realtimex-agent-flows-builder 0.4.1

AI Agent that builds workflow automations via natural language

Agent Flows Builder

AI Agent that builds workflow automations via natural language

Overview

AI Agent that constructs workflow automations through natural language descriptions, making complex workflow building accessible without JSON expertise. Transform plain English requirements into complete, executable workflow configurations.

Features

• Natural Language Interface: Describe workflows in plain English
• 8 Executor Types: API calls, LLM processing, web scraping, conditionals, loops, switches, and more
• Smart Pattern Matching: Finds and adapts existing workflow templates from documentation
• Real Filesystem Tools: Works with actual files and project documentation
• Research Specialist: Handles complex integration analysis when needed
• State Management: Persistent conversation state across workflow building sessions
• Sub-agent Delegation: Specialized agents for complex pattern analysis

Versioning

The package follows semantic versioning with a single source of truth in _version.py:

from agent_flows_builder._version import APP_VERSION

print(APP_VERSION)  # Static constant: 1.0.0

Architecture

• Master Agent: Primary workflow builder handling most tasks directly
• Research Specialist: Analyzes complex multi-executor integration patterns
• Built-in Tools: File system access, pattern search, documentation analysis
• Planning System: Task breakdown and progress tracking

Installation

As a Package (Recommended for Integration)

# Install from Git repository
pip install git+https://rtgit.rta.vn/rtlab/rtwebteam/realtimex-agent-flows-builder.git

# Or with uv
uv add git+https://rtgit.rta.vn/rtlab/rtwebteam/realtimex-agent-flows-builder.git

Environment Setup

# Create .env file in your project
REALTIMEX_AI_BASE_PATH=https://realtimexai-llm-provider.realtimex.ai
REALTIMEX_AI_API_KEY=your_realtimex_api_key_here
MCP_ACI_API_KEY=your_mcp_api_key_here
MCP_ACI_LINKED_ACCOUNT_OWNER_ID=your_mcp_account_owner_id

# Optional model configuration overrides
AGENT_MAIN_MODEL=gpt-4.1-mini
AGENT_MAIN_TEMPERATURE=0.1
AGENT_MAIN_MAX_TOKENS=8192
AGENT_RESEARCH_MODEL=gpt-4.1-mini
AGENT_RESEARCH_TEMPERATURE=0
AGENT_RESEARCH_MAX_TOKENS=8192
AGENT_FLOW_VALIDATOR_MODEL=gpt-4.1-mini
AGENT_FLOW_VALIDATOR_MAX_TOKENS=4096
AGENT_RECURSION_LIMIT=1000

For Development

Prerequisites: Python 3.11+, uv package manager

# Clone and install
git clone <repository-url>
cd agent-flows-builder

# Install production dependencies
uv sync

# Install with development dependencies (required for langgraph dev)
uv sync --group dev

# Environment setup
cp .env.example .env
# Populate REALTIMEX_AI_* and MCP_ACI_* with your credentials

# Phoenix tracing (enabled by default via RealTimeX host & API key)
# export PHOENIX_API_KEY=override_key                  # optional: custom key
# export PHOENIX_PROJECT_NAME=my-project               # optional: custom project name
# export PHOENIX_COLLECTOR_ENDPOINT=https://custom/v1/traces
# export AGENT_FLOWS_ENABLE_PHOENIX_TRACING=false      # disable tracing entirely

Usage

Integration in Your Project

from agent_flows_builder import create_flow_builder_agent

# Required API configuration
REALTIMEX_BASE_PATH = "https://your-api-endpoint.com/v1"
REALTIMEX_API_KEY = "your_secret_api_key"

# Create the agent
agent = create_flow_builder_agent(
    realtimex_ai_base_path=REALTIMEX_BASE_PATH,
    realtimex_ai_api_key=REALTIMEX_API_KEY,
)

# Build a workflow from natural language
result = agent.invoke({
    "messages": [{"role": "user", "content": "Create a workflow that gets news from an API, analyzes it with AI, and posts summaries to Slack"}]
})

# Access the generated workflow files
files = result.get("files", {})
if "flow.json" in files:
    flow_config = files["flow.json"]  # Your complete workflow configuration

# Stream responses for real-time feedback
for chunk in agent.stream({"messages": [{"role": "user", "content": "Build API workflow"}]}):
    if "messages" in chunk:
        print(chunk["messages"][-1].content)

Interactive Development

# Start development server with graph visualization
langgraph dev

Example Workflows

The agent can build workflows for:

• API Integration: Fetch data → Process → Deliver
• Content Analysis: Scrape → Analyze → Summarize → Distribute
• Data Processing: Import → Transform → Validate → Export
• Notification Systems: Monitor → Evaluate → Alert → Log

Development

Environment Setup

# Install with development dependencies
uv sync --group dev

# Activate environment  
uv shell

Development Server

# Start interactive development with graph visualization
langgraph dev

# This provides:
# - Interactive agent testing
# - Graph execution visualization  
# - State inspection
# - Real-time debugging

Code Quality

# Format code
uv run ruff format .

# Check types
uv run mypy agent_flows_builder/

# Run linter
uv run ruff check .

# Run tests
uv run pytest

Project Structure

agent_flows_builder/
├── agents/           # Master agent factory and specialist builders
├── checkpointers/    # Checkpointer implementations (e.g., SQLite)
├── config/           # Provider configuration models
├── prompts/          # Prompt templates for the master and specialists
├── resources/        # Bundled documentation and workspace helpers
├── tools/            # Filesystem, validation, and discovery tools
├── utils/            # Shared utilities (file ops, model helpers)
├── settings.py       # Typed runtime settings facade

Adding Features

Custom Tools

from langchain.tools import tool

@tool
def custom_workflow_tool(input: str) -> str:
    """Tool description for agent"""
    # Implementation
    return result

Agent Configuration

from deepagents import create_deep_agent

agent = create_deep_agent(
    tools=[your_tools],
    instructions=your_prompt,
    subagents=[specialized_agents]  # Optional
)

Testing

# Run all tests
uv run pytest

# Test specific workflow patterns
uv run pytest tests/test_workflow_patterns.py

# With coverage
uv run pytest --cov=agent_flows_builder

Code Conventions

• Type Hints: Required for all functions
• Docstrings: Google-style for public APIs
• Error Handling: Explicit error types with context
• Tool Naming: Descriptive names for agent clarity

Contributing

1. Follow existing patterns in codebase
2. Test with real workflow examples
3. Ensure agent-readable documentation
4. Validate generated workflows work correctly
5. Use langgraph dev for interactive development

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Built with Deep Agents