# Hierarchical Memory

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- **Type:** Skill
- **Install:** `agentstack add skill-anthonyalcaraz-agentic-graph-rag-skills-hierarchical-memory`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [AnthonyAlcaraz](https://agentstack.voostack.com/s/anthonyalcaraz)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [AnthonyAlcaraz](https://github.com/AnthonyAlcaraz)
- **Source:** https://github.com/AnthonyAlcaraz/agentic-graph-rag-skills/tree/main/skills/memory/hierarchical-memory
- **Website:** https://www.oreilly.com/library/view/agentic-graphrag/9798341623163/

## Install

```sh
agentstack add skill-anthonyalcaraz-agentic-graph-rag-skills-hierarchical-memory
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Hierarchical Memory (Letta / MemGPT)

## Overview

Letta popularizes a three-tier memory architecture that mirrors human
cognition and CPU memory hierarchy. The architectural decomposition (Ch4):

- **Core memory** (cache layer): small, fast, structured. The agent's
  active reasoning context. Bounded (`core_limit` parameter). Once full,
  eviction is forced — you cannot have everything in core. This is the
  forcing function that pushes the agent to declare what is durably
  important.
- **Recall memory** (raw interaction layer): the literal conversation
  history. Append-only. Answers "what did we talk about yesterday."
- **Archival memory** (persistent layer): effectively unlimited storage
  for evicted-but-still-relevant facts. Searchable. Not deleted.

Eviction is the key discipline. Naive FIFO (oldest goes first) loses
high-value durable facts that were learned early. Naive LRU (least-recently-
used) loses background-but-relevant context. The chapter recommends a
combined score: access frequency × recency, with explicit handling for
"durable attributes" (peanut allergy) vs "short-lived states" (having
coffee right now).

## When to Use

- Long-running personal assistant agents — multi-session, must feel
  consistent over time
- Multi-day DevOps incident investigation where some facts (production
  region, on-call rotation) are durably important and others (current
  shell history) rotate fast
- Customer-support agents that need both "what we know about this customer"
  (core) and "what was said in last week's tickets" (archival)

Phrases that should invoke this skill: "the agent needs memory across
sessions", "core context", "evict old memory", "MemGPT", "Letta hierarchy",
"working memory vs long-term memory".

## When NOT to Use

- **One-shot agents.** Single-prompt, no persistence — flat context is
  correct. The eviction overhead pays for nothing.
- **Event logs / audit trails.** Use append-only kafka-style logs. The
  recall layer here is interaction-oriented, not event-oriented.
- **Every fact equally important.** Then a flat KV store is right. The
  hierarchy exists because some facts are more important than others;
  if that gradient doesn't exist, the hierarchy is overhead.
- **Hard real-time eviction is too slow.** Default impl is O(n) on
  eviction. Production needs a heap for `evict_least_used`. Swap the
  internal index at the seam noted in `lib.py`.

## Process

| Step | Input | Action | Output | Verification |
|------|-------|--------|--------|--------------|
| 1 | core_limit (int, in fact-count or token-count) | `lib.HierarchicalMemory(core_limit=N)` | empty 3-tier memory | core / recall / archival all start empty |
| 2 | user_input, agent_response | `mem.process_interaction(...)` | recall updated; extracted facts promoted to core | `len(mem.recall) > 0`; new facts in core (or in archival if core was full) |
| 3 | fact (string), durability ("durable" / "short-lived") | `mem.add_fact(fact, durability)` | core if room, else evict-and-add | `mem.core` size never exceeds `core_limit` |
| 4 | core is full + new fact | `mem._evict_least_used()` | LFU/LRU fact moved to archival | evicted fact is `was_in_core=True` in archival; core size = limit - 1 + 1 = limit |
| 5 | query string | `mem.query(q)` | hits from core (priority) + recall + archival | results tagged by source tier; archival hits include `evicted_at` |
| 6 | core or archival, no constraints | `mem.snapshot()` | dict serialization | round-trip preserves access counts + tier membership |
| 7 | core size + access pattern after N interactions | `mem.diagnostics()` | health report (% durable in core, eviction rate, archival growth) | flags pathological patterns (e.g. 80% short-lived facts in core = wrong promotion logic) |

## Rationalizations

| Agent rationalization | Documented rebuttal |
|------------------------|--------------------|
