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

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

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

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

The Python Problem

I’ve spent the last decade building production systems in Go. Fast, secure, simple—everything compiles to a single binary that just works. Then came the AI revolution, and suddenly everyone said: “Just rewrite the AI layer in Python.”

That meant trading a 5MB binary for a 1.5GB container. Sacrificing type safety for runtime errors. Accepting 45-second cold starts instead of instant startup. And dealing with dependency conflicts that would make any DevOps engineer weep.

There had to be a better way.

Why We Built Aixgo

Python excels at AI research and prototyping. Libraries like LangChain, CrewAI, and AutoGen make it trivial to experiment with multi-agent workflows. But when it’s time to ship to production, these frameworks reveal their limitations:

• Bloated deployments - 1GB+ containers with 200+ dependencies
• Runtime surprises - Type errors that should have been caught at compile time
• GIL limitations - No true parallelism, even with multi-threading
• Scaling complexity - Manual queue management, service orchestration headaches
• Security vulnerabilities - Large attack surface from extensive dependency trees

Aixgo exists because production AI deserves production tooling. Go developers shouldn’t have to abandon their stack’s strengths—speed, security, simplicity, and scalability—just to build AI agents.

What is Aixgo?

Aixgo is a multi-agent framework for Go that brings production-grade orchestration to AI systems. It’s built on three core principles:

1. 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).

# 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, 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.

// 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 for local development. Scale to distributed agents with gRPC. Same code, zero changes. The runtime abstraction handles transport automatically.

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

Quick Example

Here’s a three-agent data pipeline—producer, analyzer, logger—orchestrated by a supervisor:

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, run with go run main.go, and deploy as a single binary. That’s it.

See the Quick Start Guide for the complete walkthrough.

How It Works

Aixgo uses a message-based multi-agent architecture with seven specialized agent types:

Producer, ReAct, Classifier, Aggregator, Planner, Logger, and Custom agents—each with multiple strategies for different scenarios. A supervisor orchestrates message routing, lifecycle management, and execution constraints.

Here’s a ReAct agent with tool calling:

agents:
  - name: analyst
    role: react
    model: gpt-4-turbo
    prompt: 'You are an expert data analyst.'
    tools:
      - name: query_database
        description: 'Query the database'
        input_schema:
          type: object
          properties:
            query: { type: string }

The runtime abstracts communication: Go channels for local development, gRPC/MCP for distributed production. Same code, zero changes when you scale.

For complete architecture details, patterns, and examples, see our Core Concepts Guide.

Production Performance

Here’s how Aixgo compares to equivalent Python frameworks in production workloads:

These aren’t synthetic benchmarks—these are the characteristics you get when running production AI systems at scale.

Production Features

Aixgo ships production-ready features out of the box—no plugins or add-ons required.

Observability: Full OpenTelemetry integration with Langfuse, Prometheus, distributed tracing, health checks, and structured logging. Works with Grafana, Datadog, and New Relic.

Security: Enterprise-grade security with auth framework, RBAC, rate limiting, prompt injection protection, TLS/mTLS support, audit logging, and JWT verification.

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

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

No instrumentation code required—configure and deploy:

observability:
  tracing: true
  service_name: 'my-agent-system'
  exporter: 'otlp'
  endpoint: 'localhost:4317'

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

Supported Integrations

LLM Providers (all with streaming support):

• OpenAI (GPT-4, GPT-4 Turbo, GPT-3.5)
• Anthropic (Claude 3.5 Sonnet, Claude 3 Opus/Sonnet/Haiku)
• Google (Vertex AI, Gemini)
• xAI (Grok)
• HuggingFace (Inference API, custom models)

Vector Databases:

• Firestore Vector Search ✅
• In-Memory Storage ✅
• Qdrant 🚧 (in progress)
• pgvector 🚧 (in progress)

Observability Platforms:

• OpenTelemetry (native)
• Langfuse (LLM observability)
• Prometheus (metrics)
• Grafana, Datadog, New Relic (via OTLP)

Use Cases

Aixgo is purpose-built for production AI scenarios:

Data Pipelines with AI Enrichment

Process high-throughput ETL workflows with inline AI agents for classification, enrichment, and routing—all in pure Go.

