Persistent memory & cognitive profiling for Claude Code — local-first, single-click MCP install.
Getting Started · Configuration · Examples · What's New · Science · Benchmarks · Wiki · Architecture
Companion projects:
cortex-beam-abstain (repo cortex-know-when-to-stop-training-model) — community-trained retrieval abstention model for RAG systems
zetetic-team-subagents — specialist Claude Code agents Cortex orchestrates with
automatised-pipeline — automated 11-stage pipeline (findings → PRs) that Cortex drives via run_pipeline
cortex-viz — read-only visualization MCP (galaxy graph, execution trace, wiki browser) over the same store
Independent project: Cortex is an independent, open-source project. It is not an Anthropic product and is not affiliated with, sponsored by, or endorsed by Anthropic.
Claude forgets you every time you close the tab. Every architecture decision you explained. Every debugging session where you traced a bug through four layers of abstraction. Every "remember, we decided to use event sourcing, not CRUD" correction. Gone. Next session, you're a stranger to your own tools.
Cortex is a persistent memory engine for Claude built on computational neuroscience. It remembers what you worked on, how you think, what you decided and why — not as a text dump shoved into context, but as a living memory system that consolidates, forgets intelligently, and reconstructs the right context at the right time.
It runs entirely on your machine — a local SQLite database by default (zero setup, no services to install), or PostgreSQL + pgvector when you want it. A 22 MB embedding model, no LLM in the retrieval loop, no data leaving localhost.
23 neuroscience mechanisms · 43 memory tools · 9 lifecycle hooks · a self-curating per-project wiki — all local, all open-source.
Cortex ships as a single-click MCP bundle (.mcpb). Download the latest hypermnesia-mcp.mcpb from Releases, then open it in Claude Desktop — Settings → Extensions installs it in one click.
It runs immediately on the built-in SQLite backend: zero configuration, no database to provision, nothing to set up. Memory persists to a local file under ~/.claude/methodology/. That's the whole install.
Want PostgreSQL + pgvector instead (for very large stores or a shared team database)? It's a single configuration field — see Configuration below. SQLite is the default; PostgreSQL is opt-in.
More options (Claude Code plugin, Clone, Docker)
Claude Code plugin (marketplace):
claude plugin marketplace add cdeust/Cortex
claude plugin install cortexThe plugin path also registers the lifecycle hooks (session-start context injection, compaction checkpointing, the autonomous wiki cycle) and the /cortex-setup-project command. If you point the plugin at PostgreSQL, run /cortex-setup-project once — it handles pgvector installation, database creation, the embedding-model download, profile building, codebase seeding, and hook registration.
If you configured the PostgreSQL backend, verify the connection:
python3 -m mcp_server.doctorSeven checks in two seconds: Python, the PG driver, DATABASE_URL, connection, extensions, a writable methodology dir, and the pool-capacity invariant. Exit 0 means the PostgreSQL path is ready. (On the default SQLite backend the PostgreSQL checks report "not set" and can be ignored — SQLite needs no doctor.)
Clone + setup script:
git clone https://github.com/cdeust/Cortex.git && cd Cortex
bash scripts/setup.sh # macOS / Linux
python3 scripts/setup.py # Windows / cross-platformDocker:
git clone https://github.com/cdeust/Cortex.git && cd Cortex
docker build -t cortex-runtime -f docker/Dockerfile .
docker run -it \
-v $(pwd):/workspace \
-v cortex-pgdata:/var/lib/postgresql/17/data \
-v ~/.claude:/home/cortex/.claude-host:ro \
cortex-runtimePyPI (uvx / pip) — deprecated secondary channel:
uvx hypermnesia-mcp # run the MCP server directly
pip install hypermnesia-mcp # or install into your environmentThe server is published on PyPI as hypermnesia-mcp (registry name io.github.cdeust/hypermnesia-mcp). The supported install paths are the .mcpb bundle and the Claude Code marketplace above; PyPI is kept best-effort for legacy pip / uvx users.
WSL / TLS client-cert / remote PostgreSQL: See deployment scenarios.
