
+        # To disable www redirection remove the next line
+        - traefik.http.middlewares.${STACK_NAME?Variable not set}-www-redirect.redirectregex.regex=^https?://(www.)?(${DOMAIN?Variable not set})/(.*)
+        # Redirect a domain with www to non-www
+        # To disable it remove the next line
+        - traefik.http.middlewares.${STACK_NAME?Variable not set}-www-redirect.redirectregex.replacement=https://${DOMAIN?Variable not set}/$${3}
+        # Redirect a domain without www to www
+        # To enable it remove the previous line and uncomment the next
+        # - traefik.http.middlewares.${STACK_NAME}-www-redirect.redirectregex.replacement=https://www.${DOMAIN}/$${3}
+        # Middleware to redirect www, to disable it remove the next line
+        - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-https.middlewares=${STACK_NAME?Variable not set}-www-redirect
+        # Middleware to redirect www, and redirect HTTP to HTTPS
+        # to disable www redirection remove the section: ${STACK_NAME?Variable not set}-www-redirect,
+        - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-http.middlewares=${STACK_NAME?Variable not set}-www-redirect,${STACK_NAME?Variable not set}-https-redirect
+
+  backend: &backend
+    image: "ogabrielluiz/langflow:latest"
+    build:
+      context: ../
+      dockerfile: base.Dockerfile
+    depends_on:
+      - db
+      - broker
+      - result_backend
+    env_file:
+      - .env
+    volumes:
+      - ../:/app
+      - ./startup-backend.sh:/startup-backend.sh # Ensure the paths match
+    command: /startup-backend.sh # Fixed the path
+    healthcheck:
+      test: "exit 0"
+    deploy:
+      labels:
+        - traefik.enable=true
+        - traefik.constraint-label-stack=${TRAEFIK_TAG?Variable not set}
+        - traefik.http.routers.${STACK_NAME?Variable not set}-backend-http.rule=PathPrefix(`/api/v1`) || PathPrefix(`/docs`) || PathPrefix(`/health`)
+        - traefik.http.services.${STACK_NAME?Variable not set}-backend.loadbalancer.server.port=7860
+
+  db:
+    image: postgres:15.4
+    volumes:
+      - app-db-data:/var/lib/postgresql/data/pgdata
+    environment:
+      - PGDATA=/var/lib/postgresql/data/pgdata
+    deploy:
+      placement:
+        constraints:
+          - node.labels.app-db-data == true
+    healthcheck:
+      test: "exit 0"
+    env_file:
+      - .env
+
+  pgadmin:
+    image: dpage/pgadmin4
+    networks:
+      - ${TRAEFIK_PUBLIC_NETWORK?Variable not set}
+      - default
+    volumes:
+      - pgadmin-data:/var/lib/pgadmin
+    env_file:
+      - .env
+    deploy:
+      labels:
+        - traefik.enable=true
+        - traefik.docker.network=${TRAEFIK_PUBLIC_NETWORK?Variable not set}
+        - traefik.constraint-label=${TRAEFIK_PUBLIC_TAG?Variable not set}
+        - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-http.rule=Host(`pgadmin.${DOMAIN?Variable not set}`)
+        - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-http.entrypoints=http
+        - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-http.middlewares=${STACK_NAME?Variable not set}-https-redirect
+        - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-https.rule=Host(`pgadmin.${DOMAIN?Variable not set}`)
+        - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-https.entrypoints=https
+        - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-https.tls=true
+        - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-https.tls.certresolver=le
+        - traefik.http.services.${STACK_NAME?Variable not set}-pgadmin.loadbalancer.server.port=5050
+
+  result_backend:
+    image: redis:6.2.5
+    env_file:
+      - .env
+    # ports:
+    #   - 6379:6379
+    healthcheck:
+      test: "exit 0"
+
+  celeryworker:
+    <<: *backend
+    env_file:
+      - .env
+    build:
+      context: ../
+      dockerfile: base.Dockerfile
+    command: celery -A langflow.worker.celery_app worker --loglevel=INFO --concurrency=1 -n lf-worker@%h
+    healthcheck:
+      test: "exit 0"
+    deploy:
+      replicas: 1
+
+  flower:
+    <<: *backend
+    env_file:
+      - .env
+    networks:
+      - default
+    build:
+      context: ../
+      dockerfile: base.Dockerfile
+    environment:
+      - FLOWER_PORT=5555
+
+    command: /bin/sh -c "celery -A langflow.worker.celery_app --broker=${BROKER_URL?Variable not set} flower --port=5555"
+    deploy:
+      labels:
+        - traefik.enable=true
+        - traefik.docker.network=${TRAEFIK_PUBLIC_NETWORK?Variable not set}
+        - traefik.constraint-label=${TRAEFIK_PUBLIC_TAG?Variable not set}
+        - traefik.http.routers.${STACK_NAME?Variable not set}-flower-http.rule=Host(`flower.${DOMAIN?Variable not set}`)
+        - traefik.http.routers.${STACK_NAME?Variable not set}-flower-http.entrypoints=http
+        - traefik.http.routers.${STACK_NAME?Variable not set}-flower-http.middlewares=${STACK_NAME?Variable not set}-https-redirect
+        - traefik.http.routers.${STACK_NAME?Variable not set}-flower-https.rule=Host(`flower.${DOMAIN?Variable not set}`)
+        - traefik.http.routers.${STACK_NAME?Variable not set}-flower-https.entrypoints=https
+        - traefik.http.routers.${STACK_NAME?Variable not set}-flower-https.tls=true
+        - traefik.http.routers.${STACK_NAME?Variable not set}-flower-https.tls.certresolver=le
+        - traefik.http.services.${STACK_NAME?Variable not set}-flower.loadbalancer.server.port=5555
+
+  frontend:
+    image: "ogabrielluiz/langflow_frontend:latest"
+    env_file:
+      - .env
+    # user: your-non-root-user
+    build:
+      context: ../src/frontend
+      dockerfile: Dockerfile
+      args:
+        - BACKEND_URL=http://backend:7860
+    restart: on-failure
+    deploy:
+      labels:
+        - traefik.enable=true
+        - traefik.constraint-label-stack=${TRAEFIK_TAG?Variable not set}
+        - traefik.http.routers.${STACK_NAME?Variable not set}-frontend-http.rule=PathPrefix(`/`)
+        - traefik.http.services.${STACK_NAME?Variable not set}-frontend.loadbalancer.server.port=80
+
+  broker:
+    # RabbitMQ management console
+    image: rabbitmq:3-management
+    environment:
+      - RABBITMQ_DEFAULT_USER=${RABBITMQ_DEFAULT_USER:-admin}
+      - RABBITMQ_DEFAULT_PASS=${RABBITMQ_DEFAULT_PASS:-admin}
+    volumes:
+      - rabbitmq_data:/etc/rabbitmq/
+      - rabbitmq_data:/var/lib/rabbitmq/
+      - rabbitmq_log:/var/log/rabbitmq/
+    ports:
+      - 5672:5672
+      - 15672:15672
+
+  prometheus:
+    image: prom/prometheus:v2.37.9
+    env_file:
+      - .env
+    volumes:
+      - ./prometheus.yml:/etc/prometheus/prometheus.yml
+    command:
+      - "--config.file=/etc/prometheus/prometheus.yml"
+    # ports:
+    #   - 9090:9090
+    healthcheck:
+      test: "exit 0"
+    deploy:
+      labels:
+        - traefik.enable=true
+        - traefik.constraint-label-stack=${TRAEFIK_TAG?Variable not set}
+        - traefik.http.routers.${STACK_NAME?Variable not set}-prometheus-http.rule=PathPrefix(`/metrics`)
+        - traefik.http.services.${STACK_NAME?Variable not set}-prometheus.loadbalancer.server.port=9090
+
+  grafana:
+    image: grafana/grafana:8.2.6
+    env_file:
+      - .env
+    # ports:
+    #   - 3000:3000
+    volumes:
+      - grafana_data:/var/lib/grafana
+    deploy:
+      labels:
+        - traefik.enable=true
+        - traefik.constraint-label-stack=${TRAEFIK_TAG?Variable not set}
+        - traefik.http.routers.${STACK_NAME?Variable not set}-grafana-http.rule=PathPrefix(`/grafana`)
+        - traefik.http.services.${STACK_NAME?Variable not set}-grafana.loadbalancer.server.port=3000
+
+  tests:
+    extends:
+      file: docker-compose.yml
+      service: backend
+    env_file:
+      - .env
+    build:
+      context: ../
+      dockerfile: base.Dockerfile
+    command: pytest -vv
+    healthcheck:
+      test: "exit 0"
+    # override deploy labels to avoid conflicts with the backend service
+    labels:
+      - traefik.enable=true
+      - traefik.constraint-label-stack=${TRAEFIK_TAG?Variable not set}
+      - traefik.http.routers.${STACK_NAME?Variable not set}-tests-http.rule=PathPrefix(`/api/v1`) || PathPrefix(`/docs`) || PathPrefix(`/health`)
+      - traefik.http.services.${STACK_NAME?Variable not set}-tests.loadbalancer.server.port=7861
+
+volumes:
+  grafana_data:
+  app-db-data:
+  rabbitmq_data:
+  rabbitmq_log:
+  pgadmin-data:
+
+networks:
+  traefik-public:
+    # Allow setting it to false for testing
+    external: false # ${TRAEFIK_PUBLIC_NETWORK_IS_EXTERNAL-true}

deploy/docker-compose.yml

@@ -0,0 +1,258 @@
+version: "3.8"
+
+services:
+  proxy:
+    image: traefik:v3.0
+    env_file:
+      - .env
+    networks:
+      - ${TRAEFIK_PUBLIC_NETWORK?Variable not set}
+      - default
+    volumes:
+      - /var/run/docker.sock:/var/run/docker.sock
+    command:
+      # Enable Docker in Traefik, so that it reads labels from Docker services
+      - --providers.docker
+      # Add a constraint to only use services with the label for this stack
+      # from the env var TRAEFIK_TAG
+      - --providers.docker.constraints=Label(`traefik.constraint-label-stack`, `${TRAEFIK_TAG?Variable not set}`)
+      # Do not expose all Docker services, only the ones explicitly exposed
+      - --providers.docker.exposedbydefault=false
+      # Enable the access log, with HTTP requests
+      - --accesslog
+      # Enable the Traefik log, for configurations and errors
+      - --log
+      # Enable the Dashboard and API
+      - --api
+    deploy:
+      placement:
+        constraints:
+          - node.role == manager
+      labels:
+        # Enable Traefik for this service, to make it available in the public network
+        - traefik.enable=true
+        # Use the traefik-public network (declared below)
+        - traefik.docker.network=${TRAEFIK_PUBLIC_NETWORK?Variable not set}
+        # Use the custom label "traefik.constraint-label=traefik-public"
+        # This public Traefik will only use services with this label
+        - traefik.constraint-label=${TRAEFIK_PUBLIC_TAG?Variable not set}
+        # traefik-http set up only to use the middleware to redirect to https
+        - traefik.http.middlewares.${STACK_NAME?Variable not set}-https-redirect.redirectscheme.scheme=https
+        - traefik.http.middlewares.${STACK_NAME?Variable not set}-https-redirect.redirectscheme.permanent=true
+        # Handle host with and without "www" to redirect to only one of them
+        # Uses environment variable DOMAIN
+        # To disable www redirection remove the Host() you want to discard, here and
+        # below for HTTPS
+        - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-http.rule=Host(`${DOMAIN?Variable not set}`) || Host(`www.${DOMAIN?Variable not set}`)
+        - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-http.entrypoints=http
+        # traefik-https the actual router using HTTPS
+        - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-https.rule=Host(`${DOMAIN?Variable not set}`) || Host(`www.${DOMAIN?Variable not set}`)
+        - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-https.entrypoints=https
+        - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-https.tls=true
+        # Use the "le" (Let's Encrypt) resolver created below
+        - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-https.tls.certresolver=le
+        # Define the port inside of the Docker service to use
+        - traefik.http.services.${STACK_NAME?Variable not set}-proxy.loadbalancer.server.port=80
+        # Handle domain with and without "www" to redirect to only one
+        # To disable www redirection remove the next line
+        - traefik.http.middlewares.${STACK_NAME?Variable not set}-www-redirect.redirectregex.regex=^https?://(www.)?(${DOMAIN?Variable not set})/(.*)
+        # Redirect a domain with www to non-www
+        # To disable it remove the next line
+        - traefik.http.middlewares.${STACK_NAME?Variable not set}-www-redirect.redirectregex.replacement=https://${DOMAIN?Variable not set}/$${3}
+        # Redirect a domain without www to www
+        # To enable it remove the previous line and uncomment the next
+        # - traefik.http.middlewares.${STACK_NAME}-www-redirect.redirectregex.replacement=https://www.${DOMAIN}/$${3}
+        # Middleware to redirect www, to disable it remove the next line
+        - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-https.middlewares=${STACK_NAME?Variable not set}-www-redirect
+        # Middleware to redirect www, and redirect HTTP to HTTPS
+        # to disable www redirection remove the section: ${STACK_NAME?Variable not set}-www-redirect,
+        - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-http.middlewares=${STACK_NAME?Variable not set}-www-redirect,${STACK_NAME?Variable not set}-https-redirect
+
+  backend: &backend
+    image: "ogabrielluiz/langflow:latest"
+    build:
+      context: ../
+      dockerfile: base.Dockerfile
+    depends_on:
+      - db
+      - broker
+      - result_backend
+    env_file:
+      - .env
+    volumes:
+      - ../:/app
+      - ./startup-backend.sh:/startup-backend.sh # Ensure the paths match
+    command: /startup-backend.sh # Fixed the path
+    healthcheck:
+      test: "exit 0"
+    deploy:
+      labels:
+        - traefik.enable=true
+        - traefik.constraint-label-stack=${TRAEFIK_TAG?Variable not set}
+        - traefik.http.routers.${STACK_NAME?Variable not set}-backend-http.rule=PathPrefix(`/api/v1`) || PathPrefix(`/docs`) || PathPrefix(`/health`)
+        - traefik.http.services.${STACK_NAME?Variable not set}-backend.loadbalancer.server.port=7860
+
+  db:
+    image: postgres:15.4
+    volumes:
+      - app-db-data:/var/lib/postgresql/data/pgdata
+    environment:
+      - PGDATA=/var/lib/postgresql/data/pgdata
+    deploy:
+      placement:
+        constraints:
+          - node.labels.app-db-data == true
+    healthcheck:
+      test: "exit 0"
+    env_file:
+      - .env
+
+  pgadmin:
+    image: dpage/pgadmin4
+    networks:
+      - ${TRAEFIK_PUBLIC_NETWORK?Variable not set}
+      - default
+    volumes:
+      - pgadmin-data:/var/lib/pgadmin
+    env_file:
+      - .env
+    deploy:
+      labels:
+        - traefik.enable=true
+        - traefik.docker.network=${TRAEFIK_PUBLIC_NETWORK?Variable not set}
+        - traefik.constraint-label=${TRAEFIK_PUBLIC_TAG?Variable not set}
+        - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-http.rule=Host(`pgadmin.${DOMAIN?Variable not set}`)
+        - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-http.entrypoints=http
+        - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-http.middlewares=${STACK_NAME?Variable not set}-https-redirect
+        - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-https.rule=Host(`pgadmin.${DOMAIN?Variable not set}`)
+        - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-https.entrypoints=https
+        - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-https.tls=true
+        - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-https.tls.certresolver=le
+        - traefik.http.services.${STACK_NAME?Variable not set}-pgadmin.loadbalancer.server.port=5050
+
+  result_backend:
+    image: redis:6.2.5
+    env_file:
+      - .env
+    # ports:
+    #   - 6379:6379
+    healthcheck:
+      test: "exit 0"
+
+  celeryworker:
+    <<: *backend
+    env_file:
+      - .env
+    build:
+      context: ../
+      dockerfile: base.Dockerfile
+    command: celery -A langflow.worker.celery_app worker --loglevel=INFO --concurrency=1 -n lf-worker@%h
+    healthcheck:
+      test: "exit 0"
+    deploy:
+      replicas: 1
+
+  flower:
+    <<: *backend
+    env_file:
+      - .env
+    networks:
+      - default
+    build:
+      context: ../
+      dockerfile: base.Dockerfile
+    environment:
+      - FLOWER_PORT=5555
+
+    command: /bin/sh -c "celery -A langflow.worker.celery_app --broker=${BROKER_URL?Variable not set} flower --port=5555"
+    deploy:
+      labels:
+        - traefik.enable=true
+        - traefik.docker.network=${TRAEFIK_PUBLIC_NETWORK?Variable not set}
+        - traefik.constraint-label=${TRAEFIK_PUBLIC_TAG?Variable not set}
+        - traefik.http.routers.${STACK_NAME?Variable not set}-flower-http.rule=Host(`flower.${DOMAIN?Variable not set}`)
+        - traefik.http.routers.${STACK_NAME?Variable not set}-flower-http.entrypoints=http
+        - traefik.http.routers.${STACK_NAME?Variable not set}-flower-http.middlewares=${STACK_NAME?Variable not set}-https-redirect
+        - traefik.http.routers.${STACK_NAME?Variable not set}-flower-https.rule=Host(`flower.${DOMAIN?Variable not set}`)
+        - traefik.http.routers.${STACK_NAME?Variable not set}-flower-https.entrypoints=https
+        - traefik.http.routers.${STACK_NAME?Variable not set}-flower-https.tls=true
+        - traefik.http.routers.${STACK_NAME?Variable not set}-flower-https.tls.certresolver=le
+        - traefik.http.services.${STACK_NAME?Variable not set}-flower.loadbalancer.server.port=5555
+
+  frontend:
+    image: "ogabrielluiz/langflow_frontend:latest"
+    env_file:
+      - .env
+    # user: your-non-root-user
+    build:
+      context: ../src/frontend
+      dockerfile: Dockerfile
+      args:
+        - BACKEND_URL=http://backend:7860
+    restart: on-failure
+    deploy:
+      labels:
+        - traefik.enable=true
+        - traefik.constraint-label-stack=${TRAEFIK_TAG?Variable not set}
+        - traefik.http.routers.${STACK_NAME?Variable not set}-frontend-http.rule=PathPrefix(`/`)
+        - traefik.http.services.${STACK_NAME?Variable not set}-frontend.loadbalancer.server.port=80
+
+  broker:
+    # RabbitMQ management console
+    image: rabbitmq:3-management
+    environment:
+      - RABBITMQ_DEFAULT_USER=${RABBITMQ_DEFAULT_USER:-admin}
+      - RABBITMQ_DEFAULT_PASS=${RABBITMQ_DEFAULT_PASS:-admin}
+    volumes:
+      - rabbitmq_data:/etc/rabbitmq/
+      - rabbitmq_data:/var/lib/rabbitmq/
+      - rabbitmq_log:/var/log/rabbitmq/
+    ports:
+      - 5672:5672
+      - 15672:15672
+
+  prometheus:
+    image: prom/prometheus:v2.37.9
+    env_file:
+      - .env
+    volumes:
+      - ./prometheus.yml:/etc/prometheus/prometheus.yml
+    command:
+      - "--config.file=/etc/prometheus/prometheus.yml"
+    # ports:
+    #   - 9090:9090
+    healthcheck:
+      test: "exit 0"
+    deploy:
+      labels:
+        - traefik.enable=true
+        - traefik.constraint-label-stack=${TRAEFIK_TAG?Variable not set}
+        - traefik.http.routers.${STACK_NAME?Variable not set}-prometheus-http.rule=PathPrefix(`/metrics`)
+        - traefik.http.services.${STACK_NAME?Variable not set}-prometheus.loadbalancer.server.port=9090
+
+  grafana:
+    image: grafana/grafana:8.2.6
+    env_file:
+      - .env
+    # ports:
+    #   - 3000:3000
+    volumes:
+      - grafana_data:/var/lib/grafana
+    deploy:
+      labels:
+        - traefik.enable=true
+        - traefik.constraint-label-stack=${TRAEFIK_TAG?Variable not set}
+        - traefik.http.routers.${STACK_NAME?Variable not set}-grafana-http.rule=PathPrefix(`/grafana`)
+        - traefik.http.services.${STACK_NAME?Variable not set}-grafana.loadbalancer.server.port=3000
+
+volumes:
+  grafana_data:
+  app-db-data:
+  rabbitmq_data:
+  rabbitmq_log:
+  pgadmin-data:
+
+networks:
+  traefik-public:
+    # Allow setting it to false for testing
+    external: false # ${TRAEFIK_PUBLIC_NETWORK_IS_EXTERNAL-true}

