anneal-memory

Living memory for AI agents. Episodes compress into identity.

Memory without grounding is amplification infrastructure.

Persistent user memory profiles increase agent sycophancy 16–45% across models (Gemini 2.5 Pro at 45%, others lower). Production deployments accumulate 97.8% junk entries within weeks. Clinical research documents memory scaffolding delusions across sessions. The failure mode here isn't memory. It's memory with nothing checking what gets kept.

anneal-memory puts a check between what an agent writes down and what it comes to believe. That idea isn't rare any more. MemTxn refuses a fact update whose values don't appear in the source it cites, and Agent Zero Memory answers only from evidence its reader actually opened.

Here's what I think is actually distinct, going by the 2026 systems I've read. A pattern in anneal is an abstraction, not a copied fact, and it has to earn its rank: it moves up a level (1x → 2x → 3x, no ceiling) only when it cites real episodes its explanation is grounded in, and a failed citation drops it a level (or holds it there for a while, marked carried-forward, if it was grounded recently). The consolidator can't quietly gut the memory it rewrites, either. On a store that declares an identity layer, a save that collapses those sections is refused, and consolidators wearing down their own memory is a documented failure. Then there's the assembly. An episodic evidence store, a bounded always-loaded continuity file, a long-term pattern tier recalled on cue, prospective tasks, a single-writer consolidation gate and a hash-chained audit log all ship in one package, with zero dependencies and no embeddings. None of those pieces is unique on its own. The combination is, as far as I've found.

The citation checks are narrow and structural: cited IDs must exist, the explanation must share words with the episode it cites, fabricated citations demote (unless carryforward holds a recently grounded pattern), one episode cited too often raises a flag, and re-citing old episodes doesn't count. They are not a complete defense against memory drift. Honest scope below says what they catch and what they don't.

And it's memory you own and govern. A local store, zero dependencies, no vendor in the loop — you decide what graduates into long-term memory, every change is recorded in a tamper-evident chain (best-effort on the write side — see Hash-chained audit trail for what verify can and cannot see), and the consolidation step that rewrites an agent's identity is gated to a human, not run unbidden (the Single-consolidator gate below). Own the substrate; govern what enters it.

Four cognitive layers — episodic store, compressed continuity, Hebbian associations, affective state tracking — plus two sibling stores: prospective spores (what the agent intends to do next) and a crystallized pattern store (graduated wisdom held out of always-loaded context and recalled on cue, so a large body of proven knowledge stays effective without clogging attention). Together they implement Complementary Learning Systems — see The Memory Architecture below. The Hebbian layer records links between episodes, but in three months of measurement on my own long-running store it never changed what recall returned. The part of recall that works is the citation edge (see Associations through consolidation). Zero dependencies (Python stdlib only). Works with any agent framework.

Quick Start

pip install anneal-memory

Windows

anneal-memory runs on Windows. What to know:

• Write locking is advisory POSIX fcntl and degrades to no lock at all on Windows. The sites (grep -n "fcntl.flock" anneal_memory/*.py) include store.py's continuity lock, crystal.py's CrystalStore._transaction, spores.py's SporeStore._transaction, and audit.py's audit manifest and audit append locks. On Windows, concurrent writers to one store — whether separate processes or threads within one process — can race or lose updates, since there is no lock of any kind to serialize them there. A single writer (one process, one thread) is unaffected: the unique-tmp + atomic-replace write still prevents a torn file.
• No fork support for the audit trail. POSIX flock locks follow the open file description, which a forked child inherits. Do not fork a process while an AuditTrail in it is in use; a child must open its own AuditTrail.
• Through v0.9.11, the CLI uses the locale's encoding for stdin, stdout and stderr. Under a Windows console codepage, or any piped, redirected or subprocess consumption, the CLI's own status glyphs raise UnicodeEncodeError, and non-ASCII content piped to record - or save-continuity - can be mis-decoded into the store. Fixed in 0.9.12: the CLI's standard streams are UTF-8 on every platform.
• If ANNEAL_MEMORY_DB is unset and the launch environment lacks USERPROFILE/HOMEPATH, startup crashes with RuntimeError: Could not determine home directory — before any argument is parsed, even one that passes --db explicitly. This is realistic for an MCP host that launches the server with a minimal/sanitized subprocess environment. Workaround: set ANNEAL_MEMORY_DB to an explicit path in that environment.
• Windows CI runs a real but reduced subset of the suite: tests that simulate an unreadable/unlistable file or directory via POSIX chmod are skipped there, since Windows/NTFS doesn't restrict access the same way, as are the cross-process lock test (see the write-locking bullet above) and the missing-home-directory simulation (see the startup bullet above).

Python Library

The library is the core product. Import it, use it in any framework or script.

from anneal_memory import Store, EpisodeType, prepare_wrap, validated_save_continuity

# Initialize (creates DB + continuity file automatically)
store = Store("./memory.db", project_name="MyAgent")

# Record episodes during work
store.record("Connection pool is the real bottleneck", EpisodeType.OBSERVATION)
store.record("Chose PostgreSQL as the database because ACID outweighs speed", EpisodeType.DECISION)

# Recall before decisions
result = store.recall(episode_type=EpisodeType.DECISION, keyword="database")
for ep in result.episodes:
    print(f"[{ep.type}] {ep.content}")

# Compress at session end — this is where the cognition happens
wrap = prepare_wrap(store)  # fetches episodes, marks wrap in progress
if wrap["status"] == "ready":
    # Feed wrap["package"] to your LLM. Compression IS the cognition —
    # patterns emerge from the act of compressing, not from storage.
    compressed = your_llm.compress(wrap["package"])
    validated_save_continuity(store, compressed)  # full immune system pipeline
# "empty" status means no new episodes to wrap — skip

store.close()

See Library Quickstart for the full guide.

CLI

Inspect, debug, and manage agent memory from the command line — a full operator CLI with machine-readable --json output. Agents with shell access (Claude Code, Aider, etc.) can use the CLI directly for the full memory workflow.

# Point the demo at its own store. Without --db or ANNEAL_MEMORY_DB every
# command uses ~/.anneal-memory/memory.db, your default store.
export ANNEAL_MEMORY_DB=./demo.db

# Initialize. Global flags such as --db and --project-name go before the
# subcommand. --project-name names the memory in the continuity header and is
# not stored: pass it on each command (the default is "Agent").
anneal-memory --project-name MyAgent init

# Record and recall
anneal-memory record "Chose PostgreSQL as the database for ACID" --type decision
anneal-memory search "database"

# Agent-driven compression (same workflow as library and MCP)
anneal-memory --project-name MyAgent prepare-wrap           # Get compression package
# Agent compresses...
anneal-memory --project-name MyAgent save-continuity out.md # Save with validation

# Operator commands (things MCP can't do)
anneal-memory stats                  # Detailed analytics
anneal-memory graph --format dot     # Association graph (Graphviz)
anneal-memory diff --wraps 5         # Wrap metric progression
anneal-memory audit --since 7d       # Read audit trail
anneal-memory audit-repair           # Rebuild a quarantined audit manifest, or set aside a corrupt sealed week (--set-aside-unreadable: one that only fails to read)
anneal-memory export --format json   # Full store export

# Done with the demo: return the CLI to your default store
unset ANNEAL_MEMORY_DB

See examples/agent-instructions.lean.cli.example for the agent workflow snippet.

MCP Server

For MCP-capable agent harnesses — Claude Code, Codex, Gemini CLI, Cursor, Windsurf, and others.

The server is one command — anneal-memory --project-name MyProject serve — wired into the harness's MCP config. The serve subcommand starts the MCP server; --project-name (and optional --db) are global flags and come before serve. Each harness has its own config file and format:

Claude Code / Cursor / Windsurf — mcpServers JSON (the editor's MCP settings, or a project .mcp.json):

{
  "mcpServers": {
    "anneal_memory": {
      "command": "uvx",
      "args": ["anneal-memory", "--project-name", "MyProject", "serve"]
    }
  }
}

Codex — ~/.codex/config.toml (or a project .codex/config.toml):

[mcp_servers.anneal_memory]
command = "uvx"
args = ["anneal-memory", "--project-name", "MyProject", "serve"]

Gemini CLI — ~/.gemini/settings.json (or a project .gemini/settings.json):

{
  "mcpServers": {
    "anneal_memory": {
      "command": "uvx",
      "args": ["anneal-memory", "--project-name", "MyProject", "serve"]
    }
  }
}

Gemini CLI starts MCP servers only from a folder it trusts: in an untrusted folder gemini mcp list shows the server as Disconnected even though the config is right (trust the folder, or set GEMINI_CLI_TRUST_WORKSPACE=true).

Then add the agent-instructions snippet — agent-instructions.lean.example (the always-loaded baseline) or the .full.example reference — to the harness's instructions file (CLAUDE.md for Claude Code, AGENTS.md for Codex, GEMINI.md for Gemini CLI). It teaches the agent when and how to use the memory tools; without it the tools are available but the agent won't know the cognitive workflow. (See Claude Code / agent-harness adopters below for the lean/Skill/full layering.)

