Living Brain

Overview

Living Brain is a persistent, self-learning memory and intelligence layer designed to provide shared context, continuity, and knowledge retrieval across AI agents and applications. Developed within the ChatAndBuild and ChatChat ecosystem, the platform allows connected models to retain information, surface non-obvious connections, and adapt at inference time without modifying underlying model weights.

Key Features

• Flexible agent connectivity — Integrates with applications and over 60 AI tools using a REST API, SDK, CLI, and Model Context Protocol (MCP) interfaces.
• Knowledge compilation and scoring — Converts unstructured notes, conversations, and updates into structured, source-linked markdown evaluated by memory worthiness, placement fit, and connection resonance scores.
• Multi-format context capture — Ingests URLs, raw text, and document formats including PDF, DOCX, and PPTX up to 100 MB per file, alongside native audio transcription.
• Externalized memory retrieval — Stores memory outside the language model to deliver semantic search and targeted context injection for specific tasks.
• Chat and communication integrations — Captures real-time interactions and triggers workflows through Telegram capture bots, Slack integrations, and configurable webhooks.
• Scheduled briefs and version history — Generates scheduled context summaries and maintains page version histories ranging from 30 to 90 days.

Who It's For

Living Brain is designed for software engineers, AI system architects, and technical product teams building autonomous agents or multi-agent workflows that require persistent state across sessions. It also serves development teams and enterprise organizations seeking to reduce context window bloat and unify organizational data across fragmented AI platforms.

Notable Strengths

In internal LoCoMo benchmark evaluations, the platform achieved a 464-millisecond median memory retrieval latency and 92.2% judged accuracy across 1,540 questions. In LongMemEval-S testing, its external context architecture reduced token consumption by 22.6x and cut task costs by 95% compared to a 116K full-context baseline. Furthermore, it provides transparent developer onboarding through pay-as-you-go API pricing with deep native support for the open Model Context Protocol ecosystem.