Examples Overview
AxonFlow ships with a broad example set because engineers assessing an AI control plane usually want more than a quickstart. They want to see how the platform behaves in real workflows: governed LLM requests, MCP access, policy enforcement, audit logging, workflow control, and industry-specific use cases.
This page is the public index for those examples. Use it to find the right starting point based on what you are building.
Community Example Themes
The public/community repo already covers the core runtime patterns most teams need first:
- first requests and health checks
- gateway-mode and proxy-mode integrations
- audit logging and execution tracking
- policy configuration and dynamic policies
- cost controls and cost estimation
- MAP and workflow-control examples
- MCP connector and MCP policy examples
- SDK interceptor examples
- framework integrations such as LangChain, LangGraph, CrewAI, DSPy, AutoGen, and Semantic Kernel
Good Starting Points
| Example Area | What It Helps You Validate |
|---|---|
| Hello world and health checks | Basic runtime connectivity and SDK setup |
| Audit logging | What gets recorded and how to build governed request flows |
| Gateway and proxy integrations | How AxonFlow fits into an existing application or agent stack |
| Dynamic policies | How tenant-specific policy logic changes request handling |
| Cost controls and estimation | How to keep LLM usage and budget behavior visible |
| MCP connectors and MCP policies | How to govern tool and data access in multi-agent systems |
| MAP and workflow examples | How AxonFlow supports larger multi-step and multi-agent flows |
Industry and Use-Case Pages
The public docs include solution-style pages that describe how AxonFlow applies to specific industries and regulated environments:
These pages describe the use cases, architecture patterns, and governance strategies for each domain. They are published in the public docs so that teams assessing AxonFlow can understand how the platform fits their regulatory and operational requirements. The runnable code and detailed implementations for industry-specific examples live in the enterprise repository, available with an evaluation or enterprise license.
Community vs Enterprise Examples
The example estate follows the same product story as the rest of the docs:
- community examples help you build and validate governed AI products quickly
- evaluation and enterprise examples show the workflows that become important when approval queues, compliance modules, customer-portal operations, or enterprise-only connectors enter the picture
That means you can start with public examples for architecture and product integration, then move to licensed examples when the conversation shifts toward organization-wide rollout, compliance operations, or advanced governance workflows.
Recommended Learning Path
If you are new to AxonFlow:
- Start with Getting Started.
- Pick the SDK and integration mode you want from SDK Overview.
- Use one or two public examples to validate your core request path.
- Move into policy, MCP, workflow, or industry examples based on your use case.
Why These Examples Matter
For strong engineering teams, examples are not just onboarding material. They answer high-value questions quickly:
- How much code changes if we adopt AxonFlow?
- What does a governed multi-agent flow actually look like?
- How do MCP policies, LLM routing, and audit records fit together?
- Can we use community first, then expand into evaluation or enterprise later?
That is why the examples are an important part of the docs funnel, not a side section.
