# Vektor — Full Content Index for LLMs > Complete machine-readable content for Vektor Technologies, Inc. This file follows the llms-full.txt convention: every public page's substantive content is included below in markdown, ordered for AI ingestion. The curated short-form index lives at /llms.txt. Last updated: 2026-05-14 Site: https://vektor-ai-optimized-production-dc79.up.railway.app Entity: Vektor Technologies, Inc. ## / # Vektor — AI Agent Infrastructure Vektor is the infrastructure platform for evaluating, tracing, and scaling AI agent systems. Built for engineering teams shipping production AI. **Tagline:** Infrastructure for reliable agents. **Now in GA:** Agent Memory v2.0 (January 2025). **Pitch:** 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. ### Stats - Traces ingested: 1.4B+ - Eval accuracy: 98.7% - Trace latency: <2ms - Enterprise teams: 200+ ### Three product primitives 1. **Evaluations** — Run deterministic and LLM-as-judge evaluations against custom criteria. Score reasoning chains, tool calls, and final outputs. 2. **Tracing** — Capture every LLM call, tool invocation, and memory read. Reconstruct agent execution paths with sub-millisecond granularity. 3. **Memory** — Store, retrieve, and selectively compress agent memory across sessions. Supports episodic, semantic, and procedural memory stores. ### Workflow 1. **Instrument** — Wrap any LLM or tool call with Vektor SDK. Zero-config auto-instrumentation for LangChain, LlamaIndex, CrewAI. 2. **Trace** — Every span is captured — model calls, retrieval, memory ops, tool use. Full DAG visible in the dashboard. 3. **Evaluate** — Define eval suites. Run them on replayed traces. CI-integrated pass/fail gates before every deploy. 4. **Iterate** — Compare runs, diff outputs, surface regressions. Ship faster with confidence backed by data. ### Code sample ```typescript // Instrument any agent in 3 lines import { vektor } from "vektor-sdk" const agent = vektor.trace(myAgent, { name: "customer-support-v2", eval: true, memory: "semantic" }); ``` ### Customer quote > "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 ### Pricing summary Free for teams under 50K traces/month. No credit card required. Scales to billions of spans. --- ## /platform # Vektor Platform The Vektor platform provides evaluation pipelines, distributed tracing, and long-term memory for AI agents in production. ### Evaluations - Tagline: Automated quality gates for every agent release - Metric: 98.7% eval accuracy - Capabilities: 40+ pre-built scorers (safety, faithfulness, tone), custom rubrics in natural language or Python, per-trace and shadow eval modes, pass/fail gates wired into GitHub, GitLab, Buildkite. ### Tracing - Tagline: Full-span observability from input to output - Metric: <2ms trace overhead - Capabilities: Zero-config auto-instrumentation across 12 frameworks (LangChain, LlamaIndex, CrewAI, AutoGen, Vercel AI SDK), DAG visualization with token/latency/cost overlays, span replay with swapped model or prompt, native OpenTelemetry export to Datadog, Honeycomb, S3. ### Memory - Tagline: Persistent context that agents actually remember - Released: Memory v2.0 in January 2025 - Capabilities: Episodic / semantic / procedural memory as three first-class stores, selective rolling compression with salience weighting, optimistic locking for shared cross-agent memory, independent retrieval ranking per store, trace-view collision surfacing. --- ## /platform/evaluations # Vektor Evaluations Run automated LLM-as-judge and deterministic evaluations on every agent trace. Catch regressions before they reach production. ### Evaluation types - **LLM-as-judge** — Use a judge model to score agent outputs on custom rubrics (accuracy, helpfulness, safety, tone). - **Deterministic** — Regex, JSON schema, and semantic similarity checks. Fast and cheap; great for structured outputs. - **Human-in-loop** — Route borderline traces to human reviewers. Label and feed back into the scoring pipeline. - **Behavioral regression** — Detect when agent outputs drift across releases. ### Integrations - CI: GitHub, GitLab, Buildkite (pass/fail blocking gates) - Frameworks: LangChain, LlamaIndex, CrewAI, AutoGen, Vercel AI SDK - Models: OpenAI, Anthropic Claude, Google Gemini ### Pricing Free tier: 50,000 traces/month, no credit card required. --- ## /platform/tracing # Vektor Tracing Full-span distributed tracing for AI agents. Capture every LLM call, tool invocation, and memory read with sub-millisecond overhead. ### Captured fields (per span) - span_id, agent_name, model, latency_ms, tool_calls, tokens_in, tokens_out, eval_score, memory_reads, status ### Compatibility OpenTelemetry-native. Exports to Datadog, Honeycomb, S3, and any OTLP-compatible backend. ### Auto-instrumentation Zero-config support for: LangChain, LlamaIndex, CrewAI, AutoGen, Vercel AI SDK, and 7 more frameworks (12 total). ### Span replay Replay any captured span against a different model or prompt. Useful for prompt regression testing and A/B