slowburn 0.5.3

Cost-Sustainable Concurrent Execution for Long-Horizon LLM Agents

SlowBurn 🐢🔥 - Cost-Sustainable Concurrent Execution for Long-Horizon LLM Agents

Authors: Abhishek Divekar

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_{Click the image above to watch the demo video}

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Overview

Long-horizon LLM agents (autonomous coding assistants, deep research pipelines, multi-agent simulations) issue dozens to hundreds of API calls per task. Existing tools either passively monitor spending, or hard-terminate the agent when a budget cap is reached, discarding accumulated context.

SlowBurn takes a different approach: when the budget is exhausted, the agent pauses rather than crashes. Budget exhaustion becomes a flow-control signal (backpressure), not a fatal error. The agent sleeps until the rate-limit window refills, then resumes exactly where it left off with no context loss.

What SlowBurn provides:

• CostLimit: a dollar-denominated rate limit that composes with token and request rate limits, and blocks rather than terminates when exhausted
• SlowBurnLLM: an asyncio LLM worker with automatic per-call cost tracking, multi-turn conversations, tool calling, and 100+ models via litellm (text and vision)
• Framework integrations: drop-in hooks for CrewAI, AutoGen (AG2), LangGraph, and LangChain that share a unified budget
• CostReporter: per-call, per-model cost attribution with JSON, Markdown, and LaTeX export
• Global config: all defaults centralized in slowburn_config, overridable at runtime via temp_config()

Quick Start

Create a cost-controlled LLM worker with a daily dollar budget, make calls, and inspect the cost report:

from slowburn import create_llm

# Create a cost-controlled LLM worker: $5 daily budget, asyncio execution
llm = create_llm(model="gpt-4o-mini", budget_usd=5.0, window="daily")

# Make LLM calls (concurrent on the asyncio event loop)
result = llm.call_llm(prompt="Summarize this paper...").result()

# Check costs
reporter = llm.get_reporter().result()
print(f"Cost: ${reporter.total_cost():.4f}")
print(reporter.to_markdown())

llm.stop()

Vision-Language Agents

Pass local files, URLs, or data-URLs as images for multimodal (VLM) calls:

from pathlib import Path

result = llm.call_llm(
    prompt="Describe this image in detail.",
    images=[Path("photo.jpg")],       # local files, URLs, or data-URLs
    image_detail="high",
).result()

Batch calls (concurrent)

Send multiple prompts in one call; they execute concurrently on the asyncio event loop under the same budget:

results = llm.call_llm_batch(
    prompts=["Capital of France?", "Capital of Japan?", "Capital of Brazil?"],
).result()
# All 3 execute concurrently on the event loop

Multi-turn conversations

Pass history= to maintain conversation state across turns. When history is provided, call_llm returns the full messages list (with the assistant response appended) instead of a plain string. The messages list is the conversation state; you control it, and pass it back on the next call.

In a loop (the common pattern):

llm = create_llm(model="gpt-4o-mini", budget_usd=1.0)

tasks = [
    "My name is Zephyr. I'm researching fusion energy.",
    "What are the main approaches to achieving net energy gain?",
    "Which approach is closest to commercialization?",
]

messages = []  # empty list enables multi-turn mode from the first call
for prompt in prompts:
    messages = llm.call_llm(
        prompt,
        system_prompt="You are a helpful research assistant.",
        history=messages,
    ).result()
    print(f"User:      {task}")
    print(f"Assistant: {messages[-1]['content']}\n")

llm.stop()

system_prompt is only prepended on the first call (when history has no system message yet). On subsequent calls it's a no-op, so passing it every time is safe.

With build_messages (for processing inputs before the LLM call):

build_messages constructs the messages list without calling the LLM. Pass its output directly to call_llm via prompt= (when prompt is a list of dicts, call_llm sends it as-is and returns a messages list):

messages = []
for task in tasks:
    # Build the messages list (sync, no LLM call)
    input_messages = llm.build_messages(
        prompt=task,
        system_prompt="You are a helpful assistant.",
        history=messages,
    ).result()

    # Log/inspect before sending
    print(f"Sending {len(input_messages)} messages, last 3:")
    for message in input_messages[-3:]:
        role = message["role"]
        content = str(message.get("content", ""))[:80]
        print(f"  {role}: {content}")
    save_to_disk(input_messages)

    # Send the pre-built messages to the LLM (no re-building)
    messages = llm.call_llm(prompt=input_messages).result()

Return type auto-detection: history= provided or prompt is a list of message dicts returns a messages list; a plain string prompt with no history returns a string (backward compatible). Override explicitly with return_messages=True or return_messages=False.