deploy/prometheus.yml

@@ -0,0 +1,11 @@
+global:
+  scrape_interval: 15s
+  evaluation_interval: 15s
+
+scrape_configs:
+  - job_name: prometheus
+    static_configs:
+      - targets: ["prometheus:9090"]
+  - job_name: flower
+    static_configs:
+      - targets: ["flower:5555"]

deploy/startup-backend.sh

@@ -0,0 +1,17 @@
+#!/bin/bash
+
+export LANGFLOW_DATABASE_URL="postgresql://${DB_USER}:${DB_PASSWORD}@${DB_HOST}:${DB_PORT}/${DB_NAME}"
+
+
+# Your command to start the backend
+
+# If the ENVIRONMENT variable is set to "development", then start the backend in development mode
+# else start the backend in production mode with guvicorn
+if [ "$ENVIRONMENT" = "development" ]; then
+    echo "Starting backend in development mode"
+    exec python -m uvicorn --factory langflow.main:create_app --host 0.0.0.0 --port 7860 --log-level ${LOG_LEVEL:-info} --workers 2 --reload
+else
+    echo "Starting backend in production mode"
+    exec langflow run --host 0.0.0.0 --port 7860 --log-level ${LOG_LEVEL:-info} --workers -1 --backend-only
+fi
+

docs/docs/deployment/jina-deployment.md

@@ -1,101 +0,0 @@
-# Deploy on Jina AI Cloud
-
-Langflow integrates with langchain-serve to provide a one-command deployment to [Jina AI Cloud](https://github.com/jina-ai/langchain-serve).
-
-Start by installing `langchain-serve` with 
-
-```bash
-pip install -U langchain-serve
-``` 
-
-Then, run:
-
-```bash
-langflow --jcloud
-```
-
-```text
-🎉 Langflow server successfully deployed on Jina AI Cloud 🎉
-🔗 Click on the link to open the server (please allow ~1-2 minutes for the server to startup): https://<your-app>.wolf.jina.ai/
-📖 Read more about managing the server: https://github.com/jina-ai/langchain-serve
-```
-
-**Complete (example) output:**
-
-```text
-  🚀 Deploying Langflow server on Jina AI Cloud
-  ╭───────────────────────── 🎉 Flow is available! ──────────────────────────╮
-  │                                                                          │
-  │   ID                    langflow-e3dd8820ec                              │
-  │   Gateway (Websocket)   wss://langflow-e3dd8820ec.wolf.jina.ai           │
-  │   Dashboard             https://dashboard.wolf.jina.ai/flow/e3dd8820ec   │
-  │                                                                          │
-  ╰──────────────────────────────────────────────────────────────────────────╯
-  ╭──────────────┬──────────────────────────────────────────────────────────────────────────────╮
-  │ App ID       │                     langflow-e3dd8820ec                                      │
-  ├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
-  │ Phase        │                            Serving                                           │
-  ├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
-  │ Endpoint     │          wss://langflow-e3dd8820ec.wolf.jina.ai                              │
-  ├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
-  │ App logs     │                  dashboards.wolf.jina.ai                                     │
-  ├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
-  │ Swagger UI   │          https://langflow-e3dd8820ec.wolf.jina.ai/docs                       │
-  ├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
-  │ OpenAPI JSON │        https://langflow-e3dd8820ec.wolf.jina.ai/openapi.json                 │
-  ╰──────────────┴──────────────────────────────────────────────────────────────────────────────╯
-
-  🎉 Langflow server successfully deployed on Jina AI Cloud 🎉
-  🔗 Click on the link to open the server (please allow ~1-2 minutes for the server to startup): https://langflow-e3dd8820ec.wolf.jina.ai/
-  📖 Read more about managing the server: https://github.com/jina-ai/langchain-serve
-  ```
-## API Usage (with python)
-
-You can use Langflow directly on your browser or the API endpoints on Jina AI Cloud to interact with the server.
-
-```python
-import requests
-
-BASE_API_URL = "https://langflow-e3dd8820ec.wolf.jina.ai/api/v1/predict"
-FLOW_ID = "864c4f98-2e59-468b-8e13-79cd8da07468"
-# You can tweak the flow by adding a tweaks dictionary
-# e.g {"OpenAI-XXXXX": {"model_name": "gpt-4"}}
-TWEAKS = {
-"ChatOpenAI-g4jEr": {},
-"ConversationChain-UidfJ": {}
-}
-
-def run_flow(message: str, flow_id: str, tweaks: dict = None) -> dict:
-  """
-  Run a flow with a given message and optional tweaks.
-
-  :param message: The message to send to the flow
-  :param flow_id: The ID of the flow to run
-  :param tweaks: Optional tweaks to customize the flow
-  :return: The JSON response from the flow
-  """
-  api_url = f"{BASE_API_URL}/{flow_id}"
-
-  payload = {"message": message}
-
-  if tweaks:
-      payload["tweaks"] = tweaks
-
-  response = requests.post(api_url, json=payload)
-  return response.json()
-
-# Setup any tweaks you want to apply to the flow
-print(run_flow("Your message", flow_id=FLOW_ID, tweaks=TWEAKS))
-  ```
-
-  ```json
-{
-  "result": "Great choice! Bangalore in the 1920s was a vibrant city with a rich cultural and political scene. Here are some suggestions for things to see and do:\n\n1. Visit the Bangalore Palace - built in 1887, this stunning palace is a perfect example of Tudor-style architecture. It was home to the Maharaja of Mysore and is now open to the public.\n\n2. Attend a performance at the Ravindra Kalakshetra - this cultural center was built in the 1920s and is still a popular venue for music and dance performances.\n\n3. Explore the neighborhoods of Basavanagudi and Malleswaram - both of these areas have retained much of their old-world charm and are great places to walk around and soak up the atmosphere.\n\n4. Check out the Bangalore Club - founded in 1868, this exclusive social club was a favorite haunt of the British expat community in the 1920s.\n\n5. Attend a meeting of the Indian National Congress - founded in 1885, the INC was a major force in the Indian independence movement and held many meetings and rallies in Bangalore in the 1920s.\n\nHope you enjoy your trip to 1920s Bangalore!"
-}
-  ```
-
-:::info
-
-Read more about resource customization, cost, and management of Langflow apps on Jina AI Cloud in the **[langchain-serve](https://github.com/jina-ai/langchain-serve)** repository.
-
-:::

docs/docs/guidelines/api.mdx

@@ -0,0 +1,147 @@
+import useBaseUrl from "@docusaurus/useBaseUrl";
+import ZoomableImage from "/src/theme/ZoomableImage.js";
+
+# API Keys
+
+## Introduction
+
+Langflow offers an API Key functionality that allows users to access their individual components and flows without going through traditional login authentication. The API Key is a user-specific token that can be included in the request's header or query parameter to authenticate API calls. The following documentation outlines how to generate, use, and manage these API Keys in Langflow.
+
+## Generating an API Key
+
+### Through Langflow UI
+
+{/* add image img/api-key.png */}
+
+<ZoomableImage
+  alt="Docusaurus themed image"
+  sources={{
+    light: useBaseUrl("img/api-key.png"),
+  }}
+  style={{ width: "50%", maxWidth: "600px", margin: "0 auto" }}
+/>
+
+1. Click on the "API Key" icon.
+2. Click on "Create new secret key".
+3. Give it an optional name.
+4. Click on "Create secret key".
+5. Copy the API key and store it in a secure location.
+
+## Using the API Key
+
+### Using the `x-api-key` Header
+
+Include the `x-api-key` in the HTTP header when making API requests:
+
+```bash
+curl -X POST \
+  http://localhost:3000/api/v1/process/<your_flow_id> \
+  -H 'Content-Type: application/json'\
+  -H 'x-api-key: <your api key>'\
+  -d '{"inputs": {"text":""}, "tweaks": {}}'
+```
+
+With Python using `requests`:
+
+```python
+import requests
+from typing import Optional
+
+BASE_API_URL = "http://localhost:3001/api/v1/process"
+FLOW_ID = "4441b773-0724-434e-9cee-19d995d8f2df"
+# You can tweak the flow by adding a tweaks dictionary
+# e.g {"OpenAI-XXXXX": {"model_name": "gpt-4"}}
+TWEAKS = {}
+
+def run_flow(inputs: dict,
+            flow_id: str,
+            tweaks: Optional[dict] = None,
+            apiKey: Optional[str] = None) -> dict:
+    """
+    Run a flow with a given message and optional tweaks.
+
+    :param message: The message to send to the flow
+    :param flow_id: The ID of the flow to run
+    :param tweaks: Optional tweaks to customize the flow
+    :return: The JSON response from the flow
+    """
+    api_url = f"{BASE_API_URL}/{flow_id}"
+
+    payload = {"inputs": inputs}
+    headers = {}
+
+    if tweaks:
+        payload["tweaks"] = tweaks
+    if apiKey:
+        headers = {"x-api-key": apiKey}
+
+    response = requests.post(api_url, json=payload, headers=headers)
+    return response.json()
+
+# Setup any tweaks you want to apply to the flow
+inputs = {"text":""}
+api_key = "<your api key>"
+print(run_flow(inputs, flow_id=FLOW_ID, tweaks=TWEAKS, apiKey=api_key))
+```
+
+### Using the Query Parameter
+
+Alternatively, you can include the API key as a query parameter in the URL:
+
+```bash
+curl -X POST \
+  http://localhost:3000/api/v1/process/<your_flow_id>?x-api-key=<your_api_key> \
+  -H 'Content-Type: application/json'\
+  -d '{"inputs": {"text":""}, "tweaks": {}}'
+```
+
+Or with Python:
+
+```python
+import requests
+
+BASE_API_URL = "http://localhost:3001/api/v1/process"
+FLOW_ID = "4441b773-0724-434e-9cee-19d995d8f2df"
+# You can tweak the flow by adding a tweaks dictionary
+# e.g {"OpenAI-XXXXX": {"model_name": "gpt-4"}}
+TWEAKS = {}
+
+def run_flow(inputs: dict,
+            flow_id: str,
+            tweaks: Optional[dict] = None,
+            apiKey: Optional[str] = None) -> dict:
+    """
+    Run a flow with a given message and optional tweaks.
+
+    :param message: The message to send to the flow
+    :param flow_id: The ID of the flow to run
+    :param tweaks: Optional tweaks to customize the flow
+    :return: The JSON response from the flow
+    """
+    api_url = f"{BASE_API_URL}/{flow_id}"
+
+    payload = {"inputs": inputs}
+    headers = {}
+
+    if tweaks:
+        payload["tweaks"] = tweaks
+    if apiKey:
+        api_url += f"?x-api-key={apiKey}"
+
+    response = requests.post(api_url, json=payload, headers=headers)
+    return response.json()
+
+# Setup any tweaks you want to apply to the flow
+inputs = {"text":""}
+api_key = "<your api key>"
+print(run_flow(inputs, flow_id=FLOW_ID, tweaks=TWEAKS, apiKey=api_key))
+```
+
+## Security Considerations
+
+- **Visibility**: The API key won't be retrievable again through the UI for security reasons.
+- **Scope**: The key only allows access to the flows and components of the specific user to whom it was issued.
+
+## Revoking an API Key
+
+To revoke an API key, simply delete it from the UI. This will immediately invalidate the key and prevent it from being used again.

docs/docs/guidelines/async-api.mdx

@@ -0,0 +1,73 @@
+import Admonition from "@theme/Admonition";
+
+# Asynchronous Processing
+
+## Introduction
+
+Starting from version 0.5, Langflow introduces a new feature to its API: the _`sync`_ flag. This flag allows users to opt for asynchronous processing of their flows, freeing up resources and enabling better control over long-running tasks.
+This feature supports running tasks in a Celery worker queue and AnyIO task groups for now.
+
+<Admonition type="warning" caption="Experimental Feature">
+  This is an experimental feature. The default behavior of the API is still
+  synchronous processing. The API may change in the future.
+</Admonition>
+
+## The _`sync`_ Flag
+
+The _`sync`_ flag can be included in the payload of your POST request to the _`/api/v1/process/<your_flow_id>`_ endpoint.
+When set to _`false`_, the API will initiate an asynchronous task instead of processing the flow synchronously.
+
+### API Request with _`sync`_ flag
+
+```bash
+curl -X POST \
+  http://localhost:3000/api/v1/process/<your_flow_id> \
+  -H 'Content-Type: application/json' \
+  -H 'x-api-key: <your_api_key>' \
+  -d '{"inputs": {"text": ""}, "tweaks": {}, "sync": false}'
+```
+
+Response:
+
+```json
+{
+  "result": {
+    "output": "..."
+  },
+  "task": {
+    "id": "...",
+    "href": "api/v1/task/<task_id>"
+  },
+  "session_id": "...",
+  "backend": "..." // celery or anyio
+}
+```
+
+## Checking Task Status
+
+You can check the status of an asynchronous task by making a GET request to the `/task/{task_id}` endpoint.
+
+```bash
+curl -X GET \
+  http://localhost:3000/api/v1/task/<task_id> \
+  -H 'x-api-key: <your_api_key>'
+```
+
+### Response
+
+The endpoint will return the current status of the task and, if completed, the result of the task. Possible statuses include:
+
+- _`PENDING`_: The task is waiting for execution.
+- _`SUCCESS`_: The task has completed successfully.
+- _`FAILURE`_: The task has failed.
+
+Example response for a completed task:
+
+```json
+{
+  "status": "SUCCESS",
+  "result": {
+    "output": "..."
+  }
+}
+```

docs/docs/guidelines/login.mdx

@@ -0,0 +1,128 @@
+import ThemedImage from "@theme/ThemedImage";
+import useBaseUrl from "@docusaurus/useBaseUrl";
+import ZoomableImage from "/src/theme/ZoomableImage.js";
+import ReactPlayer from "react-player";
+import Admonition from "@theme/Admonition";
+
+# Sign up and Sign in
+
+## Introduction
+
+The login functionality in Langflow serves to authenticate users and protect sensitive routes in the application. Starting from version 0.5, Langflow introduces an enhanced login mechanism that is governed by a few environment variables. This allows new secure features.
+
+## Environment Variables
+
+The following environment variables are crucial in configuring the login settings:
+
+- _`LANGFLOW_AUTO_LOGIN`_: Determines whether Langflow should automatically log users in. Default is `True`.
+- _`LANGFLOW_SUPERUSER`_: The username of the superuser.
+- _`LANGFLOW_SUPERUSER_PASSWORD`_: The password for the superuser.
+- _`LANGFLOW_SECRET_KEY`_: A key used for encrypting the superuser's password.
+- _`LANGFLOW_NEW_USER_IS_ACTIVE`_: Determines whether new users are automatically activated. Default is `False`.
+
+All of these variables can be passed to the CLI command _`langflow run`_ through the _`--env-file`_ option. For example:
+
+```bash
+langflow run --env-file .env
+```
+
+<Admonition type="info">
+  It is critical not to expose these environment variables in your code
+  repository. Always set them securely in your deployment environment, for
+  example, using Docker secrets, Kubernetes ConfigMaps/Secrets, or dedicated
+  secure environment configuration systems like AWS Secrets Manager.
+</Admonition>
+
+### _`LANGFLOW_AUTO_LOGIN`_
+
+By default, this variable is set to `True`. When enabled (`True`), Langflow operates as it did in versions prior to 0.5—automatic login without requiring explicit user authentication.
+
+To disable automatic login and enforce user authentication:
+
+```bash
+export LANGFLOW_AUTO_LOGIN=False
+```
+
+### _`LANGFLOW_SUPERUSER`_ and _`LANGFLOW_SUPERUSER_PASSWORD`_
+
+These environment variables are only relevant when `LANGFLOW_AUTO_LOGIN` is set to `False`. They specify the username and password for the superuser, which is essential for administrative tasks.
+
+To create a superuser manually:
+
+```bash
+export LANGFLOW_SUPERUSER=admin
+export LANGFLOW_SUPERUSER_PASSWORD=securepassword
+```
+
+You can also use the CLI command `langflow superuser` to set up a superuser interactively.
+
+### _`LANGFLOW_SECRET_KEY`_
+
+This environment variable holds a secret key used for encrypting the superuser's password. Make sure to set this to a secure, randomly generated string.
+
+```bash
+export LANGFLOW_SECRET_KEY=randomly_generated_secure_key
+```
+
+### _`LANGFLOW_NEW_USER_IS_ACTIVE`_
+
+By default, this variable is set to `False`. When enabled (`True`), new users are automatically activated and can log in without requiring explicit activation by the superuser.
+
+## Command-Line Interface
+
+Langflow provides a command-line utility for managing superusers:
+
+```bash
+langflow superuser
+```
+
+This command prompts you to enter the username and password for the superuser, unless they are already set using environment variables.
+
+## Sign-up
+
+With _`LANGFLOW_AUTO_LOGIN`_ set to _`False`_, Langflow requires users to sign up before they can log in. The sign-up page is the default landing page when a user visits Langflow for the first time.
+
+<ZoomableImage
+  alt="Docusaurus themed image"
+  sources={{
+    light: useBaseUrl("img/sign-up.png"),
+  }}
+  style={{ width: "50%", maxWidth: "600px", margin: "0 auto" }}
+/>
+
+## Profile settings
+
+Users can change their profile settings by clicking on the profile icon in the top right corner of the application. This opens a dropdown menu with the following options:
+
+- **Admin Page**: Opens the admin page, which is only accessible to the superuser.
+- **Profile Settings**: Opens the profile settings page.
+- **Sign Out**: Logs the user out.
+
+<ZoomableImage
+  alt="Docusaurus themed image"
+  sources={{
+    light: useBaseUrl("img/my-account.png"),
+  }}
+  style={{ width: "50%", maxWidth: "600px", margin: "0 auto" }}
+/>
+
+By clicking on **Profile Settings**, the user is taken to the profile settings page, where they can change their password and their profile picture.
+
+<ZoomableImage
+  alt="Docusaurus themed image"
+  sources={{
+    light: useBaseUrl("img/profile-settings.png"),
+  }}
+  style={{ maxWidth: "600px", margin: "0 auto" }}
+/>
+
+By clicking on **Admin Page**, the superuser is taken to the admin page, where they can manage users and groups.
+
+<ZoomableImage
+  alt="Docusaurus themed image"
+  sources={{
+    light: useBaseUrl("img/admin-page.png"),
+  }}
+  style={{ maxWidth: "600px", margin: "0 auto" }}
+
+/>

docs/docs/guides/async-tasks.mdx

@@ -0,0 +1,44 @@
+import Admonition from "@theme/Admonition";
+
+# Async API
+
+## Introduction
+
+<Admonition type="info" caption="In development">
+  This implementation is still in development. Contributions are welcome!
+</Admonition>
+
+The Async API is an implementation of the Langflow API that uses [Celery](https://docs.celeryproject.org/en/stable/)
+to run the tasks asynchronously, using a message broker to send and receive messages, a result backend to store the results and a cache to store the task states and session data.
+
+### Configuration
+
+The folder _`./deploy`_ in the [Github repository](https://github.com/logspace-ai/langflow) contains a _`.env.example`_ file that can be used to configure a Langflow deployment.
+The file contains the variables required to configure a Celery worker queue, Redis cache and result backend and a RabbitMQ message broker.
+
+To set it up locally you can copy the file to _`.env`_ and run the following command:
+
+```bash
+docker compose up -d
+```
+
+This will set up the following containers:
+
+- Langflow API
+- Celery worker
+- RabbitMQ message broker
+- Redis cache
+- PostgreSQL database
+- PGAdmin
+- Flower
+- Traefik
+- Grafana
+- Prometheus
+
+### Testing
+
+To run the tests for the Async API, you can run the following command:
+
+```bash
+docker compose -f docker-compose.with_tests.yml up --exit-code-from tests tests result_backend broker celeryworker db --build
+```

docs/docs/guides/superuser.mdx

@@ -0,0 +1,7 @@
+import ThemedImage from "@theme/ThemedImage";
+import useBaseUrl from "@docusaurus/useBaseUrl";
+import ZoomableImage from "/src/theme/ZoomableImage.js";
+import ReactPlayer from "react-player";
+
+Now, we need to explain what are the permissions the superuser gets. Once logged in, they can activate new users,
+edit them,

docs/docs/index.mdx

@@ -7,10 +7,11 @@ import useBaseUrl from "@docusaurus/useBaseUrl";
 import ZoomableImage from "/src/theme/ZoomableImage.js";
 
