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agent-sdk-go

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Basic Information

Agent SDK Go is a Go framework for building production-ready AI agents that combines LLM access, memory, tool execution, and enterprise features into a modular architecture. It is designed to let developers create agents that use multiple model providers such as OpenAI, Anthropic, Google Vertex AI, Ollama, and vLLM, and to connect to external Model Context Protocol (MCP) servers. The SDK supports persistent conversation memory with buffer and vector retrieval, optional Redis for distributed memory, declarative YAML agent and task definitions, structured JSON outputs, guardrails for safer deployments, and observability with tracing and logging. The repo includes examples, a CLI wizard, and auto-configuration capabilities that generate agent profiles and tasks from system prompts, so teams can prototype and deploy specialized agents while maintaining multi-tenancy and operational controls.

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App URL
https://github.com/Ingenimax/agent-sdk-go
Github Repository
https://github.com/Ingenimax/agent-sdk-go/blob/main/README.md

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App Details

Features
The SDK offers multi-model intelligence with seamless OpenAI, Anthropic, Vertex AI, Ollama, and vLLM integrations. It provides a modular tools system and registry for plug-and-play capabilities like web search and custom tools. Memory features include conversation buffers and vector stores with optional Redis for distribution. MCP support allows HTTP and stdio transports to connect external tool servers. Enterprise features include multi-tenancy, tracing, logging, and guardrails for safety. Configuration is declarative via YAML with support for structured JSON output and schema-driven unmarshalling into Go structs. Additional capabilities include an execution plan system for multi-step tasks, auto-generation of agent and task configs from prompts, local model management for Ollama and vLLM, and numerous examples in the cmd/examples directory.
Use Cases
The SDK helps developers and teams accelerate building reliable AI agents by providing reusable, production-grade components so they do not need to implement core plumbing. Declarative YAML and auto-configuration reduce setup time and make agents reproducible and shareable. Multi-LLM support and local model options give flexibility for latency, cost, and privacy trade-offs. Memory and vector stores enable contextual and retrieval-augmented behavior, while MCP integration lets agents call external services and tools. Enterprise features such as multi-tenancy, guardrails, tracing, and logging support secure, observable deployments. Structured output support simplifies integration with Go applications by allowing direct unmarshalling into typed structs. Examples, templates, and a task framework make it easier to create research assistants, reporting agents, and other domain-specific agents.

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