audrey

Memory that shows up before your coding agent makes the same mistake twice.

Audrey gives Codex and Claude Code one local, evidence-backed memory loop: remember what mattered, recall it automatically, check before acting, and learn from what happened next.

Your agent should remember the work, not just the chat

You fix the deploy command on Monday. On Thursday, a fresh session tries the broken version again.

You explain that this repository never commits generated files. The next agent helpfully commits them.

You discover a subtle migration rule, write it down somewhere, and still have to remember to paste it into every new conversation.

That is the gap Audrey closes.

Audrey sits beside the agent and participates in the work automatically. At the start of a session it brings back a small, relevant memory packet. When you submit a prompt, it recalls project facts, preferences, procedures, and recent risks. Before a side-effectful tool runs, Audrey checks the proposed action against prior evidence. Afterward, it links the outcome back to the exact check that preceded it.

The model does not have to remember that a memory tool exists. That is the point.

Meet Audrey Autopilot

Install Audrey once, review the hooks once, and then use Codex or Claude Code normally.

npm install -g audrey --allow-scripts=better-sqlite3,onnxruntime-node,sharp,protobufjs
audrey install --host auto

The explicit install-script list is for npm 12's safer dependency policy. It permits only the four packages Audrey needs for SQLite, local inference, and their generated runtime files. With npm 11 or earlier, the shorter npm install -g audrey is equivalent.

auto configures whichever supported CLIs are installed. You can choose one explicitly:

audrey install --host codex
audrey install --host claude-code

Restart the host after installation. Codex asks you to trust non-managed hooks once through /hooks; Claude Code may also ask you to approve project or plugin components. Audrey is automatic after that explicit install-and-trust step—never secretly installed.

Autopilot then closes the loop:

Moment	What Audrey does
Session starts	Injects a compact, agent-scoped memory briefing
You send a prompt	Recalls relevant evidence; explicitly durable phrases such as “remember that…” or “I prefer…” can become memories
Bash/edit/write is proposed	Checks exact prior failures, trusted rules, procedures, contradictions, and memory health
The tool finishes	Correlates tool_use_id to the Guard receipt and records the redacted outcome
A tool failure is reported	Forms a durable, sanitized failure memory for the next attempt
The turn stops or context compacts	Runs lightweight, due-only consolidation without holding the conversation open

Each hook event carries a host-declared timeout (30 seconds for the PreToolUse Guard check); Audrey races its own internal embedding/LLM timeout a few seconds ahead of that deadline so it can exit cleanly instead of losing the race to the host's kill. Infrastructure failures are fail-open by default: if Audrey itself errors or runs out of time, the tool call proceeds unguarded rather than freezing the session. Set AUDREY_HOOK_FAIL_CLOSED=1 to deny the action instead when the PreToolUse check fails this way; other lifecycle hooks (session start, prompt recall, post-tool bookkeeping) have no "deny" to fall back to and always degrade open regardless of this setting.

A small story about a failed deploy

The first attempt fails:

$ npm run deploy
Error: deployment target is missing

Audrey keeps a redacted trace and the exact action fingerprint. If another session proposes the same action before the problem is fixed, Guard returns a denial with evidence. Change the command or fix the target and Audrey lets the work continue. Once that exact action succeeds, the old failure no longer blocks it.

This is more useful than “the vector search found a vaguely similar error.” Audrey creates a receipt before the action, records what happened after it, and preserves the lineage between the two.

Try the complete loop without an API key or network call:

audrey demo --scenario repeated-failure

What Audrey remembers

Audrey treats memory as more than a pile of text chunks.

• Episodes are things that happened: a user decision, a tool result, a project fact, a preference.
• Semantic memories are principles supported by accumulated evidence.
• Procedural memories are ways of acting: how to retry, verify, avoid, or recover.
• Contradictions stay visible instead of being silently overwritten.
• Confidence changes with source quality, evidence, age, retrieval, interference, context, and feedback.
• Low-value memories decay; repeated evidence can consolidate into longer-lived knowledge.

Every context packet includes memory IDs, confidence, provenance where available, and a reason for inclusion. Uncertain or disputed memories are labeled as such. Retrieved content is wrapped with a simple rule: memory is evidence, not authority; current system and user instructions always win.

