agentlite-py 0.1.0

A small, focused library for building Claude-powered agents in Python.

agentlite

A small, focused library for building Claude-powered agents in Python. Minimal abstractions, prompt caching done right, permissions as a first-class concept.

from agentlite import Agent, tool

@tool
def get_weather(city: str) -> str:
    """Return current weather for a city."""
    return f"{city}: 22°C, sunny"

agent = Agent(model="claude-opus-4-7", tools=[get_weather])
agent.run("What's the weather in Istanbul?")

Why agentlite?

If you've ever tried to build a Claude agent in Python you've probably faced this choice:

• Anthropic SDK directly → fast but you write the agent loop, retry logic, permission handling, and prompt caching yourself.
• LangChain → batteries included but ~150K lines of abstractions to navigate, and debugging often means reading the framework's source.

agentlite sits in between: ~2,000 lines of focused code that handles the agent loop, tool definitions, prompt caching, and permissions — and gets out of your way for everything else.

Status

🚧 Alpha (v0.1.0) — under active development. API may change before v1.0.

Installation

pip install agentlite-py

The PyPI distribution name is agentlite-py (the bare agentlite is taken by an unrelated package). The Python import is still agentlite: from agentlite import Agent, tool.

Requires Python 3.10+. Get an Anthropic API key from console.anthropic.com.

Quick start

export ANTHROPIC_API_KEY="sk-ant-..."

from agentlite import Agent, tool

@tool
def search_files(pattern: str) -> list[str]:
    """Find files matching a glob pattern."""
    from glob import glob
    return glob(pattern)

@tool
def read_file(path: str) -> str:
    """Read a text file's contents."""
    return open(path, encoding="utf-8").read()

agent = Agent(
    model="claude-opus-4-7",
    system="You help users explore their codebase.",
    tools=[search_files, read_file],
)

agent.run("Find all Python files and summarize the largest one.")

Features

• @tool decorator — type hints become JSON Schema, docstring becomes description.
• Built-in agent loop with max_turns safety brake.
• Streaming support — agent.stream_text() for token-by-token output.
• Prompt caching by default — system prompt + tools cached automatically; typically ~80% input cost reduction on repeated requests.
• Verifiable caching — inspect response.usage.cache_read_input_tokens to confirm hits (no silent failures).
• Permission system (planned) — mark tools as requires_confirmation or read_only.
• Sub-agent support (planned) — delegate sub-tasks without polluting context.

Comparison

	LangChain	OpenAI Agents	Anthropic SDK (raw)	agentlite
LoC to read	~150K	~5K	—	~2K
Prompt caching	manual	none	manual	automatic
Permission model	none	none	none	first-class
Multi-agent	complex	handoffs	none	@subagent
Learning curve	steep	medium	low	very low

Documentation

Full docs and design notes at: https://hakansabunis.com

License

MIT © 2026 Hakan Sabunis