AgentOps.ai
Tracks and debugs AI agents with precision, streamlining development
AgentOps is a developer platform for monitoring, debugging, and deploying AI agents and LLM applications, supporting over 400 frameworks and LLMs like OpenAI, CrewAI, and LangChain. It provides tools to track agent behavior, manage costs, and optimize performance. The platform integrates via a Python SDK, requiring just two lines of code for setup, and offers a TypeScript SDK for Node.js projects.
The Session Replay feature records and replays agent runs, allowing developers to inspect LLM calls, tool interactions, and multi-agent workflows with precise timestamps. Time Travel Debugging enables stepping backward through execution states to pinpoint errors. Cost Tracking monitors token usage and expenses across agents, supporting budget management. The platform’s visualization tools generate graphs of agent interactions, aiding in performance analysis. AgentOps also logs errors and potential prompt injection attacks, ensuring a robust audit trail.
Pricing includes a free tier for small projects and flexible plans for enterprises, competitive with platforms like LangSmith and HoneyHive. LangSmith offers broader LLM debugging but is less focused on agent-specific workflows. HoneyHive provides similar observability but lacks AgentOps’ depth in time-travel debugging. The free tier suits solo developers, while enterprise plans scale for larger teams.
Some users report a learning curve for advanced features like custom trace decorators, particularly for those new to AI frameworks. The dashboard can lag with large datasets, as noted in recent Reddit feedback. Documentation is comprehensive but could include more beginner-level examples. Integration with over 400 LLMs and frameworks ensures versatility, though setup time varies by framework complexity.
To use AgentOps effectively, start with the free tier and test integrations with your primary framework. Use Session Replay for debugging and Cost Tracking to monitor expenses. Review the documentation for setup guides and join the Discord community for support. Regularly check the dashboard for performance insights and error logs to optimize your agents.
Homepage Screenshot 📸
Video Overview 🎬
What are the key features? ✨
- Session Replay: Records and replays agent runs to debug LLM calls and tool interactions.
- Time Travel Debugging: Steps backward through execution states to identify errors.
- Cost Tracking: Monitors token usage and expenses across multiple agents.
- Visualization Tools: Generates graphs of agent interactions for performance analysis.
- Framework Integrations: Supports over 400 LLMs and frameworks like OpenAI and CrewAI.
Who is it for? 🤔
Examples of what you can use it for 💡
- AI Developer: Uses Session Replay to debug a CrewAI agentโs faulty tool interactions.
- Data Scientist: Tracks token usage with Cost Tracking to optimize LLM expenses.
- Engineering Team: Visualizes multi-agent workflows to improve system performance.
- Startup Founder: Tests prototype agents with the free tier to validate ideas.
- Enterprise Manager: Audits agent logs to ensure compliance in production systems.
Pros & Cons ⚖️
- Robust debugging with Session Replay.
- Supports 400+ frameworks and LLMs.
- Free tier suits small projects.
- Dashboard lag with large datasets.
FAQs 💬
Ready to try AgentOps.ai?
Tracks and debugs AI agents with precision, streamlining development
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