 {" "}
+
 <ZoomableImage
   alt="Docusaurus themed image"
   sources={{
-    light: "img/new_langflow2.gif",
+    light: "img/new_langflow.gif",
   }}
   style={{ width: "100%" }}
 />

docs/package-lock.json

@@ -28,7 +28,7 @@
         "medium-zoom": "^1.0.8",
         "node-fetch": "^3.3.1",
         "path-browserify": "^1.0.1",
-        "postcss": "^8.4.24",
+        "postcss": "^8.4.31",
         "prism-react-renderer": "^1.3.5",
         "react": "^17.0.2",
         "react-dom": "^17.0.2",
@@ -13956,9 +13956,9 @@
       }
     },
     "node_modules/postcss": {
-      "version": "8.4.25",
+      "version": "8.4.31",
-      "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.4.25.tgz",
+      "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.4.31.tgz",
-      "integrity": "sha512-7taJ/8t2av0Z+sQEvNzCkpDynl0tX3uJMCODi6nT3PfASC7dYCWV9aQ+uiCf+KBD4SEFcu+GvJdGdwzQ6OSjCw==",
+      "integrity": "sha512-PS08Iboia9mts/2ygV3eLpY5ghnUcfLV/EXTOW1E2qYxJKGGBUtNjN76FYHnMs36RmARn41bC0AZmn+rR0OVpQ==",
       "funding": [
         {
           "type": "opencollective",

docs/package.json

@@ -34,7 +34,7 @@
     "medium-zoom": "^1.0.8",
     "node-fetch": "^3.3.1",
     "path-browserify": "^1.0.1",
-    "postcss": "^8.4.24",
+    "postcss": "^8.4.31",
     "prism-react-renderer": "^1.3.5",
     "react": "^17.0.2",
     "react-dom": "^17.0.2",

docs/sidebars.js

@@ -16,6 +16,9 @@ module.exports = {
       label: "Guidelines",
       collapsed: false,
       items: [
+        "guidelines/login",
+        "guidelines/api",
+        "guidelines/async-api",
         "guidelines/components",
         "guidelines/features",
         "guidelines/collection",
@@ -52,6 +55,7 @@ module.exports = {
       label: "Step-by-Step Guides",
       collapsed: false,
       items: [
+        "guides/async-tasks",
         "guides/loading_document",
         "guides/chatprompttemplate_guide",
         "guides/langfuse_integration",
@@ -87,7 +91,7 @@ module.exports = {
       type: "category",
       label: "Deployment",
       collapsed: false,
-      items: ["deployment/gcp-deployment", "deployment/jina-deployment"],
+      items: ["deployment/gcp-deployment"],
     },
     {
       type: "category",

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langflow"
-version = "0.5.0a1"
+version = "0.5.1"
 description = "A Python package with a built-in web application"
 authors = ["Logspace <contact@logspace.ai>"]
 maintainers = [
@@ -25,18 +25,18 @@ documentation = "https://docs.langflow.org"
 langflow = "langflow.__main__:main"
 
 [tool.poetry.dependencies]
-pandas = "^2.0.0"
 python = ">=3.9,<3.11"
-fastapi = "^0.100.0"
+fastapi = "^0.103.0"
-uvicorn = "^0.22.0"
+uvicorn = "^0.23.0"
 beautifulsoup4 = "^4.12.2"
 google-search-results = "^2.4.1"
 google-api-python-client = "^2.79.0"
 typer = "^0.9.0"
 gunicorn = "^21.2.0"
-langchain = "^0.0.274"
+langchain = "^0.0.308"
 openai = "^0.27.8"
-chromadb = "^0.3.0"
+pandas = "2.0.3"
+chromadb = "^0.3.21"
 huggingface-hub = { version = "^0.16.0", extras = ["inference"] }
 rich = "^13.5.0"
 llama-cpp-python = { version = "~0.1.0", optional = true }
@@ -49,16 +49,15 @@ fake-useragent = "^1.2.1"
 docstring-parser = "^0.15"
 psycopg2-binary = "^2.9.6"
 pyarrow = "^12.0.0"
-tiktoken = "~0.4.0"
+tiktoken = "~0.5.0"
 wikipedia = "^1.4.0"
-langchain-serve = { version = ">0.0.51", optional = true }
 qdrant-client = "^1.4.0"
 websockets = "^10.3"
 weaviate-client = "^3.23.0"
 jina = "3.15.2"
 sentence-transformers = { version = "^2.2.2", optional = true }
 ctransformers = { version = "^0.2.10", optional = true }
-cohere = "^4.21.0"
+cohere = "^4.27.0"
 python-multipart = "^0.0.6"
 sqlmodel = "^0.0.8"
 faiss-cpu = "^1.7.4"
@@ -77,7 +76,10 @@ psycopg = "^3.1.9"
 psycopg-binary = "^3.1.9"
 fastavro = "^1.8.0"
 langchain-experimental = "^0.0.8"
-alembic = "^1.11.2"
+celery = { extras = ["redis"], version = "^5.3.1", optional = true }
+redis = { version = "^4.6.0", optional = true }
+flower = { version = "^2.0.0", optional = true }
+alembic = "^1.12.0"
 passlib = "^1.7.4"
 bcrypt = "^4.0.1"
 python-jose = "^3.3.0"
@@ -86,10 +88,12 @@ pywin32 = { version = "^306", markers = "sys_platform == 'win32'" }
 loguru = "^0.7.1"
 langfuse = "^1.0.13"
 pillow = "^10.0.0"
-metal-sdk = "^2.0.2"
+metal-sdk = "^2.2.0"
 markupsafe = "^2.1.3"
 
+
 [tool.poetry.group.dev.dependencies]
+types-redis = "^4.6.0.5"
 black = "^23.1.0"
 ipykernel = "^6.21.2"
 mypy = "^1.1.1"
@@ -105,6 +109,7 @@ types-appdirs = "^1.4.3.5"
 types-pyyaml = "^6.0.12.8"
 types-python-jose = "^3.3.4.8"
 types-passlib = "^1.7.7.13"
+locust = "^2.16.1"
 pytest-mock = "^3.11.1"
 pytest-xdist = "^3.3.1"
 types-pywin32 = "^306.0.0.4"
@@ -113,7 +118,7 @@ pytest-sugar = "^0.9.7"
 
 
 [tool.poetry.extras]
-deploy = ["langchain-serve"]
+deploy = ["langchain-serve", "celery", "redis", "flower"]
 local = ["llama-cpp-python", "sentence-transformers", "ctransformers"]
 all = ["deploy", "local"]
 
@@ -125,6 +130,7 @@ testpaths = ["tests", "integration"]
 console_output_style = "progress"
 filterwarnings = ["ignore::DeprecationWarning"]
 log_cli = true
+markers = ["async_test"]
 
 
 [tool.ruff]

src/backend/langflow/__init__.py

@@ -1,7 +1,7 @@
 from importlib import metadata
 
 # Deactivate cache manager for now
-# from langflow.services.cache import cache_manager
+# from langflow.services.cache import cache_service
 from langflow.processing.process import load_flow_from_json
 from langflow.interface.custom.custom_component import CustomComponent
 
@@ -12,4 +12,4 @@ except metadata.PackageNotFoundError:
     __version__ = ""
 del metadata  # optional, avoids polluting the results of dir(__package__)
 
-__all__ = ["load_flow_from_json", "cache_manager", "CustomComponent"]
+__all__ = ["load_flow_from_json", "cache_service", "CustomComponent"]

src/backend/langflow/__main__.py

@@ -1,31 +1,29 @@
+import platform
+import socket
 import sys
 import time
-import httpx
+import webbrowser
-from langflow.services.database.utils import session_getter
-from langflow.services.utils import initialize_services
-from langflow.services.getters import get_db_manager, get_settings_manager
-from langflow.services.utils import initialize_settings_manager
-
-from multiprocess import Process, cpu_count  # type: ignore
-import platform
 from pathlib import Path
 from typing import Optional
-import socket
+
-from rich.panel import Panel
+import httpx
+import typer
+from dotenv import load_dotenv
+from langflow.main import setup_app
+from langflow.services.database.utils import session_getter
+from langflow.services.getters import get_db_service, get_settings_service
+from langflow.services.utils import initialize_services, initialize_settings_service
+from langflow.utils.logger import configure, logger
+from multiprocess import Process, cpu_count  # type: ignore
 from rich import box
 from rich import print as rprint
-from rich.table import Table
-import typer
-from langflow.main import setup_app
-from langflow.utils.logger import configure, logger
-import webbrowser
-from dotenv import load_dotenv
-
 from rich.console import Console
+from rich.panel import Panel
+from rich.table import Table
 
 console = Console()
 
-app = typer.Typer()
+app = typer.Typer(no_args_is_help=True)
 
 
 def get_number_of_workers(workers=None):
@@ -54,6 +52,18 @@ def display_results(results):
         console.print()  # Print a new line
 
 
+def set_var_for_macos_issue():
+    # OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES
+    # we need to set this var is we are running on MacOS
+    # otherwise we get an error when running gunicorn
+
+    if platform.system() in ["Darwin"]:
+        import os
+
+        os.environ["OBJC_DISABLE_INITIALIZE_FORK_SAFETY"] = "YES"
+        logger.debug("Set OBJC_DISABLE_INITIALIZE_FORK_SAFETY to YES to avoid error")
+
+
 def update_settings(
     config: str,
     cache: Optional[str] = None,
@@ -64,66 +74,20 @@ def update_settings(
     """Update the settings from a config file."""
 
     # Check for database_url in the environment variables
-    initialize_settings_manager()
+    initialize_settings_service()
-    settings_manager = get_settings_manager()
+    settings_service = get_settings_service()
     if config:
         logger.debug(f"Loading settings from {config}")
-        settings_manager.settings.update_from_yaml(config, dev=dev)
+        settings_service.settings.update_from_yaml(config, dev=dev)
     if remove_api_keys:
         logger.debug(f"Setting remove_api_keys to {remove_api_keys}")
-        settings_manager.settings.update_settings(REMOVE_API_KEYS=remove_api_keys)
+        settings_service.settings.update_settings(REMOVE_API_KEYS=remove_api_keys)
     if cache:
         logger.debug(f"Setting cache to {cache}")
-        settings_manager.settings.update_settings(CACHE=cache)
+        settings_service.settings.update_settings(CACHE=cache)
     if components_path:
         logger.debug(f"Adding component path {components_path}")
-        settings_manager.settings.update_settings(COMPONENTS_PATH=components_path)
+        settings_service.settings.update_settings(COMPONENTS_PATH=components_path)
-
-
-def serve_on_jcloud():
-    """
-    Deploy Langflow server on Jina AI Cloud
-    """
-    import asyncio
-    from importlib.metadata import version as mod_version
-
-    import click
-
-    try:
-        from lcserve.__main__ import serve_on_jcloud  # type: ignore
-    except ImportError:
-        click.secho(
-            "🚨 Please install langchain-serve to deploy Langflow server on Jina AI Cloud "
-            "using `pip install langchain-serve`",
-            fg="red",
-        )
-        return
-
-    app_name = "langflow.lcserve:app"
-    app_dir = str(Path(__file__).parent)
-    version = mod_version("langflow")
-    base_image = "jinaai+docker://deepankarm/langflow"
-
-    click.echo("🚀 Deploying Langflow server on Jina AI Cloud")
-    app_id = asyncio.run(
-        serve_on_jcloud(
-            fastapi_app_str=app_name,
-            app_dir=app_dir,
-            uses=f"{base_image}:{version}",
-            name="langflow",
-        )
-    )
-    click.secho(
-        "🎉 Langflow server successfully deployed on Jina AI Cloud 🎉", fg="green"
-    )
-    click.secho(
-        "🔗 Click on the link to open the server (please allow ~1-2 minutes for the server to startup): ",
-        nl=False,
-        fg="green",
-    )
-    click.secho(f"https://{app_id}.wolf.jina.ai/", fg="blue")
-    click.secho("📖 Read more about managing the server: ", nl=False, fg="green")
-    click.secho("https://github.com/jina-ai/langchain-serve", fg="blue")
 
 
 @app.command()
@@ -132,7 +96,7 @@ def run(
         "127.0.0.1", help="Host to bind the server to.", envvar="LANGFLOW_HOST"
     ),
     workers: int = typer.Option(
-        2, help="Number of worker processes.", envvar="LANGFLOW_WORKERS"
+        1, help="Number of worker processes.", envvar="LANGFLOW_WORKERS"
     ),
     timeout: int = typer.Option(300, help="Worker timeout in seconds."),
     port: int = typer.Option(7860, help="Port to listen on.", envvar="LANGFLOW_PORT"),
@@ -159,7 +123,6 @@ def run(
         help="Type of cache to use. (InMemoryCache, SQLiteCache)",
         default=None,
     ),
-    jcloud: bool = typer.Option(False, help="Deploy on Jina AI Cloud"),
     dev: bool = typer.Option(False, help="Run in development mode (may contain bugs)"),
     # This variable does not work but is set by the .env file
     # and works with Pydantic
@@ -190,15 +153,15 @@ def run(
     ),
 ):
     """
-    Run the Langflow server.
+    Run the Langflow.
     """
+
+    set_var_for_macos_issue()
     # override env variables with .env file
+
     if env_file:
         load_dotenv(env_file, override=True)
 
-    if jcloud:
-        return serve_on_jcloud()
-
     configure(log_level=log_level, log_file=log_file)
     update_settings(
         config,
@@ -217,7 +180,6 @@ def run(
     options = {
         "bind": f"{host}:{port}",
         "workers": get_number_of_workers(workers),
-        "worker_class": "uvicorn.workers.UvicornWorker",
         "timeout": timeout,
     }
 
@@ -351,10 +313,17 @@ def superuser(
     password: str = typer.Option(
         ..., prompt=True, hide_input=True, help="Password for the superuser."
     ),
+    log_level: str = typer.Option(
+        "critical", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL"
+    ),
 ):
+    """
+    Create a superuser.
+    """
+    configure(log_level=log_level)
     initialize_services()
-    db_manager = get_db_manager()
+    db_service = get_db_service()
-    with session_getter(db_manager) as session:
+    with session_getter(db_service) as session:
         from langflow.services.auth.utils import create_super_user
 
         if create_super_user(db=session, username=username, password=password):
@@ -373,12 +342,15 @@ def superuser(
 
 
 @app.command()
-def migration(test: bool = typer.Option(False, help="Run migrations in test mode.")):
+def migration(test: bool = typer.Option(True, help="Run migrations in test mode.")):
+    """
+    Run or test migrations.
+    """
     initialize_services()
-    db_manager = get_db_manager()
+    db_service = get_db_service()
     if not test:
-        db_manager.run_migrations()
+        db_service.run_migrations()
-    results = db_manager.run_migrations_test()
+    results = db_service.run_migrations_test()
     display_results(results)
 
 

src/backend/langflow/alembic/versions/eb5866d51fd2_change_columns_to_be_nullable.py

@@ -0,0 +1,79 @@
+"""Change columns to be nullable
+
+Revision ID: eb5866d51fd2
+Revises: 67cc006d50bf
+Create Date: 2023-10-04 10:18:25.640458
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+import sqlmodel  # noqa: F401
+
+# revision identifiers, used by Alembic.
+revision: str = "eb5866d51fd2"
+down_revision: Union[str, None] = "67cc006d50bf"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    try:
+        op.drop_table("flowstyle")
+        with op.batch_alter_table("component", schema=None) as batch_op:
+            batch_op.drop_index("ix_component_frontend_node_id")
+            batch_op.drop_index("ix_component_name")
+    except Exception:
+        pass
+
+    try:
+        op.drop_table("component")
+    except Exception:
+        pass
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    try:
+        op.create_table(
+            "component",
+            sa.Column("id", sa.CHAR(length=32), nullable=False),
+            sa.Column("frontend_node_id", sa.CHAR(length=32), nullable=False),
+            sa.Column("name", sa.VARCHAR(), nullable=False),
+            sa.Column("description", sa.VARCHAR(), nullable=True),
+            sa.Column("python_code", sa.VARCHAR(), nullable=True),
+            sa.Column("return_type", sa.VARCHAR(), nullable=True),
+            sa.Column("is_disabled", sa.BOOLEAN(), nullable=False),
+            sa.Column("is_read_only", sa.BOOLEAN(), nullable=False),
+            sa.Column("create_at", sa.DATETIME(), nullable=False),
+            sa.Column("update_at", sa.DATETIME(), nullable=False),
+            sa.PrimaryKeyConstraint("id"),
+        )
+        with op.batch_alter_table("component", schema=None) as batch_op:
+            batch_op.create_index("ix_component_name", ["name"], unique=False)
+            batch_op.create_index(
+                "ix_component_frontend_node_id", ["frontend_node_id"], unique=False
+            )
+    except Exception:
+        pass
+
+    try:
+        op.create_table(
+            "flowstyle",
+            sa.Column("color", sa.VARCHAR(), nullable=False),
+            sa.Column("emoji", sa.VARCHAR(), nullable=False),
+            sa.Column("flow_id", sa.CHAR(length=32), nullable=True),
+            sa.Column("id", sa.CHAR(length=32), nullable=False),
+            sa.ForeignKeyConstraint(
+                ["flow_id"],
+                ["flow.id"],
+            ),
+            sa.PrimaryKeyConstraint("id"),
+            sa.UniqueConstraint("id"),
+        )
+    except Exception:
+        pass
+    # ### end Alembic commands ###

src/backend/langflow/api/utils.py

@@ -59,33 +59,6 @@ def build_input_keys_response(langchain_object, artifacts):
     return input_keys_response
 
 
-def merge_nested_dicts(dict1, dict2):
-    for key, value in dict2.items():
-        if isinstance(value, dict) and isinstance(dict1.get(key), dict):
-            dict1[key] = merge_nested_dicts(dict1[key], value)
-        else:
-            dict1[key] = value
-    return dict1
-
-
-def merge_nested_dicts_with_renaming(dict1, dict2):
-    for key, value in dict2.items():
-        if (
-            key in dict1
-            and isinstance(value, dict)
-            and isinstance(dict1.get(key), dict)
-        ):
-            for sub_key, sub_value in value.items():
-                if sub_key in dict1[key]:
-                    new_key = get_new_key(dict1[key], sub_key)
-                    dict1[key][new_key] = sub_value
-                else:
-                    dict1[key][sub_key] = sub_value
-        else:
-            dict1[key] = value
-    return dict1
-
-
 def get_new_key(dictionary, original_key):
     counter = 1
     new_key = original_key + " (" + str(counter) + ")"

src/backend/langflow/api/v1/callback.py

@@ -1,15 +1,17 @@
 import asyncio
+from uuid import UUID
 
 from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler
 
-from langflow.api.v1.schemas import ChatResponse
+from langflow.api.v1.schemas import ChatResponse, PromptResponse
 
 
-from typing import Any, Dict, List, Union
+from typing import Any, Dict, List, Optional
-from fastapi import WebSocket
+from langflow.services.getters import get_chat_service
 
 
-from langchain.schema import AgentAction, LLMResult, AgentFinish
+from langflow.utils.util import remove_ansi_escape_codes
+from langchain.schema import AgentAction, AgentFinish
 from loguru import logger
 
 
@@ -17,39 +19,15 @@ from loguru import logger
 class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler):
     """Callback handler for streaming LLM responses."""
 
