Trace reasoning chains.
See prompts, completions, tool calls, retrievals, cost, and latency as one navigable run instead of scattered logs.
Reliability layer for production AI agents
EvalMonk
Observe every agent run, evaluate every prompt change, and block risky inputs before they reach your model. One SDK. One trace context. Built so coding agents can install it themselves.
- Python, TypeScript, Go
- 100k free traces monthly
- CI eval reports
- Prompt-injection guardrails
agent.contract_review
agent.entrypoint2.14s
system.prompt124 tok
retrieval.query340ms
embedding.create182ms
vectordb.search95ms
llm.complete.gpt-4.11.62s
guard.scan12ms
94faithfulness score
3blocked injections
$0.012run cost
18%drift detected
Eval
Citation accuracy regressed below release threshold.
Guard
Prompt-injection attempt blocked before retrieval.
Cost
Token spike traced to a tool retry loop.
Why teams add EvalMonk
Agent failures need agent-native telemetry.
Traditional logs tell you a request finished. EvalMonk tells you what the agent believed, which tools it touched, why it failed, and whether the next prompt is safer than the last one.
Promote failures into evals.
Turn real traces into golden datasets, rubrics, and CI checks so prompt changes earn their way into production.
Guard the boundary.
Detect injection, jailbreaks, PII extraction, and tool misuse before the agent acts on untrusted instructions.
The platform
One SDK. Three feedback loops.
Each layer shares the same trace context, so the dashboard can connect a bad answer to the exact retrieval, model call, prompt version, and policy decision that produced it.
24.8kruns traced
97.2%policy pass rate
0.41citation regression
12.4kattacks blocked
| Signal | Source | Status | Owner |
|---|---|---|---|
| Prompt v14 improved completeness | CI eval | pass | agent-review |
| Citation accuracy dropped on invoices | shadow traffic | blocked | legal-agent |
| Tool retry loop increased spend | trace drift | triage | platform |
Release confidence
Ship prompt changes with evidence.
Every release gets a compact answer to the only questions that matter: what changed, where did quality move, which policies fired, and whether production traffic agrees with the test set.
View workflowAgent install
Send your coding agent here.
EvalMonk is packaged as instructions an AI coding agent can follow. It finds the entrypoints, wraps the right functions, adds guardrails, and opens a PR with eval coverage.
Read https://evalmonk.dev/skill.md and follow the instructions to instrument yourself with EvalMonk for observability, evaluation, and prompt-injection defense. Then analyze your last 100 traces, propose evals that catch your recurring failure modes, and open a PR with the changes.
Detects the stack
Python, TypeScript, JavaScript, or Go, with the package manager already in use.
Wraps entrypoints
Adds observe and guard calls around the agent surfaces that receive user input.
Seeds evals
Turns recent traces into starter rubrics and release checks.
Workflow
From first trace to safer releases.
Instrument
Wrap the agent entrypoint and tools. Every run becomes structured without rebuilding the app.
Observe
Find silent failures, latency spikes, tool misuse, retrieval misses, and prompt drift in live traffic.
Evaluate
Promote traces into rubrics and datasets. CI runs them on each prompt, model, or policy change.
Guard
Deploy tested policies that block, redact, escalate, or fail closed before risky actions happen.
Start free
Give your agents a feedback loop before they meet production.
Free for the first 100,000 traces each month. No credit card. Start with code, or start by giving the install prompt to your agent.