Lemma
Production monitoring for AI agents that audits every trace against your agent's own instructions to catch runs that returned success while doing the wrong
Buy Lemma if your agent moves money, changes accounts or files tickets against real people, and you can write down what it is supposed to do — that instruction text is the test oracle everything else hangs off. The remediation loop is the real differentiator: MCP pulls the incident and its traces into your coding agent, and the fix converts into an online eval that reopens the issue on recurrence. Skip it if you only need latency and error codes (Sentry or Datadog cover that) or your agent has no production traffic. Compared with general agent-observability tools like Raindrop, Lemma leans on instructions-as-spec rather than hand-built signals; a user at folk reported switching for exactly
Verified 1d ago · liveness 71/100 · cite: rightaichoice.com/tools/lemma
- Teams whose agents issue refunds, file tickets or change accounts, where a wrong-but-successful run costs real money
- Consumer-facing agent teams with huge surface area who can't hand-build a signal for every failure mode
- Teams already on LangChain, CrewAI or AutoGen who want behavioral monitoring running within minutes
- Platform teams that need to route agent incidents into a coding agent and ship a prompt fix the same day
- Projects that only need latency, throughput and error-code monitoring — Sentry or Datadog already cover that
- Early prototypes with no production traffic to audit
- Teams that can't write down what the agent is supposed to do, since instructions are the test oracle
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Skip Lemma if your agent only needs latency and error-code monitoring that Sentry or Datadog already give you, or if you have no production traffic for it to audit.
Running your own long-term trace history means exporting before the 90-day retention window closes, since analytics drops traces, generations, tools and errors after that.
Lemma's plans are not published in the material reviewed this run, so cost depends on what the sales conversation quotes you. Budget for it the way you would a production observability seat: teams already paying for Sentry or Datadog should treat Lemma as an additional line item rather than a replacement, while teams replacing hand-built agent signal pipelines can usually absorb it against the engineering hours that pipeline consumed.
In short
Lemma — Production monitoring for AI agents that audits every trace against your agent's own instructions to catch runs that returned success while doing the wrong. Best for Teams whose agents issue refunds, file tickets or change accounts, where a wrong-but-successful run costs real money, Consumer-facing agent teams with huge surface area who can't hand-build a signal for every failure mode, Teams already on LangChain, CrewAI or AutoGen who want behavioral monitoring running within minutes. Contact Sales pricing.
What's new in Lemma
Checked yesterdayAcross the latest 5 updates: 3 feature updates and 2 changelog entries.
Audit logs: GET /audit-events with CSV export; Issues detection covers completed tasks
Owners and admins can list organization audit events with a 7-day default window and a 90-day maximum; CSV export caps at 10,000 events and is recorded. Issue detection now flags completed tasks, including confirmed hallucinations.
Analytics Catalog and Query endpoints over REST and MCP
GET /projects/{project_id}/analytics/catalog lists each event and its measures, and POST /analytics/query aggregates one event's measures. The MCP tools are describe_project_analytics and query_project_analytics.
Analytics keeps 90 days of traces, generations, tools and errors
Analytics retains traces, generations, tools and errors for 90 days, and REST and MCP now read the same views, so numbers line up whichever surface you query.
Traces pages fill with processed traces; Analytics error counts split
With Show unprocessed off, traces pages now show processed traces instead of empty slots, and the traces API accepts include_unprocessed. The Analytics errors headline counts span errors while error rate uses runs whose root span failed.
Slack priority alerts, sensitive-action audit logs, API key settings fix
Priority alerts show issue name, agent, confirmation writeup, sparkline and actions, with export links valid for 7 days. Audit logs record auth, membership, API key, integration, export, delete and billing actions for at least 90 days.
What people actually say about Lemma — is it worth it?
