Get up and running with MeshAI in minutes. Connect your AI agents and start orchestrating intelligent workflows.
New to MeshAI? Start with our comprehensive guide that shows you exactly how to use the platform with real examples.
View Complete GuideIncludes web dashboard walkthrough, API examples, Python/JavaScript code, and real-world use cases
Create your MeshAI account and connect your first AI agent using our simple SDK integration.
Configure agent capabilities and deploy them to your mesh network with zero-downtime updates.
Use the admin dashboard to monitor performance, debug issues, and scale your agent mesh as needed.
Integrate your existing LangChain agents with MeshAI in just a few lines of code:
from langchain.agents import AgentExecutor
from meshai import MeshAIAdapter
# Your existing LangChain agent
agent_executor = AgentExecutor.from_agent_and_tools(
agent=your_agent,
tools=your_tools
)
# Wrap with MeshAI adapter
mesh_agent = MeshAIAdapter(
agent=agent_executor,
name="my-langchain-agent",
capabilities=["text-analysis", "question-answering"]
)
# Connect to MeshAI
await mesh_agent.connect("wss://api.meshai.dev")Connect your CrewAI crews to the mesh for cross-crew collaboration:
from crewai import Crew, Agent, Task
from meshai import CrewAIAdapter
# Your existing CrewAI setup
researcher = Agent(role="Researcher", goal="Research topics")
writer = Agent(role="Writer", goal="Write content")
crew = Crew(agents=[researcher, writer], tasks=[research_task, write_task])
# Connect to MeshAI
mesh_crew = CrewAIAdapter(
crew=crew,
name="content-creation-crew",
capabilities=["research", "writing", "content-creation"]
)
await mesh_crew.connect("wss://api.meshai.dev")Build custom agents that implement the Agent Mesh Protocol directly:
from meshai import AMPAgent, Capability
class MyCustomAgent(AMPAgent):
def __init__(self):
super().__init__(
name="custom-agent",
capabilities=[
Capability("data-analysis", self.analyze_data),
Capability("report-generation", self.generate_report)
]
)
async def analyze_data(self, data):
# Your custom logic here
return {"analysis": "processed", "insights": [...]}
async def generate_report(self, analysis_results):
# Generate report from analysis
return {"report": "Generated report..."}
# Deploy your agent
agent = MyCustomAgent()
await agent.connect("wss://api.meshai.dev")