agentops

AgentOps is a monitoring and observability tool for AI agent developers, provided as a Python SDK to help developers build, evaluate, and monitor AI agents, covering the entire process from prototype to production. Its core goal is to solve the problems of AI agents running slowly, costing too much, and being unreliable.

Main capabilities and uses

• Comprehensive observability: Track AI agent performance, user interactions, and API usage.
• Real-time monitoring: Gain instant insights through session replays, metrics, and real-time monitoring tools.
• Cost control: Monitor and manage spending on LLM and API calls.
• Failure detection: Quickly identify and respond to agent failures and multi-agent interaction issues.
• Tool usage statistics: Understand how agents use external tools through detailed analytics.
• Session-level metrics: Gain an overall view of agent sessions through comprehensive statistics.
• Evaluation and debugging: The materials list an evaluation roadmap and a debugging roadmap (Roadmap), but specific feature details are not yet provided.

How to use After initializing the AgentOps client, analytics data for all LLM calls is automatically available, and all sessions can be viewed in the AgentOps dashboard. Developers can progressively add observability to agents, tools, and functions line by line, and all decorators support input/output recording, exception handling, async/await functions, generator functions, and custom attributes and names.

For CrewAI agents, simply set AGENTOPS_API_KEY in the environment, and with two lines of code you can get automatic monitoring in the AgentOps dashboard. For AG2 (formerly AutoGen) agents, set AGENTOPS_API_KEY and call agentops.init() to add full observability and monitoring. Frameworks such as CAMEL and SwarmZero likewise track and analyze by setting AGENTOPS_API_KEY and initializing AgentOps. Langchain needs to be installed as an optional dependency, then imported and the handler set, and it also supports async handlers.

Supported integrated frameworks and models (based on the materials listed)

• OpenAI Agents SDK: Native integration for Python and TypeScript, supporting the construction of multi-agent systems with tools, handoffs, and guardrails.
• CrewAI: Observability in two lines of code.
• AG2 (formerly AutoGen): Add full observability with two lines of code.
• CamelAI: Trace and analyze CAMEL agents.
• Langchain: Seamless collaboration, supports async handlers.
• Cohere (>=5.4.0): First-class support, continuous integration.
• Anthropic Python SDK (>=0.32.0): Trace agents, supports async streaming.
• Mistral Python SDK (>=0.32.0): Trace agents.
• LiteLLM (>=1.3.1): Supports 100+ LLMs, unified input/output format.
• LlamaIndex: Build context-augmented generative AI applications.
• Llama Stack Python Client (>=0.0.53): Monitor agent applications.
• SwarmZero: Multi-agent framework tracing and analysis.

Running the Full Application To run the complete AgentOps application locally (dashboard + API backend), refer to the setup guide in app/README.md. The AgentOps application is open source under the MIT license, with code located in the app directory.

Use Cases Suitable for development teams that need to monitor, evaluate, and optimize AI agents, especially for scenarios where multi-agent systems are built using the frameworks above and there is a need to track LLM call costs, detect agent failures, and analyze tool usage. Suitable for both the prototyping stage and production environments.

Limitations and Information Not Yet Provided The materials do not provide specific pricing models, prices, platform support scope (aside from the mention of Python and TypeScript integration), performance data, or effectiveness metrics. Evaluation and debugging features are only on the roadmap, and specific capabilities are not yet provided.