CONNECT APP
Build with Ollama
Artificial Intelligence (AI)
- API key
MCP
Give your agent Ollama tools
Every Ollama 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 Ollama account for each tool call — you store no tokens.
// 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": "ollama",
},
},
},
)
const mcp = new Client({ name: "my-agent", version: "1.0.0" })
await mcp.connect(transport)
const { tools } = await mcp.listTools()
// e.g. run Copy Model:
const result = await mcp.callTool({
name: "ollama-copy-model",
arguments: {
source: "Model Name",
destination: "New Model Name",
},
})# 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": "ollama",
}
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 Copy Model:
result = await session.call_tool("ollama-copy-model", {
"source": "Model Name",
"destination": "New Model Name",
})API PROXY
Call the Ollama API directly
For an endpoint with no pre-built tool, the Connect proxy forwards your request to the Ollama API with the connected user's credentials attached. You store no tokens and write no refresh logic.
const resp = await pd.proxy.get({
externalUserId: "{external_user_id}", // any stable ID for this user in your system
accountId: "apn_xxxxxxx",
url: "https://api.example.com/v1/me",
})
// Any allowed Ollama endpoint works here. Pipedream attaches the
// connected account's credentials to the outgoing request.# The path segment is the target URL, URL-safe base64 encoded:
# https://api.example.com/v1/me
curl "https://api.pipedream.com/v1/connect/{project_id}/proxy/aHR0cHM6Ly9hcGkuZXhhbXBsZS5jb20vdjEvbWU?external_user_id={external_user_id}&account_id=apn_xxxxxxx" \
-H "Authorization: Bearer {access_token}" \
-H "x-pd-environment: production"SDK
Run Ollama actions from your backend
Connect a user's Ollama account once, then run Copy Model on their behalf from your own code — TypeScript, Python, or plain HTTP.
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: "ollama-copy-model",
externalUserId: "{external_user_id}", // any stable ID for this user in your system
configuredProps: {
ollama: { authProvisionId: "apn_xxxxxxx" },
source: "Model Name",
destination: "New Model Name",
},
})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="ollama-copy-model",
external_user_id="{external_user_id}", # any stable ID for this user in your system
configured_props={
"ollama": {"authProvisionId": "apn_xxxxxxx"},
"source": "Model Name",
"destination": "New Model Name",
},
)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": "ollama-copy-model",
"configured_props": {
"ollama": { "authProvisionId": "apn_xxxxxxx" },
"source": "Model Name",
"destination": "New Model Name"
}
}'TOOLS
Ollama actions
On-demand operations your product or agent can configure and run on behalf of a connected user.
-
Copy Model
actionCopies a model, creating a model with another name from an existing model. See the documentation.Writev0.0.2 -
Create Model
actionCreate a model from a modelfile. See the documentation.Writev0.0.2 -
Delete Model
actionDelete a model and its data. See the documentationWritev0.0.2 -
Generate Chat Completion
actionGenerates the next message in a chat with a provided model. See the documentation.Writev0.0.3 -
Generate Completion
actionGenerates a response for a given prompt with a provided model. See the documentation.Writev0.0.2 -
Generate Embeddings
actionGenerate embeddings from a model. See the documentation.Writev0.0.2 -
List Local Models
actionList models that are available locally. See the documentation.Read-onlyv0.0.2 -
Pull Model
actionDownload a model from the ollama library. Cancelled pulls are resumed from where they left off, and multiple calls will share the same download progress. See the documentation.Writev0.0.2 -
Push Model to Library
actionUpload a model to a model library. Requires registering for ollama.ai and adding a public key first. See the documentation.Writev0.0.3 -
Show Model Information
actionShow information about a model including details, modelfile, template, parameters, license, and system prompt. See the documentation.Writev0.0.2
No Ollama triggers are available yet.
MULTI-APP
Use Ollama with other popular apps
Most products don't stop at one integration. Pair Ollama with the other apps your users rely on, and ship use cases that span both.
- App slug
- ollama
- Authentication
- API key
- Categories
- Artificial Intelligence (AI)
- Actions
- 10
- Triggers
- 0
- API proxy
- Available