Observational Memory By Mastra

Observational Memory by Mastra is an AI Agents & Automation tool that provides a human-inspired memory system for agentic systems. It achieves ~95% on LongMemEval with a stable context window and is open-source.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is Observational Memory by Mastra?

Observational Memory by Mastra is a cutting-edge memory system designed for AI agentic systems, drawing inspiration from human cognitive processes. It boasts an impressive score of approximately 95% on the LongMemEval benchmark, all while maintaining a completely stable context window. This system operates by compressing context into observations, much like a human brain distills complex sensory input into key takeaways. It uses a text-based, log-like message format with a three-date model for enhanced temporal reasoning and emoji-based prioritization for importance. The context window is dynamically managed, with new messages appended to a raw block until a token threshold is met, at which point they are compressed into observations. This open-source implementation is compatible with prompt caching from major LLM providers like Anthropic and OpenAI.

Best used for

Ideal for developers and data scientists who need to build AI agents with robust and efficient memory capabilities, manage long-term conversational context, and improve temporal reasoning. Especially valuable for those seeking an open-source, human-inspired memory system that performs well on benchmarks.

Common actions

manage AI agent memory

improve agent context

build AI agents

artificial intelligenceGitHubsoftware engineeringDeveloper Tools

Capabilities

Key features

Human-inspired memory system
Stable context window
Text-based observations
Log-based message format
Three-date temporal model
Emoji-based prioritization
Open-source implementation

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free to start. Pricing launches Q1 2026.

FAQs

What is the core mechanism behind Observational Memory?

Observational Memory compresses large amounts of information into concise, text-based observations, similar to how humans distill experiences. It uses a log-based message format with a three-date model for temporal reasoning and emoji-based prioritization to manage context efficiently.

How does Observational Memory ensure a stable context window?

The system breaks the context window into two blocks: observations and raw messages. New messages are added to the raw block and, once a configurable token threshold is reached, they are compressed into observations, maintaining a stable and manageable context.

Is Observational Memory compatible with existing LLM providers?

Yes, Observational Memory is designed to be compatible with prompt caching mechanisms from major Large Language Model providers such as Anthropic and OpenAI, allowing for seamless integration into existing AI agent workflows.

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