The Future of AI Agents is Go
Build and test AI systems as single binaries. Alpha release - production features coming late 2025.
Try Alpha Version →From Prototype to Planet-Scale in 60 Seconds
1. Install
go get github.com/aixgo-dev/aixgo2. Create config/agents.yaml
supervisor:
name: coordinator
model: grok-beta
max_rounds: 10
agents:
- name: data-producer
role: producer
interval: 1s
outputs:
- target: analyzer
- name: analyzer
role: react
model: grok-beta
prompt: |
You are a data analyst. Analyze incoming data and provide insights.
inputs:
- source: data-producer
outputs:
- target: logger
- name: logger
role: logger
inputs:
- source: analyzer3. Create main.go
package main
import (
"github.com/aixgo-dev/aixgo"
_ "github.com/aixgo-dev/aixgo/agents"
)
func main() {
if err := aixgo.Run("config/agents.yaml"); err != nil {
panic(err)
}
}4. Deploy anywhere
# Local development
go run main.go
# Production - single 8MB binary
go build -o agent
./agent
# Edge, Lambda, Cloud Run, Kubernetes - one binary, zero configurationWhy the Industry is Moving to Go
Python dominated AI because it was easy to prototype. Go will dominate production because it's built to ship.
Container Size
Python frameworks
1.2GB with dependencies
Aixgo
8MB single binary
Impact:
Deploy to edge devices, serverless, anywhere
Startup Performance
Python frameworks
30-45s cold start
Aixgo
<100ms instant startup
Impact:
True serverless viability, real-time response
Runtime Safety
Python frameworks
Runtime - Discover errors in production
Aixgo
Compile-time - Compiler catches errors before deploy
Impact:
Ship with confidence, sleep at night
LLM Data Validation
Python frameworks
Runtime only - Type changes found in production
Aixgo
Compile-time - Type changes caught before deploy, auto-retry
Impact:
Refactor with confidence, LLM errors auto-recover
The Go Advantage
| What Matters in Production | Python Frameworks | Aixgo |
|---|---|---|
| Deploy Anywhere | 1GB+ containers, complex deps | 8MB binary, zero deps |
| Cold Start Speed | 10-45 seconds | <100ms |
| Type Safety | Runtime discovery | Compile-time guarantees |
| Concurrency | GIL bottleneck | Native parallelism |
| Scaling Pattern | Rewrite for distribution | Same code, local → distributed |
| Operational Cost | High compute overhead | 60-70% infrastructure savings |
Built for What's Next
Aixgo isn't just another framework. It's the foundation for the next generation of AI systems.
13 Orchestration Patterns
All patterns production-ready: Supervisor, Sequential, Parallel, Router, Swarm, RAG, Reflection, Ensemble, and more. Go channels locally, gRPC for distributed.
8+ LLM Providers
OpenAI, Anthropic, Google Gemini, xAI, Vertex AI, HuggingFace, Ollama, and vLLM. Switch providers with a config change. Auto-detection by model name.
Full Observability Stack
OpenTelemetry tracing, Langfuse integration, Prometheus metrics, automatic cost tracking. Kubernetes health probes included.
Enterprise Security
4 auth modes, RBAC, SSRF protection, prompt injection defense, SIEM integration. Rate limiting and audit logging built-in.
Type-Safe Validation
Pydantic AI-style validation with automatic retry. 40-70% improvement in structured output reliability. Compile-time type checking.
60-70% Lower Infra Costs
8MB binaries, <100ms cold starts, ~50MB memory. Router pattern saves 25-50% on LLM costs. Local inference with Ollama for zero API costs.
What We're Building
- ✅ 6 LLM providers (OpenAI, Anthropic, Gemini, xAI, Vertex AI, HuggingFace)
- ✅ 7 agent types + 6 orchestration patterns
- ✅ Full observability (OpenTelemetry, Langfuse, Prometheus, Distributed Tracing)
- ✅ Complete security suite (Auth, RBAC, Rate Limiting, Injection Protection)
- ✅ Docker, Cloud Run deployment
- ✅ Kubernetes deployment (partial - manifests and RBAC)
- ✅ Phased agent startup with dependency ordering (v0.2.3+)
- ✅ Public agent package for library-style integration
- ✅ VertexAI SDK migration with production hardening
- ✅ Enhanced streaming reliability and resource management
- ✅ SSRF protection for Ollama integration
- ✅ Topological sort for eliminating startup race conditions
- 📋 Kubernetes deployment (complete - operator and automation)
- 📋 Secrets management and rotation
- 📋 Long-term memory and personalization
- 📋 Multi-modal support (Vision, Audio, Document Parsing)
- 📋 Additional vector database providers (Qdrant, pgvector)
- 📋 API stability guarantees and backward compatibility
- 📋 Kubernetes operator for automated lifecycle management
- 📋 Multi-region deployment with state replication
- 📋 Infrastructure as Code (Terraform modules)
Join the Alpha
The AI infrastructure built on Python is reaching its limits. Aixgo is what comes next. Join us in building it.
Try Alpha Version →