laghav 0.1.0

Python SDK for Laghav API prompt compression and model routing

laghav

Official Python SDK for the Laghav API — compress every prompt, route to the cheapest capable model, and see exactly where your money went.

Installation

pip install laghav

Quick start

from laghav import LaghavClient

client = LaghavClient(api_key="lgh_live_...")

response = client.complete(
    messages=[{"role": "user", "content": "Explain LLM compression in one sentence."}],
    model="auto",
)
print(response.choices[0].message.content)
print(f"Saved ${response.laghav_meta.saved_usd:.6f} on this call")

Methods

client.complete()

Full completion with compression, routing, caching, and typed LaghavResponse.

response = client.complete(
    messages=[{"role": "user", "content": "..."}],
    model="auto",          # or "claude-haiku-3", "gpt-4o-mini", etc.
    max_tokens=1000,
    laghav_options={
        "compress": True,
        "route": True,
        "cache": True,
    },
)
# response is LaghavResponse (Pydantic model)
meta = response.laghav_meta   # LaghavMeta — all fields typed

client.ask()

Single-prompt convenience wrapper — returns the response string directly.

answer = client.ask("What is prompt compression?")

client.compress()

Compress text without invoking an LLM (hits /v1/playground).

compressed = client.compress("Hello there! I hope you're doing well ...")

Streaming

for chunk in client.complete(messages=[...], stream=True):
    print(chunk.choices[0].delta.content or "", end="", flush=True)
    if chunk.laghav_meta:       # final chunk carries the meta
        print(f"\nSaved: ${chunk.laghav_meta.saved_usd:.6f}")

@shield decorator

Automatically compress any function's prompt argument before your own LLM call:

from laghav import shield, configure_global_client

configure_global_client(api_key="lgh_live_...", base_url="https://api.laghav.ai")

@shield(compress=True)
def my_llm_call(prompt: str) -> str:
    # prompt is already compressed when this runs
    return openai_client.chat.completions.create(...)

my_llm_call("Hello there! Could you possibly help me understand...")

Error handling

from laghav.errors import RateLimitError, BudgetExceededError, LaghavError

try:
    response = client.complete(messages=[...])
except RateLimitError as e:
    print(f"Rate limited — retry after {e.retry_after}s")
except BudgetExceededError as e:
    print(f"Budget cap reached: {e.budget_id}")
except LaghavError as e:
    print(f"API error [{e.status_code}] {e.code}: {e.message}")

Environment variables

Variable	Default	Description
LAGHAV_API_KEY	—	API key (required if not passed to constructor)
LAGHAV_BASE_URL	https://api.laghav.ai	Override for local development