total-agent-memory

The only memory layer that learns how you work — not just what you said. Persistent, local memory for AI coding agents: Claude Code, Codex CLI, Cursor, any MCP client. Temporal knowledge graph · procedural memory · AST codebase ingest · cross-project analogy · 3D WebGL visualization.

Why this, not mem0 / Letta / Zep / Supermemory / Cognee? → docs/vs-competitors.md

v11.0 — production memory engine

v11.0 = production memory engine: fast deterministic memory core + async AI enrichment layer. Default mode is fast: zero LLM, zero Ollama, zero network in the save/search/recall hot path.

The codebase is now split into two layers:

src/memory_core/* — deterministic facade modules (storage, embeddings, vector_store, classifier, chunker, dedup, cache, graph_links, telemetry, health, embedding_spaces). No LLM imports allowed. Enforced by tests/test_no_llm_hot_path.py.
src/ai_layer/* — every LLM-touching path (enrichment_worker, summarizer, keyword_extractor, question_generator, relation_extractor, contradiction_detector, reflection, self_improve, plus thin shims for quality_gate / coref_resolver / reranker / query_rewriter). Off-limits to memory_core.

Architecture details and full hot-path audit: docs/v11/audit.md.

Modes

MEMORY_MODE selects the runtime profile. Default is fast.

Mode	Hot-path LLM	Async enrichment	Reranker	Embed fallback	Use when
`ultrafast`	off	off	off	FastEmbed only (vector index off, FTS-only)	Throughput stress / CI
`fast` (default)	off	off	off	FastEmbed only, Ollama fallback gated	Production coding-agent loop
`balanced`	off (sync)	on	off	FastEmbed only	You want LLM-derived facets, but never on the critical path
`deep`	on (sync)	on	on (when `rerank=true`)	FastEmbed → Ollama ladder	v10.5 behaviour: quality gate / contradiction / coref / HyDE inline

deep mode reproduces v10.5.0 defaults exactly. Set MEMORY_MODE=deep if you depended on synchronous quality_gate, contradiction_detector, or coref. balanced keeps the same ergonomics but moves enrichment off-thread.

Migration from v10.5: docs/v11/MIGRATION-FROM-V10.md.

v11.0 hot-path benchmark

Warm, in-memory SQLite, MacBook M-series, MEMORY_MODE=fast, MEMORY_ALLOW_OLLAMA_IN_HOT_PATH=false:

metric	p50	p95	p99
`save_fast`	6.5	9.0	27.8
`save_fast` cached	0.3	0.4	1.1
`search_fast`	3.7	4.0	6.2
`cached_search`	0.0	0.0	0.0

llm_calls = 0, network_calls = 0 across the entire hot path. Reproduce: bin/memory-bench. CI gate: bin/memory-perf-gate. Raw artifact: docs/v11/benchmark.md.

v10.5 → v11.0 — same workload, same script

The v10.5 native bench (benchmarks/v10_5_latency.py) re-run on v11 fast against the recorded v10.5 baseline (benchmarks/results/v10_5_latency.json):

metric	v10.5 sync (with LLM)	v11.0 fast	speedup
save p95	2150.51 ms	8.51 ms	252×
save p99	2178.98 ms	11.09 ms	196×
recall p95	1424.26 ms	5.81 ms	245×
recall p99	1771.70 ms	6.75 ms	262×
LLM calls / save	2-4	0	gate
Network calls / save	1-3	0	gate

Even versus v10.5 without LLM (23.3 ms p95), v11 fast is 2.7× faster — the deterministic-only stages (quality_gate probe, contradiction candidate fetch, episodic event creation, project_wiki refresh) are now fully bypassed in fast mode and queued only when MEMORY_ENRICHMENT_ENABLED=true.

Recall quality is preserved: LongMemEval R@5 = 100% on a 30-question sample; hybrid retrieval (FTS5 + dense + RRF + base graph) is identical to v10.5 except for HyDE / analyze_query LLM expansion which is opt-in via MEMORY_MODE=deep. See docs/v11/benchmark.md for the full table including LoCoMo and per-space embedding load characteristics.

New MCP tools in v11.0

memory_save_fast · memory_search_fast · memory_explain_search · memory_warmup · memory_perf_report · memory_rebuild_fts · memory_rebuild_embeddings · memory_eval_locomo · memory_eval_recall · memory_eval_temporal · memory_eval_entity_consistency · memory_eval_contradictions · memory_eval_long_context

All previous tool names (memory_save, memory_recall, ...) continue to work unchanged.

Multi-embedding-space contract

Every vector row now records embedding_provider / embedding_model / embedding_dimension / embedding_space / content_type / language. Spaces: text / code / log / config. Single Chroma backend; per-space model swap is one env flip:

MEMORY_TEXT_EMBED_MODEL=sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
MEMORY_CODE_EMBED_MODEL=jinaai/jina-embeddings-v2-base-code   # optional
MEMORY_LOG_EMBED_MODEL=                                       # falls back to TEXT
MEMORY_CONFIG_EMBED_MODEL=                                    # falls back to TEXT

Old chunks stay searchable in their space; new chunks pick up the swapped model. Backfill one space at a time via memory_rebuild_embeddings.

v10.x sections below are preserved as legacy v10.5 behaviour — still available via MEMORY_MODE=deep. The numbers, screenshots, and benchmark blocks dated 2026-04-19 / 2026-04-25 / 2026-04-27 (v10) describe the deep-mode pipeline. v11 replaces defaults, not capabilities.

v11.0 — production memory engine
The problem it solves
60-second demo
Benchmarks — how it compares
Competitor comparison
What you get
Architecture
Install
Quick start
CLI: lookup-memory for sub-agents
MCP tools reference
TypeScript SDK
Dashboard
Update
Upgrading from v8.x to v9.0
Upgrading from v7.x to v8.0
Ollama setup
Configuration
Performance tuning
Roadmap
Support the project
Philosophy & license

The problem it solves

AI coding agents have amnesia. Every new Claude Code / Codex / Cursor session starts from zero. Yesterday's architectural decisions, bug fixes, stack choices, and hard-won lessons vanish the moment you close the terminal. You re-explain the same things, re-discover the same solutions, paste the same context into every new chat.

total-agent-memory gives the agent a persistent brain — on your machine, not in someone else's cloud.

Every decision, solution, error, fact, file change, and session summary is:

Captured — explicitly via memory_save or implicitly via hooks on file edits / bash errors / session end
Linked — automatically extracted into a knowledge graph (entities, relations, temporal facts)
Searchable — 6-stage hybrid retrieval (BM25 + dense + graph + CrossEncoder + MMR + RRF fusion), 96.2% R@5 on public LongMemEval
Private — 100% local. SQLite + FastEmbed + optional Ollama. No data leaves your machine.

60-second demo

You:     "remember we picked pgvector over ChromaDB because of multi-tenant RLS"
Claude:  ✓ memory_save(type=decision, content="Chose pgvector over ChromaDB",
                       context="WHY: single Postgres, per-tenant RLS")

[3 days later, different session, possibly different project directory:]

You:     "why did we pick pgvector again?"
Claude:  ✓ memory_recall(query="vector database choice")
         → "Chose pgvector over ChromaDB for multi-tenant RLS. Single DB
            instance, row-level security per tenant."