When a memory stops being true

Age is not the only way a memory goes wrong. A note saying "ship with npm run deploy:prod" is perfectly recent, well sourced, and completely wrong the day that script is deleted. Worse, every recall reinforces it, because retrieval counts as evidence that a memory is useful. A confidently stated, well-supported, false instruction is more damaging than no memory at all.

So Audrey checks. When a memory is written, it records the claims inside it that can be verified against the project — repository-relative paths and package script names — and keeps only the ones that resolve at that moment. That last part is what makes the signal worth anything: a claim that never resolved is a guess about a typo, while a claim that resolved once and no longer does is the world moving out from under a memory that still asserts it.

audrey ground

[audrey] Grounding memories against /home/you/project
[audrey] Checked 14: 12 still true, 2 broken, 0 repaired.
[audrey]   01K8ZQ... references a missing npm_script: deploy:prod
[audrey]   01K8ZR... references a missing path: scripts/release.mjs

Broken memories are not deleted. They keep their content, say plainly what they still refer to, and take a confidence penalty so they stop leading by default while remaining readable and repairable. They also stop being eligible for the packet's must-follow section — that is the section that can force a Guard block, and a rule naming a file that no longer exists is a rule nobody can follow.

Repair is symmetric. Restore the file or the script and the next check clears the flag. A checkout that has moved reports unknown rather than broken, because a memory should not be discredited for describing a project this machine cannot currently see.

Memories with no checkable claims are left unlabeled. Silence is not a clean bill of health, and presenting it as one would be the same mistake pointed the other way.

Everything it does, and when you'd actually use it

Most of this runs on its own once Autopilot is installed. You do not invoke recall, and you do not invoke Guard. The table is here for the parts you would reach for deliberately, and so the automatic parts are legible rather than magic.

What	When you want it	Why it helps	How
Autopilot	Always, after one install	The whole point. Memory arrives before the agent acts instead of after you notice it went wrong.	audrey install --host auto, restart the host, approve hooks once
Guard	Automatic, before any bash/edit/write	Checks the exact action fingerprint against prior failures. Not "something like this broke once" — this exact command, still broken.	Runs at PreToolUse. Manually: audrey guard --tool Bash --strict
Grounding	After deleting or renaming things a memory might mention	Confidence tells you a memory is well-sourced. Grounding tells you it is still true. A note about a script you deleted is confident and wrong.	audrey ground, or let the maintenance sweep do it
Session briefing	Automatic at session start	Small, scoped packet instead of pasting context every time. Each memory injects once per session, not every prompt.	SessionStart hook. Preview with audrey greeting
Explicit capture	When you say "remember that…" or "I prefer…"	Deliberate memories are worth more than inferred ones, and phrasing it that way is enough.	Just type it. Autopilot picks up those sentence shapes
Consolidation	Automatic when idle; manually before a long break	Repeated episodes become one principle. Otherwise the store is a pile of near-duplicates and recall gets noisy.	audrey dream
Contradictions	When two memories disagree	Neither one silently wins. Both stay visible and labeled until something resolves them.	Surfaced in packets; memory_resolve_truth to settle one
Decay	Automatic	Low-value memories fade. Reinforced ones stick around. Runs in the same sweep as consolidation.	Part of audrey dream
Promote	When a pattern deserves to be a repo rule	Moves a learned habit out of memory and into a file your team can read and review.	audrey promote --dry-run first
Impact	When you want to know whether any of this is working	Shows which memories were used and whether they helped. Answers "is this earning its keep".	audrey impact --window 30
Snapshot	Backups, or moving to a new machine	Full store in one JSON file. Import treats it as untrusted: redacted and stripped of trust markers on the way in.	AUDREY_ENABLE_ADMIN_TOOLS=1, then memory_export / memory_import
Doctor	Packets stopped arriving	The two real causes are a drifted hook entrypoint and starved consolidation. Doctor names which one.	audrey doctor
Demo	Before trusting any of this	Runs the whole loop with no API key and no network. Nothing to configure to see it work.	audrey demo --scenario repeated-failure
REST sidecar	Custom agents that are not Codex or Claude Code	Same memory runtime, same evidence contract, over HTTP. Python and JS clients included.	audrey serve with AUDREY_API_KEY set

What Audrey deliberately does not do

Audrey does not upload your memory to a hosted service by default. It does not treat every sentence as permanent truth. It does not promote instructions from arbitrary tool output into trusted policy. It does not claim that a small local benchmark proves state-of-the-art memory quality.