-    def __init__(self, websocket: WebSocket):
+    def __init__(self, client_id: str):
-        self.websocket = websocket
+        self.chat_service = get_chat_service()
+        self.client_id = client_id
+        self.websocket = self.chat_service.active_connections[self.client_id]
 
     async def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
         resp = ChatResponse(message=token, type="stream", intermediate_steps="")
         await self.websocket.send_json(resp.dict())
 
-    async def on_llm_start(
-        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
-    ) -> Any:
-        """Run when LLM starts running."""
-
-    async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> Any:
-        """Run when LLM ends running."""
-
-    async def on_llm_error(
-        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
-    ) -> Any:
-        """Run when LLM errors."""
-
-    async def on_chain_start(
-        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
-    ) -> Any:
-        """Run when chain starts running."""
-
-    async def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> Any:
-        """Run when chain ends running."""
-
-    async def on_chain_error(
-        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
-    ) -> Any:
-        """Run when chain errors."""
-
     async def on_tool_start(
         self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
     ) -> Any:
@@ -95,8 +73,14 @@ class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler):
             logger.error(f"Error sending response: {exc}")
 
     async def on_tool_error(
-        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+        self,
-    ) -> Any:
+        error: BaseException,
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        tags: Optional[List[str]] = None,
+        **kwargs: Any,
+    ) -> None:
         """Run when tool errors."""
 
     async def on_text(self, text: str, **kwargs: Any) -> Any:
@@ -104,6 +88,14 @@ class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler):
         # This runs when first sending the prompt
         # to the LLM, adding it will send the final prompt
         # to the frontend
+        if "Prompt after formatting" in text:
+            text = text.replace("Prompt after formatting:\n", "")
+            text = remove_ansi_escape_codes(text)
+            resp = PromptResponse(
+                prompt=text,
+            )
+            await self.websocket.send_json(resp.dict())
+            self.chat_service.chat_history.add_message(self.client_id, resp)
 
     async def on_agent_action(self, action: AgentAction, **kwargs: Any):
         log = f"Thought: {action.log}"
@@ -131,8 +123,10 @@ class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler):
 class StreamingLLMCallbackHandler(BaseCallbackHandler):
     """Callback handler for streaming LLM responses."""
 
-    def __init__(self, websocket):
+    def __init__(self, client_id: str):
-        self.websocket = websocket
+        self.chat_service = get_chat_service()
+        self.client_id = client_id
+        self.websocket = self.chat_service.active_connections[self.client_id]
 
     def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
         resp = ChatResponse(message=token, type="stream", intermediate_steps="")

src/backend/langflow/api/v1/chat.py

@@ -13,17 +13,16 @@ from langflow.api.v1.schemas import BuildStatus, BuiltResponse, InitResponse, St
 
 from langflow.graph.graph.base import Graph
 from langflow.services.auth.utils import get_current_active_user, get_current_user
+from langflow.services.cache.utils import update_build_status
 from loguru import logger
-from langflow.services.getters import get_chat_manager, get_session
+from langflow.services.getters import get_chat_service, get_session, get_cache_service
-from cachetools import LRUCache
 from sqlmodel import Session
-from langflow.services.chat.manager import ChatManager
+from langflow.services.chat.manager import ChatService
+from langflow.services.cache.manager import BaseCacheService
 
 
 router = APIRouter(tags=["Chat"])
 
-flow_data_store: LRUCache = LRUCache(maxsize=10)
-
 
 @router.websocket("/chat/{client_id}")
 async def chat(
@@ -31,7 +30,7 @@ async def chat(
     websocket: WebSocket,
     token: str = Query(...),
     db: Session = Depends(get_session),
-    chat_manager: "ChatManager" = Depends(get_chat_manager),
+    chat_service: "ChatService" = Depends(get_chat_service),
 ):
     """Websocket endpoint for chat."""
     try:
@@ -46,15 +45,15 @@ async def chat(
                 code=status.WS_1008_POLICY_VIOLATION, reason="Unauthorized"
             )
 
-        if client_id in chat_manager.in_memory_cache:
+        if client_id in chat_service.cache_service:
-            await chat_manager.handle_websocket(client_id, websocket)
+            await chat_service.handle_websocket(client_id, websocket)
         else:
             # We accept the connection but close it immediately
             # if the flow is not built yet
             message = "Please, build the flow before sending messages"
             await websocket.close(code=status.WS_1011_INTERNAL_ERROR, reason=message)
     except WebSocketException as exc:
-        logger.error(f"Websocket error: {exc}")
+        logger.error(f"Websocket exrror: {exc}")
         await websocket.close(code=status.WS_1011_INTERNAL_ERROR, reason=str(exc))
     except Exception as exc:
         logger.error(f"Error in chat websocket: {exc}")
@@ -72,26 +71,26 @@ async def init_build(
     graph_data: dict,
     flow_id: str,
     current_user=Depends(get_current_active_user),
-    chat_manager: "ChatManager" = Depends(get_chat_manager),
+    chat_service: "ChatService" = Depends(get_chat_service),
+    cache_service: "BaseCacheService" = Depends(get_cache_service),
 ):
     """Initialize the build by storing graph data and returning a unique session ID."""
-
     try:
         if flow_id is None:
             raise ValueError("No ID provided")
         # Check if already building
         if (
-            flow_id in flow_data_store
+            flow_id in cache_service
-            and flow_data_store[flow_id]["status"] == BuildStatus.IN_PROGRESS
+            and isinstance(cache_service[flow_id], dict)
+            and cache_service[flow_id].get("status") == BuildStatus.IN_PROGRESS
         ):
             return InitResponse(flowId=flow_id)
 
         # Delete from cache if already exists
-        if flow_id in chat_manager.in_memory_cache:
+        if flow_id in chat_service.cache_service:
-            with chat_manager.in_memory_cache._lock:
+            chat_service.cache_service.delete(flow_id)
-                chat_manager.in_memory_cache.delete(flow_id)
+            logger.debug(f"Deleted flow {flow_id} from cache")
-                logger.debug(f"Deleted flow {flow_id} from cache")
+        cache_service[flow_id] = {
-        flow_data_store[flow_id] = {
             "graph_data": graph_data,
             "status": BuildStatus.STARTED,
             "user_id": current_user.id,
@@ -104,12 +103,14 @@ async def init_build(
 
 
 @router.get("/build/{flow_id}/status", response_model=BuiltResponse)
-async def build_status(flow_id: str):
+async def build_status(
-    """Check the flow_id is in the flow_data_store."""
+    flow_id: str, cache_service: "BaseCacheService" = Depends(get_cache_service)
+):
+    """Check the flow_id is in the cache_service."""
     try:
         built = (
-            flow_id in flow_data_store
+            flow_id in cache_service
-            and flow_data_store[flow_id]["status"] == BuildStatus.SUCCESS
+            and cache_service[flow_id]["status"] == BuildStatus.SUCCESS
         )
 
         return BuiltResponse(
@@ -123,7 +124,9 @@ async def build_status(flow_id: str):
 
 @router.get("/build/stream/{flow_id}", response_class=StreamingResponse)
 async def stream_build(
-    flow_id: str, chat_manager: "ChatManager" = Depends(get_chat_manager)
+    flow_id: str,
+    chat_service: "ChatService" = Depends(get_chat_service),
+    cache_service: "BaseCacheService" = Depends(get_cache_service),
 ):
     """Stream the build process based on stored flow data."""
 
@@ -131,18 +134,18 @@ async def stream_build(
         final_response = {"end_of_stream": True}
         artifacts = {}
         try:
-            if flow_id not in flow_data_store:
+            if flow_id not in cache_service:
                 error_message = "Invalid session ID"
                 yield str(StreamData(event="error", data={"error": error_message}))
                 return
 
-            if flow_data_store[flow_id].get("status") == BuildStatus.IN_PROGRESS:
+            if cache_service[flow_id].get("status") == BuildStatus.IN_PROGRESS:
                 error_message = "Already building"
                 yield str(StreamData(event="error", data={"error": error_message}))
                 return
 
-            graph_data = flow_data_store[flow_id].get("graph_data")
+            graph_data = cache_service[flow_id].get("graph_data")
-            user_id = flow_data_store[flow_id]["user_id"]
+            cache_service[flow_id]["user_id"]
 
             if not graph_data:
                 error_message = "No data provided"
@@ -155,7 +158,7 @@ async def stream_build(
             graph = Graph.from_payload(graph_data)
 
             number_of_nodes = len(graph.nodes)
-            flow_data_store[flow_id]["status"] = BuildStatus.IN_PROGRESS
+            update_build_status(cache_service, flow_id, BuildStatus.IN_PROGRESS)
 
             for i, vertex in enumerate(graph.generator_build(), 1):
                 try:
@@ -163,7 +166,10 @@ async def stream_build(
                         "log": f"Building node {vertex.vertex_type}",
                     }
                     yield str(StreamData(event="log", data=log_dict))
-                    vertex.build(user_id)
+                    if vertex.is_task:
+                        vertex = try_running_celery_task(vertex)
+                    else:
+                        vertex.build()
                     params = vertex._built_object_repr()
                     valid = True
                     logger.debug(f"Building node {str(vertex.vertex_type)}")
@@ -179,16 +185,20 @@ async def stream_build(
                     logger.exception(exc)
                     params = str(exc)
                     valid = False
-                    flow_data_store[flow_id]["status"] = BuildStatus.FAILURE
+                    update_build_status(cache_service, flow_id, BuildStatus.FAILURE)
 
-                response = {
+                vertex_id = (
-                    "valid": valid,
+                    vertex.parent_node_id if vertex.parent_is_top_level else vertex.id
-                    "params": params,
+                )
-                    "id": vertex.id,
+                if vertex_id in graph.top_level_nodes:
-                    "progress": round(i / number_of_nodes, 2),
+                    response = {
-                }
+                        "valid": valid,
+                        "params": params,
+                        "id": vertex_id,
+                        "progress": round(i / number_of_nodes, 2),
+                    }
 
-                yield str(StreamData(event="message", data=response))
+                    yield str(StreamData(event="message", data=response))
 
             langchain_object = graph.build()
             # Now we  need to check the input_keys to send them to the client
@@ -203,14 +213,15 @@ async def stream_build(
                     "handle_keys": [],
                 }
             yield str(StreamData(event="message", data=input_keys_response))
-            chat_manager.set_cache(flow_id, langchain_object)
+            chat_service.set_cache(flow_id, langchain_object)
             # We need to reset the chat history
-            chat_manager.chat_history.empty_history(flow_id)
+            chat_service.chat_history.empty_history(flow_id)
-            flow_data_store[flow_id]["status"] = BuildStatus.SUCCESS
+            update_build_status(cache_service, flow_id, BuildStatus.SUCCESS)
         except Exception as exc:
             logger.exception(exc)
             logger.error("Error while building the flow: %s", exc)
-            flow_data_store[flow_id]["status"] = BuildStatus.FAILURE
+
+            update_build_status(cache_service, flow_id, BuildStatus.FAILURE)
             yield str(StreamData(event="error", data={"error": str(exc)}))
         finally:
             yield str(StreamData(event="message", data=final_response))
@@ -220,3 +231,19 @@ async def stream_build(
     except Exception as exc:
         logger.error(f"Error streaming build: {exc}")
         raise HTTPException(status_code=500, detail=str(exc))
+
+
+def try_running_celery_task(vertex):
+    # Try running the task in celery
+    # and set the task_id to the local vertex
+    # if it fails, run the task locally
+    try:
+        from langflow.worker import build_vertex
+
+        task = build_vertex.delay(vertex)
+        vertex.task_id = task.id
+    except Exception as exc:
+        logger.debug(f"Error running task in celery: {exc}")
+        vertex.task_id = None
+        vertex.build()
+    return vertex

src/backend/langflow/api/v1/endpoints.py

@@ -1,12 +1,17 @@
 from http import HTTPStatus
-from typing import Annotated, Any, Optional, Union
+from typing import Annotated, Optional, Union
 from langflow.services.auth.utils import api_key_security, get_current_active_user
 
+
 from langflow.services.cache.utils import save_uploaded_file
 from langflow.services.database.models.flow import Flow
 from langflow.processing.process import process_graph_cached, process_tweaks
 from langflow.services.database.models.user.user import User
-from langflow.services.getters import get_settings_manager
+from langflow.services.getters import (
+    get_session_service,
+    get_settings_service,
+    get_task_service,
+)
 from loguru import logger
 from fastapi import APIRouter, Depends, HTTPException, UploadFile, Body, status
 import sqlalchemy as sa
@@ -15,66 +20,43 @@ from langflow.interface.custom.custom_component import CustomComponent
 
 from langflow.api.v1.schemas import (
     ProcessResponse,
+    TaskResponse,
+    TaskStatusResponse,
     UploadFileResponse,
     CustomComponentCode,
 )
 
-from langflow.api.utils import merge_nested_dicts_with_renaming
-
-from langflow.interface.types import (
-    build_langchain_types_dict,
-    build_langchain_template_custom_component,
-    build_langchain_custom_component_list_from_path,
-)
 
 from langflow.services.getters import get_session
+
+try:
+    from langflow.worker import process_graph_cached_task
+except ImportError:
+
+    def process_graph_cached_task(*args, **kwargs):
+        raise NotImplementedError("Celery is not installed")
+
+
 from sqlmodel import Session
 
+
+from langflow.services.task.manager import TaskService
+
 # build router
 router = APIRouter(tags=["Base"])
 
 
 @router.get("/all", dependencies=[Depends(get_current_active_user)])
 def get_all(
-    settings_manager=Depends(get_settings_manager),
+    settings_service=Depends(get_settings_service),
 ):
+    from langflow.interface.types import get_all_types_dict
+
     logger.debug("Building langchain types dict")
-    native_components = build_langchain_types_dict()
+    try:
-    # custom_components is a list of dicts
+        return get_all_types_dict(settings_service)
-    # need to merge all the keys into one dict
+    except Exception as exc:
-    custom_components_from_file: dict[str, Any] = {}
+        raise HTTPException(status_code=500, detail=str(exc)) from exc
-    if settings_manager.settings.COMPONENTS_PATH:
-        logger.info(
-            f"Building custom components from {settings_manager.settings.COMPONENTS_PATH}"
-        )
-
-        custom_component_dicts = []
-        processed_paths = []
-        for path in settings_manager.settings.COMPONENTS_PATH:
-            if str(path) in processed_paths:
-                continue
-            custom_component_dict = build_langchain_custom_component_list_from_path(
-                str(path)
-            )
-            custom_component_dicts.append(custom_component_dict)
-            processed_paths.append(str(path))
-
-        logger.info(f"Loading {len(custom_component_dicts)} category(ies)")
-        for custom_component_dict in custom_component_dicts:
-            # custom_component_dict is a dict of dicts
-            if not custom_component_dict:
-                continue
-            category = list(custom_component_dict.keys())[0]
-            logger.info(
-                f"Loading {len(custom_component_dict[category])} component(s) from category {category}"
-            )
-            custom_components_from_file = merge_nested_dicts_with_renaming(
-                custom_components_from_file, custom_component_dict
-            )
-
-    return merge_nested_dicts_with_renaming(
-        native_components, custom_components_from_file
-    )
 
 
 # For backwards compatibility we will keep the old endpoint
@@ -87,14 +69,16 @@ def get_all(
     "/process/{flow_id}",
     response_model=ProcessResponse,
 )
-async def process_flow(
+async def process(
     session: Annotated[Session, Depends(get_session)],
     flow_id: str,
     inputs: Optional[dict] = None,
     tweaks: Optional[dict] = None,
     clear_cache: Annotated[bool, Body(embed=True)] = False,  # noqa: F821
     session_id: Annotated[Union[None, str], Body(embed=True)] = None,  # noqa: F821
+    task_service: "TaskService" = Depends(get_task_service),
     api_key_user: User = Depends(api_key_security),
+    sync: Annotated[bool, Body(embed=True)] = True,  # noqa: F821
 ):
     """
     Endpoint to process an input with a given flow_id.
@@ -125,10 +109,55 @@ async def process_flow(
                 graph_data = process_tweaks(graph_data, tweaks)
             except Exception as exc:
                 logger.error(f"Error processing tweaks: {exc}")
-        response, session_id = process_graph_cached(
+        if sync:
-            graph_data, inputs, clear_cache, session_id
+            task_id, result = await task_service.launch_and_await_task(
+                process_graph_cached_task
+                if task_service.use_celery
+                else process_graph_cached,
+                graph_data,
+                inputs,
+                clear_cache,
+                session_id,
+            )
+            if isinstance(result, dict) and "result" in result:
+                task_result = result["result"]
+                session_id = result["session_id"]
+            elif hasattr(result, "result") and hasattr(result, "session_id"):
+                task_result = result.result
+
+                session_id = result.session_id
+        else:
+            logger.warning(
+                "This is an experimental feature and may not work as expected."
+                "Please report any issues to our GitHub repository."
+            )
+            if session_id is None:
+                # Generate a session ID
+                session_id = get_session_service().generate_key(
+                    session_id=session_id, data_graph=graph_data
+                )
+            task_id, task = await task_service.launch_task(
+                process_graph_cached_task
+                if task_service.use_celery
+                else process_graph_cached,
+                graph_data,
+                inputs,
+                clear_cache,
+                session_id,
+            )
+            task_result = task.status
+
+        if task_id:
+            task_response = TaskResponse(id=task_id, href=f"api/v1/task/{task_id}")
+        else:
+            task_response = None
+
+        return ProcessResponse(
+            result=task_result,
+            task=task_response,
+            session_id=session_id,
+            backend=task_service.backend_name,
         )
-        return ProcessResponse(result=response, session_id=session_id)
     except sa.exc.StatementError as exc:
         # StatementError('(builtins.ValueError) badly formed hexadecimal UUID string')
         if "badly formed hexadecimal UUID string" in str(exc):
@@ -151,6 +180,23 @@ async def process_flow(
         raise HTTPException(status_code=500, detail=str(e)) from e
 
 
+@router.get("/task/{task_id}", response_model=TaskStatusResponse)
+async def get_task_status(task_id: str):
+    task_service = get_task_service()
+    task = task_service.get_task(task_id)
+    result = None
+    if task.ready():
+        result = task.result
+        if isinstance(result, dict) and "result" in result:
+            result = result["result"]
+        elif hasattr(result, "result"):
+            result = result.result
+
+    if task is None:
+        raise HTTPException(status_code=404, detail="Task not found")
+    return TaskStatusResponse(status=task.status, result=result)
+
+
 @router.post(
     "/upload/{flow_id}",
     response_model=UploadFileResponse,
@@ -159,7 +205,7 @@ async def process_flow(
 async def create_upload_file(file: UploadFile, flow_id: str):
     # Cache file
     try:
-        file_path = save_uploaded_file(file.file, folder_name=flow_id)
+        file_path = save_uploaded_file(file, folder_name=flow_id)
 
         return UploadFileResponse(
             flowId=flow_id,
@@ -182,6 +228,10 @@ def get_version():
 async def custom_component(
     raw_code: CustomComponentCode,
 ):
+    from langflow.interface.types import (
+        build_langchain_template_custom_component,
+    )
+
     extractor = CustomComponent(code=raw_code.code)
     extractor.is_check_valid()
 

src/backend/langflow/api/v1/flows.py

@@ -13,7 +13,7 @@ from langflow.services.database.models.flow import (
 )
 from langflow.services.database.models.user.user import User
 from langflow.services.getters import get_session
-from langflow.services.getters import get_settings_manager
+from langflow.services.getters import get_settings_service
 import orjson
 from sqlmodel import Session
 from fastapi import APIRouter, Depends, HTTPException
@@ -83,7 +83,7 @@ def update_flow(
     flow_id: UUID,
     flow: FlowUpdate,
     current_user: User = Depends(get_current_active_user),
-    settings_manager=Depends(get_settings_manager),
+    settings_service=Depends(get_settings_service),
 ):
     """Update a flow."""
 
