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

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v13.0.0 — MCP 2026-07-28, and honest benchmarks (2026-08-27)

Upgrade if you installed after the MCP Python SDK went 2.0. The 2.x line dropped the @Server.list_tools() / @Server.call_tool() decorators this server was built on, and the dependency was floored at mcp[cli]>=1.0.0 — so every fresh pip / uvx / npx / brew / docker install resolved 2.x and died at import. Existing installs kept working only because their pinned 1.x never moved.

Protocol. Tools now register through whichever API the installed SDK exposes, and the server serves both protocol eras from one process: the stateless 2026-07-28 revision — tools/list, server/discover and tools/call with no initialize handshake, protocol metadata per request — alongside the legacy handshake for clients on older SDKs. JSON-answering tools return structuredContent, so clients stop re-parsing strings, and all 74 tools carry readOnlyHint / destructiveHint / idempotentHint annotations that clients use to decide what runs without a confirmation prompt.

Claude Code plugin. The MCP server, the memory-protocol skill and the seven capture hooks now install in one step:

/plugin marketplace add vbcherepanov/total-agent-memory
/plugin install total-agent-memory@vbcherepanov

LongMemEval now measures the product. The runner had its own self-contained BM25 / RRF / MMR / CrossEncoder stack, so the published 96.2% described an algorithm rather than this software. A new --modes store — now the default — ingests each haystack into a real Store and queries Recall.search. Re-measured: 95.1% R@5, 27.6 ms per query.

Benchmarks that no longer measure themselves. Recall.search bumps recall_count on every row it returns, and the scorer adds recall_boost = min(0.3, recall_count * 0.05). Spaced repetition is wanted in normal use and fatal for measurement: successive runs against one database scored 0.547 → 0.565 → 0.588 → 0.607 R@5 without a line of retrieval code changing. Both runners now pass record_usage=False, a clean run and a re-run are byte-identical, and every number below was re-measured on that basis. The LoCoMo runner had also been printing categories 2 and 3 under each other's labels.

BEAM (ICLR 2026) is now part of the suite — retrieval across its ten memory abilities at the 100K / 500K / 1M scales, graded against each probe's source_chat_ids with no LLM in the loop.

The server was carrying ~450 MB it never used. chromadb and sentence_transformers were imported at module scope, both are fallback paths, and the second pulls in torch — so every user paid for a stack that fastembed made unnecessary. Deferring them took import server from 558 MB to 116 MB and a serving process from 1367 MB to 909 MB. Reported by d.snezhinskiy. A failed fastembed init also stops being a single log line: it now names the cache and the memory cost, because a macOS-purged model cache is the usual reason a memory server suddenly wants 1.5 GB.

Bugs worth naming — all of the "works in a checkout, silently dead when installed" kind. tree-sitter-language-pack was in no requirements file, so "AST codebase ingest, 9 languages" degraded to whole-file chunks for everyone. vocabularies/ and filters/ never made it into the wheel or the image, so canonical tag normalisation ran against an empty vocabulary and every memory_save(filter=…) was a no-op. The enrichment worker shared the Store's sqlite connection — safe for reads, not for writes — and long ingests died on cannot start a transaction within a transaction. Migration 028 failed on every fresh database and could never record itself, so it retried on every startup forever (root cause spotted by @juicetin in #12: two owners for one schema change). And ai_layer/verifier.py looked for NLI calibrations at the pre-.tam path.

Full notes in CHANGELOG.md. Earlier releases: v12.4.0 · v12.0.0 · v11.0.

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Table of contents

• v13.0.0 — what changed
• The problem it solves
• 60-second demo
• Benchmarks — how it compares
  • LoCoMo · BEAM · LongMemEval
• 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

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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), 95.1% R@5 on public LongMemEval
• Private — 100% local. SQLite + FastEmbed + optional Ollama. No data leaves your machine.

