Gain complete observability and fix failing AI agents automatically by exposing them to a continuous learning loop.
Observe. Learn. Improve.
pip install maev-sdkBuilt for engineers building on
These aren't hypothetical edge cases. They are active blind spots happening during live production runs where you currently lack visibility.
>>Agent returned fabricated API data. User filed a support ticket 3 days later.
>>Tool call triggered itself 47 times. $180 burned before rate limit hit.
>>External API returned 429. Agent retried 12 times silently. No alert fired.
>>Prompt hit 128k tokens. Agent silently dropped memory. Output was wrong.
>>$340 burned in 8 minutes on one runaway session. Caught on the billing page.
>>Agent completed the wrong subtask. Returned success. No one noticed for a week.
Maev diagnoses and auto-fixes all 10 failure categories seamlessly.
Expose your agents to a constant feedback loop that identifies weaknesses and reinforces correct behaviors autonomously
Instantly diagnose failures in production and apply hotfixes to your prompt chains and tool schemas without manual intervention
Monitor every sequence action token and tool invocation in real time to understand exactly how your agents make decisions
"Analyze the Q3 revenue report"
{ status: "ok", data: [...] }
No infrastructure to host. No complex pipelines. Initialize Maev with a single command and it immediately starts analyzing and injecting safety schemas natively during runtime.
Built on highly optimized async channels. Collection, diagnosis, and self-healing rules are processed out-of-band so your primary execution thread remains entirely untouched.
The vast majority of unexpected LLM costs come from runaway loops, duplicate requests, and context stuffing. Maev diagnoses them and applies guardrails before your billing explodes.
Catch agents stuck in repetition cycles before they drain your API budget. Maev acts as an automatic circuit breaker.
Our analyzers surface duplicate calls across traces and self-correct schemas to remove repetitive token waste.
See the exact cost per step, prompt, and tool call. Identify runaway context accumulation on the fly.
Maev automatically intercepts and fixes failing AI workflows by applying real-time learning loops. Secure your agentic architecture with a reliable platform that enforces strict behavioral guardrails autonomously, backed by total runtime observability.
FAQ
Maev exposes your AI agents to a continuous feedback loop. When an agent fails or encounters an edge case in production, Maev instantly diagnoses the root cause and synthesizes a behavioral guardrail to prevent the failure in the future.
Yes. Instead of just alerting you, Maev patches prompt instructions and tool schemas in real-time, autonomously directing the agent back on path without requiring manual intervention from engineering teams.
Absolutely. Total runtime observability is built-in. Maev captures rich execution traces for every sequence, action, tool invocation, and token used, giving you full visibility into the agent's decision timeline.
Maev is built to intercept and improve agents running on Python, LangChain, CrewAI, AutoGen, and custom n8n workflows seamlessly.
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One line of code to deploy continuous learning loops, autonomous self-healing, and total runtime observability for any AI workflow.