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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallA failed context compaction can leave an AI coding session without the summary it needs to continue. In a DEV Community post, author geco describes losing access to session memories after a compaction returned an empty summary, then building an OpenCode plugin that saves conversation data incrementally through MemPalace. The approach reduces reliance on a single compaction step; it does not guarantee that memory can never be lost.
What happened when the compaction failed
geco reports that an OpenCode session had reached 378,755 tokens of context when the author ran /compact. The model produced three output tokens, an empty summary, and a finish: length result. According to the post, the memory plugin then in use had saved everything inside that compaction call, leaving the session with no memories available to continue from. These are the author’s account of one incident, not independently verified telemetry or evidence of a general OpenCode failure rate. The DEV Community page displays “Posted on Sep 17” without a year, so the publication year is not established. Read geco’s account on DEV Community.
The underlying risk is architectural: if useful context is persisted only when compaction succeeds, a failed summary can also mean no durable record of the conversation. geco’s stated principle is: “Save incrementally, while you work — so a failed compaction costs you a summary, not your past.”
How the incremental-save design works
The project described in the post, opencode-mempalace-persistence, connects an OpenCode plugin to MemPalace, a separate local-first system for storing and searching conversation text. Instead of treating compaction as the only save point, the documented design combines several moments and routes for retaining information:
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- Periodic checkpoints: the plugin exports conversation transcripts as work proceeds. The current repository README says the default interval is every 15 human messages, with a minimum of five; this is configurable. That is a current documented default, not a guarantee that every setup uses it.
- Pre-compaction save: the plugin documents an emergency save before compaction, so the transcript can be retained even if the generated summary is empty or unusable.
- Exit and idle processing: the repository describes transcript export and MemPalace mining on idle and exit, with additional mining at startup.
- Project-separated memory: memories are organized into separate “wings” by project, helping keep one codebase’s context apart from another’s.
- Optional history backfill: existing OpenCode history can be imported. geco reports a one-time backfill of 9,448 messages into 1,735 drawers. Those are author-reported project figures, not a benchmark of retrieval quality or speed.
MemPalace describes itself as storing verbatim conversation text locally and retrieving relevant material through semantic search. Its repository says data does not leave the user’s machine unless the user opts in. The plugin repository describes an OpenCode message-transform hook that can inject identity information and search results into prompts when auto-injection is enabled. Both the hook and the save and mining behaviors are project-documented features, not independently verified guarantees. MemPalace project · OpenCode plugin repository.
What “infinite memory” means—and what it does not
“Infinite memory” is the project’s framing, not a literal claim that an AI model’s context window has no limit. The design separates durable storage from the amount of text the model can hold at once: conversations can be retained outside the active context, then searched for relevant material to bring into a later prompt. The model still needs a bounded, relevant selection to work with, and retrieval may not surface every useful detail.
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The repository distinguishes retained transcript drawers from knowledge-graph facts. Transcript backfill can be run again and, according to the project documentation, does not modify the knowledge graph. Graph facts have a different limitation: a fact can become stale and still be read as current until it is revisited. Retaining a transcript therefore does not by itself guarantee that derived facts are accurate or up to date.
The plugin repository also documents a past duplicate-export incident involving 691 overlapping files, 628,000 drawers, and 5 GB, and says a cursor-on-write correction addressed it. This is the maintainers’ account of a project issue and fix, not an independent audit or performance result. Together, these caveats are why the system is best understood as a persistence and retrieval workflow—not a promise that every failure mode is eliminated.
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Set up the plugin carefully
geco’s article outlines the process as installing MemPalace, configuring the OpenCode plugin and local MCP server, creating an identity file, and restarting OpenCode. Exact commands and configuration can change, so use the live plugin README rather than relying on a version-sensitive summary. The post names plugin version 2.1.0 and an MIT license; those are details stated in that post and may not describe the current release. The repository identifies the plugin as a community project, not as software officially maintained by the MemPalace team.
- Check compatibility and current instructions. Review the plugin repository’s stated support for OpenCode v1 and v2, along with its latest installation instructions, before changing your setup.
- Install MemPalace and configure its local MCP server. Follow the current MemPalace CLI and server guidance linked from the plugin README; do not assume the article’s brief installation outline covers every present-day prerequisite.
- Install and configure the OpenCode plugin. Add the plugin using the repository’s current configuration format. Auto-injection is off by default, so retained memories will not necessarily be inserted into prompts unless you enable and configure that behavior.
- Set a checkpoint interval that fits your workflow. The repository’s current default is every 15 human messages, with a minimum of five. Confirm the setting in your configuration rather than assuming the default applies unchanged.
- Create the identity file and restart OpenCode. Use the file format and location documented in the current README, then restart as directed so the plugin can load the configuration.
- Verify persistence and retrieval on a low-stakes session. Confirm that transcripts are being saved and that search or injection behaves as intended before relying on the setup for important work. This is a prudent configuration check, not a guarantee against every failure.
For setup details and changes, consult the plugin README and repository and the MemPalace repository.
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Who should consider this approach
This pattern is relevant if you use OpenCode for long-running work and want conversation history preserved independently of a generated compaction summary. It is especially useful to understand the distinction between keeping a transcript and retrieving the right information at the right time: incremental exports address the first problem, while search and optional prompt injection address the second. It is not evidence that this plugin prevents all memory loss, works identically under every configuration, or outperforms alternatives; the cited material does not establish comparative performance.
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