Introducing Aixgo: AI Agents in Pure Go (Alpha Release)

Your AI agent doesn’t need 1.5GB to say hello.

Python frameworks produce massive containers—1GB+, 30-second cold starts, and dependency hell. They’re built for research, not production systems that need to ship and scale.

Today, we’re launching Aixgo alpha—an AI agent framework built for Go developers who refuse to compromise on performance, security, or simplicity.

Why Aixgo?

Python excels at AI research and prototyping, but production deployments reveal critical limitations:

• Bloated deployments - 1GB+ containers with 200+ dependencies
• Runtime surprises - Type errors caught in production, not compile time
• GIL limitations - No true parallelism
• Scaling complexity - Manual orchestration overhead
• Security vulnerabilities - Large attack surface

Aixgo exists because production AI deserves production tooling. Go developers shouldn’t abandon their stack’s strengths just to build AI agents.

Core Principles

1. Single Binary Simplicity

Deploy AI agents in <10MB binaries with zero runtime dependencies.

# Python AI service
FROM python:3.11
COPY requirements.txt .
RUN pip install -r requirements.txt  # 1.2GB later...
COPY . .
CMD ["python", "main.py"]

# Aixgo service
FROM scratch
COPY aixgo-agent /
CMD ["/aixgo-agent"]  # 8MB total

2. Type-Safe Architecture

Catch errors at compile time. Go’s type system enforces contracts between agents, tools, and workflows.

// This won't compile - caught before deployment
agent := aixgo.NewAgent(
    aixgo.WithName("analyzer"),
    aixgo.WithModel(123),  // Type error: expected string, got int
)

3. Seamless Scaling

Start with Go channels locally. Scale to distributed agents with gRPC. Same code, zero changes.

// This code works locally AND distributed
supervisor := aixgo.NewSupervisor("coordinator")
supervisor.AddAgent(producer)
supervisor.AddAgent(analyzer)
supervisor.Run()  // Local: channels, Distributed: gRPC

Quick Example

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)
    }
}

Configure agents declaratively in YAML. See the Quick Start Guide for details.

Production Performance

Production Features

Observability: OpenTelemetry integration, Langfuse, Prometheus, distributed tracing, health checks, structured logging.

Security: Auth framework, RBAC, rate limiting, prompt injection protection, TLS/mTLS, audit logging, JWT verification.

Infrastructure: Model Context Protocol (MCP) support with local and gRPC transports, service discovery, dynamic tool registration.

Reliability: Circuit breakers, retry with exponential backoff, graceful degradation, workflow persistence.

No instrumentation code required—configure and deploy:

observability:
  tracing: true
  service_name: 'my-agent-system'
  exporter: 'otlp'

security:
  auth:
    enabled: true
    provider: 'jwt'
  rate_limiting:
    enabled: true
    requests_per_minute: 1000

Supported Integrations

LLM Providers: OpenAI, Anthropic (Claude), Google (Vertex AI, Gemini), xAI (Grok), HuggingFace

Vector Databases: Firestore Vector Search, In-Memory Storage, Qdrant (in progress), pgvector (in progress)

Observability: OpenTelemetry, Langfuse, Prometheus, Grafana, Datadog, New Relic

Use Cases

• Data Pipelines - High-throughput ETL with inline AI classification and enrichment
• Production APIs - Sub-millisecond P99 latency AI endpoints
• Edge Deployment - Run on IoT gateways, edge servers, embedded systems
• Multi-Agent Research - Coordinate complex workflows with supervisor orchestration
• Distributed Networks - Scale from single instance to multi-region deployment

Current Status: Alpha

What’s ready today:

• Multi-agent orchestration with 13 patterns (supervisor, sequential, parallel, router, swarm, hierarchical, RAG, reflection, ensemble, classifier, aggregation, planning, MapReduce)
• Seven agent types: Producer, ReAct, Logger, Classifier, Aggregator, Planner, Custom
• YAML-based declarative configuration with validation
• Local and distributed execution (Go channels, gRPC/MCP)
• Complete observability suite: OpenTelemetry, Langfuse, Prometheus
• Enterprise security: Auth, RBAC, rate limiting, TLS/mTLS
• Production deployment: Docker, Cloud Run, Kubernetes
• 6 LLM providers with streaming support
• Circuit breakers, retry logic, workflow persistence

In active development:

• Vector database integrations (Qdrant, pgvector)
• Long-term memory and personalization
• Multi-modal capabilities (vision, audio, document parsing)

Expect breaking changes as we evolve the API based on feedback.

When to Choose Aixgo

Choose Aixgo when:

• Deploying AI agents to production, not experimenting
• Your team uses Go for backend services
• Container size and cold start time matter
• You need type safety and compile-time error detection
• You want to avoid Python dependency overhead
• You’re building distributed multi-agent systems

Choose Python frameworks when:

• Doing exploratory research or rapid prototyping
• Need access to Python’s ML ecosystem
• Your team doesn’t have Go experience
• You need features Aixgo doesn’t support yet

Getting Started

Follow our Quick Start Guide to get running in 5 minutes. Explore the Features and join GitHub Discussions.

Roadmap

Beta Release (Q4 2025)

• Complete vector database integrations (Qdrant, pgvector)
• Long-term memory and personalization
• Enhanced error handling and validation
• Production battle-testing

v1.0 Production Release (Q1 2026)

• API stability guarantees with semantic versioning
• Kubernetes operator
• Multi-region deployment with state replication
• Terraform modules
• Multi-modal capabilities
• Performance benchmarking suite
• Production SLA commitments

See our v1.0 Compatibility Guarantee for API stability details.

Our Philosophy

Aixgo is built on a simple belief: production AI deserves production tooling.

We’re not trying to out-prototype Python. We’re trying to out-ship it. Production-first design, single binary simplicity, type safety, Go-native patterns, observable by default, open source (MIT licensed).

Read our complete Philosophy for design principles and decision criteria.

Join Us

Links:

• GitHub: github.com/aixgo-dev/aixgo
• Discussions: github.com/aixgo-dev/aixgo/discussions
• Documentation: aixgo.dev

Drop into GitHub Discussions with questions or feedback.

Where Python prototypes go to die in production, Go agents ship and scale.

Welcome to Aixgo.