Agent Kit

This library provides you with an easy way to create and run Agents.

Project Requirements

Install Python (>= 3.11):

Download from the official Python website.
Follow the installation instructions for your OS.

Acquiring an OpenAI API Key:

Sign up or log in to your OpenAI account at OpenAI API.
Navigate to the API section and generate a new API key.

Refer to the OpenAI Rate Limits for detailed information.

Supported Large Language Models (LLMs)

Here are the supported large language models in Agent:

o1-preview
o1-mini
gpt-4o
gpt-4o-mini
gpt-4-turbo
gpt-4
gpt-3.5-turbo
gpt-3.5-turbo-instruct

Installation

You can either directly install from pip:

pip install swarmzero

Or add it to your requirements.txt file:

swarmzero==x.y.z

Environment Setup

You need to specify an OPENAI_API_KEY in a .env file in this directory.
Make a copy of the .env.example file and rename it to .env.

Configuration Setup

To use a configuration file with your Agent, follow these steps:

Create a Configuration File:
- Create a TOML file (e.g., swarmzero_config.toml) in your project directory. (See swarmzero_config_example.toml).
Create an SDK Context:
- Create an instance of SDKContext with the path to your configuration file.
- The SDKContext allows you to manage configurations, resources, and utilities across your Agents more efficiently.
```
from swarmzero.sdk_context import SDKContext

sdk_context = SDKContext(config_path="./swarmzero_config.toml")
```
Specify the Configuration Path:
- When creating an Agent instance, provide the relative or absolute path to your configuration file.
- Agent will use the configuration from the SDK Context. If you have one agent you can directly pass the config_path it will create the sdk_context for you.
```
from swarmzero import Agent

simple_agent = Agent(
    name="Simple Agent",
    functions=[],
    instruction="your instructions for this agent's goal",
    # sdk_context=sdk_context
    config_path="./swarmzero_config.toml" 
)
```

Usage

First import the Agent class:

from swarmzero import Agent

Load your environment variables:

from dotenv import load_dotenv
load_dotenv()

Then create a Agent instance:

my_agent = Agent(
    name="my_agent",
    functions=[],
    instruction="your instructions for this agent's goal",
)

Then, run your agent:

my_agent.run()

Finally, call the API endpoint, /api/v1/chat, to see the result:

curl --request POST \
  --url http://localhost:8000/api/v1/chat \
  --header 'Content-Type: multipart/form-data' \
  --form 'user_id="test"' \
  --form 'session_id="test"' \
  --form 'chat_data={ "messages": [ { "role": "user", "content": "Who is Satoshi Nakamoto?" } ] }'

Advanced Usage

You configure your Agent by adding tools, you can create a swarm of agents to handle a complex task, and you can add a retriever to get semantic information. Below are some examples of how you can do it:

Adding tools

You can create tools that help your agent handle more complex tasks. Here's an example:

import os
from typing import Optional, Dict
from web3 import Web3
from swarmzero import Agent
from dotenv import load_dotenv

load_dotenv()

rpc_url = os.getenv("RPC_URL") # add an ETH Mainnet HTTP RPC URL to your `.env` file

def get_transaction_receipt(transaction_hash: str) -> Optional[Dict]:
    """
    Fetches the receipt of a specified transaction on the Ethereum blockchain and returns it as a dictionary.

    :param transaction_hash: The hash of the transaction to fetch the receipt for.
    :return: A dictionary containing the transaction receipt details, or None if the transaction cannot be found.
    """
    web3 = Web3(Web3.HTTPProvider(rpc_url))

    if not web3.is_connected():
        print("unable to connect to Ethereum")
        return None

    try:
        transaction_receipt = web3.eth.get_transaction_receipt(transaction_hash)
        return dict(transaction_receipt)
    except Exception as e:
        print(f"an error occurred: {e}")
        return None

if __name__ == "__main__":
    my_agent = Agent(
        name="my_agent",
        functions=[get_transaction_receipt]
    )

    my_agent.run()

    """
    [1] send a request:

    ```
    curl --request POST \
    --url http://localhost:8000/api/v1/chat \
    --header 'Content-Type: multipart/form-data' \
    --form 'user_id="test"' \
    --form 'session_id="test"' \
    --form 'chat_data={ "messages": [ { "role": "user", "content": "Who is the sender of this transaction - 0x5c504ed432cb51138bcf09aa5e8a410dd4a1e204ef84bfed1be16dfba1b22060" } ] }'
    ```

    [2] result:

    The address that initiated the transaction with hash 0x5c504ed432cb51138bcf09aa5e8a410dd4a1e204ef84bfed1be16dfba1b22060 is 0xA1E4380A3B1f749673E270229993eE55F35663b4.
    """

Creating a Swarm

You can create a swarm of agents to collaborate on complex tasks. Here's an example of how to set up and use a swarm:

from swarmzero.swarm import Swarm
from swarmzero.agent import Agent
from swarmzero.sdk_context import SDKContext

import asyncio

# Create SDK Context
sdk_context = SDKContext(config_path="./swarmzero_config_example.toml")


def save_report():
    return "save_item_to_csv"


def search_on_web():
    return "search_on_web"


# Create individual agents
agent1 = Agent(name="Research Agent", instruction="Conduct research on given topics", sdk_context=sdk_context,
               functions=[search_on_web])
agent2 = Agent(name="Analysis Agent", instruction="Analyze data and provide insights", sdk_context=sdk_context,
               functions=[save_report])
agent3 = Agent(name="Report Agent", instruction="Compile findings into a report", sdk_context=sdk_context, functions=[])

# Create swarm
swarm = Swarm(name="Research Team", description="A swarm of agents that collaborate on research tasks",
              instruction="Be helpful and collaborative", functions=[], agents=[agent1, agent2, agent3])


async def chat_with_swarm():
    return await swarm.chat("Can you analyze the following data: [1, 2, 3, 4, 5]")


if __name__ == "__main__":
    asyncio.run(chat_with_swarm())

Adding Retriever

You can add retriever tools to create vector embeddings and retrieve semantic information. It will create vector index for every pdf documents under 'swarmzero-data/files/user' folder and can filter files with required_exts parameter.

SwarmZero agent supports ".md", '.mdx' ,".txt", '.csv', '.docx', '.pdf' file types.
SwarmZero agent supports 4 type of retriever (basic, chroma, pinecone-serverless, pinecone-pod) and controlled with retrieval_tool parameter.

from swarmzero import Agent
from dotenv import load_dotenv

load_dotenv()

if __name__ == "__main__":
    my_agent = Agent(
        name="retrieve-test",
        functions=[],
        retrieve = True,
        required_exts = ['.md'],
        retrieval_tool='chroma'
    )

    my_agent.run()

    """
    [1] send a request:

    ```
    curl --request POST \
    --url http://localhost:8000/api/v1/chat \
    --header 'Content-Type: multipart/form-data' \
    --form 'user_id="test"' \
    --form 'session_id="test"' \
    --form 'chat_data={ "messages": [ { "role": "user", "content": "Can you summarise the documents?" } ] }'
    ```
    """

Adding Sample Prompts

Users of your agent/swarm may not always be familiar with its abilities. Providing sample prompts allows them to explore what you have built. Here's how to add sample prompts which they can use before committing to use your agent/swarm.

Default

In your swarmzero_config.toml file, create a top level entry called [sample_prompts] and add a new array to the key prompts like this:

[sample_prompts]
prompts = [
  "What can you help me do?",
  "Which tools do you have access to?",
  "What are your capabilities?"
]

Specific agents in a swarm

[target_agent_id]
model = "gpt-3.5-turbo"
timeout = 15
environment = "dev"
enable_multi_modal = true
ollama_server_url = 'http://123.456.78.90:11434'
sample_prompts = [
    "What can you help me do?",
    "Which tools do you have access to?",
    "What are your capabilities?"
]

See ./swarmzero_config.toml for an example configuration file.