@@ -91,7 +91,7 @@ def update_flow(
     if not db_flow:
         raise HTTPException(status_code=404, detail="Flow not found")
     flow_data = flow.dict(exclude_unset=True)
-    if settings_manager.settings.REMOVE_API_KEYS:
+    if settings_service.settings.REMOVE_API_KEYS:
         flow_data = remove_api_keys(flow_data)
     for key, value in flow_data.items():
         if value is not None:

src/backend/langflow/api/v1/login.py

@@ -12,7 +12,7 @@ from langflow.services.auth.utils import (
     get_current_active_user,
 )
 
-from langflow.services.getters import get_settings_manager
+from langflow.services.getters import get_settings_service
 
 router = APIRouter(tags=["Login"])
 
@@ -23,7 +23,17 @@ async def login_to_get_access_token(
     db: Session = Depends(get_session),
     # _: Session = Depends(get_current_active_user)
 ):
-    if user := authenticate_user(form_data.username, form_data.password, db):
+    try:
+        user = authenticate_user(form_data.username, form_data.password, db)
+    except Exception as exc:
+        if isinstance(exc, HTTPException):
+            raise exc
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=str(exc),
+        ) from exc
+
+    if user:
         return create_user_tokens(user_id=user.id, db=db, update_last_login=True)
     else:
         raise HTTPException(
@@ -35,9 +45,9 @@ async def login_to_get_access_token(
 
 @router.get("/auto_login")
 async def auto_login(
-    db: Session = Depends(get_session), settings_manager=Depends(get_settings_manager)
+    db: Session = Depends(get_session), settings_service=Depends(get_settings_service)
 ):
-    if settings_manager.auth_settings.AUTO_LOGIN:
+    if settings_service.auth_settings.AUTO_LOGIN:
         return create_user_longterm_token(db)
 
     raise HTTPException(

src/backend/langflow/api/v1/schemas.py

@@ -47,11 +47,30 @@ class UpdateTemplateRequest(BaseModel):
     template: dict
 
 
+class TaskResponse(BaseModel):
+    """Task response schema."""
+
+    id: Optional[str] = Field(None)
+    href: Optional[str] = Field(None)
+
+
 class ProcessResponse(BaseModel):
     """Process response schema."""
 
-    result: dict
+    result: Any
+    task: Optional[TaskResponse] = None
     session_id: Optional[str] = None
+    backend: Optional[str] = None
+
+
+# TaskStatusResponse(
+#         status=task.status, result=task.result if task.ready() else None
+#     )
+class TaskStatusResponse(BaseModel):
+    """Task status response schema."""
+
+    status: str
+    result: Optional[Any] = None
 
 
 class ChatMessage(BaseModel):
@@ -59,6 +78,7 @@ class ChatMessage(BaseModel):
 
     is_bot: bool = False
     message: Union[str, None, dict] = None
+    chatKey: Optional[str] = None
     type: str = "human"
 
 
@@ -66,6 +86,7 @@ class ChatResponse(ChatMessage):
     """Chat response schema."""
 
     intermediate_steps: str
+
     type: str
     is_bot: bool = True
     files: list = []
@@ -77,6 +98,14 @@ class ChatResponse(ChatMessage):
         return v
 
 
+class PromptResponse(ChatMessage):
+    """Prompt response schema."""
+
+    prompt: str
+    type: str = "prompt"
+    is_bot: bool = True
+
+
 class FileResponse(ChatMessage):
     """File response schema."""
 

src/backend/langflow/api/v1/users.py

@@ -13,7 +13,7 @@ from sqlalchemy.exc import IntegrityError
 from sqlmodel import Session, select
 from fastapi import APIRouter, Depends, HTTPException
 
-from langflow.services.getters import get_session
+from langflow.services.getters import get_session, get_settings_service
 from langflow.services.auth.utils import (
     get_current_active_superuser,
     get_current_active_user,
@@ -32,6 +32,7 @@ router = APIRouter(tags=["Users"], prefix="/users")
 def add_user(
     user: UserCreate,
     session: Session = Depends(get_session),
+    settings_service=Depends(get_settings_service),
 ) -> User:
     """
     Add a new user to the database.
@@ -39,7 +40,7 @@ def add_user(
     new_user = User.from_orm(user)
     try:
         new_user.password = get_password_hash(user.password)
-
+        new_user.is_active = settings_service.auth_settings.NEW_USER_IS_ACTIVE
         session.add(new_user)
         session.commit()
         session.refresh(new_user)
@@ -66,7 +67,7 @@ def read_current_user(
 def read_all_users(
     skip: int = 0,
     limit: int = 10,
-    current_user: Session = Depends(get_current_active_superuser),
+    _: Session = Depends(get_current_active_superuser),
     session: Session = Depends(get_session),
 ) -> UsersResponse:
     """
@@ -99,9 +100,11 @@ def patch_user(
             status_code=403, detail="You don't have the permission to update this user"
         )
     if user_update.password:
-        raise HTTPException(
+        if not user.is_superuser:
-            status_code=400, detail="You can't change your password here"
+            raise HTTPException(
-        )
+                status_code=400, detail="You can't change your password here"
+            )
+        user_update.password = get_password_hash(user_update.password)
 
     if user_db := get_user_by_id(session, user_id):
         return update_user(user_db, user_update, session)
@@ -164,31 +167,3 @@ def delete_user(
     session.commit()
 
     return {"detail": "User deleted"}
-
-
-# TODO: REMOVE - Just for testing purposes
-@router.post("/super_user", response_model=User)
-def add_super_user_for_testing_purposes_delete_me_before_merge_into_dev(
-    session: Session = Depends(get_session),
-) -> User:
-    """
-    Add a superuser for testing purposes.
-    (This should be removed in production)
-    """
-    new_user = User(
-        username="superuser",
-        password=get_password_hash("12345"),
-        is_active=True,
-        is_superuser=True,
-        last_login_at=None,
-    )
-
-    try:
-        session.add(new_user)
-        session.commit()
-        session.refresh(new_user)
-    except IntegrityError as e:
-        session.rollback()
-        raise HTTPException(status_code=400, detail="User exists") from e
-
-    return new_user

src/backend/langflow/components/chains/PromptRunner.py

@@ -1,7 +1,7 @@
 from langflow import CustomComponent
 
 from langchain.llms.base import BaseLLM
-from langchain import PromptTemplate
+from langchain.prompts import PromptTemplate
 from langchain.schema import Document
 
 

src/backend/langflow/core/celery_app.py

@@ -0,0 +1,11 @@
+from celery import Celery  # type: ignore
+
+
+def make_celery(app_name: str, config: str) -> Celery:
+    celery_app = Celery(app_name)
+    celery_app.config_from_object(config)
+    celery_app.conf.task_routes = {"langflow.worker.tasks.*": {"queue": "langflow"}}
+    return celery_app
+
+
+celery_app = make_celery("langflow", "langflow.core.celeryconfig")

src/backend/langflow/core/celeryconfig.py

@@ -0,0 +1,14 @@
+# celeryconfig.py
+import os
+
+langflow_redis_host = os.environ.get("LANGFLOW_REDIS_HOST")
+langflow_redis_port = os.environ.get("LANGFLOW_REDIS_PORT")
+if "BROKER_URL" in os.environ and "RESULT_BACKEND" in os.environ:
+    # RabbitMQ
+    broker_url = os.environ.get("BROKER_URL", "amqp://localhost")
+    result_backend = os.environ.get("RESULT_BACKEND", "redis://localhost:6379/0")
+elif langflow_redis_host and langflow_redis_port:
+    broker_url = f"redis://{langflow_redis_host}:{langflow_redis_port}/0"
+    result_backend = f"redis://{langflow_redis_host}:{langflow_redis_port}/0"
+# tasks should be json or pickle
+accept_content = ["json", "pickle"]

src/backend/langflow/graph/edge/base.py

@@ -1,22 +1,84 @@
 from loguru import logger
 from typing import TYPE_CHECKING
+from pydantic import BaseModel, Field
+from typing import List, Optional
 
 if TYPE_CHECKING:
     from langflow.graph.vertex.base import Vertex
 
 
+class SourceHandle(BaseModel):
+    baseClasses: List[str] = Field(
+        ..., description="List of base classes for the source handle."
+    )
+    dataType: str = Field(..., description="Data type for the source handle.")
+    id: str = Field(..., description="Unique identifier for the source handle.")
+
+
+class TargetHandle(BaseModel):
+    fieldName: str = Field(..., description="Field name for the target handle.")
+    id: str = Field(..., description="Unique identifier for the target handle.")
+    inputTypes: Optional[List[str]] = Field(
+        None, description="List of input types for the target handle."
+    )
+    type: str = Field(..., description="Type of the target handle.")
+
+
 class Edge:
     def __init__(self, source: "Vertex", target: "Vertex", edge: dict):
         self.source: "Vertex" = source
         self.target: "Vertex" = target
-        self.source_handle = edge.get("sourceHandle", "")
+        if data := edge.get("data", {}):
-        self.target_handle = edge.get("targetHandle", "")
+            self._source_handle = data.get("sourceHandle", {})
-        # 'BaseLoader;BaseOutputParser|documents|PromptTemplate-zmTlD'
+            self._target_handle = data.get("targetHandle", {})
-        # target_param is documents
+            self.source_handle: SourceHandle = SourceHandle(**self._source_handle)
-        self.target_param = self.target_handle.split("|")[1]
+            self.target_handle: TargetHandle = TargetHandle(**self._target_handle)
-
+            self.target_param = self.target_handle.fieldName
+            # validate handles
+            self.validate_handles()
+        else:
+            # Logging here because this is a breaking change
+            logger.error("Edge data is empty")
+            self._source_handle = edge.get("sourceHandle", "")
+            self._target_handle = edge.get("targetHandle", "")
+            # 'BaseLoader;BaseOutputParser|documents|PromptTemplate-zmTlD'
+            # target_param is documents
+            self.target_param = self._target_handle.split("|")[1]
+        # Validate in __init__ to fail fast
         self.validate_edge()
 
+    def validate_handles(self) -> None:
+        if self.target_handle.inputTypes is None:
+            self.valid_handles = (
+                self.target_handle.type in self.source_handle.baseClasses
+            )
+        else:
+            self.valid_handles = (
+                any(
+                    baseClass in self.target_handle.inputTypes
+                    for baseClass in self.source_handle.baseClasses
+                )
+                or self.target_handle.type in self.source_handle.baseClasses
+            )
+        if not self.valid_handles:
+            logger.debug(self.source_handle)
+            logger.debug(self.target_handle)
+            raise ValueError(
+                f"Edge between {self.source.vertex_type} and {self.target.vertex_type} "
+                f"has invalid handles"
+            )
+
+    def __setstate__(self, state):
+        self.source = state["source"]
+        self.target = state["target"]
+        self.target_param = state["target_param"]
+        self.source_handle = state.get("source_handle")
+        self.target_handle = state.get("target_handle")
+
+    def reset(self) -> None:
+        self.source._build_params()
+        self.target._build_params()
+
     def validate_edge(self) -> None:
         # Validate that the outputs of the source node are valid inputs
         # for the target node

src/backend/langflow/graph/graph/base.py

@@ -2,6 +2,7 @@ from typing import Dict, Generator, List, Type, Union
 
 from langflow.graph.edge.base import Edge
 from langflow.graph.graph.constants import lazy_load_vertex_dict
+from langflow.graph.graph.utils import process_flow
 from langflow.graph.vertex.base import Vertex
 from langflow.graph.vertex.types import (
     FileToolVertex,
@@ -19,13 +20,29 @@ class Graph:
 
     def __init__(
         self,
-        nodes: List[Dict[str, Union[str, Dict[str, Union[str, List[str]]]]]],
+        nodes: List[Dict],
         edges: List[Dict[str, str]],
     ) -> None:
         self._nodes = nodes
         self._edges = edges
+        self.raw_graph_data = {"nodes": nodes, "edges": edges}
+
+        self.top_level_nodes = []
+        for node in self._nodes:
+            if node_id := node.get("id"):
+                self.top_level_nodes.append(node_id)
+
+        self._graph_data = process_flow(self.raw_graph_data)
+        self._nodes = self._graph_data["nodes"]
+        self._edges = self._graph_data["edges"]
         self._build_graph()
 
+    def __setstate__(self, state):
+        self.__dict__.update(state)
+        for edge in self.edges:
+            edge.reset()
+            edge.validate_edge()
+
     @classmethod
     def from_payload(cls, payload: Dict) -> "Graph":
         """
@@ -44,10 +61,16 @@ class Graph:
             edges = payload["edges"]
             return cls(nodes, edges)
         except KeyError as exc:
+            logger.exception(exc)
             raise ValueError(
                 f"Invalid payload. Expected keys 'nodes' and 'edges'. Found {list(payload.keys())}"
             ) from exc
 
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, Graph):
+            return False
+        return self.__repr__() == other.__repr__()
+
     def _build_graph(self) -> None:
         """Builds the graph from the nodes and edges."""
         self.nodes = self._build_vertices()
@@ -147,7 +170,7 @@ class Graph:
     def generator_build(self) -> Generator[Vertex, None, None]:
         """Builds each vertex in the graph and yields it."""
         sorted_vertices = self.topological_sort()
-        logger.debug("Sorted vertices: %s", sorted_vertices)
+        logger.debug("There are %s vertices in the graph", len(sorted_vertices))
         yield from sorted_vertices
 
     def get_node_neighbors(self, node: Vertex) -> Dict[Vertex, int]:
@@ -204,7 +227,9 @@ class Graph:
             node_lc_type: str = node_data["node"]["template"]["_type"]  # type: ignore
 
             VertexClass = self._get_vertex_class(node_type, node_lc_type)
-            nodes.append(VertexClass(node))
+            vertex = VertexClass(node)
+            vertex.set_top_level(self.top_level_nodes)
+            nodes.append(vertex)
 
         return nodes
 

src/backend/langflow/graph/graph/utils.py

@@ -0,0 +1,230 @@
+from collections import deque
+import copy
+
+
+def find_last_node(nodes, edges):
+    """
+    This function receives a flow and returns the last node.
+    """
+    return next((n for n in nodes if all(e["source"] != n["id"] for e in edges)), None)
+
+
+def add_parent_node_id(nodes, parent_node_id):
+    """
+    This function receives a list of nodes and adds a parent_node_id to each node.
+    """
+    for node in nodes:
+        node["parent_node_id"] = parent_node_id
+
+
+def ungroup_node(group_node_data, base_flow):
+    template, flow = (
+        group_node_data["node"]["template"],
+        group_node_data["node"]["flow"],
+    )
+    parent_node_id = group_node_data["id"]
+    g_nodes = flow["data"]["nodes"]
+    add_parent_node_id(g_nodes, parent_node_id)
+    g_edges = flow["data"]["edges"]
+
+    # Redirect edges to the correct proxy node
+    updated_edges = get_updated_edges(
+        base_flow, g_nodes, g_edges, group_node_data["id"]
+    )
+
+    # Update template values
+    update_template(template, g_nodes)
+
+    nodes = [
+        n for n in base_flow["nodes"] if n["id"] != group_node_data["id"]
+    ] + g_nodes
+    edges = (
+        [
+            e
+            for e in base_flow["edges"]
+            if e["target"] != group_node_data["id"]
+            and e["source"] != group_node_data["id"]
+        ]
+        + g_edges
+        + updated_edges
+    )
+
+    base_flow["nodes"] = nodes
+    base_flow["edges"] = edges
+
+    return nodes
+
+
+def process_flow(flow_object):
+    cloned_flow = copy.deepcopy(flow_object)
+    processed_nodes = set()  # To keep track of processed nodes
+
+    def process_node(node):
+        node_id = node.get("id")
+
+        # If node already processed, skip
+        if node_id in processed_nodes:
+            return
+
+        if (
+            node.get("data")
+            and node["data"].get("node")
+            and node["data"]["node"].get("flow")
+        ):
+            process_flow(node["data"]["node"]["flow"]["data"])
+            new_nodes = ungroup_node(node["data"], cloned_flow)
+            # Add new nodes to the queue for future processing
+            nodes_to_process.extend(new_nodes)
+
+        # Mark node as processed
+        processed_nodes.add(node_id)
+
+    nodes_to_process = deque(cloned_flow["nodes"])
+
+    while nodes_to_process:
+        node = nodes_to_process.popleft()
+        process_node(node)
+
+    return cloned_flow
+
+
+def update_template(template, g_nodes):
+    """
+    Updates the template of a node in a graph with the given template.
+
+    Args:
+        template (dict): The new template to update the node with.
+        g_nodes (list): The list of nodes in the graph.
+
+    Returns:
+        None
+    """
+    for _, value in template.items():
+        if not value.get("proxy"):
+            continue
+        proxy_dict = value["proxy"]
+        field, id_ = proxy_dict["field"], proxy_dict["id"]
+        node_index = next((i for i, n in enumerate(g_nodes) if n["id"] == id_), -1)
+        if node_index != -1:
+            display_name = None
+            show = g_nodes[node_index]["data"]["node"]["template"][field]["show"]
+            advanced = g_nodes[node_index]["data"]["node"]["template"][field][
+                "advanced"
+            ]
+            if "display_name" in g_nodes[node_index]["data"]["node"]["template"][field]:
+                display_name = g_nodes[node_index]["data"]["node"]["template"][field][
+                    "display_name"
+                ]
+            else:
+                display_name = g_nodes[node_index]["data"]["node"]["template"][field][
+                    "name"
+                ]
+
+            g_nodes[node_index]["data"]["node"]["template"][field] = value
+            g_nodes[node_index]["data"]["node"]["template"][field]["show"] = show
+            g_nodes[node_index]["data"]["node"]["template"][field][
+                "advanced"
+            ] = advanced
+            g_nodes[node_index]["data"]["node"]["template"][field][
+                "display_name"
+            ] = display_name
+
+
+def update_target_handle(new_edge, g_nodes, group_node_id):
+    """
+    Updates the target handle of a given edge if it is a proxy node.
+
+    Args:
+        new_edge (dict): The edge to update.
+        g_nodes (list): The list of nodes in the graph.
+        group_node_id (str): The ID of the group node.
+
+    Returns:
+        dict: The updated edge.
+    """
+    target_handle = new_edge["data"]["targetHandle"]
+    if target_handle.get("proxy"):
+        proxy_id = target_handle["proxy"]["id"]
+        if node := next((n for n in g_nodes if n["id"] == proxy_id), None):
+            set_new_target_handle(proxy_id, new_edge, target_handle, node)
+    return new_edge
+
+
+def set_new_target_handle(proxy_id, new_edge, target_handle, node):
+    """
+    Sets a new target handle for a given edge.
+
+    Args:
+        proxy_id (str): The ID of the proxy.
+        new_edge (dict): The new edge to be created.
+        target_handle (dict): The target handle of the edge.
+        node (dict): The node containing the edge.
+
+    Returns:
+        None
+    """
+    new_edge["target"] = proxy_id
+    _type = target_handle.get("type")
+    if _type is None:
+        raise KeyError("The 'type' key must be present in target_handle.")
+
+    field = target_handle["proxy"]["field"]
+    new_target_handle = {
+        "fieldName": field,
+        "type": _type,
+        "id": proxy_id,
+    }
+    if node["data"]["node"].get("flow"):
+        new_target_handle["proxy"] = {
+            "field": node["data"]["node"]["template"][field]["proxy"]["field"],
+            "id": node["data"]["node"]["template"][field]["proxy"]["id"],
+        }
+    if input_types := target_handle.get("inputTypes"):
+        new_target_handle["inputTypes"] = input_types
+    new_edge["data"]["targetHandle"] = new_target_handle
+
+
+def update_source_handle(new_edge, g_nodes, g_edges):
+    """
+    Updates the source handle of a given edge to the last node in the flow data.
+
+    Args:
+        new_edge (dict): The edge to update.
+        flow_data (dict): The flow data containing the nodes and edges.
+
+    Returns:
+        dict: The updated edge with the new source handle.
+    """
+    last_node = copy.deepcopy(find_last_node(g_nodes, g_edges))
+    new_edge["source"] = last_node["id"]
+    new_source_handle = new_edge["data"]["sourceHandle"]
+    new_source_handle["id"] = last_node["id"]
+    new_edge["data"]["sourceHandle"] = new_source_handle
+    return new_edge
+
+
+def get_updated_edges(base_flow, g_nodes, g_edges, group_node_id):
+    """
+    Given a base flow, a list of graph nodes and a group node id, returns a list of updated edges.
+    An updated edge is an edge that has its target or source handle updated based on the group node id.
+
+    Args:
+        base_flow (dict): The base flow containing a list of edges.
+        g_nodes (list): A list of graph nodes.
+        group_node_id (str): The id of the group node.
+
+    Returns:
+        list: A list of updated edges.
+    """
+    updated_edges = []
+    for edge in base_flow["edges"]:
+        new_edge = copy.deepcopy(edge)
+        if new_edge["target"] == group_node_id:
+            new_edge = update_target_handle(new_edge, g_nodes, group_node_id)
+
+        if new_edge["source"] == group_node_id:
+            new_edge = update_source_handle(new_edge, g_nodes, g_edges)
+
+        if edge["target"] == group_node_id or edge["source"] == group_node_id:
+            updated_edges.append(new_edge)
+    return updated_edges