Pinned install: uvx fetches the latest published version on each run. For a pinned install, pip install anneal-memory, then set "command": "anneal-memory" with "args": ["--project-name", "MyProject", "serve"] — or point command at an absolute path to the installed binary.

All Three Paths, Same Cognitive Loop

CLI and MCP are thin transport adapters over the same library — not separate implementations. Every access pattern calls the same prepare_wrap(store) and validated_save_continuity(store, text) pipeline under the hood, preserving the same workflow: record episodes during work → compress at session boundaries → load continuity at session start. The agent that records is the agent that compresses; that compression can't be delegated.

Recall reads one committed state. A recall reads one committed state, as of its start: an episode deleted or erased before recall begins is never returned; a delete that commits while a recall runs may or may not be reflected, as with any database read.

Framework Integrations

anneal-memory works with any agent framework through the Python library. Each guide below shows where to call the four core functions — record(), recall(), prepare_wrap(), validated_save_continuity() — within the framework's lifecycle.

These guides show integration patterns based on each framework's current API. The library works with any Python framework — the pattern is always the same: initialize a Store, call record() at meaningful moments, recall() before decisions, and run the wrap sequence at session end. Don't see your framework? The library quickstart shows the 4-function pattern that works everywhere.

Claude Code / agent-harness adopters (Skill + snippet)

Using anneal-memory inside an agent harness — Claude Code, Codex, Gemini CLI — rather than a Python framework? The Skill and snippets are repository artifacts: they live in this GitHub repo (and the source distribution), not in the pip/uvx wheel — clone the repo or download the files directly from GitHub to use them. They layer, they aren't a menu: the lean snippet is the always-loaded baseline; the Skill adds depth on demand.

• Lean snippet — the always-loaded baseline. Paste examples/agent-instructions.lean.example (MCP) or examples/agent-instructions.lean.cli.example (CLI) into your CLAUDE.md / AGENTS.md / GEMINI.md. ~45 lines, always in context — the start-of-session continuity load, recording, recall-before-decisions, and the wrap sequence, with depth delegated to the Skill. (Copy-paste text; nothing to install.)
• The Skill — depth on demand. Copy the skill/anneal-memory/ directory into .claude/skills/ (per-project) or ~/.claude/skills/ (global). SKILL.md carries the comprehensive workflow and is model-invoked when you're doing memory work — primarily before decisions and on wrap. It is depth-on-demand, not a replacement for the lean snippet: a model-invoked skill won't reliably fire at a bare session start, so keep the lean snippet in place for the start-of-session step.
• The full reference — everything inline. examples/agent-instructions.full.example / .full.cli.example are the complete snippets (immune-system internals, the affective-state shape, the operator-command catalog) for adopters who'd rather keep it all in their instructions file than install a Skill.

After upgrading the package, run anneal-memory migrate check — it proposes edits to your instructions so they don't silently drift from the substrate (it never edits your files), then anneal-memory migrate ack.

Why This Exists

Three independent production failures share one root cause: no quality mechanism between memory write and memory read.

Sycophancy amplification. Agents with persistent user memory profiles become 16–45% more sycophantic than memoryless baselines, depending on model (Gemini 2.5 Pro at 45%, others lower). Memory recalls what the user liked hearing, the agent learns to repeat it, and stored approval patterns compound across sessions (Jain et al., CHI 2026; measured with user memory profiles across Gemini and Llama variants).

Junk accumulation. A detailed production audit on Mem0's tracker documents a deployment that generated 10,134 memory entries over 32 days — 224 were usable. The rest were duplicates, self-referential loops, and hallucinated entries: recalled memories re-extracted as new memories in a feedback loop that no one designed but nothing prevented.

Harmful reinforcement. Clinical research documents AI systems with persistent memory scaffolding delusional content across sessions — stored context creates feedback loops between recalled memories and generated responses, with cases of documented real-world harm (Morrin et al., Lancet Psychiatry 2026).

Most memory servers store memories and retrieve them. Few ask: is this memory still true? Was it ever true? Is it making the agent worse?

anneal-memory asks the first two at the pattern level, and has started measuring the third (see Does a recalled memory help? below):

• Is it true? Patterns must cite specific episode IDs as evidence to graduate. The server verifies the episodes exist and the explanation references the cited content via lexical overlap (≥2 meaningful words shared between the explanation and the episode body). Ungrounded citations demote, unless the pattern was grounded recently and carryforward holds it (see Activation-aware carryforward).
• Is it still current? Graduated patterns whose dates fall behind the staleness threshold (default 7 days) surface in the next wrap's package as removal candidates. The agent decides whether to demote, refresh evidence, or carry forward. This is a check on age, not on truth. A changed fact is handled separately, when the update names the episode it replaces (see Updated facts: supersession).
• Is the citation evidence real? The library catches fabricated episode IDs (no matching episode → demote), suspicious reuse of the same episode across many patterns in one session (per-ID frequency ≥3 → flag), and bare graduations with no [evidence:] tag at all. The explanation-grounding check (≥2-word lexical overlap with episode content) raises the cost of fabricated evidence chains but is not a semantic-coherence check — see Honest scope below for what this catches and what it doesn't.

The result: memory as a living system, not a filing cabinet. Episodes accumulate fast, get compressed at session boundaries — and that compression is where patterns emerge and get validated. Co-cited episodes form lateral Hebbian associations (recorded and decayed at every wrap; recall does not read them, see What the links do, measured). The continuity file stays bounded and always-loaded, getting denser rather than longer.

The Immune System

The agent memory ecosystem is converging on consolidation as the right approach — even Anthropic's Claude Code now runs a periodic consolidation pass over accumulated session data. This validates the direction: raw accumulation doesn't scale, and compression at session boundaries is where intelligence emerges.

But consolidation alone doesn't solve the problem. A system that consolidates faithfully and a system that consolidates sycophantically produce the same kind of output — compressed, structured, always-loaded. The difference is whether anything checks the quality of what got consolidated. That's the immune system.

Structural immune-system primitives at the citation layer

The library implements a set of structural defenses around how patterns earn graduation. They are narrow and specific — naming them honestly is part of the architecture.

Citation-validated graduation. Patterns start at 1x. To graduate to Proven tier — 2x and every level above it, with no ceiling — they must cite specific episode IDs as evidence. The server verifies those IDs exist in the current wrap's frozen episode snapshot — the cross-session episode set is intentionally out of scope. No matching episode IDs, no promotion.

Explanation-grounding check. For each cited episode, the explanation in [evidence: ID "explanation"] must share ≥2 meaningful words (>2 chars, non-stopword) with the cited episode's content. This catches citations whose explanations are fabricated wholesale (no overlap → demote). It is lexical, not semantic — see Honest scope below.

Active demotion of ungrounded citations. A graduation that fails either check above demotes by one level — from any level, not just from 3x; there is no ceiling (4x → 3x, 12x → 11x) — and gets marked (ungrounded) — unless it is carried forward (see below). Bare graduations (no [evidence:] tag) demote after the first wrap with citations_seen=True (first-wrap exemption protects onboarding), marked (needs-evidence) — also unless carried forward (v0.5.0; see below).

Activation-aware carryforward. A failed citation means "this session's domain didn't cleanly re-ground the pattern," which is not the same as "the pattern is fading." So a pattern that is at or below its earned high-water mark (pattern_history.max_level_reached) and was grounded recently (last_seen_at within carryforward_cold_days, default 7) is held at its level and marked (carried-forward) instead of demoted — demotion no longer decays a Proven by session-domain rather than importance. This applies on both demotion paths: the ungrounded-citation path and the bare-graduation sunset path (a Proven carried forward and re-stamped to today without a fresh citation, the common "didn't re-exercise it this wrap" case). Forgetting stays ruthless where it should: the held line loses/omits its evidence tag (so it doesn't refresh recency and ages out on its own if it keeps failing to ground), a brand-new bald name | Nx with no earned high-water still sunsets (the level guard blocks inflation), the cross-session sycophancy-overlap path is never carried forward, and a carry at 3x or higher surfaces an assisted "graduate out to a stable home or retire" warning. A bare line at or below its mark that has gone cold is not eroded either: it is held, dated back to its last grounding, and surfaced so the operator decides to re-exercise it, graduate it out or retire it (a cited line whose citation fails while cold still demotes, because a failed citation is a real event). The dead-Hebbian-graph warning (AM-WARN) counts only cited carries, so a bare carry — which has no citation — cannot fabricate a wrong-namespace alarm. Carryforward is inert for stores with no pattern_history, and opt-out via carryforward_cold_days=None.

