Qdrant
Dense semantic memory for Hermes, stored in a Qdrant collection you host. Each turn becomes one point — user and assistant text verbatim, no LLM summarization step — embedded on-device at 384 dimensions with fastembed (ONNX; the default install is 206 MB of runtime dependencies with no torch, versus ~1.2 GB when sentence-transformers was a hard dependency) and recalled by cosine search with an optional session_id payload filter, so a result quotes what was actually said. Registers 7 tools: qdrant_search, qdrant_upsert, qdrant_recall, qdrant_collect, qdrant_prepare, qdrant_forget (point-targeted deletion, dry-run by default, no delete-all), and md_search (local markdown knowledge index). An optional recall-indicator display, display.level (off | summary | verbose, default off), adds latency and collection to the chat indicator at summary and one numeric line per operation to the log at verbose — numbers and identifiers only, never recalled message text or search-result snippets (the md_search line also echoes the query you searched — your own input, not recalled content); it is separate from the older top-level progress knob (off | minimal | verbose), which gates status events. sentence-transformers is selectable on GPU hosts; both backends produce identical vectors for all-MiniLM-L6-v2, so switching backends needs no re-embedding. A circuit breaker (5 consecutive I/O failures, 120 s cooldown) degrades to no new memories instead of crashing, and is_available()/check_backend()/unavailable_reason() distinguish a misconfiguration from a dead server. Optional hybrid dense+sparse RRF search (default off): one config key enables dense+BM25 RRF fusion. A fresh collection declares the sparse vector at creation and an existing dense-only store gets it added in place (Qdrant >= 1.18) with one bundled backfill command; measured +0.024 recall@10 on the LongMemEval 1,000-example benchmark. INT8 quantization is not implemented. Local embedded Qdrant mode is not supported: the provider is REST-only and needs a reachable server.
Good to know before installing
All memory content (verbatim user and assistant turns) is stored in and read back from the Qdrant server at the configured url (default http://localhost:6333); QDRANT_API_KEY is only needed for Qdrant Cloud, is kept in .env and read through the Hermes secret scope, and is never written to config.yaml or qdrant.json. Disclosure — the only other outbound traffic is a one-time download of the embedding model weights (plus the 10 MB Qdrant/bm25 sparse model when hybrid is on) from huggingface.co (fastembed falls back to storage.googleapis.com/qdrant-fastembed if that fails); embedding runs on-device and no memory content goes to any other party. Disclosure — settings are stored in <HERMES_HOME>/qdrant.json (created 0600, no credentials) and last-operation status in <HERMES_HOME>/qdrant-status.json. Disclosure — md_search returns snippets of local markdown to the model from ~/.hermes/skills, the Obsidian vault at ~/Documents/hermes_default, ~/.hermes/hermes-agent/website and any path in the md_docs_roots key of qdrant.json; its index is built only when you run scripts/md_ingest.py and lives in <base Hermes home>/state/md-search/ (shared by all profiles). Disclosure — the optional semant
From the plugin's catalog listing.
How to install
- Install it. Hermes asks for any API keys it needs, then asks whether to turn it on.
hermes plugins install qdrant - Said no? Turn it on later:
hermes plugins enable qdrant - Restart Hermes so it loads the plugin.
Some plugins also need a sign-in or settings: see Full setup & settings. Memory plugins ask whether to become your memory provider instead. Steps follow the official plugins guide (opens in a new tab).
- Category
- Memory & notes
- Maintained by
- kuehnberger (Community)
- Needs
- No API keys declared
- Adds tools
qdrant_searchqdrant_upsertqdrant_recallqdrant_collectqdrant_prepareqdrant_forgetmd_search- Needs Hermes
- 0.21.5 or newer
- Version
- 0.1.8
- In the catalog
- 2026-10-02, updated 2026-10-16
Full setup & settings ↗Source code ↗
More in memory & notes
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