Lmnr
Open-source agent observability that catches failures and fixes them.
Laminar is a strong pick for teams building production agents who are tired of digging through raw spans. Signals' natural-language failure clustering is genuinely unique and saves real debugging time. The Free tier is generous enough to evaluate, but watch data limits on lower plans. If you need pure tracing, LangSmith works, but Laminar's debugger and eval loops give it an edge for agent-heavy workflows.
Verified 6d ago · liveness 79/100 · cite: rightaichoice.com/tools/lmnr
- AI agent developers building production agents with multiple tool calls
- Teams debugging complex agent failures like infinite 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
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Skip Laminar if you only build simple chat apps without multi-step agent behavior, or if you need a no-code AI monitoring solution and aren't comfortable with tracing, SQL, and agent-oriented debugging concepts.
Going past your included data (1 GB Free, 3 GB Starter, 10 GB Pro) incurs overage fees: $2/GB on Starter, $1.50/GB on Pro, which can add up if you have high-volume agents.
Laminar's freemium model is ideal for startups and individual developers just getting started with agent observability—the Free tier gives you 1 GB data and $5 in Signals credits. For growing teams, Starter at $30/month is more affordable than many competitors, while Pro at $150/month offers higher limits and Slack support. If you're on a budget, the Free tier is hard to beat, but high-volume teams may find data overages costly compared to flat-rate alternatives.
In short
Lmnr — Open-source agent observability that catches failures and fixes them. Best for AI agent developers building production agents with multiple tool calls, Teams debugging complex agent failures like infinite 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 4 days agoAcross the latest 8 updates: 7 feature updates and 1 news mention.
Manage Signals from the CLI
lmnr-cli signal create/list/get/update/delete puts Signal definitions in your terminal. Supports trigger, filter, and mode flags for firing configuration.
Flush span batches by payload size
SDKs can now export a batch when payload exceeds byte limit, alongside existing triggers. Configurable via flushBySize and maxExportBatchSizeBytes.
Trace render templates
Render templates can now render a whole trace, not just individual spans.
Evaluations UI refresh
Evaluations page gains groups sidebar, combined progression chart, aggregation picker, split run detail view, and side-by-side deltas.
Enable or disable Signals and alerts
Pause Signals with an Active switch; alerts get an on/off switch on each card. Disabled items grouped separately.
Generate render templates in the editor
Render template editor now generates JSX from a description. For traces, also writes span filters and refreshes sample data.
Claude Code and Codex plugins
One-command install traces Claude Code and OpenAI Codex CLI sessions in Laminar. Each turn becomes a trace, grouped per conversation.
How Laminar compresses agent traces by 20x
Technical post detailing how Laminar achieves 20x compression on agent traces.
What people actually say about Lmnr — is it worth it?
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) · researched Jul 15, 2026.
- +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 well maintained and how widely used is Lmnr? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Automatic capture of LLM calls, tool calls, sub-agents, costs, and tokens
- Readable transcript view for traces
- Signals: natural-language failure detection with clustering
- Signal clusters showing failure distribution and behavior
- Auto-resolution of signal clusters when issues stop recurring
- Automatic eval dataset generation from resolved error clusters
- Agent Debugger with caching and MCP for coding agents
- SQL editor for querying trace and signal data
- Custom dashboards with custom SQL
- Full-text search across span inputs, outputs, and attributes
- Labeling queues for annotation of traces and datasets
- Browser session recording for browser agents
- Slack alerts and email alerts
- Server-side PII removal
- Laminar Agent: plain-language queries via MCP, CLI, or Slack
About Lmnr