It's not just retrieval. It's procedural too:

You:     "migrate auth middleware to JWT-only session tokens"
Claude:  ✓ workflow_predict(task_description="migrate auth middleware...")
         → confidence 0.82, predicted steps:
             1. read src/auth/middleware.go + tests
             2. update session fixtures in tests/
             3. run migration 0042
             4. regenerate OpenAPI spec
           similar past: wf#118 (success), wf#93 (success)

Benchmarks — how it compares

Public LongMemEval benchmark (xiaowu0162/longmemeval-cleaned, 470 questions, the dataset everyone publishes against):

                   R@5 (recall_any) on public LongMemEval
                   ─────────────────────────────────────────
  100% ─┤
        │
  96.2% ┤  ████  ← total-agent-memory v7.0  (LOCAL, 38.8 ms, MIT)
  95.0% ┤  ████  ← Mastra "Observational"    (cloud)
        │  ████
        │  ████
  85.4% ┤  ████  ← Supermemory                (cloud, $0.01/1k tok)
        │  ████
        │  ████
        │  ████
   80%  ┤  ████
        └──────────────────────────────────────────

Reproducible: evals/longmemeval-2026-04-17.json · Runner: benchmarks/longmemeval_bench.py

Per-question-type breakdown (R@5 recall_any)

Question type	Count	Our R@5
knowledge-update	72	100.0%
single-session-user	64	100.0%
multi-session	121	96.7%
single-session-assistant	56	96.4%
temporal-reasoning	127	95.3% ← bi-temporal KG pays off
single-session-preference	30	80.0% ← weakest spot
TOTAL	470	96.2%

LoCoMo benchmark (new in v9)

Public LoCoMo benchmark (snap-research/locomo, 1986 QA across 10 long-running conversations, the dataset Mem0 / Memobase / Zep / MemMachine publish against):

              LoCoMo Acc (overall, no adversarial)
              ─────────────────────────────────────
  85% ─┤  ████  ← MemMachine        (commercial)
       │  ████
  80%  ┤  ████
       │  ████
  75%  ┤  ████  ← Memobase
       │  ████  ← Zep / Graphiti
       │  ████
  70%  ┤  ████
       │  ████
  67%  ┤  ████  ← Mem0
       │  ████
       │  ████  ← total-agent-memory v9.0  (LOCAL, MIT, gpt-4o-mini)
  60%  ┤  ████
  59%  ┤  ████  ← total-agent-memory (0.596)
       │  ████  ← LangMem (0.581)
  55%  ┤  ████
       └──────────────────────────────────────────

Rank	System	Overall (no adv)	License
1	MemMachine	0.849	Commercial
2	Memobase	0.758	Apache-2.0
3	Zep / Graphiti	0.751	Apache-2.0
4	Mem0	0.669	Apache-2.0
5	total-agent-memory v9.0	0.596	MIT
6	LangMem	0.581	MIT

Per-category breakdown (v9.0, gpt-4o-mini gen + judge):

Category	N	Acc	R@5
1 — single-hop	282	0.443	0.514
2 — temporal	321	0.564	0.717
3 — multi-hop	96	0.490	0.385
4 — open-domain	841	0.661	0.601
5 — adversarial	446	0.998 ← we lead	0.421
Overall (no adv)	1540	0.596	0.622

We lead on adversarial (0.998 vs Memobase 0.90) thanks to judge-weighted ensemble + abstain logic. Top-3 leaders win on cat 1/2 via subject-aware profile retrieval — that's our v10 target.

Reproducible: benchmarks/results/v9_diag_v1_*.json · Runner: benchmarks/locomo_bench_llm.py (15 ablation flags). Cost on gpt-4o-mini: ~$5 for full 1986 QA run with ensemble=3.

Latency profile

  p50 (warm)   ▌ 0.065 ms
  p95 (warm)   ▌▌ 2.97 ms
  LongMemEval  ▌▌▌▌▌ 38.8 ms/query   ← includes embedding + CrossEncoder rerank
  p50 (cold)   ▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌▌ 1333 ms  ← first query after process start

Warm / cold reproducible from evals/results-2026-04-17.json.

Competitor comparison

We're not replacing chatbot memory — we're occupying the coding-agent + MCP + local niche.

	mem0	Letta	Zep	Supermemory	Cognee	LangMem	total-agent-memory
Funding / status	$24M YC	$10M seed	$12M seed	$2.6M seed	$7.5M seed	in LangChain	self-funded OSS
Runs 100% local	🟡	✅	🟡	❌	🟡	🟡	✅
MCP-native	via SDK	❌	🟡 Graphiti	🟡	❌	❌	✅ 60+ tools
Knowledge graph	🔒 $249/mo	❌	✅	✅	✅	❌	✅
Temporal facts (`kg_at`)	❌	❌	✅	❌	🟡	❌	✅
Procedural memory	❌	❌	❌	❌	❌	🟡	✅ `workflow_predict`
Cross-project analogy	❌	❌	❌	❌	❌	❌	✅ `analogize`
Self-improving rules	❌	❌	❌	❌	🟡	❌	✅ `learn_error`
AST codebase ingest	❌	❌	❌	❌	🟡	❌	✅ tree-sitter 9 lang
Pre-edit risk warnings	❌	❌	❌	❌	❌	❌	✅ `file_context`
3D WebGL graph viewer	❌	❌	🟡	✅	❌	❌	✅
Price for graph features	$249/mo	free	cloud	usage	free	free	free

Full side-by-side with pricing, latency, accuracy, "when to pick each" → docs/vs-competitors.md.

What you get

Eight capabilities nobody else ships

Capability	Tool	One-liner
🧠 Procedural memory	`workflow_predict` / `workflow_track`	"How did I solve this last time?" — predicts steps with confidence
🔗 Cross-project analogy	`analogize`	"Was there something like this in another repo?" — Jaccard + Dempster-Shafer
⚠️ Pre-edit risk warnings	`file_context`	Surfaces past errors / hot spots on the file you're about to edit
🛡 Self-improving rules	`learn_error` + `self_rules_context`	Bash failures → patterns → auto-consolidated behavioral rules at N≥3
🕰 Temporal facts	`kg_add_fact` / `kg_at`	Append-only KG with `valid_from`/`valid_to` — query what was true at any point
🎯 Task workflow phases	`classify_task` / `phase_transition`	Automatic L1-L4 complexity classification, state machine across van/plan/creative/build/reflect/archive
🧩 Structured decisions	`save_decision`	Options + criteria matrix + rationale + discarded → searchable decision records with per-criterion embeddings
💸 Token-efficient retrieval	`memory_recall(mode="index")` + `memory_get`	3-layer workflow: compact IDs → timeline → batched full fetch. ~83% token saving on typical queries

Plus the basics done well

6-stage hybrid retrieval (BM25 + dense + fuzzy + graph + CrossEncoder + MMR, RRF fusion) — 96.2% R@5 public
Multi-representation embeddings — each record embedded as raw + summary + keywords + questions + compressed
AST codebase ingest — tree-sitter across 9 languages (Python, TS/JS, Go, Rust, Java, C/C++, Ruby, C#)
Auto-reflection pipeline — memory_save → LaunchAgent file-watch → graph edges appear ~30 s later
rtk-style content filters — strip noise from pytest / cargo / git / docker logs while preserving URLs, paths, code
3D WebGL knowledge graph viewer — 3,500+ nodes, 120,000+ edges, click-to-focus, filters
Hive plot & adjacency matrix — alternate graph views sorted by node type
A2A protocol — memory shared between multiple agents (backend + frontend + mobile in a team)
design-explore skill — drop-in Claude Code skill that walks L3-L4 tasks through options → criteria matrix → save_decision before code (see examples/skills/design-explore/SKILL.md)
<private>...</private> inline redaction in any saved content
Cloud LLM/embed providers with per-phase routing (OpenAI / Anthropic / OpenRouter / Together / Groq / Cohere / any OpenAI-compat)
activeContext.md Obsidian projection for human-readable session state
Phase-scoped rules (self_rules_context(phase="build")) — ~70% token reduction

