total-agent-memory
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Persistent local memory MCP server for Claude Code, Codex CLI, Cursor and any MCP client: 74 tools — temporal knowledge graph, procedural memory, episodic memory, AST codebase ingest, pre-edit guard, auto-consolidating error capture
Persistent memory for your facts, decisions and working practices. 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
Release date: 2026-09-21.
Two problems that got worse as a store grew. Dedup treated near-identical texts as repeats, so an update that changed one value ("Messi's citizenship is Argentina" → "... Armenia", "PostgreSQL 16" → "18") was dropped and the old value kept; on MemoryAgentBench FactConsolidation 14.3.0 lost 36 of 455 facts. And several queries behind every recall and save read the whole store. Measured on one machine with 200 synthetic tenants (report):
| 14.3.0 | 14.3.1 | |
|---|---|---|
Tenant-scoped memory_recall, 100k records, p50 / p95 | 781 / 1,901 ms | 25 / 35 ms |
Tenant-scoped memory_recall, 1M records, p50 / p95 | 6–64 s | 105 / 147 ms |
| HTTP calls/s, 100k records, 16 clients | 1.3 | 37 (one process), 117 (MCP_HTTP_WORKERS=4) |
| Change | How you use it |
|---|---|
| Updates are kept | Automatic. A record is a repeat only when it has the same words in the same order (case, punctuation and ё/е aside). A repeat replaces the stored record, so "A", then "B", then "A" leaves A as the latest. |
| Scoped search follows the project, not the store | Automatic. Migration 035 puts the project into the full-text index (rebuilt once, about 30 s per million records); graph seeds and available_solutions use indexes. |
| HTTP workers | MCP_HTTP_WORKERS=4 with MCP_TRANSPORT=http: four server processes on one port, stateless sessions, POSIX only. In Docker Compose: TAM_MCP_WORKERS=4. |
| Scale benchmark | benchmarks/scale_bench.py loads a synthetic multi-tenant corpus through the real save path and measures recall, save, HTTP throughput and concurrent writers. |
| MemoryAgentBench | FactConsolidation results: findings. |
Release date: 2026-09-21.
memory_answer can now check retrieved facts for contradictions with TypeSafe's Jev, a
System One model that answers typed questions with calibrated probabilities instead of
generating text. Every (supporting, opposing) pair becomes one noul question, and all of
them go in a single request. Measured on the same questions against the Claude Haiku 4.5
scorer (report):
accuracy is unchanged within noise (LongMemEval knowledge-update 35/78 vs 36/78, control
15/50 vs 16/50). The median contradiction pass drops from 3.1 s to 1.9 s, and Jev billed
$0.038 for all 78 questions.
| Change | How you use it |
|---|---|
| Jev contradiction scorer | MEMORY_CONTRADICTION_SCORER=jev plus TYPESAFE_API_KEY. The default stays llm. The client retries 408, 429, 5xx and connection errors like the official SDKs, honours retry-after-ms / Retry-After up to 60 s, and reports jev_* counters and a jev_request_ms latency histogram. |
| API key hygiene | An empty key, or one containing a newline or other control character, is rejected before any request, and the error never contains the key. (The official Python and JS SDKs echo it; reported upstream as typesafe-sdk-python#9 and typesafe-sdk-js#14.) |
| Benchmark harness | benchmarks/knowledge_update_eval.py records per-question contradiction-pass time and Jev token usage, so the two scorers can be compared on cost as well as accuracy. |
Release date: 2026-09-21.