PreviousContributing to SwarmZero NextAgent API Schema

Last updated 24 days ago

from swarmzero import Agent simple_agent = Agent( name="Simple Agent", functions=[], instruction="your instructions for this agent's goal", # sdk_context=sdk_context config_path="./swarmzero_config.toml" )

curl --request POST \ --url http://localhost:8000/api/v1/chat \ --header 'Content-Type: multipart/form-data' \ --form 'user_id="test"' \ --form 'session_id="test"' \ --form 'chat_data={ "messages": [ { "role": "user", "content": "Who is Satoshi Nakamoto?" } ] }'

import os from typing import Optional, Dict from web3 import Web3 from swarmzero import Agent from dotenv import load_dotenv load_dotenv() rpc_url = os.getenv("RPC_URL") # add an ETH Mainnet HTTP RPC URL to your `.env` file def get_transaction_receipt(transaction_hash: str) -> Optional[Dict]: """ Fetches the receipt of a specified transaction on the Ethereum blockchain and returns it as a dictionary. :param transaction_hash: The hash of the transaction to fetch the receipt for. :return: A dictionary containing the transaction receipt details, or None if the transaction cannot be found. """ web3 = Web3(Web3.HTTPProvider(rpc_url)) if not web3.is_connected(): print("unable to connect to Ethereum") return None try: transaction_receipt = web3.eth.get_transaction_receipt(transaction_hash) return dict(transaction_receipt) except Exception as e: print(f"an error occurred: {e}") return None if __name__ == "__main__": my_agent = Agent( name="my_agent", functions=[get_transaction_receipt] ) my_agent.run() """ [1] send a request: ``` curl --request POST \ --url http://localhost:8000/api/v1/chat \ --header 'Content-Type: multipart/form-data' \ --form 'user_id="test"' \ --form 'session_id="test"' \ --form 'chat_data={ "messages": [ { "role": "user", "content": "Who is the sender of this transaction - 0x5c504ed432cb51138bcf09aa5e8a410dd4a1e204ef84bfed1be16dfba1b22060" } ] }' ``` [2] result: The address that initiated the transaction with hash 0x5c504ed432cb51138bcf09aa5e8a410dd4a1e204ef84bfed1be16dfba1b22060 is 0xA1E4380A3B1f749673E270229993eE55F35663b4. """

from swarmzero.swarm import Swarm from swarmzero.agent import Agent from swarmzero.sdk_context import SDKContext import asyncio # Create SDK Context sdk_context = SDKContext(config_path="./swarmzero_config_example.toml") def save_report(): return "save_item_to_csv" def search_on_web(): return "search_on_web" # Create individual agents agent1 = Agent(name="Research Agent", instruction="Conduct research on given topics", sdk_context=sdk_context, functions=[search_on_web]) agent2 = Agent(name="Analysis Agent", instruction="Analyze data and provide insights", sdk_context=sdk_context, functions=[save_report]) agent3 = Agent(name="Report Agent", instruction="Compile findings into a report", sdk_context=sdk_context, functions=[]) # Create swarm swarm = Swarm(name="Research Team", description="A swarm of agents that collaborate on research tasks", instruction="Be helpful and collaborative", functions=[], agents=[agent1, agent2, agent3]) async def chat_with_swarm(): return await swarm.chat("Can you analyze the following data: [1, 2, 3, 4, 5]") if __name__ == "__main__": asyncio.run(chat_with_swarm())

from swarmzero import Agent from dotenv import load_dotenv load_dotenv() if __name__ == "__main__": my_agent = Agent( name="retrieve-test", functions=[], retrieve = True, required_exts = ['.md'], retrieval_tool='chroma' ) my_agent.run() """ [1] send a request: ``` curl --request POST \ --url http://localhost:8000/api/v1/chat \ --header 'Content-Type: multipart/form-data' \ --form 'user_id="test"' \ --form 'session_id="test"' \ --form 'chat_data={ "messages": [ { "role": "user", "content": "Can you summarise the documents?" } ] }' ``` """

[target_agent_id] model = "gpt-3.5-turbo" timeout = 15 environment = "dev" enable_multi_modal = true ollama_server_url = 'http://123.456.78.90:11434' sample_prompts = [ "What can you help me do?", "Which tools do you have access to?", "What are your capabilities?" ]