Noveum.ai
AI Agent Monitoring Production

Monitor Your AI Agents in Production with Noveum.ai

Production tracing and analytics for AI agents. Inspect the LLM calls, agent steps, retrievals, and tool interactions that you instrument, with links from operational signals to trace evidence.

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112 Published Evaluation Scorers
Multi-Agent Support
Operational Analytics

The Challenge

Traditional Monitoring Tools Don't Understand AI Applications

As AI agents become mission-critical, the gap between traditional observability and AI-specific needs grows wider. Without specialized monitoring, you're flying blind.

Invisible Agent Behavior

Traditional APM tools track HTTP requests, not agent decisions. You can't see why your agent chose a specific path, used certain tools, or produced particular outputs.

Uncontrolled AI Costs

Without captured model and token metadata, teams cannot attribute estimated AI cost to the projects and requests that produced it.

Undetected Hallucinations

AI agents confidently produce incorrect information. Without semantic evaluation, these errors slip through to production, damaging trust and causing real business harm.

Performance Degradation

Latency spikes, timeout errors, and degraded quality go unnoticed until users complain. By then, the damage to user experience is done.

The Cost of Missing Evidence

Disconnected logs make it harder to reconstruct an agent run, connect an evaluation failure to its source operation, and compare behavior across releases.

Trace → evalConnected Investigation Path

The Noveum Solution

Purpose-Built AI Agent Monitoring for Production

Noveum.ai connects hierarchical AI traces, operational analytics, datasets, and configured evaluation results so teams can investigate instrumented agent behavior.

AI Operations Dashboard

Review projects, recent request activity, latency, tokens, estimated cost, errors, and evaluation quality from one workspace.

Hierarchical Trace Visualization

Reconstruct the recorded flow of multi-agent workflows from parent-child spans and captured tool interactions.

Multi-Agent Workflow Support

Track instrumented CrewAI and LangGraph workflows, including recorded agent handoffs, tool calls, and shared context.

AI Cost Tracking

Inspect cost attribution by project, model, and request, then configure current NovaGuard cost caps where that beta is enabled.

Performance Analytics

P50/P95/P99 latency, throughput, error rates, and token usage trends with customizable time ranges.

Production Investigation

Filter recent traces by project, environment, status, user, session, service, and time window, then inspect the execution evidence.

Operational Signals with Trace Evidence

Use dashboard trends to choose a project and time window, then inspect the instrumented traces and scorer results behind the signal.

Hierarchical Span Tracking

Instrumented LLM calls, tool uses, retrievals, and agent steps captured with their available context and timing.

Latency Breakdown

Compare recorded duration across LLM inference, tool execution, retrieval, and other traced operations.

Token Usage Analytics

Track input/output tokens per request with cost attribution by provider.

Error Analysis

Filter error-status traces, open their spans, and inspect recorded error messages and attributes.

Key Features

Enterprise-Grade AI Agent Monitoring

Hierarchical AI Tracing

Capture the operations you instrument as hierarchical traces that preserve execution context.

  • Instrumented LLM calls with captured request and response fields
  • RAG retrieval steps and document context
  • Tool and function call execution

Operational Dashboards

Review project and organization trends in dashboards built for AI operations.

  • Request-volume and latency trends
  • Customizable time ranges and filters
  • Project and environment segmentation

Cost Analytics & Optimization

Review reported or estimated model cost and identify operations worth investigating.

  • Token usage by provider and model
  • Cost attribution by project and agent
  • Cost trends and current NovaGuard cost caps

Performance Metrics

Compare recorded request, latency, token, cost, and error trends over a selected time range.

  • Request volume and average latency
  • Token usage and estimated cost
  • Error-rate trends and trace drill-down

Multi-Framework Support

Use documented framework integrations or instrument custom Python operations.

  • LangChain, CrewAI, LangGraph, LiveKit
  • OpenAI, Anthropic, AWS Bedrock, Azure
  • Custom implementations and direct APIs

112 Published Evaluation Scorers

Apply compatible published or custom scorers to configured dataset items, then review saved decisions and source evidence.

  • Accuracy, relevance, and coherence
  • Safety, bias, and toxicity detection
  • Custom business rule evaluation

Works With Your Entire AI Stack

Use documented integrations for LangChain, LangGraph, CrewAI, LiveKit, and Pipecat, or instrument custom Python operations.

