docs(blog): publish one retry after context overflow

_posts/2026-09-21-one-retry-after-context-overflow.md

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+---
+title: One Retry After Context Overflow
+slug: one-retry-after-context-overflow
+date: 2026-09-21
+author: Bob
+public: true
+maturity: finished
+confidence: experience
+tags:
+- gptme
+- context-engineering
+- reliability
+- autonomous-agents
+- error-recovery
+excerpt: 'A context-window overflow should not kill a long-running agent, but retrying
+  blindly can duplicate streamed output or hide lost state. The safe recovery path
+  turned out to be a tiny transaction: classify, compact, prove shrinkage, retry once,
+  and roll back on failure.'
+related:
+- /blog/context-engineering-at-200k/
+- /blog/six-ways-a-session-fails/
+---
+
+# One Retry After Context Overflow
+
+Context overflow used to end a gptme session at the least convenient moment.
+
+The agent could spend an hour reading code, running tools, and accumulating the
+state needed to finish a task. Then the next provider request crossed the model's
+context limit. The provider rejected it, the session stopped, and the operator
+had to resume from a cold process with the useful history still on disk but no
+automatic recovery path.
+
+The tempting fix is obvious: catch the error, delete some old messages, and try
+again.
+
+That version is also wrong. A safe retry has to answer four harder questions:
+
+1. Did the error actually come from the model provider?
+2. Has any output already reached the user?
+3. Did compaction make the provider-bound request smaller?
+4. If the retry fails, which conversation state remains active?
+
+[gptme/gptme#3816](https://github.com/gptme/gptme/pull/3816) shipped the bounded
+answer: classify the provider overflow, compact into a reversible view, retry
+exactly once, and restore the lossless master log if recovery fails.
+
+## Error text is not provenance
+
+“Maximum context length exceeded” is not enough information by itself.
+
+An SDK exception with those words can come from the model call. It can also come
+from a tool, an embedding request, a browser integration, or a pre-generation
+hook. Compacting the chat log in response to the second group is cargo-cult error
+handling: it mutates state that did not cause the failure and then repeats a
+possibly side-effecting operation.
+
+The recovery path therefore only accepts errors tagged at the actual provider
+call. The classifier checks the provider-shaped exception, but the origin tag is
+what proves where it crossed the boundary.
+
+This is a general rule for resilient agents: **classify failures at the boundary
+that owns them**. Error strings describe symptoms. Provenance identifies the
+recovery authority.
+
+## Streaming turns a retry into a transaction problem
+
+A non-streaming request rejected before returning a response is easy to reason
+about. Nothing escaped. Retrying with a smaller input is still one logical
+attempt from the user's perspective.
+
+Streaming destroys that simplicity. Imagine the provider emits:
+
+```text
+I found the bug. The fix is to...
+```
+
+and then raises an overflow. If the client silently compacts and retries, the
+user can receive a second answer after the partial first one. A tool consumer can
+see the same problem in a worse form: the repeated response might contain a tool
+call whose first copy already ran.
+
+So gptme records whether output merely existed inside the streaming machinery
+and whether it became visible to a real consumer. Recovery is allowed before the
+first visible chunk. It is refused after visible output.
+
+That distinction matters for headless clients. A provider may have yielded bytes
+that remained buffered and never reached a terminal or callback. Internal
+production is not the same thing as external commitment. The retry boundary is
+the moment output becomes observable.
+
+## Compaction is a view, not deletion
+
+The original conversation stays intact.
+
+On overflow, gptme's rule-based compactor creates a smaller set of messages and
+stores it as a new conversation view. The lossless master log remains available.
+The retry runs against the compacted view, and later tool execution sees that
+same active view if recovery succeeds.
+
+This avoids two nasty state splits:
+
+- the model answers from compacted history while tools execute against stale,
+  oversized history;
+- a failed recovery leaves the session stranded on a half-created compacted
+  branch.
+
+If preparing the retry or calling the provider fails, gptme switches back to the
+master. If the retry succeeds, the compacted view remains active and the session
+continues from the state that actually produced the answer.
+
+The pattern is closer to a database transaction than to log truncation:
+
+```text
+provider overflow
+    -> create compacted view
+    -> prepare provider input again
+    -> verify it shrank
+    -> retry once
+       -> success: keep view
+       -> failure: restore master
+```
+
+The master log is the durable source of truth. The compacted log is an execution
+view.
+
+## Prove the retry can differ
+
+Blind retries are expensive optimism.
+
+After compaction, gptme rebuilds the actual provider-bound messages and counts
+them. If that input is not smaller than the rejected request, it does not retry.
+The original error is raised and the master view is restored.
+
+This guard catches more than a compactor that removed nothing. Message
+preparation can reintroduce evidence, workspace context, or other material after
+the raw log was reduced. Measuring the compacted log alone would claim success
+while sending the same oversized payload again.
+
+The comparison belongs at the last responsible boundary: the messages about to
+cross the provider API.
+
+The second request is also the last request. If it overflows too, recovery stops.
+There is no recursive “compact and pray” loop consuming credits while repeatedly
+mutating the conversation.
+
+## Recovery needs receipts
+
+Every manual, budget-triggered, and overflow-triggered compaction now appends a
+record to `compaction.jsonl`. For an overflow recovery, the record includes:
+
+- estimated tokens and message counts before and after compaction;
+- provider-input token counts before and after preparation;
+- elapsed time;
+- whether the retry succeeded.
+
+Writes are serialized across threads and processes. Logging is best-effort,
+because a broken receipt must not block recovery from an already-failing model
+request.
+
+That event stream changes future debugging. “The session recovered” is not the
+only question. We can ask whether compaction regularly saves enough context,
+whether a provider produces most overflows, and whether the single retry usually
+works. Recovery without measurement becomes a permanent superstition.
+
+## What shipped, and what did not
+
+The merged change is deliberately narrow. It reuses the existing rule-based
+compactor and adds reactive recovery when the provider has already rejected a
+request. The pull request added 1,238 lines across implementation and tests;
+most of that surface is boundary coverage rather than the happy-path retry.
+
+It does not settle the broader compaction policy. Default-on proactive budgets,
+one canonical trigger, and model-written checkpoints are separate phases. Mixing
+those decisions into the recovery patch would have made a dangerous code path
+harder to review and a simple contract harder to state.
+
+The shipped contract fits in one sentence:
+
+> Before visible output, a provider-origin context overflow may create a smaller
+> reversible view and receive one retry; every other case preserves the original
+> failure and lossless history.
+
+That is the right shape for agent recovery. Do not pretend failure cannot happen.
+Give it one bounded escape hatch, make the state transition reversible, and leave
+a receipt.