llmbelt 🧰

A tiny, zero-dependency tool belt for working with LLMs. The small utilities you end up re-writing on every project — token counting, cost estimation, retries, prompt templates, and text chunking — in one clean import.

• 🪶 Zero required dependencies — pure standard library.
• 🔌 Provider-agnostic — works with Anthropic, OpenAI, Gemini, or anything else.
• 🧪 Fully tested across Python 3.9–3.12.

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Install

pip install llmbelt

# Optional: exact token counts for OpenAI-family models
pip install "llmbelt[tiktoken]"

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Command line

python -m llmbelt --help          # or the installed `llmbelt` command

llmbelt tokens "Hello, world!"               # -> token count
echo "long text" | llmbelt tokens -          # read from stdin
llmbelt cost --in 1500 --out 800 -m gpt-4o-mini   # -> $0.000630
llmbelt redact "email a@b.com"               # -> "email [EMAIL]"
llmbelt env check OPENAI_API_KEY             # exit 1 + message if unset
llmbelt env report OPENAI_API_KEY            # masked value report
llmbelt env keys                             # providers with a key set

Usage

Configure from the environment

from llmbelt import get_api_key, load_dotenv, EnvConfig, llm_settings_from_env

load_dotenv()                          # zero-dep .env loader (no override by default)
key = get_api_key("openai")            # reads OPENAI_API_KEY (+ known aliases)
settings = llm_settings_from_env()     # {api_key, model, base_url, temperature, ...}

# Declarative, typed config straight from the environment:
class Settings(EnvConfig):
    __prefix__ = "APP_"
    api_key: str                       # required (no default) -> clear EnvError if unset
    timeout: float = 30.0
    debug: bool = False

cfg = Settings.from_env()              # reads APP_API_KEY, APP_TIMEOUT, APP_DEBUG

Also included: env_str/int/float/bool/list, require_env, mask_secret, EnvNamespace, expand/resolve_layers (${VAR} interpolation), env_template (generate a .env.example), and snapshot_env/freeze_env.

Count tokens

from llmbelt import count_tokens, truncate_to_tokens

count_tokens("Hello, world!")              # exact if tiktoken installed, else estimated
count_tokens("Hello", model="gpt-4o")      # use a model-specific encoding

# Trim text to fit a budget (great before sending context to an API)
truncate_to_tokens(long_document, max_tokens=4000)

Estimate cost

from llmbelt import estimate_cost, Price

estimate_cost(input_tokens=1_500, output_tokens=800, model="gpt-4o-mini")   # -> USD

# Bring your own prices (the built-in table is approximate — always verify):
my_prices = {"my-model": Price(input_per_1m=2.0, output_per_1m=6.0)}
estimate_cost(1000, 500, "my-model", pricing=my_prices)

Retry with backoff

from llmbelt import retry

@retry(attempts=5, exceptions=(ConnectionError, TimeoutError))
def call_api():
    ...   # retried with exponential backoff + jitter on failure

# Works on async functions too — awaited, with non-blocking asyncio.sleep backoff
@retry(attempts=5, exceptions=(ConnectionError, TimeoutError))
async def call_api_async():
    ...

Extract JSON from a model reply

from llmbelt import extract_json

extract_json('Sure!\n```json\n{"ok": true}\n```')   # -> {"ok": True}
extract_json('The score is {"value": 0.9}.')         # -> {"value": 0.9}
extract_json("no json here", default=None)           # -> None (else raises ValueError)

Prompt templates

from llmbelt import PromptTemplate

t = PromptTemplate("Translate {text} into {language}.")
t.render(text="hello", language="French")   # "Translate hello into French."
t.render(text="hello")                      # KeyError: Missing template variables: ['language']

Fit a conversation into the context window

from llmbelt import count_message_tokens, trim_messages

messages = [
    {"role": "system", "content": "You are concise."},
    {"role": "user", "content": "..."},
    # ... a long history ...
]

count_message_tokens(messages)                      # total tokens of the chat
trim_messages(messages, max_tokens=8000)            # drop oldest turns, keep the system prompt

Manage a chat with a self-trimming history

from llmbelt import Conversation

chat = Conversation(system="You are concise.", max_tokens=8000)
chat.user("Hello!")
chat.assistant("Hi — how can I help?")

response = client.chat(messages=chat.messages)   # plug into any SDK; auto-trims as it grows

Redact PII before sending to an LLM

from llmbelt import redact, find_pii

redact("email jane@acme.com or call 555-123-4567")
# -> "email [EMAIL] or call [PHONE]"

find_pii("card 4111 1111 1111 1111")   # -> [("CREDIT_CARD", "4111 1111 1111 1111")]

Best-effort regex redaction — a guardrail, not a compliance guarantee. Extend DEFAULT_PII_PATTERNS for your own data.