Architecture

                  ┌─────────────────────────────────────────────────┐
                  │             Your AI coding agent                │
                  │   (Claude Code · Codex CLI · Cursor · any MCP)  │
                  └──────────────────────┬──────────────────────────┘
                                         │ MCP (stdio or HTTP)
                                         │ 60+ tools
                  ┌──────────────────────▼──────────────────────────┐
                  │            total-agent-memory server             │
                  │    ┌──────────────┐  ┌────────────────────┐     │
                  │    │ memory_save  │  │  memory_recall      │     │
                  │    │ memory_upd   │  │  6-stage pipeline:  │     │
                  │    │ kg_add_fact  │  │  BM25  (FTS5)       │     │
                  │    │ learn_error  │  │  + dense (FastEmbed)│     │
                  │    │ file_context │  │  + fuzzy            │     │
                  │    │ workflow_*   │  │  + graph expansion  │     │
                  │    │ analogize    │  │  + CrossEncoder †   │     │
                  │    │ ingest_code  │  │  + MMR diversity †  │     │
                  │    └──────┬───────┘  │  → RRF fusion       │     │
                  │           │          └──────────┬──────────┘     │
                  └───────────┼─────────────────────┼────────────────┘
                              │                     │
                  ┌───────────▼─────────────────────▼────────────────┐
                  │                   Storage                         │
                  │  ┌────────────┐  ┌────────────┐  ┌─────────────┐ │
                  │  │  SQLite    │  │  FastEmbed │  │   Ollama    │ │
                  │  │  + FTS5    │  │  HNSW      │  │  (optional) │ │
                  │  │  + KG tbls │  │  binary-q  │  │  qwen2.5-7b │ │
                  │  └────────────┘  └────────────┘  └─────────────┘ │
                  └───────────────────────────────────────────────────┘
                              │
                              │ file-watch + debounce
                  ┌───────────▼────────────────────────────────────┐
                  │  Auto-reflection pipeline  (LaunchAgent)        │
                  │  triple_extraction → deep_enrichment → reprs   │
                  │  (async, 10s debounce, drains in background)   │
                  └─────────────────────────────────────────────────┘
                              │
                  ┌───────────▼─────────────────────────────────────┐
                  │  Dashboard (localhost:37737)                     │
                  │   /           - stats, savings, queue depths   │
                  │   /graph/live - 3D WebGL force-graph           │
                  │   /graph/hive - D3 hive plot                   │
                  │   /graph/matrix - adjacency matrix             │
                  └─────────────────────────────────────────────────┘

  † CrossEncoder + MMR are on-demand via `rerank=true` / `diverse=true`

Install

Two paths. Same 60+ tools, same dashboard, different deployment shapes.

IDE matrix (v10.5)

The same MCP server, same tools, same protocol — different installation locations and hook wiring per IDE. The installer (install.sh --ide <name>) automates all of it.

IDE	Skill API	Hook API	Sub-agents	Install command
Claude Code	✅	✅ full	✅	`./install.sh --ide claude-code`
Codex CLI	✅	✅	❌	`./install.sh --ide codex`
Cursor	rules-pane	❌	composer	`./install.sh --ide cursor`
Cline (VS Code)	`.clinerules/`	❌	❌	`./install.sh --ide cline`
Continue	rules file	❌	❌	`./install.sh --ide continue`
Aider	`.aider.conf.yml` read	❌ ¹	❌	`./install.sh --ide aider`
Windsurf	`.windsurfrules`	❌	cascade	`./install.sh --ide windsurf`
Gemini CLI	`.gemini/rules/`	⚠️ partial	❌	`./install.sh --ide gemini-cli`
OpenCode	`.opencode/skills/`	✅	custom	`./install.sh --ide opencode`

¹ Aider has no MCP yet — the bridge is via lookup_memory.sh / save_memory.sh shell scripts.

Full per-IDE setup, manual fallbacks, and template snippets: skills/memory-protocol/references/ide-setup.md.

Platform matrix

OS	Command	Background services
macOS 10.15+	`./install.sh --ide claude-code`	LaunchAgents (`launchctl`)
Linux (Ubuntu 22.04+, Debian 12+, Fedora 38+)	`./install.sh --ide claude-code`	systemd `--user`
WSL2 (Windows 11 + Ubuntu/Debian)	`./install.sh --ide claude-code`	systemd `--user` — requires `/etc/wsl.conf` with `[boot] systemd=true`; otherwise falls back to shell-loop autostart
Windows 10/11 native	`.\install.ps1 -Ide claude-code`	Task Scheduler

Full per-platform walkthrough, WSL2 Windows-host-vs-WSL IDE nuances, the wsl -e MCP-command pattern, IDE coverage matrix, and uninstall/diagnostic flows: docs/installation.md.

Path A — native (macOS / Linux / WSL2)

git clone https://github.com/vbcherepanov/claude-total-memory.git ~/claude-memory-server
cd ~/claude-memory-server
bash install.sh --ide claude-code   # or: cursor | gemini-cli | opencode | codex

The installer:

Clones + creates ~/claude-memory-server/.venv/
Installs deps from requirements.txt and requirements-dev.txt
Pre-downloads the FastEmbed multilingual MiniLM model
Registers the MCP server via claude mcp add-json memory ... (stored in ~/.claude.json, the canonical store Claude Code actually reads)
Copies all hooks (session-*, user-prompt-submit.sh, post-tool-use.sh, pre-edit.sh, on-bash-error.sh, etc.) into ~/.claude/hooks/ and registers them in ~/.claude/settings.json
Grants permissions.allow for 20+ mcp__memory__* tools so hook-driven calls don't prompt for confirmation
Installs background services for the current OS:
- macOS — 4 LaunchAgents (reflection, orphan-backfill, check-updates, dashboard) under ~/Library/LaunchAgents/
- Linux / WSL2 — 7 systemd --user units (*.service, *.timer, *.path) under ~/.config/systemd/user/; gracefully degrades if systemd --user is unavailable (WSL without /etc/wsl.conf)
Applies all migrations to a fresh memory.db
Starts the dashboard at http://127.0.0.1:37737

Restart Claude Code → /mcp → memory should show Connected with 60+ tools.

Path A — native (Windows 10/11)

git clone https://github.com/vbcherepanov/claude-total-memory.git $HOME\claude-memory-server
cd $HOME\claude-memory-server
powershell -ExecutionPolicy Bypass -File install.ps1 -Ide claude-code

Same 9 steps as Unix, but:

MCP config path is %USERPROFILE%\.claude\settings.json (or .cursor\mcp.json, etc.)
Hooks copied to %USERPROFILE%\.claude\hooks\ — .ps1 versions (auto-capture, memory-trigger, user-prompt-submit, post-tool-use, pre-edit, on-bash-error, session-start/end, on-stop, codex-notify)
Background services via Task Scheduler:
- total-agent-memory-reflection — every 5 min (no native FileSystemWatcher equivalent)
- total-agent-memory-orphan-backfill — daily 00:00 + 6h repetition
- total-agent-memory-check-updates — weekly Mon 09:00
- ClaudeTotalMemoryDashboard — AtLogon

Uninstall

All installers preserve ~/.claude-memory/memory.db and your config files; only services + hook registrations are removed.

./install.sh --uninstall          # macOS/Linux/WSL2 — removes LaunchAgents OR systemd units
.\install.ps1 -Uninstall          # Windows — unregisters Scheduled Tasks + cleans settings.json

Diagnose

One-shot health check — prints ✓/✗ for each subsystem (OS detect, venv, MCP import, services, dashboard HTTP, Ollama, DB migrations):

bash scripts/diagnose.sh          # macOS / Linux / WSL2
.\scripts\diagnose.ps1            # Windows

Exit code 0 = all green, 1 = something broken.

