Description
Living Brain functions as a persistent and evolving intelligence layer designed to enhance the capabilities of AI agents. It connects various contexts, surfaces non-obvious connections, and provides every agent with shared memory, context, and continuity. The platform aims to solve the "intelligence trilemma" by balancing intelligence, speed, and cost, allowing teams to achieve high performance without compromise.
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This system improves memory and accuracy, demonstrating higher performance in internal comparisons. For instance, it achieved 92.2% judged accuracy on LoCoMo across 1,540 questions and boasts a median memory retrieval time of 464 ms. Furthermore, Living Brain significantly reduces operational costs and token usage, utilizing 22.6 times fewer tokens per question and achieving 95% lower cost per task compared to a full-context baseline. It also reported 0% hallucinations on LongMemEval-S, highlighting its reliability.
Living Brain integrates across compatible AI tools, allowing users to keep their knowledge connected and accessible. It also supports connections to custom MCPs (Memory and Context Providers), enabling agents to expand their capabilities by linking to proprietary tools and data. The platform automatically transforms information into persistent, shared memory, compiling scattered conversations, files, and updates into structured, source-linked knowledge.
For pricing, Living Brain offers a "Pay as you go" API model with no subscription, charging $8 per 1M model input tokens and $0.50 per 1M output tokens, with a minimum top-up of $5. It includes one project, multiple brains, 1 GB storage, and 100,000 requests per month. The "Living Brain Workspace" is billed monthly and includes three team seats, one production project, 10 GB storage, 500,000 requests per month, and shared monthly credits. For larger organizations, a custom "Business" plan is available, offering tailored deployment, capacity, storage, data location, and dedicated support.
Living Brain supports two complementary paths for AI adaptation: retaining knowledge, corrections, and decisions to bring relevant context into future tasks without changing model weights, and using accumulated knowledge as a foundation for post-training workflows like fine-tuning open-weight models. This allows connected models to adapt to specific work at inference time, even for closed-weight models.
- CategoryAI Agent Builders
- Pricing ModelFreemium
- DeploymentCloud, On-premises, Customer Cloud, Air-gapped
- PlatformsWeb
Living Brain's Core Features
Persistent and evolving intelligence layer
Connects context and surfaces non-obvious connections
Provides shared memory, context, and continuity for AI agents
Enhances memory and accuracy for AI tasks
Reduces token usage and operational costs (95% lower cost per task)
Achieves 0% hallucinations on LongMemEval-S
Offers fast recall with 464 ms median memory retrieval
Integrates with compatible AI tools
Connects to custom Memory and Context Providers (MCPs)
Automatically turns knowledge into persistent, shared memory
Supports adaptation without changing model weights
Enables memory as a foundation for post-training
Provides REST API, SDK, CLI access
Offers file extraction and storage
Includes semantic search and connection discovery
How to use Living Brain?
Build your Living Brain: Create your first Living Brain instance.
Connect to AI tools: Link your Living Brain to compatible AI applications and agents.
Integrate custom data: Connect your own tools and data sources to expand agent capabilities.
Process information: Bring documents, conversations, and knowledge into Living Brain.
Utilize context: Make the accumulated context available to your applications and agents via API, SDK, CLI, and MCP.
Manage agents: Organize and run your team's Brains and agents within the ChatChat Workspace.
Living Brain's Use Cases
- Enhancing AI Agent Memory
- Reducing AI Operational Costs
- Improving AI Accuracy
- Integrating Knowledge Across AI Tools
- Custom AI Agent Development
- Automated Knowledge Organization
- AI Model Adaptation
- Post-Training Data Curation
