total-agent-memory

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

Version 14.3.1 — updates are no longer dropped, recall stays fast at 1M records

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):

Version 14.3.0 — Jev as the contradiction checker

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.

Version 14.2.0 — facts that change over time

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.

Version 14.1.0 — what is new

Release date: 2026-09-21.

Version 14.0.0 — what is new

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.

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

Getting started with v14

Choose local or server mode

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.

Start a server without Docker

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.

Start a server with Docker Compose

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.

Connect a remote IDE

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.

Save, search and see who changed what

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"]}

• Personal: visible only to the token's owner; this is the default for saves.
• Team: visible to members; reader can read, editor can also change records.
• Shared: readable and editable by every authenticated user of this server.
• Tags: organize topics. Access comes from 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.

Configure internal models and CPU

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.

Table of contents

• Version 14.3.1 — updates are no longer dropped, recall stays fast at 1M records
• Version 14.3.0 — Jev as the contradiction checker
• Version 14.2.0 — facts that change over time
• Version 14.1.0 — what is new
• Version 14.0.0 — what is new
• Getting started with v14
• 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

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 by default — SQLite + FastEmbed + optional local Ollama. In server mode, memory stays on your server; external LLMs receive content only when configured and used.

60-second demo

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

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

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

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

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

Benchmarks — how it compares

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.

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:

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:

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

How it scales, and what that exposed

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.

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:

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:

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:

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.

Competitor comparison

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

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.

What you get

Eight capabilities nobody else ships

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

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

These are the published distribution channels. For the unpublished v14 candidate, use the wheel or source-based Compose instructions above.

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.

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.

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

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:

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.

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):
  bash

  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:

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:

   bash

   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

Configuration

Environment variables (all optional):

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

v10 + earlier

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.

Performance tuning

v11.0 fast-mode hot path (default)

When MEMORY_MODE=fast (default):

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:

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

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

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

Trade-off — soft drop semantic

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

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

Crash recovery

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

Roadmap

Shipped in 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)

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

Non-monetary ways to help (equally appreciated)

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

Commercial / consulting

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

Philosophy

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

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

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

Contributing

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

License

MIT — see LICENSE.

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