The open-source TypeScript framework for building AI workflows and agents. Designed for Claude Code describe what you want, Claude builds it, with all the best practices already in place.

An enterprise AI development framework for building AI agents. It provides unified management of multi-provider LLMs, secure enterprise knowledge bases with high-precision retrieval, visual workflow orchestration and multi-agent coordination. Compatible with mainstream Agent Skill standards, it enables developers to efficiently build production-gra

A Python wrapper for the Chemistry Development Kit (CDK)

Java test automation framework for web, mobile, API, CLI, database, and desktop E2E testing with a fluent API and built-in reporting.

The open-source memory and observability layer for AI agents — persistent memory, loop detection, hash-chained audit trails, and a live dashboard, automatic on pip install.

TypeScript multi-agent framework that runs in your own environment: consequential actions wait for approval and every run leaves a verifiable record. Describe the goal, not the graph. 13 built-in providers (Claude, OpenAI, Gemini, DeepSeek and more) plus any OpenAI-compatible endpoint, local models included.

Open source version of Claude Managed Agents. Fastest way to build and deploy reliable AI agents, MCP tools and agent-to-agent.

AI Agent Assistant & development framework that integrates lots of IM platforms, LLMs, plugins and AI feature, and can be your openclaw alternative. ✨

The full-stack TypeScript framework to build, test, and deploy production-ready MCP servers and AI-native apps.

TUI framework and developer productivity apps in Rust 🦀

deep, reliable and confidential coding-context

Declarative management of Agent Skills on Nix

Smithers is an agentic workflow framework for defining workflows in simple TypeScript configuration files and executing them quickly, durably, and reliably

MCP Server Framework and Tool Development library for building custom capabilities into agents.

Deterministic safety solutions for probabilistic AI agents

A selective learning and memory substrate for agentic systems — typed, revisable, decayable memory with competence learning and trust-aware retrieval.

Next-gen AI memory layer with importance scoring, temporal decay, hierarchical memory, and YMYL prioritization

Fluent argument validation for fluent software development.

Java AI application development framework (supports LLM-tool,skill; RAG; MCP; Agent-ReAct,Team-Agent). Compatible with java8 ~ java26. It can also be embedded in SpringBoot, jFinal, Vert.x, Quarkus, and other frameworks.

Self-evolving memory OS for LLM & AI Agents: ultra-persistent memory, hybrid-retrieval, and cross-task skill reuse, with 35.24% token savings and DeepSeek Harness support.

The fullstack MCP framework to develop MCP Apps for ChatGPT / Claude & MCP Servers for AI Agents.

A minimal yet powerful framework for creating AI agents with full control over tools, providers, and execution flow.

Connect AI agents across any network — zero config, encrypted, skill-based routing

🔥 Java enterprise application development framework for full scenario: Restrained, Efficient, Open, Ecologicalll!!! 700% higher concurrency 50% memory savings Startup is 10 times faster. Packing 90% smaller; Compatible with java8 ~ java26; Supports LTS. (Replaceable spring)