hive agent py
1.0.0
This library provides you with an easy way to create and run Hive Agents.
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You can either directly install from pip:
pip install git+https://github.com/hivenetwork-ai/hive-agent-py.git@mainOr add it to your requirements.txt file:
hive-agent @ git+https://github.com/hivenetwork-ai/hive-agent-py@mainTo install with the optional web3 dependencies, you can specify them as follows:
pip install git+https://github.com/hivenetwork-ai/hive-agent-py.git@main#egg=hive-agent[web3]Or add it to your requirements.txt file:
hive-agent[web3] @ git+https://github.com/hivenetwork-ai/hive-agent-py@mainYou 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 HiveAgent, follow these steps:
Create a Configuration File:
hive_config.toml) in your project directory. (See hive_config_example.toml).Create an SDK Context:
from hive_agent.sdk_context import SDKContext
sdk_context = SDKContext(config_path="./hive_config.toml")Specify the Configuration Path:
HiveAgent instance, provide the relative or absolute path to your configuration file.from hive_agent import HiveAgent
import os
def get_config_path(filename):
return os.path.abspath(os.path.join(os.path.dirname(__file__), filename))
simple_agent = HiveAgent(
name="Simple Agent",
functions=[],
instruction="your instructions for this agent's goal",
sdk_context=sdk_context
#config_path=get_config_path("hive_config.toml") # ./hive_config.toml works too
)First import the HiveAgent class:
from hive_agent import HiveAgentLoad your environment variables:
from dotenv import load_dotenv
load_dotenv()Then create a HiveAgent instance:
my_agent = HiveAgent(
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 hive_agent import HiveAgent
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 = HiveAgent(
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 hive_agent.swarm import HiveSwarm
from hive_agent.agent import HiveAgent
from hive_agent.sdk_context import SDKContext
from hive_agent.llms.utils import llm_from_config
from hive_agent.utils import tools_from_funcs
from hive_agent.llms.claude import ClaudeLLM
import asyncio
# Create SDK Context
sdk_context = SDKContext(config_path="./hive_config_example.toml")
def save_report():
return "save_item_to_csv"
def search_on_web():
return "search_on_web"
#You can use the default config using default_config or the config of a specific agent by using the get_config method.
llm = llm_from_config(sdk_context.get_config("target_agent_id"))
tools = tools_from_funcs([search_on_web])
claude = ClaudeLLM(llm=llm, tools=tools)
# Create individual agents
agent1 = HiveAgent(name="Research Agent", instruction="Conduct research on given topics", sdk_context=sdk_context, functions=[search_on_web], llm=claude)
agent2 = HiveAgent(name="Analysis Agent", instruction="Analyze data and provide insights", sdk_context=sdk_context, functions=[save_report])
agent3 = HiveAgent(name="Report Agent", instruction="Compile findings into a report", sdk_context=sdk_context, functions=[])
# Create swarm
swarm = HiveSwarm(name="Research Team", description="A swarm of agents that collaborate on research tasks",
instruction="Be helpful and collaborative", functions=[], agents=[agent1, agent2, agent3], sdk_context=sdk_context)
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 'hive-agent-data/files/user' folder and can filter files with required_exts parameter.
import os
from typing import Optional, Dict
from web3 import Web3
from hive_agent import HiveAgent
from dotenv import load_dotenv
load_dotenv()
if __name__ == "__main__":
my_agent = HiveAgent(
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 hive_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 ./hive_config_example.toml for an example configuration file.
The complete tutorial can be found at ./tutorial.md.
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
