agentmesh-py 0.1.7

Python Agent Graph Orchestration Engine - Build multi-agent workflows as executable graphs

Mesh: Python Agent Graph Orchestration Engine

Build agent and multi-agent workflows as executable graphs

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Mesh is a lightweight Python library for orchestrating multi-agent workflows as executable graphs. Build complex agent systems with token-by-token streaming, state management, and seamless integration with Vel and OpenAI Agents SDK.

Documentation

📚 View Full Documentation →

Comprehensive guides and API reference:

• Getting Started - Installation and configuration
• Quick Start - Build your first graph in 5 minutes
• Core Concepts - Graphs, nodes, execution, events
• Guides - Streaming, loops, state management, variables
• Integrations - Vel, OpenAI Agents SDK
• API Reference - Complete API documentation
• Examples - Working code examples

Features

• Graph-Based Workflows: Build agent workflows as directed graphs with controlled cycles
• Dual API: Programmatic (LangGraph-style) and declarative (React Flow JSON) interfaces
• Graph Visualization: Generate Mermaid diagrams with color-coded node types
• Token-by-Token Streaming: Real-time streaming with provider-agnostic events
• Advanced Event Types: Reasoning (o1/o3), usage tracking, citations, multi-modal files, custom events
• Event Translation: Use Vel's standardized events or provider-native events
• Multiple Agent Frameworks: Auto-detection for Vel and OpenAI Agents SDK
• Multi-Provider Support: OpenAI, Anthropic, Google via Vel translation
• 7 Core Node Types: Start, End, Agent, LLM, Tool, Condition, Loop
• State Persistence: Pluggable backends (SQLite, in-memory, custom)
• Variable Resolution: Template variables for dynamic workflows
• Production Ready: Error handling, retries, and structured logging

Installation

# Basic installation
pip install agentmesh-py

# With OpenAI Agents SDK support
pip install agentmesh-py[agents]

# With FastAPI server support
pip install agentmesh-py[server]

# Development installation
pip install agentmesh-py[dev]

# All features
pip install agentmesh-py[all]

# With Vel SDK support (install separately)
pip install agentmesh-py
pip install git+https://github.com/rscheiwe/vel.git

Configuration

Create a .env file in your project root:

cp .env.example .env

Add your OpenAI API key:

OPENAI_API_KEY=sk-your-key-here

Load it in your code:

from mesh.utils import load_env
load_env()  # Loads variables from .env

Quick Start

Programmatic API (LangGraph-style)

import asyncio
from mesh import StateGraph, Executor, ExecutionContext, MemoryBackend
from mesh.nodes import LLMNode

async def main():
    # Build graph
    graph = StateGraph()
    graph.add_node("llm", None, node_type="llm", model="gpt-4")
    graph.add_edge("START", "llm")
    graph.set_entry_point("llm")

    # Compile and execute
    compiled = graph.compile()
    executor = Executor(compiled, MemoryBackend())
    context = ExecutionContext(
        graph_id="my-graph",
        session_id="session-1",
        chat_history=[],
        variables={},
        state={}
    )

    # Stream results
    async for event in executor.execute("What is 2+2?", context):
        if event.type == "token":
            print(event.content, end="", flush=True)

asyncio.run(main())

React Flow JSON (Flowise-compatible)

from mesh import ReactFlowParser, NodeRegistry, Executor

# Parse Flowise JSON
registry = NodeRegistry()
parser = ReactFlowParser(registry)
graph = parser.parse(flow_json)

# Execute
executor = Executor(graph, backend)
async for event in executor.execute(input_data, context):
    print(event)

FastAPI Server

from fastapi import FastAPI
from mesh import Executor, ExecutionContext
from mesh.streaming import SSEAdapter

app = FastAPI()

@app.post("/execute/stream")
async def execute_stream(request: ExecuteRequest):
    executor = Executor(graph, backend)
    context = ExecutionContext(...)

    adapter = SSEAdapter()
    return adapter.to_streaming_response(
        executor.execute(request.input, context)
    )

Core Concepts

Node Types

1. StartNode: Entry point to the graph
2. EndNode: Exit point from the graph
3. AgentNode: Wraps Vel or OpenAI agents with auto-detection
4. LLMNode: Direct LLM calls with streaming
5. ToolNode: Execute Python functions
6. ConditionNode: Conditional branching
7. LoopNode: Iterate over arrays

Variable Resolution

Mesh supports template variables in node configurations:

• {{$question}} - User input
• {{node_id}} - Reference node output
• {{node_id.field}} - Access nested fields
• {{$vars.key}} - Global variables
• {{$chat_history}} - Formatted chat history
• {{$iteration}} - Current iteration value (in loops)

