AgentOps: Trace, Debug, & Deploy Reliable AI Agents

AgentOps

3.5 | 18 | 0
Type:
Website
Last Updated:
2025/10/30
Description:
AgentOps is a developer platform for building reliable AI agents and LLM apps. It offers agent observability, time travel debugging, cost tracking, and fine-tuning capabilities.
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AI agent debugging
LLM observability
AI cost tracking

Overview of AgentOps

AgentOps: The Leading Platform for Reliable AI Agents

What is AgentOps? AgentOps is a comprehensive developer platform designed to build, trace, debug, and deploy reliable AI agents and Large Language Model (LLM) applications. It caters to engineers aiming to create robust and scalable AI solutions.

Key Features and Benefits

  • Agent Observability: AgentOps provides visualization tools to track events like LLM calls and multi-agent interactions, crucial for understanding agent behavior.
  • Time Travel Debugging: This feature allows developers to rewind and replay agent runs, enabling precise debugging at any point in time.
  • Debugging and Auditing: The platform maintains a full data trail of logs, errors, and prompt injection attacks, ensuring thorough auditing from prototype to production.
  • Cost Tracking: AgentOps helps manage and visualize agent spending with up-to-date price monitoring across multiple agents and LLMs.
  • Fine-Tuning: It offers the ability to fine-tune specialized LLMs up to 25x cheaper by leveraging saved completions, optimizing both performance and cost.

How does AgentOps work?

AgentOps uses a single SDK with native integrations to leading agent frameworks, including OpenAI, CrewAI, and Autogen. By integrating this SDK, developers can track every token their agent uses, monitor costs in real-time, and debug complex interactions. The platform supports over 400 LLMs, ensuring broad compatibility and flexibility.

Here’s how you can use AgentOps:

  1. Installation: Start by installing the AgentOps SDK using pip install agentops.
  2. Integration: Integrate the SDK into your AI agent or LLM application.
  3. Visualization: Visually track events such as LLM calls, tools, and multi-agent interactions.
  4. Debugging: Utilize the time travel debugging feature to rewind and replay agent runs with point-in-time precision.
  5. Cost Management: Track, save, and monitor every token your agent sees to manage costs effectively.

Why Choose AgentOps?

AgentOps stands out due to its comprehensive feature set tailored for AI agent development. Its ability to provide detailed observability, debugging tools, and cost tracking makes it an invaluable asset for developers aiming to build reliable and cost-effective AI solutions. The platform’s native integrations and support for numerous LLMs further enhance its versatility.

Who is AgentOps for?

AgentOps is designed for:

  • AI engineers building reliable agents.
  • Developers working with LLM applications.
  • Teams looking to scale enterprise-grade AI solutions.

Pricing and Plans

AgentOps offers flexible pricing plans:

  • Basic: Free up to 5,000 events, including agent agnostic SDK and LLM cost tracking.
  • Pro: Starting at $40 per month, includes unlimited events, log retention, session and event export, and dedicated support.
  • Enterprise: Custom pricing with SLA, Slack Connect, custom SSO, on-premise deployment, and SOC-2, HIPAA, NIST AI RMF compliance.

What is High Agency?

AgentOps champions the concept of 'High Agency,' encouraging developers to build AI agents that are not only powerful but also reliable and auditable. By providing the tools and insights needed to understand and optimize agent behavior, AgentOps empowers developers to create the AI solutions of the future.

Conclusion

AgentOps is the go-to platform for developers serious about building reliable, scalable, and cost-effective AI agents and LLM applications. With its robust feature set and flexible pricing, AgentOps is well-equipped to meet the demands of modern AI development. Whether you are debugging complex interactions, tracking costs, or fine-tuning LLMs, AgentOps provides the tools you need to succeed.

Keywords: AI agents, LLM apps, agent observability, debugging, cost tracking, fine-tuning, developer platform, AI solutions, machine learning, OpenAI, CrewAI, Autogen.

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