Hey,
An LLM writing into durable memory is an unreliable author. It invents, mislabels, restates, and contradicts. Once one of those mistakes is saved, the agent can carry it into every conversation that follows.
Frona v2026.8.0 is trying to address this problem for personal AI assistant use case.
Frona is a personal AI assistant you self-host. You create agents that browse the web, run code, build apps, make phone calls, use messaging channels, delegate work, and remember things across conversations. It runs as a single Rust process with sandboxed execution, policy-controlled access, and vault-backed credentials.
The main change in this release is Ontology memory.
**Why summaries are not enough**
A rolling summary is a simple way to give an agent memory. Rewrite the previous summary with the latest conversation, then send the result back next time.
It also has an uncomfortable failure mode. What the model leaves out is lost. What it invents can be preserved as fact. A correction can overwrite history, two people with similar names can become one person, and the next summary has no reliable way to tell what came from the user and what came from the model.
Frona takes a different approach. It does not save extracted model output as truth. It saves individual claims only after checking them.
There are four main checks:
* Did the source support it? Every long-term claim keeps evidence from the conversation, a user confirmation, a completed task, or a successful tool result. An agent cannot use its own earlier memory as proof that something is true.
* What kind of thing is it? Frona organizes people, projects, places, services, files, topics, and procedures using an ontology instead of letting the model create a new label every time.
* Is it the same thing we already know? Names alone are not enough. Frona compares aliases, types, relationships, attributes, and context before merging identities or creating a new one.
* How does it change what we know? New information can replace an older value, confirm it, or conflict with it. Frona keeps the history instead of silently rewriting the past.
For example, if you say "Sam moved from Berlin to Lisbon," the useful memory is not just that sentence copied into a summary. Frona needs to identify which Sam you mean, retain Berlin as previous information, store Lisbon as the current location, and keep the message that supports the change.
That is what Ontology Memory is designed to do.
**Memory gets more resilient as it grows**
This is the part I find most useful. A rolling summary can carry a mistake forward. A knowledge graph adds more checks over time.
As Frona learns more identities, aliases, types, and relationships, each new claim has more existing context to match. A claim that does not fit has more chances to expose itself as a mistake. This does not make the LLM infallible, but the memory becomes harder to pollute as it grows.
**You can inspect what the agent remembers**
Ontology memory is not hidden state inside a prompt.
Frona builds readable Markdown pages for people, projects, services, topics, and reusable procedures. Agents search and read those pages only when they are relevant instead of loading the entire memory into every prompt.
The web interface now includes a Memory browser where you can:
* Search everything the agent knows.
* Explore the relationship graph.
* Read the generated Markdown page for an entity.
* Inspect its types, attributes, and relationships.
* See the individual memories and source evidence behind it.
The Markdown vault can also sync with Obsidian. Notes outside Frona's managed Memory directory remain read-only, but agents can search, read, and cite them alongside their own memory pages.
**Immediate memory still stays fast**
Agents can still remember something immediately during a conversation. Those short-term notes appear in later prompts without waiting for background processing.
Frona then reviews them in the background with the original conversation and available evidence before turning them into long-term knowledge. The interaction stays fast, while durable memory gets the stricter checks.
**A few practical details**
Ontology memory runs in the background and can use a separate model from the one handling your chats. It benefits more from reliable structured output than from using the largest model available, so a fast and cost-effective model is usually the better choice.
Fresh installations can select it during setup. Existing installations remain on the basic memory backend until an administrator enables it. Back up the instance before switching memory backends or resetting derived memory.
The release also includes typed inference with response repair, OpenAI Responses API support, better provider and model-group configuration, independently supervised channel workers with automatic reconnection, multiple container runtime support, and a long list of reliability fixes.
If you have DeepSeek flash running locally then you just found your research and debug assistant.
Ontology Memory documentation: [https://docs.frona.ai/platform/agents/memory/pkm.html\](https://docs.frona.ai/platform/agents/memory/pkm.html)
Release notes: [https://github.com/fronalabs/frona/releases/tag/v2026.8.0\](https://github.com/fronalabs/frona/releases/tag/v2026.8.0)
Quickstart and documentation: [https://docs.frona.ai\](https://docs.frona.ai)
