Both talk about memory. Only one treats it as system architecture.

A comparison between Backboard and Letta is reasonable at first glance. Both focus on helping AI systems retain context over time. Both reject the idea that stateless prompts are enough for serious applications.

That is where the overlap mostly stops.

Letta is built around agent memory as a core abstraction. Backboard is built around memory as one layer in a full AI runtime.

That difference shapes everything else.

What Letta does well

Letta is opinionated about agent design. It emphasizes:

• Explicit memory structures

• Clear separation between short-term and long-term memory

• Developer-controlled memory injection into prompts

• Transparency into what the agent “knows”

For developers building research agents, simulations, or tightly scoped autonomous systems, this explicitness is useful. You can reason about memory like you would a data structure.

The tradeoff is that the developer becomes responsible for correctness.

Memory philosophy: explicit vs ambient

Letta treats memory as something the developer actively manages. You decide what gets stored, when it is retrieved, and how it is used by the model.

Backboard treats memory as ambient infrastructure.

By default, Backboard:

• Writes memory automatically

• Retrieves memory automatically

• Maintains state across full threads

• Preserves memory across sessions and models

You can override behavior when needed, but you do not need to orchestrate memory flows manually just to get consistent results.

This difference matters less in demos and more in production, where missed recalls and prompt drift create silent failures.

Accuracy over abstraction

Letta’s strength is conceptual clarity. Backboard’s strength is operational accuracy.

Memory is only useful if the right information is recalled at the right moment. Backboard optimizes for recall accuracy across long-running conversations, multi-step workflows, and model changes.

In practice, this means:

• Less prompt stuffing

• Fewer brittle heuristics

• More consistent agent behavior over time

If your system depends on users trusting that the assistant remembers them correctly, accuracy becomes non-negotiable.

Scope: agent framework vs AI runtime

Letta focuses on agent memory and cognition. It assumes the rest of the stack exists elsewhere.

Backboard includes that stack.

Backboard ships with:

• Stateful threads

• Long-term and short-term memory

• LLM routing across providers

• Retrieval augmented generation

• Tool calling and custom tools

• Web search

• Model switching without losing memory

• Unified usage tracking and billing

This is not about convenience. It is about reducing integration debt. Every external layer you stitch together becomes another place state can break.

Control versus leverage

Letta gives developers fine-grained control over how agents think and remember. That is valuable for experimentation and research.

Backboard gives developers leverage. You trade some manual control for systems that behave correctly by default.

This distinction mirrors a common fork in AI development:

• Do you want to design cognition?

• Or do you want to ship reliable products?

Both are valid. They just serve different goals.

When Letta makes sense

Letta is a strong fit if:

• You are building experimental or research-driven agents

• You want explicit visibility into memory structures

• You are comfortable orchestrating memory logic yourself

• Your system does not require broad stack integration

When Backboard is the better fit

Backboard is the stronger choice if:

• You need memory that works reliably in production

• You want state across sessions and models

• You need routing, retrieval, and tools to share context

• You want fewer moving parts and failure modes

• You care about long-term correctness more than theoretical purity

Bottom line

Backboard and Letta both take memory seriously.

Letta focuses on how agents think about memory.
Backboard focuses on whether systems behave correctly over time.

If you want to experiment with cognition, Letta is compelling.
If you want to deploy stateful AI systems that users depend on, Backboard is built for that reality.

Next step: decide whether your bottleneck is understanding agent behavior, or making that behavior reliable at scale.

One question to guide the decision: are you optimizing for insight into agent internals, or confidence in production outcomes?