Memory Blocks and Sleep-Time Compute

Learning Objectives

• Name the three memory tiers Letta uses (core, recall, archival) and the role of each.
• Explain the memory-block pattern: Human block, Persona block, and user-defined blocks as first-class typed objects.
• Describe what sleep-time compute is, why it sits off the critical path, and why it can run a stronger model than the primary agent.
• Implement a scripted two-agent loop where a primary agent serves responses and a sleep-time agent consolidates blocks between turns.

The Problem

MemGPT (Lesson 07) solved the virtual-memory control flow. Three production problems emerged:

1. Latency. Every memory operation sits on the critical path. If the agent has to prune, summarize, or reconcile while the user waits, tail latency blows up.
2. Memory rot. Writes accumulate. Contradicted facts stay. Retrieval drowns in stale content.
3. Structure loss. A flat archival store cannot express "the Human block is always in the prompt; the Persona block is always in the prompt; the Task block swaps per session."

Letta (letta.com) is the platform name the original MemGPT project adopted in 2024 — the paper's pattern keeps the MemGPT name — and the 2026 Letta V1 rewrite is a later, separate step. Memory blocks make structure explicit; sleep-time compute moves consolidation off the critical path.

The Concept

Three tiers

Core is the MemGPT core. Recall is the conversation buffer with its evicted tail. Archival is the external store. The split cleans up MemGPT's two-tier overloading.

Memory blocks

A block is a typed, persistent, editable section of the core tier. The original MemGPT paper defined two:

• Human block — facts about the user (name, role, preferences, goals).
• Persona block — the agent's self-concept (identity, tone, constraints).

Letta generalizes to arbitrary user-defined blocks: a Task block for the current goal, a Project block for codebase facts, a Safety block for hard constraints. Each block has an id, label, value, limit (character cap), description (so the model knows when to edit it).

Blocks are editable via the tool surface:

• block_append(label, text)
• block_replace(label, old, new)
• block_read(label)
• block_summarize(label) — condense a block that is near its limit.

Sleep-time compute

The 2025 Letta addition: run a second agent in background, off the critical path. Sleep-time agents process conversation transcripts and codebase context, write learned_context into shared blocks, and consolidate or invalidate archival records.

Properties that fall out:

• No latency cost. Primary responses do not wait for memory ops.
• Stronger model allowed. The sleep-time agent can be a more expensive, slower model because it is not latency-constrained.
• Natural consolidation window. Dedup, summarize, invalidate contradicted facts when the user is not waiting.

The shape matches how humans work: you do the task, you sleep on it, the long-term memory settles overnight.

Native reasoning

Letta V1 (letta_v1_agent, 2026) deprecates send_message/heartbeat and inline Thought: tokens in favor of native reasoning. The Responses API (OpenAI) and the Messages API with extended thinking (Anthropic) emit reasoning on a separate channel, passed through turns (encrypted across providers in production). The control loop is still ReAct. The thought trace is structural, not prompt-shaped.

Where this pattern goes wrong

• Block bloat. Infinite block_append hits the limit fast. Wire a block summarizer before the write that pushes over the cap.
• Silent drift. Sleep-time agent rewrites a block and the primary agent never notices. Version blocks and surface diffs in the trace.
• Poisoned consolidation. Sleep-time agent processes attacker-reachable content into core. Lesson 27 applies to the sleep-time surface too.

memory-blocks

Build It

code/main.py implements:

• Block — id, label, value, limit, description.
• BlockStore — CRUD + near_limit(label) helper.
• Two scripted agents — PrimaryAgent serves a turn, SleepTimeAgent consolidates between turns.
• A trace that shows a three-turn conversation with block writes, plus a sleep-time pass that summarizes a block and invalidates a stale fact.

Run it:

python3 code/main.py

The transcript shows the split: primary turns are fast and produce raw writes; the sleep pass compacts and cleans up.

Use It

• Letta (letta.com) for the reference implementation. Self-host or managed cloud.
• Claude Agent SDK skills as block-shaped knowledge — a skill is a named, versioned, retrievable block of instructions the agent loads on demand.
• Custom builds for teams that want control over the storage backend. Use the Letta API contract so you can migrate later.

Ship It

outputs/skill-memory-blocks.md generates a Letta-shaped block system with sleep-time hooks for any runtime, including safety rules and citation wiring.

Exercises

1. Add a block_summarize tool that replaces the block value with a model-generated summary when near_limit returns true. Which trigger threshold minimizes both summarization calls and block overflow?
2. Implement sleep-time dedup over archival: two records whose text has >90% token overlap collapse to one. Do it only in the sleep pass, never on the critical path.
3. Version blocks. On every write record the old value and a diff. Expose block_history(label) so operators can debug "why did the agent forget X."
4. Treat sleep-time agents as untrusted writers. When they touch the Persona or Safety block, require a second-agent review before committing.
5. Port the example to use the Letta API (letta_v1_agent). What changes in the block schema, and how does native reasoning alter the trace shape?

Key Terms

Further Reading

• Letta, Memory Blocks blog — the block pattern
• Letta, Sleep-time Compute blog — async consolidation
• Letta, Rearchitecting the Agent Loop — native reasoning rewrite
• Packer et al., MemGPT (arXiv:2310.08560) — the origin