async-bulkhead-llm

Fail-fast admission control for LLM workloads, built on async-bulkhead-ts.

Designed for services that need to enforce cost ceilings, concurrency limits, and backpressure at the boundary of their LLM calls — before request fan-out, before hitting provider rate limits, before saturation cascades.

Features

• ✅ Hard max in-flight concurrency (maxConcurrent)
• ✅ Token-aware admission — reserves against estimated input + max output tokens
• ✅ Token refund — reclaim unused budget capacity from actual usage post-completion
• ✅ Model-aware estimation — per-model character ratios for known providers
• ✅ Per-request model — mixed-model routing through a single bulkhead
• ✅ Multimodal content — text blocks counted, non-text blocks ignored
• ✅ Fail-fast by default — shed load early, never silently queue
• ✅ Opinionated profiles — 'interactive' and 'batch' presets with escape hatch
• ✅ In-flight deduplication — identical requests share one LLM call
• ✅ Custom dedup key — bring your own equivalence function
• ✅ Event hooks — on('admit' | 'reject' | 'release' | 'dedup', fn)
• ✅ Graceful shutdown — close() + drain()
• ✅ Optional AbortSignal and timeout cancellation
• ✅ bulkhead.run(request, fn) — acquire + release handled automatically
• ✅ Zero dependencies beyond async-bulkhead-ts
• ✅ ESM + CJS
• ✅ Node.js 20+

Non-goals (by design):

• ❌ No retries
• ❌ No provider SDK — bring your own client
• ❌ No distributed coordination
• ❌ No cost accounting — token estimation is for load-shedding only

Competitive Matrix (LLM Workloads)

| Capability / Library | async-bulkhead-llm | LangChain / LlamaIndex | OpenAI SDK (raw) | p-limit / Bottleneck | cockatiel / polly | |---------------------|--------------------|------------------------|------------------|---------------------|-------------------| | Primary goal | LLM admission control (cost + concurrency) | Orchestration / pipelines | API client | Concurrency / scheduling | Resilience patterns | | Fail-fast by default | ✅ Yes | ❌ No | ❌ No | ❌ No | ⚠️ Depends | | Token-aware admission | ✅ Yes (pre-admission budget) | ❌ No | ❌ No | ❌ No | ❌ No | | Token refund (post-call correction) | ✅ Yes | ❌ No | ❌ No | ❌ No | ❌ No | | Cost ceiling enforcement | ✅ Yes (tokenBudget) | ❌ No | ❌ No | ❌ No | ❌ No | | Concurrency limits | ✅ Yes | ⚠️ Indirect | ❌ No | ✅ Yes | ⚠️ Indirect | | Bounded queue (optional) | ✅ Yes | ⚠️ Internal | ❌ No | ✅ Yes | ⚠️ Indirect | | Fail-fast overload handling | ✅ Core feature | ❌ No | ❌ No | ❌ No | ⚠️ Indirect | | In-flight deduplication | ✅ Yes | ⚠️ Partial caching | ❌ No | ❌ No | ❌ No | | Custom dedup key | ✅ Yes | ❌ No | ❌ No | ❌ No | ❌ No | | Model-aware estimation | ✅ Yes | ❌ No | ❌ No | ❌ No | ❌ No | | Per-request model routing | ✅ Yes | ✅ Yes | ✅ Yes | ❌ No | ❌ No | | Multimodal-aware estimation | ✅ Yes (text-only counted) | ❌ No | ❌ No | ❌ No | ❌ No | | Abort / timeout (admission) | ✅ Yes | ⚠️ Partial | ⚠️ SDK-level | ⚠️ Partial | ✅ Yes | | Event hooks (metrics/logging) | ✅ Yes | ❌ No | ❌ No | ❌ No | ⚠️ Limited | | Graceful shutdown (drain/close) | ✅ Yes | ❌ No | ❌ No | ❌ No | ❌ No | | Retries / fallback | ❌ No | ⚠️ Yes | ❌ No | ❌ No | ✅ Yes | | LLM orchestration (chains/agents) | ❌ No | ✅ Yes | ❌ No | ❌ No | ❌ No |

Quick positioning

• LangChain / LlamaIndex → what to run (orchestration)
• OpenAI SDK → how to call the provider
• p-limit / Bottleneck → how many tasks run
• cockatiel / polly → what happens after failure
• async-bulkhead-llm → whether a request should run at all

Rule of thumb

If you want to build LLM pipelines, use LangChain.
If you want to protect your system and budget under load, use async-bulkhead-llm.

Key differentiator

Most LLM tooling optimizes execution.
async-bulkhead-llm optimizes survival under load.

