The AI Agent Framework That Ships in 8MB

Build, deploy, and scale AI agents in Go. No containers. No cold starts. No Python.

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From Prototype to Planet-Scale in 60 Seconds

1. Install

go get github.com/aixgo-dev/aixgo

2. Create config/agents.yaml

supervisor:
  name: coordinator
  model: gpt-4o-mini  # OpenAI - fast orchestration
  max_rounds: 10

agents:
  - name: data-producer
    role: producer
    interval: 1s
    outputs:
      - target: analyzer

  - name: analyzer
    role: react
    model: claude-3-5-haiku  # Anthropic - strong reasoning
    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: analyzer

3. 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 configuration

Why 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 ProductionPython FrameworksAixgo
Deploy Anywhere1GB+ containers, complex deps8MB binary, zero deps
Cold Start Speed10-45 seconds<100ms
Type SafetyRuntime discoveryCompile-time guarantees
ConcurrencyGIL bottleneckNative parallelism
Scaling PatternRewrite for distributionSame code, local → distributed
Operational CostHigh compute overhead60-70% 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

v0.3.3 Beta (Current) - ✅
February 2026
  • ✅ 8+ LLM providers (OpenAI, Anthropic, Gemini, xAI, Vertex AI, HuggingFace, Ollama)
  • ✅ 13 orchestration patterns (Supervisor, Parallel, RAG, Reflection, MapReduce, etc.)
  • ✅ Session persistence with JSONL and Redis backends
  • ✅ Full observability (OpenTelemetry, Langfuse, Prometheus)
  • ✅ Enterprise security (Auth, RBAC, TLS/mTLS, audit logging)
  • ✅ Local to distributed with zero code changes
v0.4.x Enhancements
Q2 2026
  • 📋 Session encryption at rest
  • 📋 PostgreSQL session backend
  • 📋 Conversation branching and tree navigation
  • 📋 Memory compaction and summarization
  • 📋 Additional vector databases (Qdrant, pgvector)
v1.0 Production
Q3 2026
  • 📋 API stability guarantees and semantic versioning
  • 📋 Kubernetes operator for automated lifecycle management
  • 📋 Multi-modal support (Vision, Audio, Documents)
  • 📋 Long-term memory and personalization
  • 📋 Infrastructure as Code (Terraform modules)

Try v0.3.3

The AI infrastructure built on Python is reaching its limits. Aixgo is what comes next. Join us in building it.

Get Started →