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The Everflow API is designed to work seamlessly with AI assistants, code generators, and automation frameworks. Every endpoint is defined in machine-readable OpenAPI 3.0 specifications, making it straightforward for AI tools to understand and generate correct API calls.

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Use AI assistants with the Everflow API

You can use any AI assistant with the Everflow API by providing it the relevant OpenAPI specification as context. This gives the assistant the full endpoint definitions, request/response schemas, and authentication details it needs to generate correct API calls. Try copying an endpoint spec and pasting it into your favorite AI tool, or load the llms.txt file into tools like Cursor, VS Code Copilot, or Claude Projects for persistent context across the entire documentation site.

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Example: generate a reporting script

Ask any AI assistant:
“Using the Everflow API, write a Python script that pulls my top 10 offers by revenue for the last 7 days.”
With the OpenAPI spec as context, the assistant will generate a working script using the correct endpoint, authentication, and request body: 
import requests
from datetime import datetime, timedelta

API_KEY = "your-api-key"
BASE_URL = "https://api.eflow.team/v1"

today = datetime.now().strftime("%Y-%m-%d")
week_ago = (datetime.now() - timedelta(days=7)).strftime("%Y-%m-%d")

response = requests.post(
    f"{BASE_URL}/networks/reporting/entity/table",
    headers={
        "X-Eflow-API-Key": API_KEY,
        "Content-Type": "application/json"
    },
    json={
        "from": week_ago,
        "to": today,
        "timezone_id": 90,
        "currency_id": "USD",
        "columns": [{"column": "offer"}],
        "order": {"field": "revenue", "direction": "desc"},
        "limit": 10
    }
)

for row in response.json().get("table", []):
    print(f"Offer {row['columns'][0]['id']}: ${row['reporting']['revenue']:.2f} revenue")

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Build automations with the API

The Everflow API supports common automation patterns out of the box:
  • Scheduled reporting — Pull aggregated data on a cron schedule and push to Slack, email, or a dashboard
  • Event-driven workflows — Use Webhooks to trigger actions when offers, partners, or conversions change
  • Multi-step pipelines — Chain API calls to automate complex processes like affiliate onboarding or offer lifecycle management

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Integrate with agent frameworks

The OpenAPI specifications can be loaded directly into popular agent and automation frameworks:
FrameworkHow to use
LangChainLoad the OpenAPI spec as a tool definition for your agent
CrewAIUse the spec to define API tools for your crew agents
Custom GPTsUpload the spec files as knowledge to create a specialized Everflow assistant
Claude ProjectsAdd spec files to a project for persistent API context
n8n / MakeUse the HTTP node with endpoint details from the spec
See OpenAPI Specifications for details on accessing the spec files.

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Coming soon: MCP Server

We are building an MCP (Model Context Protocol) server for Everflow. MCP is an open standard that lets AI assistants interact directly with external tools and data sources. Once released, AI agents will be able to query your Everflow data, manage offers, and generate reports through natural language — no code required.

OpenAPI Specifications

Machine-readable API specs for AI tools and agent frameworks.

LLM Context (llms.txt)

A structured summary of these docs optimized for AI assistant context.