LLM Agent Reasoning and Planning

This project is a comprehensive guide and framework for large language model prompt engineering. It provides a collection of techniques and patterns for optimizing model responses through structured system prompts, context management, and a variety of implementation patterns. The project focuses on several specialized domains, including the creation of autonomous agents through reasoning loops and the implementation of retrieval augmented generation to inject semantic context into prompts. It also provides methods for enforcing structured outputs in serialization formats like JSON or YAML for programmatic use. The resource covers high-level capabilities such as context window optimization using sliding windows, the definition of model behavior via hidden system prompts, and the use of chain-of-thought reasoning to improve logical accuracy. It further addresses the integration of dynamic data and the enforcement of output citations for information retrieval.

GLM-4.5 is a multimodal large language model and advanced reasoning system. It functions as an AI coding assistant, an autonomous AI agent, and a multimodal content generator capable of processing and generating text, images, audio, and video within a single unified system. The project is distinguished by its deep reasoning capabilities, utilizing chain-of-thought processing to solve complex mathematical, logical, and technical problems. It features an agentic architecture that allows for autonomous task execution, long-horizon goal planning, and the ability to interact with external tools and web browsers through iterative reasoning. Its capability surface includes comprehensive AI software engineering, ranging from automated code generation and bug fixing to performance optimization and documentation. The system also covers professional translation workflows, intelligent document processing, and the creation of high-resolution visual and video content. It further integrates search and indexing through retrieval-augmented generation and repository mapping. The system provides an API interface compatible with common SDKs and protocols for integration with developer tools.

OpenViking is a multi-tenant context server and knowledge base administration system designed to provide AI agents with persistent long-term memory. It enables the indexing of diverse documents and codebases to support retrieval-augmented generation, allowing agents to recall past interactions, user preferences, and learned experiences across sessions. The project is distinguished by its use of a URI-based virtual filesystem to organize memories, resources, and skills. It implements a tiered context loading system that balances retrieval precision with token budgets by structuring data into abstracts, overviews, and full details. Additionally, it supports the Model Context Protocol to expose a standardized interface for agents to read, search, and store context. The system covers a broad range of capabilities, including hybrid semantic search with cross-encoder reranking, multimodal content analysis, and automated knowledge extraction from chat sessions. It provides comprehensive security through AES-GCM transparent encryption, OAuth 2.1 authentication, and role-based access control to ensure isolation between tenants. The server can be deployed as a standalone HTTP service via Docker or Kubernetes Helm Charts, with management available through a dedicated administrative API, a terminal-based interface, and a web-based investigation studio.