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A failed compaction can leave an AI session without the summary it needs to continue. In a DEV Community post, OpenCode user geco describes losing access to session memories after a compaction returned an empty summary. The lesson is practical: save useful conversation history as work happens, rather than making compaction the only moment when memory is stored.
What happened when 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. The memory plugin in use had saved everything inside that compaction call, so the author says the session was left with no memories.
These are the author’s account of one incident, not independently verified telemetry or evidence of a failure rate across OpenCode or other AI agent systems. The DEV Community page displays “Posted on Sep 17” but does not show a year.
Why saving only at compaction is risky
Compaction condenses a long conversation so an agent can continue with less context. If a system stores its memory only as part of that operation, a failed or empty summary may leave no saved record to rely on. Incremental persistence changes the timing: useful information is saved during the session, before a compaction is needed.
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geco describes the principle this way: “Save incrementally, while you work — so a failed compaction costs you a summary, not your past.” The design aims to make a failed summary less damaging; it does not establish that all forms of memory loss can be prevented.
How the OpenCode and MemPalace workflow is designed
The project named in the post, opencode-mempalace-persistence, connects an OpenCode plugin with MemPalace, a separate local-first system for storing and searching conversation text. The post describes version 2.1.0 and an MIT license; those are details from that post and may not match the repository’s current state. The repository identifies the plugin as a community project, not software officially maintained by the MemPalace team.
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According to the plugin’s documentation, its persistence and recall workflow includes:
- Periodic checkpoints: The plugin exports conversation history and has MemPalace process it on a configurable cadence. The current README says the save interval defaults to every 15 human messages, with a stated minimum of five.
- A pre-compaction save: The plugin documents an emergency save before compaction, so earlier conversation history is not dependent on the resulting summary.
- Exit and startup processing: The repository describes transcript exports and MemPalace mining on idle, exit, and startup.
- Optional history backfill: Existing session history can be imported, and transcript backfill can be rerun.
- Project separation: The plugin documents separate memory “wings” for projects, helping keep project context distinct.
- Search-based recall: When enabled, an OpenCode message-transform hook can add identity information and relevant search results to prompts.
These are documented design and behavior, not guarantees independently tested here. MemPalace describes itself as local-first: its repository says conversation text stays on the user’s machine unless the user opts in to sending it elsewhere.
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Set up the plugin using the current repository instructions
The article’s high-level outline is to install MemPalace, configure the OpenCode plugin and local MCP server, create an identity file, and restart OpenCode. Exact setup steps can change, so use the live plugin README and MemPalace repository for current commands, configuration fields, and compatibility details. The plugin repository includes setup details for its configuration, MemPalace CLI installation, optional settings, and stated support for OpenCode v1 and v2.
One setting is especially easy to misunderstand: the current plugin README says auto-injection is off by default. Saving conversation history and automatically placing retrieved memories into later prompts are separate behaviors. Check the configuration rather than assuming that a successful save means later sessions will receive search results.
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- Install MemPalace using the CLI installation method in its current repository documentation.
- Configure the OpenCode plugin and local MCP server according to the plugin README, including any required paths and options.
- Create an identity file as described by the plugin, so the workflow has the identity context it expects.
- Review checkpoint and auto-injection settings, then restart OpenCode as the setup instructions specify.
Know what backfill can and cannot restore
The plugin distinguishes retained transcript drawers from knowledge-graph facts. Transcript backfill can be rerun and, according to the README, does not modify the knowledge graph. That makes transcript recovery different from repairing or refreshing extracted facts.
The repository warns that graph facts can become stale and may still be read as current until they are revisited. A saved transcript is therefore not the same as a perfectly current knowledge graph; important facts may need review when circumstances change.
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The article reports that the author’s one-time backfill recovered 9,448 messages across 1,735 drawers. Those figures describe that author’s project, not a typical recovery result. Separately, the plugin maintainers recount a prior duplicate-export incident involving 691 overlapping files, 628,000 drawers, and 5 GB, and say a cursor-on-write correction addressed it. That account is project-maintainer documentation, not an independent audit or performance benchmark.
What “infinite memory” means here
“Infinite memory” is a useful shorthand for retaining and searching conversation history beyond the active context window; it is not a literal guarantee of unlimited storage, perfect recall, or error-free recovery. MemPalace describes storing verbatim conversation text and retrieving it through semantic search. The plugin adds session-oriented exports and optional prompt injection, but outcomes depend on configuration and the underlying systems.
The MemPalace repository advertises “96.6% R@5 raw on LongMemEval — zero API calls” as its own benchmark claim. That is a project claim about MemPalace, not a verified benchmark of this OpenCode plugin or geco’s workflow; consult the project’s benchmark materials and date before treating it as comparative evidence.
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