src/backend/langflow/graph/vertex/base.py

@@ -1,5 +1,7 @@
 import ast
+import pickle
 from langflow.graph.utils import UnbuiltObject
+from langflow.graph.vertex.utils import is_basic_type
 from langflow.interface.initialize import loading
 from langflow.interface.listing import lazy_load_dict
 from langflow.utils.constants import DIRECT_TYPES
@@ -12,12 +14,19 @@ import types
 from typing import Any, Dict, List, Optional
 from typing import TYPE_CHECKING
 
+
 if TYPE_CHECKING:
     from langflow.graph.edge.base import Edge
 
 
 class Vertex:
-    def __init__(self, data: Dict, base_type: Optional[str] = None) -> None:
+    def __init__(
+        self,
+        data: Dict,
+        base_type: Optional[str] = None,
+        is_task: bool = False,
+        params: Optional[Dict] = None,
+    ) -> None:
         self.id: str = data["id"]
         self._data = data
         self.edges: List["Edge"] = []
@@ -26,6 +35,66 @@ class Vertex:
         self._built_object = UnbuiltObject()
         self._built = False
         self.artifacts: Dict[str, Any] = {}
+        self.task_id: Optional[str] = None
+        self.is_task = is_task
+        self.params = params or {}
+        self.parent_node_id: Optional[str] = self._data.get("parent_node_id")
+        self.parent_is_top_level = False
+
+    def reset_params(self):
+        for edge in self.edges:
+            if edge.source != self:
+                target_param = edge.target_param
+                if target_param in ["document", "texts"]:
+                    # this means they got data and have already ingested it
+                    # so we continue after removing the param
+                    self.params.pop(target_param, None)
+                    continue
+
+                if target_param in self.params and not is_basic_type(
+                    self.params[target_param]
+                ):
+                    # edge.source.params = {}
+                    edge.source._build_params()
+                    edge.source._built_object = UnbuiltObject()
+                    edge.source._built = False
+
+                    self.params[target_param] = edge.source
+
+    def __getstate__(self):
+        state_dict = self.__dict__.copy()
+        try:
+            # try pickling the built object
+            # if it fails, then we need to delete it
+            # and build it again
+            pickle.dumps(state_dict["_built_object"])
+        except Exception:
+            self.reset_params()
+            del state_dict["_built_object"]
+            del state_dict["_built"]
+        return state_dict
+
+    def __setstate__(self, state):
+        self._data = state["_data"]
+        self.params = state["params"]
+        self.base_type = state["base_type"]
+        self.is_task = state["is_task"]
+        self.edges = state["edges"]
+        self.id = state["id"]
+        self._parse_data()
+        if "_built_object" in state:
+            self._built_object = state["_built_object"]
+            self._built = state["_built"]
+        else:
+            self._built_object = UnbuiltObject()
+            self._built = False
+        self.artifacts: Dict[str, Any] = {}
+        self.task_id: Optional[str] = None
+        self.parent_node_id = state["parent_node_id"]
+        self.parent_is_top_level = state["parent_is_top_level"]
+
+    def set_top_level(self, top_level_nodes: List[str]) -> None:
+        self.parent_is_top_level = self.parent_node_id in top_level_nodes
 
     def _parse_data(self) -> None:
         self.data = self._data["data"]
@@ -68,6 +137,13 @@ class Vertex:
                     self.base_type = base_type
                     break
 
+    def get_task(self):
+        # using the task_id, get the task from celery
+        # and return it
+        from celery.result import AsyncResult  # type: ignore
+
+        return AsyncResult(self.task_id)
+
     def _build_params(self):
         # sourcery skip: merge-list-append, remove-redundant-if
         # Some params are required, some are optional
@@ -89,9 +165,11 @@ class Vertex:
             for key, value in self.data["node"]["template"].items()
             if isinstance(value, dict)
         }
-        params = {}
+        params = self.params.copy() if self.params else {}
 
         for edge in self.edges:
+            if not hasattr(edge, "target_param"):
+                continue
             param_key = edge.target_param
             if param_key in template_dict:
                 if template_dict[param_key]["list"]:
@@ -102,6 +180,8 @@ class Vertex:
                     params[param_key] = edge.source
 
         for key, value in template_dict.items():
+            if key in params:
+                continue
             # Skip _type and any value that has show == False and is not code
             # If we don't want to show code but we want to use it
             if key == "_type" or (not value.get("show") and key != "code"):
@@ -112,9 +192,10 @@ class Vertex:
                 # Load the type in value.get('suffixes') using
                 # what is inside value.get('content')
                 # value.get('value') is the file name
-                file_path = value.get("file_path")
+                if file_path := value.get("file_path"):
-
+                    params[key] = file_path
-                params[key] = file_path
+                else:
+                    raise ValueError(f"File path not found for {self.vertex_type}")
             elif value.get("type") in DIRECT_TYPES and params.get(key) is None:
                 if value.get("type") == "code":
                     try:
@@ -144,6 +225,7 @@ class Vertex:
                 else:
                     params.pop(key, None)
         # Add _type to params
+        self._raw_params = params
         self.params = params
 
     def _build(self, user_id=None):
@@ -151,13 +233,13 @@ class Vertex:
         Initiate the build process.
         """
         logger.debug(f"Building {self.vertex_type}")
-        self._build_each_node_in_params_dict()
+        self._build_each_node_in_params_dict(user_id)
         self._get_and_instantiate_class(user_id)
         self._validate_built_object()
 
         self._built = True
 
-    def _build_each_node_in_params_dict(self):
+    def _build_each_node_in_params_dict(self, user_id=None):
         """
         Iterates over each node in the params dictionary and builds it.
         """
@@ -166,9 +248,9 @@ class Vertex:
                 if value == self:
                     del self.params[key]
                     continue
-                self._build_node_and_update_params(key, value)
+                self._build_node_and_update_params(key, value, user_id)
             elif isinstance(value, list) and self._is_list_of_nodes(value):
-                self._build_list_of_nodes_and_update_params(key, value)
+                self._build_list_of_nodes_and_update_params(key, value, user_id)
 
     def _is_node(self, value):
         """
@@ -182,11 +264,31 @@ class Vertex:
         """
         return all(self._is_node(node) for node in value)
 
+    def get_result(self, user_id=None, timeout=None) -> Any:
+        # Check if the Vertex was built already
+        if self._built:
+            return self._built_object
+
+        if self.is_task and self.task_id is not None:
+            task = self.get_task()
+            result = task.get(timeout=timeout)
+            if result is not None:  # If result is ready
+                self._update_built_object_and_artifacts(result)
+                return self._built_object
+            else:
+                # Handle the case when the result is not ready (retry, throw exception, etc.)
+                pass
+
+        # If there's no task_id, build the vertex locally
+        self.build(user_id)
+        return self._built_object
+
     def _build_node_and_update_params(self, key, node, user_id=None):
         """
         Builds a given node and updates the params dictionary accordingly.
         """
-        result = node.build(user_id)
+
+        result = node.get_result(user_id)
         self._handle_func(key, result)
         if isinstance(result, list):
             self._extend_params_list_with_result(key, result)
@@ -200,7 +302,7 @@ class Vertex:
         """
         self.params[key] = []
         for node in nodes:
-            built = node.build(user_id)
+            built = node.get_result(user_id)
             if isinstance(built, list):
                 if key not in self.params:
                     self.params[key] = []
@@ -245,6 +347,7 @@ class Vertex:
             )
             self._update_built_object_and_artifacts(result)
         except Exception as exc:
+            logger.exception(exc)
             raise ValueError(
                 f"Error building node {self.vertex_type}: {str(exc)}"
             ) from exc
@@ -285,7 +388,10 @@ class Vertex:
         return f"Vertex(id={self.id}, data={self.data})"
 
     def __eq__(self, __o: object) -> bool:
-        return self.id == __o.id if isinstance(__o, Vertex) else False
+        try:
+            return self.id == __o.id if isinstance(__o, Vertex) else False
+        except AttributeError:
+            return False
 
     def __hash__(self) -> int:
         return id(self)

src/backend/langflow/graph/vertex/types.py

@@ -7,14 +7,27 @@ from langflow.interface.utils import extract_input_variables_from_prompt
 
 
 class AgentVertex(Vertex):
-    def __init__(self, data: Dict):
+    def __init__(self, data: Dict, params: Optional[Dict] = None):
-        super().__init__(data, base_type="agents")
+        super().__init__(data, base_type="agents", params=params)
 
         self.tools: List[Union[ToolkitVertex, ToolVertex]] = []
         self.chains: List[ChainVertex] = []
 
+    def __getstate__(self):
+        state = super().__getstate__()
+        state["tools"] = self.tools
+        state["chains"] = self.chains
+        return state
+
+    def __setstate__(self, state):
+        self.tools = state["tools"]
+        self.chains = state["chains"]
+        super().__setstate__(state)
+
     def _set_tools_and_chains(self) -> None:
         for edge in self.edges:
+            if not hasattr(edge, "source"):
+                continue
             source_node = edge.source
             if isinstance(source_node, (ToolVertex, ToolkitVertex)):
                 self.tools.append(source_node)
@@ -38,16 +51,16 @@ class AgentVertex(Vertex):
 
 
 class ToolVertex(Vertex):
-    def __init__(self, data: Dict):
+    def __init__(self, data: Dict, params: Optional[Dict] = None):
-        super().__init__(data, base_type="tools")
+        super().__init__(data, base_type="tools", params=params)
 
 
 class LLMVertex(Vertex):
     built_node_type = None
     class_built_object = None
 
-    def __init__(self, data: Dict):
+    def __init__(self, data: Dict, params: Optional[Dict] = None):
-        super().__init__(data, base_type="llms")
+        super().__init__(data, base_type="llms", params=params)
 
     def build(self, force: bool = False, user_id=None, *args, **kwargs) -> Any:
         # LLM is different because some models might take up too much memory
@@ -64,13 +77,13 @@ class LLMVertex(Vertex):
 
 
 class ToolkitVertex(Vertex):
-    def __init__(self, data: Dict):
+    def __init__(self, data: Dict, params=None):
-        super().__init__(data, base_type="toolkits")
+        super().__init__(data, base_type="toolkits", params=params)
 
 
 class FileToolVertex(ToolVertex):
-    def __init__(self, data: Dict):
+    def __init__(self, data: Dict, params=None):
-        super().__init__(data)
+        super().__init__(data, params=params)
 
 
 class WrapperVertex(Vertex):
@@ -86,17 +99,19 @@ class WrapperVertex(Vertex):
 
 
 class DocumentLoaderVertex(Vertex):
-    def __init__(self, data: Dict):
+    def __init__(self, data: Dict, params: Optional[Dict] = None):
-        super().__init__(data, base_type="documentloaders")
+        super().__init__(data, base_type="documentloaders", params=params)
 
     def _built_object_repr(self):
         # This built_object is a list of documents. Maybe we should
         # show how many documents are in the list?
 
         if self._built_object:
-            avg_length = sum(len(doc.page_content) for doc in self._built_object) / len(
+            avg_length = sum(
-                self._built_object
+                len(doc.page_content)
-            )
+                for doc in self._built_object
+                if hasattr(doc, "page_content")
+            ) / len(self._built_object)
             return f"""{self.vertex_type}({len(self._built_object)} documents)
             \nAvg. Document Length (characters): {int(avg_length)}
             Documents: {self._built_object[:3]}..."""
@@ -104,14 +119,51 @@ class DocumentLoaderVertex(Vertex):
 
 
 class EmbeddingVertex(Vertex):
-    def __init__(self, data: Dict):
+    def __init__(self, data: Dict, params: Optional[Dict] = None):
-        super().__init__(data, base_type="embeddings")
+        super().__init__(data, base_type="embeddings", params=params)
 
 
 class VectorStoreVertex(Vertex):
-    def __init__(self, data: Dict):
+    def __init__(self, data: Dict, params=None):
         super().__init__(data, base_type="vectorstores")
 
+        self.params = params or {}
+
+    # VectorStores may contain databse connections
+    # so we need to define the __reduce__ method and the __setstate__ method
+    # to avoid pickling errors
+    def clean_edges_for_pickling(self):
+        # for each edge that has self as source
+        # we need to clear the _built_object of the target
+        # so that we don't try to pickle a database connection
+        for edge in self.edges:
+            if edge.source == self:
+                edge.target._built_object = None
+                edge.target._built = False
+                edge.target.params[edge.target_param] = self
+
+    def remove_docs_and_texts_from_params(self):
+        # remove documents and texts from params
+        # so that we don't try to pickle a database connection
+        self.params.pop("documents", None)
+        self.params.pop("texts", None)
+
+    def __getstate__(self):
+        # We want to save the params attribute
+        # and if "documents" or "texts" are in the params
+        # we want to remove them because they have already
+        # been processed.
+        params = self.params.copy()
+        params.pop("documents", None)
+        params.pop("texts", None)
+        self.clean_edges_for_pickling()
+
+        return super().__getstate__()
+
+    def __setstate__(self, state):
+        super().__setstate__(state)
+        self.remove_docs_and_texts_from_params()
+
 
 class MemoryVertex(Vertex):
     def __init__(self, data: Dict):
@@ -124,8 +176,8 @@ class RetrieverVertex(Vertex):
 
 
 class TextSplitterVertex(Vertex):
-    def __init__(self, data: Dict):
+    def __init__(self, data: Dict, params: Optional[Dict] = None):
-        super().__init__(data, base_type="textsplitters")
+        super().__init__(data, base_type="textsplitters", params=params)
 
     def _built_object_repr(self):
         # This built_object is a list of documents. Maybe we should
@@ -211,7 +263,7 @@ class PromptVertex(Vertex):
                 self.params["input_variables"] = list(
                     set(self.params["input_variables"])
                 )
-            else:
+            elif isinstance(self.params, dict):
                 self.params.pop("input_variables", None)
 
             self._build(user_id=user_id)
@@ -258,8 +310,13 @@ class OutputParserVertex(Vertex):
 
 class CustomComponentVertex(Vertex):
     def __init__(self, data: Dict):
-        super().__init__(data, base_type="custom_components")
+        super().__init__(data, base_type="custom_components", is_task=True)
 
     def _built_object_repr(self):
+        if self.task_id and self.is_task:
+            if task := self.get_task():
+                return str(task.info)
+            else:
+                return f"Task {self.task_id} is not running"
         if self.artifacts and "repr" in self.artifacts:
             return self.artifacts["repr"] or super()._built_object_repr()

src/backend/langflow/graph/vertex/utils.py

@@ -0,0 +1,5 @@
+from langflow.utils.constants import PYTHON_BASIC_TYPES
+
+
+def is_basic_type(obj):
+    return type(obj) in PYTHON_BASIC_TYPES

src/backend/langflow/interface/agents/base.py

@@ -5,7 +5,7 @@ from langchain.agents import types
 from langflow.custom.customs import get_custom_nodes
 from langflow.interface.agents.custom import CUSTOM_AGENTS
 from langflow.interface.base import LangChainTypeCreator
-from langflow.services.getters import get_settings_manager
+from langflow.services.getters import get_settings_service
 
 from langflow.template.frontend_node.agents import AgentFrontendNode
 from loguru import logger
@@ -54,7 +54,7 @@ class AgentCreator(LangChainTypeCreator):
     # Now this is a generator
     def to_list(self) -> List[str]:
         names = []
-        settings_manager = get_settings_manager()
+        settings_service = get_settings_service()
         for _, agent in self.type_to_loader_dict.items():
             agent_name = (
                 agent.function_name()
@@ -62,8 +62,8 @@ class AgentCreator(LangChainTypeCreator):
                 else agent.__name__
             )
             if (
-                agent_name in settings_manager.settings.AGENTS
+                agent_name in settings_service.settings.AGENTS
-                or settings_manager.settings.DEV
+                or settings_service.settings.DEV
             ):
                 names.append(agent_name)
         return names

src/backend/langflow/interface/agents/custom.py

@@ -1,6 +1,6 @@
 from typing import Any, List, Optional
 
-from langchain import LLMChain
+from langchain.chains.llm import LLMChain
 from langchain.agents import (
     AgentExecutor,
     Tool,

src/backend/langflow/interface/agents/prebuilt.py

@@ -1,4 +1,4 @@
-from langchain import LLMChain
+from langchain.chains.llm import LLMChain
 from langchain.agents import AgentExecutor, ZeroShotAgent
 from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX
 from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit

src/backend/langflow/interface/base.py

@@ -2,7 +2,7 @@ from abc import ABC, abstractmethod
 from typing import Any, Dict, List, Optional, Type, Union
 from langchain.chains.base import Chain
 from langchain.agents import AgentExecutor
-from langflow.services.getters import get_settings_manager
+from langflow.services.getters import get_settings_service
 from pydantic import BaseModel
 
