How to Add File Storage to Flowise Chatflows

Flowise is an open-source platform for building LLM apps without code. It lets you build chatflows that handle documents, manage knowledge bases, and save agent outputs. However, the default setup uses a local database and the local filesystem for uploads. This causes three main issues for real-world apps:

Data Loss in Containers: If you run Flowise on Docker, Railway, or Render, the local disk is temporary. When the container restarts, your uploads and files vanish. 2.

Scaling Issues: Storing big PDFs or datasets on the same server as your app makes the instance heavy and hard to scale. 3.

Handoff Friction: If an agent generates a report, there is no easy way for a user to find it later. You usually end up with broken download links and no history for the user. Using external storage fixes this by keeping your data separate from the code running your agents.

Helpful references: Fastio Workspaces, Fastio Collaboration, and Fastio AI.

Flowise has basic support for external storage through environment variables. You can change where files are saved by updating your config.

Local Storage (Default)

By default, STORAGE_TYPE is set to local. Files go to .flowise/storage on the machine running the app. This is fine for testing on your laptop but risky for anything else.

AWS S3 Integration

You can tell

Flowise to use AWS S3 by setting STORAGE_TYPE=s3 and adding your AWS keys (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION, AWS_BUCKET_NAME).

The Good:

• Files stay safe even if the container dies. * Uses a standard industry tool.

The Bad:

• Setting up IAM permissions is a chore. * There is no interface for users to see their files. * You have to write extra code to create shareable links for users.

If your agents need to give files to people (or if people need to send files to agents), Fastio is a better way to handle it. It goes beyond simple storage by providing a branded portal for your users, global speed, and a dedicated MCP server for your agents.

Why use Fastio with Flowise? * Built-in Delivery Portals: When an agent saves a file, it shows up in a secure, branded portal. You don't have to build a frontend for file downloads. * Simple MCP Connection: Point Flowise at the Fastio MCP server (https://mcp.fast.io/mcp, or https://mcp.fast.io/mcp/key with a Bearer header) to give the agent 19 named tools, including upload, storage, find, and ai. * Workspace Intelligence: Ripley, Fastio's built-in RAG agent, can answer questions about files the chatflow stored. From Flowise, call the MCP ai tool with action ask. Industry trends suggest enterprise AI workflows increasingly require persistent file handoffs between agents and humans.

Give Your AI Agents Persistent Storage

Stop losing agent data when your system restarts. Persistent workspace storage with a branded delivery portal for your AI workflows.

Explore Agent Storage

You can connect Flowise to Fastio in two ways: with the HTTP Request Tool (simple) or the MCP Tool (for advanced agents).

Method 1: Using the HTTP Request Node

Use this for simple file uploads.

Get your API Key: Create a Fastio account, open a workspace, and generate an API key in Settings > Devices & Agents > API Keys, or with POST /current/user/auth/key/. Workspace IDs are 19-digit numeric strings. 2.

Add HTTP Request Node: Drag an HTTP Request node into your Flowise chatflow. 3.

Configure the Endpoint: * URL: https://api.fast.io/current/upload/ * Method: POST * Headers: Authorization: Bearer {api_key} * Body: multipart/form-data with name (filename), size (bytes), chunk (the file bytes from the previous node), action=create, instance_id (the workspace ID), and folder_id=root (or an existing folder node ID).

Keep the trailing slash. A successful small upload returns HTTP 201: {"result":true,"id":"<upload_id>","new_file_id":"<node_id>"}. If Flowise already has a public URL for the file, use POST https://api.fast.io/current/web_upload/ instead, with form fields source_url, file_name, profile_id, profile_type (workspace or share), and folder_id.

Fastio MCP Server Method

If your agent needs to search, read, and organize files, use the Model Context Protocol. Official guidance is that agent integrations should use the MCP server.

Point Flowise at MCP: In Flowise, use the MCP Tool node and set the server URL to https://mcp.fast.io/mcp. If you pass a Bearer header, use https://mcp.fast.io/mcp/key. 2.

Choose Your Tools: Enable named tools such as upload, storage, find, and ai. The storage tool (list, search, move, copy, delete, details) requires profile_type set to workspace or share. The ai tool with action ask returns a cited answer from workspace files. 3.

Optional HTTP fallback: An HTTP Request node can POST this JSON-RPC body to https://mcp.fast.io/mcp/key with Authorization: Bearer {api_key}:

{"jsonrpc":"2.0","id":1,"method":"tools/call",
 "params":{"name":"upload","arguments":{"action":"web-import","url":"https://example.com/report.pdf",
 "profile_type":"workspace","profile_id":"1234567890123456789"}}}

Now the agent can save its work, check for existing files, or look up information from a previous chat.

A popular use for Flowise is building an automated document pipeline. Here is how you can set one up with reliable storage:

Upload: A user drops a PDF into a Fastio Receive share using a branded share link. 2.

Trigger: Flowise long-polls GET /current/activity/poll/{entityId}?wait=95&lastactivity={timestamp}, or queries GET /current/events/search/, then starts the chatflow when a new file appears. 3.

Process: * The agent reads the PDF with GET /current/workspace/{workspace_id}/storage/{node_id}/read/, or checks metadata with the MCP storage tool (details). * The LLM (like GPT-4o or Claude) pulls out the data you need, like invoice dates or totals. 4.

Save: * The agent writes the results with POST /current/upload/ (or MCP upload). * The agent moves the original PDF to an archive folder with POST /current/workspace/{workspace_id}/storage/{node_id}/move/ (or MCP storage action move). This way, nothing gets lost in the chat window. Everything is stored, logged, and easy to find later.

When agents handle files, security is the top priority. Standard local storage doesn't give you much control over who sees what.

How Fastio secures agent data:

• Full Audit Logs: You can see every time an agent reads or writes a file.
• File Locking: This prevents two agents from trying to edit the same file at the same time, which stops data from getting corrupted.
• Isolation: Your files live in the cloud, separate from your Flowise server. This keeps your data safe even if your agent environment has a security issue. For teams in serious industries, keeping your compute (Flowise) and your storage (Fastio) separate is a key security practice.