The AI tool that remembers context across sessions
TL;DR
Standard chatbots start from zero every session, so you re-explain your project constantly and decisions get lost. An AI tool with persistent memory keeps the project state, constraints and decisions across sessions, and when several models share that memory, the whole team stays in sync. Bordet curates a canonical project state plus an append-only decisions log so context survives and nothing gets relitigated.
The single biggest friction in working with AI is that it forgets. You spend twenty minutes getting a model up to speed on your project, close the tab, and tomorrow you start from zero again. Memory across sessions is the feature that fixes this, and it is rarer than it should be.
Why most chatbots forget
A normal chat session is stateless beyond its own window. Once the conversation scrolls past the context limit, or you start a new chat, the model has no durable record of what you agreed. "Memory" features in some tools store loose facts about you, but not the evolving state of a specific project.
What "remembers context" should actually mean
For real project work, useful memory is more than a pile of saved facts. It is:
- a canonical project state: the objective, constraints, and current focus, kept current as the work moves;
- an auditable decisions log: an append-only record of what was decided and why, so settled questions stay settled;
- retrievable detail: the specific numbers and preferences surfaced again when they are relevant.
Why it matters even more with multiple models
If you run several models, memory has to be shared, not siloed per model. Otherwise the Strategist and the Critic each remember a different version of the project. A shared memory means every member, on every model, works from the same source of truth, across every session.
How Bordet handles it
Bordet's keystone memory does exactly this. After each turn, a maintainer quietly extracts what was decided, patches the canonical project state, appends decisions, and stores specific facts for retrieval, conservatively, with the source attached. The next session, any member can recall it: "Per the decisions log, we chose SQLite for zero-ops." The context carries, so you build instead of re-explaining.
That is the difference between a chatbot you restart every day and an AI team that actually remembers the project.
See it for yourself
Open a multi-LLM boardroom and put your AI team to work.