An AI-native API testing platform built on Model Context Protocol (MCP) that generates mock servers from OpenAPI v2/v3 specs. Supports Mock/Proxy/Hybrid modes with 50 MCP capabilities, integrates with LangGraph/CrewAI/LangChain, and provides enterprise-grade audit logging and performance analytics.
Reflective memory for AI agents — semantic storage with multi-format ingestion, intent tracking, and cross-session context persistence.
Human-like memory layer for AI agents featuring semantic, episodic, and procedural memory with failure-driven workflow evolution. Offers Python/JS SDKs, REST API, and LangChain/CrewAI integrations.
A Kubernetes-based AI Agent runtime platform released by McKinsey, codifying patterns for deploying, orchestrating, and evaluating agentic resources via CRDs, providing production-grade infrastructure for multi-agent systems. Currently in Technical Preview.
A Python logging tool that automatically traces code execution and generates Mermaid JS sequence diagrams via decorators, with async support, distributed tracing, FastAPI and LangChain integration.
A self-hosted personal AI gateway integrating 6 LLM providers, 3-tier auto-routing, and a persistent memory system, enabling personal life automation and dev assistance via 62 built-in tools.
A lightweight AI Agent Skills secure engine built in Rust, featuring a built-in native system-level sandbox, zero dependencies, and fully local execution. Provides three-layer security defense (install-time scan, pre-execution authorization, runtime sandbox) with 100% security test score, 40ms hot start, and ~10MB memory footprint.
A Python-based MCP protocol implementation framework providing client-server architecture for LLM tool orchestration, integrated with LangChain and ChromaDB for RAG development.
A framework for building semantic layers, context graphs, and decision intelligence systems with explainability and provenance.
Open-source self-hosted AI Agent platform featuring voice-driven workflows, episodic memory, multi-view orchestration, and enterprise-grade integrations. Users can drive complex automation via natural language while keeping data entirely in local environments.
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