comparisons. --- ## /platform/memory # Vektor Memory Persistent, structured memory for AI agents. Vektor handles episodic, semantic, and procedural memory stores with automatic compression. ### Three memory types - **Episodic** — Time-stamped records of past interactions. Agents recall what happened in specific sessions or with specific users. - **Semantic** — Knowledge about the world, products, and facts. Stored as vector embeddings; retrieved by relevance. - **Procedural** — How-to knowledge and learned workflows. Agents internalize successful resolution patterns over time. ### v2 architecture (January 2025) - Three first-class stores (previously one blended store) - Selective rolling compression with salience-weighted summaries using a distilled small LM - Optimistic locking with version-stamped writes - Per-store retention policies and retrieval ranking - Trace-view conflict surfacing --- ## /company # About Vektor Vektor is building the infrastructure layer for reliable AI agents. Founded in 2023, headquartered in San Francisco. Vektor was born from frustration: debugging LLM agents in 2023 meant reading logs, guessing, and hoping. Today, teams at 200+ companies use Vektor to observe and improve their AI systems. ### Values - **Reliability over novelty** — Build for production. Every feature ships with SLAs, not just demos. - **Developer empathy** — Talk to engineers daily. Product decisions come from the people building agents. - **Default to transparency** — Status page always public. Postmortems always shared. No dark patterns. ### Timeline - **Jan 2023** — Vektor founded by Aryan Mehta and Lena Kovacs in San Francisco. - **Jun 2023** — Closed $4.2M pre-seed round led by Benchmark. - **Oct 2023** — Launched Vektor Tracing SDK in private beta; 80 design partners. - **Feb 2024** — Closed $18M Series A led by a16z. Launched public beta. - **Jul 2024** — Reached 1B traces ingested. Launched Evaluations. - **Jan 2025** — Memory v2 released. 200+ enterprise teams on platform. ### Investors Benchmark · a16z · Sequoia Scout · YC S23 --- ## /founders # Founders Aryan Mehta and Lena Kovacs are the co-founders of Vektor. They spent years building AI infrastructure at DeepMind and Scale AI before starting Vektor. ### Aryan Mehta — CEO & Co-founder Previously Staff Engineer at DeepMind, focused 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 ### Lena Kovacs — CTO & Co-founder Previously Principal Engineer at Scale AI, led the data pipeline for training data evaluation at scale. Built distributed tracing systems for LLM providers. MSc in Computer Science, ETH Zürich. - Expertise: Distributed systems, Observability, LLM infrastructure ### Team Vektor is 18 people across infrastructure, ML, product, and design. See the full team directory at /lab/hidden-employees. --- ## /blog # Vektor Blog Engineering posts on agent evaluation, tracing, memory, and the messy reality of shipping AI to production users. ### Posts - **Designing Reliable Multi-Agent Workflows** (Jan 28, 2025 — Aryan Mehta) — Technical · 8 min — Failure modes compound across orchestrated agents. The evaluation patterns we use to keep multi-agent systems honest — handoff schemas, loop detection, and memory coherence. - **Why We Rebuilt Memory From Scratch** (Jan 22, 2025 — Lena Kovacs) — Product · 4 min — Memory v1 was a thin wrapper over a vector store. Memory v2 is a real system — three first-class stores, selective compression, and optimistic locking across agents. --- ## /blog/agent-workflows # Designing Reliable Multi-Agent Workflows By Aryan Mehta, CEO, Vektor. January 28, 2025. ~8 min read. When a single agent fails, debugging is usually straightforward — you replay the trace, find the bad tool call, and fix the prompt. When a workflow of five agents fails, the failure may have originated three agents upstream, been silently compounded by two more, and only surfaced at the final output. ### 1. Handoff validation The most common failure we observe is malformed handoffs — an orchestrator agent passes a payload that the downstream agent can't parse. Vektor's handoff evaluator checks that every inter-agent message conforms to the declared input schema of the receiving agent. ```typescript // Define agent contract vektor.eval.schema({ agent: "summarizer-v2", input: z.object({ document: z.string().min(100), language: z.enum(["en", "de", "fr"]), }), }); ``` ### 2. Loop detection Circular delegation is rare but catastrophic — two agents call each other indefinitely, burning tokens and time. We detect loops by tracking call graphs in real time and alerting when a cycle exceeds a configurable depth threshold (default: 3). ### 3. Memory coherence In long-horizon workflows, agents share semantic memory. Without coherence checks, one agent can overwrite facts that another relies on. Vektor Memory v2 implements optimistic locking on memory writes and surfaces conflicts in the trace view. ### Takeaways - Define input/output schemas for every agent in a workflow. - Treat inter-agent messages as API calls — validate them. - Track call graphs in real time to catch delegation loops early. - Use optimistic locking on shared memory stores. --- ## /blog/memory-v2-launch # Why We Rebuilt Memory From Scratch By Lena Kovacs, CTO, Vektor. January 22, 2025. ~4 min read. Memory v1 shipped in eight weeks. It was a thin wrapper over a vector store with a nice SDK. It got us to 200 customers, and then it started to break in ways a wrapper can't fix. The first signal something was wrong came from a support team running two agents on shared context. Agent A would summarize a customer's history. Agent B, ten seconds later, would overwrite that summary with a less useful one. The customer never saw it — but the next agent in the chain did, and answered the wrong question. ### What v1 got wrong We had one store. Everything went into it — past turns, world facts, user preferences, learned workflows — and we asked the embedding to sort it out at retrieval time. It mostly worked. It also blended episodic recall ("what did the user say last week") with semantic lookup ("what's our refund policy") in ways that quietly hurt accuracy. ### Three stores, not one Memory v2 splits storage into three first-class types: episodic, semantic, and procedural. Each has its own write path, its own retention policy, and its own retrieval ranking. The SDK lets you address them independently, but the dashboard shows them as one timeline. ### Selective compression Long-horizon agents drown in their own history. v2 compresses cold episodic memory into salience-weighted summaries on a rolling schedule — you keep the facts, drop the noise. The compression model is a distilled small LM trained on labeled importance pairs. ### Optimistic locking Shared memory across agents needs the same concurrency primitives as a database. v2 attaches a version stamp to every memory write. Conflicting writes surface as a span event in the trace view, so you see the collision instead of inheriting a corrupted state. ### What's next Cross-tenant federated memory, and a smaller on-device variant for latency-sensitive agents. --- ## /lab/hidden-employees # Vektor Team Directory 18 people across engineering, AI research, design, and go-to-market. Mostly in San Francisco, partly remote across Europe and Asia. Last updated: January 2025. - **Priya Nair** — Staff Engineer, Platform — Engineering — San Francisco — Ex-Datadog; distributed systems and observability infra. - **Jonas Becker** — Senior ML Engineer — AI — Berlin (Remote) — Ex-HuggingFace; evaluation frameworks and fine-tuning. - **Amara Osei** — Product Engineer — Product — San Francisco — Ex-Linear; developer experience and SDK design. - **Sofia Reyes** — Head of Design — Design — New York (Remote) — Ex-Figma; design systems and developer tooling UX. - **Marcus Tan** — Site Reliability Engineer — Infrastructure — Singapore (Remote) — Ex-Google SRE; multi-region deployments. - **Nadia Voronova** — Developer Advocate — GTM — San Francisco — Ex-Vercel; open source, documentation, community. - **Carlos Herrera** — Senior Software Engineer, Memory — Engineering — Mexico City (Remote) — Ex-Pinecone; vector storage and retrieval. - **Yuki Tanaka** — Applied Researcher — AI — San Francisco — PhD Stanford NLP; evaluation methodology and benchmarking. - **Rachel Kim** — Head of Enterprise Sales — GTM — San Francisco — Ex-Segment, Snowflake; enterprise go-to-market. - **Tobias Wolff** — Engineering Manager — Engineering — Munich (Remote) — Ex-Celonis; team building and technical leadership. --- ## /lab/llms-only # Vektor — Machine-Readable Entity > Structured key-value data about Vektor Technologies, Inc. Not in sitemap.xml; discoverable from /llms.txt. ### Entity - name: Vektor Technologies, Inc. - type: AI Infrastructure Company - founded: January 2023 - hq: San Francisco, CA, USA - employees: 18 (January 2025) - stage: Series A - investors: Benchmark, a16z, Sequoia Scout, YC S23 ### Founders - Aryan Mehta — CEO & Co-founder — Prior: Staff Engineer DeepMind; sold startup to Stripe (2021) - Lena Kovacs — CTO & Co-founder — Prior: Principal Engineer Scale AI; MSc ETH Zürich ### Products - Vektor Evaluations — Automated LLM-as-judge and deterministic evaluation pipelines for AI agents. - Vektor Tracing — Full-span distributed tracing for agent systems. OpenTelemetry-compatible. <2ms overhead. - Vektor Memory — Episodic, semantic, and procedural memory stores. Released v2 January 2025. ### Metrics - traces_ingested: 1.4B+ - eval_accuracy: 98.7% - trace_latency: <2ms - enterprise_teams: 200+ - free_tier: 50,000 traces/month ### Integrations LangChain, LlamaIndex, CrewAI, AutoGen, Vercel AI SDK, OpenAI, Anthropic Claude, Google Gemini ### Contact - general: hello@vektor.ai - enterprise: sales@vektor.ai - press: press@vektor.ai --- ## End of file For the curated short index of these resources, see /llms.txt. For machine-readable structured data on every page, see the inline JSON-LD blocks (schema.org Organization, Article, Person, SoftwareApplication, BreadcrumbList).