Cortex needs no configuration to run — the SQLite backend is the default and requires nothing. Two optional settings let you change the storage backend; in the single-click bundle they appear as fields in Claude Desktop's extension settings, and everywhere else they map to environment variables.
| Setting | Env var | Default | What it does |
|---|---|---|---|
| Storage backend | CORTEX_MEMORY_STORE_BACKEND |
sqlite* |
sqlite runs fully local with zero setup. postgresql uses an external PostgreSQL + pgvector database (set the URL below). auto tries PostgreSQL and falls back to SQLite. |
| PostgreSQL URL | CORTEX_MEMORY_DATABASE_URL |
(empty) | Only used when the backend is postgresql or auto. Example: postgresql://user:password@host:5432/cortex. Leave empty to stay on SQLite. Treated as sensitive. |
* The single-click bundle pins the backend to sqlite through the manifest. If you run the server directly (clone / Docker) without setting the variable, the underlying code default is auto — it tries PostgreSQL and falls back to SQLite.
That's the entire surface most users touch. Both backends expose the same 43 memory tools (46 with the optional automatised-pipeline + prd-spec-generator integrations) and the same retrieval contract; PostgreSQL adds server-side PL/pgSQL fusion and HNSW indexing that pays off at very large scale. Every other knob uses the CORTEX_MEMORY_ prefix — see mcp_server/infrastructure/memory_config.py.
A live, end-to-end run on the SQLite backend (43 tools registered) — store three memories, recall them by meaning, then check the store. The output is taken from the in-process FastMCP client (recall lists trimmed to the top hit). The harness writes with force: true for determinism, and the demo store already held a few earlier memories — so memory_stats totals exceed the three inserted here.
1 — Store a memory. It is stored with a heat score (force: true skips the dedup write-gate to keep the demo deterministic; omit it and a near-duplicate would be gated).
remember({
content: "Cortex stores memory in a local SQLite database by default — zero setup, no PostgreSQL required.",
tags: ["architecture", "decision"],
force: true
})
// → { stored: true, memory_id: 490, action: "stored", heat: 0.796 }2 — Recall by meaning, not keywords. The fused retrieval ranks the relevant memory first.
recall({ query: "how does cortex store memory by default?" })
// → memories[0] = {
// content: "Cortex stores memory in a local SQLite database by default — zero setup, no PostgreSQL required.",
// score: 0.0167, heat: 0.846, tags: ["architecture", "decision"]
// }3 — A different query surfaces a different memory. Stored "Anchored memories survive context compaction with maximum priority."; this recall puts it on top.
recall({ query: "what survives context compaction?" })
// → memories[0] = {
// content: "Anchored memories survive context compaction with maximum priority.",
// heat: 0.565, tags: ["compaction"]
// }4 — Inspect the store. has_vector_search: true confirms semantic search is live on SQLite.
memory_stats({})
// → { total_memories: 14, episodic_count: 8, semantic_count: 6,
// avg_heat: 0.942, has_vector_search: true }You rarely call these by hand: the lifecycle hooks (plugin install) inject the right memories at session start and capture new ones as you work. The tools are there when you want explicit control — anchor to pin an architecture constraint, consolidate to run a maintenance cycle, narrative to get the project's story so far.
v3.23.0 — single-click bundle + registry-indexer build fix. Cortex now ships as an MCP bundle (.mcpb) with a uv runtime and a selectable storage backend — SQLite by default, PostgreSQL optional — so it installs in one click with zero setup. Also: a neuro-cortex-memory console script so uv run neuro-cortex-memory resolves from a checkout; registry indexers that build with uv sync and launch via that script now start the server and register all 43 standalone tools without a PostgreSQL connection (46 when the automatised-pipeline + prd-spec-generator integrations are configured; so tools/list answers inside a DB-less container).
v3.22.0 — security + reliability hardening (P0/P1 audit). Security: a headless-authoring RCE fix (the subprocess toolset is restricted and the untrusted repo's settings/hooks are ignored), DSN/secret redaction (libpq ?password= query params and psycopg exception leaks), and pip supply-chain hardening. Fixed: the SQLite backend's incomplete heat→heat_base rename — all 11 indexes were never created (full table scans) and search/stats queried a dropped column; both are corrected, with a dedicated SQLite-backend CI job and deployment docs (WSL, TLS client-cert DATABASE_URL, remote PostgreSQL). 46 MCP tools.
