litlm

litlm is a small, notebook-first interface to LiteLLM. One function handles a prompt or a parallel batch, while keeping costs, provider metadata, failures, and retries close at hand.

from litlm import complete

answer = complete("What is 2 + 2?")

answers = complete(
    ["Summarize Ada Lovelace", "Summarize Alan Turing"],
    model="gpt-4.1-mini",
    max_concurrency=16,
)

It is designed for exploratory work where the full SDK response is useful, but SDK ceremony is not.

Install

pip install litlm

Set the keys for the providers you use:

import os

os.environ["OPENROUTER_API_KEY"] = "sk-or-..."
os.environ["NVIDIA_NIM_API_KEY"] = "nvapi-..."      # optional
os.environ["ALBERT_API_KEY"] = "..."                # optional

Why litlm

• A string in, a string-like result out.
• Lists, NumPy arrays, and Pandas Series run as ordered async batches.
• Compact progress shows cost and a bounded error breakdown.
• Partial batches stay usable and can retry only failed positions.
• Results expose usage, reasoning, cost, model, and the raw LiteLLM response.
• Bare model names can resolve through free and paid provider fallbacks.
• The typed signature and docstring work well with Jupyter completion and Shift-Tab help.

Results that remain simple

A scalar result behaves like str:

answer = complete("Write a haiku")

print(answer)
print(answer.model_used)
print(answer.cost)
print(answer.usage)
print(answer.reasoning)
print(answer.call_id)

Any other response field remains accessible through the same object.

Batch results behave like an ordinary list, so existing Python and Pandas code continues to work:

answers = complete(["Capital of France?", "Capital of Japan?"])

answers[0]
len(answers)
df["answer"] = answers
isinstance(answers, list)  # True

Resilient batches

One failed request does not discard the rest of a batch. Failed positions are empty-string-compatible objects with the original exception and prompt attached, so output order and length remain stable.

During a batch, the progress line stays bounded while showing cost, failure rate, error types, and the beginning of a representative message:

Completing: 95%|...| cost=$0.126242, ⚠ 375/755 (49.7%), Timeout×375 | Timeout Error: OpenRouter…

Retry only the positions that failed, optionally with safer settings:

answers.resume(
    timeout=180,
    num_retries=5,
    max_concurrency=8,
)

answers.failures  # failures still present after the retry

resume() updates the same list-compatible result in place. Successful answers are neither requested again nor reordered.

For the latest full provider exception:

import litlm

print(litlm.get_failure())

Or inspect every failed item and its metadata:

failures = litlm.get_failures()
print(failures[-1].error)
print(failures[-1].prompt)

Model routing

Use a bare model name when you want litlm to find a suitable route:

complete("Hello", model="gpt-4.1-mini")
complete("Hello", model="deepseek-v4-flash")
complete("Hello", model="haiku")

Depending on availability and configured keys, bare names are tried through Albert, NVIDIA NIM, OpenRouter free models, then paid OpenRouter models.

Use an exact slug when routing should be explicit:

complete("Hello", model="openrouter/anthropic/claude-sonnet-4")
complete("Hello", model="nvidia_nim/deepseek-ai/deepseek-r1")

To bypass litlm routing and call a LiteLLM provider directly, prefix the exact LiteLLM route with direct/:

complete("Hello", model="direct/gemini/gemini-3.7-flash")
complete("Hello", model="direct/openai/gpt-5.6-luna")

The returned Text.model_used records the route that answered.

Useful controls

Common options are explicit and typed; additional LiteLLM parameters pass through unchanged:

answer = complete(
    "Explain the result briefly",
    system="You are a careful mathematician.",
    model="openrouter/deepseek/deepseek-v4-flash",
    reasoning_effort="none",
    temperature=0.2,
    max_tokens=512,
    timeout=60,
)

Request and parse JSON directly:

data = complete(
    "Return a JSON object with a string field named topic",
    json=True,
)

Throttle large batches by concurrency or request starts per minute:

answers = complete(inputs, max_concurrency=12, rpm=120)

Persist or stream results as soon as each item settles without coupling litlm to an application's storage format:

def save_result(index, result):
    if not result.failed:
        checkpoint(index, str(result), result.usage)

answers = complete(inputs, max_concurrency=12, on_result=save_result)

The callback receives the original input index and a Text or Failure. BatchResult.resume() preserves original indexes when it retries failed items.

Caching

Local response caching avoids paying twice for identical calls and survives notebook restarts:

answer = complete("Expensive stable query", caching=True)

Provider-side prompt caching is separate:

complete("Question over stable context", prompt_cache=True)
complete("Question over stable context", prompt_cache="1h")
complete(
    "Question over stable context",
    cache_control={"type": "ephemeral", "ttl": "1h"},
)

History and cost

from litlm import cost_breakdown, get_history

last_result = get_history()
first_result = get_history(0)

cost_breakdown("session")
cost_breakdown("day")
cost_breakdown("week", by="day")

Cost history is lightweight and in memory for the current Python process.