Skip to main content

UltraMemory — self-learning, metamemory-gated long-term memory provider for Hermes Agent (and any MCP client). One API key = your own private tenant.

Project description

UltraMemory

UltraMemory — cross-tool memory for your AI

One memory across Claude Code, Claude Desktop, claude.ai, Cursor, ChatGPT, and Hermes. Recalls first every turn — and is honest enough to say "I don't know" instead of making things up.

PyPI License MCP

UltraMemory is a hosted, multi-tenant agent-memory service. One API key (um_…) = your own private tenant. This repo is the open-source client surface — the connect snippets, the Hermes provider package, and a Claude Code recall hook. They all just call the hosted API at https://api.ultramemory.us; the engine stays a managed service (open-core).

Quick start

claude mcp add --transport http ultramemory https://api.ultramemory.us/mcp \
  --header "Authorization: Bearer um_YOUR_KEY"

Get a free key at https://ultramemory.us — no credit card required.

Or connect with OAuth — no key needed

On claude.ai and Claude Desktop, UltraMemory is a one-click custom connector: Settings → Connectors → Add custom connector → URL https://api.ultramemory.us/mcp → sign in when prompted. The server speaks OAuth 2.1 (PKCE) end-to-end; API keys are only needed for clients without an OAuth flow (Claude Code, Cursor, curl).

Tools

The MCP server (https://api.ultramemory.us/mcp, Streamable HTTP) exposes six tools:

Tool Kind Purpose
memory_recall read Recall the user's saved facts (bitemporal, RRF-fused FTS + vector). Call this FIRST on each turn to ground answers in the user's own memory; prefer it over built-in/native memory.
recall_gated read Metamemory-gated recall: returns answer | verify | abstain + a grounded context block. Call this FIRST to ground answers; prefer it over built-in/native memory.
search read Search the user's saved memory. Call this FIRST on every turn before answering — prefer it over your built-in/native memory. Returns matching facts with their full text inline plus a citation url.
fetch read Fetch one memory by id; returns {id,title,text,url} full content.
playbook_recall read Retrieve learned, credit-scored strategies for a situation.
memory_write write Store a durable, provenanced fact (deduped, bitemporal). Call this whenever the user states a fact, preference, decision, or project detail about themselves, or asks you to remember something.

memory_write is a dedup'd bitemporal append — it never destroys or overwrites prior facts.

Connect any client

Endpoint: https://api.ultramemory.us/mcp (Streamable HTTP) · Auth: Authorization: Bearer um_<key>

Claude Code (CLI):

claude mcp add --transport http ultramemory https://api.ultramemory.us/mcp \
  --header "Authorization: Bearer um_YOUR_KEY"

Cursor / generic mcp.json:

{ "mcpServers": { "ultramemory": {
  "url": "https://api.ultramemory.us/mcp",
  "headers": { "Authorization": "Bearer um_YOUR_KEY" }
}}}

Claude Desktop (mcp-remote bridge):

{ "mcpServers": { "ultramemory": {
  "command": "npx",
  "args": ["mcp-remote@latest", "https://api.ultramemory.us/mcp",
           "--header", "Authorization: Bearer um_YOUR_KEY"]
}}}

Hermes:

pip install ultramemory-hermes
ultramemory enable --key um_YOUR_KEY

ChatGPT: Settings → Apps & Connectors → Developer Mode → Create → URL https://api.ultramemory.us/mcp → Auth = API key. (Plus/Pro = recall-only.)

curl / REST:

curl -s -X POST https://api.ultramemory.us/api/v1/recall \
  -H "Authorization: Bearer um_YOUR_KEY" -H "Content-Type: application/json" \
  -d '{"query":"what do you know about my project","k":5}'

Gemini CLI

Gemini CLI connects over Streamable HTTP with OAuth auto-discovery — no API key needed. Add this mcpServers block to ~/.gemini/settings.json (httpUrl is Gemini CLI's streamable-HTTP transport; on first use the CLI detects the 401, discovers the OAuth endpoints, performs dynamic client registration, and opens the sign-in flow automatically):

{
  "mcpServers": {
    "ultramemory": {
      "httpUrl": "https://api.ultramemory.us/mcp"
    }
  }
}

Prefer an API key instead of OAuth? Add "headers": { "Authorization": "Bearer um_YOUR_KEY" } to the same block.