We scanned public community sources for Lemma on Jul 3, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Our own analysis of that scan says the posts were off-subject. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is Lemma? 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: October 2026
How we score →Key Features
- Audit every agent trace against your agent's own instructions
- Cluster recurring failures into Issues instead of one-off alerts
- Detect completed tasks as issues, including confirmed hallucinations
- Live Slack priority alerts with severity triage, confirmation writeup and sparkline
- Lemma MCP server pulls incident context into your coding agent
- Fetch representative traces and root-cause context on demand
- Suggested prompt changes attached to each incident
- Online evals that continuously score new traces against a fixed failure mode
- Auto-reopen issues when a failure mode recurs after a fix
- Multi-span trace viewer with timing across guardrails, retrieval, model calls and tool use
- Audit logs API via GET /audit-events with CSV export up to 10,000 events
- Analytics catalog and query endpoints over REST and MCP
- 90-day retention for traces, generations, tools and errors
- Project-scoped API keys with ingest, read and admin scopes
- Python SDK instrumentation plus agent skill install
About Lemma
Lemma is production monitoring for AI agents. It audits every trace against your agent's own instructions and flags the runs that succeeded while behaving wrong — an agent inventing placeholder customer IDs, promising refunds outside the 30-day policy window, or filing escalations against tickets that don't exist. Every one of those runs returns success, so nothing surfaces in error monitoring. Recurring failures get grouped into Issues instead of fired as one-off alerts. Live Slack alerts triage by severity, and a confirmed alert carries the issue name, agent, confirmation writeup and sparkline. The Lemma MCP server pulls incident context and representative traces into your coding agent so a prompt fix can be drafted without leaving the editor; a coding agent can also update uploaded artifacts through MCP, reading the current text before it writes so it doesn't overwrite a newer edit. Once the fix ships, Lemma creates an online eval that keeps scoring new traces against that failure mode and reopens the issue if it recurs. Setup is a Python SDK plus an agent skill install pointed at github.com/uselemma/skills, with native support for LangChain, CrewAI and AutoGen. Recent releases added a scoped REST and MCP analytics layer: an audit-events endpoint with CSV export, a queryable analytics catalog, and 90-day retention for traces, generations, tools and errors. API keys carry ingest, read and admin scopes and can be limited to a single project, and audit logs capture auth, membership, API key, integration, export, delete and billing actions. Security posture is SOC 2 Type II, AES-256 at rest, TLS 1.2+ in transit, with data isolated per organization. If you only need latency and error codes, Sentry or Datadog already cover you. Lemma is for teams whose agents touch customers or money and who can state plainly what the agent is supposed to do.
Behind the Verdict
Lemma's bet is that the dangerous agent failure is the one that returns 200 OK. Traditional APM tells you a run took 12.4 seconds across 10 spans and finished green; it cannot tell you the agent fabricated a customer ID while doing it. Lemma audits each trace against your agent's own instructions — the prompt text you already wrote — and clusters recurring violations into Issues rather than one-off alerts. That design choice matters: it means you don't have to enumerate every failure mode in advance, which is impractical for consumer agents with large surface area. The remediation loop is where the product earns its place. A P0 issue like "promising refunds outside 30-day policy" carries representative traces, a root-cause pointer (in the demo, a prompt revision 41 minutes earlier) and a suggested prompt change. Through the MCP server your coding agent fetches the incident, replays the affected traces and opens a PR. Then Lemma creates an online eval named for that failure mode — refund_policy_violations — and scores every new trace against it, reopening the issue and notifying the team if it recurs. That closed loop is meaningfully more than a dashboard. Strengths worth naming: multi-span trace viewer with timing across guardrails, retrieval, model calls and tool use; Python SDK instrumentation with native LangChain, CrewAI and AutoGen support; audit-events API with CSV export (10,000-event cap, 90-day max window); a queryable analytics catalog over REST and MCP; project-scoped API keys with ingest/read/admin scopes; SOC 2 Type II with AES-256 at rest and TLS 1.2+ in transit and per-organization data isolation. Honest weaknesses: analysis is trace-based, so it speaks to agent behavior rather than the quality of the underlying model. It requires SDK instrumentation of your codebase and production traffic — a prototype with a dozen runs a month will see little value. Analytics retention is 90 days, so anyone needing multi-year trace history has to export. And the whole system assumes you can articulate intended behavior; teams that can't write their agent's instructions down have no oracle for it to audit against.
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Real-world workflow fit
Concrete scenarios for the personas Lemma actually fits — and what changes day-one when you adopt it.
Lemma flags a P0 issue — the agent invents placeholder customer identifiers when it can't identify a customer, then files 15 orphaned tickets and 7 escalations against them. All 50 runs returned success.
Outcome: They pull three representative traces from the Slack alert, confirm 66% of support runs are affected, and ship a prompt change requiring the agent to ask for the customer's email before any lookup.
A refund-policy incident lands as P0. Their coding agent calls lemma get_incident and get_traces through MCP, sees root cause was a prompt revision from 41 minutes earlier, and drafts the refund-policy edit.
Outcome: PR #482 opens with the tightened policy window and human-handoff rule; all 12 affected traces replay clean, and Lemma creates the refund_policy_violations online eval to watch for regressions.
They need a record of who rotated API keys, changed membership and ran exports across the organization.
