We built AgentUp with a - configuration-driven architecture that scales from prototype to production with its plugin ecosystem
The Architecture Layers
1. Agent Core
The runtime engine that processes A2A protocol messages and manages the execution lifecycle. Handles routing, state management, and multi-modal content processing.
2. Plugin System
Python packages that provide capabilities. Plugins are automatically wrapped with your configured middleware and security policies. No manual wiring needed.
3. Middleware Stack
Composable middleware (caching, rate limiting, retry logic) that automatically applies to all plugins.
4. Security Layer
Scope-based authorization with hierarchical permissions. The framework automatically filters which tools are available to AI based on user permissions.
5. Multi-Modal Engine
Handles text, images, and documents across different AI providers. Graceful fallbacks when models don't support certain content types.
6. Protocol Adapters
- A2A Protocol: For agent-to-agent communication
- MCP Integration: Both client (consume external tools) and server (expose capabilities)
- REST API: Standard HTTP endpoints with OpenAPI docs
7. State Management
Pluggable backends (memory, Redis/Valkey) with automatic persistence and TTL management.
How It All Works Together
When a request comes in:
- Authentication validates scope permissions
- Agent Core parses the A2A message
- AI Router determines which plugins to use (filtered by user scopes)
- Middleware wraps plugin execution (cache check, rate limit, etc.)
- Plugin executes with full context and security
- Response streams back via SSE or returns complete result
The Magic: Automatic Optimization
AgentUp automatically optimizes based on plugin characteristics:
- NETWORK plugins get retry, rate limiting, and caching
- LOCAL plugins get validation and audit logging
- AI_FUNCTION plugins get model routing and token management
No configuration needed - the framework is smart about what each plugin type needs.
Why This Architecture Works
For Developers:
- Write business logic, not infrastructure
- Standard Python packaging for plugins
- Configuration validation catches issues early
For Production:
- Enterprise security out of the box
- Automatic scaling and optimization
- Full observability and audit trails
For Teams:
- Share plugins as Python packages
- Version and manage like any dependency
- Clear separation of concerns
Real-World Impact
This architecture means you can go from idea to production agent in minutes, not weeks. The configuration approach makes agents maintainable and the plugin system makes them extensible.
We've seen teams reduce their agent development time by 80% while actually improving security and reliability.
Try It Yourself
pip install agentup
agentup agent create my-agent
cd my-agent
agentup agent serve --reload
Your at the ready agent is running at http://localhost:8000
The architecture handles the complexity so you can focus on what makes your agent unique.
Want to dive deeper? Check out the repository or join our Discord community.
What's your biggest AI agent development challenge? Let me know in the comments! 👇
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