Why Aixgo
Why we built Aixgo and what we believe about production AI. Our design principles, values, and commitment to production-grade tooling.
Production AI Deserves Production Tooling
We’re not trying to out-prototype Python. We’re trying to out-ship it.
For too long, production AI teams have been forced to choose between:
- The velocity of Python frameworks (LangChain, CrewAI, AutoGen)
- The reliability of production-grade infrastructure (Go, Rust, Java)
Aixgo exists to eliminate that choice. We believe AI agents should ship with the same performance, security, and simplicity as the rest of your production systems.
The Production Reality
| What Matters | Python Frameworks | Aixgo | Impact |
|---|---|---|---|
| Container Size | 1.2GB+ | 8MB | 150x smaller |
| Cold Start | 30-45 seconds | <100ms | 450x faster |
| Dependencies | 200+ packages | ~10 packages | 95% fewer |
| Type Safety | Runtime discovery | Compile-time | Zero production surprises |
| Memory Baseline | 512MB+ | 50MB | 10x more efficient |
The Problem We’re Solving
Python excels at AI research and prototyping. But production reveals fundamental limitations:
- Bloated deployments: 1GB+ containers, 200+ dependencies, massive security surface
- Runtime surprises: Type errors caught in production, not at compile time
- GIL bottleneck: No true parallelism for multi-agent systems
- Slow cold starts: 30-45 second startup kills serverless economics
- Scaling complexity: Manual orchestration, heavy memory footprint
Go developers shouldn’t have to abandon their stack’s strengths—speed, security, simplicity, and scalability—just to build AI agents. That meant trading a 5MB binary for a 1.5GB container, type safety for runtime errors, and instant startup for 45-second cold starts.
Aixgo exists because production AI teams deserve better.
Our Design Principles
1. Production-First, Not Research-First
API stability over rapid iteration. Performance-driven decisions. Security and observability built-in from day one. We prioritize production-hardened primitives over research experiments—fewer features, but battle-tested at scale.
Key trade-off: YAML configuration instead of Python DSLs for declarative, reviewable, deployable workflows.
2. Single Binary Simplicity
Deploy AI agents in <10MB binaries with zero runtime dependencies. No Python interpreter, no virtual environments, no Docker required (though it works great with containers).
Real impact: 8MB total deployment vs 1.2GB Python containers. Deploy to edge devices, serverless, IoT, anywhere.
3. Type Safety as a Feature
Catch errors at compile time, not in production. Go’s type system enforces contracts between agents, tools, and workflows—your IDE tells you what’s broken before your customers do.
Team velocity: Less time debugging production runtime errors, more time building features. Refactor with confidence.
4. Go-Native Patterns
We don’t port Python concepts to Go. We embrace Go’s strengths: channels for local message passing, gRPC for distributed systems, goroutines for concurrency, context for cancellation.
The abstraction that matters: Same code works locally (channels) and distributed (gRPC). Runtime picks transport automatically.
5. Observable by Default
Every agent interaction is traceable via OpenTelemetry, logged with structured context, and measurable with metrics. No instrumentation code required—just configuration.
Built-in integrations: Prometheus, Grafana, Datadog, Langfuse, New Relic. Works out of the box.
6. Open and Permissive
MIT licensed. Use in commercial products without restrictions. No vendor lock-in, no surprise license changes. Your investment is protected.
When to Choose Aixgo
Choose Aixgo When:
Deploying to production, not experimenting
- Predictable performance and resource usage matter
- Container size and cold start times are critical (serverless, edge)
- Multi-region or distributed deployments needed
Your team already uses Go
- Backend services in Go, want AI agents in same stack
- Value type safety and compile-time error detection
- Want to avoid Python dependency management overhead
Performance is non-negotiable
- Sub-100ms cold starts for serverless
- High-throughput pipelines (10,000+ req/s)
- Resource-constrained environments (IoT, edge)
You need production-grade guarantees
- Compile-time type safety
- Single binary deployments
- Minimal dependency surface
- Enterprise security requirements
Choose Python Frameworks When:
Doing exploratory research
- Rapid prototyping with frequent pivots
- Research workflows that don’t need production deployment
- Throwaway scripts and notebooks
You need Python’s ML ecosystem
- Training models with PyTorch, TensorFlow, JAX
- Data analysis with pandas, numpy, scikit-learn
- Integration with Jupyter notebooks
Your team is Python-native
- No Go experience and no interest in learning
- Existing Python infrastructure
- Python-first organizational culture
Our Commitment
Stability First
When v1.0 releases, we guarantee API stability with semantic versioning, long-term support, and clear upgrade paths. No breaking changes without major version bumps.
See our v1.0 Compatibility Promise for details.
Open Development
Public roadmap, open issue tracking, community-driven feature prioritization, transparent decision-making. Your feedback shapes Aixgo.
Long-Term Vision
Enterprise-grade multi-agent orchestration with production-proven patterns. We’re building for what ships, not what trends.
Join the Movement
Aixgo is just getting started. We’re building this in the open, learning from the community, and evolving based on real-world production usage.
If you’re tired of wrestling with Python in production, if you believe AI agents should ship with the same simplicity as the rest of your Go services, or if you just want to see what production-grade AI looks like in pure Go—join us.
Get Started:
- Quick Start Guide - Get running in 5 minutes
- Core Concepts - Understand Aixgo’s architecture
- Features - Explore what’s available today
- Proverbs - 15 production principles
Get Involved:
- GitHub - Star the repo, open issues, contribute
- Discussions - Ask questions, share ideas
- Roadmap - See what’s coming next
Where Python prototypes go to die in production, Go agents ship and scale.
This is our philosophy. Welcome to Aixgo.