Why Build a RAG AI Agent on EdgeOne Makers
The platform provides file processing capabilities, session-sticky memory for multi-turn Q&A, and LLM tool calling for retrieval — build a complete agent rag architecture without external vector databases or services.
One-Command Deployment
Deploy with `git push` for auto-deploy or `edgeone makers deploy` via CLI — no Docker, no Kubernetes, no server provisioning.
Built-in Agent Runtime
Managed runtime with session-sticky routing, up to 1-hour execution time, and in-memory state reuse — purpose-built for LLM calls and multi-step agent loops.
Integrated AI Models
Access DeepSeek, MiniMax, Hunyuan, and more through a unified AI gateway. New accounts receive 500K free tokens with zero configuration.
Full Observability
Zero-instrumentation distributed tracing — view complete call chains, LLM interactions, tool invocations, and latency metrics in local and cloud dashboards.
How to Build a RAG AI Agent in 3 Steps
Process documents into a knowledge base, define retrieval tools, and deploy — the platform handles memory, routing, and LLM orchestration for your knowledge base chatbot.
1
Write Your Agent
Build in the `agents/` directory with any framework (OpenAI SDK, Claude SDK, LangGraph, CrewAI, DeepAgents).
2
Deploy to Production
Push to Git (GitHub/GitLab/Gitee) for auto-deploy, or run `edgeone makers deploy` via CLI.
3
Go Live
Deploys globally in minutes with automatic SSL and edge routing.
Platform Capabilities for RAG AI Agent
How EdgeOne Makers enables your rag ai agent — file storage for knowledge base, tool calling for retrieval, session memory for multi-turn Q&A, and ai agents rag patterns.
Feature | Description |
|---|---|
| Agent Runtime | Hosts LLM calls, Agent loop orchestration, and business logic, with session-based routing and automatic scaling. |
| Sandbox Tools | Provides two separate yet interoperable API layers for both LLMs and developers. Browser automation, code execution, Shell, and file operations all run in an isolated sandbox environment. |
| Conversation Storage | Provides framework-compatible memory management, with unified APIs for sessions and messages. |
| Observability | Automatically collects call traces with zero-intrusion instrumentation, enabling unified trace viewing in both local and cloud dashboards. |
| Built-in Models | Access Hunyuan and other mainstream Chinese models through AI Gateway with a limited-time free token quota. |
What platform capabilities do I need for a rag ai agent?
File storage/processing for knowledge base, LLM tool calling (search + fetch) for retrieval, and session-sticky memory for multi-turn context. EdgeOne Makers provides all three — enabling agent rag architecture without external services.
Do I need a vector database to build a knowledge base chatbot?
No. The platform's file system and tool calling enable lightweight agent rag architecture — process PDFs to structured text, define search/fetch tools, and let the LLM retrieve relevant pages. No Pinecone or Weaviate required.
How does session memory help my rag ai agent?
Session-sticky routing keeps your knowledge base chatbot's conversation context alive across multiple questions — the agent remembers what was discussed and can refine answers without re-retrieving.
Is building ai agents rag free on EdgeOne Makers?
Yes. 500K model tokens/month, persistent file storage, session-sticky memory, and multi-turn conversation support — all the components for ai agents rag at zero cost.
Can I customize the retrieval logic in my rag ai agent?
Yes. You define your own tool functions (search, fetch, filter) — the platform provides the runtime and LLM access. Build any agent rag architecture pattern: keyword, semantic, hybrid, or custom.