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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)

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Benchmarks — how it compares

Everything below is retrieval: does the memory surface the passage that contains the answer, in the top-K? That is the part this project owns — answer quality is bounded above by it, and it can be graded with no LLM in the loop, which makes the numbers deterministic, free, and reproducible on your machine.

Two things to read them honestly:

• These are the default fast profile — FastEmbed, no reranker, no LLM anywhere in the path. That is what you get after install.sh, not a tuned configuration.
• Every runner passes record_usage=False. Recall.search normally bumps recall_count, and the scorer adds recall_boost = min(0.3, recall_count × 0.05) — so before v13, each re-run against the same database scored higher than the last, partly measuring its own history. A clean run and a re-run are now byte-identical.

LoCoMo — snap-research/locomo

1,536 gradable questions across 10 long-running conversations (5,882 turns ingested), plus 446 adversarial questions scored separately.

Category	N	R@1	R@5	R@10	MRR
single-hop	282	0.202	0.500	0.638	0.332
temporal	321	0.411	0.689	0.735	0.524
multi-hop	92	0.163	0.413	0.435	0.256
open-domain	841	0.363	0.633	0.712	0.479
overall	1,536	0.331	0.607	0.687	0.448

Latency p50 18.2 ms, p95 55.4 ms. Temporal is the strongest category — the bi-temporal knowledge graph earns its keep. Multi-hop is the weakest and is the v13.1 target.

Reproduce: python benchmarks/locomo_bench.py --wipe → benchmarks/results/v13-locomo-retrieval.json

BEAM — Beyond a Million Tokens, ICLR 2026

BEAM is the benchmark that starts where context windows stop: conversations of 100K / 500K / 1M tokens (a separate 10M set goes further), probed across ten distinct memory abilities. Scored here against each probe's source_chat_ids.

Scale 100K — 20 conversations, 5,732 messages, 355 gradable probes:

Ability	N	R@1	R@5	R@10	MRR
contradiction_resolution	40	0.700	1.000	1.000	0.824
temporal_reasoning	40	0.475	0.975	1.000	0.689
knowledge_update	40	0.550	0.925	0.950	0.719
multi_session_reasoning	40	0.375	0.675	0.850	0.486
information_extraction	40	0.400	0.625	0.725	0.503
summarization	36	0.167	0.444	0.556	0.267
preference_following	39	0.077	0.282	0.410	0.169
event_ordering	40	0.025	0.150	0.200	0.074
instruction_following	40	0.025	0.075	0.150	0.054
overall	355	0.313	0.575	0.651	0.423

Latency p50 17.7 ms. The shape is the useful part: contradiction resolution, temporal reasoning and knowledge update are effectively solved, while instruction_following and event_ordering are near-zero — those probes ask whether a stated instruction was followed or in what order things happened, and semantic similarity to the question does not find the message where the instruction was given. Retrieval is the wrong primitive there, and that is the roadmap item.

Scale 500K — 35 conversations, 38,058 messages, 629 gradable probes:

Ability	N	R@1	R@5	R@10	MRR
contradiction_resolution	70	0.714	0.943	0.971	0.828
knowledge_update	69	0.464	0.855	0.899	0.617
temporal_reasoning	70	0.500	0.786	0.871	0.625
multi_session_reasoning	70	0.357	0.614	0.729	0.470
information_extraction	70	0.271	0.443	0.571	0.354
preference_following	70	0.071	0.300	0.471	0.168
summarization	70	0.100	0.286	0.414	0.174
instruction_following	70	0.029	0.157	0.257	0.086
event_ordering	70	0.014	0.029	0.186	0.042
overall	629	0.280	0.490	0.596	0.373

Scale 1M — 35 conversations, 74,630 messages, 625 gradable probes:

Ability	N	R@1	R@5	R@10	MRR
knowledge_update	70	0.529	0.886	0.929	0.677
contradiction_resolution	70	0.686	0.871	0.914	0.772
temporal_reasoning	70	0.371	0.686	0.800	0.508
multi_session_reasoning	70	0.214	0.429	0.600	0.315
information_extraction	70	0.157	0.371	0.500	0.250
summarization	66	0.015	0.288	0.515	0.147
preference_following	69	0.029	0.246	0.406	0.134
event_ordering	70	0.000	0.157	0.329	0.069
instruction_following	70	0.029	0.086	0.200	0.061
overall	625	0.227	0.448	0.578	0.327

