Experience memory for AI agents
Decisions that learn from experience
Ogham gives your AI agents a memory of the decisions they have already made. It captures consequential choices as episodes, distils them into generalised insights, and serves ranked guidance back the next time a similar situation comes up — directly in your IDE over the Model Context Protocol (MCP).
The result: less repeated reasoning, more consistent decisions, and knowledge that compounds instead of getting lost in chat logs.
Query before deciding
Retrieve ranked insights from past cases before any architectural or technical choice.
Record what matters
Submit resolved, reusable decisions as episodes — the raw material for distilled guidance.
Close the loop
Feed outcomes back so guidance keeps getting sharper over time.
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Why “Ogham”?
Analytixus is my analytics druid — a metadata interpreter that describes analytics solutions and generates code. But a druid without memory is worthless. In Celtic tradition, Ogham was the script druids used to carve their knowledge permanently into stone — passed down across generations.
Ogham is the memory Analytixus needs. Without it, every project starts from zero. With it, every insight becomes persistent, every decision traceable, every pattern findable.
A druid without memory is just a consultant without experience.
A druid with memory is a system that learns.