Tool calling (ReAct agents)

create_llm accepts tools and tool_choice as first-class parameters. Combined with history=, this enables the standard tool-calling loop. The inner while loop handles tool execution; the outer loop drives multiple tasks:

llm = create_llm(
    model="gpt-4o-mini",
    budget_usd=1.0,
    tools=[{
        "type": "function",
        "function": {
            "name": "search_web",
            "description": "Search the web for information.",
            "parameters": {
                "type": "object",
                "properties": {"query": {"type": "string"}},
                "required": ["query"],
            },
        },
    }],
    tool_choice="auto",
)

tasks = ["Population of Tokyo?", "GDP of Germany?"]
messages = []

for task in tasks:
    # Send the user's task
    messages = llm.call_llm(
        prompt=task,
        system_prompt="Use tools to find real data.",
        history=messages,
    ).result()

    # Tool-calling loop: execute tools until the LLM produces a text response
    while messages[-1].get("tool_calls"):
        for tc in messages[-1]["tool_calls"]:
            result = my_tool_executor(tc["function"]["name"], tc["function"]["arguments"])
            messages.append({
                "role": "tool",
                "tool_call_id": tc["id"],
                "content": result,
            })
        # Re-submit with tool results (empty prompt = no new user message)
        messages = llm.call_llm(prompt="", history=messages).result()

    print(f"Q: {task}")
    print(f"A: {messages[-1]['content']}\n")

llm.stop()

Structured output with validators

Attach a validator function to parse and type-check the response; ValueError triggers an automatic retry:

import re

def extract_number(text: str) -> int:
    match = re.search(r"\d+", text)
    if match is None:
        raise ValueError(f"No number found: {text!r}")  # triggers retry
    return int(match.group())

answer = llm.call_llm(
    prompt="What is 17 * 3? Reply with just the number.",
    validator=extract_number,    # retries automatically on ValueError
).result()
# answer = 51 (int, not str)

Global configuration

Override defaults (temperature, budget, timeouts) for a specific run using a context manager that restores on exit:

from slowburn import slowburn_config, temp_config

# Inspect defaults
print(slowburn_config.defaults.temperature)    # 0.7
print(slowburn_config.defaults.budget_usd)     # 5.0

# Override for a specific run (restores on exit)
with temp_config(temperature=0.0, budget_usd=0.10):
    llm = create_llm(model="gpt-4o-mini")
    # temperature=0.0, budget_usd=$0.10

Framework Integrations

SlowBurn provides drop-in hooks that add backpressure-based budget enforcement to existing agent frameworks. Each hook intercepts LLM calls at the framework's extension point and routes them through a shared limit set.

AutoGen (AG2)

from slowburn.integrations.autogen import SlowBurnModelClient

assistant.register_model_client(
    model_client_cls=SlowBurnModelClient,
    limit_set=limit_set,
    reporter=reporter,
)

CrewAI

from slowburn.integrations.crewai import SlowBurnCrewAI

sb = SlowBurnCrewAI(budget_usd=5.0, max_tokens=1000)
sb.install()
crew.kickoff()
print(sb.reporter.to_markdown())

LangGraph

from slowburn.integrations.langgraph import SlowBurnMiddleware

budget = SlowBurnMiddleware(budget_usd=5.0)
agent = create_agent(model="openai:gpt-4o-mini", middleware=[budget])

LangChain

from slowburn.integrations.langchain import SlowBurnCallbackHandler

handler = SlowBurnCallbackHandler(budget_usd=5.0)
llm = ChatOpenAI(model="gpt-4o-mini", callbacks=[handler])

Case Study: Autonomous Code Improvement Agent

We deployed a ReAct agent that reads Python code, searches the web for best practices, writes improved code, and iterates three times, with every LLM call routed through SlowBurn with a $0.02-per-30s budget window.

