Why Choose EdgeOne Makers for LangChain Agent Deployment
Production-grade langchain deployment with session-sticky runtime, conversation memory, and sandbox tools — purpose-built for langchain ai agent workloads.
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 Deploy a LangChain Agent in 3 Steps
From langchain agent code to production langchain deployment in minutes — no infrastructure to manage.
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.
Complete LangChain Agent Deployment Features
Everything your langchain ai agent needs in production — runtime, tools, memory, models, and observability.
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. |
How do I deploy a langchain agent on EdgeOne Makers?
Write your langchain agent in the `agents/` directory using LangChain/LangGraph, push to Git or run CLI deploy. Your langchain deployment goes live globally in seconds.
Does langchain deployment include built-in memory?
Yes. The platform provides conversation memory with framework-adaptive APIs, automatically compatible with LangChain's memory patterns. No external database needed for your langchain ai agent.
Is langchain deployment on EdgeOne Makers free?
Yes. Free tier includes 200,000 executions/month, 500K model tokens, 40 concurrent sessions, and up to 1-hour execution time. Perfect for most langchain agent workloads.
Can my langchain agent use the platform's sandbox tools?
Yes. Built-in sandbox tools (browser, code execution, shell, file I/O) are available to your langchain ai agent through the platform's tool interface, compatible with LangChain's tool abstractions.
Does langchain deployment support both JS and Python?
Yes. EdgeOne Makers supports langchain agent deployment in both JavaScript/TypeScript and Python, with the same platform features available in both runtimes.