"Mary loves red", saved in May, and "Mary no longer likes red; she has fallen for green",
saved in August: memory_answer now says green, previously red. Measured with Claude
Haiku 4.5 on the same questions (report):
LongMemEval knowledge-update rose from 12/78 to 35/78, a Russian + English update suite from
8/30 to 22/30, and five other LongMemEval categories from 11/50 to 16/50.
| Change | How you use it |
|---|---|
| Recording dates reach the reader | memory_answer's reader and verifier see when each record was saved. The latest record about the same subject gives the current value unless its text describes the past, and a value stays current until a later record changes it. Answers come back in the language of the question. |
| Contradictions are resolved, not refused | A hard contradiction now passes both sides, with their dates, to the reader. MEMORY_CONTRADICTION_POLICY=abstain restores the 14.1.0 refusal. The contradiction scorer sees the question, so a conflict about someone else no longer blocks the answer. |
| Russian word forms | The lexical recall tier stems Cyrillic terms (Snowball, new dependency snowballstemmer): "Маша" in a question finds "Маше" in a record, and claim grounding accepts the inflected name. |
| One timestamp format | Records are stored as 2026-09-21T08:21:37.622445Z (UTC). Migration 034 rewrites older rows: +00:00 and fraction-less values are reformatted, and zone-less values, which older versions wrote in local time, are converted with that zone's DST rules. The instants do not change, so atomic facts are not rebuilt. |
Release date: 2026-09-21.
| Change | How you use it |
|---|---|
Negative retrieval in memory_answer | Before reading, the grounded reader runs a second, contradiction-seeking search: a small model inverts the question, and each (supporting, opposing) pair — at most 5 × 5 — is scored in one batched call. Score ≥ 0.60 answers Not enough information without picking a side; 0.30–0.60 answers with a caveat; below 0.30 the answer is unchanged. The verdict is returned under negative. MEMORY_NEGATIVE_RETRIEVAL=false turns it off. |
memory_answer on Anthropic and Ollama | Both providers now return schema-constrained output (forced tool call / JSON-schema format). Before this, Claude Haiku wrapped JSON in a markdown fence and memory_answer failed with Reader returned invalid grounded evidence. |
Release date: 2026-09-15 · Status: release candidate; registry publication pending. Use the prepared wheel or this source checkout for v14. The general package-manager commands below follow their published channels and do not guarantee v14 before publication.
| Change | How you use it |
|---|---|
| Personal, team and shared memory | Install one server; give Vasya and Petya separate tokens. Select a scope when saving; search all areas you can access. Each area has its own database, graph and index. |
| Authorship and revision history | The token identifies the author and client. See who changed a record, when and why; revision checks prevent overwriting another person's edit. |
| Remote MCP and team web interface | Connect an IDE through the lightweight Python bridge. In the browser, select a scope, search, browse, save, edit and inspect history. The interface displays the product name, version and release date. |
| Lower CPU pressure | Fast mode remains the default. Embedding and optional PyTorch models default to one compute thread. The team server retains three workspace workers to avoid repeated model loading. |
| Configurable internal LLMs | Use Ollama, an OpenAI-compatible endpoint or Anthropic for internal text tasks; explicitly select a vision model for images. |
| Safer retrieval and answers | Scoped context, model-aware vector search and privacy-safe write intents. The optional grounded reader checks supporting evidence and rejects contradictory claims; it is not the default answer path. |
| Installation and packaging fixes | Wheel, source archive and Docker checks cover Linux, Windows and macOS; the source archive now includes test fixtures and installation support files. |
Validation: 2,162 tests passed in the checkout; 2,145 passed from the source archive. Native wheel checks passed on Ubuntu, Windows and macOS; 12 browser scenarios passed across Chromium, Firefox and WebKit. The expanded CI matrix still requires execution. See the final verification report for skips, exact platforms and artifact hashes.
Performance limits: BGE is optional. On the measured Linux ARM64 setup, its one-thread p95 was about 2,179 ms, above the 200 ms target. Limiting threads reduces parallel CPU load; it does not eliminate CPU work. No top-10 ranking or new default answer-quality improvement is claimed. CPU measurements.
