Workflow Rate Limiting And Throttling

via “request rate limiting with queue-based throttling and quota tracking”

Create and manage Linear issues and projects via MCP.

Unique: Implements queue-based rate limiting with request batching to maximize throughput while respecting Linear's 1400 req/hr quota. Transparent to MCP tools — all rate limiting happens in the LinearMCPClient abstraction layer.

vs others: More sophisticated than naive request delays because it batches requests and tracks quota, and simpler than implementing per-user rate limiting because it uses a shared quota model suitable for single-workspace deployments.

via “rate limiting and quota management with tier-based access”

Access to GPT-4o, o1/o3, DALL-E 3, Whisper, embeddings — function calling, assistants, fine-tuning.

via “rate-limiting-and-throttling-with-multi-level-enforcement”

Unified API for 100+ LLM providers — OpenAI format, load balancing, spend tracking, proxy server.

Unique: Implements a hierarchical rate limiting system where limits cascade from organization → team → user, with per-model overrides. Uses Redis token bucket algorithm (increment counter, check against limit, decrement on success) with configurable window sizes (minute, hour, day). Supports both request-count limits and token-consumption limits, enabling fine-grained control over LLM usage.

vs others: More granular than API Gateway rate limiting (which typically only does per-IP); supports token-based limits unlike request-count-only systems; hierarchical enforcement is unique vs flat rate limit structures

via “rate-limited request throttling with per-tool quotas”

Search the web privately via DuckDuckGo MCP.

Unique: Implements dual-quota rate limiting (30 req/min search, 20 req/min content) at the MCP tool execution layer rather than at HTTP client level, providing tool-specific throttling that reflects actual service impact. Integrated into FastMCP framework's tool decorator pattern, making limits transparent to MCP clients without additional configuration.

vs others: More granular than generic HTTP rate limiters (separate quotas per tool); simpler than distributed rate limiting systems (no Redis/external state needed); integrated into MCP protocol layer vs requiring separate middleware.

via “concurrency control with per-function and per-key limits”

Event-driven durable workflow engine.

Unique: Implements distributed concurrency control via Redis Lua scripts with atomic compare-and-swap operations, supporting both global and per-key limits without requiring external coordination services. Lease-based locking prevents deadlocks from crashed executors.

vs others: More flexible than simple rate limiting (supports per-key limits) while avoiding the complexity of distributed consensus systems like Zookeeper.

via “rate limiting and fairness scheduling for llm api calls”

Distributed task queue for AI workloads.

Unique: Implements hierarchical rate limiting (workflow, step, action levels) with fairness scheduling specifically optimized for LLM API calls, using token bucket algorithms to enforce quotas while allowing bursts. Prevents single workflows from starving others in multi-tenant systems.

vs others: More sophisticated than simple queue-based rate limiting; purpose-built for LLM fairness vs generic rate limiting libraries.

via “concurrency control and rate limiting per task”

Background jobs framework for TypeScript.

Unique: Implements distributed concurrency control via Redis-based locking that coordinates limits across multiple worker instances, with both per-task concurrency caps and time-window-based rate limiting — unlike Bull which only supports per-queue concurrency.

vs others: Provides fine-grained per-task concurrency control across distributed workers, whereas traditional job queues require manual rate limiting logic in task code.

via “rate limiting and quota management with tiered access”

Gen-3 Alpha video generation API.

Unique: Implements tiered quota systems with quota pooling support for teams, allowing shared budget management across multiple API keys. Rate limit headers provide real-time quota visibility for client-side backoff implementation.

vs others: Offers more granular quota management than simple per-minute rate limits, enabling better resource allocation for teams and organizations with complex usage patterns.

via “rate-limiting-and-throttling-with-distributed-state”

Python SDK, Proxy Server (AI Gateway) to call 100+ LLM APIs in OpenAI (or native) format, with cost tracking, guardrails, loadbalancing and logging. [Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, HuggingFace, VLLM, NVIDIA NIM]

Unique: Implements distributed rate limiting using Redis with support for multiple limit strategies (requests/minute, tokens/hour, cost/day), with automatic HTTP 429 responses and retry-after headers, enabling fair resource allocation across multi-tenant deployments

vs others: More sophisticated than simple request counting; supports token-based and cost-based limits in addition to request counts, enabling fine-grained control over LLM usage

via “billing and quota management with usage tracking and rate limiting”

Open-source no-code automation tool.

Unique: Implements quota enforcement at the execution engine level with real-time tracking, preventing quota overages before they occur rather than charging retroactively — a feature essential for multi-tenant SaaS deployments

vs others: More granular than simple API rate limiting because it tracks workflow-level metrics (runs, API calls) in addition to HTTP request rates, enabling fair resource allocation in multi-tenant environments

via “rate-limiting-and-quota-enforcement”

Headless browser infrastructure for AI agents — stealth mode, CAPTCHA solving, session recording.

