Swarms API Documentation

The Swarms API provides endpoints to interact with various language models, manage agent configurations, and handle token counting. This documentation covers the available endpoints, input and output models, and detailed examples for each endpoint.

URL: https://api.swarms.world

Key Features

• Dynamic Model Switching: Easily switch between different language models based on user input.
• Token Counting: Efficiently count tokens using the tiktoken library.
• Agent Configuration: Configure and run agents with detailed settings for various tasks.
• CORS Handling: Support for Cross-Origin Resource Sharing (CORS) to allow web-based clients to interact with the API.

Endpoints

/v1/models

Method: GET

Response Model: List[str]

Description: This endpoint returns a list of available model names. It is useful for clients to query and understand which models are available for use.

Response Example:

[
    "OpenAIChat",
    "GPT4VisionAPI",
    "Anthropic"
]

Example Usage:

import requests

response = requests.get("http://api.swarms.world/v1/models")
print(response.json())

/v1/agent/completions

Method: POST

Request Model: AgentInput

Response Model: AgentOutput

URL: http://api.swarms.world/v1/agent/completions

Description: This endpoint handles the completion request for an agent configured with the given input parameters. It processes the request and returns the completion results.

Request Example:

{
    "agent_name": "Swarm Agent",
    "system_prompt": "Summarize the following text",
    "agent_description": "An agent that summarizes text",
    "model_name": "OpenAIChat",
    "max_loops": 1,
    "autosave": false,
    "dynamic_temperature_enabled": false,
    "dashboard": false,
    "verbose": false,
    "streaming_on": true,
    "saved_state_path": null,
    "sop": null,
    "sop_list": null,
    "user_name": "User",
    "retry_attempts": 3,
    "context_length": 8192,
    "task": "This is a sample text that needs to be summarized."
}

Response Example:

{
    "agent": {
        "agent_name": "Swarm Agent",
        "system_prompt": "Summarize the following text",
        "agent_description": "An agent that summarizes text",
        "model_name": "OpenAIChat",
        "max_loops": 1,
        "autosave": false,
        "dynamic_temperature_enabled": false,
        "dashboard": false,
        "verbose": false,
        "streaming_on": true,
        "saved_state_path": null,
        "sop": null,
        "sop_list": null,
        "user_name": "User",
        "retry_attempts": 3,
        "context_length": 8192,
        "task": "This is a sample text that needs to be summarized."
    },
    "completions": {
        "choices": [
            {
                "index": 0,
                "message": {
                    "role": "Swarm Agent",
                    "content": "The sample text summarizes how to perform text summarization using an agent.",
                    "name": null
                }
            }
        ],
        "stream_choices": null,
        "usage_info": {
            "prompt_tokens": 10,
            "completion_tokens": 15,
            "total_tokens": 25
        }
    }
}

Example Usage:

import requests
from pydantic import BaseModel
from typing import List

class AgentInput(BaseModel):
    agent_name: str = "Swarm Agent"
    system_prompt: str = None
    agent_description: str = None
    model_name: str = "OpenAIChat"
    max_loops: int = 1
    autosave: bool = False
    dynamic_temperature_enabled: bool = False
    dashboard: bool = False
    verbose: bool = False
    streaming_on: bool = True
    saved_state_path: str = None
    sop: str = None
    sop_list: List[str] = None
    user_name: str = "User"
    retry_attempts: int = 3
    context_length: int = 8192
    task: str = None

agent_input = AgentInput(task="Generate a summary of the provided text.")
response = requests.post("http://api.swarms.world/v1/agent/completions", json=agent_input.dict())
print(response.json())

Models

AgentInput

The AgentInput class defines the structure of the input data required to configure and run an agent.

Parameter	Type	Default	Description
agent_name	str	"Swarm Agent"	The name of the agent.
system_prompt	str or None	None	The system prompt to guide the agent's behavior.
agent_description	str or None	None	A description of the agent's purpose.
model_name	str	"OpenAIChat"	The name of the language model to use.
max_loops	int	1	The maximum number of loops the agent should perform.
autosave	bool	False	Whether to enable autosave functionality.
dynamic_temperature_enabled	bool	False	Whether dynamic temperature adjustment is enabled.
dashboard	bool	False	Whether to enable the dashboard feature.
verbose	bool	False	Whether to enable verbose logging.
streaming_on	bool	True	Whether to enable streaming of responses.
saved_state_path	str or None	None	Path to save the agent's state.
sop	str or None	None	Standard operating procedures for the agent.
sop_list	List[str] or None	None	A list of standard operating procedures.
user_name	str	"User"	The name of the user interacting with the agent.
retry_attempts	int	3	Number of retry attempts for failed operations.
context_length	int	8192	Maximum context length for the model's input.
task	str or None	None	The task description for the agent to perform.

AgentOutput

The AgentOutput class defines the structure of the output data returned by the agent after processing a request.

Parameter	Type	Description
agent	AgentInput	The input configuration used to create the agent.
completions	ChatCompletionResponse	The response generated by the agent.

Functions

count_tokens

The count_tokens function counts the number of tokens in a given text using the tiktoken library.

Parameters:

• text (str): The text to be tokenized and counted.

Returns:

• int: The number of tokens in the text.

Example Usage:

text = "This is a sample text to count tokens."
token_count = count_tokens(text)
print(f"Token count: {token_count}")

model_router

The model_router function switches to the specified language model based on the provided model name.

Parameters:

• model_name (str): The name of the model to switch to.

Returns:

• An instance of the specified language model.

Example Usage:

model_name = "OpenAIChat"
model_instance = model_router(model_name)

Additional Information and Tips

• Error Handling: Ensure robust error handling by catching exceptions and returning meaningful HTTP status codes and messages.
• Model Selection: When adding new models, update the model_router function and the /v1/models endpoint to include the new model names.
• Token Management: Keep track of token usage to optimize API costs and manage rate limits effectively.