AgentOps

Name: AgentOps Introduction Video
Uploaded: 2025-04-28T16:03:02Z
Duration: 1 min 33 s
Description: Leading AI Agent Observability Company

About AgentOps

Launched Jan 16, 2025

Introduction Video

Description

Leading AI Agent Observability Company

Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including CrewAI, Langchain, Autogen, AG2, and CamelAI

AgentOps Key Features

Visualize: Visually track events such as LLM calls, tools, and multi-agent interactions.
Time Travel Debugging: Rewind and replay agent runs with point in time precision.
Debug and Audit: Keep a full data trail of logs, errors, and prompt injection attacks from prototype to production.
Token Counts: Track, save, and monitor every token your agent sees.
Cost Tracking: Manage and vizualize agent spend with up-to-date price monitoring.
Fine-tuning: Fine-tune specialized LLMs up to 25x cheaper on saved completions.

AgentOps Use Cases

From open source AI agent developer tools like AgentOps to Fortune 500 enterprises, we help clients create safe, reliable, and scalable AI agents.

Pros

Comprehensive monitoring capabilities for AI agents, allowing detailed insights into performance.
Built in Python SDK which is developer-friendly and integrates easily with existing systems.
Tracks LLM costs effectively, providing users with budget management tools.
Benchmarking feature helps in comparing AI agent performance across different platforms.
Seamless integration with popular LLMs and agent frameworks such as CrewAI, Langchain, Autogen, AG2, and CamelAI.

Cons

Might require a certain level of technical expertise to fully utilize all features, making it less accessible for non-technical users.
Limited support for languages other than Python, which could restrict its use in diverse development environments.
Potentially high costs associated with the extensive features and integrations available.