# Show HN: TenseMem – deterministic memory for AI agents

> Source: <https://github.com/tensemem/tensemem>
> Published: 2026-10-22 05:19:23+00:00

**Memory that knows its tenses.** Embedded, deterministic memory that AI agents can safely act on.

One SQLite file. No server required. The model proposes typed facts; the engine decides what is current.

```
pip install tensemem
python
from tensemem import FakeExtractor, Memory

mem = Memory(llm=FakeExtractor())
mem.register_predicate("lives_in")
added = mem.add("u1", "I live in Delhi")
ctx = mem.context(user_id="u1", min_seq=added.seq, wait=1.5)
print(ctx.text)  # fenced block the model can read
print(ctx.freshness.is_current)  # True once extract finished
```

`FakeExtractor` is rule-based and needs no API key. Same API with `tensemem[openai]`, `tensemem[anthropic]`, `tensemem[ollama]`, `tensemem[litellm]`, or Gemini (` GEMINI_API_KEY` in the process environment).

Similarity search returns both “I live in Delhi” and “I moved to Bangalore” and leaves the model to guess. TenseMem keeps one current value per slot, says whether the read is fresh, and refuses to learn from its own output.

- **One current truth per ONE slot.** A new city supersedes the old one. History still answers “where did I live before?”
- **Every read reports freshness.**`as_of_seq` ,`pending` ,`is_current` . Wait for a message, or see that it is not applied yet.
- **No self-echo.** Recalled`context()` is fenced. Extract needs a verbatim quote from the user.
- **Corrections stick.**`correct()` restores the displaced value and survives`rebuild()` with a newer model.
- **The engine decides.** The LLM emits typed ops. Deterministic rules apply them.
- **No infrastructure.** Default store is one SQLite file inside your process.

```
mem.add("u1", "I live in Delhi")
mem.set(user_id="u1", predicate="lives_in", value="Bangalore")

print(mem.state("u1").values)
# {"lives_in": "Bangalore"}

history = mem.history("u1", predicate="lives_in")
# ACTIVE Bangalore, SUPERSEDED Delhi

bangalore = next(f for f in history.facts if f.object == "Bangalore")
mem.correct(bangalore.id)  # that move was never true
print(mem.state("u1").values)
# {"lives_in": "Delhi"}
php
flowchart LR
  E[episodes] --> X[extractor proposes ops]
  X --> R[resolver]
  R --> F[facts]
  F --> C["context() + freshness"]
  C -.->|fence stripped| X
```

Every surface is an adapter over `Memory` / `AsyncMemory`. The model proposes typed ops; `resolve()` decides mutations; `apply()` writes one SQLite file. Facts are a rebuildable projection of an append-only episode log.

Editable source: [docs/architecture.excalidraw](https://github.com/tensemem/tensemem/blob/main/docs/architecture.excalidraw). File map: [Architecture](https://github.com/tensemem/tensemem/blob/main/docs/architecture.md).

| If you are changing… | Start here | 
|---|---|
| Python / CLI / REST / MCP API | `src/tensemem/memory.py` ,`cli/main.py` ,`server.py` ,`mcp.py` | 
| Studio UI | `studio/app/` (board, memories, playground, echo, ops) and`studio/lib/api.ts` | 
| Extract prompt or worker | `pipeline/prompts/extract_v1.md` ,`pipeline/worker.py` ,`pipeline/extractor.py` | 
| What an op is allowed to do | `core/resolver.py` (pure),`pipeline/normalize.py` ,`pipeline/policy.py` ,`pipeline/guard.py` | 
| A new LLM or embedder | `providers/llm/` ,`providers/embed/` — keys in process env only | 
| SQLite schema or reads | `storage/sqlite/` (one writer; ADR 0003 / 0020) | 
| Freshness, fence, corrections | `storage/sqlite/snapshot.py` ,`security/fence.py` ,`correct()` | 
| Invariants | `tests/core/` ,`tests/property/` ,`tests/golden/` — default pytest never calls a hosted model | 

Do not auto-register unknown predicates, skip `keep_ops`, or teach the engine from fenced `context()` text. Those are locked by tests.

