Observational Memory Might Kill RAG for Long-Running Agents
Mastra's open-source memory system replaces retrieval with background compression, scoring highest on LongMemEval at a fraction of the cost. If you're building agents that need to remember, pay attention.
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'Observational memory' cuts AI agent costs 10x and outscores RAG on long-context benchmarks
Mastra open-sourced 'observational memory,' a new approach to AI agent memory that replaces RAG retrieval with continuous background compression of conversation history, achieving state-of-the-art scores on LongMemEval at a fraction of the cost.
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Every team building AI agents has hit the same wall: the agent forgets everything between sessions. The standard fix is RAG — stuff a vector database with conversation chunks, retrieve the relevant ones when needed, pray the embeddings surface the right context. It works. Barely. And it's expensive, slow, and brittle for anything that needs to maintain state across weeks or months.
Mastra's observational memory takes a fundamentally different approach. Instead of retrieving past context on demand, two background agents — an Observer and a Reflector — continuously compress conversation history into a dated observation log using a traffic-light priority system. No retrieval step. No embedding search. The agent just... remembers.
Why this matters for builders
The numbers are striking: 94.87% on LongMemEval with gpt-5-mini (the highest score ever recorded on that benchmark), at 5-40x compression on tool-heavy workloads. That's not an incremental improvement over RAG — it's a category shift. The cost reduction alone changes which agent architectures are economically viable.
Think about the use cases that have been stuck in prototype hell because memory was too expensive or unreliable: SRE agents that need months of incident context, customer support agents that should know your entire history, coding assistants that remember your codebase conventions across sessions. Observational memory doesn't just make these cheaper — it makes them possible at production quality.
The team behind this came from Gatsby (which they sold to Netlify), so they understand developer tooling and open-source adoption. The fact that this is fully open-source matters. Memory infrastructure for agents has been a gap that every team fills with custom solutions. A good open-source default could consolidate the ecosystem the way LangChain did for chains — or the way MCP is doing for tool connectivity right now.
The bigger picture
This week saw three pieces of the agentic stack fall into place simultaneously: Anthropic donated MCP to an independent foundation, standardising how agents connect to tools. OpenAI launched Frontier, standardising how enterprises manage fleets of agents. And now Mastra solves how agents maintain context over time.
The production stack for AI agents in 2026 is no longer theoretical. The question for product teams isn't whether to build with agents — it's which memory, orchestration, and connectivity layers to bet on. If you're still hand-rolling RAG pipelines for long-running agent state, this is the week to reconsider.
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