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Mastra Blog — Announcing Mastra Code: A Coding Agent That Never Compacts

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2026-03-17

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Mastra Mastra Code LibSQL Mastra Blog — Announcing Observational Memory Mastra Observational Memory: Dual-Agent Compression with Traffic Light Priority Encoding Achieves 95 GPT-4o Mastra — Changelog 2026-03-04 Mastra stepExecutionPath + supportsConcurrentUpdates: Hardening Parallel Agent Workflows Against Sta SoftBank Adobe Replit LangChain Inngest LongMemEval GPT-5-mini

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2026-03-17 / SKILLSMastra Code: Terminal Coding Agent with Observational Memory That Eliminates Context Compaction (Launched Feb 27, 2026)Mastra Code is a terminal-based coding agent (TUI, no IDE plugin required) built on top of Mastra's observational memory system — dynamically compressing context into semantic observations rather than discarding history when the token limit approaches. It connects to 70+ AI models, monitors tool outputs (file edits, executions) to retain architectural patterns and variable dependencies beyond standard token limits, and uses LibSQL-based storage for cross-session continuity. This makes it the only major coding agent that claims full context continuity across arbitrarily long sessions without compaction-induced amnesia.
2026-03-17 / SKILLSMastra Observational Memory: Dual-Agent Compression with Traffic Light Priority Encoding Achieves 95% on LongMemEvalMastra's open-source observational memory uses two background agents — Observer and Reflector — to continuously compress conversation history into a dated log using red/yellow/green emoji priority markers that LLMs parse with unusual efficiency. This architecture enables 5–40x compression for tool-heavy agents and unlocks provider-side prompt caching (normally unavailable to RAG-based agents), cutting token costs by 4–10x. Benchmarks show 94.87% on LongMemEval with GPT-4o, surpassing the previous SOTA by 2.6 points.
2026-03-15 / SKILLSMastra stepExecutionPath + supportsConcurrentUpdates: Hardening Parallel Agent Workflows Against State Corruption

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