`tensemem bench` scores the packaged 101 conversations with `FakeExtractor` (no network):

| System | Current-state accuracy | Stale-fact leak | Freshness honesty | Contamination | 
|---|---|---|---|---|
| TenseMem | 1.00 | 0.00 | 1.00 | 0.00 | 
| Concatenated transcript | 0.37 | 1.00 | 0.00 | n/a | 
| Token-overlap retrieval | 0.37 | 1.00 | 0.00 | n/a | 

Reproduce with `tensemem bench`. Methodology: [TenseBench](https://github.com/tensemem/tensemem/blob/main/docs/tensebench.md).

```
tensemem inspect --user u1
tensemem history --user u1 lives_in
tensemem search --user u1 "where did I live"
```

[TenseMem Studio](https://github.com/tensemem/tensemem/blob/main/docs/studio.md) is a local Next.js inspector over `tensemem serve` (board, memories table, playground, compare, echo). It is not a chatbot. Secrets stay in the serve process environment — never in the Token field. Studio lives in the git checkout, not on PyPI.

```
git clone https://github.com/tensemem/tensemem.git
cd tensemem
pip install "tensemem[server]"
```

Terminal 1 — HTTP:

```
tensemem --db ./studio.db serve --port 8080
```

Terminal 2 — Studio:

```
cd studio && npm install && npm run dev
```

| Extra | What you get | 
|---|---|
| `tensemem[openai]` /`[anthropic]` /`[ollama]` /`[litellm]` | Hosted or local extract | 
| `tensemem[server]` | `tensemem serve` REST + Studio backend | 
| `tensemem[mcp]` | stdio or `tensemem mcp --http` | 
| `tensemem[langgraph]` | `TenseMemStore` (`put` /`search` ) | 
| `tensemem[llamaindex]` | `TenseMemChatMemory` (`put` /`get` ) | 
| `tensemem[local]` | FastEmbed vectors for search | 

Gemini extract uses `GEMINI_API_KEY` (or `GOOGLE_API_KEY`) in the environment of the process that runs extract. No extra package beyond the server if you use the built-in Gemini adapter.

`--token` / `TENSEMEM_TOKEN` is optional bearer auth on HTTP. Bind stays loopback unless you put a TLS proxy in front.

**0.1.0 alpha.** The Python API, CLI, REST, MCP, and Studio are usable on a single SQLite file. The default store is SQLite; a second SQL dialect is not shipped. Multi-tenant hosted Studio is not in this tree.

[Quickstart](https://github.com/tensemem/tensemem/blob/main/docs/quickstart.md) · [Concepts](https://github.com/tensemem/tensemem/blob/main/docs/concepts.md) · [Architecture](https://github.com/tensemem/tensemem/blob/main/docs/architecture.md) · [API](https://github.com/tensemem/tensemem/blob/main/docs/api.md) · [CLI](https://github.com/tensemem/tensemem/blob/main/docs/cli.md) · [REST](https://github.com/tensemem/tensemem/blob/main/docs/server.md) · [Studio](https://github.com/tensemem/tensemem/blob/main/docs/studio.md) · [MCP](https://github.com/tensemem/tensemem/blob/main/docs/mcp.md) · [Operations](https://github.com/tensemem/tensemem/blob/main/docs/ops.md) · [ADRs](https://github.com/tensemem/tensemem/blob/main/docs/adr)

`uvx mkdocs serve` previews the same pages locally.

Default tests need no API keys and never call a model. From a git checkout:

```
git clone https://github.com/tensemem/tensemem.git
cd tensemem
uv sync
uv run ruff check .
uv run ruff format --check .
uv run pyright
uv run pytest
```

Studio (optional, when you change `studio/`):

```
cd studio && npm ci && npm run typecheck && npm run build
```

Live extract against a real model (skipped unless both are set):

```
uv sync --extra openai
TENSEMEM_LIVE=1 OPENAI_API_KEY=… uv run pytest -m live
```

Model keys belong in the process that runs extract (your script, or the `tensemem serve` terminal). They are never valid values for Studio **Token**.

| Variable | Used for | 
|---|---|
| `GEMINI_API_KEY` or`GOOGLE_API_KEY` | Gemini extract | 
| `OPENAI_API_KEY` | OpenAI extract / embeddings ( `tensemem[openai]` ) | 
| `ANTHROPIC_API_KEY` | Anthropic extract ( `tensemem[anthropic]` ) | 
| `TENSEMEM_LLM` | Default extractor name for `tensemem serve` (`fake` ,`gemini` ,`openai` , …) | 
| `TENSEMEM_LITELLM_MODEL` | Override Gemini / LiteLLM model id | 
| `TENSEMEM_TOKEN` | Optional bearer for `tensemem serve` /`tensemem mcp --http` (not a model key) | 
| `TENSEMEM_URL` | Studio rewrite target (default `http://127.0.0.1:8080` ) | 
| `TENSEMEM_LIVE` | Set to `1` with`OPENAI_API_KEY` to run`pytest -m live` | 

Ollama needs a local Ollama process, not an env key. Details: [Studio](https://github.com/tensemem/tensemem/blob/main/docs/studio.md), [REST](https://github.com/tensemem/tensemem/blob/main/docs/server.md), [SECURITY.md](https://github.com/tensemem/tensemem/blob/main/SECURITY.md).

See [CONTRIBUTING.md](https://github.com/tensemem/tensemem/blob/main/CONTRIBUTING.md). `main` is protected; every change is a pull request. Merges wait until **CI**, **PR quality**, and CodeQL are green.