Path B — Docker (everything containerized, cross-platform)

git clone https://github.com/vbcherepanov/claude-total-memory.git
cd claude-total-memory
bash install-docker.sh --with-compose

Brings up 5 services:

Service	Role	Exposed
`mcp`	MCP server (HTTP transport)	`127.0.0.1:3737/mcp`
`dashboard`	Web UI	`127.0.0.1:37737`
`ollama`	Local LLM runtime	`127.0.0.1:11434`
`reflection`	File-watch queue drainer	internal
`scheduler`	Ofelia cron (backfill + update check)	internal

First run pulls qwen2.5-coder:7b (~4.7 GB) + nomic-embed-text (~275 MB) — 5–10 min cold start.

GPU note: Docker Desktop on macOS doesn't forward Metal. Native install is faster on Mac. On Linux with NVIDIA Container Toolkit, uncomment the deploy.resources.reservations.devices block in docker-compose.yml.

Verify (both paths)

memory_save(content="install works", type="fact")
memory_stats()

Open http://127.0.0.1:37737/ — dashboard, knowledge graph, token savings.

Quick start

v11 default is MEMORY_MODE=fast. No LLM, no Ollama, no network in the save/search/recall hot path. To restore v10.5 synchronous-LLM behaviour set export MEMORY_MODE=deep. Mode switching: LAUNCH.md § Tuning.

Once installed, in any Claude Code / Codex CLI / Cursor session:

1. Resume where you left off (auto on session start, but you can also invoke)

session_init(project="my-api")
→ {summary: "yesterday: migrated auth middleware to JWT",
   next_steps: ["update OpenAPI spec", "notify frontend team"],
   pitfalls: ["don't revert migration 0042 — dev DB already migrated"]}

2. Save a decision (agent does this automatically after hooks are registered)

memory_save(
  type="decision",
  content="Chose pgvector over ChromaDB for multi-tenant RLS",
  context="WHY: single Postgres instance, per-tenant row-level security",
  project="my-api",
  tags=["database", "multi-tenant"],
)

3. Recall across sessions / projects

memory_recall(query="vector database choice", project="my-api", limit=5)
→ RRF-fused results from 6 retrieval tiers

4. Predict approach before starting a task

workflow_predict(task_description="migrate auth middleware to JWT-only")
→ {confidence: 0.82, predicted_steps: [...], similar_past: [...]}

5. Check a file's risk before editing (auto via hook, also manual)

file_context(path="/Users/me/my-api/src/auth/middleware.go")
→ {risk_score: 0.71, warnings: ["last 3 edits caused test failures in ..."], hot_spots: [...]}

6. Get full stats

memory_stats()
→ {sessions: 515, knowledge: {active: 1859, ...}, storage_mb: 119.5, ...}

CLI: `lookup-memory` for sub-agents

New in v9. Bash-friendly memory search for sub-agent workflows where launching the full MCP server would be overkill (e.g. Bash(lookup-memory "fix slow Wave query") from inside a Claude Code agent prompt).

Two equivalent commands ship with the package (registered as [project.scripts] entries — installed automatically by ./install.sh or ./update.sh):

lookup-memory "Caroline researched"          # human-readable bullets
ctm-lookup "Caroline researched"             # alias

lookup-memory --project myproj --limit 5 "auth flow"
lookup-memory --type solution --tag reusable "fix bug"
lookup-memory --json "claude code hooks"     # structured stdout for piping

How it works: opens the same $CLAUDE_MEMORY_DIR/memory.db the running MCP server uses → BM25 ranking via FTS5 → falls back to LIKE on older DBs. Zero deps beyond the package. No Ollama, no rag_chat.py, no ChromaDB required for the CLI path. Works on macOS, Linux, Windows.

$ lookup-memory --project locomo_0 --limit 2 "adoption"
1. [synthesized_fact|locomo_0] Caroline is researching adoption agencies.
2. [synthesized_fact|locomo_0] Melanie congratulates Caroline on her adoption.

Why two names? lookup-memory matches the legacy bash script that older docs and sub-agent prompts reference (~/claude-memory-server/ollama/lookup_memory.sh). ctm-lookup is the project-prefixed canonical form. Both call into claude_total_memory.lookup:main.

Migration note: v7/v8 docs that pointed at ~/claude-memory-server/ollama/lookup_memory.sh should be updated — the bash version still works for users with a manual install, but ./install.sh / ./update.sh clients on v9+ now get lookup-memory on PATH directly via the package's [project.scripts] entry.

MCP tools reference (60+ tools)

Tool categories

Core retrieval (9): memory_save, memory_recall, memory_get, memory_update, memory_delete, memory_history, memory_extract_session, memory_relate, memory_search_by_tag

Knowledge graph (8): kg_add_fact, kg_invalidate_fact, kg_at, kg_timeline, memory_graph, memory_graph_index, memory_graph_stats, memory_concepts

Episodic / session (6): memory_episode_save, memory_episode_recall, session_init, session_end, memory_timeline, memory_history

Procedural / workflows (4): workflow_learn, workflow_predict, workflow_track, classify_task

Task phases (4, v8.0): task_create, phase_transition, task_phases_list, complete_task

Decisions (1, v8.0): save_decision

Intents (3, v8.0): save_intent, list_intents, search_intents

Self-improvement (5): self_rules, self_rules_context, self_insight, self_patterns, self_error_log, rule_set_phase (v8.0)

Pre-edit guard / error learning (3): file_context, learn_error, self_error_log

Analogy / cross-project (2): analogize, ingest_codebase

Reflection / consolidation (4): memory_reflect_now, memory_consolidate, memory_forget, memory_observe

Stats / export (5): memory_stats, memory_export, memory_self_assess, memory_context_build, benchmark

Skills (3): memory_skill_get, memory_skill_update, file_context

Total: 60+ tools. Each is documented below with input schema and example.

Token-efficient 3-layer workflow

When you only know the topic but not which records matter, use progressive disclosure:

Index — memory_recall(query="auth refactor", mode="index", limit=20) → ~2 KB of {id, title, score, type, project, created_at} per hit. No content, no cognitive expansion.
Timeline — memory_recall(query="auth refactor", mode="timeline", limit=5, neighbors=2) → top-K hits padded with ±neighbours from the same session, sorted chronologically.
Fetch — memory_get(ids=[3622, 3606]) → full content for ONLY the IDs you chose (max 50 per call, detail="summary" truncates to 150 chars).

Typical saving: 80-90 %% fewer tokens vs memory_recall(detail="full", limit=20) when you end up using 2-3 of the 20 hits.

Core memory (15)

memory_recall · memory_get · memory_save · memory_update · memory_delete · memory_search_by_tag · memory_history · memory_timeline · memory_stats · memory_consolidate · memory_export · memory_forget · memory_relate · memory_extract_session · memory_observe

Knowledge graph (6)

memory_graph · memory_graph_index · memory_graph_stats · memory_concepts · memory_associate · memory_context_build

Episodic memory & skills (4)

memory_episode_save · memory_episode_recall · memory_skill_get · memory_skill_update

Reflection & self-improvement (7)

memory_reflect_now · memory_self_assess · self_error_log · self_insight · self_patterns · self_reflect · self_rules · self_rules_context

Temporal knowledge graph (4)

kg_add_fact · kg_invalidate_fact · kg_at · kg_timeline

Procedural memory (3)

workflow_learn · workflow_predict · workflow_track

Pre-flight guards & automation (8)

file_context (pre-edit risk scoring) · learn_error (auto-consolidating error capture) · session_init / session_end · ingest_codebase (AST, 9 languages) · analogize (cross-project analogy) · benchmark (regression gate)

Full JSON schemas: python -m claude_total_memory.cli tools --json or open the dashboard at localhost:37737/tools.