It enforces:

• concurrency ceilings
• token budget ceilings
• fail-fast admission

before a request ever reaches your provider.

Install

npm install async-bulkhead-llm

Quick Start

import { createLLMBulkhead } from 'async-bulkhead-llm';

const bulkhead = createLLMBulkhead({
  model:          'claude-sonnet-4',
  maxConcurrent:  10,
});

const request = {
  messages: [{ role: 'user', content: 'Summarise this document...' }],
  max_tokens: 1024,
};

const result = await bulkhead.run(request, async () => {
  return callYourLLMProvider(request);
});

What's New in v3

Stats are now namespaced

bulkhead.stats() now separates:

• underlying bulkhead stats → stats().bulkhead
• LLM-layer request stats → stats().llm

This avoids conflating base bulkhead slot lifecycle with higher-level LLM request outcomes such as 'budget_limit'.

What's New in v2

Token Refund

v1 reserved input + maxOutput tokens and held that full reservation until release. Most calls use far fewer output tokens than max_tokens. v2 reclaims the difference:

// Via run() — extract usage from your provider's response
const result = await bulkhead.run(
  request,
  async () => callLLM(request),
  {
    getUsage: (response) => ({
      input:  response.usage.input_tokens,
      output: response.usage.output_tokens,
    }),
  },
);

// Via acquire() — pass usage at release time
const r = await bulkhead.acquire(request);
if (r.ok) {
  try {
    const response = await callLLM(request);
    return response;
  } finally {
    r.token.release({
      input:  response.usage.input_tokens,
      output: response.usage.output_tokens,
    });
  }
}

When usage is reported, the refund is reserved - (actual input + actual output). Budget capacity is returned immediately. Without usage, behavior is identical to v1.

Multimodal Content

content may now be a plain string or an array of content blocks:

const request = {
  messages: [{
    role: 'user',
    content: [
      { type: 'text', text: 'Describe this image...' },
      { type: 'image', source: { type: 'base64', data: '...' } },
    ],
  }],
  max_tokens: 1024,
};

Built-in estimators extract text from text blocks and ignore non-text blocks. Token estimates for multimodal requests are a lower bound. Provide a custom estimator for accurate multimodal estimation.

Per-Request Model

Route multiple models through a single bulkhead with accurate per-model estimation:

const bulkhead = createLLMBulkhead({
  model:         'claude-sonnet-4',  // default for estimation
  maxConcurrent: 20,
  tokenBudget:   { budget: 500_000 },
});

// Estimator uses request.model when present
await bulkhead.run(
  { model: 'claude-haiku-4-5', messages, max_tokens: 512 },
  async () => callLLM(request),
);

Custom Dedup Key

const bulkhead = createLLMBulkhead({
  model:         'claude-sonnet-4',
  maxConcurrent: 10,
  deduplication: {
    keyFn: (request) => {
      // Your own equivalence logic
      return hash(request.messages);
    },
  },
});

The default key now includes messages, max_tokens, and model. Return an empty string to opt a specific request out.

Event Hooks

const off = bulkhead.on('release', ({ reservedTokens, refundedTokens, usage }) => {
  metrics.histogram('llm.tokens.reserved', reservedTokens);
  metrics.histogram('llm.tokens.refunded', refundedTokens);
});

// Later:
off(); // unsubscribe

Events: 'admit', 'reject', 'release', 'dedup'.

Graceful Shutdown

// In your SIGTERM handler:
bulkhead.close();       // reject all pending; block future admission
await bulkhead.drain(); // wait for in-flight work to complete

Profiles

Two built-in presets cover the common cases. Explicit options always override preset defaults.

// Interactive — fail-fast, no waiting (default)
const bulkhead = createLLMBulkhead({
  model:         'claude-sonnet-4',
  maxConcurrent: 10,
  profile:       'interactive',
});

// Batch — bounded queue, 30s timeout
const bulkhead = createLLMBulkhead({
  model:         'claude-sonnet-4',
  maxConcurrent: 4,
  profile:       'batch',
});

// Escape hatch — plain object
const bulkhead = createLLMBulkhead({
  model:         'claude-sonnet-4',
  maxConcurrent: 4,
  profile:       { maxQueue: 5, timeoutMs: 5_000 },
});

Token Budget

const bulkhead = createLLMBulkhead({
  model:         'claude-sonnet-4',
  maxConcurrent: 10,
  tokenBudget: {
    budget: 200_000,
  },
});

Token reservations are calculated pre-admission from input + maxOutput. Capacity is returned when each request completes. When TokenUsage is provided at release, the refund reclaims unused capacity immediately.

