swarmzero
v0.0.3
This library provides you with an easy way to create and run AI Agents and Swarms of Agents.
Supported LLM Providers:
You can either directly install with pip:
pip install swarmzeroOr can either directly install with poetry:
poetry add swarmzeroOr add it to your requirements.txt file:
...
swarmzero==x.y.z
...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.
To use a configuration file with your Agent, follow these steps:
Create a Configuration File:
swarmzero_config.toml) in your project directory. (See swarmzero_config_example.toml).Create an SDK Context:
from swarmzero.sdk_context import SDKContext
sdk_context = SDKContext(config_path="./swarmzero_config.toml")Specify the Configuration Path:
Agent instance, provide the relative or absolute path to your configuration file.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"
)More detailed examples can be found at https://github.com/swarmzero/examples
First import the Agent class:
from swarmzero import AgentLoad 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?" } ] }'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.
"""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())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.
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?" } ] }'
```
"""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.
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?"
][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_example.toml for an example configuration file.
If you want to contribute to the codebase, you would need to set up your dev environment. Follow these steps:
OPENAI_API_KEY from OpenAIcurl -sSL https://install.python-poetry.org | python3 -
export PATH="$HOME/.local/bin:$PATH"poetry shellpoetry install --no-roottests/ directory:cd tests/pytestpytest tests/path/to/test_module.pypytest -vpytest -spip install coverage pytest-cov
pytest --cov --cov-report=htmlReports file tests/htmlcov/index.html
Open http://localhost:8000/docs with your browser to see the Swagger UI of the API.
https://swarmzero.ai