v3.21.0 — visualization extracted to cortex-viz. The entire visualization stack — the galaxy graph, execution trace, the Knowledge / Board / Wiki / Pipeline views, and their HTTP server — moves to a standalone companion MCP, cortex-viz, which reads this same store read-only. Cortex is a focused memory engine again (−50k lines). Breaking: the open_visualization, get_methodology_graph, and query_workflow_graph MCP tools are removed from Cortex — install cortex-viz to get them back (its /cortex-visualize skill replaces the old one). 46 MCP tools remain; no memory, retrieval, or wiki behaviour changed; full suite green (3214 tests).
v3.20.0 — graph intelligence + memory knowledge-updates. The codebase graph gains Leiden community detection, centrality and god-node analysis, and native tree-sitter symbol extraction across 7 languages — no automatised-pipeline dependency required. Memory learns to handle knowledge updates: a memory that supersedes prior knowledge records an explicit supersession edge so recall ranks the newest version above what it replaces; a MinHash entity-dedup engine (with AST-symbol origin flagging) collapses near-duplicate entities during a consolidate-time merge cycle; and a new include_related recall mode returns a memory's graph neighbours in one call.
v3.19.0 — memory hygiene + scoring integrity. A fix to an auto-capture scoring inversion at all three roots: prospective-trigger injection no longer harvests garbage keyword triggers from raw tool dumps; WRRF fusion excludes mechanical freshness (post_tool_capture) from the hot/recency pools so churn isn't mistaken for importance; and rate_memory feedback now wires into rank as a metamemory confidence prior (Kraaij 2002). Oversized auto-captures store a deterministic gist plus a content-addressed artifact pointer — full output one Read away, no truncation. Benchmarks regression-free (LongMemEval R@10 98.4% / MRR 0.9124; LoCoMo MRR 0.8278).
→ Full changelog and release notes
Cortex doesn't store memories the way a database stores rows. It treats them the way a brain treats experiences. Every mechanism traces to a published paper — a 72-reference bibliography (docs/papers/bibliography.md).
Memories have temperature. Every memory starts hot. Access it and it stays hot; ignore it and it cools. Below a threshold it compresses: full text → summary → keywords → fades entirely. This is rate-distortion optimal forgetting — the framework your brain uses to decide what's worth keeping. Important memories resist compression; surprising ones get a heat boost; boring, redundant ones quietly disappear. (Anderson & Lebiere 1998; Ebbinghaus 1885)
Storage has a gatekeeper. Not everything deserves to be remembered. Cortex maintains a predictive model of what it already knows and only stores information that violates its expectations. Tell it the same thing twice and the write gate blocks the second attempt. This is predictive coding — the mechanism your neocortex uses to filter sensory input. Only prediction errors get through. (Friston 2005; Bastos et al. 2012)
Retrieval changes the memory. When you recall a memory in a new context, Cortex compares the retrieval context against the storage context, and if there's enough mismatch it reconsolidates — updates the memory to reflect what's true now. Nader et al. showed in 2000 that retrieved memories become labile and can be rewritten. Your codebase evolves, and so do Cortex's memories of it. (Dudai 2012; Nader et al. 2000)
Emotional memories are stronger. Frustration during debugging, urgency in a production incident — Cortex detects emotional valence and encodes those memories with more force. They decay slower, compress later, and surface faster, like how you remember your worst outage in vivid detail but not last Tuesday's standup. (Wang & Bhatt 2024; Yerkes-Dodson 1908)
Background consolidation runs like sleep. When you're away, a consolidation cycle decays old memories, compresses verbose ones, promotes recurring patterns into general knowledge (episodic → semantic transfer), discovers entity relationships, and runs "dream replay" where related memories are compared and new connections emerge. (McClelland et al. 1995; Foster & Wilson 2006; Buzsáki 2015)
Similar memories stay distinct. Pattern separation, modeled on the dentate gyrus, keeps "Tuesday's standup" separate from "Wednesday's standup" even though they're nearly identical — without it, retrieval returns the same generic match for every similar query. (Leutgeb et al. 2007; Yassa & Stark 2011)
The two arXiv-ready papers go deeper: Thermodynamic Memory vs. Flat-Importance Stores (PDF, 34 pages) · Stage-Aware Context Assembly (PDF, 39 pages).
Monday. You spend an hour debugging a webhook handler. After tracing through four layers, you find the root cause: a race condition in the Redis session store where TTL expiry can fire between the auth check and the permission lookup. You discuss the fix with Claude, decide on an approach, implement it. Session ends.