How it scales, and what that exposed

Scale	Messages	R@5	search p50	ingest
100K	5,732	0.575	17.7 ms	25.6 msg/s
500K	38,058	0.490	58.5 ms	10.8 msg/s
1M	74,630	0.448	411.5 ms	5.0 msg/s

Recall decays gracefully — 13× the haystack costs 12.7 points of R@5, and the abilities that hold up (knowledge update, contradiction resolution) hold up at every scale. The two curves that do not decay gracefully are the interesting part, and they have separate causes.

Ingest — found and fixed. Throughput fell 5× across the three scales on identical code. The cause was ours: graph/auto_link.py runs on every save and constructed a fresh ConceptExtractor each time. The node-name cache lives on the instance, so it was thrown away immediately and the whole graph_nodes table was re-read per write — 1,000 saves triggered 1,000 full table reads (~139 million rows at the 139k nodes this ingest reaches). Fixed in v13.0.1; counting reads rather than timing makes the check load-independent, and it is now 1 read per 1,000 saves. The ingest column above was measured before that fix and is kept as the record of the problem.

Search — open. p50 grew 7× between 500K and 1M for 2× the data. Store._binary_search loads the binary vectors of every active record into numpy on each query, so search is linear in store size. That is a different problem from the ingest one and is not fixed; an ANN index over the binary vectors is the obvious answer and has not been built yet. Stated rather than buried, because 411 ms is a real number a user would feel.

Reproduce: python benchmarks/beam_bench.py --scale 100K --wipe → v13-beam-100K.json · v13-beam-500K.json · v13-beam-1M.json

LongMemEval — xiaowu0162/longmemeval-cleaned

470 questions across six question types, re-measured for v13 through the product: each question's haystack is ingested into a real Store and queried with Recall.search, the same path an agent takes.

Question type	Count	R@5 (recall_any)
knowledge-update	72	100.0%
multi-session	121	98.3%
single-session-user	64	95.3%
single-session-assistant	56	94.6%
temporal-reasoning	127	92.9%
single-session-preference	30	80.0%
total	470	95.1%

Also recall_all@5 85.7% (every required fragment, not just one), NDCG@5 88.9%, 27.6 ms per query.

This replaces the 96.2% we published before, and the difference matters more than the 1.1 points. Until v13 this runner used its own self-contained BM25 / RRF / MMR / CrossEncoder stack, so the number described an algorithm, not this software. --modes store drives the shipping path and is now the default. The old modes remain for ablations.

For reference on the same set, Mastra "Observational" reports 95.0% and Supermemory 85.4% — both cloud services.

Reproduce: python benchmarks/longmemeval_bench.py --modes store → evals/longmemeval-2026-08-27-v13-store.json

On end-to-end accuracy numbers

Systems in this space usually publish LoCoMo accuracy — a generator answers from the retrieved context and an LLM judges it. We publish it too, with the two caveats that make it meaningful.

One LLM-judged run is a sample, not a measurement. Temperature 0 does not make the API deterministic and OpenAI documents seed as best-effort, so the runner takes --seed and we report three runs:

Category	N	mean	min	max	spread
single-hop	282	0.366	0.358	0.372	0.014
temporal	321	0.426	0.424	0.427	0.003
multi-hop	96	0.292	0.281	0.302	0.021
open-domain	841	0.570	0.567	0.573	0.006
adversarial	446	0.904	0.899	0.908	0.009
overall (no adversarial)	1,540	0.486 ± 0.002	0.484	0.488	0.005
overall (all)	1,986	0.579 ± 0.002	0.578	0.582	0.004

gpt-4o generator, gpt-4o-mini judge, seeds 1/2/3. Retrieval was byte-identical across all three — only generation and judging vary.

