# Living Brain Launches AI Memory Layer for Persistent Context A new knowledge vault architecture reduces token costs while improving AI agent consistency. - **Source:** [Dynamic Business](https://dynamicbusiness.com) - **Published:** 2026-11-11 - **Topic:** [technology](https://brifing.si/c/technology) - **Original article:** - **On Brifing:** https://brifing.si/s/86153581-4320-4bc7-b27c-a6dfb068947e ## Decoupling Memory from Models Living Brain introduces a persistent knowledge memory layer designed to store team context externally from specific language models. By capturing conversations, code, and documents into a centralized vault, it allows AI agents to access shared information via REST APIs, SDKs, or MCP links, ensuring consistent behavior across various integrated development tools. ## Architectural Integration Capabilities The platform integrates with popular models like ChatGPT and Claude, alongside coding tools such as Cursor and Codex. Developers utilize CLI tools and SDKs to plug agents into this shared memory, enabling learned rules and decisions to carry over seamlessly between different sessions, projects, and distinct AI-driven task environments. ## Quantifiable Performance Gains Internal benchmarks including LoCoMo and LongMemEval-S demonstrate significant efficiency improvements. Living Brain achieves up to a 22.6x reduction in token usage per question compared to full-context baselines. This optimization results in 95% lower costs per task while maintaining high judge-rated accuracy and ensuring low latency during critical data retrieval processes. ## Memory Health Monitoring To maintain high-quality knowledge, the system employs specific health metrics including Freshness, Strain, and Readiness. These scores help administrators identify outdated or overburdened memory branches, preventing degradation over time. This proactive management ensures that the information retrieved by agents remains relevant, accurate, and aligned with the current organizational needs. ## Tiered Pricing and Access Pricing options cater to diverse needs, starting with a usage-based API model at $8 per million input tokens. The Workspace plan costs $99 monthly for small teams, providing 10 GB of storage and 500,000 requests. Enterprise plans offer custom capacity, dedicated infrastructure, and specific deployment options like air-gapped or private cloud environments. ## Targeting Engineering and Management The solution benefits engineering teams requiring rule consistency across code deployments and project managers handling extensive research or roadmaps. By preserving organizational memory, it helps businesses avoid context loss during platform switches or team onboarding. It serves professionals who frequently find themselves repeating inputs due to poor AI memory retention. ## Governance and Compliance Needs For regulated industries, the Business plan provides critical data governance features. It includes support for audit logs, data residence controls, and contractually defined service level agreements. These tools allow organizations to maintain strict compliance while leveraging AI agents, ensuring that sensitive information remains secure within the defined enterprise infrastructure boundaries. ## Strategic Evaluation for Adoption Decision-makers should evaluate Living Brain by estimating expected token usage and integration overhead with existing workflows. While it offers potential for streamlined collaboration and lower operating costs, the complexity of the organization determines the necessity of enterprise-grade controls. Properly implemented, it addresses the fundamental challenge of maintaining context across tools. ## Sources covering this story - https://dynamicbusiness.com/ai-tools/living-brain-ai-memory-layer.html — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._