Installation

Core package

pip install agentx-python python-dotenv

Requires Python 3.9+.

Framework-specific packages

Install only what your agent needs.

Framework	Install
OpenAI	`pip install openai`
OpenAI Agents SDK	`pip install openai-agents`
Anthropic	`pip install anthropic`
Google Gemini	`pip install google-genai`
LangChain	`pip install langchain langchain-openai`
LlamaIndex	`pip install llama-index llama-index-llms-openai`
CrewAI	`pip install crewai`
AutoGen / AG2	`pip install pyautogen` (or `pip install ag2` for the AG2 fork)

These are plain framework packages; evaluating an agent doesn’t need any agentx-python[extra] install. Those extras are only for tracing auto-instrumentation (AgentXCallbackHandler, patch_anthropic_client, etc.), which evaluation examples don’t use.

For Google, install google-genai, not the deprecated google-generativeai package.

Environment

export AGENTX_API_KEY="agtx_local_a1b2c3d4e5f60718293a4b5c6d7e8f9012345678"

Then in code:

from agentx import AgentX

client = AgentX.from_env()
client.ping()  # optional but recommended: fails fast on a wrong URL or key

Constructing the client never touches the network (standard SDK behavior - a bad base_url or api_key won’t error until first use), and trace delivery is fire-and-forget, so a misconfigured client would otherwise only surface as a one-time warning in logs. client.ping() makes one cheap authenticated call and raises AgentXConnectionError (bad URL) or AgentXAuthError (rejected key) with an actionable message - call it once at startup of anything long-running. See Python SDK Overview for the full initialization options, including self-hosted base_url overrides.

Pre-Deployment Quality Gate

Build & Run

Scoring & Datasets

Framework Examples

Core package

Framework-specific packages

Environment

​Core package

​Framework-specific packages

​Environment

Core package

Framework-specific packages

Environment