How Estimation Works

The default estimator is createModelAwareTokenEstimator seeded with the bulkhead's model. It uses per-model character-to-token ratios for known model families, falling back to a flat 4.0 ratio for unknown models.

v2 checks request.model first (when present), then falls back to the bulkhead-level defaultModel.

Known model families: claude-3-5-haiku, claude-3-5-sonnet, claude-3-*, claude-sonnet-4, claude-opus-4, claude-haiku-4, claude-*-4-5, gpt-4o, gpt-4-turbo, gpt-4.1, gpt-4, gpt-3.5, o1, o3, o4-mini, gemini-1.5, gemini-2, gemini-2.5.

Custom Estimator

import { Tiktoken } from 'tiktoken';

const enc = new Tiktoken(/* your model encoding */);

const bulkhead = createLLMBulkhead({
  model:         'claude-sonnet-4',
  maxConcurrent: 10,
  tokenBudget: {
    budget:    200_000,
    estimator: (request) => ({
      input:     enc.encode(request.messages.map(m =>
        typeof m.content === 'string'
          ? m.content
          : m.content.filter(b => b.type === 'text').map(b => b.text).join('')
      ).join('')).length,
      maxOutput: request.max_tokens ?? 2_048,
    }),
  },
});

Estimator Utilities

import {
  naiveTokenEstimator,
  createModelAwareTokenEstimator,
  extractTextLength,
} from 'async-bulkhead-llm';

// Flat 4.0 ratio, multimodal-safe
const est1 = naiveTokenEstimator(request);

// Per-model ratios
const estimate = createModelAwareTokenEstimator(
  { 'my-azure-deployment': 3.7 },
  {
    defaultModel:   'claude-sonnet-4',
    outputCap:      2_048,
    onUnknownModel: (model) => console.warn(`Unknown model: ${model}`),
  },
);

// Utility for multimodal content
const charCount = extractTextLength(message.content);

Deduplication

const bulkhead = createLLMBulkhead({
  model:         'claude-sonnet-4',
  maxConcurrent: 10,
  deduplication: true,
});

const [r1, r2] = await Promise.all([
  bulkhead.run(request, async () => callLLM(request)),
  bulkhead.run(request, async () => callLLM(request)), // deduped
]);

v2 default key: JSON.stringify({ m: request.messages, t: request.max_tokens, o: request.model }). Requests with different max_tokens or model are not conflated.

Custom key:

deduplication: {
  keyFn: (request) => myHash(request.messages),
}

Return "" from keyFn to opt a specific request out.

Cancellation

const ac = new AbortController();

await bulkhead.run(
  request,
  async (signal) => callLLM(request, { signal }),
  { signal: ac.signal },
);

ac.abort();

Bound waiting time:

await bulkhead.run(request, async () => callLLM(request), { timeoutMs: 5_000 });

Manual Acquire / Release

const r = await bulkhead.acquire(request);

if (!r.ok) {
  // r.reason: 'concurrency_limit' | 'queue_limit' | 'budget_limit'
  //         | 'timeout' | 'aborted' | 'shutdown'
  return respond503(r.reason);
}

try {
  const response = await callLLM(request);
  return response;
} finally {
  // v2: pass usage for refund
  r.token.release({
    input:  response.usage.input_tokens,
    output: response.usage.output_tokens,
  });
}

Handling Rejections

import { LLMBulkheadRejectedError } from 'async-bulkhead-llm';

try {
  await bulkhead.run(request, async () => callLLM(request));
} catch (err) {
  if (err instanceof LLMBulkheadRejectedError) {
    // err.reason: 'concurrency_limit' | 'queue_limit' | 'budget_limit'
    //           | 'timeout' | 'aborted' | 'shutdown'
    return respond503(`Shed: ${err.reason}`);
  }
  throw err;
}

Stats

const s = bulkhead.stats();

s.bulkhead.inFlight
s.bulkhead.pending
s.bulkhead.maxConcurrent
s.bulkhead.maxQueue
s.bulkhead.closed                    // true after close()

s.llm.admitted
s.llm.released
s.llm.rejected
s.llm.rejectedByReason?.budget_limit                        // true after close()

s.tokenBudget?.budget
s.tokenBudget?.inFlightTokens
s.tokenBudget?.available
s.tokenBudget?.totalReserved        // v3.1: cumulative tokens reserved at admission
s.tokenBudget?.totalConsumed        // v3.1: cumulative actual tokens consumed (usage required)
s.tokenBudget?.totalRefunded         // v2: cumulative refunded tokens

s.deduplication?.active
s.deduplication?.hits

type LLMStats = {
  bulkhead: Stats;
  llm: {
    admitted: number;
    released: number;
    rejected: number;
    rejectedByReason: Partial<Record<LLMRejectReason, number>>;
  };
  tokenBudget?: {
    budget: number;
    inFlightTokens: number;
    available: number;
    totalReserved: number;
    totalConsumed: number;
    totalRefunded: number;
  };
  deduplication?: {
    active: number;
    hits: number;
  };
};

