tether

A shared memory layer for personal agents, across devices. tether is an MCP server backed by a local SQLite file. Any MCP-compatible agent can remember, recall, link, and forget durable notes — facts about you, your projects, your preferences — so context follows you instead of dying with each session.

It runs local-only with zero configuration. Point it at a hosted libSQL/Turso primary and the same file becomes an embedded replica that syncs your memory across every device in near-real-time.

Why

The near future is personal agents living across many devices — laptop, desktop, phone. For that to feel like one assistant rather than several amnesiac ones, memory has to be a substrate that follows you: readable and writable from every device and from any agent, not siloed inside a single tool.

tether is that substrate. It is deliberately a convenience layer — it makes an agent more useful when present, and never breaks the agent's work when degraded.

Related MCP server: AI-MemoryHub MCP Server

Status

v0.6.1. The core (four memory verbs + boot index + FTS5) shipped in v0.1; since then recall has grown a semantic arm, consolidation, an associative usage graph, a self-organizing store, and opt-in crystallization — each additive and each degrading cleanly to plain keyword recall. Every feature below is implemented.

Design and rationale start at docs/superpowers/specs/2026-07-03-tether-design.md; the associative core, seed-dominant recall, self-organizing store (Tier B1), and crystallization (Tier B2) each have their own design doc under docs/superpowers/specs/, with matching plans in docs/superpowers/plans/.

Design at a glance

• Four memory verbs: remember · recall · link · forget. (Enabling crystallization adds one reflection-control tool, dismiss_cluster — not a memory operation.)

• Upsert on write so the store doesn't rot into near-duplicates.

• Rich recall (id, type, title, body, tags, updated_at, plus a via receipt saying why each hit surfaced) so an agent can judge staleness and cite what it updates. body is a query-centered excerpt, not the whole memory — see Excerpts.

• An auto-loaded boot index — a compact one-line-per-memory list surfaced to the agent each session, so memory helps even when the agent doesn't think to search.

• Local-first, sync optional — the local path is untouched when no backend is configured; degradation never throws.

• Hybrid search, associative on top — FTS5 keyword hits and local static embeddings are fused, then a usage graph (explicit links, learned co-recall, semantic neighbours) pulls in connected memories. Every layer is additive and degrades to plain keyword recall.

• Safe under parallel tool calls — an agent that fires several remember/recall calls at once gets atomic, correctly-reported results; see Performance and durability.

Install

Requires Python ≥3.10 on Linux, macOS, or Windows.

Register it with Claude Code — with uv:

claude mcp add tether -- uvx tether-memory

…or install it first:

pip install tether-memory
claude mcp add tether -- tether-memory

(The package is named tether-memory on PyPI — tether was already reserved as a common brand name. tether in claude mcp add tether -- ... is just the label Claude Code uses to refer to this server; it doesn't need to match the installed command.)

Or add it as a Claude Code plugin (this repo doubles as a one-plugin marketplace; the plugin runs uvx --from 'tether-memory[semantic]', so semantic recall comes along):

/plugin marketplace add sidyellur/tether
/plugin install tether@tether

It is also listed in the MCP Registry as mcp-name: io.github.sidyellur/tether.

By default memory lives in a local SQLite file at ~/.local/share/tether/memory.db — on Windows, %LOCALAPPDATA%\tether\memory.db (override either with TETHER_DB, or set XDG_DATA_HOME, which is honored on every platform). No accounts, no network — this is the whole tool for a single machine.

Project awareness

tether knows which project it is serving: Claude Code sets CLAUDE_PROJECT_DIR in every MCP server's environment, and tether takes the directory's name as the project (override or disable with TETHER_PROJECT). That drives three things, none of which need configuration:

• The boot index leads with this project. The auto-loaded index opens with a # This project (<name>) section, then # Everything else, so the agent starts a session already looking at the decisions and gotchas for the repo it is in rather than whatever you touched last, anywhere.

• Work memories are tagged automatically. project, feedback and reference memories get a proj:<name> tag unless the agent passes a proj: tag itself. user memories are about you, not the work, and stay global. The tag is an ordinary tag: recall(tags="proj:<name>") lists a project's memories deterministically.

• Recall prefers this project on a near-tie. A hit tagged with the current project ranks a few places ahead of an equally-good hit from another project; it never outranks a clearly better match, and untagged memories are neither boosted nor penalized.

Nothing falls back to the working directory: outside Claude Code (or with TETHER_PROJECT=off) tether behaves exactly as before.

Sync across devices (optional)

Point tether at a Turso / libSQL database and the local file becomes an embedded replica — local-speed reads, writes that propagate to your other devices. Install the extra and set two env vars:

pip install 'tether-memory[sync]'
export TETHER_SYNC_URL='libsql://<your-db>.turso.io'
export TETHER_SYNC_TOKEN='<your-auth-token>'

If the backend is unreachable, tether logs sync offline and keeps working against the local file. Sync is a single-primary design: every write goes to the hosted primary and replicas only pull, so a device that was merely offline catches up (including tombstones from forget) on its next pull and can never resurrect a memory another device forgot. The one gap: writes made while degraded land only in that device's local file and are not merged back later (#99 tracks reconciliation).