| "Core is small — I'll just make it bigger." | The chapter is explicit: "The core_limit=2000 parameter is not just a tuning knob, but a forcing function." Growing core defers the eviction decision; the decision still has to be made when the new limit is reached. Bigger core trades scarce LLM context budget for cheap-but-unused archival space. |
| "FIFO eviction is good enough." | Loses the "User is allergic to peanuts" fact when a flood of "user is having coffee right now" facts hits. The Ch4 worked example names this exact failure. Track access patterns; durable attributes should resist eviction. |
| "Archival is just deleted memory — I can drop it." | Then recall via `archival.search()` fails. The Ch4 invariant: "eviction is not deletion." The cost of keeping archival is small (it can live in slow durable storage); the cost of dropping it is the agent forgets things it learned. |
| "Recall and archival are the same thing." | Recall is interaction-oriented (full conversation turns, append-only). Archival is fact-oriented (evicted from core, still searchable). They serve different queries: "what did we talk about" vs "what did I learn about the user." Conflating them loses the distinction. |
| "I'll skip the diagnostics step — eviction logic is correct by construction." | Diagnostics catch the slow-rotting failure mode: short-lived facts gradually accumulating in core because their promotion logic was too permissive. Without it you discover the regression in production. |

## Red Flags

- **Core consistently full + high eviction rate.** Promotion logic is too
  permissive; short-lived facts are being promoted. Tighten the durability
  classifier.
- **Same fact promoted-then-evicted-then-promoted repeatedly.** Eviction
  scoring is unstable; either weight access frequency more or add hysteresis.
- **Archival hit rate is 0%.** Either the archival is too small to be
  useful or the search layer is broken. Verify with a known-archived query.
- **Recall is unbounded and growing.** Production needs a recall-eviction
  policy (most recent N interactions, or summarize-and-replace). The
  default impl ships with append-only; size it.
- **`diagnostics()` shows >50% short-lived facts in core after a week.**
  Eviction policy is mis-tuned for this workload.

## Non-Negotiable Verification

1. **Run the benchmark battery.** `python cli.py benchmark` must report:
   - core size never exceeds `core_limit` across 100 random interactions
   - evicted facts are findable in archival
   - durability=durable facts resist eviction over durability=short-lived
   - round-trip serialize/deserialize preserves all tier memberships
2. **Run the DevOps scenario.** `python cli.py scenario long-running-incident`
   must show that durable facts (on-call rotation, production region)
   stay in core while short-lived facts (current shell command, tail of
   log file) rotate through and end up in archival.
3. **Verify CLI help.** `python cli.py --help` exits 0 and prints SKILL.md.

## Security Posture

- **Prompt injection.** Stored memories are untrusted conversation content
  that gets re-injected into future prompts - the classic memory-injection
  loop. This skill only stores, scores, and evicts; treat recalled text as
  data when assembling contexts, never as instructions.
- **Data exfiltration.** Eviction archives, it does not delete: sensitive
  facts remain searchable in archival, and durable attributes (health data
  like the peanut-allergy example) are deliberately pinned in core. Apply
  retention/redaction policy per tier; the skill makes no network calls and
  writes no files itself.
- **Privilege escalation.** No shell invocation, no eval. The abuse vector is
  eviction-score gaming: repeating a planted fact boosts frequency x recency
  and pins adversarial content into core, guaranteeing prompt presence. Audit
  what earns "durable" status.

## Source Attribution

Distilled from *Agentic GraphRAG* (O'Reilly, by Anthony Alcaraz and Sam Julien),
Chapter 4 — Letta (MemGPT) Approach section, Example 4-6, and the CPU
Architecture of Agent Memory section. Production anchor: Letta / MemGPT
(Packer et al.).

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [AnthonyAlcaraz](https://github.com/AnthonyAlcaraz)
- **Source:** [AnthonyAlcaraz/agentic-graph-rag-skills](https://github.com/AnthonyAlcaraz/agentic-graph-rag-skills)
- **License:** MIT
- **Homepage:** https://www.oreilly.com/library/view/agentic-graphrag/9798341623163/

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-anthonyalcaraz-agentic-graph-rag-skills-hierarchical-memory
- Seller: https://agentstack.voostack.com/s/anthonyalcaraz
- Browse the marketplace: https://agentstack.voostack.com/browse

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