# Memory Blocks and Sleep-Time Compute

> Discrete functional memory blocks the model can edit directly, and a sleep-time agent that consolidates memory asynchronously while the primary agent is idle. These two ideas are how you scale memory beyond one conversation.

**Type:** Build
**Languages:** Python (stdlib)
**Prerequisites:** Phase 14 · 07 (MemGPT)
**Time:** ~75 minutes

## 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

| Tier | Scope | Where it lives | Written by |
|------|-------|----------------|------------|
| Core | Always visible | Inside the main prompt | Agent tool call + sleep-time rewrites |
| Recall | Conversation history | Retrievable | Automatic turn logging |
| Archival | Arbitrary facts | Vector + KV + graph | Agent tool call + sleep-time ingest |

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.

```figure
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

| Term | What people say | What it actually means |
|------|----------------|------------------------|
| Memory block | "Editable prompt section" | Typed, persistent, LLM-editable segment of core memory |
| Human block | "User memory" | Facts about the user, pinned in core |
| Persona block | "Agent identity" | Self-concept, tone, constraints, pinned in core |
| Sleep-time compute | "Async memory work" | Second agent doing consolidation off the critical path |
| Core / Recall / Archival | "Tiers" | Three-layer memory split: always-visible / conversation / external |
| Block limit | "Cap" | Character limit per block; forces summarization |
| Native reasoning | "Thinking channel" | Provider-level reasoning output, not prompt-level `Thought:` |
| Learned context | "Sleep output" | Facts the sleep-time agent writes into shared blocks |

## Further Reading

- [Letta, Memory Blocks blog](https://www.letta.com/blog/memory-blocks) — the block pattern
- [Letta, Sleep-time Compute blog](https://www.letta.com/blog/sleep-time-compute) — async consolidation
- [Letta, Rearchitecting the Agent Loop](https://www.letta.com/blog/letta-v1-agent) — native reasoning rewrite
- [Packer et al., MemGPT (arXiv:2310.08560)](https://arxiv.org/abs/2310.08560) — the origin