Cache calls so you don't pay twice

from llmbelt import cached

@cached(ttl=3600)            # remember results for an hour; unhashable args are fine
def ask(prompt: str):
    ...                      # identical prompt -> served from cache, no API call

# works on async functions too
@cached()
async def ask_async(prompt): ...

Stay under rate limits

from llmbelt import RateLimiter

limiter = RateLimiter(rate=60, per=60)   # 60 requests per minute

@limiter                                  # decorator
def call_api(): ...

with limiter:                             # or a context manager
    call_api()

Chunk text for RAG

from llmbelt import chunk_text, chunk_by_tokens, split_text

chunks = chunk_text(document, chunk_size=1000, overlap=100)
# overlapping chunks so answers aren't split across a boundary

# Budget by tokens instead of characters (exact with tiktoken installed):
chunks = chunk_by_tokens(document, chunk_size=500, overlap=50)

# Smarter: break on paragraph/sentence/word boundaries instead of mid-word
chunks = split_text(document, chunk_size=1000, overlap=100)

Track spend across calls

from llmbelt import CostTracker

tracker = CostTracker()
tracker.add(input_tokens=1_500, output_tokens=800, model="gpt-4o-mini")
tracker.add(2_000, 1_200, "gpt-4o-mini")

print(tracker)          # "2 calls, 5,500 tokens, $0.0019"
tracker.summary()       # {"calls": 2, "input_tokens": ..., "cost_usd": ...}

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API reference

Function	Description
get_env / env_str / env_int / env_float / env_bool / env_list	Typed environment readers (defaults, required, casting)
get_api_key(provider) / has_api_key / available_providers	Resolve LLM provider API keys from env
load_dotenv / parse_dotenv / find_dotenv	Zero-dependency .env loader
require_env / mask_secret / env_report	Fail-fast validation + secret-safe reporting
EnvConfig	Declarative typed config from the environment
EnvNamespace / collect_prefixed	Group prefixed variables into one object/dict
expand / resolve_layers	${VAR} interpolation + layered resolution
llm_settings_from_env(provider)	Assemble an LLM client config from env
env_template / env_template_from_config	Generate a .env.example
snapshot_env / diff_env / freeze_env / FrozenEnv	Snapshot, diff, freeze env config
count_tokens(text, model=None)	Exact (tiktoken) or estimated token count
estimate_tokens(text)	Dependency-free heuristic count
truncate_to_tokens(text, max_tokens, model=None)	Trim text to a token budget
estimate_cost(input_tokens, output_tokens, model, pricing=None)	USD cost estimate
CostTracker(pricing=None)	Accumulate tokens + USD cost across many calls
retry(attempts, base_delay, backoff, jitter, exceptions, ...)	Backoff retry decorator (sync and async)
PromptTemplate(template)	Templating with missing-variable validation
chunk_text(text, chunk_size, overlap)	Overlapping text chunks (by character)
chunk_by_tokens(text, chunk_size, overlap, model=None)	Overlapping text chunks (by token budget)
split_text(text, chunk_size, overlap, separators=None)	Boundary-aware chunks (paragraph/sentence/word)
extract_json(text, default=...)	Parse the first JSON value out of an LLM reply
count_message_tokens(messages, model=None)	Token count of a chat-format message list
trim_messages(messages, max_tokens, model=None, keep_system=True)	Trim a conversation to a token budget
Conversation(system=None, max_tokens=None, model=None)	Stateful chat history that self-trims to a budget
redact(text, patterns=None, mask="[{label}]")	Mask PII/secrets in text
find_pii(text, patterns=None)	List (label, match) PII detections
cached(maxsize, ttl)	Memoize calls on an args hash (sync + async)
RateLimiter(rate, per, capacity=None)	Token-bucket throttle (gate / context manager / decorator)

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Development

git clone https://github.com/YoungAlpaccino/llmbelt
cd llmbelt
pip install -e ".[dev]"
pytest          # run tests
ruff check .    # lint

Publishing to PyPI (maintainer notes)

python -m build
twine upload dist/*

Before first publish: confirm the name llmbelt is free on PyPI. If taken, rename in pyproject.toml, the src/ folder, and imports (a single find-and-replace).

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License

MIT — see LICENSE. Use it anywhere, including commercially.