The judge needed two guards, and they point opposite ways.

Refusals scored as correct answers. On ~100 of the 1,540 non-adversarial questions per run, the judge answered YES to "Not mentioned in the conversation." against golds like Sweden, June 2023, Single — F1 exactly 0.00. Almost certainly the adversarial rule bleeding across, since the judge is told to accept a refusal when the gold also indicates no information. Per category the inflation runs 3.2 pp (open-domain) to 14.3 pp (temporal).

Hallucinations scored as correct abstentions. 99.6% of LoCoMo's adversarial golds are the empty string. The judge accepts almost any fluent answer against an empty reference, so 27–30 invented answers per run scored correct — inflating the one category we used to lead on.

Both are rules rather than judgements — on categories 1–4 the gold is a fact, so a refusal cannot be right; with an empty gold, only a refusal can be — so both now run deterministically at judging time. Effect: no-adv 0.551 → 0.486, adversarial 0.966 → 0.904, all 0.645 → 0.579. The table above is corrected.

How noisy is the rest? Aligning all 1,986 questions across the three seeds:

	share
generator's answer differed between seeds	12.5%
judge's verdict differed	5.1%
judge flipped on an identical answer	2.7%

The aggregate holds within ±0.005 because those flips roughly cancel, not because the instrument is precise. Quoting one run to three decimals — as we did before — is not supported by the data.

Not comparable to the 90%+ figures some competitors publish: different generators, judges, prompts and question subsets. And on this evidence, an unguarded LLM judge can be worth six points on its own. The retrieval numbers above remain our primary metric because they are checkable without an API key.

benchmarks/results/v13-locomo-llm-3seeds.json · Runner: benchmarks/locomo_bench_llm.py

Do the retrieval numbers mean anything? — negative controls

A retrieval score with no floor under it is not a claim. Every LoCoMo run now scores three degenerate baselines on the same questions:

Baseline	R@1	R@5	R@10
random — ten turns from the same conversation	0.001	0.012	0.023
first — the ten earliest turns	0.000	0.023	0.039
recency — the ten most recent turns	0.001	0.003	0.011
the pipeline	0.331	0.607	0.687

27× the best degenerate baseline. The controls run in the same pass as the metric, so the floor ships with the number rather than living in a script somebody stops running.

Latency profile

  p50 (warm)   ▌ 0.065 ms
  p95 (warm)   ▌▌ 2.97 ms
  LoCoMo       ▌▌▌ 18.2 ms/query    ← full hybrid retrieval over 5,882 records
  BEAM 100K    ▌▌▌ 17.7 ms/query    ← over 5,732 messages
  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.

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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	🟡	❌	❌	✅ 74 tools, MCP 2026-07-28
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

On competitors' benchmark numbers. mem0 now publishes 92.5 on LoCoMo and 94.4 on LongMemEval. Those are end-to-end accuracy with their own generator, judge and prompts — not comparable to the retrieval numbers above, and not independently reproducible without their stack. We publish retrieval because the runner, the corpus and the gold labels are all public and you can re-run them on your laptop without an API key. Where a project has not published on a benchmark, we write "—" rather than inventing a number.

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

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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) — 95.1% 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

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Architecture

                  ┌─────────────────────────────────────────────────┐
                  │             Your AI coding agent                │
                  │   (Claude Code · Codex CLI · Cursor · any MCP)  │
                  └──────────────────────┬──────────────────────────┘
                                         │ MCP (stdio or HTTP)
                                         │ 74 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

