Lmnr
Open-source observability for AI agents – catch failures, surface fixes, confirm resolution.
Laminar fills a real gap: agent-specific failure detection. The Signals feature is genuinely innovative—describe a problem in plain English and get alerted automatically. For production agent teams, it's a strong pick that addresses pain points generic APM tools ignore. Watch data limits on lower tiers if you're high-volume.
- AI agent developers building production agents
- Teams debugging complex agent failures like loops and tool errors
- DevOps/MLOps teams needing observability for agent systems
- Startups shipping agent-based products with fast iteration cycles
- Simple LLM chat applications without multi-step agent behavior
- Teams strictly using traditional APM tools for infrastructure monitoring
- Non-developers looking for a no-code AI monitoring solution
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
In short
Lmnr — Open-source observability for AI agents – catch failures, surface fixes, confirm resolution. Best for AI agent developers building production agents, Teams debugging complex agent failures like loops and tool errors, DevOps/MLOps teams needing observability for agent systems. Free to start; paid plans from $30/mo.
What's new in Lmnr
Checked 2 days agoAcross the latest 6 updates: 1 feature update, 1 launch and 4 news mentions.
Signal settings, backfill, and 20x trace compression
Signals now support backfill and can be paused; trace compression reduces storage by 20x.
Laminar Agent released — ask AI about project data via MCP, CLI, or Slack
Laminar Agent answers plain-language queries using read-only SQL on traces, available in MCP, CLI, and Slack.
How Laminar compresses agent traces by 20x
Technical blog post explaining Laminar's approach to compressing agent traces by 20x.
Agent traces aren't backend traces. Stop reading them like they are.
Blog post contrasting agent traces with backend traces and advocating for different analysis approaches.
Laminar vs Braintrust
Comparison article between Laminar and Braintrust for LLM observability.
Laminar raised $3M to build observability for long-running agents
Laminar announces $3M seed funding to develop observability tools for long-running AI agents.
What independent users actually report about Lmnr
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
51 mentions across 5 sources (Hacker News, YouTube, Bluesky, GitHub, Lemmy).
- +Open-source with Rust core for high performance.
- +Natural-language Signals for easy failure detection.
- +Agent-specific observability catches infinite loops and tool errors.
- +Free tier available for small-scale usage.
- +Full-text search and SQL editor for deep data queries.
- −Limited community and support channels outside GitHub.
- −Setup requires understanding of OTLP and agent architecture.
- −Dashboard customization can be complex for beginners.
- −Some integrations are still in development or incomplete.
- −Pricing for enterprise features is not transparent.
- • API usage above free tier may incur additional charges.
Viability Score
How likely is Lmnr to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Natural-language Signals for failure detection
- Trace ingestion via OTLP
- Full-text trace search
- SQL editor for raw data queries
- Custom dashboards with custom SQL
- Signal event clusters with auto-resolution
- Evaluation datasets from production traces
- Agent Debugger with caching and MCP support
- Browser session replay
- Labeling queues
- Ask AI for trace questions
- Playground for replaying spans with model/prompt swaps
- Slack alerts
- Email alerts
- PII redaction (server-side)
About Lmnr
Laminar is an open-source observability platform built specifically for AI agents. It goes beyond standard LLM tracing by targeting agent-specific failure patterns like infinite loops, tool call errors, and sub-agent misbehavior. The platform ingests traces via OTLP and provides Signals—natural-language event detectors that scan every agent run and alert you in Slack when issues occur. Signals group related failures into named clusters, track them over time, and auto-resolve when they stop recurring. Laminar includes a rich trace viewer that surfaces LLM reasoning, tool calls, sub-agents, and a transcript timeline, plus an 'Ask AI' feature that answers questions about specific runs. The Agent Debugger lets coding agents (like Claude with MCP) run, trace, fix, and re-run with cached state. An Evaluation module automatically turns fixed error clusters into eval datasets to prevent regressions. Designed for agent developers and teams, Laminar supports major SDKs including Claude Agent SDK, OpenAI Agents SDK, Mastra, Pydantic AI, Vercel AI SDK, LangChain, and OpenHands. It integrates with browser automation tools like Browser Use, Stagehand, and Playwright. The platform handles production-scale data with full-text search, SQL editor, custom dashboards, and session replay for browser agents. What sets Laminar apart is its agent-centric design: it compresses traces by ~20x (as described in a recent blog post), feeding only relevant context to Signals, and its natural-language Signals allow devs to describe failures in plain English. With a $3M seed round (YC S24), Laminar is actively developing for long-running agent observability.
