### Run LangGraph Agent Example

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/mcp-docs/implement-with-langgraph.mdx

This function demonstrates how to initiate the LangGraph agent execution. It defines a configuration dictionary containing placeholder API keys and then calls the `run_explorium_agent` function to start the agent process. The `if __name__ == "__main__"` block ensures this example runs when the script is executed directly.

```python
async def run_langgraph():
    """Example usage of the Explorium LangGraph"""
    
    # Configuration with API keys
    config = {
        "configurable": {
            "explorium_api_key": "your-explorium-api-key",
            "anthropic_api_key": "your-anthropic-api-key"
        }
    }
    
    await run_explorium_agent(config)

# Run the agent
if __name__ == "__main__":
    asyncio.run(run_langgraph())
```

--------------------------------

### Quick Start: Initialize MCP Client and Load Tools

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/mcp-docs/implement-with-langgraph.mdx

A minimal example to initialize the MultiServerMCPClient with Explorium's URL and API key, create a session, and load available MCP tools. Ensure you replace 'your-explorium-api-key' with your actual key.

```python
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_mcp_adapters.tools import load_mcp_tools

async def quick_start():
    # Initialize MCP client
    client = MultiServerMCPClient({
        "explorium": {
            "transport": "streamable_http",
            "url": "https://mcp.explorium.ai/mcp",
            "headers": {
                "api_key": "your-explorium-api-key"
            }
        }
    })
    
    # Create session and load tools
    async with client.session("explorium") as session:
        tools = await load_mcp_tools(session)
        print(f"Loaded {len(tools)} Explorium tools")
        for tool in tools[:5]:
            print(f"  - {tool.name}")

# Run the example
asyncio.run(quick_start())
```

--------------------------------

### Basic Chatbot Query Example

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/integrations/n8n/explorium-prospects-search-chatbot.mdx

Start a conversation with the chatbot by providing a specific search query. This example demonstrates a simple request for VPs of Sales.

```text
"Find me VPs of Sales at software companies in the US"
```

--------------------------------

### Example Request (cURL)

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/reference/prospects/events/get_prospects_enrollments.mdx

Use this cURL command to make a GET request to the API to retrieve prospect enrollments. Ensure you replace placeholders with your actual API key and partner ID.

```shell
curl -X GET \
  "https://api.explorium.ai/v1/prospects/events/enrollments" \
  -H "API_KEY: your_api_key_here" \
  -H "parnter_id: your_partner_id"
```

--------------------------------

### New Product Launch Event Query

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/reference/businesses/events/types/new-product-launch.mdx

This example demonstrates how to query the New Product Launch event using the Explorium API. It specifies the event type, business IDs, and a start timestamp. Note that this event is only available for recent announcements within the last quarter.

```APIDOC
## POST /v1/businesses/events

### Description
Queries for businesses that have announced a new product launch. This event is only available for recent announcements within the last quarter.

### Method
POST

### Endpoint
/v1/businesses/events

### Request Body
- **event_types** (array[string]) - Required - List of event types to query. For this event, use `"new_product"`.
- **business_ids** (array[string]) - Required - List of business IDs to filter the events by.
- **timestamp_from** (datetime) - Required - The start of the time range for the events. For new product launches, this should be within the last quarter.

### Request Example
```json
{
  "event_types": [
    "new_product"
  ],
  "business_ids": [
    "8adce3ca1cef0c986b22310e369a0793"
  ],
  "timestamp_from": "2025-01-01T10:03:03.050Z"
}
```

### Response
#### Success Response (200)
- **product_name** (string) - Name of the company's newly announced product.
- **product_description** (string) - Details about the product.
- **event_time** (datetime) - The timestamp indicating when the event actually occurred.
- **snippet** (string) - Snippet of the published article or report on the company's new product.
- **title** (string) - Title of the published news report or article on the company's new product.
- **link** (url) - Link to the article announcing the company's new product.
- **event_name** (string) - Name of the event related to the company's new product launch.
```

--------------------------------

### Body Params - Try Me Example

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/reference/businesses/match_businesses.mdx

