AgentX Python

[](https://pypi.org/project/agentx-python/)

The official Python SDK for AgentX — build, chat with, and orchestrate AI agents in a few lines of code.

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Contents

• [Why AgentX](#why-agentx)
• [Installation](#installation)
• [Authentication](#authentication)
• [Quick start](#quick-start)
• [Working with agents](#working-with-agents)
• [List agents](#list-agents)
• [Start a conversation](#start-a-conversation)
• [Chat (streaming and non-streaming)](#chat-streaming-and-non-streaming)
• [Workforce (multi-agent orchestration)](#workforce-multi-agent-orchestration)
• [Agent Evaluations](#custom-agent-evaluations) — LLM-as-a-judge, cosine / Jaccard similarity
• [Links](#links)

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Why AgentX

• Simple mental model — Agent → Conversation → Message.
• Chain-of-thought is built in, no extra plumbing.
• Bring any LLM — works across major open and closed-source vendors.
• Batteries included — voice (ASR/TTS), image generation, document/CSV/Excel/OCR, RAG with built-in re-ranking.
• MCP support — connect any Model Context Protocol server.
• Multi-agent orchestration — workforces of agents with a designated manager, across LLM vendors.
• Agent Evaluations — score any agent (LangChain, CrewAI, OpenAI, Anthropic, HTTP, …) with LLM-as-a-judge ratings plus optional cosine and Jaccard similarity metrics.
• A2A — Each agent can be published with agent-to-agent protocol compatible.

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Installation

pip install --upgrade agentx-python

Requires Python 3.9 or newer.

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Authentication

Get your API key at app.agentx.so, then either pass it inline or expose it as an environment variable.

# Option A — pass the key inline
from agentx import AgentX
client = AgentX(api_key="your-api-key-here")

# Option B — set AGENTX_API_KEY in your environment, then:
client = AgentX.from_env()

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Quick start

from agentx import AgentX

client = AgentX.from_env()

# Pick an existing agent and chat with it
agent = client.list_agents()[0]
conversation = agent.new_conversation()
print(conversation.chat("Hello! What can you help me with?"))

That's it. The remaining sections show the same primitives in more detail.

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Working with agents

List agents

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

Start a conversation

agent = client.get_agent(id="")

# Either resume an existing conversation…
existing = agent.list_conversations()
last = existing[-1]
for msg in last.list_messages():
    print(msg)

# …or start a fresh one
conversation = agent.new_conversation()

Chat (streaming and non-streaming)

# Blocking — returns the full response once it's ready
response = conversation.chat("What is your name?")
print(response)

# Streaming — yields ChatResponse objects as the model produces them
for chunk in conversation.chat_stream("Hello, what is your name?"):
    if chunk.text:
        print(chunk.text, end="")

Each ChatResponse chunk exposes the agent's text and, where applicable, its cot (chain-of-thought) reasoning, along with any retrieved references and tasks.

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Workforce (multi-agent orchestration)

A workforce is a team of agents coordinated by a designated manager agent. Workforces can mix LLM vendors and route work between specialists.

workforces = client.list_workforces()
workforce = workforces[0]

print(f"Workforce: {workforce.name}")
print(f"Manager:   {workforce.manager.name}")
print(f"Agents:    {[a.name for a in workforce.agents]}")

# Chat with the workforce — the manager decides which agent(s) to delegate to
conversation = workforce.new_conversation()
for chunk in workforce.chat_stream(conversation.id, "How can you help me with this project?"):
    if chunk.text:
        print(chunk.text, end="")

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Custom agent evaluations

Evaluate any AI agent — LangChain, CrewAI, AutoGen, LlamaIndex, OpenAI, Anthropic, HTTP endpoints, or plain Python — using AgentX as the scoring and reporting backend. Includes optional cosine and Jaccard similarity metrics alongside LLM-graded ratings.

report = (
    client.evaluations
    .run(dataset_id="evds_…", subject={"kind": "custom_agent", "framework": "raw_python"})
    .execute(my_agent_fn)
    .finalize()
    .analyze()
)

print(report.average_rating)       # LLM-graded score, 0–10
print(report.cosine_similarity)    # embedding cosine, 0–1 (None if not enabled)
print(report.jaccard_similarity)   # token-set overlap, 0–1 (None if not enabled)

See [EVALUATIONS.md](EVALUATIONS.md) for the full guide — dataset builder, framework adapters, similarity metrics, and the complete API reference.

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Links

• Dashboard — app.agentx.so
• Website — agentx.so
• PyPI — agentx-python
• Evaluations docs — [EVALUATIONS.md](EVALUATIONS.md)

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: AgentX-ai
• Source: AgentX-ai/AgentX-Python
• License: MIT
• Homepage: https://pypi.org/project/agentx-python/

Install and usage instructions live in the source repository linked above.