agentapps

AgentApps

A flexible multi-agent orchestration framework for building intelligent agent applications with a visual Flow Builder.

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Screenshots

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Features

🤖 Simple Agent Creation — Clean, intuitive agent setup
👥 Team Collaboration — Multiple agents working together
🔄 Sequential Workflows — Automatic multi-step execution
🛠️ Built-in Tools — Web search, scraping, calculations
🎯 Custom Tools — Easy 3rd-party integrations (Jira, Slack, GitHub, SMTP and more)
📊 Streaming Support — Real-time responses
🔍 Web Search — DuckDuckGo integration
🌐 Web Scraping — Extract content from any URL
🌟 Multi-Model Support — OpenAI, Google Gemini, xAI Grok, Ollama (local)
🗂️ Platform Tables — Built-in structured data storage
⏰ Triggers & Scheduler — Run agents on a schedule or via webhook
🐚 Shell Tool — Execute server commands from agents (opt-in)
🔐 Role-Based Access — Admin, Editor, Operator, Viewer roles
🔑 SSO / Microsoft Azure AD — Single Sign-On support
🏗️ AI Builder — Describe an agent in plain English and it builds itself
🌍 Portal — End-user chat interface for your deployed agents
🔌 MCP Client Tool — Connect to any MCP server and use its tools (New)
📌 Pinned Response Cards — Pin agent responses to the Portal hero page (New)
💾 Session Persistence — Responses saved even if you navigate away (New)

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Links

• GitHub: https://github.com/91Abdul/agentapps
• PyPI: https://pypi.org/project/agentapps
• Website: https://www.agentappsai.com
• Issues: https://github.com/91Abdul/agentapps/issues
• Discord: https://discord.gg/xAUYb2vujP
• LinkedIn Group: https://www.linkedin.com/groups/14471903/
• Support: ak@agentappsai.com

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Installation

pip install agentapps

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Quick Start — Flow Builder UI

agentapps-flow

This starts the server and automatically opens the visual Flow Builder in your browser at http://localhost:7860.

# Custom port
agentapps-flow --port 8080

# Don't open browser automatically
agentapps-flow --no-browser

# Dev mode with hot-reload
agentapps-flow --reload

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Secure Login

1. CLI flag

agentapps-flow --password mysecretpassword

2. Environment variable (recommended for servers)

export AGENTAPPS_PASSWORD=mysecretpassword
agentapps-flow

3. Auto-generated password

If no password is set, one is auto-generated and printed to the console:

╔══════════════════════════════════════════════╗
║   🔑 Auto-generated password:                ║
║      xK9mP2nQvR4sT7uW                        ║
╚══════════════════════════════════════════════╝

Passwords are stored as secure hashes — never in plaintext.

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Shell Tool (opt-in)

Enable agents to run shell commands on the server (Windows cmd, Mac/Linux bash, PowerShell):

agentapps-flow --enable-shell

Or via environment variable:

AGENTAPPS_ENABLE_SHELL=true agentapps-flow

Optionally restrict the working directory:

AGENTAPPS_SHELL_DIR=/path/to/scripts agentapps-flow --enable-shell

Shell tool is disabled by default. All commands are logged and protected by a blocklist. Use with care on shared servers.

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Supported Models

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Quick Start with OpenAI

from agentapps import Agent
from agentapps.model import OpenAIChat
from agentapps.tools import SearchSummaryTool

agent = Agent(
    name="Research Assistant",
    role="Search and analyze information",
    model=OpenAIChat(id="gpt-4o", api_key="your-openai-key"),
    tools=[SearchSummaryTool()],
    instructions=["Always include sources"],
    show_tool_calls=True
)

agent.print_response("What is the latest news about AI?")

Get an OpenAI API key: https://platform.openai.com/api-keys

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Quick Start with Gemini

from agentapps import Agent
from agentapps.model import GeminiChat
from agentapps.tools import SearchSummaryTool

agent = Agent(
    name="Research Assistant",
    role="Search and analyze information",
    model=GeminiChat(id="gemini-2.0-flash-exp", api_key="your-google-api-key"),
    tools=[SearchSummaryTool()],
    instructions=["Always include sources"],
    show_tool_calls=True
)

agent.print_response("What is the latest news about AI?")