Provides example body parameters for matching businesses. Use this to test the business matching API.

```yaml
name: Apple
domain: apple.com

name: Microsoft
domain: microsoft.com
```

--------------------------------

### Example Response

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/reference/prospects/events/get_prospects_enrollments.mdx

This is an example of the JSON response you will receive when successfully retrieving prospect enrollments. It includes response context and a list of active enrollment configurations.

```json
{
  "response_context": {
    "correlation_id": "1234",
    "request_status": "success",
    "time_took_in_seconds": 0.515
  },
  "enrollments": [
    {
      "enrollment_id": "en_8d52c3f1",
      "enrollment_key": "tech_executives_monitor",
      "prospect_ids": ["20ae6cbf564ee683e66685e429844a5ff8ffc30f", "4c485f009d59e319dc039cdf3e935b85014e6a33"],
      "event_types": ["prospect_changed_role", "prospect_changed_company"]
    },
    {
      "enrollment_id": "en_7d31a9c2",
      "enrollment_key": "sales_prospects",
      "prospect_ids": ["fd4c46716295a2e4731417eee802a883280e4d57", "a7bbe0674c63338e62ae4c10751ae19da5723e5a"],
      "event_types": ["prospect_job_start_anniversary", "prospect_changed_company"]
    }
  ]
}
```

--------------------------------

### Basic Example: Find Companies

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/mcp-docs/implement-with-openai.mdx

A complete example demonstrating how to find companies using specific criteria with AgentSource MCP and OpenAI. Replace API keys with your actual keys.

```python
from openai import OpenAI

# Initialize client
client = OpenAI(api_key="your-openai-api-key")

# Make a request to find banks
response = client.responses.create(
    model="gpt-4.1",
    tools=[{
        "type": "mcp",
        "require_approval": "never",
        "server_label": "explorium",
        "server_url": "https://mcp.explorium.ai/mcp",
        "headers": {
            "api_key": "YOUR_AGENTSOURCE_API_KEY"
        }
    }],
    input="Find 10 banks in the US with less than 5000 employees that use Azure"
)

# The response will contain the results
print(response)
```

--------------------------------

### Install LangGraph, Langchain Anthropic, and MCP Adapters

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/mcp-docs/implement-with-langgraph.mdx

Install the necessary Python packages for LangGraph, Anthropic LLM integration, and MCP adapters. For Databricks, use %pip install in separate cells and restart the kernel.

```bash
pip install langgraph langchain_anthropic langchain_mcp_adapters
```

```python
%pip install langgraph
%pip install langchain_anthropic
%pip install langchain_mcp_adapters
```

```python
dbutils.library.restartPython()
```

--------------------------------

### Example cURL Request

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/reference/businesses/events/get_businesses_enrollments.mdx

Use this cURL command to make a GET request to the API endpoint. Include your API key and partner ID in the headers for authentication and identification.

```shell
curl -X GET \
  "https://api.explorium.ai/v1/businesses/events/enrollments" \
  -H "API_KEY: your_api_key_here" \
  -H "partner_id: your_partner_id"
```

--------------------------------

### Business ID Example

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/reference/businesses/enrichments/business_challenges.mdx

This is an example of a business ID that can be used as a parameter.

```text
business_id: 8adce3ca1cef0c986b22310e369a0793
```

--------------------------------

### Prospect Fetch Response Example (JSON)

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/reference/quick-starts/use_case_prospecting.mdx

This is an example of the JSON response structure when fetching prospects. It includes pagination and a list of prospects with basic details like name and company.

```json
{
  "total_results": 56,
  "data": [
    {
      "prospect_id": "xyz123456789abc",
      "full_name": "Jane Marketing",
      "job_title": "Marketing Manager",
      "company_name": "Example Software Inc.",
      "emails": [
        "[email protected]"
      ],
      ...
    },
    ...
  ]
}
```

--------------------------------

### Fetch Prospects API Response Example

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/reference/prospects/fetch_prospects.mdx