Raw prompt events and tool bodies are not retained by default. Audrey stores hashes, bounded summaries, fingerprints, and redaction metadata. Explicit user-memory language is persisted intentionally; tool failure memories are sanitized first. Admin export/import/forget/promote surfaces are disabled unless AUDREY_ENABLE_ADMIN_TOOLS=1.

At-rest encryption, identity-bound tenant authorization, rate limiting, and regulated retention remain deployment responsibilities today. They are not hidden behind a “production ready” badge.

Why a team might actually want this

Fewer repeated mistakes

Guard checks memory at the point where it can change an action, not after the damage is done. Exact failure fingerprints avoid the noisy “one Bash command failed, so all Bash commands are suspicious” behavior.

Continuity across agent sessions

Audrey is not tied to one model vendor. Codex and Claude Code use the same memory runtime and the same evidence contract. MCP, REST, JavaScript, and Python clients make the core usable in custom agents too.

Evidence a human can inspect

Allow, warn, and block decisions carry receipts and evidence IDs. Outcome records connect back to those receipts. Teams can ask not only “what did the agent remember?” but “which memory changed this action, and was that useful?”

Local control

The default store is SQLite, FTS5, and sqlite-vec. Local embeddings are the default. Cloud embedding or LLM providers require explicit configuration.

A safer shared store

Agent-scoped recall now continues through validation, contradiction detection, interference, affect, failure lookup, capsules, greetings, Guard, and REST request routing. Hidden retrieval candidates do not reinforce themselves; only memories actually surfaced to the caller receive retrieval bookkeeping (usage count and last-reinforced timestamp for semantic and procedural memories). Explicit validation feedback (memory_validate / /v1/validate) separately adjusts salience based on how a memory actually performed, not merely on being recalled.

Vector candidates are partitioned by agent before nearest-neighbor ranking, so one busy agent cannot crowd another out of a bounded search. For hard tenant boundaries, still use a distinct AUDREY_DATA_DIR per tenant or security domain.

See it before installing anything

npm exec --yes --package=audrey --allow-scripts=better-sqlite3,onnxruntime-node,sharp,protobufjs -- audrey demo --scenario repeated-failure

That command runs from the npm cache, exercises the full SQLite-backed Guard loop, and leaves host configuration unchanged.

Where we want to take it

The ambition is a temporal evidence graph for agents: immutable observations, explicit validity windows, source trust, evolving claims, scoped procedures, and outcome-calibrated policy. The defensible part is not storing more text. It is knowing what was believed, why, in which context, for how long, and whether acting on it helped.

Near-term work includes durable background cognition jobs, tenant namespaces bound to credentials, memory quarantine and taint propagation, public long-horizon evaluations, encrypted backup options, and a persistent local daemon that removes per-hook model startup entirely.

If that is the kind of agent infrastructure you want to build, open an issue or start with the demo. Audrey is MIT licensed, and the product boundary is intentionally inspectable.

Technical reference

Everything below is the machinery. The short version above is the product.

Requirements and packages

• Node.js 20+
• npm package: audrey
• Python client: audrey-memory
• Default storage: local SQLite + FTS5 + sqlite-vec
• Default embeddings: local 384-dimensional model

npm install audrey
pip install audrey-memory

For a project install with npm 12, approve Audrey's reviewed dependency scripts in the project root and rebuild once if npm reported that it blocked them:

npm install-scripts approve better-sqlite3 onnxruntime-node sharp protobufjs
npm rebuild

For Autopilot, prefer a global or otherwise stable installation. Hook and MCP configuration pins the actual Node executable and Audrey entrypoint; an ephemeral npx cache is not a durable production runtime.