Laminar is an open-source agent observability platform that automatically captures LLM calls, tool calls, sub-agents, costs, and tokens, presenting them in a readable transcript view. It's built for developers shipping production AI agents, particularly those using Claude, OpenAI, LangChain, and other major agent frameworks. The platform goes beyond basic tracing to surface failure modes you didn't anticipate, using a feature called Signals to cluster similar failures into distinct patterns. This gives you a high-level overview of your agent's failure distribution, so you can see whether a reported issue has occurred before and understand its impact at scale. Signals work by analyzing every agent run with a natural-language approach: you describe a problem in plain English, and Laminar identifies related failures, clusters them, and alerts you in Slack. When a cluster stops recurring, Laminar auto-resolves it, and reopens it if the issue returns. Each resolved error cluster can automatically become an eval dataset, letting you run evals after a change to catch regressions and iterate with confidence. The Agent Debugger integrates with Claude and other coding agents via MCP and CLI, allowing your coding agent to query traces, evals, and signals with SQL, replay from any checkpoint, and verify fixes quickly. Laminar ingests traces via OTLP and supports major SDKs including Claude Agent SDK, OpenAI Agents SDK, Mastra, Pydantic AI, Vercel AI SDK, LangChain, OpenHands SDK, and browser automation tools like Browser Use, Stagehand, and Playwright. The platform includes full-text search across span inputs/outputs/attributes, a SQL editor, custom dashboards with custom SQL, labeling queues, browser session recording, and server-side PII removal. A differentiator is trace compression, which reduces storage costs by about 20x, and Signals are billed by tokens used during analysis, not by your agent's token usage. Pricing is freemium, with a Free tier at $0/month, Starter
Behind the Verdict
We've seen a lot of LLM observability tools, and Laminar stands out by tackling agent-specific failure patterns rather than just showing traces. The Signals feature is the reason to consider it: describe a problem in plain English, and it clusters similar failures across your runs, auto-resolves when they stop, and reopens if they return. That's a workflow-saver for teams dealing with non-deterministic agent behavior. The Agent Debugger is another differentiator—being able to let Claude or another coding agent query your traces with SQL, replay checkpoints, and verify fixes in seconds feels like the future of debugging. Recent updates only strengthen this: evals now run inside the debugger, and trace render templates let you build custom views with SQL filters. We'd reach for Laminar when you're building complex agents with multiple tool calls, sub-agents, or browser automation, and when failure modes are hard to anticipate. It's also great if you're already using eval-driven development—the auto-generation of eval datasets from resolved error clusters closes the loop between production issues and regression testing. But if your use case is simple LLM chat apps with single calls, Laminar's features are overkill; a lighter tracing tool will do. Also, if your team relies on traditional APM for infrastructure monitoring, Laminar won't replace that—it's agent-focused. Compared to LangSmith, Laminar offers more agent-specific insight (Signals, debugger) but LangSmith has a longer track record and broader ecosystem integrations. Phoenix is a solid open-source alternative but lacks Laminar's natural-language clustering and debugger-driven fix loop. Where Laminar bites: the Free tier's 1 GB data and 7-day retention limit you if you're high-volume, and Signals credits are
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Real-world workflow fit
Concrete scenarios for the personas Lmnr actually fits — and what changes day-one when you adopt it.
Deploy a new agent version and receive a Slack alert that the agent is stuck in a loop.
Outcome: Open the trace, see the repeated web_search calls, use Laminar Agent to ask 'why did this fail?', get a clear explanation, and use the Debugger to let Claude fix the code and re-run with caching—resolving the issue in minutes.
Need to track failure modes across your agent fleet.
Outcome: Set up a Signal for 'agent reports success but didn't complete the task', get clusters of similar failures, and auto-create eval datasets from resolved clusters to prevent regressions.
Shipping a new agent product and need to ensure reliability before launch.
Outcome: Use Laminar's free tier to instrument your agent with the Claude Agent SDK, monitor for unexpected failures, and use the transcript view to debug issues quickly—iterating fast without a huge upfront cost.
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 custom dashboards.
- Use Laminar Agent to ask plain-English questions about traces via Slack, CLI, or MCP.
- Compress agent trace storage by up to 20x to reduce costs.
Models Under the Hood
as of 2026-08-27
Limitations
- Laminar is an open-source observability platform for AI agents.
- It automatically captures LLM calls, tool calls, sub-agents, costs, and tokens, and provides Signals to detect and cluster failures.
- Pricing plans include data limits (1 GB free, 3 GB Starter, 10 GB Pro), overage charges, and retention periods (7 days, 30 days, 6 months).
- Signals usage is billed based on tokens analyzed by an internal Signals Agent.
as of 2026-08-19
Verification history
We have re-verified Lmnr 6 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
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.