 from langflow.template.field.base import TemplateField
@@ -27,11 +27,11 @@ class LangChainTypeCreator(BaseModel, ABC):
     @property
     def docs_map(self) -> Dict[str, str]:
         """A dict with the name of the component as key and the documentation link as value."""
-        settings_manager = get_settings_manager()
+        settings_service = get_settings_service()
         if self.name_docs_dict is None:
             try:
                 type_settings = getattr(
-                    settings_manager.settings, self.type_name.upper()
+                    settings_service.settings, self.type_name.upper()
                 )
                 self.name_docs_dict = {
                     name: value_dict["documentation"]

src/backend/langflow/interface/chains/base.py

@@ -3,7 +3,7 @@ from typing import Any, Dict, List, Optional, Type
 from langflow.custom.customs import get_custom_nodes
 from langflow.interface.base import LangChainTypeCreator
 from langflow.interface.importing.utils import import_class
-from langflow.services.getters import get_settings_manager
+from langflow.services.getters import get_settings_service
 
 from langflow.template.frontend_node.chains import ChainFrontendNode
 from loguru import logger
@@ -31,7 +31,7 @@ class ChainCreator(LangChainTypeCreator):
     @property
     def type_to_loader_dict(self) -> Dict:
         if self.type_dict is None:
-            settings_manager = get_settings_manager()
+            settings_service = get_settings_service()
             self.type_dict: dict[str, Any] = {
                 chain_name: import_class(f"langchain.chains.{chain_name}")
                 for chain_name in chains.__all__
@@ -45,8 +45,8 @@ class ChainCreator(LangChainTypeCreator):
             self.type_dict = {
                 name: chain
                 for name, chain in self.type_dict.items()
-                if name in settings_manager.settings.CHAINS
+                if name in settings_service.settings.CHAINS
-                or settings_manager.settings.DEV
+                or settings_service.settings.DEV
             }
         return self.type_dict
 

src/backend/langflow/interface/custom/constants.py

@@ -1,7 +1,7 @@
-from langchain import PromptTemplate
+from langchain.prompts import PromptTemplate
 from langchain.chains.base import Chain
 from langchain.document_loaders.base import BaseLoader
-from langchain.embeddings.base import Embeddings
+from langchain.schema.embeddings import Embeddings
 from langchain.llms.base import BaseLLM
 from langchain.schema import BaseRetriever, Document
 from langchain.text_splitter import TextSplitter
@@ -45,7 +45,7 @@ DEFAULT_CUSTOM_COMPONENT_CODE = """from langflow import CustomComponent
 
 from langchain.llms.base import BaseLLM
 from langchain.chains import LLMChain
-from langchain import PromptTemplate
+from langchain.prompts import PromptTemplate
 from langchain.schema import Document
 
 import requests

src/backend/langflow/interface/custom/custom_component.py

@@ -4,7 +4,7 @@ from fastapi import HTTPException
 from langflow.interface.custom.constants import CUSTOM_COMPONENT_SUPPORTED_TYPES
 from langflow.interface.custom.component import Component
 from langflow.interface.custom.directory_reader import DirectoryReader
-from langflow.services.getters import get_db_manager
+from langflow.services.getters import get_db_service
 from langflow.interface.custom.utils import extract_inner_type
 
 from langflow.utils import validate
@@ -95,7 +95,20 @@ class CustomComponent(Component, extra=Extra.allow):
 
         build_method = build_methods[0]
 
-        return build_method["args"]
+        args = build_method["args"]
+        for arg in args:
+            if arg.get("type") == "prompt":
+                raise HTTPException(
+                    status_code=400,
+                    detail={
+                        "error": "Type hint Error",
+                        "traceback": (
+                            "Prompt type is not supported in the build method."
+                            " Try using PromptTemplate instead."
+                        ),
+                    },
+                )
+        return args
 
     @property
     def get_function_entrypoint_return_type(self) -> List[str]:
@@ -176,25 +189,25 @@ class CustomComponent(Component, extra=Extra.allow):
         return validate.create_function(self.code, self.function_entrypoint_name)
 
     def load_flow(self, flow_id: str, tweaks: Optional[dict] = None) -> Any:
-        from langflow.processing.process import build_sorted_vertices_with_caching
+        from langflow.processing.process import build_sorted_vertices
         from langflow.processing.process import process_tweaks
 
-        db_manager = get_db_manager()
+        db_service = get_db_service()
-        with session_getter(db_manager) as session:
+        with session_getter(db_service) as session:
             graph_data = flow.data if (flow := session.get(Flow, flow_id)) else None
         if not graph_data:
             raise ValueError(f"Flow {flow_id} not found")
         if tweaks:
             graph_data = process_tweaks(graph_data=graph_data, tweaks=tweaks)
-        return build_sorted_vertices_with_caching(graph_data)
+        return build_sorted_vertices(graph_data)
 
     def list_flows(self, *, get_session: Optional[Callable] = None) -> List[Flow]:
         if not self.user_id:
             raise ValueError("Session is invalid")
         try:
             get_session = get_session or session_getter
-            db_manager = get_db_manager()
+            db_service = get_db_service()
-            with get_session(db_manager) as session:
+            with get_session(db_service) as session:
                 flows = session.query(Flow).filter(Flow.user_id == self.user_id).all()
             return flows
         except Exception as e:
@@ -209,8 +222,8 @@ class CustomComponent(Component, extra=Extra.allow):
         get_session: Optional[Callable] = None,
     ) -> Flow:
         get_session = get_session or session_getter
-        db_manager = get_db_manager()
+        db_service = get_db_service()
-        with get_session(db_manager) as session:
+        with get_session(db_service) as session:
             if flow_id:
                 flow = session.query(Flow).get(flow_id)
             elif flow_name:

src/backend/langflow/interface/document_loaders/base.py

@@ -1,7 +1,7 @@
 from typing import Dict, List, Optional, Type
 
 from langflow.interface.base import LangChainTypeCreator
-from langflow.services.getters import get_settings_manager
+from langflow.services.getters import get_settings_service
 from langflow.template.frontend_node.documentloaders import DocumentLoaderFrontNode
 from langflow.interface.custom_lists import documentloaders_type_to_cls_dict
 
@@ -31,12 +31,12 @@ class DocumentLoaderCreator(LangChainTypeCreator):
             return None
 
     def to_list(self) -> List[str]:
-        settings_manager = get_settings_manager()
+        settings_service = get_settings_service()
         return [
             documentloader.__name__
             for documentloader in self.type_to_loader_dict.values()
-            if documentloader.__name__ in settings_manager.settings.DOCUMENTLOADERS
+            if documentloader.__name__ in settings_service.settings.DOCUMENTLOADERS
-            or settings_manager.settings.DEV
+            or settings_service.settings.DEV
         ]
 
 

src/backend/langflow/interface/embeddings/base.py

@@ -2,7 +2,7 @@ from typing import Dict, List, Optional, Type
 
 from langflow.interface.base import LangChainTypeCreator
 from langflow.interface.custom_lists import embedding_type_to_cls_dict
-from langflow.services.getters import get_settings_manager
+from langflow.services.getters import get_settings_service
 
 from langflow.template.frontend_node.base import FrontendNode
 from langflow.template.frontend_node.embeddings import EmbeddingFrontendNode
@@ -33,12 +33,12 @@ class EmbeddingCreator(LangChainTypeCreator):
             return None
 
     def to_list(self) -> List[str]:
-        settings_manager = get_settings_manager()
+        settings_service = get_settings_service()
         return [
             embedding.__name__
             for embedding in self.type_to_loader_dict.values()
-            if embedding.__name__ in settings_manager.settings.EMBEDDINGS
+            if embedding.__name__ in settings_service.settings.EMBEDDINGS
-            or settings_manager.settings.DEV
+            or settings_service.settings.DEV
         ]
 
 

src/backend/langflow/interface/importing/utils.py

@@ -3,7 +3,7 @@
 import importlib
 from typing import Any, Type
 
-from langchain import PromptTemplate
+from langchain.prompts import PromptTemplate
 from langchain.agents import Agent
 from langchain.base_language import BaseLanguageModel
 from langchain.chains.base import Chain
@@ -144,6 +144,8 @@ def import_chain(chain: str) -> Type[Chain]:
 
     if chain in CUSTOM_CHAINS:
         return CUSTOM_CHAINS[chain]
+    if chain == "SQLDatabaseChain":
+        return import_class("langchain_experimental.sql.SQLDatabaseChain")
     return import_class(f"langchain.chains.{chain}")
 
 

src/backend/langflow/interface/initialize/loading.py

@@ -1,7 +1,7 @@
 import json
 import orjson
 from typing import Any, Callable, Dict, Sequence, Type, TYPE_CHECKING
-
+from langchain.schema import Document
 from langchain.agents import agent as agent_module
 from langchain.agents.agent import AgentExecutor
 from langchain.agents.agent_toolkits.base import BaseToolkit
@@ -40,12 +40,23 @@ if TYPE_CHECKING:
     from langflow import CustomComponent
 
 
+def build_vertex_in_params(params: Dict) -> Dict:
+    from langflow.graph.vertex.base import Vertex
+
+    # If any of the values in params is a Vertex, we will build it
+    return {
+        key: value.build() if isinstance(value, Vertex) else value
+        for key, value in params.items()
+    }
+
+
 def instantiate_class(
     node_type: str, base_type: str, params: Dict, user_id=None
 ) -> Any:
     """Instantiate class from module type and key, and params"""
     params = convert_params_to_sets(params)
     params = convert_kwargs(params)
+
     if node_type in CUSTOM_NODES:
         if custom_node := CUSTOM_NODES.get(node_type):
             if hasattr(custom_node, "initialize"):
@@ -100,7 +111,7 @@ def instantiate_based_on_type(class_object, base_type, node_type, params, user_i
     elif base_type == "vectorstores":
         return instantiate_vectorstore(class_object, params)
     elif base_type == "documentloaders":
-        return instantiate_documentloader(class_object, params)
+        return instantiate_documentloader(node_type, class_object, params)
     elif base_type == "textsplitters":
         return instantiate_textsplitter(class_object, params)
     elif base_type == "utilities":
@@ -289,6 +300,13 @@ def instantiate_embedding(node_type, class_object, params: Dict):
 
 def instantiate_vectorstore(class_object: Type[VectorStore], params: Dict):
     search_kwargs = params.pop("search_kwargs", {})
+    # clean up docs or texts to have only documents
+    if "texts" in params:
+        params["documents"] = params.pop("texts")
+    if "documents" in params:
+        params["documents"] = [
+            doc for doc in params["documents"] if isinstance(doc, Document)
+        ]
     if initializer := vecstore_initializer.get(class_object.__name__):
         vecstore = initializer(class_object, params)
     else:
@@ -303,7 +321,9 @@ def instantiate_vectorstore(class_object: Type[VectorStore], params: Dict):
     return vecstore
 
 
-def instantiate_documentloader(class_object: Type[BaseLoader], params: Dict):
+def instantiate_documentloader(
+    node_type: str, class_object: Type[BaseLoader], params: Dict
+):
     if "file_filter" in params:
         # file_filter will be a string but we need a function
         # that will be used to filter the files using file_filter
@@ -323,6 +343,11 @@ def instantiate_documentloader(class_object: Type[BaseLoader], params: Dict):
             raise ValueError(
                 "The metadata you provided is not a valid JSON string."
             ) from exc
+
+    if node_type == "WebBaseLoader":
+        if web_path := params.pop("web_path", None):
+            params["web_paths"] = [web_path]
+
     docs = class_object(**params).load()
     # Now if metadata is an empty dict, we will not add it to the documents
     if metadata:

src/backend/langflow/interface/initialize/vector_store.py

@@ -8,7 +8,7 @@ from langchain.vectorstores import (
     SupabaseVectorStore,
     MongoDBAtlasVectorSearch,
 )
-
+from langchain.schema import Document
 import os
 
 import orjson
@@ -201,11 +201,16 @@ def initialize_chroma(class_object: Type[Chroma], params: dict):
         if "texts" in params:
             params["documents"] = params.pop("texts")
         for doc in params["documents"]:
+            if not isinstance(doc, Document):
+                # remove any non-Document objects from the list
+                params["documents"].remove(doc)
+                continue
             if doc.metadata is None:
                 doc.metadata = {}
             for key, value in doc.metadata.items():
                 if value is None:
                     doc.metadata[key] = ""
+
         chromadb = class_object.from_documents(**params)
     if persist:
         chromadb.persist()

src/backend/langflow/interface/listing.py

@@ -1,19 +1,4 @@
-from langflow.interface.agents.base import agent_creator
+from langflow.services.getters import get_settings_service
-from langflow.interface.chains.base import chain_creator
-from langflow.interface.document_loaders.base import documentloader_creator
-from langflow.interface.embeddings.base import embedding_creator
-from langflow.interface.llms.base import llm_creator
-from langflow.interface.memories.base import memory_creator
-from langflow.interface.prompts.base import prompt_creator
-from langflow.interface.text_splitters.base import textsplitter_creator
-from langflow.interface.toolkits.base import toolkits_creator
-from langflow.interface.tools.base import tool_creator
-from langflow.interface.utilities.base import utility_creator
-from langflow.interface.vector_store.base import vectorstore_creator
-from langflow.interface.wrappers.base import wrapper_creator
-from langflow.interface.output_parsers.base import output_parser_creator
-from langflow.interface.retrievers.base import retriever_creator
-from langflow.interface.custom.base import custom_component_creator
 from langflow.utils.lazy_load import LazyLoadDictBase
 
 
@@ -33,24 +18,10 @@ class AllTypesDict(LazyLoadDictBase):
         }
 
     def get_type_dict(self):
-        return {
+        from langflow.interface.types import get_all_types_dict
-            "agents": agent_creator.to_list(),
+
-            "prompts": prompt_creator.to_list(),
+        settings_service = get_settings_service()
-            "llms": llm_creator.to_list(),
+        return get_all_types_dict(settings_service=settings_service)
-            "tools": tool_creator.to_list(),
-            "chains": chain_creator.to_list(),
-            "memory": memory_creator.to_list(),
-            "toolkits": toolkits_creator.to_list(),
-            "wrappers": wrapper_creator.to_list(),
-            "documentLoaders": documentloader_creator.to_list(),
-            "vectorStore": vectorstore_creator.to_list(),
-            "embeddings": embedding_creator.to_list(),
-            "textSplitters": textsplitter_creator.to_list(),
-            "utilities": utility_creator.to_list(),
-            "outputParsers": output_parser_creator.to_list(),
-            "retrievers": retriever_creator.to_list(),
-            "custom_components": custom_component_creator.to_list(),
-        }
 
 
 lazy_load_dict = AllTypesDict()

src/backend/langflow/interface/llms/base.py

@@ -2,7 +2,7 @@ from typing import Dict, List, Optional, Type
 
 from langflow.interface.base import LangChainTypeCreator
 from langflow.interface.custom_lists import llm_type_to_cls_dict
-from langflow.services.getters import get_settings_manager
+from langflow.services.getters import get_settings_service
 
 from langflow.template.frontend_node.llms import LLMFrontendNode
 from loguru import logger
@@ -34,12 +34,12 @@ class LLMCreator(LangChainTypeCreator):
             return None
 
     def to_list(self) -> List[str]:
-        settings_manager = get_settings_manager()
+        settings_service = get_settings_service()
         return [
             llm.__name__
             for llm in self.type_to_loader_dict.values()
-            if llm.__name__ in settings_manager.settings.LLMS
+            if llm.__name__ in settings_service.settings.LLMS
-            or settings_manager.settings.DEV
+            or settings_service.settings.DEV
         ]
 
 

src/backend/langflow/interface/memories/base.py

@@ -2,7 +2,7 @@ from typing import Dict, List, Optional, Type
 
 from langflow.interface.base import LangChainTypeCreator
 from langflow.interface.custom_lists import memory_type_to_cls_dict
-from langflow.services.getters import get_settings_manager
+from langflow.services.getters import get_settings_service
 
 from langflow.template.frontend_node.base import FrontendNode
 from langflow.template.frontend_node.memories import MemoryFrontendNode
@@ -49,12 +49,12 @@ class MemoryCreator(LangChainTypeCreator):
             return None
 
     def to_list(self) -> List[str]:
-        settings_manager = get_settings_manager()
+        settings_service = get_settings_service()
         return [
             memory.__name__
             for memory in self.type_to_loader_dict.values()
-            if memory.__name__ in settings_manager.settings.MEMORIES
+            if memory.__name__ in settings_service.settings.MEMORIES
-            or settings_manager.settings.DEV
+            or settings_service.settings.DEV
         ]
 
 

src/backend/langflow/interface/output_parsers/base.py

@@ -4,7 +4,7 @@ from langchain import output_parsers
 
 from langflow.interface.base import LangChainTypeCreator
 from langflow.interface.importing.utils import import_class
-from langflow.services.getters import get_settings_manager
+from langflow.services.getters import get_settings_service
 
 from langflow.template.frontend_node.output_parsers import OutputParserFrontendNode
 from loguru import logger
@@ -24,7 +24,7 @@ class OutputParserCreator(LangChainTypeCreator):
     @property
     def type_to_loader_dict(self) -> Dict:
         if self.type_dict is None:
-            settings_manager = get_settings_manager()
+            settings_service = get_settings_service()
             self.type_dict = {
                 output_parser_name: import_class(
                     f"langchain.output_parsers.{output_parser_name}"
@@ -35,8 +35,8 @@ class OutputParserCreator(LangChainTypeCreator):
             self.type_dict = {
                 name: output_parser
                 for name, output_parser in self.type_dict.items()
-                if name in settings_manager.settings.OUTPUT_PARSERS
+                if name in settings_service.settings.OUTPUT_PARSERS
-                or settings_manager.settings.DEV
+                or settings_service.settings.DEV
             }
         return self.type_dict
 

src/backend/langflow/interface/prompts/base.py

@@ -5,7 +5,7 @@ from langchain import prompts
 from langflow.custom.customs import get_custom_nodes
 from langflow.interface.base import LangChainTypeCreator
 from langflow.interface.importing.utils import import_class
-from langflow.services.getters import get_settings_manager
+from langflow.services.getters import get_settings_service
 
 from langflow.template.frontend_node.prompts import PromptFrontendNode
 from loguru import logger
@@ -21,7 +21,7 @@ class PromptCreator(LangChainTypeCreator):
 
     @property
     def type_to_loader_dict(self) -> Dict:
-        settings_manager = get_settings_manager()
+        settings_service = get_settings_service()
         if self.type_dict is None:
             self.type_dict = {
                 prompt_name: import_class(f"langchain.prompts.{prompt_name}")
@@ -36,8 +36,8 @@ class PromptCreator(LangChainTypeCreator):
             self.type_dict = {
                 name: prompt
                 for name, prompt in self.type_dict.items()
-                if name in settings_manager.settings.PROMPTS
+                if name in settings_service.settings.PROMPTS
-                or settings_manager.settings.DEV
+                or settings_service.settings.DEV
             }
         return self.type_dict
 

src/backend/langflow/interface/retrievers/base.py

@@ -4,7 +4,7 @@ from langchain import retrievers
 
 from langflow.interface.base import LangChainTypeCreator
 from langflow.interface.importing.utils import import_class
-from langflow.services.getters import get_settings_manager
+from langflow.services.getters import get_settings_service
 
 from langflow.template.frontend_node.retrievers import RetrieverFrontendNode
 from loguru import logger
@@ -49,12 +49,12 @@ class RetrieverCreator(LangChainTypeCreator):
             return None
 
     def to_list(self) -> List[str]:
-        settings_manager = get_settings_manager()
+        settings_service = get_settings_service()
         return [
             retriever
             for retriever in self.type_to_loader_dict.keys()
-            if retriever in settings_manager.settings.RETRIEVERS
+            if retriever in settings_service.settings.RETRIEVERS
-            or settings_manager.settings.DEV
+            or settings_service.settings.DEV
         ]
 