Writes push immediately. Reads also pull, debounced to at most once every TETHER_SYNC_READ_INTERVAL seconds (default 30) — so a device that only asks things still sees what your other devices wrote, instead of staying frozen at its own startup state until it happens to write something. The read-path pull is bounded much more tightly than the write-path one: if the backend is slow, the recall returns local data and the pull lands for the next read rather than making you wait.

One thing to know about replicas: libSQL forwards every write to the hosted primary, and with the associative graph on (the default) recall writes too — it records what was recalled together so memories can wire up over time. On a replica that makes each recall a few network round-trips on top of the local search. If that matters more to you than learned associations, TETHER_ASSOC=0 makes recall read-only again.

Keyword search

The keyword arm is SQLite FTS5 over title, body and tags, ranked by bm25. Ask in plain language: a memory that contains some of the query's words is a hit, and one that contains more of them ranks higher, so "how do we run the integration tests?" finds the note that says "pytest runs the tests" (common function words are ignored). The index stems English words, so tests matches test and deciding matches decided. Stemming is English-only; turn it off for a store in another language and tether rebuilds the index on the next start.

Measured on the LoCoMo long-conversation benchmark (one memory per dialogue turn, 1,536 questions, "did recall return the turns that answer it" in the top 10), the keyword arm alone finds the evidence for 61% of questions, against 54% for a textbook BM25 over the same text. That harness ships in the repo — see Benchmarks.

Semantic search (optional)

By default recall is hybrid: keyword (FTS5) results are fused with semantic (vector) results, so a query finds relevant memories even when the exact words differ ("automobile" recalls a note about your "car"). Semantic recall runs a small static embedding model locally — no network, no API key, nothing to hang on. Install the extra:

pip install 'tether-memory[semantic]'

Without the extra (or with TETHER_SEMANTIC=0), tether runs keyword-only FTS5 — semantic is a pure add-on and never a requirement. The first run embeds existing memories once (a one-time backfill); after that it is incremental.

Environment:

Consolidation (optional)

tether keeps a superseded fact rather than overwriting it: when a memory is replaced, the old one is marked no longer current (retained for history) and excluded from recall and the boot index. Recall also gently favors more recent facts. Two opt-in behaviors go further:

Consolidation never deletes — forget soft-deletes (see Tools), and only the admin CLI's purge is permanent. All of this degrades to plain keyword recall when the semantic extra is absent.

Associative recall (optional)

recall doesn't just return keyword/semantic matches — it follows a usage graph to related memories, so asking about one thing surfaces its connected context. The graph's edges come from three local, deterministic sources — no LLM, no network:

• semantic — nearest neighbours by embedding (needs the [semantic] extra),

• explicit — the link() verb,

• hebbian — memories you recall together get wired together over time.

Every hit carries a via receipt saying why it surfaced (a direct match, or the edge it came through), and two optional recall args tune it:

Associative recall is seed-dominant: the top direct matches are locked in place, and associations only fill the slots below them — so turning association on never demotes a hit that keyword/semantic search already ranked highly.

With TETHER_ASSOC=0 (or budget=0, or an empty graph), recall behaves exactly as before — associative recall is purely additive and never breaks a lookup.

Self-organizing store (optional)

As a store grows, tether keeps it legible using the same usage graph:

• Hub-curated boot-index. The auto-loaded memory index is capped once it passes TETHER_BOOT_INDEX_CAP (default 50) — the cap always applies, so a large store never gets an unbounded index. With a graph, above the cap it shows two labeled slices — load-bearing memories (highest behavioral degree: explicit links + learned co-recall, never mere similarity) and the most recent ones — so the index stays small and shows what actually matters. Without a graph (TETHER_ASSOC=0), it falls back to a plain most-recent-N list instead of the hub/recency split. Below the cap it's the full newest-first list either way.

• Forgetting-by-disconnection (opt-in, TETHER_FORGET). A bounded sweep runs every TETHER_FORGET_INTERVAL writes and soft-archives memories that are both old (TETHER_FORGET_AGE_DAYS, default 90) and behaviorally isolated (no explicit/hebbian edge — semantic similarity doesn't count). Archived memories drop out of recall and the boot-index but are retained and reversible (it reuses the same mark-invalid machinery as consolidation; nothing is deleted). Safety rails: never runs without a live behavioral graph, below 2 × CAP memories, or more than TETHER_FORGET_MAX_PER_SWEEP (default

  10. per sweep.

With TETHER_FORGET off (default) and a normal store size, recall and the boot-index behave exactly as before.

Crystallization (optional, off by default)

With TETHER_CRYSTALLIZE=1, tether reflects: it detects dense clusters of related memories and offers them for naming. Read tether://crystallization during a reflection pass (it is pull-only, never auto-loaded) to get candidate clusters; name a real principle with remember(..., crystallizes=[source_ids]) — which writes the principle and links it over its sources — or drop a candidate with dismiss_cluster(id_a, id_b). Clusters are seeded by explicit links + usage (semantic similarity fills out membership), so this finds "these belong together" structure, not mere topical similarity. tether finds the structure; your agent supplies the words.