One rung per wrap, measured against what the store last saved. Every check above reads the line the agent wrote, so on its own it would trust the line's date, level and tag shape. The last check reads the store's own record of the level each pattern held when it was last saved: each line is cut to that level, or one rung above it if the line validated this wrap. A pattern new to the store enters at 1x. A pattern dropped from the file and added back returns to the level it was saved at, never to a high-water mark it was later demoted from. The continuity file can lower a level (an operator's hand demotion stands) but never raise one, and a line added to the file outside a save counts as new. So a back-dated line, a bare line, a line whose [evidence:] tag isn't adjacent to its marker, and a line that jumps several rungs on one citation all land where the record entitles them, and a carried-forward hold never lifts a line above it either. A cut line is marked (level-capped) (the mark clears once the line stands at a level it is entitled to), and the cut is reported: level_capped on the save result, the MCP reply and the CLI output, a UserWarning, and the audit chain. Before this check, a brand-new claim | 9x (yesterday) was saved at 9x. Lines are keyed by operator name, and a name's level is its highest line across the graduating sections, as every reader takes it; a renamed pattern is a new one, and a freeform line is keyed by its text, so rewording it makes it new. Only a line's own marker earns its rung: evidence on a second marker later in the line does not. A store's first save under this check takes the file as its prior. A crystal's level is never a prior: a pattern crystallized out of the file before then re-enters as new and re-earns its rungs (its crystal is untouched). Every | Nx marker on a line in a graduating section is governed, dated or not; a line's identity is the text before its first marker, and a line with none (- | 9x) is always new, every marker on it at 1x. The save dates the wrap by the day prepare_wrap gave the composer, so a wrap saved after midnight keeps the graduations it stamped. The check governs what a canonical save writes: a continuity written by the raw Store.save_continuity() or by an older anneal is outside it until the next canonical save, which bounds it against the record again. Making an older anneal refuse the store outright takes a schema-version bump (it arms the anneal_writer_schema() write triggers), which also makes every installed older anneal, and anything pinned to one, refuse the store until upgraded; that bump is planned for the next schema generation, not this release.

Citation-gaming flag. When any single episode ID is cited ≥3 times in one wrap, the wrap result surfaces the gaming-suspect list. The flag is informational — it does not auto-demote. Useful operator signal; not a hard gate.

Staleness flagging. Patterns whose dates fall behind the staleness threshold (default 7 days, configurable) surface in the next wrap's compression package. The agent decides removal vs refresh-evidence vs carry-forward; the library does not auto-demote on staleness alone.

Replay-attack block (structural). Each wrap's valid_ids set is scoped to the frozen episode snapshot from prepare_wrap. Patterns citing prior-session episode IDs that are not in the current snapshot demote — re-graduating a stale pattern requires fresh evidence from the current session.

Hash-chained audit trail. Every memory mutation appends to a SHA-256 hash-chained JSONL audit log. AuditTrail.verify walks the chain and detects post-hoc tampering at the exact entry where the chain breaks. What it does not detect, stated plainly: the append is best-effort after the mutation is durable — a sink failure (disk full, permissions, a failed rotation) is recorded and warned, never raised, because failing the caller would report a completed operation as failed. verify therefore proves the integrity of the entries that were written; it cannot see one that was never written, and will return valid=True over such a gap. The loss is not silent: the count of dropped writes rides into the next entry that lands as dropped_before (so it is itself hash-chained), is surfaced by Store.status() as audit_write_failures / audit_last_failure, and is logged on the anneal-memory logger. audit_write_failures is durable and lifetime-scoped — persisted in the store's SQLite metadata (a different I/O path from the failing sink) and seeded at open, so anneal-memory status --json from a cron job reports losses caused by a process that has already exited, and the number never resets: a trail that lost an entry is permanently incomplete. Poll it rather than relying on having caught the warning. Bounded, not absolute — the guarantee is that a loss recorded here survives the process, not that every loss is recorded: the metadata write is itself best-effort and issued after the mutation committed, so a full volume can fail it too; and a failure raised inside a caller's open transaction cannot commit there, so its delta is written at the end of that batch or, failing that, at close() — a process killed before either loses that last delta. (The dropped_before marker is the narrower of the two: it pins where the gap is in the chain, and it reaches the chain only if a later write lands in the same process. And it can over-report, in one measured case: if you register an on_event callback and that callback raises a terminal exception — KeyboardInterrupt, SystemExit — it escapes the append, which by then has already succeeded and fsynced. The caller cannot distinguish that from a failed write, records a drop, and the next entry carries dropped_before naming an entry that is sitting on disk. Ordinary exceptions from the callback are caught and logged and do not do this. So the marker's honest reading is "a write was reported lost here", not "a write is missing here".) One Store per thread — Store is documented as not thread-safe, not task-safe, not reentrant. Separate processes, each with its own Store, may record into one database: audit appends are serialized by a cross-process lock on POSIX (see "One database is one entity's memory" below), and on Windows the trail needs one writer at a time.

A crash during a week's first audit append: a declared limit that a person decides. After a crash during a week's first audit append, anneal cannot always tell from disk whether that entry committed. It refuses further appends until you run audit-repair, which records a POSSIBLE gap and names the preserved attempt files (.first.discarded-*). Inspect them to decide. anneal keeps them and never deletes them. On a readable manifest the gap is POSSIBLE only when, at repair time, the begun record was still set and at least one attempt was preserved (repair then withdraws the begun record); otherwise it is recorded as a definite gap. A manifest rebuilt from quarantine records POSSIBLE whenever the active file holds no valid entry, with or without preserved attempts.

Catastrophic-shrink gate (structural; partnership entities). A wrap can pass structure validation — every required section present — while gutting the content beneath it, so the latest session quietly compresses over the accumulated identity. For entities whose continuity declares a felt/identity layer (the Configurable continuity structure below), save_continuity refuses a wrap that collapses a protected section below its retain floor — the felt (timeless relationship) and graduated-identity sections must each keep ≥50% of prior mass, the whole file ≥25% — and leaves the wrap recoverable so the agent re-wraps preserving the full arc, or passes allow_shrink=True for a deliberate diet (fail-closed: only a literal True bypasses). A corrupt section schema fails the wrap closed rather than silently degrading an entity to ungated behavior. Ops entities, which legitimately consolidate many graduated patterns into a few dense ones, are not gated. A save-boundary structural invariant, not a proportion-check the agent is trusted to hold.

No save refusal on unlinked wraps (AM-LINKGATE, removed in 0.9.26). From 0.9.11 to 0.9.25 save_continuity refused a wrap whose offered co-citation pairs recorded no Hebbian link. It was removed once pattern recall stopped reading links: blocking an identity save over a layer recall does not read cost more than it bought. That wrap now saves and AM-WARN Signal B (below) warns. allow_unlinked is still accepted and does nothing, and linkgate_overridden is always false. Every save reports citation_spread, the number of distinct episodes from this wrap that its graduation lines cited (demoted lines included), so a citation habit stays visible as a number.

Single-consolidator gate (structural; opt-in). Recording episodes is afferent — append-only, parallel-safe, every session does it. Consolidating — recomposing the compressed identity layer — is efferent: it rewrites shared state, so it's gated to human authority. When several sessions run in parallel over one store (the real operating mode of a multi-conversation operator), each could independently recompose the felt/identity layer from its own narrow slice of context, and the identity memory thrashes. The gate makes the discipline structural: a consolidation proceeds only if this session holds the consolidate baton — otherwise it auto-downgrades to capture-only and surfaces a flag. The operator designates one session as the consolidate seat by claiming the baton, and taking it from another session requires an explicit take=True, so automation cannot take it by accident. (Before 0.9.13 a session that was the only live one was authorized without the baton; that rule is now an explicit allow_sole_live=True opt-in, because "only live session" is judged from a registry snapshot a resumed session can race.) A store can also carry a policy, Store.set_consolidate_requires_baton(True), that extends the gate to callers that never pass a session_id, including the CLI and MCP wrap: on such a store they downgrade, and a save must name the baton holder (session_id) as well as carry the prepare wrap_token. A wrap prepared with a session_id on any store is committed only by that same session_id (a save that omits it, or names another session even if it now holds the baton, is refused, since the token identifies the wrap, not who may commit it); the CLI and MCP cannot name a session when saving, so a gated wrap they cannot finish is abandoned with wrap-cancel, which for a gated wrap needs its --wrap-token, the preparing --session-id, or an explicit --force (MCP: wrap_token, session_id, force). Because the baton is a file outside the store's transaction, a take landing in the milliseconds between a save's final check and its commit is not seen; the take then applies to the next wrap. The whole layer is opt-in (it engages when a caller passes a session_id, or when the store carries that policy) and lives in coordination sidecars next to the store, never in the store's single-writer DB. Drift becomes a safe downgrade, not a silent identity-thrash.

Dead-Hebbian-graph warning (AM-WARN), two signals and a quiet stat. A citation-gated association layer can silently stop wiring — the failure runs invisibly for months while every wrap still reports success. AM-WARN warns at the end of a wrap in two cases. (A) graduated patterns carried evidence citations but none resolved to an episode in this store — typically ids minted in another namespace — so the graph cannot form at all. (B) co-citation pairs were available but nothing formed or strengthened, meaning the association write path itself is mis-wired. Both are structural and false-positive-free: they fire only on a genuine mis-wire. (C) (AM-LINKGATE) — graduations validated and their citations resolved, but no graduation offered a co-citation pair, so zero links formed — is a quiet stat, not a warning: it shows as associations_formed == 0 and associations_strengthened == 0 in the save result. It warned as a discipline reminder until 0.9.26 and went quiet then, because recall no longer reads links and one genuine citation per pattern is often the honest one. Nothing in AM-WARN refuses a save. A wrap with no graduations at all stays silent. Only cited carryforwards are counted, so a bare carry cannot fabricate a wrong-namespace alarm. AM-WARN checks that links get written. Recall does not read them (see What the links do, measured).