Thursday. Different project, but a user reports intermittent logouts. You open Claude. Before you even describe the bug, Cortex has already injected three memories: Monday's race-condition analysis, a decision from two weeks ago to use Redis for all session state, and a lesson from an older session about TTL edge cases in distributed caches.
Claude doesn't just have your conversation history. It has context — it connects the current problem to past decisions and skips the part where you re-explain your architecture.
Three weeks later. Those debugging sessions have consolidated into a general pattern: "authentication edge cases involving TTL-based caches." The specific Redis commands compressed to a summary, the debugging steps faded, the principle survived. Your next auth issue starts with institutional knowledge, not a blank page.
We tested Cortex against three published benchmarks. All scores are retrieval-only — no LLM reader in the evaluation loop. We measure whether the right memory shows up, not whether a model can generate a good answer from it.
LongMemEval (Wu et al., ICLR 2025): 500 human-curated questions embedded in ~40 sessions of conversation history (~115k tokens). The paper's best retrieval hit 78.4% Recall@10.
| Cortex | What it means | |
|---|---|---|
| Recall@10 | 98.4% | The right memory shows up in the top 10 for nearly every question |
| MRR | 0.9124 | The correct memory is usually ranked first or second — retrieval rank only, no LLM reader |
n=500, E1 v3 verification campaign — per-row JSONs with code SHAs in benchmarks/results/ablation/longmemeval-s_v3/. Re-verified on a clean DB 2026-06-10.
| Category | MRR | R@10 |
|---|---|---|
| Single-session (assistant) | 1.000 | 100.0% |
| Multi-session reasoning | 0.962 | 100.0% |
| Knowledge updates | 0.925 | 100.0% |
| Temporal reasoning | 0.926 | 98.5% |
| Single-session (user) | 0.814 | 94.3% |
| Single-session (preference) | 0.668 | 93.3% |
Knowledge updates score near-perfect because the retrieval stack's recency signal and update-intent routing push the newest version of a fact above older ones.
LoCoMo (Maharana et al., ACL 2024): 1,986 questions across 10 conversations — adversarial trick questions, multi-hop queries needing evidence from multiple turns, and temporal reasoning.
| Cortex | What it means | |
|---|---|---|
| Recall@10 | 94.2% | Right memory in top 10 over 9 times out of 10 |
| MRR | 0.8278 | The correct memory is typically ranked first — retrieval rank only, no LLM reader |
n=1986, BASELINE_NO_CONSOLIDATION, post-plasticity-fix — docs/benchmarks/e1-v3-locomo-results-post-fix.md.
| Category | MRR | R@10 |
|---|---|---|
| Adversarial | 0.881 | 96.0% |
| Open-domain | 0.875 | 96.9% |
| Multi-hop | 0.779 | 90.3% |
| Single-hop | 0.741 | 94.0% |
| Temporal | 0.577 | 78.3% |
No LLM at query time. Five signals fused — vector similarity, full-text search, trigram matching, thermodynamic heat, recency — then reranked by a cross-encoder. On PostgreSQL the fusion runs server-side in PL/pgSQL; on SQLite the same five signals are fused in-process.
BEAM (Tavakoli et al., ICLR 2026) is the hardest long-term memory benchmark published: 10 conversations, each spanning 10 million tokens, probed across 10 memory abilities — including three no prior benchmark tests: contradiction resolution, event ordering, and instruction following. (Question counts vary by split: 196 on 10M, 395 on the current 100K.)
Every system in the paper collapses at this scale; the best reported (LIGHT on Llama-4-Maverick) scores 0.266 end-to-end. The collapse is measurable — and structured assembly resists it. Same code, same day, clean database, 35 conversations per split:
| Split | Flat WRRF | With Context Assembler | Δ |
|---|---|---|---|
| 500K (699 Qs) | 0.500 | 0.570 | +0.070 |
| 1M (695 Qs) | 0.466 | 0.535 | +0.069 |
Measured 2026-06-11 — benchmarks/results/beam_crossover/RESULTS.md. Flat retrieval degrades as the corpus doubles (0.500 → 0.466); the assembler holds a durable +0.07. At small scale it is net-flat (April 100K: 0.591 flat vs 0.602 assembled, 200-Q split since re-based to 395) — the value is scale-dependent, not universal.
