In a production incident-response agent, I traced HTTP 429 errors to an oversized prompt containing retrieved memory records and an uncapped completion request. My fix was to keep complete records in persistent memory while sending the model a short, task-specific summary, cap output at 700 tokens, and bound retries. Sriyamshu Reddy describes this approach and its reported results in a DEV Community article published September 29, 2026; the figures below are his account, not independently verified benchmarks.
Why the agent hit a rate limit
Reddy’s agent called Groq’s openai/gpt-oss-120b endpoint and encountered an 8,000 Tokens Per Minute (TPM) quota. The reported 429 response showed 6,793 tokens already used and 2,664 requested. In his account, two implementation choices drove the pressure: the prompt included indented JSON serialized from rich memory records, and the request had no explicit output-token cap.
Each memory object in this workflow held 15 metadata attributes. Three serialized records exceeded 4,000 characters. Those details describe Reddy’s system, not a typical memory record or a universal Groq quota. The useful general lesson is narrower: retrieved context and requested output both draw on the request’s token budget, so verbose formatting and unconstrained generation can make a request harder to fit within a provider’s limits.
Keep durable memory rich and inference context small
The change was to separate what the agent stores from what it sends on each inference call. Full-fidelity records stayed in persistent Hindsight memory; the prompt received a compact projection selected for the current task. Reddy’s formatter included at most the top three memories and organized each around five useful fields:
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- Problem
- Error
- Failed attempts
- Successful fix
- Root cause
In the author’s comparison, this reduced roughly 3,500 characters of JSON to about 400 characters of denser text. Character counts are not token counts, and actual tokenization depends on the content and model; the figures illustrate the reported formatting change rather than a portable token-saving guarantee.
Choose what earns space in the prompt
A concise projection should preserve information that can change the next decision, rather than every stored attribute. For an incident-response task, the prior failure, the fix that worked, and the underlying cause are often more actionable than repeated metadata. Keep the source record available for follow-up or audit, but avoid sending it wholesale when a task-specific summary can answer the current question.
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Bound output and make 429 handling finite
Reddy’s client explicitly set a 700-token output ceiling. This puts a limit on requested generation; it does not guarantee a request will fit a provider’s TPM window, because prompt size, other usage, provider accounting, and timing also matter.
For a 429 response, the client read Retry-After and retried once only when the indicated delay was positive and no more than three seconds. If that condition was not met or the retry did not resolve the request, it returned a deterministic fallback. This is the behavior described for his client, not a promise that every API returns the same header or applies identical token accounting.
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A practical request sequence
- Retrieve only memories relevant to the current task, then project them into concise fields instead of serializing raw records.
- Set an explicit maximum output-token value appropriate to the task; Reddy used 700 tokens in this workflow.
- On HTTP 429, honor a usable
Retry-Aftervalue only within a defined short bound. Retry once under Reddy’s rule: delay greater than zero and at most three seconds. - If the request still cannot proceed, return a deterministic fallback rather than retrying indefinitely or leaving the caller without a defined response.
What the reported results show—and do not show
Reddy reports that two consecutive investigations used 3,058 tokens combined and completed without a rate-limit error. His telemetry lists prompt/completion totals of 871/612 tokens for the first call and 875/700 for the second. He also reports a prompt-size reduction of more than 80% and zero 429 errors after the change.
These are figures from one author’s production account, not a controlled comparison or independently verified benchmark. The account supports the plausibility of reducing prompt pressure in that workflow; it does not establish that the same memory structure, output cap, or retry rule will eliminate rate limits elsewhere. Quotas, concurrent usage, request mix, provider policies, and model behavior can differ.
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Implementation decisions to make for your own agent
| Decision | Reddy’s reported choice | What to evaluate in your system |
|---|---|---|
| Memory sent to inference | At most three task-relevant memories, rendered as concise problem, error, failed attempts, successful fix, and root cause fields. | How much prompt budget the projection uses and whether it retains the details needed for the next decision. |
| Durable storage | Keep full-fidelity records in persistent Hindsight memory. | Whether the complete record remains retrievable when the short context is insufficient. |
| Completion reservation | Explicit 700-token output ceiling. | Whether the ceiling matches the response task and leaves enough room under the provider’s applicable limits. |
| 429 handling | Read Retry-After; retry once only for a positive delay of at most three seconds, then use a deterministic fallback. |
Whether your API supplies a usable delay, what bounded retry policy fits the service, and how callers should behave on fallback. |
Reddy’s concise recommendations capture the design intent: “Decouple persistence from context delivery” and “Treat inference context like L1 cache.” They are useful principles from his case, not formal standards or evidence that any particular framework is best.
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