MCP Servers · micro

go-micro

Go Micro is a Go framework for building AI agents and microservices together in a single runtime. It provides tools, memory, workflows, and service discovery so agents can call services as tools and other agents can reach them via standard protocols (MCP, A2A).

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Source: GitHub — github.com/micro/go-micro

23k

GitHub stars

2.4k

Forks

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	micro/go-micro
Owner	micro
Primary language	Go
License	Apache-2.0 — OSI-approved
Stars	23k
Forks	2.4k
Open issues	85
Latest release	v6.3.18 (2026-07-07)
Last updated	2026-07-08
Source	https://github.com/micro/go-micro

What go-micro is

Go Micro combines an agent harness with a service framework, allowing endpoint metadata to become AI tool schemas, agents to register as discoverable services, and deterministic workflows to be orchestrated alongside agent logic. It integrates with multiple LLM providers and exposes agents via Model Context Protocol and A2A protocol.

Quickstart

Get the go-micro source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/micro/go-micro.git2cd go-micro3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

AI-driven microservice orchestration

Build systems where agents dynamically call internal services as tools, with automatic tool discovery from RPC endpoints and stateful agent memory across conversations.

Rapid agent prototyping with service backends

Scaffold and iterate on agent-backed applications without separate infrastructure; agents and services share the same runtime, deployment model, and observability.

Multi-agent workflows with durable execution

Combine agent reasoning (for unstructured tasks) with deterministic durable flows (for known paths), with agents able to delegate to other agents or specialized services mid-conversation.

Implementation considerations

Requires Go 1.x toolchain; CLI can be installed without Go, but building agents and services requires Go environment and knowledge of the framework API.
Agent behavior depends on LLM provider choice and model capabilities; different providers (Anthropic, OpenAI, Gemini, Vertex, Groq) may yield different planning/delegation quality.
Service-to-tool conversion is automatic via endpoint metadata, but tool schema quality and agent reasoning depends on RPC signature clarity and human-written descriptions.
Memory and state are built into the framework but require explicit configuration (in-memory vs. persistent storage); production deployments should evaluate persistence strategy.
Durable workflows are supported but require developer discipline; deterministic code paths must be wrapped in flow constructs for recovery guarantees.

When to avoid it — and what to weigh

Simple REST API with no agent logic needed — If your use case is a straightforward microservice without AI reasoning, Go Micro's agent harness adds unnecessary complexity; use a lightweight web framework instead.
Non-Go technology stack — Go Micro is Go-native. Integrating agents written in Python, Node, or other languages requires custom A2A bridging; not a first-class concern of the framework.
Offline-only or air-gapped deployment — The framework assumes connectivity to LLM providers and supports community models via local endpoints, but the primary workflow is provider-based (Anthropic, OpenAI, Gemini).
Mature production workloads with existing microservice infrastructure — Adopting Go Micro in an organization already running Kubernetes + gRPC or similar requires rearchitecting; not a drop-in replacement for mature systems.

License & commercial use

Apache License 2.0 (Apache-2.0): permissive OSI-approved license permitting commercial use, modification, and distribution with attribution and no warranty.

Apache-2.0 is a permissive open-source license that allows commercial use without restriction. The README explicitly offers paid support, consulting, training, and retainers from maintainers. Use of external LLM providers (Anthropic, OpenAI, Gemini) incurs separate commercial fees and terms. Verify that your deployment model complies with the specific LLM provider's terms of service.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Good
Assessment confidence	High

Security considerations

Agents execute tool calls via RPC, so security depends on service endpoint validation, input sanitization, and access control. Framework does not document threat modeling, agent guardrails enforcement, or audit logging for tool invocations. LLM provider API keys are required in environment; no built-in secret management beyond standard Go practices. Durable workflows and cross-agent calls introduce distributed trust assumptions; review authorization model for multi-tenant scenarios. No security audit or CVE history provided in data.

Alternatives to consider

LangChain (Python) / LangChain.Go

LangChain is the dominant agent framework but primarily Python; Go bindings exist but less mature. LangChain focuses on RAG and prompt chains; less emphasis on service-to-agent integration and durable workflows.

Semantic Kernel (C# / Python)

Microsoft's agent framework with strong plugin/skill model and durable execution via orchestration. Not Go-native; integration with Go services requires separate API layer.

AutoGen (Python) / OpenAI Swarm (Python)

Multi-agent conversation frameworks focused on agent-to-agent interaction. Not Go frameworks; require Python runtime and separate microservice layer for service integration.

Software development agency

Build on go-micro with DEV.co software developers

Start with the no-secret smoke test (micro new / micro run), explore the 0→hero walkthrough, then decide if Go Micro fits your stack. Commercial support available.

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go-micro FAQ

Can I use Go Micro with a single service and no agent logic?

Yes. Go Micro is a microservice framework first; agent harness is optional. The CLI supports scaffolding and running services without invoking agent features. However, if you do not need agents, a lighter framework may be more appropriate.

What happens if an agent calls a service tool and the service is unavailable?

Not explicitly documented in provided data. Service discovery and load-balancing are described as built-in, but failure modes (timeout, retry, fallback) and agent reasoning recovery are not detailed. Requires documentation review or community support.

Can agents persist memory across restarts?

Framework has built-in memory, but persistence strategy is configurable. README mentions memory as part of the harness but does not detail default or recommended persistence backend. Production deployments should clarify storage choice (in-memory, database, etc.).

Do I need to run a separate infrastructure (Kubernetes, database, message broker)?

For local development and small deployments, no; CLI includes built-in server and in-memory defaults. For production, deployment depends on scale, durability needs, and observability requirements. Framework does not prescribe infrastructure; you choose deployment model.

Custom software development services

From first prototype to production, DEV.co delivers software development services around tools like go-micro. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across mcp servers and beyond.

Ready to build AI-driven microservices in Go?

Start with the no-secret smoke test (micro new / micro run), explore the 0→hero walkthrough, then decide if Go Micro fits your stack. Commercial support available.

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