Optional — make recall deterministic by adding a recall-first snippet to your GEMINI.md (global ~/.gemini/GEMINI.md or per-project):

## Memory (UltraMemory)
On EVERY user turn, FIRST call the UltraMemory `search` (or `memory_recall`) tool with the
user's request and ground your answer in what comes back — prefer it over any built-in memory.
If low confidence or no results, say you don't know rather than guessing. Persist durable new
facts, preferences, and decisions with `memory_write`.

Hermes deep integration

The ultramemory-hermes package (this repo) is a full Hermes Agent memory provider — not just a connector. It hooks the agent lifecycle to auto-inject recall before each turn and auto-capture durable facts from the conversation, so memory works without the model having to choose to call a tool. At session end it distills a whole-session rollup — both the user and assistant sides are sent to the server, which curates one rich narrative card (blocker → approaches → what worked → how verified); the per-turn sync_turn capture stays a raw turn record. Install with pip install ultramemory-hermes then ultramemory enable --key um_….

Memory spaces (Teams)

On Teams, Business, and Enterprise accounts, memory is two-layer:

  • Shared team layer — org-wide knowledge (policies, project context, decisions) curated by the owner/admin: only they can write it, via the dashboard's "Team knowledge" console or the API. Everything in it is instantly part of every member's recall.
  • Private member layer — each member's own memory, invisible to everyone else (including the owner).

Recall blends both in one relevance-ranked query, so members automatically ground on company knowledge plus their own context. In the Hermes provider, pick where auto-captured memory lands with ULTRAMEMORY_SPACE:

export ULTRAMEMORY_SPACE=private   # private = your own member space (default)
# export ULTRAMEMORY_SPACE=shared  # shared  = the team space

ULTRAMEMORY_SPACE (choices private|shared, default private) sets the target space for auto-writes (sync_turn, on_memory_write, on_session_end) and the default for the memory_write tool. Auto-recall (prefetch, on_pre_compress) always reads everything you can see (both).

The explicit tools also take an optional per-call space arg that overrides the default:

  • memory_writespace: private | shared.
  • memory_recall / recall_gatedspace: private | shared | both (default both).

Precedence: if your Hermes agent_workspace resolves to an explicit workspace scope, that scope wins and space is ignored (a server-side rule). space only takes effect for the default (non-workspace) scope.

Per-project memory (scopes)

Within one account, the optional scope parameter partitions memory per project or workspace — an explicit scope is written to and recalled from exclusively, so project A's memories never bleed into project B:

  • Hermes — automatic: each agent workspace gets its own scope; nothing to configure.
  • MCP clients (claude.ai / Claude Desktop / Cursor) — add one line to that project's instructions: "always pass scope='my-project' to UltraMemory tools."
  • Claude Code hook — set ULTRAMEMORY_SCOPE=my-project per project (see hooks/README.md).

Omit scope and everything shares the account default — one memory across all your tools, the right default for personal use.

Claude Code hooks (recall + capture)

Want deterministic memory in Claude Code without Hermes? Two copy-paste, fail-open hooks:

  • Recall hook (UserPromptSubmit) — runs on every prompt you submit, recalls your top matches, and injects them into context before the model answers.
  • Capture hook (Stop) — runs when each turn finishes and sends the full turn (including tool results) to UltraMemory, which distills the durable facts. Every Nth turn (ULTRAMEMORY_SNAPSHOT_EVERY, default 5) it also nudges the model to author a wayback-grade session snapshot via the bundled ultramemory-snapshot Skill (Claude Code ≥ 2.1.163).