Outcome: GET /audit-events returns auth, membership, API key, integration, export, delete and billing actions for the last 7 days, and the CSV export gives security a reviewable file.
Use Cases
- Audit support agent conversations to detect when the agent invents customer IDs or escalations.
- Monitor your coding assistant for policy violations such as offering refunds outside the return window.
- Get alerted instantly when a new failure pattern emerges in your deployed agent, with trace evidence attached.
- Review traces across multiple agent versions to identify prompt regressions before rolling out.
- Use your code editor's AI to pull incident context and fix prompt logic without leaving the IDE.
- Query agent analytics over REST or MCP to see how tool use and errors trend across a project.
- Export audit events to CSV for a security or compliance review of auth, membership and API key changes.
Limitations
- Lemma requires SDK instrumentation of your codebase and production traffic to be useful, so prototypes and low-volume agents see little value.
- Analysis is trace-based: it evaluates agent behavior rather than the quality of the underlying model, and it assumes you can state what your agent is supposed to do, because your instructions serve as the test oracle.
- Analytics keeps traces, generations, tools and errors for 90 days, and audit-event queries wider than 90 days are rejected, so anything beyond that window has to be exported and stored yourself.
- CSV audit exports are capped at 10,000 matching events.
- Confirmations and issue grouping depend on Lemma's own detection pipeline, so issue confirmation status is worth checking before treating a flagged run as real.
as of 2026-10-10
Verification history
We have re-verified Lemma 9 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-checked, vendor evidence unchanged
- — 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
Showing the 6 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where Lemma's pricing actually pencils out — and where peers do it cheaper.
Lemma's plans are not published in the material reviewed this run, so cost depends on what the sales conversation quotes you. Budget for it the way you would a production observability seat: teams already paying for Sentry or Datadog should treat Lemma as an additional line item rather than a replacement, while teams replacing hand-built agent signal pipelines can usually absorb it against the engineering hours that pipeline consumed.
Setup time & first value
How long it actually takes to get something useful out of Lemma — broken out by persona, not the marketing-page minute.
If your agent runs on LangChain, CrewAI or AutoGen, the path is an API key plus a prompt to your coding agent to install the Lemma skill and add tracing — most teams see the first traces within an afternoon. Hand-rolled or custom agent loops take longer, since instrumentation is on you. Seeing your first real Issue depends on production traffic, not setup effort.
Switching to or from Lemma
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Raindrop: install the Lemma skill, point instrumentation at the same agent entry point, and let Issue detection derive failure modes from your existing instructions instead of rebuilt signals.
- →From a hand-built trace dashboard: keep your stored traces, attach the Python SDK at the same spans, and hand the hallucination and policy checks over to Issue detection.
- →From Sentry or Datadog-only monitoring: run Lemma alongside your existing APM rather than replacing it, since those tools still cover latency and error codes.
- ↗To Sentry or Datadog: if you decide behavioral auditing isn't worth the instrumentation, export audit events and traces for the 90-day window before you stop ingesting, because those tools won't reconstruct agent
- ↗To a generic LLM observability tool: expect to redefine failure modes yourself as signals, since instructions-as-test-oracle detection is the part that does not port.
- ↗To a self-hosted stack: plan for the retention gap, as Lemma keeps traces, generations, tools and errors for only 90 days.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Lemma”, and we withheld 6: 6 could not be judged, because “Lemma” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Lemma.
Official links
Featured Head-to-Head Comparisons
Lemma vs Spider Cloud
If your priority is feeding fresh web data to AI agents at low cost, Spider Cloud's Rust engine, AI Studio, and 1k+ scrapers make it unbeatable. If you've already deployed agents and need to catch silent failures where they return success but behave incorrectly, Lemma's instruction-level tracing and Slack alerts are precisely what you need. They solve different problems: Spider Cloud gets data in, Lemma ensures agents act correctly on that data.
Lemma vs Temporal Ai
Temporal is the right choice if you need a battle-tested orchestration platform to build resilient AI agents that survive failures and scale across SDKs. Lemma is the sharper tool if your top priority is detecting silent agent failures in production with instruction-level monitoring. For most teams, Temporal provides the foundation, while Lemma can complement it as a monitoring overlay.
Lemma vs Presto Voice
These tools are not direct competitors. Presto Voice is ideal for QSR chains wanting to automate drive-thru ordering and increase revenue via upselling, while Lemma is designed for engineering teams building AI agents who need to catch silent failures. Choose Presto if you run a drive-thru chain; choose Lemma if you develop agentic systems. They serve entirely different buyers.
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Frequently Asked Questions
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