Get a Gemini API key: https://makersuite.google.com/app/apikey

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Quick Start with Grok xAI

from agentapps import Agent
from agentapps.model import GrokChat
from agentapps.tools import SearchSummaryTool

agent = Agent(
    name="Research Assistant",
    role="Search and analyze information",
    model=GrokChat(id="grok-3-mini", api_key="your-xai-api-key"),
    tools=[SearchSummaryTool()],
    instructions=["Always include sources"],
    show_tool_calls=True
)

agent.print_response("What is the latest news about AI?")

Get a Grok API key: https://console.x.ai

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Available Tools

SearchSummaryTool

Search the web and get detailed snippets:

from agentapps.tools import SearchSummaryTool

agent = Agent(
    name="Searcher",
    model=OpenAIChat(id="gpt-4o", api_key="key"),
    tools=[SearchSummaryTool()]
)

WebScraperTool

Scrape content from URLs:

from agentapps.tools import WebScraperTool

agent = Agent(
    name="Scraper",
    model=OpenAIChat(id="gpt-4o", api_key="key"),
    tools=[WebScraperTool()]
)

CalculatorTool

Perform calculations:

from agentapps.tools import CalculatorTool
agent = Agent(name="Calculator", model=OpenAIChat(id="gpt-4o", api_key="key"), tools=[CalculatorTool()])

MCPClientTool (New)

Connect to any MCP server and use its tools:

from agentapps.tools import MCPClientTool

tool = MCPClientTool(
    server_url="http://localhost:8000/mcp",
    tool_name="add_numbers",
    tool_description="Add two numbers together",
    auth_type="none"   # or "bearer" / "apikey"
)

agent = Agent(name="MCP Agent", model=OpenAIChat(id="gpt-4o", api_key="key"), tools=[tool])
agent.print_response("Add 42 and 58")

Or use the MCP Tool node in the Flow Builder — enter the server URL, click Discover Tools, select a tool, and it auto-fills all fields.

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Team Agents

Create teams that work together sequentially:

from agentapps import Agent
from agentapps.model import OpenAIChat
from agentapps.tools import SearchSummaryTool, WebScraperTool

search_agent = Agent(
    name="Search Agent",
    role="Search the web",
    model=OpenAIChat(id="gpt-4o", api_key="your-key"),
    tools=[SearchSummaryTool()]
)

scraper_agent = Agent(
    name="Scraper Agent",
    role="Read web pages",
    model=OpenAIChat(id="gpt-4o", api_key="your-key"),
    tools=[WebScraperTool()]
)

team = Agent(
    team=[search_agent, scraper_agent],
    instructions=[
        "First, search for relevant URLs",
        "Then, scrape content from those URLs",
        "Finally, provide a comprehensive answer"
    ],
    show_tool_calls=True
)

team.print_response("Research NVIDIA's latest AI developments")

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Custom Tools

Create your own tools easily:

from agentapps import Tool

class WeatherTool(Tool):
    def __init__(self):
        super().__init__(
            name="get_weather",
            description="Get weather for a city"
        )

    def execute(self, city: str) -> str:
        return f"Weather in {city}: Sunny, 72°F"

    def get_parameters(self):
        return {
            "type": "object",
            "properties": {
                "city": {"type": "string", "description": "City name"}
            },
            "required": ["city"]
        }

agent = Agent(
    name="Weather Agent",
    model=OpenAIChat(id="gpt-4o", api_key="key"),
    tools=[WeatherTool()]
)

You can also register custom tools via the Flow Builder UI — no restart required.

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Portal — End User Interface (Enhanced)

The Portal is the end-user interface for interacting with your deployed agents.

Hero Page

• Bento-grid layout — Quick Links, Popular Agents, Inbox, Calendar
• Agent chips — one-click access to agents, each starts a fresh chat
• Search bar — type a prompt and route to the right agent automatically
• Click the agentapps. logo to return to hero from any chat

Pinned Response Cards (New)

Pin any agent response directly to the hero page:

• Click the 📌 pin icon on any assistant message
• Pinned cards appear in a horizontal strip above the bento grid
• Expand (↗) to read the full response in a modal
• Refresh (🔄) to re-run the original prompt and update the card
• Unpin (✕) to remove the card
• Maximum 6 pinned cards per user