Quickstart — pick one

Channel	Command	What it does
npx (Node)	npx -y total-agent-memory connect claude-code	Zero-install. Bootstraps a Python venv in ~/.tam/.venv via uv (or python3 fallback), pulls the PyPI server, wires the MCP entry into your IDE. Replace claude-code with codex / cursor / cline / continue / aider / windsurf / gemini-cli / opencode.
uvx (Python via uv)	uvx total-agent-memory	One-off run with no install. Best for trying without commitment.
pipx (Python isolated)	pipx install total-agent-memory	Installs the total-agent-memory, tam, tam-lookup, lookup-memory binaries on PATH in an isolated venv.
brew (macOS / Linuxbrew)	brew install vbcherepanov/tap/total-memory	Bottle-style install with tam and legacy claude-total-memory symlinks.
Docker (multi-arch)	docker run -p 37737:37737 -v ~/.tam:/data ghcr.io/vbcherepanov/total-agent-memory:13.0.0	Containerized (linux/amd64 + linux/arm64). Dashboard on :37737.
Claude Code plugin	/plugin marketplace add vbcherepanov/total-agent-memory /plugin install total-agent-memory@vbcherepanov	Installs the MCP server, the memory-protocol skill and all seven capture hooks in one step, from inside Claude Code. The bootstrap reuses an existing install if it finds one, so nothing is downloaded twice.
Manual clone	git clone https://github.com/vbcherepanov/total-agent-memory ~/total-agent-memory && cd ~/total-agent-memory && ./install.sh --ide claude-code	Full control. Lets you hack on the server, run benchmarks, and pick which background services to enable. Detailed walkthrough below.

All seven channels land at the same MCP server. The npx and ./install.sh paths additionally configure IDE-specific MCP entries and hooks. Other channels start the server bare — you wire the IDE afterwards (see docs/installation.md).

Upgrade from v11.x? Whatever channel you pick will auto-migrate ~/.claude-memory/ → ~/.tam/ on first run and keep a symlink for backward compat. No manual data move required.

---

Detailed paths (manual / Docker / per-IDE)

Two manual paths. Same 74 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/total-agent-memory.git ~/total-agent-memory
cd ~/total-agent-memory
bash install.sh --ide claude-code   # or: cursor | gemini-cli | opencode | codex

The installer:

1. Clones + creates ~/total-agent-memory/.venv/
2. Installs deps from requirements.txt and requirements-dev.txt
3. Pre-downloads the FastEmbed multilingual MiniLM model
4. Registers the MCP server via claude mcp add-json memory ... (stored in ~/.claude.json, the canonical store Claude Code actually reads)
5. 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
6. Grants permissions.allow for 20+ mcp__memory__* tools so hook-driven calls don't prompt for confirmation
7. 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)
8. Applies all migrations to a fresh memory.db
9. Starts the dashboard at http://127.0.0.1:37737

Restart Claude Code → /mcp → memory should show Connected with 74 tools.

Path A — native (Windows 10/11)

git clone https://github.com/vbcherepanov/total-agent-memory.git $HOME\total-agent-memory
cd $HOME\total-agent-memory
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
  • TotalAgentMemoryDashboard — AtLogon

Uninstall

All installers preserve ~/.tam/memory.db (legacy installs: ~/.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/total-agent-memory.git
cd total-agent-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
tam-lookup "Caroline researched"             # short canonical alias
ctm-lookup "Caroline researched"             # legacy alias (v11.x and earlier)

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 $TAM_MEMORY_DIR/memory.db (legacy: $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 three names? lookup-memory matches the legacy bash script that older docs and sub-agent prompts reference (~/claude-memory-server/ollama/lookup_memory.sh, legacy install path). tam-lookup is the new project-prefixed canonical form (v12+). ctm-lookup is the v11.x prefixed name, kept as a legacy alias. All three call into total_agent_memory.lookup:main (v11.x and earlier: claude_total_memory.lookup:main, still importable via deprecation shim).

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 (and tam-lookup) on PATH directly via the package's [project.scripts] entry.

---

MCP tools reference (74 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: 74 tools. Each is documented below with input schema and example.

Every tool carries MCP behaviour annotations — 38 are marked readOnlyHint, and memory_delete / memory_forget / memory_update / kg_invalidate_fact plus the two rebuild tools are marked destructiveHint. Clients use these to decide what may run without a confirmation prompt. Tools that answer in JSON also return it as structuredContent, so you do not have to parse the text.