Host configuration

Preview or apply lifecycle hooks independently:

audrey hook-config claude-code
audrey hook-config claude-code --apply --scope local
audrey hook-config claude-code --apply --scope project
audrey hook-config claude-code --apply --scope user

audrey hook-config codex
audrey hook-config codex --apply --scope project
audrey hook-config codex --apply --scope user

Claude Code scope mapping follows the host’s terminology:

• local → .claude/settings.local.json
• project → .claude/settings.json
• user → ~/.claude/settings.json

Codex supports project .codex/hooks.json and user ~/.codex/hooks.json; it has no local hook scope. Audrey preserves unrelated hooks, replaces older Audrey-owned handlers, writes a private timestamped backup, and is idempotent on repeat installation. Project-adjacent backup names match *.audrey-*.bak; keep that pattern ignored because a host config can contain unrelated credentials.

Audrey respects CLAUDE_CONFIG_DIR and CODEX_HOME. Generated hooks pin the stable Node executable, Audrey entrypoint, data directory, agent identity, and non-secret provider choices used at install time. With local embeddings, an Autopilot install performs one warmup so the first real hook is not also the first model load; set AUDREY_DISABLE_WARMUP=1 to skip it.

Generate MCP configuration without applying it:

audrey mcp-config codex
audrey mcp-config generic
audrey mcp-config vscode

Remove Audrey-owned MCP registrations and hooks with the same host and scope you installed:

audrey uninstall --host auto --scope user
audrey uninstall --host claude-code --scope local
audrey uninstall --host codex --scope project

Add --dry-run to preview uninstall without changing either host. Add --mcp-only only when you intentionally want to preserve Audrey hooks.

Autopilot safety contract

The shared hook adapter normalizes current Codex and Claude Code payloads.

• Context injection is bounded by AUDREY_CONTEXT_BUDGET_CHARS (default 4000; Autopilot uses a conservative 3200-character packet unless overridden).
• Prompt and tool retrieval queries are bounded before embedding. Large edits carry hashes and lengths instead of file bodies; exact Guard identity uses a full redacted digest rather than a truncated prefix.
• The generated default hooks guard and observe Bash, Edit, MultiEdit, Write, NotebookEdit, apply_patch, and every mcp__* tool from connected MCP servers, excluding Audrey's own memory tools so the Guard never guards itself.
• Pre/post correlation uses session_id + tool_use_id, so parallel tool calls do not attach to the wrong receipt.
• Claude PostToolUseFailure and Codex responses that explicitly expose a non-zero exit normalize to the same failure path. Current Codex hooks can omit Bash exit status; Audrey records an opaque result as unknown, never as invented success.
• On an internal error, every hook logs to stderr and emits {} (no opinion, so the tool proceeds). Only PreToolUse changes behavior under AUDREY_HOOK_FAIL_CLOSED=1, emitting a deny decision instead; context-injection and post-tool hooks have no deny path and always emit {}.
• Stop hooks always emit valid JSON and never continue or block a completed turn.

Codex hook interception is a guardrail, not a complete shell-policy boundary. The current host contract does not intercept every richer unified_exec path and may omit the exit status of silent Bash failures. See the Codex hooks documentation. Use the Guard receipt as evidence, and keep sandboxing, approvals, CI, and deployment controls in place.

JavaScript API

import { Audrey, MemoryController } from 'audrey';

const memory = new Audrey({
  dataDir: './audrey-data',
  agent: 'payments-agent',
  embedding: { provider: 'local', dimensions: 384 },
});

await memory.encode({
  content: 'Stripe returns HTTP 429 above 100 requests per second.',
  source: 'direct-observation',
  tags: ['stripe', 'rate-limit'],
  context: { service: 'billing' },
});

const capsule = await memory.capsule('increase Stripe throughput', {
  scope: 'agent',
  budgetChars: 3000,
});

const guard = new MemoryController(memory);
const before = await guard.beforeAction({
  action: 'deploy the billing worker',
  tool: 'Bash',
  command: 'npm run deploy:billing',
  cwd: process.cwd(),
});

console.log(before.decision, before.evidenceIds);
await memory.closeAsync();

REST sidecar

AUDREY_AGENT=payments-agent audrey serve
curl http://127.0.0.1:7437/health

Core routes:

Need	Route
Encode an episode	POST /v1/encode
Recall memory	POST /v1/recall
Build a context packet	POST /v1/capsule
Check before an action	POST /v1/preflight
Create a Guard receipt	POST /v1/guard/before
Close a Guard receipt	POST /v1/guard/after
Consolidate and decay	POST /v1/dream
Health and index state	GET /v1/status
Promote learned procedures to rule files (admin)	POST /v1/promote

Use AUDREY_API_KEY for any non-loopback deployment. X-Audrey-Agent scopes encode, recall, capsules, preflight, Guard, consolidation, and greetings inside a trusted deployment; it is a routing header, not an authentication boundary. Bind agent/tenant identity at your gateway rather than trusting an arbitrary public header. Every route also accepts a per-call agent field in the JSON body as a fallback for callers (such as the Python client) that cannot set a header per request; the header wins whenever both are present.

