Every decision, direction, and discovery from your AI sessions is captured, structured, and readable by your next agent via MCP. No re-explaining. No context lost. Your agent starts informed.
the problem
New session, empty context. You re-explain the project, re-state constraints, re-establish what was decided. Your agent has no memory — you are the memory.
Your agent reads structured knowledge via MCP before you type a word. Architecture, constraints, what was tried and rejected — already loaded. You pick up where you left off.
Important calls are buried across 20 chat sessions. You remember making the decision but not where — or exactly what you decided.
The longer the project, the more you repeat yourself. Your agent doesn’t remember last session — you spend the first 10 minutes being its memory.
You could keep a doc. But flat text has no structure — no connections, no contradictions surfaced. And your agent can’t query it. It needs structured knowledge, not notes.
how it works
Key-value pairs, vector embeddings, flat text. They recall fragments. But recalling isn’t the same as understanding — your agent gets facts without structure, context, or relationships.
Every item is classified by intent — decision, feedback, directive, idea. Items connect to each other. Contradictions are detected. The result is a knowledge graph, not a memory dump.
No folders. No tags. No maintenance. Knowledge self-organizes into areas and topics. Summaries update as items accumulate. The structure evolves with your project — you don’t manage it.
Via MCP, your agent navigates areas, topics, and summaries — not a flat list of memories. It understands what was decided, what conflicts, and what’s still open. That’s the difference.
your agent reads it
When your agent connects, it receives pre-organized structure — areas, topics, summaries. It doesn’t need to know what to ask for. The knowledge is navigable, not hidden behind a search query.
Reads are database queries. No LLM calls. Nothing on top of whatever you’re already running.
built for
Every positioning discussion, competitive insight, and product decision accumulates. Three months of AI sessions become structured knowledge your next agent can draw from via MCP.
Architecture decisions, tradeoffs, rejected approaches — captured once, available always. Your coding agent knows what you already tried and why you moved on.
Findings from different sessions link automatically. Contradictions surface. Your research agent queries accumulated knowledge — not 50 separate chat windows.
Capture what matters. Your next agent session reads it back via MCP — decisions, constraints, direction. All of it.
MCP-native · self-organizing · zero read cost
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