State Management

# In-memory (for development)
from mesh.backends import MemoryBackend
backend = MemoryBackend()

# SQLite (for production)
from mesh.backends import SQLiteBackend
backend = SQLiteBackend("mesh_state.db")

Advanced Usage

Conditional Branching

from mesh.nodes import Condition

def check_success(output):
    return "success" in str(output).lower()

graph.add_node("condition", [
    Condition("success", check_success, "success_handler"),
    Condition("failure", lambda x: not check_success(x), "error_handler"),
], node_type="condition")

Loop Processing

graph.add_node("loop", None, node_type="loop",
               array_path="$.items", max_iterations=100)

Custom Tools

def my_tool(input: str, multiplier: int = 2) -> str:
    return input * multiplier

graph.add_node("tool", my_tool, node_type="tool",
               config={"bindings": {"multiplier": 3}})

Agent Integration

# Vel Agent (with streaming)
from vel import Agent as VelAgent
vel_agent = VelAgent(
    id="assistant",
    model={
        "provider": "openai",
        "name": "gpt-4",
        "temperature": 0.7,
    },
)
graph.add_node("agent", vel_agent, node_type="agent")

# Stream the output
async for event in executor.execute("Hello!", context):
    if event.type == "token":
        print(event.content, end="", flush=True)

# OpenAI Agents SDK (with Vel translation by default)
from agents import Agent
openai_agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant"
)
graph.add_node("agent", openai_agent, node_type="agent", config={"model": "gpt-4"})

# Or use native events
graph.add_node("agent", openai_agent, node_type="agent",
               use_native_events=True, config={"model": "gpt-4"})

Event Translation

By default, Mesh uses Vel's event translation for consistent event handling across providers:

# Default: Vel-translated events (consistent across providers)
graph.add_node("agent", agent, node_type="agent")

# All providers emit the same event types
async for event in executor.execute(input, context):
    if event.type == "token":  # Same for OpenAI, Anthropic, Google
        print(event.content, end="", flush=True)

# Optional: Use provider's native events
graph.add_node("agent", agent, node_type="agent", use_native_events=True)

See Event Translation Guide for details.

Architecture

┌─────────────────────────────────────────────┐
│           User Application                  │
│       (FastAPI/Flask/Django)                │
└────────────────┬────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────┐
│            MESH LIBRARY                     │
├─────────────────────────────────────────────┤
│  ┌──────────────┐    ┌──────────────┐      │
│  │  Parsers     │    │  Builders    │      │
│  │  - ReactFlow │    │  - StateGraph│      │
│  └──────┬───────┘    └──────┬───────┘      │
│         └──────────┬─────────┘              │
│                    ▼                         │
│         ┌──────────────────┐                │
│         │  Graph Compiler  │                │
│         └────────┬─────────┘                │
│                  ▼                           │
│         ┌──────────────────┐                │
│         │ Execution Engine │                │
│         │  - Queue-based   │                │
│         │  - Streaming     │                │
│         └────────┬─────────┘                │
│                  ▼                           │
│      ┌───────────────────────┐              │
│      │   Node Implementations│              │
│      │  Agent│LLM│Tool│...   │              │
│      └───────────────────────┘              │
└─────────────────────────────────────────────┘

Examples

See the examples/ directory:

• simple_agent.py - Basic LLM usage with streaming
• vel_agent_streaming.py - Vel agent with token-by-token streaming
• openai_agent_streaming.py - OpenAI Agents SDK with streaming
• event_translation_comparison.py - Compare Vel vs native events
• react_flow_parse.py - Parse Flowise JSON
• fastapi_server.py - Full API server with streaming

Guides:

• Event Translation Guide - Detailed event translation documentation
• Vel Integration Guide - Vel-specific integration
• OpenAI Agents Integration - OpenAI SDK integration
• Native Events Implementation - use_native_events flag details

Development

# Clone repository
git clone https://github.com/rscheiwe/mesh.git
cd mesh

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install development dependencies
pip install -e ".[all]"

# Run tests
pytest

# Run tests with coverage
pytest --cov=mesh --cov-report=html

# Format code
black mesh/
ruff check mesh/

# Type checking
mypy mesh/

For AI Assistants: See CLAUDE.md for comprehensive development context and architecture details.

Roadmap

• WebSocket streaming support
• Distributed execution
• Visual graph editor
• LangChain/LlamaIndex integration
• Human-in-the-loop approval nodes
• Sub-graph composition

Contributing

Contributions welcome! Please read our contributing guidelines and submit PRs.

License

MIT License - see LICENSE file for details.

Credits

Inspired by:

• Flowise - React Flow execution patterns
• LangGraph - StateGraph API design
• Vel - Event translation layer

Support

• Documentation: https://rscheiwe.github.io/mesh
• GitHub Issues: https://github.com/rscheiwe/mesh/issues
• GitHub Repository: https://github.com/rscheiwe/mesh