This is an example of the JSON response when fetching prospects. It includes prospect details, pagination information, and total results.

```json
{
  "prospects": [
    {
      "prospect_id": "0a48b12d25abfc597d92a8a626697198202bc3bc",
      "professional_email_hashed": null,
      "first_name": "Nikitha",
      "last_name": "Lakshmanan",
      "full_name": "Nikitha Lakshmanan",
      "country_name": "united states",
      "region_name": null,
      "city": "san francisco bay area",
      "linkedin": "linkedin.com/in/ACoAAB_PD-QB8ChlFofVGx6sezeUUk32eAy7EBo",
      "experience": [
        "Student research assistant",
        "usc shm lab",
        "Mobile application developer",
        "Senior software engineering manager",
        "Senior software engineer",
        "Software engineer",
        "Software engineering intern",
        "Staff software engineer"
      ],
      "skills": [
        "Java",
        "C++",
        "C",
        "Microsoft office",
        "Programming",
        "Python",
        "Ios",
        "Swift",
        "Unix",
        "Sql",
        "Matlab",
        "Linux",
        "Start-up environment"
      ],
      "interests": null,
      "company_name": "Apple",
      "company_website": "apple.com",
      "company_linkedin": "linkedin.com/company/apple",
      "job_department": "Engineering",
      "job_department_array": [
        "engineering"
      ],
      "job_department_main": "Engineering",
      "job_seniority_level": [
        "manager"
      ],
      "job_level_array": [
        "manager"
      ],
      "job_level_main": "manager",
      "job_title": "Senior software engineering manager",
      "business_id": "8adce3ca1cef0c986b22310e369a0793",
      "linkedin_url_array": [
        "linkedin.com/in/ACoAAB_PD-QB8ChlFofVGx6sezeUUk32eAy7EBo",
        "linkedin.com/in/nikitha-lakshmanan"
      ]
    }
  ],
  "total_results": 10,
  "page": 1,
  "total_pages": 1
}
```

--------------------------------

### Business Fetch Response Example (JSON)

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/reference/quick-starts/use_case_prospecting.mdx

This is an example of the JSON response structure when fetching business data. It includes pagination details and a list of businesses with basic information.

```json
{
  "total_results": 12000,
  "page": 1,
  "total_pages": 120,
  "data": [
    {
      "business_id": "8adce3ca1cef0c986b22310e369a0793",
      "name": "Example Software Inc.",
      "domain": "examplesoftware.com",
      "country_name": "united states",
      "number_of_employees_range": "11-50",
      ...
    },
    ...
  ]
}
```

--------------------------------

### Example API Response

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/reference/businesses/events/get_businesses_enrollments.mdx

This is an example of a successful response from the Get Business Events Enrollments endpoint. It includes response context and a list of active enrollments, detailing their IDs, keys, associated business IDs, and event types.

```json
{
  "response_context": {
    "correlation_id": "1234",
    "request_status": "success",
    "time_took_in_seconds": 0.515
  },
  "enrollments": [
    {
      "enrollment_id": "en_7b429a01",
      "enrollment_key": "my_b2b_saas_monitor",
      "business_ids": ["8adce3ca1cef0c986b22310e369a0793", "665595bbb4e724de6f8bc705a5b84753"],
      "event_types": ["ipo_announcement", "new_funding_round", "new_product"]
    },
    {
      "enrollment_id": "en_9c31b8d2",
      "enrollment_key": "fintech_competitors",
      "business_ids": ["2222", "3333"],
      "event_types": ["new_partnership", "new_investment"]
    }
  ]
}
```

--------------------------------

### Example Request - First Page

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/reference/pagination.mdx

Make a POST request to the businesses endpoint with `next_cursor` set to `null` to retrieve the first page of results.

```bash
curl -X POST "https://api.explorium.ai/v1/businesses" \
  -H "api_key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "filters": {
      "country_code": { "values": ["US"] },
      "company_size": { "values": ["51-200"] }
    },
    "mode": "full",
    "page_size": 100,
    "next_cursor": null
  }'
```

--------------------------------

### Example Response for Get Active Credits Summary

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/reference/credits/get_active_credits_summary.mdx

