SwarmZero SDK

Name: swarmzero
Author: swarmzero

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

Supported LLM Providers:

Project Requirements

Python >= 3.11

Installation

You can either directly install with pip:

pip install swarmzero

Or can either directly install with poetry:

poetry add 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 or YAML file (e.g., swarmzero_config.toml or swarmzero_config.yaml) in your project directory. (See swarmzero_config_example.toml or swarmzero_config_example.yaml).
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 SwarmZero Agents more efficiently.
```
from swarmzero.sdk_context import SDKContext

sdk_context = SDKContext(config_path="./swarmzero_config.toml")
# or use a YAML file
# sdk_context = SDKContext(config_path="./swarmzero_config.yaml")
```

Specify the Configuration Path:

When creating a 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" 
    # or use a YAML file
    # config_path="./swarmzero_config.yaml" 
)

Usage

More detailed examples can be found at https://github.com/swarmzero/examples

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?" } ] }'

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")
# or
# sdk_context = SDKContext(config_path="./swarmzero_config_example.yaml")


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())

Workflow

You can orchestrate agents, swarms, and tools in a flexible workflow. Each step can run sequentially, in parallel, conditionally, or in a loop.

from swarmzero import Workflow, WorkflowStep, StepMode
from swarmzero.sdk_context import SDKContext
import asyncio

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

# agent1, agent2, agent3, and agent4 are pre-defined Agent instances

workflow = Workflow(
    name="Research Workflow",
    description="Research and Analysis Pipeline",
    instruction="Demo workflow",
    sdk_context=sdk_context,
    steps=[
        # Sequential step - runs agent1
        WorkflowStep(runner=agent1.chat),
        # Parallel step - runs agent2 and agent3
        WorkflowStep(runner=[agent2.chat, agent3.chat], mode=StepMode.PARALLEL),
        # Loop step - repeats until condition is met
        WorkflowStep(
            runner=agent4.chat,
            mode=StepMode.LOOP,
            condition=lambda res: "done" in res,
            max_iterations=5,
        ),
    ],
)

async def run_workflow():
    return await workflow.run("Start research")

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

Nested Workflows

Workflow steps can themselves be workflows. This allows complex pipelines to be composed from smaller, reusable ones.

inner = Workflow(
    name="Inner",
    steps=[WorkflowStep(runner=agent1.chat)],
)

outer = Workflow(
    name="Outer",
    steps=[WorkflowStep(runner=inner)],
)

async def run_nested():
    return await outer.run("start")

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

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
fro

swarmzero

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