AgentOps

Records complete agent execution traces including LLM calls, tool invocations, and multi-agent interactions, enabling developers to rewind and replay agent runs with point-in-time precision. The platform captures full event sequences and renders them in a visual timeline interface, allowing inspection of intermediate states, prompts, and responses at any execution point without re-running the agent.

Tracks token consumption and spending across 400+ LLM providers and models by intercepting LLM API calls through the AgentOps SDK, maintaining up-to-date pricing data for each model, and aggregating costs across multiple agents and sessions. The platform provides real-time cost visualization, token counting for every LLM interaction, and cost-per-session breakdowns to identify expensive agent behaviors.

Provides a web-based dashboard for visualizing agent metrics, session replays, cost trends, and error logs with interactive charts, timelines, and drill-down capabilities. The dashboard enables non-technical stakeholders to understand agent behavior and performance without accessing raw logs or code.

Offers self-hosted deployment on AWS, GCP, or Azure, and on-premise deployment for organizations with data residency or security requirements. The platform provides containerized deployment options and infrastructure-as-code templates, enabling organizations to run AgentOps in their own cloud or on-premise environments while maintaining data sovereignty.

Analyzes saved LLM completions from agent runs and identifies opportunities to fine-tune specialized models on frequently-repeated completion patterns, claiming to reduce inference costs by up to 25x. The platform presumably identifies common prompt-completion pairs and recommends fine-tuning targets, though the exact mechanism for cost calculation and fine-tuning workflow is not documented.

Captures and logs all agent actions (LLM calls, tool invocations, errors, prompt injections) in an immutable audit trail with timestamps and metadata, supporting compliance frameworks including SOC-2, HIPAA, and NIST AI RMF at the Enterprise tier. The platform provides role-based access control, custom SSO integration, and Slack Connect for audit notifications, enabling organizations to demonstrate compliance with regulatory requirements.

Provides tools to benchmark and compare agent performance across multiple dimensions (cost, latency, success rate, token efficiency) by aggregating metrics from multiple agent runs and sessions. The platform claims to have tested 400+ agents and provides guidance on agent selection, though specific benchmarking methodology and available metrics are not detailed in documentation.

Provides a single Python SDK (`pip install agentops`) that integrates with multiple agent frameworks through a plugin/hook architecture, capturing events from any framework without requiring framework-specific code changes. The platform claims 'one SDK, many integrations' and supports native integrations with 'top agent frameworks' (specific frameworks not listed), enabling developers to add observability to existing agents with minimal code modifications.

Monitors LLM spending in real-time and triggers alerts when costs exceed configured thresholds, enabling developers to detect runaway spending or unexpected cost spikes. The platform provides budget tracking and visualization, though specific alert mechanisms (email, Slack, webhooks) and budget enforcement capabilities are not detailed in documentation.

Captures and visualizes interactions between multiple agents in a coordinated system, including message passing, tool sharing, and sequential or parallel execution patterns. The platform traces the full execution graph of multi-agent systems, enabling developers to understand how agents coordinate and where bottlenecks or failures occur in complex agent networks.

Implements a freemium pricing model based on event volume (free tier: 5,000 events/month; Pro: unlimited events at $40+/month), where each LLM call, tool invocation, or agent action counts as an event. The platform tracks event consumption in real-time and enforces tier limits, enabling developers to understand observability costs and scale usage as needed.

Captures errors, exceptions, and agent failures with full execution context (preceding LLM calls, tool invocations, prompts, responses), enabling developers to understand root causes without manual log analysis. The platform logs stack traces, error messages, and the complete execution path leading to failure, providing rich debugging information for production issues.