This JSON object shows the structure of the response when retrieving your active credits summary. It includes details on allocated credits, remaining credits, and response context.

```json
{
  "response_context": {
    "correlation_id": "5618d686ecb849fda660c3023acf3120",
    "request_status": "success",
    "time_took_in_seconds": 0.074
  },
  "allocated_credits": 1000,
  "remaining_credits": 964
}
```

--------------------------------

### Multi-turn Conversation: Initial Query

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/mcp-docs/implement-with-openai.mdx

Initiate a multi-turn conversation by making an initial query to find VPs at a company. This sets up the context for subsequent follow-up questions.

```python
# Initial query
response1 = client.responses.create(
    model="gpt-4.1",
    tools=[{
        "type": "mcp",
        "require_approval": "never",
        "server_label": "explorium",
        "server_url": "https://mcp.explorium.ai/mcp",
        "headers": {
            "api_key": "YOUR_AGENTSOURCE_API_KEY"
        }
    }],
    input="Find all VPs at Tesla"
)
```

--------------------------------

### Install MCP Python SDK

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/mcp-docs/python-sdk-implementation-guide.mdx

Install the MCP Python SDK using pip. You may need to restart your Python kernel after installation, especially in environments like Jupyter or Databricks.

```shell
pip install mcp
```

```python
# For Jupyter/Databricks
dbubbles.library.restartPython()
```

--------------------------------

### Install OpenAI Package

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/mcp-docs/implement-with-openai.mdx

Install the necessary OpenAI Python package using pip.

```shell
pip install openai
```

--------------------------------

### Full cURL Request Example

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/reference/businesses/fetch_businesses.mdx

A comprehensive cURL command demonstrating how to fetch businesses with pagination, filtering, and exclusion parameters.

```shell
curl -X POST \
  "https://api.explorium.ai/v1/businesses" \
  -H "API_KEY: your_api_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "mode": "full",
    "size": 10000,
    "page_size": 100,
    "page": 1,
    "exclude": ["00000230f8f8d86e167e189c7c3b4bd6"],
    "filters": {
      "country_code": {
        "values": ["us"]
      },
      "company_size": {
        "values": ["11-50", "51-200"]
      }
    },
    "request_context": {}
  }'
```

--------------------------------

### Initialize OpenAI Client

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/mcp-docs/implement-with-openai.mdx

Initialize the OpenAI client with your API key. Ensure you replace 'your-openai-api-key' with your actual key.

```python
from openai import OpenAI

# Initialize the client with your OpenAI API key
client = OpenAI(api_key="your-openai-api-key")
```

--------------------------------

### Prospect Statistics Request Example

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/reference/prospects/prospects_stats.mdx

An example JSON request body for fetching prospect statistics with multiple filters applied.

```json
{
  "filters": {
    "job_department": {
      "values": ["engineering", "sales", "marketing"]
    },
    "region_country_code": {
      "values": ["us-ca", "us-ny", "us-tx"]
    },
    "company_size": {
      "values": ["51-200", "201-500"]
    }
  }
}
```

--------------------------------

### Tool Chaining Example

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/integrations/claude/claude-desktop-integration.mdx

Demonstrates how Claude can automatically chain multiple tools to fulfill a complex request. This is useful for multi-step analysis or data retrieval.

```text
"Find the top 5 competitors of Salesforce and compare their employee counts and technologies"

This triggers:
1. match_businesses (Salesforce)
2. enrich_businesses_competitive_landscape
3. match_businesses (for each competitor)
4. enrich_businesses_firmographics
5. enrich_businesses_technographics
```

--------------------------------

### Example Response - First Page

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/reference/pagination.mdx

The response includes `data`, `total_results`, and `page` information. The `page.next_cursor` is used to fetch subsequent pages.

```json
{
  "response_context": {
    "correlation_id": "def456",
    "request_status": "success",
    "time_took_in_seconds": 0.38
  },
  "data": [ ... ],
  "total_results": 12350,
  "page": {
    "size": 100,
    "next_cursor": "eyJzZWFyY2hfYWZ0ZXIiOiBbMTcwOTEyMzQ1Nl19"
  }
}
```

--------------------------------

### Example Request Body with Filters

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/reference/businesses/fetch_businesses.mdx