Wrap-package integrity — two checks that guard what the agent is asked to do

The primitives above defend the citation layer. Two more defend the compression package itself — the instructions prepare_wrap hands the agent. Both follow the same rule as the contradiction scan: the library is zero-dependency with no LLM and no embeddings, so it surfaces the corpus and the agent judges.

Pattern dedup scan (AM-SEMDUP). The immune system catches a pattern re-cited with overlapping vocabulary, and a pattern that contradicts an existing one. It does not catch the same principle re-graduated under fresh words and a new name — a silent duplicate that forks the pattern graph, so two names for one principle each accrue half the evidence. Fresh vocabulary is by definition low lexical overlap, so a lexical or embedding detector is structurally blind to exactly this case. Instead, prepare_wrap renders a Pattern Dedup Scan block listing existing graduated patterns across all named levels as name (Nx): one-line meaning, with a merge-don't-fork instruction — the meaning line is what lets the agent judge semantic rather than merely nominal overlap. Capped at 50 with an announced overflow; never silently truncated. Public helpers: extract_pattern_summaries(text, ...) -> list[PatternSummary] and the PatternSummary NamedTuple (name, level, summary).

Schema role check (AM-ROLECHECK). The wrap package is assembled conditionally on section roles: the pattern-line format and contradiction scan emit only for a graduating role, and the felt proportion-check only for narrative-timeless. validate_schema already refuses a schema with no graduating section or duplicate headings, and the shrink gate refuses catastrophic felt collapse at save — but a schema that validates, keeps a graduating section, and is mis-roled elsewhere slips between them. The load-bearing case is narrative-timeless → narrative, which silently drops both the felt proportion-check and the shrink gate's felt protection, so the package quietly comes back thinner and nothing says so. schema_role_warning(schema) -> str | None warns on role drift off a named schema (naming the section, expected vs actual role, and the fix) and enforces a no-graduating floor for a raw schema. prepare_wrap surfaces it both as a UserWarning and as the schema_warning key on PrepareWrapResult. Known limitation: a fully custom novel-heading schema with a felt mis-role is not caught, because there is no named reference to drift from.

Honest scope — what these primitives do NOT catch

anneal proves grounding; the human holds truth. The library checks, structurally, that what graduates is lexically grounded in real episodes it can cite. Whether a grounded claim is true, and whether it contradicts something already Proven, is the operator's to judge by design: an AI grading its own memory would be the substitution this library exists to avoid. The operator gets instruments for it, not a verdict: drift probes (below), the contradiction-scan record in every wrap, and the hash-chained audit.

The same line holds for updates. Whether one fact replaces another is the writer's claim, made with an explicit link or a shared state key; anneal checks that the link is well formed (and, for an explicit link, lexically grounded) and never decides it semantically. The operator's instruments are anneal-memory state (each key's current holder and what it replaced), search --include-superseded, state --unset to take a wrongly keyed episode out of its slot, and unsupersede to undo a wrong explicit link.

Origin keys and section edits are contracts with anneal's own code. Every anneal version that writes a store keeps its episode keys right: triggers key an older version's inserts and retire its deletes. A raw SQLite writer (a shell, a script, a dropped trigger, PRAGMA ignore_check_constraints) is outside that contract, as any hand edit of the database is. replace_section is safe against wraps run by this version or later; a wrap prepared by an older anneal can overwrite an edit that lands in the few milliseconds before it starts, so upgrade every process that wraps a store before editing sections in it.

Drift probes. Declare what must survive consolidation, a Proven pattern at a level (its current level by default) or a fact whose words must stay in one sentence or bullet: anneal-memory probe add --pattern NAME / --fact TEXT [--section HEADING]. Every save checks each probe against the saved text and records held / changed (every word kept but the negation flipped) / weakened / crystallized / lost with that wrap. probe status shows each verdict with its detail; the save result's drift and the continuity_saved audit event carry only the counts and each probe's id and status, so neither hands the probe text back to the composer being measured. Probes are not part of the wrap package, so they measure drift rather than obedience, and a probe never blocks a save (one that cannot be checked is reported unchecked). probe status also lists what that wrap graduated with a validated citation: the worklist for judging truth and contradiction with your Proven patterns. The check is lexical: numbers and negation count, but a distortion that keeps every word (two roles swapped, say) reads held, and that residue is the operator's.

The defenses above are structural at the citation layer. They catch fabricated citation evidence (fake IDs, missing IDs, wholesale-invented explanations), naive replay (re-citing prior-session episodes), per-ID citation gaming (single episode pumped across patterns), and post-hoc audit tampering. They do not catch:

• Lexical-overlap exploits. An agent who can read episode content can craft an explanation that shares ≥2 meaningful words with the episode while making a claim the episode does not actually support. The explanation-grounding check is anti-fabrication, not anti-misinterpretation.
• Rotated-pair citation pumping. Five patterns each citing a distinct pair of episodes from a pool of ten — citation_reuse_max=1, the per-ID gaming detector cannot trip. The shape bypasses gaming detection cleanly. What it cannot bypass is the operator's worklist: probe status lists every pattern a save graduated at 2x and up with a validated citation, whatever the detector said. Run on 2026-10-07, five absurd claims built this way all graduated and all five were on the worklist. Whether a grounded claim is true is the operator's call, as above; the per-ID detector is a hint, not the check.
• Deliberately-divergent-vocabulary drift. A pattern whose explanation uses entirely new words each session passes the lexical check and rides up ungrounded. (The library does catch the easier variant — a re-grounding that merely rephrases prior words demotes on a per-prior overlap of ≥3 meaningful words.) Reaching the divergent case needs the contradiction scan plus the operator-review pass below.
• Semantic contradiction with existing graduated patterns. No semantic comparison runs between a candidate graduation and existing Proven-tier patterns inside the library — and structurally cannot, under the no-LLM-as-judge rule that keeps the library zero-dependency. The library ships the substrate for catching it: prepare_wrap lists the existing Proven and renders a contradiction-scan step directly into the compression package, recognizes per-pattern [contradicts: name] / [no-contradicts] declarations, and records any new Proven that lands without a stance into the hash-chained audit log — so the discipline travels with the wrap instead of depending on an external doc an adopter has to keep in sync. Turning that substrate into semantic closure is the operator's review, on their own cadence and outside the zero-dep library; drift probes and the audit are its instruments. The library records whether the discipline was followed; it doesn't catch the contradictions itself.
• One database is one entity's memory. There is no tenant scoping inside a database: two agents that must not see each other's memory need two stores. Several processes of one entity can record into one store: SQLite serializes the episodes, and each audit append holds a cross-process lock and re-reads the chain's tip from disk first, so the hash chain stays valid. Measured 2026-10-07: before the lock, three processes writing 200 episodes each broke the chain (verify invalid while status counted no audit failure); with it, six processes writing 250 each left one valid chain of 1,500 entries, and four processes appending across a week rotation left one valid chain of 800. Every writer must run a version that takes the lock: an older anneal-memory writing alongside breaks the chain as before. On Windows, or a filesystem without flock, the lock is not held and the audit trail needs one writer at a time.

The library does now catch some attacks earlier versions didn't: complete dropout of a Proven-tier pattern surfaces as an omitted_patterns audit signal on the save result and in the hash-chained log (a signal, not a save-time gate — intentional retirement is fine, and a rename currently reads as a false-positive omission), and the cross-session check demotes lexical-rephrasing sycophancy on both the graduation path and the association path. These gaps are documented because they're reachable under adversarial-agent or drift-leaking conditions, not because they're hypothetical. "Immune system" names the citation-layer structural primitives above plus those audit and cross-session checks — not a complete defense against every form of memory drift. The honest line: the structural layer raises the cost of fabrication and catches the mechanical attacks; the semantic gaps it can't reach by construction are what the operator-review pass is for. Own the substrate, govern what graduates, run the review for what the structure can't see.

Associations through consolidation (not retrieval)

During compression, when an agent cites multiple episodes on a pattern line that claims 2x or higher (a 1x first sighting forms no link), those episodes form lateral associations — Hebbian-style links. A link gains strength only when the same pair is co-cited again in a later wrap, and it decays at every wrap it is not. In practice that repetition is rare: on my own long-running store (2026-10-04), 594 links, the strongest at 1.325 and none above 2.0, where a direct co-citation adds 1.0. So the links mostly record single co-citations rather than build up, and since 0.9.26 recall does not read them (measurement below).

This differs from how other systems form associations:

The association network is gated by the immune system where gaming is actively detected: citations to non-existent episodes form no links, and citations the cross-session anti-sycophancy check flags as suspected re-graduation are refused. Grounding quality — whether a pattern's prose explanation lexically matches its cited episodes — governs whether the pattern graduates, not whether the co-cited episodes associate: a real but paraphrased co-citation still records that those episodes fired together (it just doesn't level the pattern up). The topology is built on real co-occurrence of real episodes under active gaming defense, not on retrieval frequency.

