Superagentx
Move from idea to production in hours with policy-driven autonomous AI agents. Unified Control Plane: Centralised tools, MCPs, models, data, and policies with consistent observability and governance.
Installation
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Documentation
SuperAgentX: is an open-source, modular agentic AI framework that enables AI agents to plan, act, and execute real-world workflowsβwith built-in human approval, governance, and auditability. Unlike traditional chatbots, SuperAgentX is designed for action, not just conversation.
β¨ Why SuperAgentX?
SuperAgentX enables AI agents to:
- Execute multi-step workflows
- Interact with browsers, APIs, databases, tools & MCPs
- Pause for human approval before sensitive actions
- Persist execution state, memory, and audit logs
All while keeping humans firmly in control.
Quick start
pip install superagentx
π§ Core Capabilities
πΉ Massive Model & Tool Support
- β 100+ LLMs supported (OpenAI, Azure OpenAI, Gemini, Claude, Bedrock, OSS models)
- β 10,000+ MCP (Model Context Protocol) tools supported
- β Browser Agents using real browser automation (Playwright)
πΉ Agentic AI (Beyond Chatbots)
Agents can:
- Understand goals
- Plan execution steps
- Call tools dynamically
- Run sequential or parallel workflows
- Retry, reflect, and recover
πΉ Human-in-the-Loop Governance
A built-in Human Approval Governance Agent:
- Pauses sensitive actions
- Requests explicit approval
- Resumes or aborts execution
- Persists decisions for audit
β‘οΈ AI cannot act blindly.
ποΈ Persistent Data Store & Memory
Supported Data Stores
- π SQLite β lightweight, local workflows
- π PostgreSQL β production-grade, multi-tenant systems
Stored Data
- Workflow state
- Agent decisions
- Human approvals
- Tool outputs
- Audit logs
- Context & memory snapshots
π§© Example: AI Food Ordering with Approval
- Plan order
- Calculate total
- Generate checkout summary
- Pause for approval
- Browser agent completes checkout
- Persist confirmation & logs
Browser AI Agent
Install Playwright for Browser AI Automation
pip install playwright
playwright install
Example 1
import asyncio
import json
from superagentx.agent import Agent
from superagentx.agentxpipe import AgentXPipe
from superagentx.browser_engine import BrowserEngine
from superagentx.llm import LLMClient
from superagentx.prompt import PromptTemplate
async def main():
print("SuperAgentX β Food Checkout & Payment Automation")
# ------------------------------------------------------------------
# LLM SETUP
# ------------------------------------------------------------------
llm = LLMClient(
llm_config={
"model": "gemini/gemini-3-pro-preview",
"temperature": 0.1
}
)
# ------------------------------------------------------------------
# AGENT 1: FOOD & SNACKS CHECKOUT AGENT
# ------------------------------------------------------------------
checkout_system_prompt = """
You are a food & snacks checkout agent. Simulate Food & Snacks Checkout with items.
Task:
- Select food and snack items
- Decide quantities
- Calculate total amount
- Prepare checkout summary for payment
Rules:
- DO NOT generate any payment or card details
- DO NOT mention CVV, card numbers, or expiry
- Output ONLY valid JSON
JSON Schema:
{
"items": [
{
"name": string,
"category": "food | snack",
"quantity": number,
"price_per_unit": number
}
],
"currency": "USD",
"total_amount": number,
"checkout_note": string
}
"""
checkout_prompt = PromptTemplate(system_message=checkout_system_prompt)
checkout_agent = Agent(
name="Food Checkout Agent",
role="Food & Snacks Checkout Planner",
goal="Prepare checkout summary",
llm=llm,
prompt_template=checkout_prompt,
max_retry=1
)
# ------------------------------------------------------------------
# AGENT 2: BROWSER REVIEW & PAY AGENT
# ------------------------------------------------------------------
browser_system_prompt = """
You are a browser automation agent responsible for review and payment.
Input:
- You will receive a checkout summary JSON from the previous agent.
Target Payment Form URL:
https://superagentxai.github.io/payment-demo.github.io/
Task:
1. Review checkout summary (items & total) and MUST set the price from result.total_amount
2. Show checkout summary in the popup with price
3. Generate DUMMY credit card details for testing:
- 16-digit test card number
- Future expiry (MM/YY)
- 3-digit CVV
- Realistic cardholder name & address
4. Fill the payment form using generated card details
5. Submit the form
Rules:
- Change the Price value in the submit button with the actual amount from result.total_amount in USD:.
