Long-Term Memory
Every model has a context window — a limit on how much conversation it can "see" at once. Long-Term Memory (LTM) keeps a running recap of the story so far and injects it into the prompt, so the model remembers chapter one by the time you reach chapter ten.
How it works
As a chat grows, Pyre periodically summarizes what's happened and stores it as a checkpoint. The accumulated recap rides along in the system prompt, compact enough to fit while preserving the threads that matter — who's who, what's been established, where the story stands.
The auto-summarizer fires every N messages (the threshold is configurable; set it to 0 to turn auto-summarize off globally). You can also summarize on demand at any time.
A story, not a synopsis
Each checkpoint is written as the next paragraph of one ongoing chapter, not a standalone bullet list. Concatenated, the recap reads as a single unbroken narrative — which keeps the model's continuation in tone, instead of feeling like it's reading meeting minutes.
Branch-aware
Pyre's chats branch: you can re-roll replies and rewrite your own turns without destroying the timeline that followed. Memory respects that. Each checkpoint is fingerprinted to the branch it was taken on:
- Re-rolling a past message invalidates checkpoints for the new branch, but they stay valid on the original branch when you navigate back.
- Stale checkpoints from abandoned branches sit harmless and hidden.
So your recap always reflects the timeline you're actually playing — not a tangle of every path you explored.
Staying in control
| Action | What it does |
|---|---|
| Summarise now | Generate a checkpoint immediately, even with auto-summarize off. |
| Retry (per checkpoint) | Regenerate a single summary you didn't like. |
| Edit | Adjust a checkpoint's text by hand. |
| Delete | Remove an individual checkpoint, or wipe all of them. |
| Per-chat toggle | Turn memory on or off for one specific chat. |
You can also tune the summary prompt template and the recap's length budget.
When something goes wrong, you'll know
If the summarizer hits a provider error (an auth failure, a timeout, an empty reply), Pyre surfaces it instead of silently giving up — so you can fix the root cause (often the model or key) rather than wondering why memory stopped.
Memory vs the other state tools
LTM is the narrative memory — the prose recap. If you also want structured state (locations, inventory, relationship beats tracked as fields) or authorial direction (planned story beats), those are separate, optional tools:
- Live Sheet — a structured, auto-updating state panel.
- Script — story-direction beats you want the model to steer toward.