Metadata-Version: 2.4
Name: token-throttle
Version: 1.4.0
Summary: Simple Multi-Resource Rate Limiting That Saves Unused Tokens. Rate limit API requests across different resources and workers without wasting your quota. Reserve tokens upfront, get refunds for what you don't use, and avoid over-limiting.
License-File: LICENSE
Requires-Python: >=3.12
Requires-Dist: frozendict>=2.4.6
Requires-Dist: pydantic>=2.11.3
Provides-Extra: loguru
Requires-Dist: loguru>=0.7.3; extra == 'loguru'
Provides-Extra: redis
Requires-Dist: redis>=5.2.1; extra == 'redis'
Provides-Extra: tiktoken
Requires-Dist: tiktoken>=0.4.0; extra == 'tiktoken'
Description-Content-Type: text/markdown

# token-throttle

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**Multi-resource rate limiting for LLM APIs.** Reserve tokens before you call, refund what you don't use, stay under the limit across workers.

Works with any LLM provider and any client library — token-throttle limits the _rate_, not the _client_.

```bash
pip install "token-throttle[redis,tiktoken]>=1.4.0,<1.5.0"   # OpenAI + Redis (recommended)
pip install "token-throttle[redis]>=1.4.0,<1.5.0"            # Any provider + Redis
pip install "token-throttle>=1.4.0,<1.5.0"                   # Any provider + in-memory
```

## Quickstart

### OpenAI (built-in helpers)

```python
from openai import AsyncOpenAI
from token_throttle import create_openai_redis_rate_limiter

client = AsyncOpenAI()
limiter = create_openai_redis_rate_limiter(
    redis_client, rpm=10_000, tpm=2_000_000,
)

# 1. Reserve capacity (blocks until available)
request = dict(model="gpt-4.1", messages=[{"role": "user", "content": "Hi"}])
reservation = await limiter.acquire_capacity_for_request(**request, extra_usage=None)

# 2. Make the API call
response = await client.chat.completions.create(**request)

# 3. Refund unused tokens
await limiter.refund_capacity_from_response(reservation, response)
```

### Any provider (manual usage)

```python
from token_throttle import RateLimiter, Quota, UsageQuotas, RedisBackendBuilder
from token_throttle import PerModelConfig

limiter = RateLimiter(
    lambda model: PerModelConfig(
        quotas=UsageQuotas([
            Quota(metric="requests", limit=1_000, per_seconds=60),
            Quota(metric="input_tokens", limit=80_000, per_seconds=60),
            Quota(metric="output_tokens", limit=20_000, per_seconds=60),
        ]),
    ),
    backend=RedisBackendBuilder(redis_client),
)

# Works with Anthropic, Gemini, local models — anything
reservation = await limiter.acquire_capacity(
    model="claude-sonnet-4-20250514",
    usage={"requests": 1, "input_tokens": 500, "output_tokens": 4_000},
)

response = await call_your_llm(...)  # Use whatever client you want

await limiter.refund_capacity(
    actual_usage={"requests": 1, "input_tokens": 480, "output_tokens": 1_200},
    reservation=reservation,
)
# Unused 2,800 output tokens returned to the pool
```

## Why token-throttle

**The problem:** You're running parallel LLM calls (batch processing, agents, multiple services sharing a key). Simple rate limiters waste throughput because they reserve worst-case tokens and never give them back. You hit 429s or crawl at half capacity.

**The solution:** Reserve before you call, refund after. Actual usage is tracked, not estimated maximums.

| Feature | Details |
|---------|---------|
| **Multi-resource limits** | Limit requests, tokens, input/output tokens — simultaneously, each with its own quota |
| **Multiple time windows** | e.g., 1,000 req/min AND 10,000 req/day on the same resource |
| **Reserve & refund** | Reserve max expected usage upfront, refund the difference after the call completes |
| **Distributed** | Redis backend with atomic locks — safe across workers and processes |
| **Per-model quotas** | Different limits per model via `model_family`; the built-in OpenAI helper auto-groups date-suffixed variants (e.g. gpt-4o-20241203 → gpt-4o) |
| **Pluggable** | Bring your own backend (ships with Redis and in-memory). Sync and async APIs |
| **Observability** | Callbacks for wait-start, wait-end, consume, refund, and missing-state events |

## How it works

token-throttle implements a [token bucket](https://en.wikipedia.org/wiki/Token_bucket) algorithm (capacity refills linearly over time, capped at the quota limit).

- **Acquire** — blocks until enough capacity is available, then atomically reserves it
- **Call** — make your API request with any client
- **Refund** — report actual usage; unused tokens return to the pool immediately

The Redis backend uses sorted locking to prevent deadlocks when acquiring multiple resource buckets simultaneously.

