Rig

OpenRig is an open-source, self-hosted multi-agent harness for coordinating AI coding agents such as Claude Code and Codex as one managed team. It uses a local daemon, CLI, terminal UI, MCP server, SQLite, tmux, and runtime adapters. Users define agent topologies in YAML with pods, seats, communication edges, and continuity policies, then use commands such as `rig up`, `rig send`, `rig broadcast`, `rig chatroom`, `rig down --snapshot`, and `rig discover`/`rig adopt` to launch, coordinate, inspect, snapshot, restore, or take over existing sessions. Its MCP tools let agents manage their own topology, while the TUI exposes topology, seat, project, feed, terminal, and system views. OpenRig requires Node.js and tmux; herdr, cmux, and Docker are optional for terminal workspaces and service-backed rigs. It is distributed through the `@openrig/cli` npm package and licensed under Apache 2.0.

Rust is a systems programming language and toolchain developed by the Rust project. Its compiler, standard library, documentation, package manager and build tool Cargo, formatter rustfmt, linter Clippy, and editor support are maintained in the main repository. The language uses a rich type system and ownership model to enforce memory and thread safety at compile time, while targeting fast, memory-efficient software for critical services, embedded devices, and integration with other languages. Rust is primarily distributed under the MIT and Apache 2.0 licenses, with portions under BSD-like licenses.

Ratel is an in-process context-engineering platform for AI agents. It catalogs tools, skills, and persistent facts, then searches the catalog on each turn and progressively discloses only the capabilities relevant to the request instead of placing every tool schema and instruction in the context window. Its retrieval supports BM25, semantic, and hybrid ranking without requiring a vector database. The project includes a Rust core, TypeScript and Python SDKs, an MCP server, and a CLI. Tools can be registered directly or ingested from an MCP server; skills can associate instructions with tools, while registered facts are re-injected when they are no longer fresh in the conversation. The repository describes the system as reducing token usage and mitigating tool overload.

RSIAgent is an open-source, training-free multi-agent framework for recursive self-improvement in unfamiliar digital environments. It uses a Curriculum Agent, Actor Agent, and Verifier Agent while keeping model parameters fixed. Its learning loop has two stages: broad recursive self-exploration, in which agents perform diverse projects and consolidate verified experience, followed by deep recursive self-exploration, in which the Curriculum Agent selects focused practice around gaps and fragile successes. The Actor Agent interacts with software through executable Python or Bash programs and visual observations; the Verifier Agent independently checks task requirements and resulting environments. Procedures, scripts, successful experiences, and failure lessons are stored in persistent memory, which is frozen and reused for downstream task execution. The repository provides runtimes and benchmark integrations for OSWorld-V2 and Agents' Last Exam, along with setup, smoke-test, batch execution, reporting, recovery, and validation utilities. It requires Python 3.12 and a Linux host with Docker and /dev/kvm for the documented benchmark runs, and is licensed under Apache License 2.0.

OpenRAG is an open-source agentic Retrieval-Augmented Generation platform for ingesting, processing, filtering, and searching timestamped Markdown, documents, and other knowledge through an AI-backed conversational interface. It uses Langflow for document ingestion and retrieval workflows, Docling for parsing messy real-world data, and OpenSearch for semantic and enterprise search; its agentic workflows support re-ranking and multi-agent coordination. The platform is built with FastAPI and Next.js, runs with Docker or Podman, provides Python and TypeScript/JavaScript SDKs for chat integration, and includes a built-in Model Context Protocol server at the /mcp endpoint exposing RAG chat, semantic search, document ingestion, knowledge filters, and settings-management tools to compatible AI assistants.