- Card details must be generated ONLY by you
- Use dummy/test card numbers only (e.g., 4111 1111 1111 1111)
- Do NOT persist card data
- Do NOT assume submission success
- Extract confirmation text ONLY if visible
Output JSON:
{
"submission_status": "success | failed",
"reviewed_total_amount": number,
"confirmation_text": string | null
}
"""
browser_prompt = PromptTemplate(system_message=browser_system_prompt)
browser_engine = BrowserEngine(
llm=llm,
prompt_template=browser_prompt,
headless=False # set True in CI
)
browser_agent = Agent(
name="Review & Pay Agent",
role="Browser Payment Executor",
goal="Review checkout and pay using credit card",
llm=llm,
human_approval=True, # governance point
prompt_template=browser_prompt,
engines=[browser_engine],
max_retry=2
)
# ------------------------------------------------------------------
# PIPELINE: AGENT 1 β AGENT 2
# ------------------------------------------------------------------
pipe = AgentXPipe(
agents=[checkout_agent, browser_agent], # Sequence Agent Workflow
workflow_store=True
)
result = await pipe.flow(
query_instruction="Checkout food and snacks, then review and pay using credit card."
)
formatted_result = [
{
"agent_name": r.name,
"agent_id": r.agent_id,
"goal_satisfied": r.is_goal_satisfied,
"result": r.result
}
for r in result
]
print(" Final Result (Formatted JSON)")
print(json.dumps(formatted_result, indent=2))
return
if __name__ == "__main__":
asyncio.run(main())
Example 2
import asyncio
from superagentx.agent import Agent
from superagentx.browser_engine import BrowserEngine
from superagentx.llm import LLMClient
from superagentx.prompt import PromptTemplate
async def main():
llm_client: LLMClient = LLMClient(llm_config={'model': 'gpt-4.1', 'llm_type': 'openai'})
prompt_template = PromptTemplate()
browser_engine = BrowserEngine(
llm=llm_client,
prompt_template=prompt_template,
)
query_instruction = ("Which teams have won more than 3 FIFA World Cups, and which team is most likely to win the "
"next one?")
fifo_analyser_agent = Agent(
goal="Complete user's task.",
role="You are a Football / Soccer Expert Reviewer",
llm=llm_client,
prompt_template=prompt_template,
max_retry=1,
engines=[browser_engine]
)
result = await fifo_analyser_agent.execute(
query_instruction=query_instruction
)
print(result)
asyncio.run(main())
Run
Key Features
π Open-Source Framework: A lightweight, open-source AI framework built for multi-agent applications with Artificial General Intelligence (AGI) capabilities.
π― Goal-Oriented Multi-Agents: This technology enables the creation of agents with retry mechanisms to achieve set goals. Communication between agents is Parallel, Sequential, or hybrid.
ποΈ Easy Deployment: Offers WebSocket, RESTful API, and IO console interfaces for rapid setup of agent-based AI solutions.
β¨οΈ Streamlined Architecture: Enterprise-ready scalable and pluggable architecture. No major dependencies; built independently!
π Contextual Memory: Uses SQL + Vector databases to store and retrieve user-specific context effectively.
π§ Flexible LLM Configuration: Supports simple configuration options of various Gen AI models.
π€ Extendable Handlers: Allows integration with diverse APIs, databases, data warehouses, data lakes, IoT streams, and more, making them accessible for function-calling features.
π Agentic RPA (Robotic Process Automation) β SuperAgentX enables computer-use automation for both browser-based and desktop applications, making it an ideal solution for enterprises looking to streamline operations, reduce manual effort, and boost productivity.
Getting Started
pip install superagentx
Usage - Example SuperAgentX Code
This SuperAgentX example utilizes two handlers, Amazon and Walmart, to search for product items based on user input from the IO Console.
- It uses Parallel execution of handler in the agent
- Memory Context Enabled
- LLM configured to OpenAI
- Pre-requisites
Environment Setup
$ python3.12 -m pip install poetry
$ cd <path-to>/superagentx
$ python3.12 -m venv venv
$ source venv/bin/activate
(venv) $ poetry install
Documentation
License
SuperAgentX is released under the MIT License.
π€ Contributing
Fork β Branch β Commit β Pull Request
Keep contributions modular and documented.
π¬ Connect
β Star the repo and join the community!
