Search Results for "squid-graph"

CodeGraph is a local-first code intelligence tool that gives AI coding agents a pre-indexed understanding of a repository. Instead of forcing agents to repeatedly scan files with grep, glob, and read commands, it builds a searchable knowledge graph of symbols, relationships, call graphs, and code structure. The project uses deterministic parsing rather than LLM summaries, which makes its indexed data more predictable and grounded in the actual source tree. It is designed for tools such as Claude Code, Codex, Cursor, and OpenCode, with MCP support for agent access. CodeGraph stores project data locally, which helps reduce token usage and repeated file exploration. ...

...Model routing lets you connect to dozens of providers (OpenAI, Anthropic, Gemini, and others) through a single standardized interface, while agents orchestrate LLM calls and tools to solve open-ended tasks with internal reasoning loops. When explicit control is needed, Mastra’s workflow engine uses a graph-style API (.then(), .branch(), .parallel()) to orchestrate multi-step processes.

...The system automatically records what agents do, compresses that activity into searchable memory, and injects relevant context when a new coding session begins. It combines keyword search, vector search, confidence scoring, lifecycle management, and knowledge graph querying to retrieve useful memories without overloading the context window. The project also includes hooks, an API, import and export support, audit trails, and team sharing workflows. Overall, agentmemory is designed to make AI coding assistants more continuous, context-aware, and useful across long-running software projects.

...Instead of relying on traditional sliding-window truncation or lossy summarization, it introduces a lossless architecture that preserves all historical messages while maintaining usable context within token limits. The system stores every interaction in a persistent database and incrementally summarizes older content into a hierarchical directed acyclic graph, allowing efficient compression without discarding information. This structure enables agents to dynamically reconstruct detailed context by expanding summaries when needed, effectively simulating perfect long-term memory.

AI Maestro is a framework designed to orchestrate AI workflows and coordinate multiple components into cohesive systems. It focuses on enabling structured interaction between different AI modules, allowing them to collaborate on complex tasks. The system emphasizes modular design, enabling developers to build workflows by combining independent components. It supports automation, allowing tasks to be executed with minimal manual intervention. The framework is designed to be flexible,...

Understand-Anything is an AI-driven tool designed to help users deeply understand any topic by generating structured explanations, summaries, and breakdowns. It focuses on transforming complex or unfamiliar subjects into clear, step-by-step explanations that are easier to grasp. The system leverages language models to provide layered insights, allowing users to explore topics at different levels of detail. It is particularly useful for learning, research, and quick comprehension of new...

...Unlike traditional text-based search tools that mainly match keywords, Mantic.sh understands code structure and meaning by combining syntactic heuristics with neural semantic reranking to produce results that reflect conceptual relevance, which helps find functions, definitions, and patterns that literal search might miss. It uses local embeddings and code graph awareness so that queries like “authentication flow” return not just superficially matching text but contextually related code across multiple repositories. The tool supports navigational commands such as go-to-definition and find-references, simplifying large-scale refactoring, comprehension, and exploration tasks for teams.