Build Go-Native Agents with Protocol-Lattice

Overview

Protocol-Lattice provides a comprehensive, composable Go framework for building native AI agents. It unifies models, memory, tools, guardrails, and workflows into a single runtime, allowing developers to build the exact agent infrastructure their service needs. The framework starts with a small agent core and scales as the application grows.

Benefits

The framework is built on one runtime supporting five key systems. These systems include models for integration with various AI model providers, memory for context management and history storage, tools for system integration with safety boundaries, workflows for orchestration and control flow, and guardrails for safety and policy enforcement. The agent core provides a focused API for the full request cycle, enabling developers to generate responses, stream outputs, attach files, call tools, and coordinate specialists. All operations are performed directly from Go code. The initialization is straightforward, allowing developers to create an agent instance with just a few lines of code. The framework supports multiple model providers, allowing developers to change models without modifying their agent logic. Supported providers include OpenAI, Gemini, Anthropic, OpenRouter, and Ollama. Developers can use hosted APIs, route across providers, or keep development local. The memory system keeps useful context close by combining short-term sessions for immediate conversation context and pluggable stores for durable, searchable history. This architecture ensures that relevant information remains accessible while maintaining flexibility in storage solutions. The framework enables connection to real systems with explicit boundaries through structured tools, UTCP, CodeMode, checkpoints, and input and output guardrails. The safety model follows a tool call to policy to result pattern, ensuring that all interactions are properly validated and controlled. Production policies can be composed once and applied to any provider through model middleware. Key capabilities include timeout to bound the complete operation and prevent indefinite execution, retry to recover from transient setup failures automatically, rate limit to wait for capacity or reject early to manage resource usage, and token budget to stop requests before token spend runs away and control costs. The framework makes orchestration visible in code by routing work between functions, tools, and agents without hiding control flow. Key workflow features include route to use emitted events to select the next node in the workflow, join to collect predecessor outputs before continuing to the next step, and bound to set step limits and timeouts for predictable, reliable runs. Your first agent can be created with just one package. The framework includes a dummy model and in-memory store to keep local setup safe and key-free. This approach allows developers to start with minimal configuration and expand functionality as their application requirements grow.

Use Cases

Developers can use this framework to build AI agents that integrate with various systems. The framework is suitable for applications that require context management and history storage. It is ideal for scenarios where developers need to connect to real systems with explicit boundaries. The framework is useful for applications that require orchestration and control flow. It is suitable for applications that need safety and policy enforcement. Developers can use this framework to build agents that generate responses, stream outputs, attach files, call tools, and coordinate specialists. The framework is suitable for applications that require integration with various AI model providers. It is ideal for scenarios where developers need to change models without modifying their agent logic. The framework is useful for applications that require durable, searchable history. Developers can use this framework to build agents that keep useful context close. The framework is suitable for applications that require connection to real systems with explicit boundaries. It is ideal for scenarios where developers need to validate and control all interactions. The framework is useful for applications that require production policies to be composed once and applied to any provider. Developers can use this framework to build agents that bound the complete operation and prevent indefinite execution. The framework is suitable for applications that require recovery from transient setup failures automatically. It is ideal for scenarios where developers need to manage resource usage. The framework is useful for applications that require stopping requests before token spend runs away and control costs. Developers can use this framework to build agents that route work between functions, tools, and agents without hiding control flow. The framework is suitable for applications that require using emitted events to select the next node in the workflow. It is ideal for scenarios where developers need to collect predecessor outputs before continuing to the next step. The framework is useful for applications that require setting step limits and timeouts for predictable, reliable runs. Developers can use this framework to build agents that start with minimal configuration and expand functionality as their application requirements grow.

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