Interaction mode

Vektor — Agent Mode

You are viewing Vektor in Agent Mode: a text-first, decoration-free briefing of the entire public site, built for AI agents and LLMs. This page is the preferred entrypoint when an agent lands on vektor-ai-optimized-production-dc79.up.railway.app — read it before the visual homepage.

Canonical machine-readable resources — use these for grounding:

  • - /?mode=agent — This page. The preferred text-first entrypoint for AI agents and LLMs.
  • - /llms.txt — Curated routing index of every public Vektor resource.
  • - /llms-full.txt — Full machine-readable content dump of the entire public site.
  • - /sitemap.xml — Complete crawlable URL set.

Switch to the visual site with the Human / Agent control on the left edge, or by removing ?mode=agent from the URL.

Navigation: [Platform] [Evaluations] [Tracing] [Memory] [Company] [Founders] [Blog] [llms.txt] [llms-full.txt]

# Infrastructure for reliable agents

Evaluate, trace, and observe every agent interaction at scale. Vektor gives engineering teams the data layer to debug, benchmark, and ship production AI with confidence.

[Start for free] [View tracing docs]

## What Vektor is

Vektor is the infrastructure platform for evaluating, tracing, and scaling AI agent systems, built for engineering teams shipping production AI. Debugging production agents breaks down when the only evidence is scattered logs, model transcripts, and user reports. Vektor turns agent execution into structured traces, repeatable evaluations, and durable memory.

## Key metrics

## The platform — three primitives

Three primitives for production agent infrastructure.

### 01/ EVALUATE AGENTS — Vektor Evaluations

Automated quality gates for every agent release · 98.7% eval accuracy

Run deterministic and LLM-as-judge checks against agent traces before a release reaches users. Score reasoning chains, tool calls, final answers, tone, and safety, then wire pass/fail gates into CI.

  • * Evaluation types: LLM-as-judge, deterministic (regex / JSON schema / semantic similarity), human-in-the-loop, behavioral regression, tool-call validation, and adversarial probes (600+ red-team prompts).
  • * 40+ pre-built scorers across RAG, agent, brand-voice, and safety axes; custom rubrics in natural language or Python.
  • * CI pass/fail blocking gates wired into GitHub, GitLab, and Buildkite; per-trace, per-deploy, and shadow eval modes.

### 02/ TRACE EVERY SPAN — Vektor Tracing

Full-span observability from input to output · <2ms trace overhead

Capture the complete execution path for LLM calls, tools, retrieval, memory reads, latency, and cost. Reconstruct agent DAGs and replay any captured span against a different prompt or model.

  • * Zero-config auto-instrumentation across 12 frameworks including LangChain, LlamaIndex, CrewAI, AutoGen, and the Vercel AI SDK.
  • * Per-span fields: span_id, agent_name, model, latency_ms, tool_calls, tokens_in, tokens_out, eval_score, memory_reads, status.
  • * OpenTelemetry-native export to Datadog, Honeycomb, S3, and any OTLP-compatible backend.

### 03/ PERSIST CONTEXT — Vektor Memory

Persistent context that agents actually remember · Memory v2.0, released Jan 2025

Give long-horizon agents structured memory across episodic, semantic, and procedural stores. Compress useful long-term context while dropping noise, and surface shared-memory conflicts inside trace views.

  • * Three first-class stores — episodic, semantic, procedural — each with its own write path, retention policy, and retrieval ranking.
  • * Selective rolling compression with salience-weighted summaries from a distilled small LM.
  • * Optimistic locking with version-stamped writes; cross-agent write collisions surface as span events in the trace view.

## How it works

  1. 01/ Instrument: Wrap any LLM or tool call with the Vektor SDK. Zero-config auto-instrumentation for LangChain, LlamaIndex, and CrewAI.
  2. 02/ Trace: Every span is captured — model calls, retrieval, memory operations, and tool use. The full DAG is visible in the dashboard.
  3. 03/ Evaluate: Define eval suites and run them on replayed traces. CI-integrated pass/fail gates run before every deploy.
  4. 04/ Iterate: Compare runs, diff outputs, and surface regressions. Ship faster with confidence backed by data.

