agentops

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  <em>Observability and DevTool platform for AI Agents</em>
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AgentOps helps developers build, evaluate, and monitor AI agents, from prototype to production. This repository implements the **Model Context Protocol (MCP)**, a core component of AgentOps that enables seamless integration with various AI agent frameworks and LLMs.

## Key Features 🚀

AgentOps provides the following key features for AI agent development:

*   **Replay Analytics and Debugging**: Step-by-step agent execution graphs for in-depth analysis.
*   **LLM Cost Management**: Track spending with LLM foundation model providers.
*   **Agent Benchmarking**: Test your agents against 1,000+ evaluations.
*   **Compliance and Security**: Detect common prompt injection and data exfiltration exploits.
*   **Framework Integrations**: Native integrations with CrewAI, AG2 (AutoGen), Camel AI, & LangChain.

## Key Integrations 🔌

<div align="center" style="background-color: white; padding: 20px; border-radius: 10px; margin: 0 auto; max-width: 800px;">
  <div style="display: flex; flex-wrap: wrap; justify-content: center; align-items: center; gap: 30px; margin-bottom: 20px;">
    <a href="https://docs.agentops.ai/v2/integrations/openai_agents_python"><img src="docs/images/external/openai/agents-sdk.svg" height="45" alt="OpenAI Agents SDK"></a>
    <a href="https://docs.agentops.ai/v1/integrations/crewai"><img src="docs/v1/img/docs-icons/crew-banner.png" height="45" alt="CrewAI"></a>
    <a href="https://docs.ag2.ai/docs/ecosystem/agentops"><img src="docs/images/external/ag2/ag2-logo.svg" height="45" alt="AG2 (AutoGen)"></a>
    <a href="https://docs.agentops.ai/v1/integrations/microsoft"><img src="docs/images/external/microsoft/microsoft_logo.svg" height="45" alt="Microsoft"></a>
  </div>

  <div style="display: flex; flex-wrap: wrap; justify-content: center; align-items: center; gap: 30px; margin-bottom: 20px;">
    <a href="https://docs.agentops.ai/v1/integrations/langchain"><img src="docs/images/external/langchain/langchain-logo.svg" height="45" alt="LangChain"></a>
    <a href="https://docs.agentops.ai/v1/integrations/camel"><img src="docs/images/external/camel/camel.png" height="45" alt="Camel AI"></a>
    <a href="https://docs.llamaindex.ai/en/stable/module_guides/observability/?h=agentops#agentops"><img src="docs/images/external/ollama/ollama-icon.png" height="45" alt="LlamaIndex"></a>
    <a href="https://docs.agentops.ai/v1/integrations/cohere"><img src="docs/images/external/cohere/cohere-logo.svg" height="45" alt="Cohere"></a>
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## Quick Start ⌨️

```bash
pip install agentops

Session replays in 2 lines of code

Initialize the AgentOps client and automatically get analytics on all your LLM calls.

Get an API key

import agentops

# Beginning of your program (i.e. main.py, __init__.py)
agentops.init( < INSERT YOUR API KEY HERE >)

...

# End of program
agentops.end_session('Success')

All your sessions can be viewed on the AgentOps dashboard
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<details> <summary>Agent Debugging</summary> <a href="https://app.agentops.ai?ref=gh"> <img src="docs/images/external/app_screenshots/session-drilldown-metadata.png" style="width: 90%;" alt="Agent Metadata"/> </a> <a href="https://app.agentops.ai?ref=gh"> <img src="docs/images/external/app_screenshots/chat-viewer.png" style="width: 90%;" alt="Chat Viewer"/> </a> <a href="https://app.agentops.ai?ref=gh"> <img src="docs/images/external/app_screenshots/session-drilldown-graphs.png" style="width: 90%;" alt="Event Graphs"/> </a> </details> <details> <summary>Session Replays</summary> <a href="https://app.agentops.ai?ref=gh"> <img src="docs/images/external/app_screenshots/session-replay.png" style="width: 90%;" alt="Session Replays"/> </a> </details> <details> <summary>Summary Analytics</summary