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Crivello goes deeper into production agent memory architecture than almost anyone talking publicly: a background memory agent updating a file-based knowledge graph every 15 minutes, tree structures that reach billions of tokens within two LLM calls, and 85% cache hit rates from careful prompt design. There's also a striking data point on the economics — internal inference spend approaching payroll — and a genuinely contrarian policy take from someone whose own product runs on DeepSeek. At over two hours, jump to the technical sections.

• A background 'napping' memory agent runs every 15 minutes to update a file-based knowledge graph, with separate personal and workspace memory layers
• Tree data structures with ~100 children per node let the system reach billions of tokens of context within two LLM calls
• Lindy's internal inference spend is approaching payroll cost and will likely cross over in 3-6 months, with productivity tripling while headcount stays flat
• Context beats raw intelligence: a smart agent without your company's context is less useful than a mediocre coworker with full context

Parte de Edición Nº 004: OpenClaw's near-burnout, agent teams that ship 99.9% of PRs, and the math behind prompt caching