# Pipeless Recommendations

```json
{
  "name": "Pipeless Recommendations",
  "slug": "pipeless",
  "url": "https://composio.dev/toolkits/pipeless",
  "markdown_url": "https://composio.dev/toolkits/pipeless.md",
  "logo_url": "https://logos.composio.dev/api/pipeless",
  "categories": [
    "ai & machine learning",
    "analytics & data"
  ],
  "is_composio_managed": false,
  "updated_at": "2026-10-04T15:39:01.810Z"
}
```

![Pipeless Recommendations logo](https://logos.composio.dev/api/pipeless)

## Description

Securely connect your AI agents and chatbots (Claude, ChatGPT, Cursor, etc) with Pipeless Recommendations MCP or direct API to query activity feeds, generate personalized recommendations, update user preferences, and export recommendation analytics through natural language.

## Summary

Pipeless Recommendations is a graph-based activity feed and personalization service.
Use it to generate real-time, personalized recommendations from your application events.

## Categories

- ai & machine learning
- analytics & data

## Toolkit Details

- Tools: 10

## Images

- Logo: https://logos.composio.dev/api/pipeless

## Authentication

- **Api Key**
  - Type: `api_key`
  - Description: Api Key authentication for Pipeless Recommendations.
  - Setup:
    - Configure Api Key credentials for Pipeless Recommendations.
    - Use the credentials when creating an auth config in Composio.

## Suggested Prompts

- List top personalized recommendations for user
- Recommend next actions based on activity graph
- Export feed of top trending items

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `PIPELESS_CREATE_EVENT` | Create Event | Record one typed relationship event for Pipeless activity and recommendation algorithms. Referenced object records may be created or retained and are not removed when the event is later deleted. |
| `PIPELESS_CREATE_EVENTS_BATCH` | Create Events Batch | Record 1 to 10 relationship events in one Pipeless request. Referenced object records may be created or retained and are not removed when the events are later deleted. |
| `PIPELESS_DELETE_EVENT` | Delete Event | Delete event records matching an exact start object, relationship type and timestamp, and end object. Wildcard deletion is not supported by this tool. |
| `PIPELESS_LIST_ACTIVITY_FEED` | List Activity Feed | Return one cursor-controlled chronological page of actions by objects followed by a specified object. |
| `PIPELESS_LIST_OBJECT_ACTIVITY` | List Object Activity | Return one cursor-controlled page of incoming or outgoing relationship activity involving a specific object. |
| `PIPELESS_LIST_RECENT_EVENTS` | List Recent Events | Return the most recent events sent to the app, up to the requested limit; Pipeless provides no continuation cursor for this read. |
| `PIPELESS_LIST_RECOMMENDED_CONTENT` | List Recommended Content | Return a bounded personalized list of content recommendations for an object using configurable positive, negative, tag, author, follow, and dismissal signals. |
| `PIPELESS_LIST_RELATED_CONTENT` | List Related Content | Return content objects related to a target content object using optional tag and collaborative-interaction signals. |
| `PIPELESS_LIST_USERS_TO_FOLLOW` | List Users To Follow | Return a bounded personalized list of user or account objects that the specified object may want to follow. |
| `PIPELESS_RANK_CONTENT` | Rank Content | Rank a supplied set of content IDs for an object and report which IDs Pipeless could rank. |

## Supported Triggers

None listed.

## Installation and MCP Setup

### Path 1: SDK Installation

#### Path 1, Step 1: Install Composio

Install the Composio SDK, the OpenAI provider, and the OpenAI SDK
```python
pip install composio composio_openai openai
```

```typescript
npm install @composio/core @composio/openai openai
```

#### Path 1, Step 2: Create a Composio session

Initialize Composio with the OpenAI Responses provider and create a session scoped to Pipeless Recommendations
```python
import json
from openai import OpenAI
from composio import Composio
from composio_openai import OpenAIResponsesProvider

composio = Composio(provider=OpenAIResponsesProvider())
client = OpenAI()

session = composio.create(user_id="your-user-id", toolkits=["pipeless"])
tools = session.tools()
```

```typescript
import OpenAI from "openai";
import { Composio } from "@composio/core";
import { OpenAIResponsesProvider } from "@composio/openai";

const composio = new Composio({ provider: new OpenAIResponsesProvider() });
const client = new OpenAI();

const session = await composio.create("your-user-id", { toolkits: ["pipeless"] });
const tools = await session.tools();
```