Illustrates how to specify filters for company size in the request body.

```json
{
    "filters": {
        "company_size": {
            "values": ["11-50", "51-200"]
        }
      }
}
```

--------------------------------

### Request Body Example

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/reference/credits/get_credit_consumption_aggregation.mdx

This is an example of a request body for the credit consumption aggregation endpoint. It specifies the date range, resolution, and timezone for the aggregation.

```json
{
  "from_date": "2025-08-23T00:00:00Z",
  "to_date": "2025-08-24T00:00:00Z",
  "resolution": "hour",
  "timezone": "America/New_York",
  "key_name": "production-key"
}
```

--------------------------------

### Businesses Autocomplete - Body Params Example

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/reference/businesses/autocomplete/businesses_autocomplete.mdx

Example of body parameters for the Businesses Autocomplete API. Use this to specify search fields and queries.

```text
field: country
query: unit
```

--------------------------------

### Query New Office Opening Events

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/reference/businesses/events/types/new-office-opening.mdx

This example demonstrates how to query for new office opening events using the Explorium API. It specifies the event type and provides a timestamp for recent expansions.

```APIDOC
## POST /v1/businesses/events

### Description
Retrieves information about new office opening events for businesses.

### Method
POST

### Endpoint
/v1/businesses/events

### Parameters
#### Request Body
- **event_types** (array of strings) - Required - The type of event to query, use `"new_office"` for this event.
- **business_ids** (array of strings) - Required - A list of business IDs to retrieve events for.
- **timestamp_from** (string) - Required - The start of the time range for the events. For new office openings, this should be set to the past 3 months.

### Request Example
```json
{
  "event_types": [
    "new_office"
  ],
  "business_ids": [
    "8adce3ca1cef0c986b22310e369a0793"
  ],
  "timestamp_from": "2025-01-01T10:03:03.050Z"
}
```

### Response
#### Success Response (200)
- **event_time** (datetime) - The timestamp indicating when the event actually occurred.
- **purpose_of_new_office** (string) - Short description of the new office's purpose, extracted from the article.
- **link** (url) - Link to article announcing the company's new office.
- **office_location** (string) - Text extracted from the article indicating where the new office is located.
- **number_of_employees** (integer) - Number of employees working from the new office's location.
- **title** (string) - Title of published news report or article on company's new office.
- **snippet** (string) - Short excerpt or highlight text summarizing the award or announcement.
- **event_name** (string) - Name of the event related to company's new office.

#### Response Example
```json
{
  "events": [
    {
      "event_time": "2024-03-15T09:00:00Z",
      "purpose_of_new_office": "Engineering Hub",
      "link": "https://example.com/news/company-opens-new-sf-office",
      "office_location": "San Francisco, CA",
      "number_of_employees": 150,
      "title": "TechCorp Expands to San Francisco with New Engineering Hub",
      "snippet": "TechCorp announced the opening of its new San Francisco office, focusing on AI research and development.",
      "event_name": "new_office"
    }
  ]
}
```
```

--------------------------------

### Match Businesses using MCP SDK

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/mcp-docs/python-sdk-implementation-guide.mdx

Use the `match_businesses` tool to find business IDs by name or domain. This example demonstrates preparing input data, calling the tool, and processing the results to extract matched business information.

```python
async def match_businesses_example():
    api_key = os.environ.get('EXPLORIUM_API_KEY', 'your-api-key')
    headers = {"Authorization": f"Bearer {api_key}"}

    async with sse_client(
        url="https://mcp.explorium.ai/sse",
        headers=headers
    ) as streams:
        read_stream, write_stream = streams

        async with ClientSession(read_stream, write_stream) as session:
            await session.initialize()

            # Prepare businesses to match
            match_input = {
                "businesses_to_match": [
                    {"name": "Google"},
                    {"domain": "microsoft.com"},
                    {"name": "Amazon", "domain": "amazon.com"}
                ]
            }

            # Call the match_businesses tool
            result = await session.call_tool("match_businesses", arguments=match_input)

            # Process results
            if hasattr(result, 'content') and result.content:
                data = json.loads(result.content[0].text)
                print(f"Matched {data['total_matches']} businesses:")
                for business in data['matched_businesses']:
                    print(f"  • {business['input']} <Icon icon="arrow-right" /> ID: {business['business_id']}")

asyncio.run(match_businesses_example())
```

