MCP e chiamate agli strumenti
graphify-labs/graphify
Graphify is a local-first CLI tool and AI assistant skill that parses codebases, docs, SQL schemas, and PDFs into a queryable knowledge graph using deterministic AST parsing, eliminating the need for vector stores or embeddings.
★ 104K⑂ 10,1KPython
Apache-2.0Q98
MCP e chiamate agli strumenti
headroomlabs-ai/headroom
Headroom is a local-first context compression layer that reduces token usage for AI agents by compressing tool outputs, logs, files, and RAG chunks before they reach the LLM. It offers library, proxy, and MCP server modes and claims to preserve answer accuracy while cutting tokens by 15-95% depending on workload.
★ 65K⑂ 4,9KPython
Apache-2.0Q98
MCP e chiamate agli strumenti
langchain4j/langchain4j
An idiomatic, open-source Java library for building LLM-powered applications on the JVM, offering a unified API over popular LLM providers and vector stores with support for tool calling, MCP, agents, and RAG.
★ 12,8K⑂ 2,4KJava
Apache-2.0Q98
MCP e chiamate agli strumenti
neomjs/neo
A self-evolving software organism that combines a multi-threaded frontend application engine with an end-to-end AI engineering team operating via cross-model swarm coordination, persistent memory, and runtime application embodiment.
★ 3,3K⑂ 231JavaScript
MITQ98
MCP e chiamate agli strumenti
askimo-ai/askimo
A native desktop AI client for chat, local RAG, multi-step AI workflows (Plans), and agent skills, supporting multiple cloud and local LLM providers while keeping user files strictly on the machine.
★ 334⑂ 71Kotlin
AGPL-3.0Q94
MCP e chiamate agli strumenti
juspay/neurolink
A TypeScript integration platform providing a unified API for 30+ AI providers and 100+ models, enabling provider swapping, multi-modal voice processing, RAG, memory, and MCP-native tool integration.
★ 121⑂ 124TypeScript
MITQ93
MCP e chiamate agli strumenti
mtrnix/metronix-memory
Self-hosted memory infrastructure for AI agents featuring MCP-native integration, hybrid RAG, a temporal knowledge graph, and an ontology layer, designed for local-model friendliness and durable, agent-scoped context.
★ 39⑂ 7Python
Apache-2.0Q86