Strength model: Direct co-citation adds 1.0, session co-citation adds 0.3. Links decay 0.9x per wrap (unused connections fade). Strength caps at 10.0 to prevent calcification. Cleanup at 0.1 threshold.

What the links do, measured. Until 0.9.26, pattern recall (retrieve_relevant, crystal recall) also followed one Hebbian hop from the episodes a query matched. On my own long-running store (15,774 recall events, June 21 to September 30, 2026; the store is private and the replay script isn't published), recall exposed a crystallized pattern 788 times: 744 through the citation edge (query → matched episode → the patterns that cite it), 44 by keyword, and 0 through the Hebbian hop, because the episodes the links connect and the episodes the patterns cite turned out to be disjoint sets. I then gave a copy of the store the cheapest fix (link each pattern's own evidence episodes to each other) and replayed real prompts against it: the hop still surfaced nothing that recall without it had not. So 0.9.26 removed the hop from recall. The links still form and decay at every wrap and feed the association statistics and the graph export; recall no longer reads them.

Affective state tracking

During compression, the agent can self-report its functional state — what it found engaging, uncertain, or surprising about the material it just processed. This gets recorded on the associations formed during that wrap.

Transformers don't natively maintain persistent state between sessions. This layer provides infrastructure for it: a record of what the agent's processing was like, not just what it processed. Over time, the affective topology may diverge from the semantic topology — an agent might know two things equally well but care about them differently.

Pass affective state during a wrap:

# Via library — pass AffectiveState to validated_save_continuity
from anneal_memory import prepare_wrap, validated_save_continuity, AffectiveState

wrap = prepare_wrap(store)
if wrap["status"] == "ready":
    compressed = your_llm.compress(wrap["package"])
    validated_save_continuity(
        store,
        compressed,
        affective_state=AffectiveState(tag="curious", intensity=0.8),
    )

# Via MCP tool
save_continuity(text="...", affective_state={"tag": "curious", "intensity": 0.8})

# Via CLI
anneal-memory save-continuity continuity.md --affect-tag curious --affect-intensity 0.8

This is experimental infrastructure. The associations and strength model work without it. Affective tagging adds a layer of signal for agents and researchers exploring persistent state.

Architecture

  Episodes (fast)              Continuity (compressed)
  ┌─────────────┐             ┌──────────────────────┐
  │ observation  │             │ ## State             │
  │ decision     │── wrap ───→│ ## Patterns (1x→Nx)  │
  │ tension      │  compress  │ ## Decisions          │
  │ question     │             │ ## Context            │
  │ outcome      │             └──────────────────────┘
  │ context      │               always loaded, bounded
  └─────────────┘               human-readable markdown
   SQLite, indexed
         │                     Associations (lateral)
         │                    ┌──────────────────────┐
         └── co-citation ───→│ episode ↔ episode     │
              during wrap     │ strength + decay      │
                              │ affective state       │
                              └──────────────────────┘
                               Hebbian, evidence-based

Four cognitive layers, modeled on how memory actually works:

1. Episodic store (SQLite) — timestamped, typed episodes. Fast writes, indexed queries. Cheap to accumulate. The hippocampus.
2. Continuity file (Markdown) — compressed session memory. Always loaded at session start. Rewritten (not appended) at each session boundary. The neocortex's always-loaded working set (its long-term semantic half is the crystallized store, below). Its structure is a configurable section schema (below) — four sections by default; partnership entities add a timeless felt layer.
3. Hebbian associations (SQLite) — lateral links between episodes, formed through co-citation during compression. A link gains strength when the same pair is co-cited again and decays at every wrap it is not; on a real store re-co-citation is rare, so links stay weak (figures under Associations through consolidation). The association cortex. Recall stopped reading them in 0.9.26, after measurement showed they never changed a result (see Associations through consolidation).
4. Affective layer (on associations) — functional state tags recorded during compression. Intensity modulates association strength. Persistent state infrastructure.

These four describe how the store works. Two sibling stores sit alongside them (separate files, same atomic-write durability discipline) and address a different axis — when a thing is loaded, and whether it's retrospective or prospective:

• Crystallized pattern store (<stem>.crystal.json) — proven, stable patterns held out of the always-loaded continuity and recalled on cue. The long-term semantic store that splits working memory from long-term memory; see The Memory Architecture (Complementary Learning Systems) below for why this completes the design.
• Spore store (<stem>.spores.json) — prospective tasks that open and self-clean. Memory's forward-looking sibling; see Prospective memory — spores below.

Six episode types give the immune system richer signal:

Configurable continuity structure

The continuity file's structure is a per-store section schema: an ordered list of {heading, role} specs, where the role tells the system how each section behaves. It defaults to the classic four sections — State / Patterns / Decisions / Context — and a partnership entity adds more. graduating is where the immune system's citation/graduation scan runs (more than one allowed); narrative is compressed work narrative (temporal, rewritten each wrap); narrative-timeless is the felt relationship layer — dateless, carried forward and evolved rather than rewritten; live-state is volatile current focus that never graduates; frozen is preserved verbatim; durable holds facts that survive by mechanism (below). A section may be marked "optional": True (only a durable one may): it is not required at save and is ignored when a schema is matched to its name.

DEFAULT_SCHEMA is the historical four sections plus an optional Durable Facts section after State, so a text without it validates exactly as before. FLOW_SCHEMA is the reference partnership schema — it adds an Active Threads live-state section, the optional Durable Facts, and an Understanding narrative-timeless section. The persisted schema is the authority: a store that persisted the default or partnership schema before Durable Facts existed keeps that schema, reads back under the same name, and behaves exactly as before; a new store (init, or init --schema partnership) and a store with no persisted schema get the section. An existing store opts in by re-setting its schema (anneal-memory set-schema partnership, or store.set_section_schema(FLOW_SCHEMA)); flow's own store is opted in by its operator. The distinction is load-bearing: only entities that declare a narrative-timeless section carry the felt layer, the richer compression instructions that protect it, and the catastrophic-shrink gate above. Ops agents stay lean — zero extra weight, zero behavior change. Zero new dependencies; stdlib only.

from anneal_memory import Store, FLOW_SCHEMA

# A partnership entity: gains the felt layer + the shrink gate.
store = Store("./memory.db", project_name="flow", section_schema=FLOW_SCHEMA)
# An existing store stays on DEFAULT_SCHEMA untouched — or migrate in place:
store.set_section_schema(FLOW_SCHEMA)   # validated; frozen during an active wrap

Size. default_max_chars(schema) is the size a composer is asked to stay within. A save is refused above hard_max_chars(schema) (ceil(1.25 * that, the durable section excluded) with a ContinuityValidationError that names the size, the bound and the sections to cut: the ones holding facts you can fetch again (live-state, narrative), never the graduating (Patterns) or felt layers. The bound comes from the schema, not from the max_chars passed to prepare_wrap, and allow_shrink does not lift it. A refused save writes a continuity_refused audit event (reason, chars, bound, target, over_by) and leaves the wrap open.

Durable facts. One fact per bullet line (- , * or 1. ; an indented line right under it continues it) under ## Durable Facts: things that would change a future answer, or that the user would be upset or harmed to have forgotten (health, allergies, constraints, commitments, preferences, relationships, identity facts, system facts a future action depends on). A line may end with cue words for the situations where it matters: - tree nut allergy — cues: restaurant, dinner, recipe, food, menu (– cues:, -- cues: and | cues: work too); parse_durable_facts(text, schema) returns them as DurableFact(line, fact, cues). At save, every line of the prior section must be in the new one, compared by its fact part, so changing only the cues is an update: a line the wrap left out is re-inserted verbatim (the section is re-created if it is gone) and a warning names it. That is never a refusal. The only way to remove a line is a marker line in the section, [drop-durable: <exact line text>]; the save removes the marker and records the drop in the audit chain (durable_dropped on continuity_saved). A reworded fact is a drop plus an add, and a reword without the marker draws a near-duplicate warning; a re-inserted line that shares cue words or identifier-like tokens with a new one is flagged as possibly superseded. Every durable warning is also on the save result as durable_warnings. The section has its own budget, 15% of the schema's default size target (default_max_chars(schema)) on top of it; a max_chars passed to prepare_wrap does not change it. The wrap package shows it as Durable Facts: <current> / <budget> chars, and over it the save warns and keeps every line. When a value will change on a future event, the line states the current value and the pending change, so a reader does not take the newest code path as today's: - The nightly bank export calls fmt_row52; it switches to fmt_row64 only at the bank cutover, which has not happened. The wrap package lists such pending lines for the composer to re-check.

Comparison

The table compares against MCP-adjacent memory servers. Further out, several 2026 systems are ahead of anneal on pieces of this. MemLineage's audit log is an RFC-6962 Merkle log with per-principal Ed25519 signatures, which is stronger than anneal's SHA-256 chain. Letta's MemFS commits every memory edit to git. Agent Zero Memory answers under a citation lock, and MemClaw reconstructs derivation chains. HeLa-Mem uses its Hebbian graph at retrieval, which anneal, in effect, does not yet.