A crystallized principle becomes a boot-index hub and is reachable from its sources in recall. Note: this makes "named" a third importance signal alongside "used" and "linked" — deliberate, since an agent judging something principle-worthy is a strong signal.

Tools

Plus three resources: the auto-loaded tether://memory-index (a compact one-line-per-memory index surfaced each session), the pull-only tether://status (runtime config: semantic/sync state, memory and edge counts, DB path — for debugging what's actually active), and, with TETHER_CRYSTALLIZE, the pull-only tether://crystallization (candidate clusters for a reflection pass).

Excerpts

recall returns a relevance-centered excerpt of each memory's body, not the whole thing — the window is centered on the first query term that appears, so you see why the memory matched rather than just its opening lines. A hit that was cut also carries truncated: true and body_chars (the real length), and you fetch the one memory you actually want in full with recall(id=N).

This is the search-engine shape: the result list is an index of pointers with enough text to judge relevance, not a payload of documents. It matters because memories can be large — a single 44KB journal memory made unrelated queries cost ~57–67KB per call while the retrieval itself took under a millisecond. The response was fat, not the engine:

A memory shorter than the excerpt width is returned whole and unmarked, exactly as before.

Performance and durability

tether is meant to be invisible in an agent's loop, so the hot paths are measured and kept flat as the store grows. Numbers below are from a local SQLite store with the semantic and associative layers on, single process:

A few things that make this hold:

• Writes don't scale with the store. The embedding matrix used for semantic search and neighbour wiring is kept in memory and patched row by row on every write, rather than re-read from SQLite. It is rebuilt only when vectors change wholesale (a model change, a backfill) or when another process has written to the file (a CLI purge, a second server, a sync pull) — SQLite's data_version counter catches that.

• Parallel tool calls are serialized. MCP runs each tool call on its own thread, and agents issue calls in parallel. All Store operations take one lock, so a recall and a remember arriving together are each atomic: no interleaved transactions, no half-committed writes, and action is always right.

• Commits don't fsync. Local connections run WAL with synchronous=NORMAL: still safe against corruption, but the last few transactions can be lost if the machine loses power before a checkpoint (an application crash loses nothing). Every remember and, with the graph on, every recall commits, so this is one disk sync saved per call.

• Search stays cheap. Vector search is a single numpy matmul over the in-memory matrix — well under a millisecond at thousands of memories, which is why there is no vector-index extension to install. Keyword cost is FTS5's: proportional to how many memories match the query.

Costs to expect once: the first boot after installing the [semantic] extra (or changing the model) embeds every existing memory and wires its neighbours, which takes a second or two per few thousand memories. The boot index and tag-only lookups scan the store on each call; both are fast at typical sizes (under 10 ms at 2,000 memories) and are the next things on the list to cache.

Benchmarks

Two harnesses live in bench/, both runnable without an API key:

• python -m bench.locomo — retrieval-only evaluation on LoCoMo, ten long multi-session conversations with ~1,500 questions each labelled with the dialogue turns that answer it. Every turn becomes a memory; the score is whether recall(question) returns those turns (recall@k, MRR), per condition: keyword only, keyword + semantic, and the full associative path, next to a textbook BM25 baseline. No LLM is involved, so the number measures the one thing a memory layer controls — did it hand the agent the right facts — and is not comparable to the LLM-judged "accuracy" figures memory vendors publish on the same dataset. The data (~1.5 MB) is downloaded on first use. Install the [semantic] extra to measure the semantic and associative conditions with the real model.

• python -m bench.run — tether's own associative-recall evaluation: a controlled corpus of tasks whose members are used together, measuring what the usage graph adds over keyword + semantic search after simulated use. Needs the [semantic] extra.

Export and permanent deletion

forget never deletes data — it soft-deletes, like consolidation. Two admin operations, deliberately kept off the MCP tool surface so an agent can't trigger them, live in a small CLI instead:

tether export                    # dump all current memories to JSON (stdout)
tether export -o backup.json     # ...or to a file
tether import backup.json        # merge an export back into the store
tether restore <id>              # un-forget a soft-deleted memory
tether purge <id> --yes          # permanently delete a memory (bypasses forget)

import replays records through the normal write path, so it upserts on type+title like remember does — importing into a non-empty store merges rather than duplicating. Ids are not preserved (an id in the file may map to a different one here); links are remapped accordingly, and a link pointing outside the file is dropped rather than pointed at the wrong memory. The report tells you what happened: {"created", "updated", "skipped", "linked", "dropped_links"}.

restore clears valid_to, reversing a forget (or a consolidation, or a forgetting sweep). It refuses if a newer memory has since claimed the same type+title, naming the blocker rather than failing opaquely.

purge refuses to run without --yes. All commands honor the same TETHER_DB/TETHER_SYNC_* env vars as the server.

License

MIT