TypeScript SDK

For Node.js / browser / any TS project that isn't an MCP-native agent:

npm i @vbch/total-agent-memory-client

import { connectStdio } from "@vbch/total-agent-memory-client";

const memory = await connectStdio();

await memory.save({
  type: "decision",
  content: "Picked pgvector over ChromaDB for multi-tenant RLS",
  project: "my-api",
});

const hits = await memory.recallFlat({
  query: "vector database choice",
  project: "my-api",
  limit: 5,
});

Also ships LangChain adapter example, procedural-memory integration, and HTTP transport (for team / serverless setups).

Package repo: github.com/vbcherepanov/total-agent-memory-client

Dashboard (localhost:37737)

/ — live stats, queue depths, token savings from filters, representation coverage
/graph/live — 3D WebGL force-graph (Three.js), 3,500+ nodes / 120,000+ edges, click-to-focus, type filters, search
/graph/hive — D3 hive plot, nodes on radial axes by type
/graph/matrix — canvas adjacency matrix sorted by type
/knowledge — paginated knowledge browser, tag filters
/sessions — last 50 sessions with summaries + next steps
/errors — consolidated error patterns
/rules — active behavioral rules + fire counts
SSE-pill in header — live reconnect indicator

Screenshots → docs/screenshots/ (coming)

Update

cd ~/claude-memory-server
./update.sh

7 stages:

Pre-flight — disk check + DB snapshot (keeps last 7)
Source pull (git) or SHA-256-verified tarball
Deps — pip install -r requirements.txt -r requirements-dev.txt (only if hash changed)
Full pytest suite — aborts with snapshot if red
Schema migrations — python src/tools/version_status.py
LaunchAgent reload — reflection + backfill + update-check
MCP reconnect notification — in-app /mcp → memory → Reconnect

Manual equivalent:

cd ~/claude-memory-server
git pull
.venv/bin/pip install -r requirements.txt -r requirements-dev.txt
.venv/bin/python src/tools/version_status.py
.venv/bin/python -m pytest tests/
# in Claude Code: /mcp → memory → Reconnect

Upgrading from v8.x to v9.0

v9 is backward compatible. Existing v8 calls and DB schema work unchanged — v9 is an infra release that adds pluggable backends, a public CLI for sub-agents, and LoCoMo benchmark wiring. Nothing is forcibly enabled.

One-command upgrade

cd ~/claude-memory-server && ./update.sh
# pulls v9 src, installs new entry-points (ctm-lookup / lookup-memory),
# keeps existing memory.db untouched.

After upgrade, verify the new CLI is on PATH:

lookup-memory --limit 1 "any-query-from-your-history"

What's new (no action required)

lookup-memory / ctm-lookup CLI now installed alongside claude-total-memory MCP server (registered as [project.scripts] so ./install.sh and ./update.sh put them on PATH automatically). Sub-agent prompts that reference the legacy ~/claude-memory-server/ollama/lookup_memory.sh script keep working; new prompts should prefer the package-installed name.
Embedding backends stay on fastembed by default. Switch via V9_EMBED_BACKEND=openai-3-large (set MEMORY_EMBED_API_KEY) — costs ~$0.10/5k rows for re-embed, expected R@5 lift on conversational data.
Reranker backend stays on ce-marco by default. V9_RERANKER_BACKEND=bge-v2-m3 (or off) switches at runtime.
Subject-aware retrieval is opt-in via --subject-aware in benchmarks/locomo_bench_llm.py. Future: surface as MCP tool flag.
No migrations. Schema unchanged from v8.

What requires manual action

Re-embed (only if switching embedding model, otherwise skip):

python -m scripts.reembed --backend openai-3-large --confirm

Old bash sub-agent prompts that hardcode ~/claude-memory-server/ollama/lookup_memory.sh "query" will keep working. To ride the new package install, replace with lookup-memory "query".

Breaking changes

None. All v8 MCP tools, env vars, hooks, and DB tables behave identically.

Upgrading from v7.x to v8.0

v8.0 is backward compatible — your existing v7 installation keeps working unchanged. All new features are opt-in via MCP tool calls or env vars.

One-command upgrade

cd ~/claude-memory-server && ./update.sh
# Applies migrations 011-013 idempotently, restarts LaunchAgents, updates dependencies

Then restart Claude Code: /mcp restart memory.

What changes automatically

Migrations 011–013 apply on MCP startup (privacy_counters, task_phases, intents). Zero-downtime, idempotent.
Existing memory_save calls keep working — they now additionally strip <private>...</private> sections if present.
Existing memory_recall calls keep working — default mode is still "search". New mode="index" is opt-in.
Existing session_end calls keep working — auto_compress=False by default. Pass auto_compress=True to opt in.
Existing self_rules_context calls keep working — default returns all rules (no phase filter).

What requires manual setup

1. Cloud providers (only if you want to replace/augment Ollama):

export MEMORY_LLM_PROVIDER=openai       # or "anthropic"
export MEMORY_LLM_API_KEY=sk-...
export MEMORY_LLM_MODEL=gpt-4o-mini     # or "claude-haiku-4-5"

See Cloud providers for OpenRouter / per-phase routing / Cohere examples.

2. Install additional hooks (for UserPromptSubmit capture + citation):

./install.sh --ide claude-code   # re-run installer; it now registers user-prompt-submit.sh hook

The hook is additive — existing hooks keep working.

3. activeContext.md Obsidian integration (if you want markdown projection):

export MEMORY_ACTIVECONTEXT_VAULT=~/Documents/project/Projects   # default
# Disable: export MEMORY_ACTIVECONTEXT_DISABLE=1

Each session_end writes <vault>/<project>/activeContext.md.

Breaking changes

None. All v7 MCP tool signatures are preserved. New parameters are optional with safe defaults.

Embedding dimension note

If you switch to a cloud embedding provider (MEMORY_EMBED_PROVIDER=openai/cohere), the server will refuse to start if existing DB embeddings have a different dimension than the new provider returns. This is deliberate — it prevents silent data corruption.

Either:

Keep MEMORY_EMBED_PROVIDER=fastembed (default 384d) and only change the LLM provider, OR
Re-embed the DB: python src/tools/reembed.py --provider openai --model text-embedding-3-small

New MCP tools in v8.0

Quick reference — see full docs in MCP tools reference:

Tool	Purpose
`classify_task(description)`	Returns {level 1-4, suggested_phases, estimated_tokens}
`task_create(task_id, description)`	Starts state machine in "van" phase
`phase_transition(task_id, new_phase, artifacts?)`	Moves task through van/plan/creative/build/reflect/archive
`task_phases_list(task_id)`	Chronological phase history
`save_decision(title, options, criteria_matrix, selected, rationale, ...)`	Structured decision with per-criterion indexing
`memory_get(ids, detail)`	Batched full-content fetch for IDs from `memory_recall(mode="index")`
`save_intent` / `list_intents` / `search_intents`	UserPromptSubmit-captured prompts
`rule_set_phase(rule_id, phase)`	Tag a rule for phase-scoped loading

Extended tools:

memory_recall(mode="index"|"timeline", decisions_only=False, ...) — 3-layer token-efficient workflow
session_end(auto_compress=True, transcript=None, ...) — LLM-generated summary
self_rules_context(phase="build"|"plan"|...) — phase filter
save_knowledge(...) — now strips <private>...</private> sections automatically

Rollback plan

v8.0 doesn't remove any v7 functionality. If you hit an issue, you can:

Set env var to revert behaviour:

export MEMORY_LLM_PROVIDER=ollama           # revert to local LLM
export MEMORY_EMBED_PROVIDER=fastembed      # revert to local embeddings
export MEMORY_ACTIVECONTEXT_DISABLE=1       # disable markdown projection
export MEMORY_POST_TOOL_CAPTURE=0           # disable opt-in capture (default anyway)

Migrations 011/012/013 are additive (no DROP / ALTER on existing tables), so DB downgrade is not destructive — old code continues reading older tables.
Worst case: git checkout v7.0.0 && ./update.sh --skip-migrations.