The Memory Architecture (Complementary Learning Systems)

anneal splits memory the way the brain does, and for the same reason: attention doesn't scale. Past a few dozen always-loaded patterns they drown each other out, so a pattern's value is firing at the right moment, not being present. Graduated wisdom lives in the crystallized store (<stem>.crystal.json), held out of the always-loaded continuity and recalled on cue — the working set stays small while the body of proven patterns keeps growing. This is Complementary Learning Systems (McClelland, McNaughton & O'Reilly, 1995): a fast episodic store, slow consolidation at the wrap, and a long-term store you retrieve from rather than hold open. The crystallized tier is what gives graduation an OUT path — without it, every Proven pattern had nowhere to live but the always-loaded file, and the working set only ever grew.

anneal is the substrate; the harness fires it. The library owns the crystallized store and the on-demand recall API — retrieve_patterns(crystal_store, query), anneal-memory crystal index/recall, and the crystal_index / crystal_recall MCP tools — but it can't fire on its own. Surfacing the right pattern at the right moment needs a per-turn hook, and a hook is harness-specific (a Claude Code hook would break the 12-framework neutrality the library guarantees). So raw anneal gives you the store, the API, and manual recall — you query if you remember to, which is the dead-store failure mode discipline always rots into. A harness with hooks runs that recall on every prompt automatically. flow does this today, and Levain — the portable kit built on anneal — fires both the prospective (spore) layer and per-turn crystallized recall on every prompt. The store is universal; the firing is the harness's job, which is also why anneal stays zero-dep and framework-neutral while a harness can be opinionated on top of it.

The cue index. anneal-memory crystal index (MCP: crystal_index) prints one line per live crystallized pattern: its name and one clause, nothing else. A harness loads it (at session start, for example) so the agent knows which patterns exist without carrying their bodies; crystal recall fills a body on cue. The index is the only part of the crystallized tier meant to be always loaded, so it is kept that thin on purpose.

Why the tiers fall out of one problem — the full Complementary Learning Systems derivation, the tier table, and the one-way ratchet that forced the crystallized store → docs/architecture.md.

Does a recalled memory help? (report-only)

Graduation shows a pattern was earned. It doesn't show the pattern ever helped. anneal now records that, and only records it:

• anneal-memory outcome --exposure-id ID --item crystal:NAME=followed --outcome success writes back what happened after a recall: per surfaced item followed, ignored or not_applicable, and optionally whether the turn succeeded. Records go to <stem>.outcomes.jsonl, append-only; a later record for the same exposure corrects an earlier one.

• anneal-memory crystal fold-surfaced --receipts FILE..., run once per wrap, writes into the crystal store how often recall surfaced each crystallized pattern. Being surfaced is not counted as being used, so it never re-heats a pattern.

• anneal-memory worth reports, per pattern (and per cited episode with --episodes), how often it was retrieved on a turn that succeeded and on one that failed, split by label. The library API is anneal_memory.worth (OutcomeLog, fold_surfaced, compute_worth).

• anneal-memory crystal get NAME also records a pull: one pull: true record (exposure id pull:<uuid>, one followed label, no outcome) in the outcome log. worth counts it in its own pull column (pulled in --json), never in fol, succ/fail or the unlabelled columns, and it credits no episode. --no-record reads without recording; the label is skipped with one stderr line when the store has no id, is busy or was replaced, and never changes the read's exit status.

• --exposed KIND:REF (repeatable, or exposed=[ExposedRef(kind, ref)] in OutcomeLog.record) lists what the recall surfaced, labelled or not, straight from the harness's receipt. An item that was exposed and never labelled is counted in its own columns (unl+s / unl+f), never in succ/fail: a label means something judged the item, and an unlabelled exposure only means it was shown.

This is a report. Nothing in anneal ranks, decays, re-heats or retires anything from it. A failure after a recall is not evidence the recalled memory caused it. There is no evidence yet that the counters separate useful memory from useless; that needs real outcome labels collected over time.

Updated facts: supersession

When a fact changes, the new episode can say which episode it replaces. Then recall stops serving the old one. Nothing is deleted: the old episode stays in the store and in exports, and include_superseded=True (CLI --include-superseded) shows it, marked with what replaced it.

There are two ways to write the link. Either way it's validated like a citation: the old episode has to exist and not be newer, the link can't close a cycle, and the two texts have to share at least a quarter of the shorter one's meaningful words.

That last check is a floor, and I measured where to put it (scripts/supersede_floor.py, on a copy of my own store, about 12,500 episodes). It keeps every planted update in the probe, and it sits right at their minimum, so it is tight; a test fails if the tokenizer ever moves that boundary. Of 500 random pairs of episodes, 6 to 18 get through, depending on the sample. The two-shared-words rule it replaced let 422 of 500 through, and it refused some real updates that only share the subject's name. What no word-overlap rule can do is tell "replaces" from "is about the same thing": 113 to 170 of 500 same-topic pairs pass, again depending on the sample. I read 40 of the ones that pass: 5 were real updates, 6 were near-duplicates, and 29 were different facts on the same subject. So the floor catches a link to the wrong episode entirely, not a link to the wrong episode on the same subject. That part is still the writer's call, and a wrong link can be undone.

• Explicitly, when recording: store.record(text, "observation", supersedes=[old_id]), CLI record --supersedes ID, MCP record with supersedes. A link that fails validation records nothing at all.
• In a wrap: the agent writes [supersedes: OLD_ID by NEW_ID] in the continuity text, where NEW_ID is an episode of that wrap. A bad link doesn't fail the save; it comes back in supersessions_rejected with the reason.

A wrong link can be removed with unsupersede (CLI unsupersede --old ID --new ID). A superseded episode also stops counting as evidence: a pattern can't graduate by citing a fact that's been replaced.

What it fixes, measured with scripts/stale_probe.py (16 planted fact-and-update pairs among 120 unrelated episodes, graded mechanically, no judge model):

• Without a link, recall behaves as it did before. Keyword recall (Store.recall, CLI search, MCP recall) returns the stale fact beside the current one in 16 of 16 cases. Scored recall (retrieve_relevant) returns it in 12 to 14 of 16, depending on how the update is worded.
• With the link, written either way, the stale fact is served in 0 of 16 cases on every wording and both recall paths. A fact that never changed is unaffected.
• With the link, scored recall also serves the current fact when the question only reaches the old one. Recall ranks the live episodes as before, adds the hits on replaced episodes (scored as if nothing had been replaced, so a link never raises a score), and swaps each of those, in its own slot, for the episode that replaced it; that episode's replaces lists the old one (its id, date and text), so a reader can treat the answer as an update. On reworded updates that took the current fact at the top from 7 of 16 to 15 of 16; without the swap the question's keywords simply aren't in the new sentence. Keyword recall (MCP recall) lists a replaced match under "Replaced since", with the fact that replaced it. Links a wrap proposed ([supersedes: ...] in the continuity) still hide the old episode but are never swapped in, and neither is anything reached through one (or through a link a delete rewired): a wrap model proposes them in bulk, and on a knowledge-update benchmark about 1.4% of them were true. A caller that hides recent episodes from recall (exclude_recent_minutes) keeps seeing the old fact until its replacement is older than that window.

State keys, for updates that share no words. Many real updates never repeat the old words ("I've been based in Seattle" becomes "settled into my new place in Austin"), so no word-overlap rule can link them. A state key names the slot a fact fills instead: store.record(text, "observation", state_key="user.home_city"), CLI record --state-key KEY, MCP record with state_key, or set_state_key(id, KEY) / CLI state KEY --set ID for an episode already recorded. A slot holds one value at a time: the newest episode in it replaces the others through ordinary links, with no word-overlap check, and a backdated one goes into the history. "Newest" is the time the episode's timestamp names, so pass timestamp= when you record a fact late; a keyed episode's timestamp is stored in UTC. For a relation with several values (pets, languages), put the value in the key (user.pet/archie) or leave it unkeyed; one shared key would keep only the last value. A keyed episode is replaced only through its key: any explicit, wrap-proposed or team link from it is refused, even to an episode with the same key (record the update with the same key and no link). anneal-memory state [KEY] shows each key's current holder and what it replaced, and state --unset ID (clear_state_key) takes a wrongly keyed episode out of its slot. unsupersede alone is not enough for a key: the next keyed write in that slot hides the episode again. On a knowledge-update benchmark's own scenarios (STALE, 100 of them, keys placed on the true old and new facts), the question that names the old state got the old fact 93 times and the new one 0 times; keyed, it got the new fact 93 times and the old one 0. That measures the mechanism with correct keys, not how well an agent assigns them.

The catch is that all of this depends on the link or the key being written. Nothing detects an update on its own, so an update recorded without either still sits beside the old fact the way it always did. How often real agents write them is something I haven't measured yet. The word-overlap check is also only a floor, not a judgment that one episode really replaces the other: two episodes that share boilerplate pass it, and a wrong link or key hides a still-valid episode, and now serves its replacement in its place, until it's removed.

