bu-agent-sdk

An agent is just a for-loop.

The simplest possible agent framework. No abstractions. No magic. Just a for-loop of tool calls. The framework powering BU.app.

Install

BASH
uv sync  

or

BASH
uv add bu-agent-sdk  

Quick Start

PYTHON
import asyncio  
from bu_agent_sdk import Agent, tool, TaskComplete  
from bu_agent_sdk.llm import ChatAnthropic

@tool("Add two numbers")  
async def add(a: int, b: int) -> int:  
    return a + b

@tool("Signal task completion")  
async def done(message: str) -> str:  
    raise TaskComplete(message)

agent = Agent(  
    llm=ChatAnthropic(model="claude-sonnet-4-20250514"),  
    tools=[add, done],  
)

async def main():  
    result = await agent.query("What is 2 + 3?")  
    print(result)

asyncio.run(main())  

Philosophy

The Bitter Lesson: All the value is in the RL'd model, not your 10,000 lines of abstractions.

Agent frameworks fail not because models are weak, but because their action spaces are incomplete. Give the LLM as much freedom as possible, then vibe-restrict based on evals.

Features

Done Tool Pattern

The naive "stop when no tool calls" approach fails. Agents finish prematurely. Force explicit completion:

PYTHON
@tool("Signal completion")  
async def done(message: str) -> str:  
    raise TaskComplete(message)

agent = Agent(  
    llm=llm,  
    tools=[..., done],  
    require_done_tool=True,  # Autonomous mode  
)

Ephemeral Messages

Large tool outputs (browser state, screenshots) blow up context. Keep only the last N:

PYTHON
@tool("Get browser state", ephemeral=3)  # Keep last 3 only  
async def get_state() -> str:  
    return massive_dom_and_screenshot  

Simple LLM Primitives

~300 lines per provider. Same interface. Full control:

PYTHON
from bu_agent_sdk.llm import ChatAnthropic, ChatOpenAI, ChatGoogle

# All implement BaseChatModel  
agent = Agent(llm=ChatAnthropic(model="claude-sonnet-4-20250514"), tools=tools)  
agent = Agent(llm=ChatOpenAI(model="gpt-4o"), tools=tools)  
agent = Agent(llm=ChatGoogle(model="gemini-2.0-flash"), tools=tools)  

Context Compaction

Auto-summarize when approaching context limits:

PYTHON
from bu_agent_sdk.agent import CompactionConfig

agent = Agent(  
    llm=llm,  
    tools=tools,  
    compaction=CompactionConfig(threshold_ratio=0.80),  
)

Dependency Injection

FastAPI-style, type-safe:

PYTHON
from typing import Annotated  
from bu_agent_sdk import Depends

def get_db():  
    return Database()

@tool("Query users")  
async def get_user(id: int, db: Annotated[Database, Depends(get_db)]) -> str:  
    return await db.find(id)  

Streaming Events

PYTHON
from bu_agent_sdk.agent import ToolCallEvent, ToolResultEvent, FinalResponseEvent

async for event in agent.query_stream("do something"):  
    match event:  
        case ToolCallEvent(tool=name, args=args):  
            print(f"Calling {name}")  
        case ToolResultEvent(tool=name, result=result):  
            print(f"{name} -> {result[:50]}")  
        case FinalResponseEvent(content=text):  
            print(f"Done: {text}")  

Claude Code in 100 Lines

A sandboxed coding assistant with dependency injection:

PYTHON
import asyncio  
import subprocess  
from dataclasses import dataclass  
from pathlib import Path  
from typing import Annotated

from bu_agent_sdk import Agent  
from bu_agent_sdk.llm import ChatAnthropic  
from bu_agent_sdk.tools import Depends, tool


@dataclass  
class SandboxContext:  
    """All file operations restricted to root_dir."""  
    root_dir: Path  
    working_dir: Path

    def resolve_path(self, path: str) -> Path:  
        resolved = (self.working_dir / path).resolve()  
        resolved.relative_to(self.root_dir)  # Raises if escapes  
        return resolved


def get_sandbox() -> SandboxContext:  
    raise RuntimeError("Override via dependency_overrides")


@tool("Execute shell command")  
async def bash(command: str, ctx: Annotated[SandboxContext, Depends(get_sandbox)]) -> str:  
    result = subprocess.run(command, shell=True, capture_output=True, text=True, cwd=ctx.working_dir)  
    return result.stdout + result.stderr or "(no output)"


@tool("Read file contents")  
async def read(path: str, ctx: Annotated[SandboxContext, Depends(get_sandbox)]) -> str:  
    return ctx.resolve_path(path).read_text()


@tool("Write file contents")  
async def write(path: str, content: str, ctx: Annotated[SandboxContext, Depends(get_sandbox)]) -> str:  
    ctx.resolve_path(path).write_text(content)  
    return f"Wrote {len(content)} bytes"


@tool("Find files by glob pattern")  
async def glob(pattern: str, ctx: Annotated[SandboxContext, Depends(get_sandbox)]) -> str:  
    files = [str(f.relative_to(ctx.root_dir)) for f in ctx.working_dir.glob(pattern)]  
    return "\n".join(files) or "No matches"


@tool("Signal task completion")  
async def done(message: str) -> str:  
    from bu_agent_sdk.agent import TaskComplete  
    raise TaskComplete(message)


async def main():  
    # Create sandbox  
    root = Path("./sandbox")  
    root.mkdir(exist_ok=True)  
    ctx = SandboxContext(root_dir=root.resolve(), working_dir=root.resolve())

    agent = Agent(  
        llm=ChatAnthropic(model="claude-sonnet-4-20250514"),  
        tools=[bash, read, write, glob, done],  
        system_prompt=f"Coding assistant. Working dir: {ctx.working_dir}",  
        dependency_overrides={get_sandbox: lambda: ctx},  
    )

    print("Agent ready. Ctrl+C to exit.")  
    while True:  
        task = input("\n> ")  
        async for event in agent.query_stream(task):  
            if hasattr(event, "tool"):  
                print(f"  → {event.tool}")  
            elif hasattr(event, "content") and event.content:  
                print(f"\n{event.content}")


asyncio.run(main())  

See bu_agent_sdk/examples/claude_code.py for the full version with grep, edit, and todo tools.

Examples

See bu_agent_sdk/examples/ for more:

• claude_code.py - Full Claude Code clone with sandboxed filesystem
• dependency_injection.py - FastAPI-style dependency injection

The Bitter Truth

Every abstraction is a liability. Every "helper" is a failure point.

The models got good. Really good. They were RL'd on computer use, coding, browsing. They don't need your guardrails. They need:

• A complete action space
• A for-loop
• An explicit exit
• Context management

The bitter lesson: The less you build, the more it works.

License

MIT

Credits

Built by Browser Use. Inspired by reverse-engineering Claude Code and Gemini CLI.

Source: https://github.com/browser-use/agent-sdk