agentine 0.1.16

agentine is a lightweight Python library that enables quick and easy creation of model-agnostic AI agents.

agentine

agentine is a Python framework to build AI agents and chatbots using large language models (LLMs). It supports agents, groups of agents, synchronous and asynchronous calls, JSON and text responses, and includes a basic CLI chatbot.

Installation

pip install agentine

Features

• Compatible with multiple LLM providers and models
• Supports synchronous and asynchronous calls, including streaming
• Outputs responses as text or structured JSON
• Switch LLM providers instantly with a single line of code
• Easy to update configuration (provider, model, temperature, API key, etc.)

Quick Start

Note: agentine uses OpenAI as the default LLM provider. If you prefer another provider, see the Advanced Usage section below.

1. Basic LLM: chat and chat_async

from agentine.llm import LLM, Message

llm = LLM(api_key="your-openai-api-key")

messages = [
    Message(role="system", content="You are a helpful assistant."),
    Message(role="user", content="Say hello in one short sentence."),
]

# chat (sync)
reply = llm.chat(messages)
print("LLM chat:", reply.content)

# chat_async (async)
import asyncio
async def run():
    reply_async = await llm.chat_async(messages)
    print("LLM chat async:", reply_async.content)

asyncio.run(run())

2. Basic Agent: work, stream, JSON, simple CLI chatbot

from agentine.agent import Agent
from agentine.llm import Message

agent = Agent(instruction="You are a helpful assistant.")
agent.llm.api_key = "sk-your-openai-api-key"

past_conversation = [
    Message(role="user", content="Hello!"),
    Message(role="assistant", content="Hi there! How can I help?"),
]

# work
response = agent.work(
    query="Who is the first person walking on the Moon?",
    messages=past_conversation,
)
print("Agent work (text):", response.content)

# stream
for chunk in agent.stream(query="Write one short sentence about the Moon."):
    print(chunk.content, end="")
print()

# JSON agent
json_agent = Agent(
    instruction=(
        "You are a helpful assistant. Always respond with a JSON object "
        "with exactly two keys: 'first_name' and 'last_name'."
    ),
    response_format="json_object"
)
json_agent.llm.api_key = "sk-your-openai-api-key"
json_response = json_agent.work("Who is the first person walking on the Moon?")
print("Agent work (json):", json_response.data)

3. CLI chatbot (agent as client)

from agentine.agent import Agent
from agentine.chatbot import Chatbot

agent = Agent(
    instruction="You are a helpful assistant."
)
agent.llm.provider = "openai"

chatbot = Chatbot(client=agent)
chatbot.cli_run()

Advanced Usage

1. Async support

from agentine.agent import Agent
import asyncio

agent = Agent(instruction="You are a helpful assistant.")
agent.llm.provider = "openai"

async def main():
    # work_async
    result = await agent.work_async("Say hello in 5 words.")
    print(result.content)

    # stream_async
    async for chunk in agent.stream_async(query="One short sentence about Mars."):
        print(chunk.content, end="")
    print()

asyncio.run(main())

2. Update LLM config on the fly

from agentine.agent import Agent

agent = Agent(instruction="You are a helpful assistant.")

# Switch provider – model/api_key/base_url will auto-adjust from known provider config
agent.llm.provider = "gemini"

# Change model and sampling params on the fly
agent.llm.model = "gemini-2.5-flash"
agent.llm.temperature = 0.2

# Optionally set API key directly (overrides env var)
# agent.llm.api_key = "sk-..."

# Use immediately
print(agent.work("Give a 10-word haiku about oceans.").content)

3. Custom LLM config (params not built-in)

agentine allows extra LLM parameters (passed through to the provider). Examples:

from agentine.agent import Agent

agent = Agent(instruction="You are a helpful assistant.")
agent.llm.provider = "openai"

# These fields are not predefined in LLM but are supported by many providers
agent.llm.top_p = 0.2
agent.llm.frequency_penalty = 0.3

result = agent.work("Write a single concise sentence about the Sun.")
print(result.content)

4. Change the default provider via agentine.config

You can change the framework-wide default provider before creating any LLM or Agent instances:

from agentine import config

# Must be set BEFORE creating LLM/Agent instances
config.DEFAULT_LLM_PROVIDER = "gemini"

from agentine.llm import LLM

# New instances now default to the provider above
llm = LLM()  # defaults to Gemini now

# Provide API key via env (e.g., GEMINI_API_KEY) or set programmatically
# llm.api_key = "your-gemini-api-key"

This affects only newly created instances; existing ones keep their current provider.

Configuration

agentine reads API keys from environment variables by default:

• OPENAI_API_KEY
• GROQ_API_KEY
• GEMINI_API_KEY
• PERPLEXITY_API_KEY

You can also set API keys and providers programmatically on the agent's LLM instance.

Contributing

Contributions are welcome. Please open issues or pull requests on GitHub.

License

This project is licensed under the MIT License. See the LICENSE file for details.

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If you have questions or need help, feel free to open an issue or contact the maintainer.