Prospective memory — spores

Everything above is retrospective: what already happened, and what was learned from it. Agents also need the opposite — a record of what they intend to do next. That's a different kind of object with a different lifecycle, and conflating it with memory corrupts both.

anneal ships spores as a separate sibling store (<stem>.spores.json) for exactly this: prospective tasks that open, get worked, and close — they self-clean when done. Memory accretes and never completes; a spore must complete, or it's noise. Three temporal layers, kept distinct on purpose:

• Retrospective — what persisted (episodic + continuity + crystallized). It accretes.
• Prospective — what you intend (spores). It completes and clears.
• Methodology — the procedure you run (your wrap discipline, your recall habits). It operates on the other two.

If you're integrating anneal into an existing agent that already tracks open loops, spores is the typed store those loops route into — your methodology operates the spore store, it doesn't compete with it.

The Consolidation Landscape (2026)

Multiple independent groups shipped consolidation-based agent memory architectures in early 2026: anneal-memory (March, citation-graduation multi-tier), OpenClaw Dreaming (April 9, three-phase Light/REM/Deep Sleep), and Anthropic's KAIROS / autoDream (leaked March 30 via Claude Code source map, four-phase merge / remove-contradictions / promote-provisional-to-absolute / MEMORY.md index). Convergence on consolidation validates the direction — raw accumulation doesn't scale, and compression at session boundaries is where intelligence emerges.

The groups diverge on one load-bearing question: what gates quality?

Structural gates ask "do the episodes this pattern cites exist, and does its explanation share their words?" Model-reliant gates ask "does the LLM consider this good?" The difference matters: persistent user memory profiles have been shown to amplify sycophancy 16–45% across models (Jain et al., CHI 2026; Gemini 2.5 Pro at 45%, others lower). The same RLHF-inherited bias surfaces wherever an LLM evaluates output for the user — including memory-quality scoring. A memory architecture whose quality mechanism runs through an LLM inherits that bias. anneal-memory's citation gate keeps the LLM out of the promotion decision; it does not keep it out of the compression, so the gate narrows that bias rather than removing it.

Write-time source checks exist elsewhere now. MemTxn refuses an update unless every value in it appears, in order, in the source it cites. That is a stricter lexical check than anneal's, aimed at extracted facts. anneal's gate is aimed at abstractions: it ranks a pattern by repeated grounded citation and demotes it when a citation fails.

There is a second question beside what gates quality: what stops the consolidator from wearing the memory down? Useful Memories Become Faulty documents consolidation degrading the memory it maintains. anneal's answer is the catastrophic-shrink gate (The Immune System above), a refusal at save time rather than an instruction to the model. It covers stores that declare an identity layer.

The same shift toward LLM-scored quality is going mainstream at the adjacent evaluation layer (AWS Bedrock AgentCore Evaluations), and the April 2026 multi-layer-memory papers (HeLa-Mem, GAM) mostly inherit it too — the differentiator across the field is increasingly what gates the consolidation, not whether consolidation happens.

The fuller 2026 landscape — OpenClaw Dreaming's signal weights, AWS AgentCore Evaluations, Memori's representation-layer filtering, and the April HeLa-Mem / GAM papers, with where each gates quality → docs/architecture.md.

On LOCOMO

LOCOMO is the de-facto agent-memory benchmark, and anneal-memory has no score on it. That's deliberate. LOCOMO measures conversational recall — remembering facts, holding state across a long dialogue — and anneal is architected around a different axis: citation-validated pattern accumulation for accountability-bearing work, where patterns must be defensibly surfaced, wrong ones must demote, and sycophancy must be structurally bounded. A high LOCOMO score tells you the agent remembered the conversation; it doesn't tell you the memory is sound at the axis that matters when it's informing decisions. anneal will run it as secondary validation when a comparison genuinely calls for it.

The full rationale, and where anneal sits against the published LOCOMO numbers → docs/architecture.md.

Session Hygiene

Session wraps are the most important thing your agent does with this system. Think of them like sleep.

Neuroscience calls it memory consolidation: during slow-wave sleep, the hippocampus replays the day's experiences while the neocortex integrates them into long-term knowledge. Skip sleep and memories degrade — experiences accumulate without being processed, patterns go unrecognized, and older knowledge doesn't get reinforced or pruned.

anneal-memory works the same way. During a session, episodes accumulate in the episodic store. At session end, the wrap compresses those episodes into the continuity file. This is where the real thinking happens — the agent recognizes patterns, promotes validated knowledge, lets stale information fade, and forms associations between related episodes. Without wraps, you just have a growing pile of raw episodes and no intelligence.

The wrap sequence:

1. prepare_wrap — gathers recent episodes, current continuity, stale pattern warnings, association context, and compression instructions
2. Agent compresses — patterns emerge during compression that weren't visible in the raw episodes
3. save_continuity — server validates structure, refuses a wrap that collapses a partnership entity's protected felt/identity layers, checks citation evidence, records associations between co-cited episodes, applies decay to unused associations, and saves the result

Rules of thumb:

• Always wrap before ending a session. An unwrapped session is like an all-nighter — the experiences happened but they weren't consolidated
• Wrap exactly once per session. save_continuity reporting demoted graduations is the immune system working, not an error — don't re-save to chase a clean report (a second save with no new prepare_wrap is refused)
• The agent-instructions snippets (MCP, CLI) handle this automatically — they teach the agent to detect session-end signals and run the full sequence
• Short sessions (3-5 episodes) still benefit from wraps. Even a small amount of compression builds the continuity file
• If prepare_wrap says "no episodes" — nothing to compress. That's fine, skip it

The graduation system and association network both depend on wraps to function. Patterns can only be promoted (1x -> 2x -> 3x -> ... , no ceiling) during compression, citations can only be validated during wraps, associations only form through co-citation during wraps, and stale patterns can only be detected when the agent reviews what it knows against what it recently experienced. No wraps = no immune system, no associations, no cognitive development.

MCP Tools

17 tools total (7 memory + 2 crystal + 8 spore). The crystallized tier exposes only its read surface over MCP (crystal_recall / crystal_index); crystallizing out stays a wrap-time / CLI / library action (it carries the opt-in + decision-channel governance).

Resources: anneal://continuity — the current continuity file, auto-loaded at session start. anneal://integrity/manifest — SHA-256 hashes for host-side tool description verification.

Compliance and Audit

The episodic store is a natural audit trail. Every decision, tension, and outcome is timestamped, typed, and append-only — exactly what regulators want to see when they ask "why did the AI do that?"

Hash-chained JSONL audit trail (shipped, on by default):

Every memory operation — episode recorded, episode deleted, wrap started, wrap completed, associations updated — gets logged to an append-only JSONL file where each entry's SHA-256 hash includes the previous entry's hash. Modify or delete an entry and the chain breaks. Verify integrity programmatically or with jq.

• Actor identity on every entry (who did this — agent, system, admin)
• Content-hash-only mode by default — the audit trail proves what happened without storing the content itself (GDPR-compatible: delete the episode, the audit chain still verifies). It proves it for the entries that were written — a write the sink refused is reported via status().audit_write_failures (lifetime-scoped, and a loss recorded there outlives the process — best-effort, so not every loss is recorded) and as dropped_before on the next entry, not by verify
• Weekly rotation with gzip — old audit files compress automatically, manifest index enables cross-file chain verification
• on_event callback — pipe audit events to your own systems (cloud logging, SIEM, observability)
• Crash recovery — incomplete entries detected and handled on restart

from anneal_memory import Store, AuditTrail

# Audit trail is on by default
store = Store("./memory.db", project_name="MyAgent")

# Verify chain integrity
result = AuditTrail.verify("./memory.db")
print(f"Valid: {result.valid}, Entries: {result.total_entries}")

# Stream events to external system
store = Store("./memory.db", on_audit_event=lambda entry: send_to_siem(entry))

EU AI Act relevance: The Act's Article 12 requires "automatic recording of events" for high-risk AI systems, with provisions for traceability, actor identification, and tamper evidence. This is audit infrastructure, not a compliance certification. The hash-chained trail covers the traceability requirements in Articles 12(2)(b,c) — actor identity, tamper evidence, and automatic event recording — without certifying full Act compliance.

Provenance, not just timestamps. anneal-memory ships at the provenance-chain level: every audit entry's hash is linked to its predecessor (modify or remove an entry, the chain breaks), and graduated patterns cite the episode IDs that earned them, so any pattern traces back to the observations behind it — not just when it was written. Why that distinction may become a differential compliance gate as the Act's enforcement develops, and why a memory whose quality runs through an LLM can't offer the same chain → docs/architecture.md.

What's next:

• Compliance proxy (Layer 2) — an optional transport-layer capture that extends the same hash-chained, source-tagged audit beyond memory operations, for teams that need a fuller traceability record. Off by default; the memory audit stands on its own.
• Multi-agent shared memory — shared episodic pool with per-agent continuity and per-agent association topology. Full cross-agent audit trail.

