Reasonix

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.

DeepSeek (深度求索) is an AI research project and model provider that develops and open-sources large foundation models (notably named DeepSeek-V4 and DeepSeek-R1) and provides hosted inference via a chat interface (“DeepSeek AI”) and an API. It publishes open-source model releases and offers a hosted API for text-based prompt inference; video reports state DeepSeek models can be exposed as external backends and routed into the Codex model picker via Codex Router.

Exo is an AI agent harness for recursive self-improvement, providing tools, tasks, integrations, and a layered architecture that separates executive policy, protected state, and sandbox execution. It can inspect its own code and runtime logs, incrementally modify prompts, memory, tools, and harness policy, clone itself, and manage a lineage of clones. An event log remains outside these modifications as a canonical record intended to prevent recursive loops.

Prime Agent is an open-source command-line coding and research agent for general and long-running tasks, developed by Prime Intellect. It combines recursive language-model workflows through an RLM harness with a persistent Python REPL, treating prompts as variables and invoking tools and recursive subagents programmatically. A continual harness stores supplemental prompts, memories, skill descriptions, and reusable subagent specifications as durable session state that can be refined through small, evidence-backed updates without changing the immutable base system prompt. The agent supports importable Python skills, parallel or background child agents, messaging between running agents, automatic context compaction, persistent goals, heartbeats, schedules, detached sessions, autonomous operation, and retained subagents.

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.