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Hi mcp-agent team!
We built ApeRAG using mcp-agent as our core agent framework.
๐ Live demo: https://rag.apecloud.com/
About ApeRAG
Production-ready RAG platform with Graph RAG, vector search, and intelligent AI agents.
How We Use mcp-agent
Agent Sessions:
from mcp_agent.agents.agent import Agent
from mcp_agent.workflows.llm.augmented_llm_openai import OpenAIAugmentedLLM
# Per-chat agent sessions with memory
self.agent = Agent(name="aperag_agent", server_names=["aperag"])
self.llm = await self.agent.attach_llm(OpenAIAugmentedLLM)MCP App Factory:
from mcp_agent.app import MCPApp
from mcp_agent.config import Settings, MCPSettings
mcp_app = MCPApp(name="aperag_agent", settings=Settings(
mcp=MCPSettings(servers={"aperag": MCPServerSettings(...)})
))Event Processing:
from mcp_agent.logging.listeners import EventListener
class AgentEventListener(EventListener):
async def handle_event(self, event: Event):
# Route tool results to chat sessions by trace_idProduction Features
- Multi-tenant sessions with isolated memory
- Custom MCP server for knowledge base operations
- Real-time tool processing with event routing
- Internationalization (English/Chinese)
- Enterprise error handling and retry logic
Scale
Currently serving thousands of users in production with mcp-agent powering our intelligent chat system.
Would love to be featured as an example! The project is open source (Apache 2.0).
Thanks for the amazing framework! ๐
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