MemGPT in Production 2026: Hard Lessons from Real Deployments
A year of MemGPT (now Letta) in production has taught teams hard lessons. The five patterns that stop your memory layer from drifting and burning your budget.
Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
A year of MemGPT (now Letta) in production has taught teams hard lessons. The five patterns that stop your memory layer from drifting and burning your budget.
Salesforce's MCP server gives agents safe read/write access to leads, opportunities, and accounts. The architecture and the field-level security gotchas that bite teams.
We modeled 10,000 concurrent voice agent WebSockets on Cloudflare. With hibernation and the 20:1 message ratio, the bill lands surprisingly low. Here is the line-by-line math.
Production prompts change constantly and break quietly. Here is how to version, deploy, and roll back chat agent prompts in 2026 — with instant revert and zero redeploy.
Real users generate noise. Synthetic checks generate signal. Here's how to run a fake voice call against your agent every minute and catch regressions before customers do.
Transcoding RTP to WebSocket is more CPU-intensive than people expect. For AI voice in 2026, where you place the transcode (edge near the carrier vs central near the model) decides your cost-per-minute.
Helicone has processed 2B+ LLM calls and ships both as a managed cloud and as fully self-hostable open source. Here is the actual decision tree for 2026.
Rosie's $49–$299 plans target micro-businesses and don't sign BAAs. For dental/clinic use you need HIPAA controls — here's the full build.
Pipecat is the Python-first framework for STT-LLM-TTS pipelines with 100+ AI services as plugins, ultra-low latency, distributed Subagents, and direct Twilio integration. Here is the 2026 build pattern.