Full v14 release notes · Changelog and previous releases
| Mode | Install and use |
|---|---|
| Local, one person | Follow native or Docker installation, then Quick start. Memory and models run on your machine; the local MCP catalogue has 74 tools. |
| Server, multiple people | Follow the setup below. Memory and models run on the server; clients need only Python and their token. The remote catalogue exposes eight core tools. |
The server instructions work with the prepared v14 wheel on Linux, macOS and Windows. Run commands in a directory where you can create the data folder and token files.
Install the candidate wheel in a dedicated environment. Linux/macOS:
python3 -m venv .venv
. .venv/bin/activate
python -m pip install ./dist/total_agent_memory-14.3.1-py3-none-any.whl
Windows PowerShell, using the environment directly without changing the execution policy:
py -3 -m venv .venv
.\.venv\Scripts\python.exe -m pip install .\dist\total_agent_memory-14.3.1-py3-none-any.whl
$env:PATH = "$PWD\.venv\Scripts;$env:PATH"
Then, on any of these systems:
tam-team --root ./team-data user-add vasya 'Vasya'
tam-team --root ./team-data user-add petya 'Petya'
tam-team --root ./team-data team-add engineering 'Engineering'
tam-team --root ./team-data member vasya engineering editor
tam-team --root ./team-data member petya engineering editor
tam-team --root ./team-data token-create vasya --client codex --out ./vasya.token
tam-team --root ./team-data token-create petya --client cursor --out ./petya.token
tam-team --root ./team-data serve --host 127.0.0.1 --port 3738
Open http://127.0.0.1:3738/ and sign in with the contents of your token file. Keep each token private; give Petya his own token, not Vasya's. For access from other machines, configure an HTTPS reverse proxy to the server's /mcp/ endpoint and web interface. Permissions, backup, restore and server configuration.
From this checkout, with port 3738 available:
docker compose -f docker-compose.team.yml build
docker compose -f docker-compose.team.yml run --rm team-memory python /app/src/team_memory/cli.py user-add vasya 'Vasya'
docker compose -f docker-compose.team.yml run --rm team-memory python /app/src/team_memory/cli.py token-create vasya --client codex --out /team-data/vasya.token
docker compose -f docker-compose.team.yml up -d
docker compose -f docker-compose.team.yml cp team-memory:/team-data/vasya.token ./vasya.token
The web interface is at http://127.0.0.1:3738/. The token copied to the host is a credential: restrict file access to its owner. To add Petya, a team and membership, use the same CLI subcommands shown above through docker compose -f docker-compose.team.yml run --rm team-memory python /app/src/team_memory/cli.py.
Compose keeps data and model caches in persistent volumes. Set TAM_TEAM_PORT, TAM_TEAM_MAX_WORKERS and LLM settings in .env as needed; see .env.example. Plan at least 4 GiB RAM for three warm MiniLM workers and measure your own workload. Docker server details.
Copy src/team_memory/remote.py to the client and supply its personal token file. This bridge uses only the Python standard library. For clients with an mcpServers configuration:
{
"mcpServers": {
"total-agent-memory": {
"command": "python3",
"args": ["/absolute/path/remote.py"],
"env": {
"TAM_REMOTE_URL": "https://YOUR_SERVER/mcp/",
"TAM_REMOTE_TOKEN_FILE": "/absolute/path/vasya.token"
}
}
}
}
Replace the paths and server address. On Windows use the path to python.exe and Windows file paths. Local testing may use http://127.0.0.1:3738/mcp/; remote connections require HTTPS. If the package is installed on the client, tam-remote is also available. Client configuration details.