Plans compared
For each published Lmnr tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Solo developer or small experiment trying Laminar for the first time, with light traces and no need for multi-project or multi-seat support.
What this tier adds
Starting free tier: 1 GB data, $5 in Signals credits, 7-day retention, 1 project, 1 seat.
Starter
$30/mo
Ideal for
Early-stage startup or small team that needs more data (3 GB), longer retention (30 days), and unlimited projects/seats without paying for high-volume features.
What this tier adds
Adds 2 GB more data, 23 more days retention, unlimited projects and seats, and email support.
Pro
$150/mo
Ideal for
Growing team with serious production agents that needs 10 GB data, 6-month retention, and Slack support for faster debugging.
What this tier adds
Adds 7 GB more data, retention extended to 6 months, and Slack support instead of email-only.
Enterprise
Custom
Ideal for
Large organizations that need custom limits, on-premise deployment, and dedicated support for compliance or scale.
What this tier adds
Custom limits, on-premise availability, and dedicated support, with no set data or retention caps.
Where the pricing makes sense
The company stage and team size where Lmnr's pricing actually pencils out — and where peers do it cheaper.
Laminar's freemium model is ideal for startups and individual developers just getting started with agent observability—the Free tier gives you 1 GB data and $5 in Signals credits. For growing teams, Starter at $30/month is more affordable than many competitors, while Pro at $150/month offers higher limits and Slack support. If you're on a budget, the Free tier is hard to beat, but high-volume teams may find data overages costly compared to flat-rate alternatives.
Setup time & first value
How long it actually takes to get something useful out of Lmnr — broken out by persona, not the marketing-page minute.
For a developer familiar with SDKs, you can be up and running in under 10 minutes: sign up, copy the setup command, and instrument your agent with the Claude Agent SDK or OpenAI Agents SDK. Full-text search and traces appear immediately. Setting up Signals takes another 15–30 minutes to define your failure descriptions and configure alerts.
Switching to or from Lmnr
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From LangSmith: Export your traces via OTLP and import into Laminar using the OTLP endpoint. Re-create your eval datasets and prompts in Laminar's datasets feature.
- ↗To LangSmith: Export traces from Laminar via the SQL editor or API, then import into LangSmith using its SDK. You may need to re-create dashboards and alerts manually.
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
Tracing · Lmnr
Full product docs from laminar.sh
- Documentationlaminar.sh
Signals · Lmnr
Full product docs from laminar.sh
- Documentationlaminar.sh
Debugger · Lmnr
Full product docs from laminar.sh
- Documentationlaminar.sh
Evaluations · Lmnr
Full product docs from laminar.sh
- Resourcelaminar.sh
Blog · Lmnr
Helpful link from laminar.sh
Tutorials & Learning
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
Lmnr vs Spider Cloud
Choose Lmnr if your pain point is debugging agent loops, tool errors, or sub-agent misbehavior — its Signal-based failure detection and Agent Debugger are uniquely built for that. Choose Spider Cloud if what you need is fast, cheap, and reliable web scraping with AI extraction, especially to feed data into RAG pipelines or LLMs. They solve very different problems; the right pick depends on whether you're building agents or feeding them data.
Lmnr vs Presto Voice
Lmnr and Presto Voice serve entirely different domains: Lmnr is for developers building and debugging AI agents, while Presto Voice is for QSR chains automating drive-thru ordering. Pick Lmnr if you need open-source observability with agent-specific failure detection and trace compression; choose Presto Voice if you run a multi-location restaurant and want voice AI that up-sells and integrates with your POS.
Lmnr vs Temporal Ai
Choose Lmnr if you need deep visibility into agent failures like loops and tool errors, with natural-language signals and auto-resolution. Choose Temporal AI if your priority is ensuring multi-step workflows survive infrastructure crashes and require complex retry/Saga patterns. They complement each other – many teams use both.
Alternatives to Lmnr
View all
Arize Phoenix
Open-source LLM agent observability with tracing, evals, and experiments
Langfuse
Open-source LLM observability for tracing, evaluating, and optimizing AI agents end-to-end.
Evidently AI
Open-source AI evaluation and observability for LLMs, RAG, agents, and predictive ML.
Frequently Asked Questions
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