Unique: Implements per-project rate limits (5 RPS Fetch, 2 RPS Search) with tier-based enforcement; however, quota exceeded behavior and burst capacity are undocumented, making it difficult to design resilient agents

vs others: Standard rate limiting approach but less transparent than documented APIs (no published retry strategy or burst capacity); custom limits for enterprise provide flexibility but lack of documentation limits adoption

via “rate limiting and quota management”

Run ML models via API — thousands of models, pay-per-second, custom model deployment via Cog.

Unique: Rate limiting is enforced at the API gateway level with per-user and per-organization granularity, preventing abuse without requiring application-level logic.

vs others: More transparent than cloud provider rate limiting (clear headers and error messages) but less flexible than custom quota systems; comparable to API gateway solutions like Kong or AWS API Gateway.

via “rate limiting and execution quota management”

Visual workflow automation platform.

Unique: Implements account-level execution quotas tied to pricing plans and provides rate limiting per module to prevent API abuse, with quota monitoring and alerts in the dashboard

vs others: More transparent than Zapier's task counting because Make clearly shows operation counts per scenario; simpler than managing Airflow resource limits because quotas are enforced automatically

via “rate limiting and quota management”

Opinionated MCP Framework for TypeScript (@modelcontextprotocol/sdk compatible) - Build MCP Agents, Clients and Servers with support for ChatGPT Apps, Code Mode, OAuth, Notifications, Sampling, Observability and more.

Unique: Implements rate limiting as a declarative middleware layer with multiple strategies (token bucket, sliding window) and quota scopes (per-user, per-IP, global), eliminating the need to implement rate limiting logic in individual tools

vs others: More flexible than fixed rate limits because it supports multiple strategies and scopes, whereas naive implementations use a single global limit that cannot adapt to different user tiers or resource types

via “per-tool rate limiting with request throttling”

A Model Context Protocol (MCP) server that provides web search capabilities through DuckDuckGo, with additional features for content fetching and parsing.

Unique: Implements independent per-tool rate limits (30 req/min search, 20 req/min content) with transparent request delay rather than rejection, allowing LLMs to continue operating without error handling logic — rate limits are enforced at the MCP tool invocation layer rather than at HTTP client level

vs others: Simpler than distributed rate limiting (Redis-backed) for single-instance deployments; more user-friendly than hard rejections because LLMs don't need to implement retry logic

via “rate limit management and dry-run testing”

Convert documentation websites, GitHub repositories, and PDFs into Claude AI skills with automatic conflict detection

Unique: Implements intelligent rate limit management with automatic backoff and retry logic, plus dry-run mode for safe testing without side effects. Provides quota tracking to estimate API usage before execution.

vs others: Provides built-in rate limit management and dry-run testing, whereas most tools require manual rate limit handling or lack testing modes.

via “rate limiting and quota management per provider”

Unify and supercharge your LLM workflows by connecting your applications to any model. Easily switch between various LLM providers and leverage their unique strengths for complex reasoning tasks. Experience seamless integration without vendor lock-in, making your AI orchestration smarter and more ef

Unique: Rate limiting is provider-specific and integrated with routing, allowing the framework to automatically select providers with available quota; supports both hard limits (reject) and soft limits (queue)

vs others: More sophisticated than generic rate limiting because it's provider-aware and can queue requests rather than failing them, enabling better utilization of available quota

via “rate limiting and quota enforcement per user/tool/api key”

** - Enterprise MCP gateway with SSO, RBAC, audit trails, and token vaults for secure, centralized AI agent access control. Deploy via Helm charts on-premise or in your cloud. [webrix.ai](https://webrix.ai)

Unique: Implements MCP-aware rate limiting with per-user, per-tool, and per-API-key quotas enforced at the gateway layer, with optional Redis backend for distributed deployments and support for burst allowances

vs others: More granular than network-level rate limiting (which applies uniformly to all traffic) and more MCP-native than generic API gateway rate limiting, enabling tool-specific and user-specific quotas without tool code changes

via “rate limiting and quota enforcement with tillit api compliance”

Local MCP server for Tillit API using @modelcontextprotocol/sdk. Provides 195+ tools and 48+ resources for complete Tillit API access with built-in documentation.

Unique: Implements Tillit-aware rate limiting that tracks API call counts per operation type and enforces quotas with optional persistence for distributed deployments. Exposes rate limit status to Claude for intelligent request batching.

vs others: More sophisticated than naive per-request rate limiting, with operation-specific tracking and visibility into quota consumption that enables proactive capacity management.

via “rate limiting and request throttling per configuration”

** - Discover, extract, and interact with the web - one interface powering automated access across the public internet.

Unique: Implements configurable per-server rate limiting with queue-based request throttling, allowing teams to enforce quota constraints without external rate-limiting services, and exposing rate-limit metadata to agents for intelligent backoff

vs others: Provides built-in rate limiting (vs external rate-limit services), and exposes limit status to agents (vs silent failures when quota exceeded)