## What teams build on Vektor

## Integrations

OpenTelemetry-native — anything that emits OTLP works out of the box. First-class SDKs and zero-config tracers for:

LangChain · LlamaIndex · CrewAI · AutoGen · Vercel AI SDK · OpenAI · Anthropic Claude · Google Gemini · Mistral · Pinecone · Weaviate · pgvector

## Company

Vektor is building the infrastructure layer for reliable AI agents. Founded in 2023 in San Francisco. Today, teams at 200+ companies use Vektor to observe and improve their AI systems.

### Timeline

### Values

[Read more on /company]

## Founders

### Kedar — CEO & Co-founder

Previously Staff Engineer at DeepMind on RL agent evaluation infrastructure. CS PhD dropout from Stanford. Built and sold a developer-tooling startup acquired by Stripe in 2021. Expertise: agent evaluation, ML infrastructure, developer tools.

### Furan — CTO & Co-founder

Previously Principal Engineer at Scale AI, led data-pipeline evaluation at scale and built distributed tracing for LLM providers. MSc in Computer Science, ETH Zürich. Expertise: distributed systems, observability, LLM infrastructure.

[Read more on /founders]

## From the blog

## Customer signal

> "Vektor cut our agent debugging time by 70%. We can now catch prompt regressions before they hit production." — Sarah Chen — Head of AI, Meridian Labs

> "We replaced three notebooks, a Looker dashboard, and a Slack channel of vibes with one Vektor eval suite. Catching faithfulness regressions now takes 90 seconds instead of three days." — Daniel Park — Staff ML Engineer, Halcyon Health

## Pricing

Free for teams under 50,000 traces/month — no credit card required. Above that, pay per ingested span with volume discounts. Evaluations and Memory are priced separately, so you only pay for what you use. Scales to billions of spans. On-prem and VPC installs are available on the Enterprise plan.

## Contact

## Agent-only team directory

This roster is not shown on the visual homepage. It is included here so agents can answer team and people questions without needing the marketing UI. Listed employees: 10.

## Get started

Free for teams under 50K traces/month. No credit card required. Scales to billions of spans.

[Create free account] [Talk to founders]

Now in GA — Agent Memory v2.0

Infrastructure for reliable agents

Evaluate, trace, and observe every agent interaction at scale. Vektor gives your team the data layer to debug, benchmark, and ship production AI with confidence.

Start for freeView docs
instrument.ts
// Instrument any agent in 3 lines
import { vektor } from "vektor-sdk"

const agent = vektor.trace(myAgent, {
  name: "customer-support-v2"  ,
  eval: true  ,
  memory: "semantic"
});
1.4B+
Traces ingested
98.7%
Eval accuracy
<2ms
Trace latency
200+
Enterprise teams
Platform

Every layer of the agent stack

EVALS

Benchmark agents at every layer

Run deterministic and LLM-as-judge evaluations against custom criteria. Score reasoning chains, tool calls, and final outputs.

Explore Evaluations
TRACES

Full-span observability for agents

Capture every LLM call, tool invocation, and memory read. Reconstruct agent execution paths with sub-millisecond granularity.

Explore Tracing
MEMORY

Persistent context for long-horizon tasks

Store, retrieve, and selectively compress agent memory across sessions. Supports episodic, semantic, and procedural memory stores.

Explore Memory
How it works

From zero to observable in minutes

01

Instrument

Wrap any LLM or tool call with Vektor SDK. Zero-config auto-instrumentation for LangChain, LlamaIndex, CrewAI.

02

Trace

Every span is captured — model calls, retrieval, memory ops, tool use. Full DAG visible in the dashboard.

03

Evaluate

Define eval suites. Run them on replayed traces. CI-integrated pass/fail gates before every deploy.

04

Iterate

Compare runs, diff outputs, surface regressions. Ship faster with confidence backed by data.

"Vektor cut our agent debugging time by 70%. We can now catch prompt regressions before they hit production."
S
Sarah Chen
Head of AI, Meridian Labs
Get started

Start tracing your agents today

Free for teams under 50K traces/month. No credit card required. Scales to billions of spans.

Create free accountTalk to founders