Iteration	Calls	Input Tokens	Output Tokens	Cost
1: Best practices	9	25K	3K	$0.02
2: Type hints	15	68K	9K	$0.04
3: Edge cases	15	62K	7K	$0.03
Total	39	155K	19K	$0.09

Between iterations, backpressure paused the agent for ~18 seconds until the budget window refilled. Execution resumed with no loss of context.

Comparison with Alternatives

Feature	SlowBurn	AgentBudget	LiteLLM	Langfuse	Prompto
Budget exhaustion	Pauses	Terminates	Terminates	---	---
Concurrent execution	Asyncio	---	---	---	Async
Cost tracking	Per-call	Session	Per-key	Trace	---
Dollar rate limit	Yes	---	---	---	---
Framework hooks	4	2	Proxy	Many	---
Infrastructure	Zero	Zero	Proxy	Server	Zero
Paper-ready export	Markdown + LaTeX	---	---	---	---

Project Structure

slowburn/
├── src/slowburn/
│   ├── __init__.py                 # create_llm() entry point
│   ├── config.py                   # SlowBurnConfig, temp_config(), _NO_ARG sentinel
│   ├── constants.py                # Literal type aliases (ImageDetailLevel, ToolChoiceOption, etc.)
│   ├── llm_worker.py               # SlowBurnLLM asyncio worker (text, vision, multi-turn, tools)
│   ├── cost_accounting.py          # estimate_input_tokens(), cost_controlled_call()
│   ├── limits.py                   # CostLimit (dollar-denominated rate limit)
│   ├── pricing.py                  # PricingCache (litellm + OpenRouter pricing)
│   ├── reporter.py                 # CostReporter (JSON, Markdown, LaTeX export)
│   └── integrations/
│       ├── autogen.py              # AutoGen (AG2) ModelClient
│       ├── crewai.py               # CrewAI event bus / hooks middleware
│       ├── langchain.py            # LangChain callback handler
│       └── langgraph.py            # LangGraph agent middleware
├── demos/
│   ├── Demo.ipynb                      # Interactive demo notebook
│   ├── demo_native_research_agent.py   # Research agent with web search
│   ├── demo_native_code_agent.py       # Code improvement agent
│   ├── demo_crewai_research_team.py    # CrewAI multi-agent demo
│   ├── demo_autogen_debate.py          # AutoGen debate demo
│   ├── demo_langchain_reflection.py    # LangChain chain demo
│   └── demo_langgraph_plan_execute.py  # LangGraph agent demo
└── README.md

Installation

pip install slowburn

With framework integrations:

pip install "slowburn[crewai]"       # CrewAI
pip install "slowburn[autogen]"      # AutoGen (AG2)
pip install "slowburn[langgraph]"    # LangGraph
pip install "slowburn[langchain]"    # LangChain

Everything:

pip install "slowburn[all]"         

From source (development)

git clone https://github.com/adivekar-utexas/slowburn.git
cd slowburn
pip install -e ".[dev]"

# Set API key
cp .env.example .env
# Edit .env with your OPENROUTER_API_KEY or OPENAI_API_KEY

Running tests

# Unit tests (mocked, no API key needed)
pytest tests/ --ignore=tests/test_e2e_real_llm.py --ignore=tests/test_e2e_vision.py -v

# Full suite including real LLM calls (requires API key in .env)
pytest tests/ -v --timeout=120

Running demos

# Interactive notebook
jupyter notebook demos/Demo.ipynb

# Research agent (terminal)
cd demos && python demo_native_research_agent.py

# Code improvement agent (terminal)
cd demos && python demo_native_code_agent.py

Citation

If you use SlowBurn in your research, please cite:

@misc{divekar2026slowburn,
  author       = {Divekar, Abhishek},
  title        = {{SlowBurn}: Cost-Sustainable Concurrent Execution for Long-Horizon {LLM} Agents},
  year         = {2026},
  howpublished = {\url{https://github.com/adivekar-utexas/slowburn}},
}

License

MIT