Python client

from audrey_memory import Audrey

memory = Audrey(base_url="http://127.0.0.1:7437", agent="payments-agent")
memory_id = memory.encode(
    "Stripe returns HTTP 429 above 100 requests per second.",
    source="direct-observation",
)
results = memory.recall("Stripe rate limit", limit=5)
memory.close()

The Python package is a client for the REST sidecar; the memory runtime remains in the Node process.

Memory and retrieval pipeline

episode
  ├─ transactional SQLite + vector + FTS write
  ├─ agent-scoped interference / resonance / validation
  ├─ reinforcement or contradiction evidence
  ├─ sleep-time consolidation into semantic or procedural memory
  └─ grounding checks that stored claims still hold in the project

query
  ├─ bounded vector candidates
  ├─ FTS5 lexical candidates
  ├─ reciprocal-rank fusion and confidence scoring
  ├─ context / affect / recency / interference modifiers
  └─ final-only retrieval bookkeeping

Agent-scoped vector search uses a native sqlite-vec partition key before nearest-neighbor ranking, not post-filtered whole-store candidates. If fusion underfills, Audrey makes one bounded partition-local retry. Semantic and procedural retrieval counts update only as final results are yielded; deduplicated, over-limit, and unconsumed stream candidates receive no authority boost.

MCP surface

Audrey exposes 23 MCP tools plus status, recent-memory, and principle resources and briefing/recall/reflection prompts. The main groups are:

• capture: memory_encode, memory_reflect, memory_observe_tool
• retrieval: memory_recall, memory_capsule, memory_greeting
• action safety: memory_preflight, memory_guard_before, memory_guard_after, memory_reflexes
• lifecycle: memory_consolidate, memory_dream, memory_decay, memory_resolve_truth, memory_ground
• governance: memory_validate, memory_promote, memory_forget, memory_export, memory_import, memory_status, memory_introspect

The server also sends host instructions explaining the Guard receipt loop when lifecycle hooks are unavailable.

Environment variables

Variable	Default	Purpose
AUDREY_DATA_DIR	~/.audrey/data	SQLite store; use a distinct directory per tenant/security boundary
AUDREY_AGENT	host-specific	Logical memory owner used for scoped operations
AUDREY_EMBEDDING_PROVIDER	local	local, gemini, openai, or mock
AUDREY_LLM_PROVIDER	unset	anthropic, openai, or mock for reflection/consolidation; unset (or auto) uses local heuristics only, never an ambient ANTHROPIC_API_KEY/OPENAI_API_KEY
AUDREY_LLM_MODEL	provider default	Explicit LLM model override
AUDREY_DEVICE	gpu	Local embedding device; falls back to CPU
AUDREY_CONTEXT_BUDGET_CHARS	4000	Maximum default capsule size
AUDREY_AUTOPILOT_SCOPE	agent	agent or explicit cross-agent shared recall for hooks
AUDREY_PACKET_FORMAT	compact	Injected packet style: compact line format or verbose key=value
AUDREY_PACKET_DELTA	1	Inject each memory once per session; 0 resends full packets every prompt
AUDREY_HOOK_FAIL_CLOSED	0	Deny guarded actions when Audrey itself fails
AUDREY_API_KEY	unset	Bearer token for REST access
AUDREY_HOST	127.0.0.1	REST bind address
AUDREY_PORT	7437	REST port
AUDREY_ENABLE_ADMIN_TOOLS	0	Enable export, import, forget, and promote operations
AUDREY_ENABLE_SHARED_SCOPE	0	Allow explicit cross-agent REST recall; admin tools also enable it
AUDREY_PROFILE	0	Include stage timing diagnostics
AUDREY_DISABLE_WARMUP	0	Disable MCP embedding warmup
AUDREY_PRAGMA_DEFAULTS	1	Set 0 to use better-sqlite3 PRAGMA defaults

Provider secrets are never embedded in generated hook commands. --include-secrets applies only to MCP registration; prefer host environment injection or a secret manager.

