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Graph/rate-limit-reset-hammering

Pattern

Rate Limit Reset Hammering

rate-limit-reset-hammering

Rate limiting and budget controllers reset on restart or on placeholder-empty streams, so clients/proxies keep retrying after 429 with fixed delays and permissive “headroom” checks, causing repeated hammering and premature OOM kills under memory limits.

Node Details

Public graph metadata

Updated7/6/2026

Connections

30 linked nodes

Unhandled Runtime Error Error: Maximum call stack size exceeded — trying to fetch data on the server instead of the client so I went with the hydrate method. Tension: worked fine fetching on client. Outcome: same result.

Vercel Hobby bandwidth usage does not reset after a month

your token-counting / budget-calibration state had no durable backing. Tension: it just exposes that your token-counting / budget-calibration state had no durable backing. Outcome: The fix is to stop relying on process memory for state that has to survive a restart.

AWS Lambda functions using the same Sentry DSN/client key began receiving Sentry rate-limiting errors (HTTP 429).

requests with the forbidden fields sailed through to the write path. Tension: accepting them would let one client rewrite server-derived state. Outcome: surfacing as a 500 from an unrelated downstream failure in our integration test.

If I proxy 6 streams, then my FastAPI app stops accepting any further requests — testing the application through a web browser. Outcome: all subsequent requests would get stalled and queued, until a connection is available.

as soon as RAM utilisation hits ~4-5GB, the child process dies — If I run the fastapi server with workers ( `fastapi start src/api.py --port 3000 --workers 2` ). Tension: The machine has 8 cores and 64GB of RAM. Outcome: The behavior is consistent irrespective of the number of workers ( workers > 1 ).

The idea is to update this model once hourly without blocking the API's ability to respond. Tension: update this model once hourly without blocking the API's ability to respond to CPU-bound tasks. Outcome: this is all good.

the application simply stops responding to webhooks and starts returning a 504 error — running on GCP's Cloud Run. Tension: The only way to "restore" the service is by restarting the Cloud Run container. Outcome: The last message is "Instantiating PubSub client...", then the POD stucks.

intermittently logged `[REDACTED]` after fresh browser loads and route changes — A custom activity/chat overlay. Tension: The page checked the gateway browser client for an open WebSocket and then immediately sent RPCs such as session subscribe/list/history. Outcome: The client flag represented the raw socket state, not completion of the gateway's first-frame `connect` handshake.

Implementing MCP StreamableHTTP transport in Hono. Tension: Inspecting the handler shows the transport map is rebuilt per request.

force-kills the process as "stalled" even though it is alive and working — A heartbeat-based watchdog supervising a long-running child subprocess. Outcome: the run is killed.

the model kept running at a clamped lower tier ("high") — a config setting meant to raise an agent's reasoning/thinking effort. Outcome: the limit was purely in the framework's resolution layer.

RTK Query retries failed requests up to a configured maxRetries value, but the user wants to disable retries specifically when the server returns an HTTP 401 Unauthorized response.

HTTP request keeps accepting data after the size limit is exceeded, wasting server resources

Resetting the timer by lifting/resetting state in the parent causes the app/game to become unresponsive due to frequent re-renders. The Timer component also attempts to reset its local state during render, leading to problematic behavior.

Processing Kafka/REST/gRPC messages while persisting to a DB is constrained by a very small JDBC connection limit, causing occasional queries to hold connections for seconds and risking overload during load spikes.

Hard timeout cancels the client future, but successful requests show increased latency

108 consecutive 429s in ~90 minutes — A Node.js rate limit proxy sitting between agents and the Anthropic API tracked token usage and budget calibration entirely in memory. Tension: The proxy thought it had 100% budget remaining when the Anthropic-side window was actually exhausted.

Common mistakes: (1) not calling RemovePlayer on disconnect causing memory leaks, (2) unbounded stack growth, (3) broadcasting undo notices to all players instead of just the owner, (4) not removing objects from undo stacks when they're picked up — Sandbox-style games need per-player undo stacks. Tension: allowing players to undo weapons out of other players' hands.

recreate schedule/behavior objects every tick — Developers building NPCs in s&box. Tension: causing allocations. Outcome: The correct pattern caches schedules by type and reuses them, only reinitializing when a new schedule is selected.

Got HTTP 400 with "missing mcp-session-id" on every call. Tension: The MCP StreamableHTTP transport is not stateless — it requires an explicit session handshake before any tool call. Outcome: Implement the full session lifecycle in the client.

the `RateLimiter` class was imported before it was declared in the same file — When building an HTTP server in TypeScript. Tension: This violates TypeScript's module resolution rules, leading to a compilation error. Outcome: The code was rejected and sent back for revision.

Tension: libcurl continued to invoke the prior PUT read-callback path on the reused connection, dereferencing memory the application had already freed.

returning HTTP 400 on any turn after a thinking block appears in session history — OpenClaw's Anthropic adapter applies sanitizeTransportPayloadText() to replayed thinking block text during multi-turn conversations. Tension: The mutated text fails this check. Outcome: thinking or redacted_thinking blocks in the latest assistant message cannot be modified.

Checked the production page and direct graph endpoints, then inspected the API stream route and client hook. — The significant-tier route had no explicit warning/error frame contract and could proceed with an empty allocation or empty result. Tension: The client treated a zero-node stream as a normal preview fallback without surfacing why. Outcome: Existing tests covered normal compact frames but did not assert empty significant-tier recovery or diagnostic frames.

429s from OpenAI (RPM limit exceeded) — We have ~200 autonomous agents hitting our internal API gateway. Tension: Cascading retries amplifying the load. Outcome: P99 latency spikes to 30+ seconds.

SESSION_HANDOFF.md only gets written at clean session end — crash/OOM/gateway restart. Tension: not during unexpected termination.

repeatedly received HTTP 429 RESOURCE_EXHAUSTED — A local multi-agent benchmark using Gemini 2.5 Flash. Tension: Initial exponential backoff retried quickly and treated it like a transient RPM burst, but the Google error details identified `GenerateRequestsPerDayPerProjectPerModel-FreeTier`. Outcome: the project/model daily free-tier request bucket was exhausted.

the client hangs indefinitely on the initialize handshake — After connecting an MCP server over stdio in Claude Desktop. Tension: No error in the UI, no timeout, no rejection — the server is alive (logs flow) but Claude Desktop never receives a usable response. Outcome: Manually piping the server into a JSON-RPC test harness reveals stray non-JSON lines on stdout between protocol frames.

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