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Open source UI visual tool to build LLM apps.

Artificial Intelligence (AI)

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MCP

Give your agent FlowiseAI tools

Every FlowiseAI action is exposed as an MCP tool on Pipedream's remote server. Point a client at it with your end user's ID and Connect resolves that user's FlowiseAI account for each tool call — you store no tokens.

Pipedream Connect

// accessToken: mint a short-lived token with the Connect SDK — see the MCP guide
const transport = new StreamableHTTPClientTransport(
  new URL("https://remote.mcp.pipedream.net/v3"),
  {
    requestInit: {
      headers: {
        Authorization: `Bearer ${accessToken}`,
        "x-pd-project-id": "{project_id}",
        "x-pd-environment": "production",
        "x-pd-external-user-id": "{external_user_id}", // any stable ID for this user in your system
        "x-pd-app-slug": "flowiseai",
      },
    },
  },
)

const mcp = new Client({ name: "my-agent", version: "1.0.0" })
await mcp.connect(transport)

const { tools } = await mcp.listTools()

// e.g. run Make Prediction:
const result = await mcp.callTool({
  name: "flowiseai-make-prediction",
  arguments: {
    flowId: "Flow ID",
    question: "Question",
  },
})

# access_token: mint a short-lived token with the Connect SDK — see the MCP guide
headers = {
    "Authorization": f"Bearer {access_token}",
    "x-pd-project-id": "{project_id}",
    "x-pd-environment": "production",
    "x-pd-external-user-id": "{external_user_id}",  # any stable ID for this user in your system
    "x-pd-app-slug": "flowiseai",
}

async with streamablehttp_client("https://remote.mcp.pipedream.net/v3", headers=headers) as (read, write, _):
    async with ClientSession(read, write) as session:
        await session.initialize()
        tools = await session.list_tools()

        # e.g. run Make Prediction:
        result = await session.call_tool("flowiseai-make-prediction", {
            "flowId": "Flow ID",
            "question": "Question",
        })

MCP guide →

SDK

Run FlowiseAI actions from your backend

Connect a user's FlowiseAI account once, then run Make Prediction on their behalf from your own code — TypeScript, Python, or plain HTTP.

Pipedream Connect

import { PipedreamClient } from "@pipedream/sdk"

const pd = new PipedreamClient({
  projectId: process.env.PIPEDREAM_PROJECT_ID!,
  clientId: process.env.PIPEDREAM_CLIENT_ID!,
  clientSecret: process.env.PIPEDREAM_CLIENT_SECRET!,
  projectEnvironment: "production",
})

const result = await pd.actions.run({
  id: "flowiseai-make-prediction",
  externalUserId: "{external_user_id}", // any stable ID for this user in your system
  configuredProps: {
    flowiseai: { authProvisionId: "apn_xxxxxxx" },
    flowId: "Flow ID",
    question: "Question",
  },
})

from pipedream import Pipedream

pd = Pipedream(
    client_id="{oauth_client_id}",
    client_secret="{oauth_client_secret}",
    project_id="{project_id}",
    project_environment="production",
)

result = pd.actions.run(
    id="flowiseai-make-prediction",
    external_user_id="{external_user_id}",  # any stable ID for this user in your system
    configured_props={
        "flowiseai": {"authProvisionId": "apn_xxxxxxx"},
        "flowId": "Flow ID",
        "question": "Question",
    },
)

curl -X POST https://api.pipedream.com/v1/connect/{project_id}/actions/run \
  -H "Content-Type: application/json" \
  -H "X-PD-Environment: production" \
  -H "Authorization: Bearer {access_token}" \
  -d '{
    "external_user_id": "{external_user_id}",
    "id": "flowiseai-make-prediction",
    "configured_props": {
      "flowiseai": { "authProvisionId": "apn_xxxxxxx" },
      "flowId": "Flow ID",
      "question": "Question"
    }
  }'