At 10M tokens the gap widens — and the assembler needs no labels:
| Configuration | MRR | vs. flat WRRF (0.353) |
|---|---|---|
| Flat WRRF baseline | 0.353 | — |
Assembler, oracle stage labels (BEAM plan_id) |
0.429 | +21.5% |
| Assembler, temporal stage detection (timestamps only) | 0.471 | +33.4% |
2026-04 family, same code revision, 196 Qs / 10 conversations — benchmarks/beam/variance/assembler_10m_stagefixed.txt and assembler_10m_temporal.txt. Reproduced 2026-06-11 on current code (fresh DBs, same 196 Qs): oracle 0.496, temporal 0.523 — the temporal advantage persists across code revisions (benchmarks/results/beam10m_paired/RESULTS.md).
The finding that surprised us: label-free temporal day-level partitioning outperforms BEAM's ground-truth topic labels (0.471 vs 0.429). Temporal proximity is a stronger stage signal than topic boundaries for conversational memory, so the Stage-Aware Context Assembly architecture deploys without any oracle metadata. It was originally designed in September 2025 for 9-page PRDs on Apple Intelligence's 4,096-token window (ai-prd-builder, commit 462de01) — one month before the BEAM paper existed — because the problem is the same at both scales: you can't fit everything in context, so you have to be smart about what goes in.
Honest caveat: BEAM defines no retrieval MRR metric — the paper uses LLM-as-judge nugget scoring. Our "MRR" is a retrieval proxy (rank of the first substring-matching memory); LIGHT's scores are end-to-end QA. The two are not commensurable, so we make no head-to-head BEAM claim and use BEAM only for within-system, same-harness comparisons.
Running benchmarks yourself
pip install -e ".[postgresql,benchmarks,dev]"
python benchmarks/beam/run_benchmark.py --split 100K # ~10 min
CORTEX_USE_ASSEMBLER=1 python benchmarks/beam/run_benchmark.py --split 10M
python benchmarks/locomo/run_benchmark.py # ~40 min
python benchmarks/longmemeval/run_benchmark.py --variant s # ~45 minAll scores on a fresh database (DROP + CREATE per run), TRUNCATE between conversations, FlashRank preflight verified. Full methodology: docs/papers/research-post-context-assembly.md.
Claude has a 200k/1M token context window. During long sessions, when it fills, it compacts: summarizes older messages, strips tool outputs, paraphrases instructions. Important nuance evaporates; decisions you anchored early dissolve into vague summaries.
Hippocampal Replay fixes this — named after the phenomenon where your brain replays important experiences during sleep to consolidate them. It treats compaction as "sleep" and replays what matters when Claude "wakes up." Before compaction hits, a hook drains your active context — what you were working on, which files were open, what decisions you'd made, what errors were unresolved — and stores it as a checkpoint. After compaction, a second hook reconstructs context intelligently: the latest checkpoint, anything you'd anchored as critical, the hottest project memories, and predictions about what you'll need next.
You can be explicit about what matters:
cortex:anchor({ content: "We're using event-sourcing. All state changes go through the event bus.", reason: "Architecture constraint" })
Anchored memories get maximum protection — they always survive compaction, no matter what.
The compaction checkpoint, session-start injection, and the autonomous wiki cycle are lifecycle hooks registered by the Claude Code plugin install. The single-click
.mcpbbundle is a Directory connector — it delivers the 43 memory tools but no hooks (the MCPB format carries none). For the automatic session-lifecycle memory (session-start injection, auto-capture, compaction checkpointing, the autonomous wiki cycle), install the Claude Code plugin (see More options); the plugin also auto-registers the 3 upstream-integration tools when automatised-pipeline / prd-spec-generator are present (46 total).
Cortex's wiki is a self-curating per-project knowledge base, not a memory dump. Every project the registry knows is driven toward 42 canonical documentation scopes (product overview, architecture, services, API, data flow, operations, decisions, onboarding, security, testing, configuration … ), and every source file toward 13 canonical sections (Purpose · Public API · Dependencies · Callers · How it works · Invariants · What can go wrong · Tests · Sequence diagram · Flow diagram · Parameters · Request example · Response example).