Ask your agent to call memory_scopes first to list available areas. These are example arguments for the remote memory_save tool:
{"content":"My investigation notes", "scope":{"kind":"personal"}, "tags":["release-v14"]}
{"content":"Engineering release checklist", "scope":{"kind":"team","team_id":"engineering"}, "tags":["release-v14"]}
{"content":"Company-wide onboarding guide", "scope":{"kind":"shared"}, "tags":["onboarding"]}
reader can read, editor can also change records.scope and membership; reserved scope:, team: and user: tags are managed by the server.Call memory_recall with {"query":"release checklist"} to search all accessible areas, or add a scope to narrow the search. Use memory_get and memory_history with the returned record's id and scope to inspect content, author and changes. memory_update requires those fields plus expected_revision, content and reason; use the new ID returned by the update for subsequent operations. The author is taken from the token automatically.
The remote tools are memory_scopes, memory_save, memory_recall, memory_get, memory_update, memory_delete, memory_history and memory_export. History and export are paginated. Full behavior and retry rules.
Fast mode works without an LLM. To use a local model for optional internal tasks, set these environment variables (or .env for Compose):
MEMORY_LLM_ENABLED=true
MEMORY_LLM_PROVIDER=ollama
MEMORY_LLM_MODEL=YOUR_INSTALLED_MODEL
OLLAMA_URL=http://127.0.0.1:11434
MEMORY_EMBED_THREADS=1
MEMORY_TORCH_THREADS=1
For Docker, the Ollama address must be reachable from the container; the team profile defaults to http://host.docker.internal:11434. For another compatible server, select MEMORY_LLM_PROVIDER=openai-compatible and set MEMORY_LLM_API_BASE, MEMORY_LLM_MODEL and, if required, MEMORY_LLM_API_KEY. Compatibility means Chat Completions, with JSON Schema support for structured tasks. Set MEMORY_VISION_MODEL separately for images. Restart after changing environment settings. Provider settings and CPU limits.
Optional remote LLM providers receive the content used in those tasks. Keep Ollama local or set MEMORY_LLM_ENABLED=false if that content must stay on your own infrastructure.
lookup-memory for sub-agentsAI 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:
memory_save or implicitly via hooks on file edits / bash errors / session endYou: "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)
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:
fast profile — FastEmbed, no reranker, no LLM
anywhere in the path. That is what you get after install.sh, not a tuned
configuration.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.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 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 |
| 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
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 storedrives 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
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
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.
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.
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.
| 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 |
memory_save → LaunchAgent file-watch → graph edges appear ~30 s laterdesign-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 contentactiveContext.md Obsidian projection for human-readable session stateself_rules_context(phase="build")) — ~70% token reduction ┌─────────────────────────────────────────────────┐
│ 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`
These are the published distribution channels. For the unpublished v14 candidate, use the wheel or source-based Compose instructions above.
| 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:14.3.1 | 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).
The reranker is an extra, not a dependency. A base install is 97 packages
and ~113 MB of wheels: fastembed runs the embeddings through ONNX and no torch
is resolved anywhere. The CrossEncoder / BGE reranker needs the torch stack,
which on Linux drags in the whole nvidia-cu* set — 147 packages and ~3.1 GB —
so it ships separately, and the default MEMORY_MODE=fast does not use it. Turn
it on with MEMORY_MODE=deep (or MEMORY_RERANK_ENABLED=true) and install it:
pip install "total-agent-memory[rerank]" # pip / uvx / pipx
pip install -r requirements-rerank.txt # clone / Docker
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.
Two manual paths. Same 74 tools, same dashboard, different deployment shapes.
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.
| 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.
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:
~/total-agent-memory/.venv/requirements.txt and requirements-dev.txtclaude mcp add-json memory ... (stored in ~/.claude.json, the canonical store Claude Code actually reads)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.jsonpermissions.allow for 20+ mcp__memory__* tools so hook-driven calls don't prompt for confirmationreflection, orphan-backfill, check-updates, dashboard) under ~/Library/LaunchAgents/--user units (*.service, *.timer, *.path) under ~/.config/systemd/user/; gracefully degrades if systemd --user is unavailable (WSL without /etc/wsl.conf)memory.dbhttp://127.0.0.1:37737Restart Claude Code → /mcp → memory should show Connected with 74 tools.
