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 qdrant-clients-sdkgit 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/qdrant-clients-sdk)<a href="https://agentmods.dev/skills/truera/trulens/qdrant-clients-sdk"><img src="https://agentmods.dev/badge/skills/truera/trulens/qdrant-clients-sdk/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/qdrant-clients-sdk"><img src="https://agentmods.dev/badge/skills/truera/trulens/qdrant-clients-sdk.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 | $0.00028 | $0.00778 |
| Opus 5.5 | $0.00011 | $0.00311 |
| Sonnet 5.5 | $0.00006 | $0.00156 |
| Haiku 4.5 | $0.00003 | $0.00078 |
Grade A, and why
qdrant-clients-sdk scanned grade A with 1 finding 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 16d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -X GET "https://skills.qdrant.tech/snippets/search?language=python&query=how+to+upload+points" Copies of this mod
2 near-identical copies found in the catalogue:
- qdrant-clients-sdk — 100% identical, 0 lines differ
- qdrant-clients-sdk — 92% identical, 7 lines differ
How it starts
The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Qdrant Clients SDK
Qdrant has the following officially supported client SDKs:
- Python — qdrant-client · Installation:
pip install qdrant-client[fastembed] - JavaScript / TypeScript — qdrant-js · Installation:
npm install @qdrant/js-client-rest - Rust — rust-client · Installation:
cargo add qdrant-client - Go — go-client · Installation:
go get github.com/qdrant/go-client - .NET — qdrant-dotnet · Installation:
dotnet add package Qdrant.Client - Java — java-client · Available on Maven Central: https://central.sonatype.com/artifact/io.qdrant/client
API Reference
All interaction with Qdrant can happen through the REST API or gRPC API. We recommend using the REST API if you are using Qdrant for the first time or working on a prototype.
- REST API - OpenAPI Reference - GitHub
- gRPC API - gRPC protobuf definitions
Code examples
To obtain code examples for a specific client and use case, you can send a search request to the library of curated code snippets for the Qdrant client.
curl -X GET "https://skills.qdrant.tech/snippets/search?language=python&query=how+to+upload+points"
Available languages: python, typescript, rust, java, go, csharp
Response example:
## Snippet 1
*qdrant-client* (vlatest) — https://skills.qdrant.tech/md/documentation/manage-data/points/
Uploads multiple vector-embedded points to a Qdrant collection using the Python qdrant_client (PointStruct) with id, payload (e.g., color), and a 3D-like vector for similarity search. It supports parallel uploads (parallel=4) and a retry policy (max_retries=3) for robust indexing. The operation is idempotent: re-uploading with the same id overwrites existing points; if ids aren’t provided, Qdrant auto-generates UUIDs.
client.upload_points(
collection_name="{collection_name}",
points=[
models.PointStruct(
id=1,
payload={
"color": "red",
},
vector=[0.9, 0.1, 0.1],
),
models.PointStruct(
id=2,
payload={
"color": "green",
},
vector=[0.1, 0.9, 0.1],
),
],
parallel=4,
max_retries=3,
)
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.
- 16d ago First seen · 75 lines · 28 tokens per session scan A 345e36f058b0
qdrant-clients-sdk is a skill published in the GitHub repository truera/trulens (3,595 stars, last pushed today), licensed MIT. It adds 28 tokens to every session and 778 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-24.
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