GreenNode AgentBase
Start with a real example
You want to build a customer support AI Agent: it takes questions via chat, looks up orders in a database, sends notifications through Slack, and remembers what was discussed in last week's conversation.
Sounds straightforward β but in practice you'll need to handle:
Where does the agent run? Containers, servers, autoscaling, CI/CD deployments...
Where do credentials live? Database passwords, Slack tokens, API keys β you can't hardcode them.
Can the agent call any tool it wants? You need guardrails to prevent it from accidentally calling a data-deletion API.
What did the LLM cost this month? No dashboard, no way to know when you're about to exceed budget.
When something breaks in production? No centralized logs, no visibility into which requests failed.
AgentBase handles all of this β so you can focus entirely on your agent's logic.
What is AgentBase?
GreenNode AgentBase is a purpose-built infrastructure platform for AI Agents β providing the full operational, security, and governance layer needed to take an agent from code to production.
AgentBase consists of the following modules:
Agent Runtime
Deploy and operate agents β container lifecycle management, versioning, rollback, scaling
Marketplace
Deploy pre-built agents (OpenClaw and templates) with 1 click, no code required
Access Control
Manage Agent Identity and store credentials (API Key, OAuth2) β automatically injected into the agent at runtime
MCP Governance
Control all MCP tool calls from agents β authentication and authorization via MCP Gateway + Policy Group
Protect & Govern
Rate Limiting by model or API Key β prevent agents from consuming excessive resources
Memory
Give agents cross-session memory β Short-Term (conversation history) and Long-Term (semantic search)
Container Registry
Private image registry automatically created per org β stores container images for Custom Agents
Team & Permissions
Manage members with 4 roles (Root / Admin / Member / Viewer) and granular permission control
Usage & Budget
Dashboard tracking requests, tokens, and cost by agent/model/provider; set budget limits and automated alerts
Two ways to get started
No code β use immediately: Go to the Marketplace, select OpenClaw, enter your API key and chat channel β the agent is running in minutes.
Build your own agent: Package your agent as a Docker image, push it to Container Registry, and deploy via Agent Runtime. Add credentials in Access Control, and attach an MCP Gateway if the agent needs to call external tools.
Who is it for?
AI Engineers / Developers
Focus on agent logic β infra, credentials, and observability are built in
Startups / Product Teams
Ship AI products faster.
Enterprises
Cost control, team-based access, enterprise-grade credential security
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