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agent argocd

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Multi-Agent Platforms & Frameworks
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Basic Information

This repository provides an LLM-powered agent that integrates with ArgoCD to allow programmatic, language-model-driven interactions with ArgoCD APIs and resources. The project implements a LangGraph ReAct agent that discovers and calls ArgoCD tools surfaced via a first-party MCP server generated from ArgoCD’s OpenAPI spec. It is designed to be used with external user clients via the A2A protocol and to enforce ArgoCD API token based RBAC plus optional secondary external authentication. The codebase includes local development instructions for running a KinD cluster with ArgoCD, instructions for obtaining API tokens, Docker and Makefile targets for local runs, and an evaluation harness for behavior testing. The repo was later moved under a larger ai-platform-engineering umbrella and is archived as read-only.

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App URL
https://github.com/cnoe-io/agent-argocd
Github Repository
https://github.com/cnoe-io/agent-argocd/blob/main/README.md

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App Details

Features
Implements a LangGraph ReAct agent graph that binds LLM reasoning to API tools exposed by an MCP server. Uses a generated MCP server produced by the openapi-mcp-codegen utility to provide typed, version-aligned tooling for ArgoCD. Integrates langchain-mcp-adapters to glue MCP tools to the agent runtime. Supports the Google A2A protocol for client-to-agent transport and offers Docker images and Makefile targets for running the agent locally or in containers. Enforces ArgoCD token-based RBAC and supports external authentication for access control. Includes local dev recipes using KinD and ArgoCD example deployment steps and provides automated evaluations via agentevals for strict trajectory testing.
Use Cases
The agent lets operators and developers interact with ArgoCD through natural language or programmatic A2A clients, reducing manual CLI or UI work when creating apps, syncing resources, or invoking ArgoCD APIs. The generated MCP server and adapters produce a predictable tool surface that preserves API version compatibility and supply chain traceability. Docker and local development targets make it straightforward to test the agent against a local ArgoCD instance. Token-based RBAC and optional secondary authentication help keep operations secure. The included evaluation suite and CI checks support regression testing and behavior validation, making the project useful for teams building or validating LLM-driven automation for Kubernetes continuous deployment workflows.

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