# Enrich Layer

```json
{
  "name": "Enrich Layer",
  "slug": "enrich_layer",
  "url": "https://composio.dev/toolkits/enrich_layer",
  "markdown_url": "https://composio.dev/toolkits/enrich_layer.md",
  "logo_url": "https://logos.composio.dev/api/enrich_layer",
  "categories": [
    "data & analytics"
  ],
  "is_composio_managed": false,
  "updated_at": "2026-10-05T05:32:22.902Z"
}
```

![Enrich Layer logo](https://logos.composio.dev/api/enrich_layer)

## Description

Securely connect your AI agents and chatbots (Claude, ChatGPT, Cursor, etc) with Enrich Layer MCP or direct API to enrich people, look up companies, verify schools, and find professional-network data through natural language.

## Summary

Enrich Layer is a data enrichment API for person, company, school, and professional-network lookups.
Use it to turn sparse lead, account, or candidate data into rich, useful profiles.

## Categories

- data & analytics

## Toolkit Details

- Tools: 9

## Images

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

## Authentication

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

## Suggested Prompts

- Enrich a prospect from LinkedIn profile
- Find company details from domain
- Lookup school information for candidate

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `ENRICH_LAYER_CHECK_EMAIL_TYPE` | Check Email Type | Check whether an email address is disposable and whether it belongs to a free email provider. A live request consumed 1 Enrich Layer credit despite the provider documentation describing this endpoint as free. |
| `ENRICH_LAYER_ENRICH_COMPANY` | Enrich Company | Return a structured company profile from its LinkedIn company URL. Uses cache when present and costs 1 credit on a successful base request. |
| `ENRICH_LAYER_ENRICH_PERSON` | Enrich Person | Return a structured person profile from exactly one LinkedIn, Twitter/X, or Facebook profile URL. Uses cache when present and costs 1 credit on a successful base request. |
| `ENRICH_LAYER_FIND_PERSON_BY_EMAIL` | Find Person By Email | Resolve social profile URLs for a person from a personal or work email address. Costs 3 credits on a match; the default superficial lookup avoids charging when no result is found. |
| `ENRICH_LAYER_FIND_PERSON_BY_ROLE` | Find Person By Role | Return the single person profile URL that most closely matches a role at a named company. Costs 3 credits on any successful 200 response, including no match. |
| `ENRICH_LAYER_FIND_SOCIAL_PROFILES_BY_PHONE` | Find Social Profiles By Phone | Find LinkedIn, Facebook, and Twitter/X profile URLs associated with one E.164 phone number. Costs 3 credits when profiles are found; grouped live balance evidence is consistent with no charge for a 200 no-result response. |
| `ENRICH_LAYER_GET_CREDIT_BALANCE` | Get Credit Balance | Return the connected Enrich Layer account's current credit balance without consuming credits. Use before or after paid enrichment calls to manage cost. |
| `ENRICH_LAYER_RESOLVE_COMPANY` | Resolve Company | Resolve a LinkedIn company profile URL from a company domain, company name, or both. Costs 2 credits on any successful 200 response, including a null result. |
| `ENRICH_LAYER_RESOLVE_PERSON` | Resolve Person | Return the closest person profile URL from a first name and company name or domain, or null when no match is found. Costs 2 credits on a match; the cost-safe default does not charge for null results. |

## 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 Enrich Layer
```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=["enrich_layer"])
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: ["enrich_layer"] });
const tools = await session.tools();
```

#### Path 1, Step 3: Run Enrich Layer tools with your agent

Send a request, execute the Enrich Layer 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": "Enrich this LinkedIn profile URL with person and company details"}],
)

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: "Enrich this LinkedIn profile URL with person and company details" }],
});

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 Enrich Layer and read its MCP URL and headers
```python
from composio import Composio

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

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

const composio = new Composio();
const { mcp } = await composio.create("your-user-id", {
  toolkits: ["enrich_layer"],
  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 Enrich Layer 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 Enrich Layer tools.",
    tools=[],
    allowed_tools=["mcp__composio__*"],
)

async def main():
    async with ClaudeSDKClient(options=options) as client:
        await client.query("Enrich this LinkedIn profile URL with person and company details")
        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: "Enrich this LinkedIn profile URL with person and company details",
  stopWhen: stepCountIs(10),
});

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

## Why Use Composio?

### 1. AI Native Enrich Layer Integration

- Supports both Enrich Layer MCP and direct API based integrations
- Structured, LLM-friendly schemas for reliable person, company, school, and professional-network lookups
- Rich coverage for enrichment workflows like lead research, candidate screening, account intelligence, and profile lookup

### 2. Managed Auth

- Secure API key handling without hard-coding Enrich Layer credentials in your agent code
- Central place to manage, scope, and revoke Enrich Layer access
- Per user and per environment credentials, so development and production stay cleanly separated

### 3. Agent Optimized Design

- Tools are shaped for AI agents, so prompts like “enrich this LinkedIn profile” map cleanly to Enrich Layer actions
- Tools are tuned using real error and success rates to improve reliability over time
- Comprehensive execution logs so you always know what enrichment ran, when, and on whose behalf

### 4. Enterprise Grade Security

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

## Use Enrich Layer with any AI Agent Framework

Choose a framework you want to connect Enrich Layer with:

None listed.

## Related Toolkits

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- [Api sports](https://composio.dev/toolkits/api_sports) - Api sports is a comprehensive sports data platform covering 2,000+ competitions with live scores and 15+ years of stats. Instantly access up-to-date sports information for analysis, apps, or chatbots.
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- [Beaconchain](https://composio.dev/toolkits/beaconchain) - Beaconchain is a real-time analytics platform for Ethereum 2.0's Beacon Chain. It provides detailed insights into validators, blocks, and overall network performance.
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- [Bigpicture io](https://composio.dev/toolkits/bigpicture_io) - BigPicture.io offers APIs for accessing detailed company and profile data. Instantly enrich your applications with up-to-date insights on 20M+ businesses.

## Frequently Asked Questions

### Do I need my own developer credentials to use Enrich Layer with Composio?

Yes, Enrich Layer 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)