TruLens is an open-source system for tracing and evaluating LLM applications and AI agents. It records each step's inputs, outputs, latency, tokens, and cost, then uses evaluations to find failures and compare application versions.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add truera/trulens --skill hybrid-searchgit clone --depth 1 https://github.com/truera/trulensWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/truera/trulens/hybrid-search)<a href="https://agentmods.dev/skills/truera/trulens/hybrid-search"><img src="https://agentmods.dev/badge/skills/truera/trulens/hybrid-search/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/truera/trulens/hybrid-search"><img src="https://agentmods.dev/badge/skills/truera/trulens/hybrid-search.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00095 | $0.00797 |
| Opus 5.5 | $0.00038 | $0.00319 |
| Sonnet 5.5 | $0.00019 | $0.00159 |
| Haiku 4.5 | $0.00010 | $0.00080 |
Grade A, and why
qdrant-hybrid-search scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 22d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
2 near-identical copies found in the catalogue:
- qdrant-hybrid-search — 100% identical, 9 lines differ
- qdrant-hybrid-search — 94% identical, 9 lines differ
How it starts
The opening of the file, as written. The whole thing — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hybrid Search in Qdrant
Hybrid search means running two or more different searches in parallel and combining their results into one.
In Qdrant this is powered by the Query API via prefetch: each prefetch runs exactly one type of search independently, and the outer query combines results from parallel prefetches.
Prefetches can be nested and searches can be multi-stage, all pipeline happening in one request through Query API. See Universal Query API for examples.
Identify the user's problem and pick building blocks:
- What can go into one prefetch, e.g. power one search, in Search Types
- How to combine results of these searches (RRF, DBSF, FormulaQuery, reranking) in Combining Searches
Based on what you've picked, test your approach:
- Configure Qdrant collection with named vectors, where each named vector usually corresponds to one representation (different embedding models or different vector types) of a data point.
- Construct a hybrid search request with Query API from your building blocks. You can search independently among one type of vectors, with
prefetch+using, like shown in examples in Hybrid Queries documentation. - Evaluate hybrid search quality on real user data and provide user with improvements and tradeoffs (speed/resources).
How Isolated Are Parallel Searches?
Use when: different tenants share one collection and you need to understand hybrid search isolation guarantees.
If user wants to isolate/share hybrid search pipelines between tenants, consider that:
- Indexes (sparse, payload and dense) and IDF modifier for sparse vectors are computed independently per shard, not per tenant, by default — payload-based tenant partitioning alone does not isolate IDF statistics. On Qdrant 1.19 or newer, the
idfsearch param can scope IDF statistics to a payload-filtered corpus (requires a payload index on the filtered field), giving each tenant properly isolated BM25 scoring instead of shard-wide statistics. - Prefetch runs independently per shard to retrieve #limit results, so for collection-level prefetches if collection has several shards, Qdrant will always prefetch under the hood #limit * #shard results. Final results are merged based on scores.
- In nested prefetches (deeper than 1 level), methods described in "Combining Searches" might be done on a shard level first, then per-shards results once again will be merged based on scores.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 22d ago First seen · 40 lines · 95 tokens per session scan A 12ff074420f5
qdrant-hybrid-search is a skill published in the GitHub repository truera/trulens (3,602 stars, last pushed today), licensed MIT. It adds 95 tokens to every session and 797 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-24.
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