What makes it autonomous — no cron, no daemon, no manual invocation:
- A
SessionStarthook spawns a backgroundconsolidatecycle every 6 hours (TTL stamp at~/.claude/methodology/.last_consolidate). The agent runs because you opened Claude Code, and stops when nothing is left to author. - A curation-gap detector + headless authoring worker. Each file-doc page declares its missing sections in frontmatter (
curation_gaps:); the worker drains them by invokingclaude -p(your existing credentials, no API key), which calls the codebase-intelligence MCP tools —codebase_context,codebase_impact,codebase_query— to ground each section in the real call graph before writing. - Missing-anchor authoring. When a project has no architecture / services / api / data-flow / operations / ADR / PRD page, the worker authors it from the source tree (structure + README + manifest +
CLAUDE.md), same grounding. - Drift detection. Pages whose cited source moved, whose mtime is stale (>60 days), or whose body is off-template are flagged and re-authored in place. Deletion is never the policy; visibility is — a yellow banner shows
⚠ Page N% curated — M sections still missingand exactly what belongs in each. - ADRs as task-records. Every completed task (≥1 commit at session end) auto-drafts an ADR with five mandatory sections (Entry / Mandatory / How / Result / Serves) from commit subjects + the session's memories; the worker refines it next cycle.
- Per-project dashboards at
wiki/_dashboards/<project>.mdshow slot-fill rate, file coverage %, open gaps, and the queue for the next cycle.
This isn't documentation you write — it's documentation Cortex authors and verifies for you, every 6 hours, until every project reaches full scope coverage and every source file has all 13 sections filled.
Every page is editable in place in a full scientific writing environment — the same markdown that feeds the memory pipeline, with a rendering layer on top that never steals your content into a proprietary format. Your .md files stay grep-able, diffable, and git-versioned.
- CodeMirror 6 split-pane editor — syntax-highlighted markdown on the left, fully-rendered article on the right, atomic round-trip to the
.mdfile on disk. - Structured frontmatter —
kind/domain/scope/status/authored_by/provenance/created/updated/last_reviewed. Real metadata: the coverage audit, dashboards, and wiki view all read it. [[wiki/path]]cross-references rendered as clickable links (bare slugs route to filtered search), with a backlinks footer.- Mermaid diagrams with a 🔍 lens — viewport-sized viewer with wheel-zoom, drag-pan, and keyboard shortcuts.
- LaTeX math via KaTeX, BibTeX citations (
[@friston2010]→(Friston 2010)with an auto APA bibliography), and figure / equation / table auto-numbering with cross-refs. - Pandoc export — one click to PDF (via LaTeX), TEX, DOCX, or HTML. Journal-submittable from the same source.
The wiki's editor, galaxy graph, and views render through the standalone cortex-viz MCP, which reads this same store read-only.
Cortex works with teams of specialized agents — and it uses one itself: the headless wiki worker is a Claude agent that drains the curation queue every six hours (see the autonomous wiki). Memory is shared across a team via Wegner's transactive-memory model (1987): teams store more than individuals because each member specializes.
- Specialization — each agent writes to its own
agent_topic. Engineer's debugging notes don't clutter tester's recall; the wiki worker writes toagent_topic=wiki-curationso its drafts stay out of interactive recall. - Coordination — decisions auto-protect and propagate. When engineer decides "use Redis over Memcached," every agent sees it at next session start. The ADRs the worker drafts at session end are the cross-agent shared memory.
- Directory — entity-based queries span all topics. "What do we know about the reranker?" returns results from engineer, tester, researcher, and the worker's drafts alike.
Works with any custom agents. See zetetic-team-subagents for a ready-made team of specialists, each with scoped memory.
Clean Architecture with strict dependency rules — inner layers never import outer layers.
| Layer | What lives here | Modules |
|---|---|---|
| shared/ | Pure utilities (text, hash, similarity, types) | 18 |
| core/ | Neuroscience + retrieval + wiki-curation logic | 177 |
| core/context_assembly/ | Structured context assembler + stage detector | 10 |
| infrastructure/ | SQLite + PostgreSQL stores, embeddings, file I/O, MCP client | 59 |
| handlers/ | MCP tools + consolidation cycles (43 MCP-exposed; 46 with upstream integrations) | 105 |
| hooks/ | Lifecycle automation (incl. autonomous consolidate spawn) | 9 registered |
| server/ | MCP tool registration + composition roots | — |
| observability/ | Prometheus text-format metrics | 2 |
Storage: SQLite by default (a single local file, zero setup) or PostgreSQL 15+ with pgvector (HNSW) and pg_trgm. Both back the same 43 tools and the same WRRF fusion of five signals — vector search, FTS, trigram, heat, recency. On PostgreSQL it runs server-side in PL/pgSQL stored procedures; on SQLite the equivalent fusion runs in-process (vector search included, as the live memory_stats has_vector_search flag confirms).
