→Aixgo · Open source · MIT
The AI Agent Framework That Ships in <20MB
Build, deploy, and scale AI agents in Go. No containers. No cold starts. No Python.
Status
Stable. The agent types, the orchestration patterns and the provider integrations are implemented; the latest release is named below, and the blog says what each one changed.
One Go module. Nothing to install alongside it at runtime.
MIT · 13 patterns · 8+ providers · one binary
- <20MB
- Single binary, no interpreter and no dependencies to ship
- <100ms
- Cold start, which is what makes serverless viable
- 13
- Orchestration patterns, all implemented
- 8+
- LLM providers behind one interface
01Quick start
Four steps from an empty directory to a running agent.
The configuration describes the agents and the wiring between them. The Go file does nothing but hand that configuration to the runtime.
Install the module
go get github.com/aixgo-dev/aixgoDescribe the agents
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: analyzerPoint main at the config
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) } }Build it and run it anywhere
# Local development go run main.go # Production - single <20MB binary go build -o agent ./agent # Edge, Lambda, Cloud Run, Kubernetes - one binary, zero configuration
02Why Go
What changes when an agent ships as a compiled binary.
Python won prototyping because it is quick to write. The costs arrive later, at the point where the thing has to be deployed and kept running.
Container size
- Python frameworks
- 1.2GB with dependencies
- Aixgo
- <20MB single binary
- What that buys you
- Deploy to edge devices, serverless, anywhere
Startup performance
- Python frameworks
- 30-45s cold start
- Aixgo
- <100ms instant startup
- What that buys you
- True serverless viability, real-time response
Runtime safety
- Python frameworks
- Runtime - discover errors in production
- Aixgo
- Compile-time - compiler catches errors before deploy
- What that buys you
- 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
- What that buys you
- Refactor with confidence, LLM errors auto-recover
03Side by side
The six differences that show up in production.
| What matters in production | Python frameworks | Aixgo |
|---|---|---|
| Deploy anywhere | 1GB+ containers, complex deps | <20MB 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 to distributed |
| Operational cost | High compute overhead | 60-70% infrastructure savings |
04In the binary
What you get without adding a dependency.
05Releases
Latest release: v0.7.4 · 2 May 2026
This line maintains itself: a workflow reads the GitHub API, a gate checks what it wrote, and a daily run fails loudly if the deployed page ever falls behind. No one edits it by hand. Release notes · All releases · What each release changed
06Also from us
Aixgo builds agents. The other two deal with what agents do next.
Code
An autonomous coding agent in your own GitHub. Issue in, pull request out, a human merges. It runs in your Actions, on your runners, with your secrets.
Beta · v0.5.0 · Apache-2.0
Aixgate
A deny-by-default sandbox for AI coding agents. Run Claude Code, Cursor, Aider or OpenAI Codex without giving them your .env, ~/.aws or ~/.ssh, blocked at the syscall boundary, even under prompt injection.
v0.1 proof of concept · MIT
Start with the quick start.
Four steps, ending with a running multi-agent system you can read top to bottom.
MIT · issues and discussions welcome