Ollama setup (optional but recommended)

Without Ollama: works fully — raw content is saved, retrieval via BM25 + FastEmbed dense embeddings.

With Ollama: you also get LLM-generated summaries, keywords, question-forms, compressed representations, and deep enrichment (entities, intent, topics).

brew install ollama     # or: curl -fsSL https://ollama.com/install.sh | sh
ollama serve &
ollama pull qwen2.5-coder:7b        # default — best quality/speed on M-series
ollama pull nomic-embed-text        # optional, alternative embedder

Cloud providers (optional)

Use OpenAI, Anthropic, or any OpenAI-compat endpoint (OpenRouter, Together, Groq, DeepSeek, LM Studio, llama.cpp) instead of local Ollama.

OpenAI:

export MEMORY_LLM_PROVIDER=openai
export MEMORY_LLM_API_KEY=sk-...
export MEMORY_LLM_MODEL=gpt-4o-mini

Anthropic:

export MEMORY_LLM_PROVIDER=anthropic
export MEMORY_LLM_API_KEY=sk-ant-...
export MEMORY_LLM_MODEL=claude-haiku-4-5

OpenRouter (100+ models via one endpoint):

export MEMORY_LLM_PROVIDER=openai
export MEMORY_LLM_API_BASE=https://openrouter.ai/api/v1
export MEMORY_LLM_API_KEY=sk-or-...
export MEMORY_LLM_MODEL=anthropic/claude-haiku-4.5

Per-phase routing (cheap model for bulk, quality for compression):

export MEMORY_TRIPLE_PROVIDER=openai
export MEMORY_TRIPLE_MODEL=gpt-4o-mini
export MEMORY_ENRICH_PROVIDER=anthropic
export MEMORY_ENRICH_MODEL=claude-haiku-4-5

Embeddings (dimension must match existing DB or re-embed required):

export MEMORY_EMBED_PROVIDER=openai
export MEMORY_EMBED_MODEL=text-embedding-3-small  # 1536d
# or Cohere:
export MEMORY_EMBED_PROVIDER=cohere
export MEMORY_EMBED_API_KEY=...

Model choice

Model	Size	Use case
`qwen2.5-coder:7b`	4.7 GB	default — best quality/speed ratio
`qwen2.5-coder:32b`	19 GB	highest quality, needs 32 GB+ RAM
`llama3.1:8b`	4.9 GB	general-purpose alternative
`phi3:mini`	2.3 GB	low-RAM machines

Configuration

Environment variables (all optional):

v11.0 — Memory mode + multi-embedding-space

Variable	Default	Purpose
`MEMORY_MODE`	`fast`	`ultrafast\|fast\|balanced\|deep`. Selects hot-path profile. See Modes.
`MEMORY_USE_LLM_IN_HOT_PATH`	`false`	Master switch for sync LLM stages in `save_knowledge` / `Recall.search`. `MEMORY_MODE=deep` flips this to `true`.
`MEMORY_ALLOW_OLLAMA_IN_HOT_PATH`	`false`	Re-enables the silent FastEmbed → Ollama fallback ladder when FastEmbed is unavailable.
`MEMORY_RERANK_ENABLED`	`false`	Honour caller's `rerank=true`. When `false`, CrossEncoder rerank is hard-disabled even if a tool call requests it.
`MEMORY_ENRICHMENT_ENABLED`	`false`	Run the async enrichment worker. Default-ON in `balanced` / `deep`.
`MEMORY_TEXT_EMBED_MODEL`	`sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`	Model for `embedding_space=text`.
`MEMORY_CODE_EMBED_MODEL`	empty → falls back to TEXT model	Model for `embedding_space=code`. The row still records `space=code` so a future swap is config-only.
`MEMORY_LOG_EMBED_MODEL`	empty → TEXT	Model for `embedding_space=log`.
`MEMORY_CONFIG_EMBED_MODEL`	empty → TEXT	Model for `embedding_space=config`.
`MEMORY_DEFAULT_EMBEDDING_SPACE`	`text`	Space for unclassified content.

v10 + earlier

Variable	Default	Purpose
`MEMORY_DB`	`~/.claude-memory/memory.db`	SQLite location
`MEMORY_LLM_ENABLED`	`auto`	`auto\|true\|false\|force` — LLM enrichment toggle
`MEMORY_LLM_MODEL`	`qwen2.5-coder:7b`	Ollama model for enrichment
`MEMORY_LLM_PROBE_TTL_SEC`	`60`	Cache TTL for Ollama availability probe
`MEMORY_LLM_TIMEOUT_SEC`	`60`	Global fallback timeout for Ollama requests (s)
`MEMORY_TRIPLE_TIMEOUT_SEC`	`30`	Timeout for deep triple extraction (s)
`MEMORY_ENRICH_TIMEOUT_SEC`	`45`	Timeout for deep enrichment (s)
`MEMORY_REPR_TIMEOUT_SEC`	`60`	Timeout for representation generation (s)
`MEMORY_TRIPLE_MAX_PREDICT`	`2048`	`num_predict` cap for triple extraction
`OLLAMA_URL`	`http://localhost:11434`	Ollama endpoint
`MEMORY_EMBED_MODE`	`fastembed`	`fastembed\|sentence-transformers\|ollama`
`DASHBOARD_PORT`	`37737`	HTTP dashboard port
`MEMORY_MCP_PORT`	`3737`	HTTP MCP transport port (Docker path)
`MEMORY_ASYNC_ENRICHMENT`	`false`	v10.1 — move quality gate / contradiction / entity dedup / episodic / wiki to a background worker. See Performance tuning
`MEMORY_ENRICH_TICK_SEC`	`0.1`	Worker tick interval (clamp `0.01..5`)
`MEMORY_ENRICH_BATCH`	`5`	Rows claimed per tick (clamp `1..50`)
`MEMORY_ENRICH_MAX_ATTEMPTS`	`3`	Retries before flipping a row to `failed`
`MEMORY_ENRICH_STALE_AFTER_SEC`	`60`	Seconds before a `processing` row is reclaimed (worker crash recovery)

CPU-only / WSL hosts: if Ollama keeps timing out, lower MEMORY_TRIPLE_MAX_PREDICT before raising timeouts. install-codex.sh writes conservative defaults automatically. For 30-40s save latency on WSL2 → set MEMORY_ASYNC_ENRICHMENT=true — see below.

Full config: see claude_total_memory/config.py.

Performance tuning

v11.0 fast-mode hot path (default)

When MEMORY_MODE=fast (default):

metric	p50	p95	p99
`save_fast`	6.2	8.9	11.4
`save_fast` cached	0.3	0.4	1.4
`search_fast`	3.4	4.7	6.0
`cached_search`	3.1	3.4	3.6

llm_calls=0, network_calls=0. Reproduce: ./bin/memory-bench. Regression gate: ./bin/memory-perf-gate. Architecture rationale and per-stage audit: docs/v11/audit.md. Raw bench artifact: docs/v11/benchmark.md.

If your numbers do not match the table, run ./bin/memory-bench --warmup first — cold FastEmbed import dominates the first call.

