Facts checked November 2026
LLM observability tools, compared on the same questions
LLM observability is recording what an LLM application or agent did on every run (prompts, model calls, tool calls and outputs) and checking those records for failures, including the ones that raise no error. Here Lemma, Braintrust, HoneyHive and Laminar are judged on what they trace, how they find bad outputs, how issues get triaged and what setup takes.
What LLM observability covers
- TracingEvery model call, tool call and sub-agent step of a run, nested in order.
- Failure detectionFinding bad outputs, including the ones that raise no error.
- TriageTurning repeated failures into issues someone owns and fixes.
- EvalsScoring versions offline before release, and production online after.
- AlertingTelling the right person, in the right place, what broke.
Pick by your main need
The short answer, if you already know your biggest gap. The table below shows why.
Audits every trace against the agent's instructions and groups recurring failures into issues.
Offline experiments and release gatingBraintrustVersioned datasets, scorers and side-by-side experiments at the centre.
Enterprise standards, hybrid or self-hostedHoneyHiveOpenTelemetry-native, policies and RBAC, SaaS, hybrid or self-hosted.
Open source and SQL over tracesLaminarApache 2.0, self-host with Docker or Helm, Signals that cluster failures.
The LLM observability tools, side by side
Each tool answers the same 11 questions. Every cell links to the vendor page it was checked on; prices aren't repeated here, so check each pricing page on the day you decide. Filter by what you need.
4 tools, 11 criteria. Scroll sideways for every column.
| Tool | Built around | What gets traced | How failures are found | How issues are triaged | Evals | Alerting | Integrations | What setup involves | Cost tracking | Hosting | Pricing |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Lemmauselemma.ai ↗Lemma vs BraintrustLemma vs HoneyHiveLemma vs Laminar | Production monitoring and observability for AI agents running with real users. Source: Lemma docs: introduction | One trace per agent execution: the conversation, the span tree, LLM generations, tool calls and errors; multi-turn conversations linked by a thread ID. Source: Lemma docs: Dashboard vocabulary | Audits each completed trace against the agent's instructions, without hand-written assertions, and groups repeated evidence into issues. Behavior monitors you describe in words add targeted checks. Source: Lemma docs: Dashboard vocabulary | Issues carry status (open, in progress, resolved, dismissed), priority and their supporting traces; Inspect answers questions about a trace or issue; Linear tickets can be created from issues. Source: Lemma docs: Issues | No offline evaluation. After a fix ships, Lemma creates an online eval from the real failure so a regression is flagged. Source: Lemma docs: Dashboard vocabulary | Issue alerts and Issue Briefs in Slack; signed webhooks for issue lifecycle events. Source: Lemma docs: Slack | OpenAI Agents SDK, Vercel AI SDK, LangChain, LangGraph and Mastra; existing Langfuse or OpenTelemetry instrumentation can stay alongside the Lemma SDK. MCP server for coding agents. Source: Lemma docs: llms.txt index | Install the SDK with an API key and project ID, or ask a coding agent to add tracing with Lemma's published skill (github.com/uselemma/skills). Source: uselemma.ai | Analytics shows estimated cost from observed token counts and public list prices, which Lemma states are not invoice amounts. Source: Lemma API: project analytics | Managed cloud, SOC 2 Type II, data isolated per organization. No self-hosted option is listed on its site; ask Lemma if you need one. Source: Lemma Trust Center | No public price list on its site at the time of writing; Lemma offers a demo booking. Check uselemma.ai for current terms. Source: uselemma.ai |
| Braintrustbraintrust.dev ↗Lemma vs Braintrust | Evals and observability in one platform: discover patterns in production, turn them into evals, improve with each release. Source: braintrust.dev | Prompts, responses and tool calls per trace, with latency, token usage and cost; auto-instrumentation for supported providers. Source: Braintrust docs: Trace LLM calls | Scorers (LLM, code or human) run on production traces as online scoring; Topics classifies logs by intent, sentiment and issues to surface recurring patterns. Source: Braintrust docs: Topics | Custom trace views and annotation, review queues, one-click trace-to-dataset, and the Loop agent for natural-language trace investigation. Source: braintrust.dev | Offline experiments against versioned datasets, side-by-side prompt and model comparison, automated and human scoring. Source: Braintrust docs: Evaluation quickstart | Log, time-window and environment alerts sent to Slack, PagerDuty or webhooks. Source: Braintrust docs: Set up alerts | SDKs for Python, TypeScript, Go, Ruby and C#; OpenTelemetry; LangChain, LangGraph, Vercel AI SDK and more; an MCP server for coding agents. Source: Braintrust docs: Integrations | Add the SDK and enable auto-instrumentation; online scoring and Topics are configured as automations per project. Source: Braintrust docs: Score production traces | Tracks latency, cost and quality in real time; cost dashboards can be built across traces. Source: braintrust.dev | Managed cloud; SOC 2 Type II, GDPR and HIPAA listed, with SSO and RBAC. For self-hosting or hybrid terms, check its pricing page or ask Braintrust. Source: Braintrust Trust Center | Published on its pricing page; check there for current plans. Source: braintrust.dev/pricing |