LangChainLangGraphCrewAILiveKitPipecatOpenAIAnthropicAWS BedrockAzure OpenAIOpenTelemetry

Use Cases

Built for Production AI Workloads

Use supported integrations and custom instrumentation across chatbots, RAG applications, voice systems, and multi-agent workflows.

Enterprise Customer Support Chatbots

Monitor AI-powered support agents handling thousands of customer interactions daily. Track resolution quality, response times, and escalation patterns.

ChatbotSupportReal-time

Multi-Agent Research Systems

Trace complex research workflows where multiple agents collaborate: researchers, analysts, and synthesizers working together autonomously.

Multi-AgentResearchCrewAI

RAG-Based Document Processing

Monitor retrieval-augmented generation pipelines processing millions of documents. Track retrieval quality, context relevance, and generation accuracy.

RAGDocumentsLangChain

Autonomous Workflow Automation

Observe AI agents automating complex business processes: from data extraction to decision making to action execution.

AutomationWorkflowAgents

AI-Powered Data Analysis

Track instrumented data-analysis agents through captured inputs, outputs, tool calls, and report-generation steps.

AnalyticsInsightsPython

Quick Start

Start with One Instrumented Operation

Install the Python SDK, set an API key, and instrument a representative operation or attach a supported framework integration.

  1. 1

    Install the SDK

  2. 2

    Add Your API Key

  3. 3

    Instrument the Operation

Install the SDK

Add our lightweight Python SDK to your project with a single command.

pip install noveum-trace

Add Your API Key

Set your API key as an environment variable. Organization is inferred automatically.

export NOVEUM_API_KEY=your-api-key

Instrument the Operation

Attach a Noveum callback to the LangChain invocation you want to trace, or use a context manager for custom code.

llm = ChatOpenAI(callbacks=[callback_handler])

No Credit Card Required

Ready to Monitor Your AI Agents?

Start on the Free plan, verify your first trace, and expand instrumentation when the captured evidence is useful.

Start FreeRead the Documentation

FAQ

Frequently Asked Questions

Everything you need to know about AI Agent Monitoring with Noveum.ai.

What is AI Agent Monitoring?

AI Agent Monitoring is a specialized observability solution designed to track, trace, and analyze AI agents running in production environments. Unlike traditional APM tools, it understands AI-specific metrics like token usage, LLM latency, agent decision paths, and tool interactions.

How does Noveum.ai differ from traditional monitoring tools?

Traditional monitoring tools track HTTP requests and system metrics. Noveum.ai adds hierarchical traces for instrumented AI workflows, captured token and cost context when available, a library of 112 published scorers for configured evaluation, and integrations for LangChain, LangGraph, CrewAI, LiveKit, and Pipecat.

Which AI frameworks does Noveum.ai support?

Noveum.ai ships ready-made integrations for LangChain, LangGraph, CrewAI, LiveKit and Pipecat, plus direct API integrations with OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, and Google Cloud (Vertex AI). Anything else works through the Python SDK's context managers, which are framework-agnostic.

How does cost tracking work in Noveum.ai?

Noveum.ai derives cost from captured provider, model, and token metadata when that data is available. Dashboards break usage down by project, model, and request. NovaGuard's current beta adds cost-cap policies for an enforcing integration; it is separate from NovaPilot recommendations.

Can Noveum.ai monitor multi-agent systems?

Yes. For instrumented multi-agent workflows, hierarchical traces can show the recorded parent-child relationships among agent steps, tool calls, and LLM interactions. Coverage depends on the framework callbacks and custom spans you configure.

Is my data secure with Noveum.ai?

Noveum supports encryption and organization-scoped access controls, with enterprise deployment options available for qualifying engagements. Exact data flows, storage boundaries, controls, and operational ownership depend on the selected architecture. SOC 2 Type II certification is in progress; review current evidence with Noveum during security assessment.

How long does it take to set up Noveum.ai?

Install the Python SDK, set the API key, and instrument one representative operation. Confirm the resulting spans and captured fields in Explorer before expanding the integration.

Can I start for free?

Yes. The Free plan includes monthly usage allowances and does not require a credit card. See the pricing page for the current limits and included features.

Start Monitoring Your AI Agents Today

Instrument a representative operation, inspect its trace, and expand coverage once the captured fields are correct.

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