Token-efficient 3-layer workflow

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

1. 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.
2. Timeline — memory_recall(query="auth refactor", mode="timeline", limit=5, neighbors=2) → top-K hits padded with ±neighbours from the same session, sorted chronologically.
3. 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 total_agent_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 → the dashboard is at http://localhost:37737 once installed.

---

Update

cd ~/total-agent-memory   # legacy clones: ~/claude-memory-server
./update.sh

7 stages:

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

Manual equivalent:

cd ~/total-agent-memory   # legacy clones: ~/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 ~/total-agent-memory && ./update.sh   # legacy clones: ~/claude-memory-server
# pulls v9 src, installs new entry-points (tam, tam-lookup, lookup-memory; legacy: ctm-lookup),
# 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 / tam-lookup / ctm-lookup (legacy) CLI now installed alongside total-agent-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 ~/total-agent-memory && ./update.sh   # legacy clones: ~/claude-memory-server
# 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:

1. 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)

2. Migrations 011/012/013 are additive (no DROP / ALTER on existing tables), so DB downgrade is not destructive — old code continues reading older tables.

3. 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 Performance tuning.
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	~/.tam/memory.db (legacy installs: ~/.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 total_agent_memory/config.py.

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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.

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Roadmap

Shipped in v13.0.0 (2026-08-27)

• ✅ MCP SDK 2.x compatibility — the blocker: every install created after mcp 2.0 shipped was dead on arrival. Tools register through either SDK era; dependency bounded >=1.9,<3.
• ✅ Protocol revision 2026-07-28 — stateless era served end-to-end (tools/list / server/discover / tools/call with no handshake), legacy handshake era from the same process, structuredContent on JSON-answering tools, behaviour annotations on all 74.
• ✅ Claude Code plugin — /plugin install total-agent-memory@vbcherepanov wires the MCP server, the skill and seven hooks in one step.
• ✅ Reproducible benchmarks — record_usage=False stops runs from measuring their own history; category labels in the LoCoMo runner corrected.
• ✅ BEAM (ICLR 2026) added to the suite at 100K / 500K / 1M.
• ✅ tree-sitter-language-pack is now an actual dependency — AST ingest had been silently degrading to whole-file chunks for every user.
• ✅ Enrichment worker owns its sqlite connection — long ingests no longer die on cannot start a transaction within a transaction.

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 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 / tam-lookup / ctm-lookup (legacy) 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}

Next — what the v13 numbers say to fix

The benchmarks point at specific gaps rather than a general "make retrieval better", so the roadmap names them:

• instruction_following R@5 = 0.075, event_ordering = 0.150 (BEAM). These probes ask whether a stated instruction was followed or in what order things happened. Semantic similarity to the question does not find the message where the instruction was given — retrieval is the wrong primitive. Needs a directive index (statements of the form "always/never/from now on") and ordering-aware traversal over the episodic graph.
• multi-hop R@5 = 0.413 (LoCoMo). Weakest category, and the one where the leaders win. Query decomposition without putting an LLM back in the hot path is the open design question.
• single_session_preference R@5 = 0.80 (LongMemEval), preference_following = 0.282 (BEAM). The same weakness from two directions: preferences are stated once, in passing, and never restated.
• BEAM-10M. The 1M scale runs today; 10M is the interesting claim.
• Search is linear in store size. BEAM 1M measured p50 411 ms against 58 ms at 500K — Store._binary_search loads every active record's binary vector into numpy per query. An ANN index over those vectors is the obvious answer. This is the largest open performance item.
• Profile the write path — done in v13.0.1: auto_link constructed a ConceptExtractor per save and threw away its node cache, re-reading the whole graph_nodes table on every write.

Planned

• GitHub Actions: install smoke tests + a nightly retrieval gate, so a regression in R@5 fails CI the way bin/memory-perf-gate already fails on latency.
• has_llm() per-phase provider caching.

Under research

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

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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.

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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.

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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.

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License

MIT — see LICENSE.

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