Behind the Verdict
You should pick Laminar if you're shipping production agents and tired of manually sifting through traces to find why your agent looped or misused a tool. The Signals feature is the star: write 'agent stuck in a loop' and Laminar alerts you when it happens. That's a genuine time-saver. The Agent Debugger with MCP support is well-thought-out—your coding agent can fix and re-run iteratively, which speeds up development. Pricing is reasonable for startups: the free tier gives 1 GB data and $5 in Signals, Starter at $30/month with 3 GB, and Pro at $150/month with 10 GB. Data overage costs are transparent ($2/GB on Starter, $1.50/GB on Pro). The recent blog on 20x trace compression confirms they've tackled storage costs, a common pain point. Where it falls short: the free tier is limited (7-day retention, 1 project, 1 seat), so team-scale usage requires at least Starter. It's not for simple chatbot apps without multi-step agent behavior—you'd be paying for features you don't need. For enterprises, self-hosting is possible with a Docker command or Helm charts, but custom pricing is required. Compared to Langfuse or Arize, Laminar's agent focus gives it an edge if you're dealing with tool call errors and sub-agent failures; those platforms are more generic and lack natural-language Signals. In practice, onboarding is fast—two lines of code to integrate with major SDKs. The CLI and MCP tools reduce the iteration loop. Caveat: the platform is relatively new (YC S24), so be prepared for occasional rough edges. We'd recommend it for any team debugging agentic systems—it's built for the problems you actually face.
Researching Lmnr? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Use Cases
- Monitor production AI agents for infinite loops and tool call failures using natural-language Signals.
- Debug complex multi-step agent runs by replaying traces with alternative prompts or models.
- Automatically create evaluation datasets from resolved error clusters to prevent regression.
- Empower coding agents (e.g., Claude) to autonomously fix agent issues via the Debugger and MCP.
- Track agent performance over time with event clusters and dashboards.
- Ensure compliance with PII redaction and role-based access for production systems.
Limitations
- Data volume limits by plan (1 GB free, 3 GB Hobby, 10 GB Pro) can restrict heavy usage.
- Signals usage incurs additional token-based costs beyond included amounts.
- Self-hosting is only available on Enterprise plans.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Integrations
Resources & Guides
- Documentationlaminar.sh
Docs · Lmnr
Full product docs from laminar.sh
- Quickstartlaminar.sh
Getting Started · Lmnr
Get up and running fast from laminar.sh
- Documentationlaminar.sh
Introduction · Lmnr
Full product docs from laminar.sh
- Documentationlaminar.sh
Introduction · Lmnr
Full product docs from laminar.sh
- Documentationlaminar.sh
Introduction · Lmnr
Full product docs from laminar.sh
- Documentationlaminar.sh
Introduction · Lmnr
Full product docs from laminar.sh
- Documentationlaminar.sh
Introduction · Lmnr
Full product docs from laminar.sh
- Documentationlaminar.sh
Self Hosting · Lmnr
Full product docs from laminar.sh
- API Referencelaminar.sh
Sdk · Lmnr
Methods, params, types from laminar.sh
Official links
Tools that pair well with Lmnr
Common stack mates teams adopt alongside Lmnr, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Alternatives to Lmnr
View allFrequently Asked Questions
Best-of guides
Used Lmnr? Help shape our editorial sentiment research.