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Keymem

Associative key-graph memory for LLM agents — recall by association, not vector similarity.

Connector by donggyun112 · GitHub account verified by the MCP Registry

IndexedMITLocal (stdio)★ 3
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Works with Claude Code, Codex, VS Code, Cursor, Windsurf, Claude Desktop. Read the repository before you give any tool access to your data.

npx agentmarket install connector/donggyun112-keymem

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About

Associative key-graph memory for LLM agents — recall by association, not vector similarity.

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions

What this server can do and what it asks for, from its tool list, install config and code. Checked Oct 25, 2026.

Tools
5 read · 2 write · 0 delete · 3 not yet classified
Secrets
None asked for
Install scripts
None found
Runs as
Node.js package

Tools by what they do 10

Read from the source code; the running server may list different tools.

Read 5 · Look things up without changing anything.

  • browse_keys
  • dismiss
  • read_key
  • read_memory
  • recall

Write 2 · Create, change, send or run things.

  • confirm_memory
  • correct

Not yet classified 3 · We could not tell from the name and description.

  • forget
  • remember
  • remember_batch

Network hosts it talks to 3

  • github.com · found in code
  • glama.ai · found in code
  • huggingface.co · found in code

Files it touches

  • /Users/dongkseo/project/super-memory/.worktrees/direct-hydrate-shadow/bench/assoc-fixture.json
  • /Users/dongkseo/project/super-memory/.worktrees/direct-hydrate-shadow/bench/fixture.json
  • Your home directory (~)
  • Any path the agent passes to its tools

Tools (10)

Read from the source code; the running server may differ. Descriptions are the server’s own words; hidden characters are shown as [U+…].

  • browse_keys

    Browse the vocabulary of one namespace when recall has no hit or you need an entry point. Returns active key clusters with hubs first, then by linked-memory count. This is index metadata only; continue with read_key(key_id, query, namespace) and read_memory(memory_id, via_key_id, namespace).

  • confirm_memory

    Confirm that a memory is still current using explicit present evidence. Never call this merely because read_memory returned the content. Use only after a current user assertion, an authoritative current source, or direct observation. Refreshes validity but does not change content or key links. An exact normalized restatement passed as recall's `context` is already auto-confirmed there (check memories[0].auto_confirmed) — this tool is for evidence recall can't see: read_memory results, authoritative sources, or direct observation.

  • correct

    Update outdated information. Use when user corrects you or info changes (e.g. moved cities, changed job). Old version is preserved but weakened — never lost. Omit keys to keep the same search terms. Omit decay_profile and ttl_seconds to preserve the predecessor's policies; provide either to replace that policy. related_to links the updated memory to other memory IDs.

  • dismiss

    Tell keymem a recalled memory was surfaced by the WRONG key — the fact may be fine, it just should not have come up for this query. Pass the memory_id and the key_id it arrived under (recall returns both). Weakens that one key->memory link so the pairing ranks lower next time, and cancels any pending alias learning for it. The memory itself, its other keys, and its content are untouched, and the link is floored rather than severed, so nothing becomes unreachable. Use correct() when the fact changed and forget() when it is simply wrong.

  • forget

    Permanently delete a memory. Only use for completely wrong information. For outdated info, use correct() instead — it preserves history.

  • read_key

    List the memories stored under one key (concept), ranked. Returns the canonical key, its aliases, and hub metadata plus ranked memory IDs, metadata, and validity — never memory content. Pass a focused query for the missing fact and the active namespace when known: handles are then ranked by content relevance, which is essential for hubs. Each memory's score is content_relevance × link_weight × depth_factor × freshness_factor when query is passed (link_weight × depth_factor × freshness_factor otherwise); content_relevance is normalized lexical relevance or reranker relevance, never body-vector …

  • read_memory

    Read the full content and validity of one stored memory (selected via read_key). Returns the memory and all connected key clusters so exploration can continue Key → Memory → Key. Pass via_key_id from the selected key: the read records access and only that traversed edge is Hebbian-reinforced. Reading does not change content depth or confirm that the content is current.

  • recall

    Search long-term memory for what is already known about the user, project, or topic — call this before your first reply and whenever the topic shifts. Always pass the active namespace when known. Returns {status, query, namespace, keys, memories}: ranked key clusters plus one passive Top-1 memory selected under the top key. The memory includes validity, matched_key, and connected_keys, each with a relevance score (cosine of that key to your query/context, sorted high→low). Before answering, check whether the Top-1 memory supplies every fact the question needs. If a fact is missing, follow an u…

  • remember

    MANDATORY END-OF-TURN GATE: before replying, save every durable fact newly revealed this turn (names, preferences, decisions, corrections, project facts, goals). A durable fact left unsaved is a bug; save silently in the same turn. Save nothing only after consciously confirming that nothing durable appeared. Before writing, recall() the topic in the same namespace and reuse returned canonical concepts or aliases. Use 3-6 diverse ATOMIC concept keys of 1-2 words each, never memory-specific phrases (use 'Nexora' and 'portfolio', not 'Nexora portfolio'); 3+-word keys are flagged in hints.phrase_k…

  • remember_batch

    MANDATORY END-OF-TURN GATE: when a turn reveals multiple durable facts, save them silently before replying. A durable fact left unsaved is a bug. Recall each topic first, reuse canonical concept-level keys (ATOMIC, 1-2 words each — never phrases), and register cross-lingual forms together. Each item: {content, keys, key_types?, namespace?, ttl_seconds?, decay_profile?, related_to?}; decay_profile defaults to standard. Returns saved IDs and is more efficient than multiple remember() calls.

Signals

Stars
3
Forks
0
Open issues
0
Last commit
Oct 22, 2026 (8 days ago)
Last release
Oct 22, 2026 (8 days ago)

From GitHub (fetched Oct 24, 2026).

Details

Registry name
io.github.donggyun112/keymem
Version
0.17.0
Auth
No auth
Packages
npm keymem
License
MIT
First seen
Oct 24, 2026
Last checked
Oct 25, 2026

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Keymem MCP server. Agent Mart. https://agentmart.dev/connectors/donggyun112-keymem (last updated 2026-10-25)