Session Persistence (Improved)

• Agent responses saved server-side regardless of client connection
• Navigate away during a long-running agent task — response appears when you return
• Works with approval workflows — approve from inbox, result saves automatically
• Execution logs and Recent Runs updated even after page refresh

Approvals Inbox

• Pending approval notifications visible on the hero page
• Approve or reject agent actions without staying on the chat page

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Examples

Stock Analysis

agent = Agent(
    name="Stock Analyst",
    role="Analyze stocks",
    model=OpenAIChat(id="gpt-4o", api_key="key"),
    tools=[SearchSummaryTool()],
    instructions=["Include price targets and analyst ratings"]
)

agent.print_response("Analyze NVDA stock with latest news and recommendations")

Research Team

research_team = Agent(
    team=[search_agent, scraper_agent],
    instructions=[
        "Search for academic sources",
        "Read full articles",
        "Provide a comprehensive summary with citations"
    ]
)

research_team.print_response("What are the latest breakthroughs in quantum computing?")

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API Reference

Agent

Agent(
    name: str = "Agent",
    role: str = "General Assistant",
    model: Model = None,
    tools: List[Tool] = None,
    instructions: List[str] = None,
    team: List[Agent] = None,
    show_tool_calls: bool = False,
    markdown: bool = False,
    temperature: float = None
)

Methods

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Role-Based Access Control

The platform supports four roles assignable per user:

Manage users and assign roles from the Admin → Users section in the Flow Builder.

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Triggers & Scheduler

Run agents automatically on a schedule or via external webhook:

• Scheduler — run agents every N minutes, hourly, daily, or weekly
• Webhook — trigger agents via HTTP from any external service (Jira, Zapier, Slack, etc.)
• Email — poll IMAP inbox and trigger on new messages
• API Poll — monitor any external API and trigger on condition

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MCP Client Tool (New)

Connect to any MCP (Model Context Protocol) server directly from the Flow Builder.

Supported transports: Streamable HTTP (JSON-RPC 2.0)
Auth options: None, Bearer Token, API Key
Compatible with: FastMCP, official MCP servers, any compliant server

In the Flow Builder:

1. Drag MCP Tool node to canvas
2. Enter server URL + auth settings
3. Click Discover Tools — auto-connects and lists available tools
4. Click a tool — Tool Name, Description, Schema auto-fill
5. Connect to Agent node and save

In Python:

from agentapps.tools import MCPClientTool

tool = MCPClientTool(
    server_url="https://mcp.example.com",
    tool_name="search",
    tool_description="Search for information",
    tool_schema={
        "type": "object",
        "properties": {"query": {"type": "string"}},
        "required": ["query"]
    },
    auth_type="bearer",
    auth_value="your-token"
)

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HTTPS / SSL

Secure HTTPS is required for production webhooks.

Option 1 — Auto self-signed certificate (easiest)

pip install cryptography
agentapps-flow --ssl-self-signed

Option 2 — Bring your own certificate (Let's Encrypt / purchased)

agentapps-flow --ssl-cert /path/to/cert.pem --ssl-key /path/to/key.pem

Option 3 — Let's Encrypt

pip install certbot
certbot certonly --standalone -d yourdomain.com

agentapps-flow \
  --ssl-cert /etc/letsencrypt/live/yourdomain.com/fullchain.pem \
  --ssl-key  /etc/letsencrypt/live/yourdomain.com/privkey.pem

Option 3 — Standard HTTPS port (443)

agentapps-flow --port 443 --ssl-self-signed

On Linux/macOS, binding to port 443 requires root or sudo. Consider using a reverse proxy (nginx, Caddy) for production.

Using with ngrok (recommended for local webhooks)

# Terminal 1
agentapps-flow

# Terminal 2
ngrok http 7860

Use the https://xxxx.ngrok-free.app URL as your webhook base URL.

Webhook URL format

https://your-domain.com/webhook/<project-name>?token=<bearer-token>

Tokens are generated in the Flow Builder under ☰ Menu → 🔗 Webhooks.

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All CLI Flags

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Requirements

• Python >= 3.8
• An API key from OpenAI, Google Gemini, xAI Grok — or a local Ollama instance

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License

MIT License

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Contributing

Contributions welcome! Please feel free to submit a Pull Request on GitHub.