 

src/backend/langflow/interface/run.py

@@ -1,22 +1,9 @@
-from typing import Any, Dict, Tuple
+from typing import Dict, Tuple
-from langflow.services.cache.utils import memoize_dict
 from langflow.graph import Graph
 from loguru import logger
 
 
-@memoize_dict(maxsize=10)
+def build_sorted_vertices(data_graph) -> Tuple[Graph, Dict]:
-def build_langchain_object_with_caching(data_graph):
-    """
-    Build langchain object from data_graph.
-    """
-
-    logger.debug("Building langchain object")
-    graph = Graph.from_payload(data_graph)
-    return graph.build()
-
-
-@memoize_dict(maxsize=10)
-def build_sorted_vertices_with_caching(data_graph) -> Tuple[Any, Dict]:
     """
     Build langchain object from data_graph.
     """
@@ -29,7 +16,7 @@ def build_sorted_vertices_with_caching(data_graph) -> Tuple[Any, Dict]:
         vertex.build()
         if vertex.artifacts:
             artifacts.update(vertex.artifacts)
-    return graph.build(), artifacts
+    return graph, artifacts
 
 
 def build_langchain_object(data_graph):

src/backend/langflow/interface/text_splitters/base.py

@@ -1,7 +1,7 @@
 from typing import Dict, List, Optional, Type
 
 from langflow.interface.base import LangChainTypeCreator
-from langflow.services.getters import get_settings_manager
+from langflow.services.getters import get_settings_service
 from langflow.template.frontend_node.textsplitters import TextSplittersFrontendNode
 from langflow.interface.custom_lists import textsplitter_type_to_cls_dict
 
@@ -31,12 +31,12 @@ class TextSplitterCreator(LangChainTypeCreator):
             return None
 
     def to_list(self) -> List[str]:
-        settings_manager = get_settings_manager()
+        settings_service = get_settings_service()
         return [
             textsplitter.__name__
             for textsplitter in self.type_to_loader_dict.values()
-            if textsplitter.__name__ in settings_manager.settings.TEXTSPLITTERS
+            if textsplitter.__name__ in settings_service.settings.TEXTSPLITTERS
-            or settings_manager.settings.DEV
+            or settings_service.settings.DEV
         ]
 
 

src/backend/langflow/interface/toolkits/base.py

@@ -4,7 +4,7 @@ from langchain.agents import agent_toolkits
 
 from langflow.interface.base import LangChainTypeCreator
 from langflow.interface.importing.utils import import_class, import_module
-from langflow.services.getters import get_settings_manager
+from langflow.services.getters import get_settings_service
 
 from loguru import logger
 from langflow.utils.util import build_template_from_class
@@ -30,7 +30,7 @@ class ToolkitCreator(LangChainTypeCreator):
     @property
     def type_to_loader_dict(self) -> Dict:
         if self.type_dict is None:
-            settings_manager = get_settings_manager()
+            settings_service = get_settings_service()
             self.type_dict = {
                 toolkit_name: import_class(
                     f"langchain.agents.agent_toolkits.{toolkit_name}"
@@ -38,7 +38,7 @@ class ToolkitCreator(LangChainTypeCreator):
                 # if toolkit_name is not lower case it is a class
                 for toolkit_name in agent_toolkits.__all__
                 if not toolkit_name.islower()
-                and toolkit_name in settings_manager.settings.TOOLKITS
+                and toolkit_name in settings_service.settings.TOOLKITS
             }
 
         return self.type_dict

src/backend/langflow/interface/tools/base.py

@@ -15,7 +15,7 @@ from langflow.interface.tools.constants import (
     OTHER_TOOLS,
 )
 from langflow.interface.tools.util import get_tool_params
-from langflow.services.getters import get_settings_manager
+from langflow.services.getters import get_settings_service
 
 from langflow.template.field.base import TemplateField
 from langflow.template.template.base import Template
@@ -67,7 +67,7 @@ class ToolCreator(LangChainTypeCreator):
 
     @property
     def type_to_loader_dict(self) -> Dict:
-        settings_manager = get_settings_manager()
+        settings_service = get_settings_service()
         if self.tools_dict is None:
             all_tools = {}
 
@@ -77,8 +77,8 @@ class ToolCreator(LangChainTypeCreator):
                 tool_name = tool_params.get("name") or tool
 
                 if (
-                    tool_name in settings_manager.settings.TOOLS
+                    tool_name in settings_service.settings.TOOLS
-                    or settings_manager.settings.DEV
+                    or settings_service.settings.DEV
                 ):
                     if tool_name == "JsonSpec":
                         tool_params["path"] = tool_params.pop("dict_")  # type: ignore

src/backend/langflow/interface/types.py

@@ -1,7 +1,7 @@
 import ast
 import contextlib
 from typing import Any, List
-from langflow.api.utils import merge_nested_dicts_with_renaming
+from langflow.api.utils import get_new_key
 from langflow.interface.agents.base import agent_creator
 from langflow.interface.chains.base import chain_creator
 from langflow.interface.custom.constants import CUSTOM_COMPONENT_SUPPORTED_TYPES
@@ -303,9 +303,9 @@ def build_langchain_template_custom_component(custom_component: CustomComponent)
         logger.debug("Updated attributes")
         field_config = build_field_config(custom_component)
         logger.debug("Built field config")
-        add_extra_fields(
+        entrypoint_args = custom_component.get_function_entrypoint_args
-            frontend_node, field_config, custom_component.get_function_entrypoint_args
+
-        )
+        add_extra_fields(frontend_node, field_config, entrypoint_args)
         logger.debug("Added extra fields")
         frontend_node = add_code_field(
             frontend_node, custom_component.code, field_config.get("code", {})
@@ -317,6 +317,8 @@ def build_langchain_template_custom_component(custom_component: CustomComponent)
         logger.debug("Added base classes")
         return frontend_node
     except Exception as exc:
+        if isinstance(exc, HTTPException):
+            raise exc
         raise HTTPException(
             status_code=400,
             detail={
@@ -433,6 +435,24 @@ def build_invalid_menu(invalid_components):
     return invalid_menu
 
 
+def merge_nested_dicts_with_renaming(dict1, dict2):
+    for key, value in dict2.items():
+        if (
+            key in dict1
+            and isinstance(value, dict)
+            and isinstance(dict1.get(key), dict)
+        ):
+            for sub_key, sub_value in value.items():
+                if sub_key in dict1[key]:
+                    new_key = get_new_key(dict1[key], sub_key)
+                    dict1[key][new_key] = sub_value
+                else:
+                    dict1[key][sub_key] = sub_value
+        else:
+            dict1[key] = value
+    return dict1
+
+
 def build_langchain_custom_component_list_from_path(path: str):
     """Build a list of custom components for the langchain from a given path"""
     file_list = load_files_from_path(path)
@@ -446,3 +466,51 @@ def build_langchain_custom_component_list_from_path(path: str):
     invalid_menu = build_invalid_menu(invalid_components)
 
     return merge_nested_dicts_with_renaming(valid_menu, invalid_menu)
+
+
+def get_all_types_dict(settings_service):
+    native_components = build_langchain_types_dict()
+    # custom_components is a list of dicts
+    # need to merge all the keys into one dict
+    custom_components_from_file: dict[str, Any] = {}
+    if settings_service.settings.COMPONENTS_PATH:
+        logger.info(
+            f"Building custom components from {settings_service.settings.COMPONENTS_PATH}"
+        )
+
+        custom_component_dicts = []
+        processed_paths = []
+        for path in settings_service.settings.COMPONENTS_PATH:
+            if str(path) in processed_paths:
+                continue
+            custom_component_dict = build_langchain_custom_component_list_from_path(
+                str(path)
+            )
+            custom_component_dicts.append(custom_component_dict)
+            processed_paths.append(str(path))
+
+        logger.info(f"Loading {len(custom_component_dicts)} category(ies)")
+        for custom_component_dict in custom_component_dicts:
+            # custom_component_dict is a dict of dicts
+            if not custom_component_dict:
+                continue
+            category = list(custom_component_dict.keys())[0]
+            logger.info(
+                f"Loading {len(custom_component_dict[category])} component(s) from category {category}"
+            )
+            custom_components_from_file = merge_nested_dicts_with_renaming(
+                custom_components_from_file, custom_component_dict
+            )
+
+    return merge_nested_dicts_with_renaming(
+        native_components, custom_components_from_file
+    )
+
+
+def merge_nested_dicts(dict1, dict2):
+    for key, value in dict2.items():
+        if isinstance(value, dict) and isinstance(dict1.get(key), dict):
+            dict1[key] = merge_nested_dicts(dict1[key], value)
+        else:
+            dict1[key] = value
+    return dict1

src/backend/langflow/interface/utilities/base.py

@@ -1,11 +1,11 @@
 from typing import Dict, List, Optional, Type
 
-from langchain import SQLDatabase, utilities
+from langchain import utilities
 
 from langflow.custom.customs import get_custom_nodes
 from langflow.interface.base import LangChainTypeCreator
 from langflow.interface.importing.utils import import_class
-from langflow.services.getters import get_settings_manager
+from langflow.services.getters import get_settings_service
 
 from langflow.template.frontend_node.utilities import UtilitiesFrontendNode
 from loguru import logger
@@ -27,18 +27,18 @@ class UtilityCreator(LangChainTypeCreator):
         from the langchain.chains module and filtering them according to the settings.utilities list.
         """
         if self.type_dict is None:
-            settings_manager = get_settings_manager()
+            settings_service = get_settings_service()
             self.type_dict = {
                 utility_name: import_class(f"langchain.utilities.{utility_name}")
                 for utility_name in utilities.__all__
             }
-            self.type_dict["SQLDatabase"] = SQLDatabase
+            self.type_dict["SQLDatabase"] = utilities.SQLDatabase
             # Filter according to settings.utilities
             self.type_dict = {
                 name: utility
                 for name, utility in self.type_dict.items()
-                if name in settings_manager.settings.UTILITIES
+                if name in settings_service.settings.UTILITIES
-                or settings_manager.settings.DEV
+                or settings_service.settings.DEV
             }
 
         return self.type_dict

src/backend/langflow/interface/utils.py

@@ -10,7 +10,7 @@ from langchain.base_language import BaseLanguageModel
 from PIL.Image import Image
 from loguru import logger
 from langflow.services.chat.config import ChatConfig
-from langflow.services.getters import get_settings_manager
+from langflow.services.getters import get_settings_service
 
 
 def load_file_into_dict(file_path: str) -> dict:
@@ -36,7 +36,7 @@ def pil_to_base64(image: Image) -> str:
     return img_str.decode("utf-8")
 
 
-def try_setting_streaming_options(langchain_object, websocket):
+def try_setting_streaming_options(langchain_object):
     # If the LLM type is OpenAI or ChatOpenAI,
     # set streaming to True
     # First we need to find the LLM
@@ -64,11 +64,11 @@ def extract_input_variables_from_prompt(prompt: str) -> list[str]:
 
 def setup_llm_caching():
     """Setup LLM caching."""
-    settings_manager = get_settings_manager()
+    settings_service = get_settings_service()
     try:
-        set_langchain_cache(settings_manager.settings)
+        set_langchain_cache(settings_service.settings)
     except ImportError:
-        logger.warning(f"Could not import {settings_manager.settings.CACHE}. ")
+        logger.warning(f"Could not import {settings_service.settings.CACHE_TYPE}. ")
     except Exception as exc:
         logger.warning(f"Could not setup LLM caching. Error: {exc}")
 
@@ -80,7 +80,7 @@ def set_langchain_cache(settings):
     if cache_type := os.getenv("LANGFLOW_LANGCHAIN_CACHE"):
         try:
             cache_class = import_class(
-                f"langchain.cache.{cache_type or settings.CACHE}"
+                f"langchain.cache.{cache_type or settings.LANGCHAIN_CACHE}"
             )
 
             logger.debug(f"Setting up LLM caching with {cache_class.__name__}")

src/backend/langflow/interface/vector_store/base.py

@@ -4,7 +4,7 @@ from langchain import vectorstores
 
 from langflow.interface.base import LangChainTypeCreator
 from langflow.interface.importing.utils import import_class
-from langflow.services.getters import get_settings_manager
+from langflow.services.getters import get_settings_service
 
 from langflow.template.frontend_node.vectorstores import VectorStoreFrontendNode
 from loguru import logger
@@ -44,12 +44,12 @@ class VectorstoreCreator(LangChainTypeCreator):
             return None
 
     def to_list(self) -> List[str]:
-        settings_manager = get_settings_manager()
+        settings_service = get_settings_service()
         return [
             vectorstore
             for vectorstore in self.type_to_loader_dict.keys()
-            if vectorstore in settings_manager.settings.VECTORSTORES
+            if vectorstore in settings_service.settings.VECTORSTORES
-            or settings_manager.settings.DEV
+            or settings_service.settings.DEV
         ]
 
 

src/backend/langflow/interface/wrappers/base.py

@@ -1,6 +1,6 @@
 from typing import Dict, List, Optional
 
-from langchain import requests, sql_database
+from langchain.utilities import requests, sql_database
 
 from langflow.interface.base import LangChainTypeCreator
 from loguru import logger

src/backend/langflow/jcloud.yml

@@ -1,2 +0,0 @@
-instance: C4
-autoscale_min: 1

src/backend/langflow/lcserve.py

@@ -1,15 +0,0 @@
-# This file is used by lc-serve to load the mounted app and serve it.
-
-import os
-
-# Use the JCLOUD_WORKSPACE for db URL if it's provided by JCloud.
-if "JCLOUD_WORKSPACE" in os.environ:
-    os.environ[
-        "LANGFLOW_DATABASE_URL"
-    ] = f"sqlite:///{os.environ['JCLOUD_WORKSPACE']}/langflow.db"
-
-from langflow.main import setup_app
-from langflow.utils.logger import configure
-
-configure(log_level="DEBUG")
-app = setup_app()

src/backend/langflow/processing/process.py

@@ -2,10 +2,11 @@ import json
 from pathlib import Path
 from langchain.schema import AgentAction
 from langflow.interface.run import (
-    build_sorted_vertices_with_caching,
+    build_sorted_vertices,
     get_memory_key,
     update_memory_keys,
 )
+from langflow.services.getters import get_session_service
 from loguru import logger
 from langflow.graph import Graph
 from langchain.chains.base import Chain
@@ -13,6 +14,8 @@ from langchain.vectorstores.base import VectorStore
 from typing import Any, Dict, List, Optional, Tuple, Union
 from langchain.schema import Document
 
+from pydantic import BaseModel
+
 
 def fix_memory_inputs(langchain_object):
     """
@@ -65,7 +68,7 @@ def get_result_and_thought(langchain_object: Any, inputs: dict):
             langchain_object.verbose = True
 
         if hasattr(langchain_object, "return_intermediate_steps"):
-            langchain_object.return_intermediate_steps = True
+            langchain_object.return_intermediate_steps = False
 
         fix_memory_inputs(langchain_object)
 
@@ -93,26 +96,19 @@ def get_build_result(data_graph, session_id):
     # otherwise, build the graph and return the result
     if session_id:
         logger.debug(f"Loading LangChain object from session {session_id}")
-        result = build_sorted_vertices_with_caching.get_result_by_session_id(session_id)
+        result = build_sorted_vertices(data_graph=data_graph)
         if result is not None:
             logger.debug("Loaded LangChain object")
             return result
 
     logger.debug("Building langchain object")
-    return build_sorted_vertices_with_caching(data_graph)
+    return build_sorted_vertices(data_graph)
-
-
-def clear_caches_if_needed(clear_cache: bool):
-    if clear_cache:
-        build_sorted_vertices_with_caching.clear_cache()
-        logger.debug("Cleared cache")
 
 
 def load_langchain_object(
     data_graph: Dict[str, Any], session_id: str
 ) -> Tuple[Union[Chain, VectorStore], Dict[str, Any], str]:
     langchain_object, artifacts = get_build_result(data_graph, session_id)
-    session_id = build_sorted_vertices_with_caching.hash
     logger.debug("Loaded LangChain object")
 
     if langchain_object is None:
@@ -140,6 +136,7 @@ def generate_result(langchain_object: Union[Chain, VectorStore], inputs: dict):
             raise ValueError("Inputs must be provided for a Chain")
         logger.debug("Generating result and thought")
         result = get_result_and_thought(langchain_object, inputs)
+
         logger.debug("Generated result and thought")
     elif isinstance(langchain_object, VectorStore):
         result = langchain_object.search(**inputs)
@@ -152,22 +149,34 @@ def generate_result(langchain_object: Union[Chain, VectorStore], inputs: dict):
     return result
 
 
-def process_graph_cached(
+class Result(BaseModel):
+    result: Any
+    session_id: str
+
+
+async def process_graph_cached(
     data_graph: Dict[str, Any],
     inputs: Optional[dict] = None,
     clear_cache=False,
     session_id=None,
-) -> Tuple[Any, str]:
+) -> Result:
-    clear_caches_if_needed(clear_cache)
+    session_service = get_session_service()
-    # If session_id is provided, load the langchain_object from the session
+    if clear_cache:
-    # else build the graph and return the result and the new session_id
+        session_service.clear_session(session_id)
-    langchain_object, artifacts, session_id = load_langchain_object(
+    if session_id is None:
-        data_graph, session_id
+        session_id = session_service.generate_key(
-    )
+            session_id=session_id, data_graph=data_graph
+        )
+    # Load the graph using SessionService
+    graph, artifacts = session_service.load_session(session_id, data_graph)
+    built_object = graph.build()
     processed_inputs = process_inputs(inputs, artifacts)
-    result = generate_result(langchain_object, processed_inputs)
+    result = generate_result(built_object, processed_inputs)
+    # langchain_object is now updated with the new memory
+    # we need to update the cache with the updated langchain_object
+    session_service.update_session(session_id, (graph, artifacts))
 
-    return result, session_id
+    return Result(result=result, session_id=session_id)
 
 
 def load_flow_from_json(

src/backend/langflow/server.py

@@ -4,6 +4,8 @@ from gunicorn.app.base import BaseApplication  # type: ignore
 class LangflowApplication(BaseApplication):
     def __init__(self, app, options=None):
         self.options = options or {}
+
+        self.options["worker_class"] = "uvicorn.workers.UvicornWorker"
         self.application = app
         super().__init__()
 

src/backend/langflow/services/auth/factory.py

@@ -1,12 +1,12 @@
 from langflow.services.factory import ServiceFactory
-from langflow.services.auth.service import AuthManager
+from langflow.services.auth.service import AuthService
 
 
-class AuthManagerFactory(ServiceFactory):
+class AuthServiceFactory(ServiceFactory):
-    name = "auth_manager"
+    name = "auth_service"
 
     def __init__(self):
-        super().__init__(AuthManager)
+        super().__init__(AuthService)
 
-    def create(self, settings_manager):
+    def create(self, settings_service):
-        return AuthManager(settings_manager)
+        return AuthService(settings_service)

src/backend/langflow/services/auth/service.py

@@ -2,11 +2,11 @@ from langflow.services.base import Service
 from typing import TYPE_CHECKING
 
 if TYPE_CHECKING:
-    from langflow.services.settings.manager import SettingsManager
+    from langflow.services.settings.manager import SettingsService
 
 
-class AuthManager(Service):
+class AuthService(Service):
-    name = "auth_manager"
+    name = "auth_service"
 
-    def __init__(self, settings_manager: "SettingsManager"):
+    def __init__(self, settings_service: "SettingsService"):
-        self.settings_manager = settings_manager
+        self.settings_service = settings_service