Legacy: v10.5 deep-mode `memory_save` latency

The synchronous v10 hot path runs five LLM-bound stages inline so a drop verdict can block the INSERT and a contradiction supersede commits in the same transaction. On macOS with a warm Ollama that's ~340 ms median; on a WSL2 box without GPU/CoreML each LLM round-trip can stretch the same call into 30–40 seconds.

v10.1 ships an opt-in inbox/outbox worker that moves the heavy stages out of band:

sync   : privacy → canonical_tags → INSERT → embed → enqueue → return
worker : quality_gate → entity_dedup_audit → contradiction → episodic → wiki

Enable it in your env:

export MEMORY_ASYNC_ENRICHMENT=true
# Optional knobs (defaults shown):
export MEMORY_ENRICH_TICK_SEC=0.1
export MEMORY_ENRICH_BATCH=5
export MEMORY_ENRICH_MAX_ATTEMPTS=3
export MEMORY_ENRICH_STALE_AFTER_SEC=60

Restart the MCP server. A background daemon thread now consumes enrichment_queue; you can watch it on the dashboard panel ⚡ v10.1 enrichment worker.

Bench v10.5 (10-record corpus × 2 rounds, with LLM stages on)

memory_save latency:

	min	p50	p95	p99	max	mean
sync (default)	17.5 ms	25.3 ms	2150.5 ms	2179.0 ms	2186.1 ms	348.0 ms
async (`MEMORY_ASYNC_ENRICHMENT=true`)	18.1 ms	22.3 ms	26.7 ms	27.4 ms	27.5 ms	22.7 ms

memory_recall latency: p50 ≈ 3-5 ms in both modes (steady state), with cold-cache p95 outliers on the first warmup hit.

p95 collapses 80× with async (2150 ms → 27 ms). On WSL2 with a slow Ollama, the same shape holds — sync p95 of 30-40 s becomes async p95 of ~300-1000 ms (LLM moves out of the hot path entirely).

Reproduce: ./.venv/bin/python benchmarks/v10_5_latency.py --rounds 2 --with-llm. Full report: benchmarks/v10_5_results.md.

Trade-off — soft drop semantic

When async is on, a quality_gate drop no longer prevents the INSERT (we already committed in the sync path). Instead the row is marked status='quality_dropped' after the worker scores it. memory_recall ignores that status (idx_knowledge_status_quality is added in migration 020). Audit history stays in quality_gate_log so nothing is lost.

If you need strict pre-INSERT gating (e.g. compliance), keep the default sync path.

Crash recovery

Rows stuck in processing longer than MEMORY_ENRICH_STALE_AFTER_SEC (default 60 s) are flipped back to pending automatically — covers worker process kills mid-stage. The pre-existing write_intents outbox still covers a crash before INSERT.

Roadmap

Shipped in v11.0 (2026-04-27) — production memory engine

✅ Default MEMORY_MODE=fast — zero LLM, zero Ollama, zero network in save/search/recall hot path. Set MEMORY_MODE=deep to restore v10.5 behaviour.
✅ Memory Core / AI Layer split — src/memory_core/* is deterministic; src/ai_layer/* owns every LLM-bound code path. Enforced by tests/test_no_llm_hot_path.py.
✅ 4 modes: ultrafast / fast / balanced / deep. Single env flag.
✅ Multi-embedding-space contract — every vector row records provider / model / dimension / space / content_type / language. Spaces: text / code / log / config. Single Chroma backend; per-space model swap is config-only.
✅ Embed fallback ladder gated — silent Ollama fallback in Store.embed requires MEMORY_ALLOW_OLLAMA_IN_HOT_PATH=true.
✅ New MCP tools: memory_save_fast, memory_search_fast, memory_explain_search, memory_warmup, memory_perf_report, memory_rebuild_fts, memory_rebuild_embeddings, memory_eval_locomo, memory_eval_recall, memory_eval_temporal, memory_eval_entity_consistency, memory_eval_contradictions, memory_eval_long_context.
✅ Migrations 021 (embedding_spaces) + 022 (embedding_cache_v11) — idempotent on next start.
✅ Benchmark suite: bin/memory-bench (artifact docs/v11/benchmark.md) + bin/memory-perf-gate for CI.

Shipped in v10.5 (2026-04-27)

✅ Universal memory-protocol skill — single canonical SKILL.md + 4 references (tool cheatsheet for all 60+ MCP tools, workflow recipes for 15 common situations, hooks reference, per-IDE setup) + 4 templates (Claude Code settings.json, Codex config.toml, Cursor .mdc, Cline .md). Same content for every IDE; only the wiring differs.
✅ install.sh --ide extended to 9 IDEs: claude-code, codex, cursor, cline, continue, aider, windsurf, gemini-cli, opencode. New helpers: register_mcp_cline / continue / aider / windsurf + _json_merge_mcp_nested for the dotted-key case (cline.mcpServers).
✅ Cross-platform hardening — all bash scripts pass bash -n under macOS bash 3.2 (default). Replaced ${var,,} lowercase bashism in update.sh with tr '[:upper:]' '[:lower:]'. Verified with shellcheck.
✅ Sub-agent memory protocol — universal header for any sub-agent (php-pro, golang-pro, vue-expert, etc.) with mandatory memory_recall before / memory_save after. Full template in skills/memory-protocol/references/subagent-protocol.md.
✅ v10.5 latency benchmark — benchmarks/v10_5_latency.py with apples-to-apples sync vs async comparison. Demonstrates 80× p95 reduction (2150 ms → 27 ms) when async is enabled with LLM stages on.

Shipped in v10.1 (2026-04-27)

✅ Async enrichment worker — opt-in MEMORY_ASYNC_ENRICHMENT=true moves quality gate / entity dedup / contradiction detector / episodic linking / wiki refresh to a background thread. Drops max save latency 5.4× on macOS, 60–100× on WSL2. See Performance tuning.
✅ enrichment_queue table with stale-processing recovery (rows stuck >60 s in processing flip back to pending).
✅ Dashboard panel for worker health: depth, throughput/min, p50/p95 ms per task, oldest pending age, recent failures.
✅ _binary_search ValueError fix — np.argpartition requires kth STRICTLY < N; tiny test projects (pool ≤ 50) used to silently break contradiction_log.
✅ coref_resolver RU→EN translation fix — prompt explicitly pins output language (Do NOT translate).

Shipped in v10.0 (2026-04-27)

✅ 10 Beever-Atlas-inspired features in one push: quality gate (Beever 6-Month Test), canonical tag vocabulary, importance boost in recall, opt-in coref resolution, contradiction auto-detection with supersede, write-intent outbox + reconciler, embedding-based entity dedup, episodic save events in the graph, smart query router (relational vs lexical), per-project Markdown wiki digest.
✅ 5 SQLite migrations (015–019) applied automatically on restart.
✅ 11 new env knobs, all with safe fail-open defaults.
✅ Tests: 971 → 1124 (+153).

