Lemma provides an observability and evaluation platform for AI agents, converting execution traces into searchable embeddings and enabling natural‑language queries to identify failure patterns. The system automatically clusters issues, integrates synthetic benchmarks with real‑user feedback, and links results to run IDs for data‑driven improvements, with SDKs for TypeScript and Python and enterprise‑grade security.
Funding
Funding not disclosed
Founders
Product
Problem
AI agents often produce unpredictable failures—such as hallucinations, incorrect tool usage, or missed intents—that are difficult to detect, reproduce, and remediate in production environments. Without systematic observability, regressions go unnoticed until users experience degraded performance, leading to lost trust and higher support costs.
Solution
Lemma delivers an observability and evaluation platform built specifically for AI agents, turning raw execution traces into searchable, indexed embeddings. Engineers can query traces in plain English to surface failure patterns, compare model versions, and drill into the exact spans that caused errors. Continuous clustering automatically surfaces emerging issues without manual labeling, while integrated evaluation pipelines combine synthetic metrics with real‑user signals to quantify performance. The platform links user feedback and experiment outcomes to a unique runId, enabling automated, data‑driven improvements to the agent’s behavior. Native SDKs for TypeScript and Python, plus OpenTelemetry‑compatible instrumentation, make integration into existing stacks trivial. Security and compliance are enforced through SOC 2 Type II controls, AES‑256 encryption at rest, and TLS 1.2+ in transit, ensuring that proprietary data remains isolated per organization.
Target Audience
The primary users are ML engineers, AI product developers, and DevOps teams that build, deploy, and maintain LLM‑powered agents in enterprise or consumer applications. It also serves data‑science platforms and AI‑focused SaaS providers seeking systematic monitoring and continuous improvement of their conversational agents.
Features
- Trace embedding and semantic indexing that supports natural‑language search across millions of agent runs.
- Automatic clustering of failure patterns, surfacing new issue categories without predefined labels.
- Combined evaluation framework that merges synthetic benchmark scores with live user feedback (e.g., task adherence, user frustration flags).
- Run‑ID based feedback loop allowing developers to attach ratings, metrics, or experiment results directly to the originating execution.
- First‑class SDKs for TypeScript and Python with one‑line instrumentation (`wrapAgent`) and built‑in OpenTelemetry support.
- Pre‑built integrations for Vercel AI SDK, OpenAI Agents, Langfuse, Arize Phoenix, and Azure Monitor, enabling simultaneous trace export to existing observability tools.
- Enterprise‑grade security: SOC 2 Type II audited, end‑to‑end AES‑256 encryption, TLS 1.2+ transport, and per‑organization data isolation.