Living Brain
Living Brain is an AI a persistent memory and context layer API that gives agents shared memory, context and continuity for multi-agent workflows. It connects conversations, files and tools into structured, source-linked knowledge for teams and developers.
About Living Brain
Living Brain is an AI a persistent and self-learning memory layer exposed via API, SDK, CLI, MCP and a web workspace. It compiles scattered conversations, files and updates into structured, source-linked knowledge and applies memory policies so connected agents retrieve only the most relevant context at runtime. The site lists measured internal benchmarks such as a 464 ms median memory retrieval time and a 3.7 s median total answer time on LoCoMo, plus reported reductions of 22.6× fewer tokens per question and 95% lower cost per task versus a 116K-token full-context baseline in LongMemEval-S runs.
Integration and deployment surface across developer workflows: one project supports multiple brains, with API/SDK/CLI access, an optional workspace for teams, and connectors to supported tools and custom MCPs. Living Brain supports two adaptation paths: inference-time adaptation by supplying retrieved context to closed-weight models, and a post-training path where accumulated memory can be curated into training and evaluation datasets for fine-tuning where provider access allows. Benchmarks reported on the site are internal evaluations and the documentation invites contact for benchmark details, tool compatibility and post-training options.
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Who is using Living Brain?
Key Features
- Persistent, shared memory accessible via API, SDK, CLI and MCP for multi-agent continuity
- Compiler that ingests conversations and files and produces structured, source-linked knowledge
- Configurable memory policies and scoring (e.g. Memory Worthiness, Placement Fit, Connection Resonance) to decide what is retained and surfaced
- Measured low-latency retrieval (464 ms median) and median answer times (3.7 s) reported on LoCoMo internal runs
- Token and cost savings reported in internal LongMemEval-S tests: 22.6× fewer tokens and 95% lower cost per task versus a 116K-token full-context baseline
- Workspace for teams with seats, storage, request quotas, webhooks and Slack/Telegram capture for operationalising brains
Use Cases
- Provide agents with persistent, source-linked memory so they can recall prior decisions and context
- Compile scattered conversations and documents into structured knowledge for search and retrieval
- Reduce input token usage and inference cost by retrieving only relevant context per task
- Support inference-time adaptation for closed-weight models by supplying curated context at runtime
- Curate accumulated memory and feedback into datasets for fine-tuning or post-training evaluation