Production API Services

Ship AI endpoints with Go’s performance characteristics: sub-millisecond P99 latency, predictable memory usage, efficient resource utilization.

Edge Deployment

Run AI agents on resource-constrained devices: IoT gateways, edge servers, embedded systems—anywhere you need minimal footprint and instant startup.

Multi-Agent Research Systems

Coordinate complex workflows: research assistants, data analysis pipelines, autonomous decision-making systems with supervisor orchestration.

Distributed Agent Networks

Scale from single instance to multi-region deployment with automatic transport selection—Go channels locally, gRPC for distributed.

Current Status: Alpha Release

Aixgo is currently in alpha release. We’re building in public and shipping features as they stabilize.

What’s ready today:

• Multi-agent orchestration with supervisor pattern and 6 orchestration strategies (parallel, sequential, reflection, MapReduce, planning, classification)
• Seven agent types: Producer, ReAct, Logger, Classifier, Aggregator, Planner, and custom agents
• Multiple strategies per agent type (ZeroShot, Consensus, Chain-of-Thought, Tree-of-Thought, etc.)
• YAML-based declarative configuration with validation
• Local execution with Go channels and distributed mode with gRPC/MCP transport
• Complete observability suite: OpenTelemetry, Langfuse, Prometheus, distributed tracing
• Enterprise security: Auth framework, RBAC, rate limiting, injection protection, TLS/mTLS
• Production deployment: Docker, Cloud Run, Kubernetes manifests
• 6 LLM providers with streaming: OpenAI, Anthropic, Google Gemini, xAI, Vertex AI, HuggingFace
• Vector databases: Firestore Vector Search (complete), Memory storage (complete)
• Circuit breakers, retry with exponential backoff, workflow persistence

In active development:

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

We’re shipping early because we believe production AI developers need this tool now. Expect breaking changes as we evolve the API based on community feedback.

When to Choose Aixgo Over Python

Aixgo isn’t trying to replace Python for AI research. Choose Aixgo when:

• You’re deploying AI agents to production, not experimenting
• Your team already uses Go for backend services
• Container size and cold start time matter (serverless, edge)
• You need type safety and compile-time error detection
• You want to avoid the operational overhead of managing Python dependencies
• You’re building distributed multi-agent systems that need to scale

Choose Python frameworks when:

• You’re doing exploratory research or rapid prototyping
• You need access to Python’s ML ecosystem (training, data analysis)
• Your team doesn’t have Go experience and isn’t willing to learn
• You need features that Aixgo doesn’t support yet

Getting Started

Ready to try Aixgo? Follow our Quick Start Guide to get running in 5 minutes, explore the Features to see what’s available, and join the conversation in GitHub Discussions.

Roadmap

We’re building toward production stability with a clear path to v1.0.

Beta Release (Q4 2025)

Focus: Feature completion and stability

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

v1.0 Production Release (Q1 2026)

Focus: API stability and enterprise readiness

• API stability guarantees with semantic versioning
• Kubernetes operator for production orchestration
• Multi-region deployment with state replication
• Terraform and IaC modules for infrastructure
• Multi-modal capabilities (vision, audio, document parsing)
• Performance benchmarking suite
• Production SLA commitments

See our v1.0 Compatibility Guarantee for our commitment to API stability.

Beyond v1.0

• Plugin marketplace for community extensions
• Advanced deployment patterns (blue/green, canary)
• Enterprise support tier (optional)
• Extended LLM provider ecosystem

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, and open source (MIT licensed).

Read our complete Philosophy to understand our design principles, when to choose Aixgo, and our commitment to production-grade AI tooling.

Join Us

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.

Links:

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

Questions? Feedback? Ideas?

Drop into GitHub Discussions and let’s talk. We’re listening, learning, and shipping.

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

Welcome to Aixgo.