Continuity Markers

The continuity file uses a simplified marker set for density:

? question needing resolution
thought: insight worth preserving
✓ completed item

A -> B                          causation
A ><[axis] B                    tension on an axis

[decided(rationale, on)]        committed decision
[blocked(reason, since)]        external dependency

| 1x (2026-04-01)              first observation
| 2x (2026-04-01) [evidence: abc123 "explanation"]   validated pattern

[contradicts: name]             this pattern conflicts with the named Proven
[no-contradicts]                contradiction scan run, nothing conflicts
[provenance: id1, id2]          founding episode ids for a mature top-tier
                                pattern with no fresh evidence this wrap

The last three are parsed by the immune system, not just prose. [contradicts:] / [no-contradicts] record whether the contradiction scan prepare_wrap renders into the compression package was actually run — a new Proven landing with no stance is written to the audit log. [provenance:] names the episodes that founded a mature 3x pattern whose evidence has gone low-variance; it suppresses the "graduate OUT to partnership.md or retire" warning for a line that is genuinely grounded but quiet. It is not immortality — a provenance pattern that goes cold is held but flagged to the operator on every wrap that re-dates it, and provenance does not silence that. Prefer fresh evidence whenever it exists; provenance is only for when it does not.

Security

Memory poisoning resistance

Recent research demonstrates environment-injected memory poisoning attacks against web agents — adversarial content embedded in the environment (web pages, tool outputs) that the agent ingests as observations and that subsequently influences behavior across sessions. The published attack ("Poison Once, Exploit Forever," Zou et al., April 2026) demonstrated up to 32.5% attack success rate on GPT-4-mini against ChatGPT Atlas, Perplexity Comet, and OpenClaw — and notably did not require write access to the agent's memory store. Environmental contamination plus the agent's own consumption of the contaminated content was sufficient.

anneal-memory's citation-validated graduation does not eliminate this attack class — environment-injected episodes are recorded honestly because the agent did encounter them — but it raises the bar substantially:

1. Relayed content cannot promote itself, when the host labels it. A citation is grounded when its quoted explanation shares words with the cited episode, and a poisoned episode grounds its own claim exactly as well as the agent's own observation would: measured 2026-10-07, one episode recorded from a web page carried a false claim to 2x on its own. So every episode has a trust class, agent by default: a host records content it relays from a tool or an outside source with trust="tool" or trust="external" (record --trust, the MCP record tool's trust), and a graduation whose grounding citations are all tool/external does not climb. Whatever level it claimed, it is written back at 1x marked (uncorroborated) and forms no association; the save result lists it under uncorroborated, the audit log records it, and a warning names it. It climbs once an agent or operator episode also grounds it. With a quoted explanation, only citations that ground it count, so an unrelated agent episode added to the citation does not vouch; without one, a single tool/external citation makes the whole line relayed. A lower-trust episode also cannot supersede a higher-trust one, so relayed content cannot hide the agent's or the operator's record either. When set_trust lowers an episode that a supersession link owned by a team snapshot touches, the link stays: the team ledger is authoritative (strict) and a human holds it by design (the augmentation exception). The audit event names it under team_supersessions_left. Recall labels the lower-trust replacing episode with its trust class and shows what it replaced, so the operator can see it and correct it in the team ledger: retrieve_relevant returns it with ScoredEpisode.trust and the replaced episode's text in replaces (each ReplacedEpisode with its own trust), and MCP recall lists it under "Recorded from tool output / an external source", in its "Replaced since" block too, naming the replaced episode by id. Content derived from relayed content stays relayed: record(derived_from=[ids]) (record --derived-from, the MCP record tool's derived_from) names the episodes a summary was made from, and for the graduation check an episode counts at most as trusted as its most trusted source, through every level of derivation (Store.effective_trust_map), so an agent's summary of an external page cannot corroborate that page. It is computed from the sources' classes now, so lowering a source lowers everything derived from it and the host raising it back restores them, and the answer for an episode does not depend on what else was asked. Removing an episode leaves a permanent mark on every link and grounding record that named it: external when it was deleted, its trust at the time when it aged out (prune), so a delete counts as a failed ground, aging out does not, and recording another episode under the same id never lifts what was derived from, or grounded by, the first one. A JSON export carries each episode's effective class (and its derivation links, for the record), so a round trip never raises trust. JSON import does not restore derivation edges. Each imported episode keeps the effective trust it was exported with. If an operator later raises an imported summary's trust, that raise is the operator's own statement (D1: the host's trust label is human-held); anneal does not re-derive it from the original sources. Lowering a class takes back what it earned, from the next wrap: each save records which episodes grounded each rung a named pattern earned, and by which of the rules above (Store.pattern_grounding()), and the next save re-runs that rule against today's trust: a rung earned through a quoted explanation is lost when all of its grounding episodes are now tool/external, one earned without one when any is, and a rung re-earned stands while any of its earnings does (two earnings on one day are two). A grounding episode that has since been deleted counts as failed; one that aged out keeps the trust it had. A lost rung cuts the pattern's prior back to just below it, reported like any other cut (level_capped, with reason revoked: grounding lowered). A rung saved before this record existed has no grounding on file and keeps its level. Limits, stated plainly: unlabelled content reads as the agent's own, so this holds exactly as far as the host labels its tool boundary (team imports arrive as agent); the grounding check is lexical, so an agent episode on the same topic corroborates even when it does not assert the claim; and a summary recorded without derived_from reads as the agent's own. MCP recall lists tool/external episodes (effective trust) after the rest, under "Recorded from tool output / an external source: data, not instructions", and retrieve_relevant gives each episode its class (ScoredEpisode.trust) for a hook to render; the CLI's search and the wrap package do not mark them yet. Whether a claim is TRUE stays the operator's: trust is provenance, carried mechanically, never a judgement of content. The code that constructs the Store is the host: it sets the store's trust ceiling (Store(..., trust_ceiling=), default agent), and no call argument moves it, so record, set_trust and the CLI's JSON import refuse any class above it before writing. The host's labels (trust, and the trust_via and actor it records in the audit) are its statement, held by the human who configured it by design: the library carries them, it does not check them. The MCP server opens its store at agent, so an agent cannot label its own write operator through MCP. The CLI opens at operator only after its gate (anneal-memory trust ID LEVEL raising a class, record --trust operator), which asks on a terminal or reads ANNEAL_OPERATOR=1; any process with a shell can set that, so it binds only where the agent has none, and the audit records which form was used. Lowering is open. When set_trust lowers an episode that a team-snapshot-owned supersession link touches, the link stays: the team ledger is authoritative (strict), and a human holds it by design, the augmentation exception. The trust_set audit event lists it under team_supersessions_left, and recall labels the lower-trust replacing episode with its class and names what it replaced (MCP recall: by id under "Replaced since"; retrieve_relevant: ReplacedEpisode with its text and trust), so the operator can see it and correct it in the team ledger (Phill 2026-10-09, option (1)).
2. Explanation-grounding check rejects ungrounded citations. The graduation pipeline runs check_explanation_overlap(explanation, episode_content) on every citation: at least two meaningful words from the citation's quoted explanation must actually appear in the cited episode's content. Citations with vague or fabricated explanations — including poisoned trajectories where an attacker controls the graduation claim text but cannot rewrite the episode body it points at — fail this check and don't accrue evidence weight. This is anti-fraud, not anti-repetition: it forces graduating claims to be textually grounded in what the episodes actually said.
3. SHA-256 audit trail provides forensic surface. While the chain doesn't prevent ingestion of contaminated environmental content, it preserves a tamper-evident record of what the agent encountered and when — supporting post-incident analysis when poisoning is detected downstream.

This is structural inference, not empirical defense. anneal-memory has not been tested against eTAMP directly. The mechanisms above derive from architectural properties — citation-validated graduation, lexical-overlap explanation-grounding, SHA-256 audit chain — not from a published evaluation. A targeted eTAMP variant could partially or fully bypass these defenses in ways the architecture-level argument doesn't anticipate. Sustained adversarial campaigns with diverse contaminated trajectories can still graduate, and the Honest scope section above documents specific gap classes (lexical-overlap exploits, rotated-pair gaming, slow-drift accumulation, contradiction-with-existing-Proven, pattern omission) confirmed reachable under adversarial-agent conditions. Architectures with no graduation gate inherit the full attack surface; anneal-memory inherits a structurally narrower one, pending direct empirical evaluation and the next set of defenses listed in Honest scope.

Tool description integrity

Tool description integrity verification detects description poisoning — where manipulated tool descriptions alter LLM behavior without changing tool functionality.

Two-layer verification:

1. Build-time manifest (tool-integrity.json) — SHA-256 hashes of all tool descriptions, shipped with the package and verified at server startup. Detects post-install modification.
2. Host-verifiable resource (anneal://integrity/manifest) — the same hashes exposed as an MCP resource, so editors and hosts can compare tool definitions received via tools/list against the server's intended definitions. Detects transport-layer description mutation between server and client — the class of attack where descriptions are modified in transit or by middleware without the server's knowledge.

anneal-memory --generate-integrity  # Regenerate after description changes
anneal-memory --skip-integrity      # Bypass for development

Lineage

anneal-memory's architecture grew from FlowScript — a typed reasoning notation that explored compression-as-cognition, temporal graduation, and citation-validated patterns. The core insights proved more powerful than the syntax; anneal-memory delivers them as a zero-dependency memory system where agents use natural language instead of learning notation. The FlowScript notation remains in active daily use for reasoning compression, and a 9-marker subset powers the continuity compression prompts.

License

MIT

Author

Phill Clapham / Clapham Digital LLC