Laminar vs OpenLIT

Laminar is an open-source, OpenTelemetry-native observability and debugging platform for AI agents. It traces LLM calls, tools, custom functions, and sub-agents as readable transcripts. Signals turn plain-language outcomes or failures into structured events across traces. Its debugger replays runs from cache to a selected point, while evaluations run against datasets locally or in CI to catch regressions. Laminar provides realtime traces, full-text search, raw SQL through its UI, CLI, or MCP, and custom dashboards. Integrations include OpenAI, Anthropic, Gemini, Cohere, LangChain, Pydantic AI, Claude Code, and Playwright. Deployment is available through managed cloud or self-hosting, including AWS or GCP with Helm charts. The Free plan is free with 1 GB data and seven-day retention. Starter costs $30 a month; Pro costs $150 a month.

OpenLIT is an open-source platform for tracing and evaluating LLM activity, managing prompts, and tracking costs. Built on OpenTelemetry, it can capture model calls, tool use, and retrievals, with filters for user, session, cost, latency, and metadata. Evaluations can use an LLM judge, heuristics, or human review on production traffic or experiments. Prompt Hub provides versioning, dynamic variables, API retrieval, unlimited prompt fetching, and AI-assisted improvement. OpenGround supports side-by-side model comparisons for latency, tokens, and cost, including comparisons using a user’s own key. Coding agent monitoring covers sessions, costs, tokens, repository activity, and code impact. The site lists more than 56 integrations across LLMs, agent frameworks, vector databases, and GPUs. OpenLIT accepts OTLP data and can be self-hosted with Docker Compose or Kubernetes Helm. Its Apache 2.0 license and site description note that self-hosting keeps prompts, traces, and secrets in the user’s environment. The OSS plan costs nothing and includes unlimited self-hosted usage, users, projects, and environments.