AI memory is the system that allows models to preserve information over time. It includes parametric memory inside the model weights and non-parametric memory stored outside the model through tools like databases, embeddings, and state layers. Memory is still one of the hardest unsolved problems in AI, and most approaches fail at scale. Backboard treats memory as a configurable infrastructure layer designed for accuracy, persistence, and cross-model continuity.

Why Memory Matters

Models are strong at generating answers but weak at remembering. When they forget context, users repeat themselves, workflows break, and trust drops. Even models with 1M-token windows cannot reliably maintain long-term context. Research from Anthropic and OpenAI shows accuracy decay as conversations grow due to approximate attention and compression limits. Larger windows help, but they do not replace persistent memory.

Parametric vs Non-Parametric Memory

Parametric Memory

• Stored inside model weights
• Learned during training
• Static and difficult to update
• Covers general knowledge but not personal or session-specific information

Non-Parametric Memory

• Stored outside the model
• Dynamic, persistent, controllable
• Includes transcripts, embeddings, session data, threads, and structured state
• Powers RAG, agent architectures, and context managers

Non-parametric systems tend to fail when scaling to millions of tokens, especially when retrieval is inconsistent or state management is improvised.

Why Memory Is Hard

Scale: Storing data is cheap. Finding the correct slice is not.
Retrieval: Semantic search fails if queries do not match embedding characteristics.
State Management: Most agents collapse under long histories due to drift and noisy context injection.
Privacy: Scattered storage across tools creates compliance issues.

How Backboard Solves the Problem

Backboard is built around a principle: memory should behave like a reliable, configurable database for AI.

Stateful Threads

Each conversation or agent runs inside a thread with persistent continuity. Developers get stable long-term context without manual stitching.

Portable Memory

Memory follows the user across 2,200+ models. This eliminates vendor lock-in and enables optimal routing.

Persistent Storage With High Recall

Everything is stored unless configured otherwise. Retrieval accuracy remains high thanks to configurable embedding models, vector DBs, and dimensions. Backboard currently holds the world’s highest validated LoCoMo score for long-context memory.

Configurability

Memory can be tuned per use case.
Examples:
• strict recall vs broader semantic recall
• selective write rules
• custom embedding models and storage
• fine control of context injection

Production Reliability

Backboard includes a unified API, privacy controls, anonymization, and reproducible benchmarks. It removes the need for custom glue code.

How Backboard Compares to Other Approaches

RAG

RAG is good for document lookup, not long-term memory.
• Strength: factual retrieval from known sources
• Weakness: poor with unstructured conversational history, drift, and personal context
Backboard can use RAG components, but it layers stateful threads and persistent memory on top to maintain continuity across tasks.

MemGPT

MemGPT introduced the idea of hierarchical memory with a scratchpad and long-term store.
• Strength: creative architecture for dynamic memory management
• Weakness: heavy prompting logic, custom reasoning loops, difficult to operationalize
Backboard takes the same core idea but delivers it as an API with configurable memory, multiple storage options, and cross-model portability.

Letta

Letta focuses on agent state, tool usage, and planning.
• Strength: strong agent workflows and tool orchestration
• Weakness: less focused on massive-scale, multi-model long-term memory
Backboard complements Letta by supplying a high-accuracy, persistent memory layer that agents can read from and write to.

In short:
• RAG retrieves facts
• MemGPT structures agent memory
• Letta orchestrates agent behavior
• Backboard provides the reliable long-term memory that each of them needs

Why This Matters for the Future

Systems that remember will outperform systems that reset their context every time. Long-term continuity becomes the differentiator for personal assistants, business agents, and enterprise workflows. Memory is not a feature. It is infrastructure.

Next Steps

• Explore the LoCoMo benchmark
• Review API docs for memory threads
• Sign Up!