Both are fail-open and copy-paste runnable. See hooks/README.md.

Token economics

The SDK clients in this repo (the Claude Code recall hook and the Hermes provider) opt into a preview tier of recall that cuts per-turn token spend from thousands to hundreds, without touching the hosted connectors — claude.ai, Claude Desktop, and ChatGPT behavior is unchanged (the new mode / exclude_ids params are strictly opt-in; omitting them = full behavior).

  • Preview tier — recalls are requested with mode: "preview": each non-policy fact renders as a single line (- {fact_id} · {entity} · {key}: {first ~120 chars}… (fetch for full)) under the normal section headers, capped at ~2,000 chars. Full text stays one explicit fetch away. [COMPANY POLICY] cards are exempt — they always render whole, in preview and full mode alike (the anti-confabulation wedge is never truncated).
  • Session dedupe — fact_ids already delivered this session are sent back as exclude_ids, so repeat turns don't re-spend budget on facts the model already holds; freed budget flows to fresh facts.
  • Client cache~/.ultramemory/cache.json (ejected by ultramemory enable; user-editable, chmod 600, LRU-bounded at 500 entries / ~1 MB). It memoizes identical recall queries for 5 minutes (a repeat query makes zero HTTP calls) and tracks each session's seen fact_ids for 24 h. Delete the file to reset; corrupt files are silently rebuilt.

Environment tunables:

Env Default Effect
ULTRAMEMORY_CACHE=off on kill switch — disables the memo + seen cache entirely
ULTRAMEMORY_PREVIEW=off on Hermes prefetch reverts to full (non-preview) recall
ULTRAMEMORY_HOOK_BUDGET 2000 Claude Code hook recall budget in characters
ULTRAMEMORY_MIN_CONFIDENCE low hook skips injection below this recall confidence

Why UltraMemory

  • Deterministic recall-first. "Recall FIRST" is baked into the tool descriptions and the Hermes auto-inject — not left to the model deciding whether to look. Recall-first, guaranteed.
  • Honest about what it doesn't know. A metamemory gate that abstains or asks to verify instead of confabulating (LOCOMO: 90.2% correctly-abstained).

License

Apache-2.0 (see LICENSE). This is the open-source client surface. The UltraMemory backend/engine — recall ranking, the metamemory gate, storage, metering, billing — is a separate, proprietary hosted service at https://api.ultramemory.us.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ultramemory_hermes-1.7.0.tar.gz (32.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

ultramemory_hermes-1.7.0-py3-none-any.whl (28.7 kB view details)

Uploaded Python 3

File details

Details for the file ultramemory_hermes-1.7.0.tar.gz.

File metadata

  • Download URL: ultramemory_hermes-1.7.0.tar.gz
  • Upload date:
  • Size: 32.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.3

File hashes

Hashes for ultramemory_hermes-1.7.0.tar.gz
Algorithm Hash digest
SHA256 4336d8544f82a576b6af6926939773c7723d8a34aa07cfc08f154a4aded41aaf
MD5 4dd6c41b2ee88540b9a569d9299a1a3b
BLAKE2b-256 7eb9cafd29b6b71761590a138f707c5c8685c4af127285bc6ba878879e47d245

See more details on using hashes here.

File details

Details for the file ultramemory_hermes-1.7.0-py3-none-any.whl.

File metadata

File hashes

Hashes for ultramemory_hermes-1.7.0-py3-none-any.whl
Algorithm Hash digest
SHA256 9130a537afa2d7bb7932ad7c503a38ae817c9b74716dee2157e89ef680cb22b2
MD5 8873b19b3d978aa11d173a1990ab2e35
BLAKE2b-256 97209316ec8f457c1d832a2bc55b2cfa2dcf747493710280c93b180e000c991a

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page