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\n \n \"Logo\"\n \n
\n\n
\n Observability and DevTool platform for AI Agents\n
\n\n
\n\n
\n \n \"git\n \n \"PyPI\n \n \"License:\n \n
\n\n

\n \n \"Twitter\"\n \n \n \"Discord\"\n \n \n \"Dashboard\"\n \n \n \"Documentation\"\n \n \n \"Chat\n \n

\n\nA TypeScript implementation of the AgentOps SDK that exports GenAI conventional OpenTelemetry data to standards-compliant OTel collectors. This SDK provides automatic instrumentation for multiple agent frameworks and AI libraries.\n\n
\n \n
\n\n## Features\n\n- ๐Ÿ”Œ **Plugin Architecture**: Dynamic loading and configuration of instrumentors\n- ๐Ÿค– **GenAI Support**: Built-in support for OpenTelemetry GenAI semantic conventions\n- ๐Ÿ“Š **Standards Compliant**: Exports to any OpenTelemetry-compatible collector\n- ๐Ÿ› ๏ธ **Framework Agnostic**: Instrument multiple agent frameworks simultaneously\n- ๐Ÿ”ง **TypeScript First**: Full TypeScript support with comprehensive type definitions\n- ๐Ÿ’ธ **LLM Cost Management**: Track spend with LLM foundation model providers\n- ๐Ÿงช **Agent Benchmarking**: Test your agents against 1,000+ evals\n- ๐Ÿ” **Compliance and Security**: Detect common prompt injection and data exfiltration exploits\n\n## Installation\n\n```bash\nnpm install agentops\n```\n\n## Quick Start\n\n### 1. Set your API key (recommended)\n\n```bash\nexport AGENTOPS_API_KEY=your-api-key\n```\n\n### 2. Initialize the SDK\n\n```typescript\nimport { agentops } from 'agentops';\n\nawait agentops.init();\n\n// Your AI agent code here - instrumentation happens automatically!\n```\n\n### Alternative: Pass API key explicitly\n\n```typescript\nimport { agentops } from 'agentops';\n\nawait agentops.init({\n apiKey: 'your-api-key'\n});\n```\n\n## Building\n\nTo build the project from source:\n\n```bash\nnpm install\nnpm run build\n```\n\nThis will compile the TypeScript source code to JavaScript in the `dist/` directory.\n\n## Running the Example\n\nThe repository includes an OpenAI Agents example that demonstrates the SDK in action:\n\n1. First, create a `.env` file in the `examples/openai-agents-example` directory:\n\n```bash\ncd examples/openai-agents-example\ncat > .env << EOF\nAGENTOPS_API_KEY=your-agentops-api-key\nOPENAI_API_KEY=your-openai-api-key\nEOF\n```\n\n2. Then run the example:\n\n```bash\nnpm install\nnpm run dev\n```\n\nThe example will:\n1. Initialize AgentOps instrumentation\n2. Create a weather assistant agent with tool calling capabilities\n3. Execute a sample query\n4. Export telemetry data to the AgentOps platform\n\n## OpenAI Agents Support\n\nAgentOps provides first-class support for the [OpenAI Agents SDK](https://github.com/openai/openai-agents-js/), automatically instrumenting:\n\n- **Agent Lifecycle**: Track agent creation, execution, and completion\n- **LLM Generation**: Capture model requests, responses, and token usage\n- **Function Calls**: Monitor tool usage and function execution\n- **Audio Processing**: Observe speech-to-text and text-to-speech operations\n- **Handoffs**: Track agent-to-agent communication and workflow transitions\n- **Custom Events**: Capture domain-specific agent behaviors\n\n### Automatic Instrumentation\n\nSimply initialize AgentOps before using the OpenAI Agents SDK:\n\n```typescript\nimport { agentops } from 'agentops';\nimport { Agent, run } from '@openai/agents';\n\n// Initialize AgentOps first\nawait agentops.init();\n\n// Create your agent with tools and instructions\nconst agent = new Agent({\n name: 'My Assistant',\n instructions: 'You are a helpful assistant.',\n tools: [/* your tools */],\n});\n\n// Run the agent - instrumentation happens automatically\nconst result = await run(agent, \"Hello, how can you help me?\");\nconsole.log(result.finalOutput);\n```\n\nAll agent interactions will be automatically captured and exported to your AgentOps dashboard with full OpenTelemetry semantic conventions.\n\n## Debug Logging\n\nTo see detailed instrumentation and tracing logs:\n\n```bash\nDEBUG=agentops:* node your-app.js\n```\n\n## Why AgentOps? ๐Ÿค”\n\nWithout the right tools, AI agents are slow, expensive, and unreliable. Our mission is to bring your agent from prototype to production. Here's why AgentOps stands out:\n\n- **Comprehensive Observability**: Track your AI agents' performance, user interactions, and API usage.\n- **Real-Time Monitoring**: Get instant insights with session replays, metrics, and live monitoring tools.\n- **Cost Control**: Monitor and manage your spend on LLM and API calls.\n- **Failure Detection**: Quickly identify and respond to agent failures and multi-agent interaction issues.\n- **Tool Usage Statistics**: Understand how your agents utilize external tools with detailed analytics.\n- **Session-Wide Metrics**: Gain a holistic view of your agents' sessions with comprehensive statistics.\n\nAgentOps is designed to make agent observability, testing, and monitoring easy.\n\n## Star History\n\nCheck out our growth in the community:\n\n\"Logo\"\n\n","readmeFilename":"README.md","contributors":[{"name":"Travis Dent","email":"tcdent@gmail.com"},{"name":"Braelyn Boynton","email":"bboynton97@gmail.com"}]}