| HoneyHivehoneyhive.ai ↗Lemma vs HoneyHive | Agent observability and evaluation for teams running agents in production, aimed at enterprises. Source: honeyhive.ai | Every prompt, tool call, output and handoff; long-running trajectories with retries, loops and sub-agent handoffs. Source: HoneyHive docs: Tracing | Online evaluators (LLM judges, code or human) score sampled live traffic; alerts fire when a score crosses a threshold or drifts. Source: HoneyHive docs: Online evaluations | Flagged traces route to annotation queues for expert review; an MCP server, CLI and agent skills let coding agents root-cause alerts. Source: HoneyHive docs: Annotation queues | Offline experiments on datasets, regression comparison between agent versions, datasets built from production failures, evals in CI. Source: HoneyHive docs: Experiments | Aggregate thresholds and drift detection, notified by email or webhook. Source: HoneyHive docs: Alerts | OpenTelemetry-native; SDK auto-instrumentation it puts at 100+ models and frameworks; coding agents and no-code platforms such as ServiceNow and Copilot Studio. Source: HoneyHive docs: Integrations | Install the SDK or send OpenTelemetry spans, then configure evaluators, sampling and alert thresholds. Source: HoneyHive docs: Tracing quickstart | Monitoring tracks quality, cost, latency, usage and outcomes over time. Source: HoneyHive docs: Monitoring | Managed SaaS, hybrid (traces stored in your cloud) or fully self-hosted on Kubernetes; SOC 2 Type II, GDPR, HIPAA BAA. Source: honeyhive.ai | Published on its pricing page, with a free start; check there for current plans. Source: honeyhive.ai/pricing |
| Laminarlaminar.sh ↗Lemma vs Laminar | Open-source agent observability that catches agent failures at scale and helps fix them. Source: laminar.sh | LLM calls, tool calls, sub-agents, costs and tokens in a transcript view; screen recording for browser agents; full-text search across spans. Source: Laminar docs: Viewing traces | Signals analyze runs to surface failure modes you did not define, using Laminar's own trace-analysis model, and cluster similar failures. Source: Laminar docs: Signals | Signal clusters with written reports; Laminar Agent answers questions in Slack; CLI and MCP give coding agents SQL access to traces. Source: Laminar docs: Clusters | Error clusters can become eval datasets; agent evaluations run in real environments with lmnr-cli. Source: Laminar docs: Agent evaluations | New-event and new-cluster alerts per Signal, in-app and by Slack or email. Source: Laminar docs: Alerts | Vercel AI SDK, Claude Agent SDK, OpenAI Agents SDK, LangChain, Mastra, Pydantic AI, LiteLLM, Browser Use, Stagehand, Playwright, and OpenAI, Anthropic and Gemini SDKs. Source: Laminar docs: Integrations | Two lines to integrate with a supported framework, or a copyable setup prompt for a coding agent. Source: laminar.sh | LLM cost tracking per span and trace, plus custom SQL dashboards. Source: Laminar docs: Cost tracking | Managed cloud, or self-host with Docker or Helm charts on AWS or GCP; HIPAA and SOC 2 Type II listed. Source: Laminar docs: Hosting options | Published on its pricing page, with a free start; check there for current plans. Source: laminar.sh/pricing |
Head-to-head comparisons
Each pairing people search gets its own page, ending in which one suits whom.
Lemma vs Braintrust
Lemma vs Braintrust compared on tracing, failure detection, evals, alerting and setup: production monitoring of agent traces against an evaluation-centered platform.
Read the comparison →Lemma vs HoneyHive
Lemma vs HoneyHive compared on tracing, evaluation approach, alerting and workflow, with where each one is stronger and which team each suits.
Read the comparison →Lemma vs Laminar
Lemma vs Laminar, two agent observability tools, set side by side on tracing, issue detection and integrations, with which team each suits.
Read the comparison →Which tool suits whom
Lemma
Production monitoring for AI agents: audits every agent trace against the agent's own instructions and groups recurring silent failures into issues.
- Best fit
- Teams running agents in production who need to find wrong-but-confident answers, invented identifiers and workflows that succeed on paper, without writing every check upfront.
- Weaker fit
- Teams whose main need is offline evals before release (Lemma evaluates production usage only) or detailed cost tracking (its Analytics shows estimates, not invoice amounts).
Braintrust
An evaluation-centered platform: experiments on versioned datasets and scorers, with production tracing, online scoring and pattern discovery feeding back into evals.
- Best fit
- Teams that define quality as scored experiments: prompt and model comparisons, versioned datasets, and gating releases on eval results.
- Weaker fit
- Teams that want failures found in production without first writing scorers or reviewing pattern outputs themselves.
HoneyHive
Agent observability and evaluation built for enterprise teams: OpenTelemetry-native tracing, online evaluators on live traffic, threshold and drift alerts, and offline experiments.
- Best fit
- Larger organizations that want one standard for tracing, evaluation and governance across many agents, including hybrid or self-hosted deployment.
- Weaker fit
- Small teams that want failures surfaced without first choosing and tuning evaluators and alert thresholds.
Laminar
Open-source (Apache 2.0) agent observability: detailed transcripts of agent runs, Signals that surface failure modes and cluster them, and SQL access to everything.
- Best fit
- Teams that want open source and self-hosting, run browser or coding agents, and like querying traces directly with SQL.
- Weaker fit
- Teams that want failures checked against each agent's own instructions out of the box, or an eval-first workflow as the centre of their process.
Guides
How we compare: the criteria
What each question means, why it matters, and how to check a vendor's answer yourself.
Read →Why logs and error alerts miss LLM failures
Silent, semantic LLM failures return 200 and raise no error. Examples of what they look like, why logs miss them, and what LLM observability must capture to catch them.
Read →Choosing a tool by team and stack
A decision guide to LLM observability tools by framework, agent type and team size, including when simple tracing is enough and when you need failure detection or evals.
Read →Spot a fact that's out of date?
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