MCP Fusion - The TypeScript framework for secure MCP servers.

Multi-language agent runtime and library for execution scope management, lifecycle events, and middleware on tool and LLM calls.

Open-World Self-Evolution for LLM Agents — agents that build both their skills and their own verification signals from scratch, with no target-task supervision. (Code coming soon.)

Semi-Structured Agentic Framework. Workflows build themselves as agents discover what needs to be done, not what you predicted upfront.

OpenBrowser is a framework for intelligent browser automation. It combines direct CDP communication with a CodeAgent architecture, where the LLM writes Python code executed in a persistent namespace, to navigate, interact with, and extract information from web pages autonomously.

Declarative management of Agent Skills on Nix

Open-source control plane for your AI agents. Connect tools, hire agents, track every token and dollar

Apache Camel is an open source integration framework with 350+ connectors. Write routes in Java, YAML, or XML. Run on Spring Boot, Quarkus, or standalone. Apache License 2.0.

Markdown that runs — one file, any agent.

Typed React command-center components, live examples, and a portable coding-agent skill.

Universal memory runtime for AI agents

NVIDIA Design System and UI Agent Harness for AI/ML Factories, Robotics, and Autonomous Vehicles

Composable agent runtime with enforced isolation boundaries

Python, LlamaIndex, LangChain, 15 Property Graph, 4 RDF , 10 Vector, OpenSearch, Elasticsearch, Alfresco, Nuxeo DBs. 14 data sources (10 auto-sync), KG auto-building, Ontologies, LLMs, Docling, LlamaParse, LiteParse, GraphRAG, RAG, Hybrid Search, AI Chat. TypeScript React, Vue, Angular frontends, REST, MCP Server. Options: Langflow, CocoIndex

Persistent Claude Code agents with scheduling, sessions, memory, and Telegram.

A Lisp with first-class LLM primitives, implemented in Rust

Python toolkit, MCP server, and agent skills for reproducible, auditable clickstream and event log analytics. Helps AI agents, data scientists and analysts build, validate, and cross-check product analytics, quantitative UX, customer journeys, graph-based user flows, behavioral segmentation, A/B tests, process mining models, Markov chain simulation

🔬 A Researcher&Agent-Friendly Framework for Time Series Analysis. Train Any Model on Any Dataset!

Rails Engine with MCP compliant Spec.

Deploy production-ready AI services in minutes. One YAML file for agents, RAG pipelines, and MCP servers — run anywhere. Inspired by docker-compose.

Multi-agent research automation framework for LLM agents, with adversarial lab meetings, paper-review rounds, auditable Markdown workflows, an autonomous runtime watchdog, and a pixel-art web dashboard.

Open-source customer money path for usage-based SaaS — authorize customer spend before paid work runs.

Fluent argument validation for fluent software development.