Concurrency (PostgreSQL): psycopg_pool.ConnectionPool with two latency classes — interactive_pool (min=2, max=8) for recall/remember/anchor, batch_pool (min=1, max=2) for consolidate/ingest. Tool handlers run on worker threads via asyncio.to_thread; per-tool admission semaphores bound fan-out. Heat is computed at read time by effective_heat(), so homeostatic maintenance writes one scalar per domain per run instead of N rows.
Configuration: select the backend with CORTEX_MEMORY_STORE_BACKEND (sqlite / postgresql / auto); set CORTEX_MEMORY_DATABASE_URL for the PostgreSQL path. All other parameters use the CORTEX_MEMORY_ prefix — see mcp_server/infrastructure/memory_config.py. Wiki cycle TTL is CORTEX_CONSOLIDATE_TTL_HOURS (default 6h).
Every benchmark headline above is backed by a per-mechanism ablation campaign — full n, single-seed, with code SHAs, dirty flags, manifests, and per-row JSON preserved:
- LongMemEval-S, 17 rows, n=500 —
docs/benchmarks/e1-v3-results.md. Per-mechanism deltas at the calibrated equilibrium + category-specialization analysis. - LoCoMo, 14 rows, n=1986 —
docs/benchmarks/e1-v3-locomo-results.md(pre-fix) anddocs/benchmarks/e1-v3-locomo-results-post-fix.md(post plasticity result-shape fix). Two-baseline design (NO_CONSOLIDATION / WITH_CONSOLIDATION).
The full per-mechanism evidence lives in the thermodynamic paper (§6.3); the BEAM decay dose-response (§6.4) documents a re-scoped negative result after a dirty-store confound was caught and traced. Thermodynamic Memory vs. Flat-Importance Stores (PDF, 34 pages) · Stage-Aware Context Assembly (PDF, 39 pages).
Runs 100% locally — MCP over stdio, the storage backend (SQLite file or PostgreSQL on localhost) never leaves your machine (the optional cortex-viz companion binds its server to 127.0.0.1). No data leaves your machine. SafeSkill scan: 94/100 (code 97, content 88 — docs/safeskill-report.json).
Cortex is local-first: your memories, conversations, and profiles stay on your machine — stored in a local SQLite database (~/.claude/methodology/memory.db) by default, or in a PostgreSQL database you control. Cortex sends no memories, content, or telemetry to the author, Anthropic, or any third party. The only outbound network activity is a one-time download of open-source embedding/reranking models from Hugging Face (model files only), plus any integrations you explicitly configure. Full policy: PRIVACY.md.
- Issues & bug reports: GitHub Issues
- Security disclosures: see SECURITY.md
- Contact: admin@ai-architect.tools
pytest # 3,000+ tests
ruff check . # Lint
ruff format --check . # FormatMIT
The paper PDFs on main are the canonical artefacts (arXiv IDs forthcoming, endorsement in progress):
@software{cortex2026,
title={Cortex: Persistent Memory for Claude Code},
author={Deust, Clement},
year={2026},
url={https://github.com/cdeust/Cortex}
}
@unpublished{deust2026thermodynamic,
title={Thermodynamic Memory vs. Flat-Importance Stores:
Why Long-Term Retrieval Collapses Without Decay},
author={Deust, Clement},
year={2026},
note={arXiv ID forthcoming, endorsement in progress},
url={https://github.com/cdeust/Cortex/blob/main/docs/arxiv-thermodynamic/main.pdf}
}
@unpublished{deust2026context,
title={Stage-Aware Context Assembly for Long-Context Memory Retrieval},
author={Deust, Clement},
year={2026},
note={arXiv ID forthcoming, endorsement in progress},
url={https://github.com/cdeust/Cortex/blob/main/docs/arxiv-context-assembly/main.pdf}
}