Nexus Labs
Official@nexus-labs-automation
Custom AI Agents & Automations
Agent Skills by Nexus Labs
Showing 14 vetted skills indexed across 1 GitHub repositories.
nexus-labs-automationhuman-in-the-loop
Capture human approval, clarification, and escalation events as structured spans in AI agent workflows.
nexus-labs-automationtool-call-tracking
Capture tool name, summarized inputs, results, latency, and success status in structured traces.
nexus-labs-automationmemory-rag-instrumentation
Instrument RAG memory operations with span attributes for LangChain and LlamaIndex.
nexus-labs-automationerror-retry-tracking
Instrument error handling and retries with telemetry attributes and a reusable retry wrapper.
nexus-labs-automationsession-conversation-tracking
Link sessions, conversations, and turns into cohesive traces.
nexus-labs-automationprompt-versioning
Track prompt versions and A/B test variants against production performance metrics.
nexus-labs-automationtoken-cost-tracking
Track token usage and costs across agents with per-call attribution and budget alerts.
nexus-labs-automationmulti-agent-coordination
Trace supervisor-worker handoffs and hierarchies in multi-agent workflows.
nexus-labs-automationguardrails-safety
Instrument AI agent inputs and outputs with safety and policy guardrails.
nexus-labs-automationllm-call-tracing
Trace LLM API calls to capture model, latency, tokens, cost, and success status.
nexus-labs-automationinstrumentation-planning
Plan observable metrics and spans for AI agent systems using a five-tier framework.
nexus-labs-automationproduction-eval-strategy
Sample production traffic and run asynchronous evaluations with regression detection.
nexus-labs-automationdecision-tracing
Trace agent decision points with options, actions, and context.
nexus-labs-automationevaluation-quality
Measure agent quality across automated evaluations, human feedback, and trend analysis.
Frequently Asked Questions About Nexus Labs
FAQPage SchemaWhat specific tasks are enabled by Nexus Labs instrumentation?▼
These capabilities enable granular monitoring of reasoning chains, including token cost attribution, RAG memory operation tracking, and multi-step supervisor-worker handoff visibility. You can capture human approval events, manage prompt versioning, and implement safety guardrails to ensure production reliability and performance consistency across complex distributed reasoning architectures.
Which personas benefit from these observability frameworks?▼
These capabilities are designed for MLOps engineers, reliability architects, and technical leads responsible for maintaining production-grade reasoning systems. They provide the necessary telemetry to debug decision-making logic, manage operational budgets, and ensure that complex multi-step processes remain performant and compliant under high-traffic production conditions.
What are the prerequisites for integrating these telemetry patterns?▼
Integration requires an existing environment utilizing LangChain or LlamaIndex frameworks for reasoning tasks. Users must implement the provided telemetry wrappers and span attributes within their existing codebase to capture trace data, ensuring that session identifiers and context are correctly propagated across all internal decision points and external model calls.