Llumo provides an observability and debugging platform for enterprise LLM agents, offering the Eval360™ engine that delivers low‑cost, high‑accuracy evaluation of agent behavior based on millions of real‑world interactions. The platform records full end‑to‑end traces—including inputs, reasoning steps, tool calls, latency, and token costs—allowing teams to visualize decision flows, identify failure modes, and apply actionable remediation before deployment.
Funding
$953.6K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
3OSVFounders
Product
Problem
Enterprises deploying large language model (LLM) agents often encounter silent failures, hallucinations, and performance regressions that are hard to detect before they affect users or violate compliance. Lack of end‑to‑end tracing, reliable evaluation, and actionable debugging tools makes it difficult to move experimental AI into stable production environments.
Solution
Llumo offers a platform that makes AI agents observable, testable, and reliable in production. Its Eval360™ engine provides low‑cost, high‑accuracy evaluation of agent behavior using a model trained on millions of real‑world interactions, pinpointing failure modes and their root causes. The platform records full agent traces—including inputs, reasoning steps, tool calls, latency, and token costs—so teams can visualize decision flows and understand why an agent behaved a certain way. Users can quickly create custom evaluations from templates, simulate changes before deployment, and receive actionable remediation recommendations. Integrated dashboards, SDKs for popular frameworks (LangChain, LlamaIndex, Guardrails), and extensive security controls enable seamless adoption across development pipelines and compliance‑sensitive environments.
Target Audience
Primary customers are enterprise AI product teams and engineering groups building production LLM applications, agentic pipelines, and Retrieval‑Augmented Generation (RAG) systems that require reliable performance and compliance.
Features
- Full‑stack agent tracing with visual graphs of reasoning, retrieval, tool calls, latency, and cost
- Eval360™ evaluation engine trained on 2M+ real agent behaviors, delivering 30% higher accuracy at 20× faster debugging speed
- Custom evaluation creation using ready‑made templates and scoring presets, plus simulation of changes pre‑deployment
- Actionable root‑cause analysis that lists specific issues and prioritized fixes for reliable agent behavior
- Native integrations with LangChain, LlamaIndex, Guardrails, plus SDKs for Python and JavaScript and OpenTelemetry support
- Comprehensive observability suite: session tracking, token usage monitoring, SLA metrics, and historical data access
- Enterprise‑grade security features including AES‑256 encryption, SOC 2 Type II, GDPR DPA, SSO (Okta, Azure AD), RBAC, and on‑premise hosting options