Production checklist

• Give every tenant or hard isolation domain its own AUDREY_DATA_DIR.
• Pin embedding and LLM providers explicitly.
• Back up the store before provider, dimension, or version migrations.
• Put the REST sidecar behind authentication and rate limits; do not expose an agent-selection header as identity.
• Leave REST shared scope disabled unless cross-agent retrieval is intentional and authorized by your own identity layer.
• Keep credentials and regulated raw content out of encoded memories.
• Decide retention, deletion, encryption, and audit policy before regulated use.
• Monitor audrey status --json --fail-on-unhealthy.
• Keep the hook runtime on a stable installed path.
• Load-test concurrent writers for your topology; SQLite WAL is not a distributed coordination layer.

npm audit --omit=dev reports two high-severity advisories against sharp, pulled in as a hard dependency of @huggingface/transformers for the local embedding runtime. There is no patched release compatible with the version range that package declares. Audrey never imports sharp — it is image-preprocessing code that a text-only embedding pipeline does not reach — but the package is installed, so the advisory is genuine and unresolved rather than dismissed. Running with AUDREY_EMBEDDING_PROVIDER set to a hosted provider avoids the dependency path entirely.

Benchmarks and evidence

Run the release gates locally:

npm test
npm run bench:memory:check
npm run bench:guard:check
npm run bench:guard:publication:verify
npm run smoke:cli
npm run pack:check

GuardBench currently contains ten local, deterministic pre-action scenarios covering repeated failures, procedures, scope changes, recovery, redaction, conflicting instructions, and noisy stores. The checked-in v1 methodology uses a mock 64-dimensional embedding provider and exists to catch regressions. A perfect local pass is not a claim about real-provider latency or production false-positive rates.

Latest local result in this checkout: 10/10 scenarios passed, 100% prevention rate, 0% false-block rate, 0 raw secret leaks, 0 published artifact leaks, and 3.805ms / 13.445ms p50/p95 Guard latency under the mock-provider methodology.

benchmarks/perf-snapshot.js measures encode and hybrid-recall p50/p95/p99 at configurable corpus sizes with machine and provider provenance. Run it on the hardware and embedding provider you plan to operate; hosted-provider latency is dominated by its network round trip.

The longer-term public evaluation target includes LongMemEval, MemoryAgentBench, and adversarial memory-poisoning cases. Relevant design directions include bitemporal knowledge graphs in Zep/Graphiti, evolving memory organization in A-MEM, and sleep-time agent compute in Sleep-time Compute.

Development

git clone https://github.com/Evilander/Audrey.git
cd Audrey
npm ci
npm run build
npm run lint
npm run format:check
npm test

See CONTRIBUTING.md, SECURITY.md, and docs/MEMORY_BENCHMARKING.md.

Maintainer release gates

These commands are intentionally documented because the paper and release evidence ledger verifies them against the public source tree:

npm run bench:guard:zep
npm run bench:guard:external:dry-run
npm run bench:guard:external:evidence
npm run bench:guard:external:evidence:strict

npm run paper:arxiv:compile
npm run paper:arxiv:compile:strict
npm run paper:launch-results
npm run paper:launch-results:strict

npm run release:cut:plan
npm run release:cut:apply
npm run release:readiness
npm run release:readiness:strict
npm run python:release:check

Live Zep runs require ZEP_API_KEY; ZEP_GUARDBENCH_INGEST_DELAY_MS tunes ingestion settling time. The external dry-run matrix proves adapter shape without credentials, while external evidence verification distinguishes pending runs from verified live evidence.

Publication packaging performs an absolute-path sweep, reserves an X URL reserve in social copy, and checks submitted artifact-url targets. Release readiness separately reports source-control state, live remote-head verification, npm registry/auth readiness, and PyPI publish readiness.

MIT licensed. Built for agents that should get better at the work without becoming less accountable.