The Brain
Behind Every
AI Agent.
Persistent, structured memory for any AI agent — built by you, or hired from someone else. It never explains its context twice.
AI agents forget.
Every session
starts from zero.
Project structure, coding conventions, past decisions — explained once, forgotten by the next session. The agent reasons well. It just can’t accumulate experience.
One pipeline, five steps, running on every request.
Every conversation intercepted.
Your agent's requests pass through AtlasOS on the way to the LLM — invisibly, with no added round trip the user notices.
Conversations become structured facts.
An LLM-powered pass reads each exchange, scores what matters, and converts confirmed statements into entity → value pairs.
Three stores. One Postgres.
Vector embeddings, a temporal knowledge graph, and structured key-values — all in a single database, no extra infra to run.
Only what's relevant.
Semantic, entity, and temporal search run together. Results are ranked, deduplicated, and cut to what the query actually needs.
Your agent arrives informed.
Ranked memories are formatted into the system prompt, budgeted to fit the model's context window, before the request continues.
Structured Knowledge,
Not Chat Logs.
Conversations distilled into entity → value facts — searchable, editable, always current. No vector dumps. No chat history bloating your context window.
One Line.
Any Agent.
Change one URL. Memory injection and extraction happen transparently in the background — no wrappers, no middleware, no code changes beyond a base_url swap.
Memory That
Updates Itself.
When context changes, old facts are invalidated — not deleted, not duplicated. Your agent always operates on what’s current, not conflicting history from months ago.
One Memory Layer.
Every Model.
Create an agent
Sign up, create your agent profile, and get your API key.
Connect in one line
Change base_url in your agent's code. That's literally it.
It learns automatically
Every session builds memory. Your agent never starts from zero.
# No other code changes needed.