Shipped in v9.0 (2026-04-25)

✅ lookup-memory / ctm-lookup CLI — bash entry-point for sub-agents, registered as [project.scripts] and installed by ./install.sh / ./update.sh (replaces manual ~/claude-memory-server/ollama/lookup_memory.sh)
✅ Pluggable embedding backends: openai-3-small, openai-3-large (3072d), bge-m3, e5-large, locomo-tuned-minilm (fine-tuned on user data)
✅ Pluggable reranker backends: ce-marco, bge-v2-m3, bge-large, off (env V9_RERANKER_BACKEND, hot-swap)
✅ Subject-aware retrieval — LLM extracts (subject, action) from question → SQL graph lookup → DIRECT FACTS prepended to context (LoCoMo cat 1/2 lift)
✅ Judge-weighted ensemble — category-aware scoring rubric + abstain logic for LoCoMo-style adversarial gold
✅ Fine-tune embedding pipeline (scripts/finetune_embedding.py) — mine triplets from your data, train on top of MiniLM via sentence-transformers
✅ Few-shot pair mining (scripts/mine_locomo_fewshot.py) — augment per-category prompts with held-in (Q,A) pairs
✅ Schema-specific graph extractor (closed canonical predicate vocabulary, optional)
✅ SSL fix for macOS Python.org installs — urllib requests now use certifi by default
✅ HTTP retry with exponential backoff for embedding providers (5xx/timeout)
✅ LoCoMo benchmark integration (benchmarks/locomo_bench_llm.py with 14 ablation flags)

Shipped in v8.0 (2026-04-19)

✅ Task workflow phases (L1-L4 classifier + 6-phase state machine)
✅ Structured save_decision with criteria matrix + multi-representation criterion indexing
✅ Cloud LLM/embed providers (OpenAI, Anthropic, Cohere, any OpenAI-compat)
✅ session_end(auto_compress=True) via LLM provider
✅ Progressive disclosure: memory_recall(mode="index") + memory_get(ids)
✅ activeContext.md Obsidian live-doc projection
✅ Phase-scoped rules via tag filter
✅ <private>...</private> inline redaction
✅ HTTP citation endpoints /api/knowledge/{id} + /api/session/{id}
✅ UserPromptSubmit + PostToolUse (opt-in) capture hooks
✅ Unified install.sh --ide {claude-code|cursor|gemini-cli|opencode|codex}

Planned (v8.1+)

Plugin marketplace publish (when Claude Code API opens)
has_llm() per-phase provider caching
GitHub Actions: install smoke tests + LongMemEval nightly

Under research

"Endless mode" — continuous session without hard boundaries (virtual sessions by idle >N hours)
MLX local LLM integration (A1 plan from memory #3583)
Speculative decoding for local path (+1.5-1.8× LLM speed)

Support the project

total-agent-memory is, and will always be, free and MIT-licensed. No paid tier, no gated features, no "enterprise edition". The benchmarks on this page are the entire product.

If it's saving you hours of context-pasting every week and you want to help keep development going — or just say thanks — a donation means a lot.

What your support funds

	Goal
☕ $5 — a coffee	One evening of focused OSS work
🍕 $25 — a pizza	A new MCP tool end-to-end (design, code, tests, docs)
🎧 $100 — a weekend	A major feature: e.g. the preference-tracking module that closes the 80% gap on LongMemEval
💎 $500+ — a sprint	A release cycle: new subsystem + migrations + docs + benchmark artifact

Non-monetary ways to help (equally appreciated)

⭐ Star the repo — GitHub discovery runs on this
🐦 Share benchmarks on X / HN / Reddit — reach matters more than donations
🐛 Open issues with repro cases — bug reports are pure gold
📝 Write a blog post about how you use it
🔧 Submit a PR — fixes, new tools, new integrations
🌍 Translate the README — first docs in RU / DE / JA / ZH very welcome
💬 Tell your team — peer recommendations convert 10× better than marketing

Commercial / consulting

Building something that would benefit from a custom integration, on-prem deployment, or team-shared memory? Email vbcherepanov@gmail.com — open to contract work and partnerships.
AI / dev-tools company whose roadmap overlaps? Same email — happy to talk.

Philosophy

MIT forever. No commercial-license switch, no VC money, no dark patterns. The memory layer belongs to the developers using it, not to a SaaS vendor.

Local-first is the product. If you want a cloud memory service, mem0 and Supermemory are great. If you want your data on your disk, untouched by anyone else — this.

Honest benchmarks. Every number on this page is reproducible from the artifacts in evals/ and the scripts in benchmarks/. If you can't reproduce a claim, open an issue — it's a bug.

Contributing

Open an issue before a large PR — saves everyone time.
pytest tests/ must stay green. Add tests for new tools.
Update evals/scenarios/*.json if you change retrieval behavior.
Docs-only / typo PRs welcome without discussion.

License

MIT — see LICENSE.

Built for coding agents. Runs on your machine. Free forever.
Compare to mem0 / Letta / Zep / Supermemory · Benchmark artifact · TypeScript SDK · Donate

Name		Name	Last commit message	Last commit date
Latest commit History 32 Commits
.github		.github
benchmarks		benchmarks
bin		bin
claude_total_memory		claude_total_memory
codex-skill		codex-skill
docker		docker
docs		docs
evals		evals
examples		examples
filters		filters
hooks		hooks
launchagents		launchagents
migrations		migrations
scripts		scripts
skills/memory-protocol		skills/memory-protocol
src		src
systemd		systemd
tests		tests
vocabularies		vocabularies
.codex		.codex
.dockerignore		.dockerignore
.env.example		.env.example
.gitignore		.gitignore
.last-requirements.sha256		.last-requirements.sha256
AGENTS.md.template		AGENTS.md.template
CHANGELOG.md		CHANGELOG.md
CLAUDE.md.template		CLAUDE.md.template
Dockerfile		Dockerfile
LAUNCH.md		LAUNCH.md
LICENSE		LICENSE
README.md		README.md
SYSTEM-OVERVIEW.md		SYSTEM-OVERVIEW.md
agent-rules.md.template		agent-rules.md.template
codex-global-rules.md.template		codex-global-rules.md.template
docker-compose.yml		docker-compose.yml
global-rules.md.template		global-rules.md.template
install-codex.ps1		install-codex.ps1
install-codex.sh		install-codex.sh
install-docker.sh		install-docker.sh
install.ps1		install.ps1
install.sh		install.sh
pyproject.toml		pyproject.toml
requirements-dev.txt		requirements-dev.txt
requirements.txt		requirements.txt
setup.sh		setup.sh
update.sh		update.sh

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total-agent-memory

v11.0 — production memory engine

Modes

v11.0 hot-path benchmark

v10.5 → v11.0 — same workload, same script

New MCP tools in v11.0

Multi-embedding-space contract

Table of contents

The problem it solves

60-second demo

Benchmarks — how it compares

Per-question-type breakdown (R@5 recall_any)

LoCoMo benchmark (new in v9)

Latency profile

Competitor comparison

What you get

Eight capabilities nobody else ships

Plus the basics done well

Architecture

Install

IDE matrix (v10.5)

Platform matrix

Path A — native (macOS / Linux / WSL2)

Path A — native (Windows 10/11)

Uninstall

Diagnose

Path B — Docker (everything containerized, cross-platform)

Verify (both paths)

Quick start

CLI: lookup-memory for sub-agents

MCP tools reference (60+ tools)

Tool categories

Token-efficient 3-layer workflow

TypeScript SDK

Dashboard (localhost:37737)

Update

Upgrading from v8.x to v9.0

One-command upgrade

What's new (no action required)

What requires manual action

Breaking changes

Upgrading from v7.x to v8.0

One-command upgrade

What changes automatically

What requires manual setup

Breaking changes

Embedding dimension note

New MCP tools in v8.0

Rollback plan

Ollama setup (optional but recommended)

Cloud providers (optional)

Model choice

Configuration

v11.0 — Memory mode + multi-embedding-space

v10 + earlier

Performance tuning

v11.0 fast-mode hot path (default)

Legacy: v10.5 deep-mode memory_save latency

Bench v10.5 (10-record corpus × 2 rounds, with LLM stages on)

Trade-off — soft drop semantic

Crash recovery

Roadmap

Shipped in v11.0 (2026-04-27) — production memory engine

Shipped in v10.5 (2026-04-27)

Shipped in v10.1 (2026-04-27)

Shipped in v10.0 (2026-04-27)

Shipped in v9.0 (2026-04-25)

Shipped in v8.0 (2026-04-19)

Planned (v8.1+)

Under research

Support the project

What your support funds

Non-monetary ways to help (equally appreciated)

Commercial / consulting

Philosophy

CLI: `lookup-memory` for sub-agents

Legacy: v10.5 deep-mode `memory_save` latency