--------------------------------

### Refined Chatbot Query Example

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/integrations/n8n/explorium-prospects-search-chatbot.mdx

Refine your search by adding more criteria to the existing conversation. This example adds 'directors' and filters by company size.

```text
"Can you also include directors and filter for companies with 100+ employees?"
```

--------------------------------

### Example Support Request Structure

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/reference/support-help-center.mdx

This JSON structure outlines the information to include when submitting a support request, detailing the issue, steps taken, and expected/actual responses.

```json
{
"issue": "Received a 403 error when calling the Fetch Business Events API",
"steps_to_replicate": "Sent a request to \`/businesses/events\` with valid parameters",
"exact_request_send": "<copy the exact request payload here>",
"expected_response": "A list of recent business events",
"actual_response": {
"error": "You have insufficient credits to perform this operation."
},
"correlation_id": "4826416550f648e58cc9e2ceee2529b7"
}
```

--------------------------------

### Test API Key with Python

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/reference/setup/getting_your_api_key.mdx

This Python script demonstrates how to set up the necessary headers and payload for a request to the /v1/prospects endpoint using your API key.

```python
import requests

url = "https://api.explorium.ai/v1/prospects"

payload = {
    "mode": "full",
    "page": 1
}
headers = {
    "accept": "application/json",
    "content-type": "application/json",
    "api_key": "YOUR_API_KEY"
}
```

--------------------------------

### Rate Limit Response Example

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/reference/rate-limit.mdx

This is an example of a 429 Too Many Requests response, including headers and a JSON body indicating the rate limit has been exceeded.

```bash
HTTP/1.1 429 Too Many Requests
Retry-After: 60
X-RateLimit-Limit: 200
X-RateLimit-Remaining: 0
X-RateLimit-Reset: 1234567890
Content-Type: application/json

{
  "error": "rate_limit_exceeded",
  "message": "API rate limit exceeded. Please retry after 60 seconds.",
  "retry_after": 60
}
```

--------------------------------

### Manage Connections with Context Managers in Python

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/mcp-docs/python-sdk-implementation-guide.mdx

Illustrates using asynchronous context managers (`async with`) for the `sse_client` and `ClientSession` to ensure proper connection handling and automatic cleanup.

```python
async with sse_client(url, headers) as streams:
    async with ClientSession(*streams) as session:
        # Your operations here
        pass  # Connection automatically closed
```

--------------------------------

### Example Prospecting Request (cURL)

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/reference/prospects/fetch_prospects.mdx

Example cURL request to fetch prospects with specific filters for job level and department, and presence of email and phone number.

```curl
{
  "request_context": {},
  "mode": "full",
  "size": 10,
  "page_size": 10,
  "page": 1,
  "exclude": [
    "a5fbfab4d7221b144efecbe83e55cbf80b64bd50"
  ],
  "filters": {
    "has_email": {
      "value": true
    },
    "has_phone_number": {
      "value": true
    },
    "job_level": {
      "values": [
        "owner",
        "cxo",
        "vp",
        "director",
        "senior",
        "manager",
        "partner",
        "non-managerial",
        "founder",
        "training",
        "c-suite"
      ]
    },
    "job_department": {
      "values": [
        "real estate",
        "Customer service",
        "Trades",
        "design",

```

--------------------------------

### Example Request - Next Page

Source: https://github.com/explorium-ai/explorium-mintlify-docs/blob/main/reference/pagination.mdx

To retrieve the next page, pass the `next_cursor` obtained from the previous response in the request payload.

```bash
curl -X POST "https://api.explorium.ai/v1/businesses" \
  -H "api_key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "filters": {
      "country_code": { "values": ["US"] },
      "company_size": { "values": ["51-200"] }
    },
    "mode": "full",
    "page_size": 100,
    "next_cursor": "eyJzZWFyY2hfYWZ0ZXIiOiBbMTcwOTEyMzQ1Nl19"
  }'
```