The system stores information independently of individual language models, allowing users to save knowledge through one assistant and retrieve it through another. The company reports 92.2% accuracy on LoCoMo, 464 ms median retrieval latency, and up to 22.6× fewer tokens per question.
Technical Specifications
| Parameter | Living Brain |
| Architecture | Model-independent persistent memory |
| LoCoMo accuracy | 92.2% |
| Median retrieval latency | 464 ms |
| Token reduction | Up to 22.6× |
| Supported agents | ChatGPT, Claude, Cursor, Codex, custom agents |
| Integration | MCP, REST API, CLI |
| Retrieval | Semantic search and keyword fallback |
| Knowledge structure | Linked pages and knowledge graph |
| Input formats | Text, URLs, conversations, PDF, DOCX, PPTX |
| SDKs | Python, TypeScript |
| Authentication | OAuth, API keys |
How It Works
Living Brain separates memory storage from LLM inference.
- Capture: Agents submit conversations, documents, and project information.
- Processing: Information is organized into knowledge pages with relationships.
- Storage: Memory persists independently of the originating model.
- Retrieval: Connected agents retrieve relevant information without loading complete conversation histories.
Memory updates do not require retraining or modifying model weights.
Comparison With Traditional AI Memory
Living Brain is not a replacement for a specific previous model. It replaces the conventional approach of maintaining separate memory within individual assistants.
| Feature | Traditional AI Memory | Living Brain |
| Memory storage | Assistant-specific | Shared persistent storage |
| Cross-model access | Limited | Supported |
| Context transfer | Manual or application-dependent | Automatic retrieval |
| Memory updates | Platform-dependent | Independent of model weights |
| Token consumption | Often requires history or summaries | Up to 22.6× lower, claimed |
| Integration | Provider-specific | MCP and API |
Benchmark Comparison
| Memory system | Reported LoCoMo accuracy |
| Living Brain | 92.2% |
| Backboard | 90.1% |
| Make0 Standard | 83.64% |
| SuperLocalMemory V3.3 | 70.4% |
These are independently reported results, not a controlled head-to-head evaluation. Differences in models and benchmark configurations limit direct comparisons.
API and Integration
Living Brain provides an MCP endpoint for supported AI assistants:
Its REST API supports memory ingestion, search, knowledge management, and retrieval. Search responses can include relevance scores, excerpts, document references, and metadata.
Limitations
The announcement does not establish:
- The exact methodology behind the 92.2% LoCoMo score.
- The baseline used to calculate the 22.6× token reduction.
- Retrieval performance under high concurrency or with very large memory stores.
- Whether the 464 ms latency includes network overhead and processing beyond retrieval.
Conclusion
Living Brain provides a shared memory layer for multiple AI assistants without modifying their underlying models. Its main technical claims are 92.2% LoCoMo accuracy, 464 ms retrieval latency, and up to 22.6× lower token usage.
Cross-model memory sharing is the primary architectural difference from assistant-specific memory systems. The performance claims still require independent verification.