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:
%USERPROFILE%\.claude\settings.json (or .cursor\mcp.json, etc.)%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)total-agent-memory-reflection — every 5 min (no native FileSystemWatcher equivalent)total-agent-memory-orphan-backfill — daily 00:00 + 6h repetitiontotal-agent-memory-check-updates — weekly Mon 09:00TotalAgentMemoryDashboard — AtLogonAll 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
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.
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.
memory_save(content="install works", type="fact")
memory_stats()
Open http://127.0.0.1:37737/ — dashboard, knowledge graph, token savings.
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 setexport 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, ...}
lookup-memory for sub-agentsNew 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.
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.
When you only know the topic but not which records matter, use progressive disclosure:
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.memory_recall(query="auth refactor", mode="timeline", limit=5, neighbors=2) → top-K hits padded with ±neighbours from the same session, sorted chronologically.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.
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
memory_graph · memory_graph_index · memory_graph_stats · memory_concepts · memory_associate · memory_context_build
memory_episode_save · memory_episode_recall · memory_skill_get · memory_skill_update
memory_reflect_now · memory_self_assess · self_error_log · self_insight · self_patterns · self_reflect · self_rules · self_rules_context
kg_add_fact · kg_invalidate_fact · kg_at · kg_timeline
workflow_learn · workflow_predict · workflow_track
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.
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
/ — 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 countsScreenshots → the dashboard is at http://localhost:37737 once installed.
cd ~/total-agent-memory # legacy clones: ~/claude-memory-server
./update.sh
7 stages:
pip install -r requirements.txt -r requirements-dev.txt (only if hash changed)python src/tools/version_status.py/mcp → memory → ReconnectManual 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
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.
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"
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.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.ce-marco by default. V9_RERANKER_BACKEND=bge-v2-m3 (or off) switches at runtime.--subject-aware in benchmarks/locomo_bench_llm.py. Future: surface as MCP tool flag.python -m scripts.reembed --backend openai-3-large --confirm
~/claude-memory-server/ollama/lookup_memory.sh "query" will keep working. To ride the new package install, replace with lookup-memory "query".None. All v8 MCP tools, env vars, hooks, and DB tables behave identically.
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.
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.
memory_save calls keep working — they now additionally strip <private>...</private> sections if present.memory_recall calls keep working — default mode is still "search". New mode="index" is opt-in.session_end calls keep working — auto_compress=False by default. Pass auto_compress=True to opt in.self_rules_context calls keep working — default returns all rules (no phase filter).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.
None. All v7 MCP tool signatures are preserved. New parameters are optional with safe defaults.
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:
MEMORY_EMBED_PROVIDER=fastembed (default 384d) and only change the LLM provider, ORpython src/tools/reembed.py --provider openai --model text-embedding-3-smallQuick 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 workflowsession_end(auto_compress=True, transcript=None, ...) — LLM-generated summaryself_rules_context(phase="build"|"plan"|...) — phase filtersave_knowledge(...) — now strips <private>...</private> sections automaticallyv8.0 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.
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
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 | 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 |
Environment variables (all optional):
| 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_NEGATIVE_RETRIEVAL | true | memory_answer runs the contradiction-seeking second search (one inversion call + one batched scoring call over at most 5×5 pairs). false skips it. |
MEMORY_CONTRADICTION_POLICY | resolve | What memory_answer does when that search finds a hard contradiction. resolve hands both sides, with their recording dates, to the reader, which answers with the latest value and names the one it replaced. abstain answers Not enough information without reading (the 14.1.0 behaviour). |
MEMORY_CONTRADICTION_SCORER | llm | Who scores the (supporting, opposing) pairs of that search. llm uses the reasoning provider. jev sends every pair as one noul question in a single request to TypeSafe's System One API (model Jev); needs TYPESAFE_API_KEY. If the scorer fails, the pass reports no contradiction and the answer proceeds. |
TYPESAFE_API_KEY | unset | Key for MEMORY_CONTRADICTION_SCORER=jev. Empty keys and keys with control characters are rejected before any request, without echoing the key. |
TYPESAFE_BASE_URL / TYPESAFE_DEFAULT_MODEL | https://api.typesafe.ai / jev-latest | Endpoint and model for the Jev scorer (self-hosted servers speaking /v1/systemone work too). |
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. |
| 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_PREDICTbefore raising timeouts.install-codex.shwrites conservative defaults automatically. For 30-40s save latency on WSL2 → setMEMORY_ASYNC_ENRICHMENT=true— see below.