#### Path 1, Step 3: Run Pipeless Recommendations tools with your agent

Send a request, execute the Pipeless Recommendations tool calls through the session, and print the final answer
```python
response = client.responses.create(
    model="gpt-5.6-sol",
    tools=tools,
    input=[{"role": "user", "content": "List top personalized recommendations for user"}],
)

while True:
    tool_calls = [o for o in response.output if o.type == "function_call"]
    if not tool_calls:
        break
    results = composio.provider.handle_tool_calls(response=response, session=session)
    response = client.responses.create(
        model="gpt-5.6-sol",
        tools=tools,
        previous_response_id=response.id,
        input=[
            {"type": "function_call_output", "call_id": call.call_id, "output": json.dumps(results[i])}
            for i, call in enumerate(tool_calls)
        ],
    )

print(response.output_text)
```

```typescript
let response = await client.responses.create({
  model: "gpt-5.6-sol",
  tools,
  input: [{ role: "user", content: "List top personalized recommendations for user" }],
});

while (response.output.some((o) => o.type === "function_call")) {
  const outputs = await composio.provider.handleToolCalls(session, response.output);
  response = await client.responses.create({
    model: "gpt-5.6-sol",
    tools,
    previous_response_id: response.id,
    input: outputs,
  });
}

console.log(response.output_text);
```

### Path 2: MCP Server Setup

#### Path 2, Step 1: Install Composio

Install the Composio SDK and your agent framework
```python
pip install composio claude-agent-sdk
```

```typescript
npm install @composio/core ai @ai-sdk/mcp @ai-sdk/openai
```

#### Path 2, Step 2: Create a session with MCP enabled

Create a session scoped to Pipeless Recommendations and read its MCP URL and headers
```python
from composio import Composio

composio = Composio()
session = composio.create(user_id="your-user-id", toolkits=["pipeless"], mcp=True)
```

```typescript
import { Composio } from "@composio/core";

const composio = new Composio();
const { mcp } = await composio.create("your-user-id", {
  toolkits: ["pipeless"],
  mcp: true,
});
```

#### Path 2, Step 3: Connect your agent to the MCP server

Pass the session's MCP URL and headers to your agent and run a Pipeless Recommendations request
```python
import asyncio
from claude_agent_sdk import ClaudeSDKClient, ClaudeAgentOptions, AssistantMessage, TextBlock

options = ClaudeAgentOptions(
    mcp_servers={
        "composio": {
            "type": "http",
            "url": session.mcp.url,
            "headers": session.mcp.headers,
        }
    },
    system_prompt="You are a helpful assistant with access to Pipeless Recommendations tools.",
    tools=[],
    allowed_tools=["mcp__composio__*"],
)

async def main():
    async with ClaudeSDKClient(options=options) as client:
        await client.query("List top personalized recommendations for user")
        async for message in client.receive_response():
            if isinstance(message, AssistantMessage):
                for block in message.content:
                    if isinstance(block, TextBlock):
                        print(block.text)

asyncio.run(main())
```

```typescript
import { createMCPClient } from "@ai-sdk/mcp";
import { openai } from "@ai-sdk/openai";
import { generateText, stepCountIs } from "ai";

const client = await createMCPClient({
  transport: { type: "http", url: mcp.url, headers: mcp.headers },
});

const { text } = await generateText({
  model: openai("gpt-5.6-sol"),
  tools: await client.tools(),
  prompt: "List top personalized recommendations for user",
  stopWhen: stepCountIs(10),
});

console.log(text);
await client.close();
```

## Why Use Composio?

### 1. AI Native Pipeless Recommendations Integration

- Supports both Pipeless Recommendations MCP and direct API based integrations
- Structured, LLM-friendly schemas for reliable tool execution
- Rich coverage for reading, writing, and querying your Pipeless Recommendations data

### 2. Managed Auth

- Built-in OAuth handling with automatic token refresh and rotation
- Central place to manage, scope, and revoke Pipeless Recommendations access
- Per user and per environment credentials instead of hard-coded keys

### 3. Agent Optimized Design

- Tools are tuned using real error and success rates to improve reliability over time
- Comprehensive execution logs so you always know what ran, when, and on whose behalf

### 4. Enterprise Grade Security

- Fine-grained RBAC so you control which agents and users can access Pipeless Recommendations
- Scoped, least privilege access to Pipeless Recommendations resources
- Full audit trail of agent actions to support review and compliance

## Use Pipeless Recommendations with any AI Agent Framework

Choose a framework you want to connect Pipeless Recommendations with:

- [ChatGPT](https://composio.dev/toolkits/pipeless/framework/chatgpt)
- [Claude Cowork](https://composio.dev/toolkits/pipeless/framework/claude-cowork)
- [Hermes](https://composio.dev/toolkits/pipeless/framework/hermes-agent)
- [Atomic Agent](https://composio.dev/toolkits/pipeless/framework/atomic-agent)

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## Frequently Asked Questions

### Do I need my own developer credentials to use Pipeless Recommendations with Composio?

Yes, Pipeless Recommendations requires you to configure your own API key credentials. Once set up, Composio handles secure credential storage and API request handling for you.

### Can I use multiple toolkits together?

Yes! Composio's Tool Router enables agents to use multiple toolkits. [Learn more](https://docs.composio.dev/tool-router/overview).

### Is Composio secure?

Composio is SOC 2 and ISO 27001 compliant with all data encrypted in transit and at rest. [Learn more](https://trust.composio.dev).

### What if the API changes?

Composio maintains and updates all toolkit integrations automatically, so your agents always work with the latest API versions.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)