## Configuration

### Quotas

```python
from token_throttle import Quota, UsageQuotas, SecondsIn

quotas = UsageQuotas([
    Quota(metric="requests", limit=2_000, per_seconds=SecondsIn.MINUTE),
    Quota(metric="tokens", limit=3_000_000, per_seconds=SecondsIn.MINUTE),
    Quota(metric="requests", limit=10_000_000, per_seconds=SecondsIn.DAY),
])
```

`per_seconds` accepts integer seconds. Use `SecondsIn.MINUTE` (60), `SecondsIn.HOUR` (3600), `SecondsIn.DAY` (86400), or any integer.

### Per-model configuration

```python
def get_config(model_name: str) -> PerModelConfig:
    if model_name.startswith("gpt"):
        return PerModelConfig(
            quotas=UsageQuotas([
                Quota(metric="requests", limit=10_000, per_seconds=60),
                Quota(metric="tokens", limit=2_000_000, per_seconds=60),
            ]),
            usage_counter=OpenAIUsageCounter(),  # text-only: counts payload + instructions/tools/schema + output budget
            model_family=openai_model_family_getter(model_name),
        )
    # ... other providers

limiter = RateLimiter(get_config, backend=RedisBackendBuilder(redis_client))
```

Models that share a `model_family` must also share the same live quota definition. If two model names need different limits, give them different `model_family` values instead of reusing one family name.

`OpenAIUsageCounter` handles text-only OpenAI requests. It counts `input`,
`inputs`, or `messages`, plus prompt-bearing request context such as
`instructions`, tool/function definitions, and structured output schemas.
Image/audio/file inputs are still unsupported; pass usage manually for those.

Custom `usage_counter` callables receive the same kwargs you pass to
`acquire_capacity_for_request()`. They can accept `**request` for the whole
payload or only the named request fields they use; fixed-signature counters do
not need to accept unrelated kwargs like `model`.

### Backends

```python
# Distributed (multiple workers/processes)
from token_throttle import RedisBackendBuilder
backend = RedisBackendBuilder(redis_client)

# Single process (no Redis needed)
from token_throttle import MemoryBackendBuilder
backend = MemoryBackendBuilder()
```

Both backends are available in sync (`SyncRedisBackendBuilder`, `SyncMemoryBackendBuilder`) and async variants.

### Dynamic rate limits

Adjust bucket limits at runtime without rebuilding the limiter — useful for
adaptive rate limiting (e.g., reacting to `x-ratelimit-*` response headers):

```python
# After at least one acquire/record call for this model:
await limiter.set_max_capacity(
    model="gpt-4o",
    metric="tokens",
    per_seconds=60,
    value=5000,
)
```

For Redis backends the new limit is written to Redis, so all processes
sharing the same Redis see the change within ~1 second. This persisted Redis
value is an explicit runtime override; static quota changes from your config do
not rewrite it automatically.

If a callable config removes a bucket and later re-adds it, the re-added
bucket starts from the static quota in the current config. Runtime overrides
from earlier `set_max_capacity()` calls do not survive a remove-and-readd;
call `set_max_capacity()` again if you want the override restored.

### Timeout

By default, `acquire_capacity` blocks until enough capacity is available.
Use `timeout` to fail fast or cap the wait:

```python
# Non-blocking: check if capacity is available without waiting
try:
    reservation = await limiter.acquire_capacity(
        model="gpt-4o",
        usage={"requests": 1, "tokens": 500},
        timeout=0,  # Fail immediately if no capacity
    )
except TimeoutError:
    # Handle: retry later, use cheaper model, skip, etc.
    pass

# Bounded wait: wait up to 5 seconds
reservation = await limiter.acquire_capacity(
    model="gpt-4o",
    usage={"requests": 1, "tokens": 500},
    timeout=5.0,  # Raise TimeoutError after 5s
)

Async callbacks are awaited within the remaining timeout budget. Sync
callbacks run inline and cannot be preempted, so a slow sync wait callback can
push wall-clock time past the requested timeout before `TimeoutError` is
raised.
```

## Sync API

```python
from token_throttle import SyncRateLimiter, SyncMemoryBackendBuilder

limiter = SyncRateLimiter(get_config, backend=SyncMemoryBackendBuilder())

reservation = limiter.acquire_capacity(model="gpt-4.1", usage={"requests": 1, "tokens": 500})
response = call_llm_sync(...)
limiter.refund_capacity(actual_usage={"requests": 1, "tokens": 320}, reservation=reservation)
```

## Links

- Originally a rewrite of [openlimit](https://github.com/shobrook/openlimit)

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