Full config: see total_agent_memory/config.py.
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.
memory_save latencyThe 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.
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.
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.
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.
mcp 2.0 shipped was dead on arrival. Tools register through either SDK era;
dependency bounded >=1.9,<3.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./plugin install total-agent-memory@vbcherepanov
wires the MCP server, the skill and seven hooks in one step.record_usage=False stops runs from
measuring their own history; category labels in the LoCoMo runner corrected.tree-sitter-language-pack is now an actual dependency — AST ingest
had been silently degrading to whole-file chunks for every user.cannot start a transaction within a transaction.MEMORY_MODE=fast — zero LLM, zero Ollama, zero network in save/search/recall hot path. Set MEMORY_MODE=deep to restore v10.5 behaviour.src/memory_core/* is deterministic; src/ai_layer/* owns every LLM-bound code path. Enforced by tests/test_no_llm_hot_path.py.ultrafast / fast / balanced / deep. Single env flag.text / code / log / config. Single Chroma backend; per-space model swap is config-only.Store.embed requires MEMORY_ALLOW_OLLAMA_IN_HOT_PATH=true.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.bin/memory-bench (artifact docs/v11/benchmark.md) + bin/memory-perf-gate for CI.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).bash -n under macOS bash 3.2 (default). Replaced ${var,,} lowercase bashism in update.sh with tr '[:upper:]' '[:lower:]'. Verified with shellcheck.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.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.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)._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).015–019) applied automatically on restart.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)openai-3-small, openai-3-large (3072d), bge-m3, e5-large, locomo-tuned-minilm (fine-tuned on user data)ce-marco, bge-v2-m3, bge-large, off (env V9_RERANKER_BACKEND, hot-swap)scripts/finetune_embedding.py) — mine triplets from your data, train on top of MiniLM via sentence-transformersscripts/mine_locomo_fewshot.py) — augment per-category prompts with held-in (Q,A) pairsurllib requests now use certifi by defaultbenchmarks/locomo_bench_llm.py with 14 ablation flags)save_decision with criteria matrix + multi-representation criterion indexingsession_end(auto_compress=True) via LLM providermemory_recall(mode="index") + memory_get(ids)activeContext.md Obsidian live-doc projection<private>...</private> inline redaction/api/knowledge/{id} + /api/session/{id}install.sh --ide {claude-code|cursor|gemini-cli|opencode|codex}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.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.auto_link constructed a
ConceptExtractor per save and threw away its node cache, re-reading the
whole graph_nodes table on every write.bin/memory-perf-gate already fails on
latency.has_llm() per-phase provider caching.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.
| 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 |
vbcherepanov@gmail.com — open to contract work and partnerships.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.
pytest tests/ must stay green. Add tests for new tools.evals/scenarios/*.json if you change retrieval behavior.MIT — see LICENSE.
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Persistent local memory MCP server for Claude Code, Codex CLI, Cursor and any MCP client: 74 tools — temporal knowledge graph, procedural memory, episodic memory, AST codebase ingest, pre-edit guard, auto-consolidating error capture
The pypi package total-agent-memory receives a total of 168 weekly downloads. As such, total-agent-memory popularity was classified as not popular.
We found that total-agent-memory demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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