Event Hooks

// Subscribe
const off = bulkhead.on('admit', ({ request, reservedTokens }) => {
  console.log(`Admitted: ${reservedTokens} tokens reserved`);
});

// Unsubscribe
off();

| Event | Payload | |-----------|---------| | admit | { request, reservedTokens } | | reject | { request, reason } | | release | { request, reservedTokens, refundedTokens, usage? } | | dedup | { request } |

Listeners are synchronous and fire-and-forget. Exceptions are silently caught.

Graceful Shutdown

bulkhead.close();        // stop admission, reject pending waiters
await bulkhead.drain();  // wait for in-flight work to finish

close() is synchronous, idempotent, and irreversible. drain() resolves when inFlight and pending both reach zero.

API Reference

createLLMBulkhead(options)

type LLMBulkheadOptions = {
  model:          string;
  maxConcurrent:  number;
  maxQueue?:      number;
  timeoutMs?:     number;
  profile?:       'interactive' | 'batch' | LLMBulkheadPreset;
  tokenBudget?: {
    budget:      number;
    estimator?:  TokenEstimator;
    outputCap?:  number;
  };
  deduplication?: boolean | DeduplicationOptions;
};

type DeduplicationOptions = {
  keyFn?: (request: LLMRequest) => string;
};

bulkhead.run(request, fn, options?)

run<T>(
  request:  LLMRequest,
  fn:       (signal?: AbortSignal) => Promise<T>,
  options?: {
    signal?:    AbortSignal;
    timeoutMs?: number;
    getUsage?:  (result: T) => TokenUsage | undefined;
  },
): Promise<T>

bulkhead.acquire(request, options?)

acquire(
  request:  LLMRequest,
  options?: { signal?: AbortSignal; timeoutMs?: number },
): Promise<LLMAcquireResult>

type LLMAcquireResult =
  | { ok: true;  token: LLMToken }
  | { ok: false; reason: LLMRejectReason };

type LLMToken = { release(usage?: TokenUsage): void };

bulkhead.stats()

bulkhead.close()

bulkhead.drain()

bulkhead.on(event, listener)

on<K extends keyof LLMEventMap>(
  event:    K,
  listener: (payload: LLMEventMap[K]) => void,
): () => void    // returns unsubscribe

Types

type LLMRequest     = { model?: string; messages: LLMMessage[]; max_tokens?: number };
type LLMMessage     = { role: string; content: string | ContentBlock[] };
type ContentBlock   = TextContentBlock | OpaqueContentBlock;
type TextContentBlock  = { type: 'text'; text: string };
type OpaqueContentBlock = { type: string; [key: string]: unknown };

type TokenEstimate  = { input: number; maxOutput: number };
type TokenEstimator = (request: LLMRequest) => TokenEstimate;
type TokenUsage     = { input: number; output: number };

type LLMRejectReason =
  | 'concurrency_limit'
  | 'queue_limit'
  | 'budget_limit'
  | 'timeout'
  | 'aborted'
  | 'shutdown';

Migration

v2 → v3

Update stats access from top-level fields to the bulkhead block:

const s = bulkhead.stats();

// v2
s.inFlight;
s.pending;

// v3
s.bulkhead.inFlight;
s.bulkhead.pending;

LLM-layer counters are now available separately:

const s = bulkhead.stats();
s.llm.admitted;
s.llm.rejected;
s.llm.rejectedByReason?.budget_limit;

v1 → v2

See CHANGELOG for the v2 migration guide.

Design Notes

This library enforces backpressure at the boundary of your LLM calls. It does not replace higher-level concerns:

• Retries — compose a retry library around the bulkhead, not inside it
• Cost accounting — use provider response metadata; estimation here is for gating only
• Distributed rate limiting — use a shared token bucket; bulkheads are per-process
• Model selection — route above the bulkhead layer; each bulkhead instance is model-specific

Token estimation is deliberately approximate. The refund mechanism improves budget utilization but does not make estimation exact — it corrects after the fact based on actual usage.

Compatibility

• Node.js: 20+
• Module formats: ESM and CommonJS

License

Apache License 2.0 © 2026