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A fast way to build AI Agents and create agent workforces

The official AgentX Python SDK for AgentX

Why build AI agents with AgentX?

• Simplicity — Agent → Conversation → Message structure.
• Chain-of-thought built in.
• Choose from most open and closed-source LLM vendors.
• Built-in Voice (ASR, TTS), Image Gen, Document, CSV/Excel, OCR, and more.
• Support for all running MCP (Model Context Protocol) servers.
• RAG with built-in re-rank.
• Multi-agent workforce orchestration.
• Multiple agents working together with a designated manager agent.
• Cross-LLM-vendor, multi-agent orchestration.
• A2A — agent-to-agent protocol (coming soon)

Installation

pip install --upgrade agentx-python

Quick Start

from agentx import AgentX

client = AgentX(api_key="your-api-key-here")

agents = client.list_agents()
print(f"You have {len(agents)} agents")

if agents:
    agent = agents[0]
    conversation = agent.new_conversation()
    response = conversation.chat("Hello! What can you help me with?")
    print(response)

Usage

Provide an api_key inline or set AGENTX_API_KEY as an environment variable. Get your API key at https://app.agentx.so

Agent

from agentx import AgentX

client = AgentX(api_key="<your api key here>")
print(client.list_agents())

Conversation

my_agent = client.get_agent(id="<agent id here>")

existing_conversations = my_agent.list_conversations()
last_conversation = existing_conversations[-1]
msgs = last_conversation.list_messages()
print(msgs)

Chat

a_conversation = my_agent.get_conversation(id="<conversation id here>")

response = a_conversation.chat_stream("Hello, what is your name?")
for chunk in response:
    print(chunk)

*cot stands for chain-of-thought

Workforce

from agentx import AgentX

client = AgentX(api_key="<your api key here>")

workforces = client.list_workforces()
workforce = workforces[0]
print(f"Workforce: {workforce.name}")
print(f"Manager: {workforce.manager.name}")
print(f"Agents: {[agent.name for agent in workforce.agents]}")

Chat with Workforce

conversation = workforce.new_conversation()
response = workforce.chat_stream(conversation.id, "How can you help me with this project?")
for chunk in response:
    if chunk.text:
        print(chunk.text, end="")

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Custom Agent Evaluations

See EVALUATIONS.md for full documentation — installation, dataset builder, framework examples (OpenAI, Anthropic, Google, LangChain, CrewAI, AutoGen, LlamaIndex, HTTP endpoints), and the complete API reference.