OpenRath is a framework for building LLM-powered AI agent applications with a torch-like API design. It centers on the Session abstraction (analogous to a Tensor) that flows through Sandboxes (execution environments), Memories (persistent state), and Tools (callable operations), orchestrated by Agents and Workflows. The framework provides session lineage tracking, sandboxed tool execution, memory persistence, and LLM provider abstraction, enabling the creation of multi-agent workflows with durable state and traceable provenance. The architecture is modular and extensible, featuring pluggable backends for sandboxes (local, OpenSandbox) and memory systems (local, OpenViking), a registry pattern for dynamic backend loading, and lazy evaluation of sessions for performance. Key innovations include write-ahead logging for crash-resistant session persistence, resource-keyed parallelism for safe concurrent tool execution, and unified garbage collection across session, sandbox, and memory domains. The framework is designed for production use, with extensive testing, clear documentation, and active maintenance.
MiroThinker is a deep research agent framework designed for complex, multi-step tasks requiring external tool use. The system orchestrates LLM reasoning with a modular suite of MCP (Model Context Protocol) tools including web search, code execution, vision, audio, and reasoning capabilities. It features sophisticated context management through failure experience summarization and sliding window truncation to handle long conversations. The agent iteratively generates tool calls, executes them via MCP servers, and integrates results until producing a final answer. The repository includes benchmark evaluation scripts for GAIA, BrowseComp, HLE, and other research benchmarks, trace collection utilities for model improvement, and a Gradio demo for interactive use. Architecture centers on an orchestrator managing the agent loop, a pipeline for step execution, tool executors handling MCP server interactions, and LLM abstractions supporting Anthropic and OpenAI APIs with prompt caching.
GEO Optimizer is a Go library for Generative Engine Optimization that improves content visibility in AI search engines. It features a layered architecture with LLM abstraction, rule-based content scoring, and pluggable optimization strategies. The system optimizes content through a pipeline that applies strategies like structure enhancement, schema generation, and FAQ creation, using LLMs to generate optimized content while tracking improvements through scoring mechanisms. Key design patterns include the strategy interface for interchangeable optimizations and modular separation of concerns in the scoring system.