Agent API

This sections documents the Agent API


About

The Agent API is the next evolution of Katoshi - Allowing you to send complex commands to your AI Agents using natural language via simple POST requests or using webhooks. Read more about agent capabilities here:

Agents

Quick Start

POST https://api.katoshi.ai/agent?id=USER_ID

Replace USER_ID with your unique ID, which can be found in the Settings Page

Headers

Name
Type
Description

Content-Type*

String

application/json

Request Body

Name
Type
Description

api_key*

String

Katoshi API Key

agent_id*

Number

ID of your agent

message*

String

Message sent to your agent

task_id

Number

ID of a task

The message field is required if no task_id is specified

Example Request

{
  "message": "Long BTC with $11 if the 15m RSI is oversold",
  "api_key": "12345678-1234-1234-1234-123456789012",
  "agent_id": 7
}

For TradingView, add the Request JSON directly in the alert message field.


task_id

Passing a task_id parameter allows you to send predefined messages to your agent.

  • Create tasks in your agent settings first, then reference the task_id in your request.

Example Scenario

Task message

Open a long position with $50 size, 1% stop loss, and 3% take profit when the following conditions are met:
- The 15m RSI is oversold
- The funding rates is negative
- There is no other open perpetual positions in my account

Request

{
  "message": "Apply this strategy for BTC:",
  "api_key": "12345678-1234-1234-1234-123456789012",
  "agent_id": 7,
  "task_id": 2
}

Final Message

Apply this strategy for BTC:

Open a long position with $50 size, 1% stop loss, and 3% take profit when the following conditions are met:
- The 15m RSI is oversold
- The funding rates is negative
- There is no other open perpetual positions in my account

The message parameter is optional when using task_id; but can be included to append it to the task message to create dynamic reuests.


Testing

Test your commands using the Agents chat interface in Katoshi.

Results can vary tremendously based on your selected LLM model, system prompt, and message sent.

Agents

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