neatlogs
Neatlogs is an AI observability platform that helps engineering teams monitor, debug, and resolve failures in AI agent runs. It imports traces from existing tools like LangSmith, Langfuse, and OpenTelemetry, then enriches them with customer feedback and business context to surface root causes. The platform automatically groups related failures into incidents, reducing time-to-resolution for production agent workflows.
- Developer Tools
- Artificial Intelligence
- Data & Analytics
- Software Only
- AI Agents
Funding
Founders
Founder details are not available yet.
Product
Problem
AI agent runs are complex, multi-step processes that often fail in ways traditional monitoring tools miss. Teams lack visibility into the full context of agent behavior, including customer feedback and business logic, making it difficult to identify root causes and resolve incidents quickly.
Solution
Neatlogs provides an AI observability platform that imports traces from existing tools or allows direct instrumentation, then enriches those traces with customer feedback and business context. The platform monitors every agent run, automatically surfaces failures, and groups related cases into a single incident for streamlined troubleshooting. By connecting technical trace data with business context, Neatlogs gives teams a complete picture of what happened and why, reducing time-to-resolution and improving agent reliability. The platform supports importing traces from LangSmith, Langfuse, Braintrust, Arize, and Phoenix, and also supports OpenTelemetry, with SDKs for Python and TypeScript.
Target Audience
Primary customers are engineering and product teams building and operating AI agents, including those at companies like VWO, AB Tasty, Runway, and CFO.ai, who need observability into complex agent workflows.
Features
- Trace import from existing platforms (LangSmith, Langfuse, Braintrust, Arize, Phoenix) or direct instrumentation via OpenTelemetry
- SDKs for Python and TypeScript for flexible integration
- Context enrichment by connecting customer feedback and business logic to agent runs
- Automatic failure detection across every run with incident grouping for related cases
- Detailed run visualization showing step counts, durations, and detection alerts
- Live trace monitoring with production-level visibility into agent behavior