Open Source · Go · Production-Grade
Develop and Orchestrate AI Agents
A unified system for developing and orchestrating production AI agents in Go.
Built for infrastructure engineers, not notebook demos.
The Problem Is Not Intelligence.
It's Infrastructure.
The world doesn't need another way to call an LLM. What it needs is a unified way to develop and orchestrate AI agents in production — where the agent you define is the agent that gets monitored, governed, and scaled.
80–90%
of AI agent projects never leave the pilot phase. (RAND, 2025)
This isn't a failure of AI — it's a failure of infrastructure.
LangChain
Ecosystem is vast, but drowns you in abstraction layers
CrewAI
Great mental model, but falls apart in production
AutoGen
Pioneered multi-agent — then got rewritten twice
Most frameworks
Python-only, prototype-first, production as an afterthought
GoGrid is here to fix that.
What We Believe
Seven convictions that shape every decision in GoGrid.
Clarity Over Cleverness
Understanding and maintaining code is the bottleneck, not writing it. GoGrid follows the Go philosophy: explicit over implicit, readable over concise.
Production Is the Point
Monitoring, cost tracking, error recovery, security, backward compatibility — built in from day one. Not bolted on after the demo works.
Memory Is First-Class
Memory is as fundamental to an agent as a file system is to an OS. Shared memory pools, state ownership transfer, and monitorable storage — all primitives.
One Architecture Does Not Fit All
Five composable patterns — single, team, pipeline, graph, dynamic — because we refuse to force your problem into our ideology.
Agents Are Infrastructure
At scale, agents are long-running, stateful, concurrent, networked, mission-critical. Infrastructure demands a language built for infrastructure.
No Lock-In. Ever.
Swapping models is a config change, not a rewrite. Open source. We will never paywall core features to push a managed platform.
Stability Is a Feature
Backward-compatible, gradual updates. Your agents won't break when you upgrade. We version our APIs. We deprecate before we remove.
5 Orchestration Patterns
Different problems demand different architectures. GoGrid supports all five — and they compose.
Single Agent
A well-scoped agent with a small number of tools. The recommended starting point for any GoGrid project.
Team (Chat Room)
Multiple domain experts collaborating in real-time — concurrent execution, debate, and consensus via pub/sub messaging with shared memory. Optional coordinator agent synthesizes the final decision.
Pipeline (Linear)
Sequential handoff between specialists. Each agent completes its work, yields state to the next, and terminates cleanly.
Graph
Like a pipeline with loops (re-do, clarify) and parallel branches that merge. Bounded agents with visible data flow.
Dynamic Orchestration
A Runtime enables agents to spawn child agents, teams, pipelines, or graphs at runtime. Resource governance, async futures, and aggregate metrics.
All patterns are composable. A graph node can contain a team. A team member can spawn a pipeline.
See It in Action
Define agents in YAML. Run, trace, and track costs — one CLI, zero friction.
gogrid init
Scaffold a project
gogrid.yaml
Configure in YAML
gogrid run
Run agents
gogrid trace
Trace & cost
Why Go?
AI agents are infrastructure — building them and running them should be one system. That system demands a language built for infrastructure.
Kubernetes. Docker. Prometheus. Terraform. The most critical infrastructure of the modern internet runs on Go.
Who It's For
Infrastructure Engineers
Building production AI systems, not prototypes
Platform Teams
Deploying multi-tenant agent workloads at scale
Companies
That need agents they can monitor, audit, secure, and trust
Frustrated Developers
Tired of framework churn, abstraction bloat, and the demo-to-production gap
If you're building a weekend hackathon project, there are simpler tools.
If you're building something that needs to run in production, at scale, for real users — GoGrid is for you.