AI tool comparison
Craft Agents OSS vs RLM
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Developer Tools
Craft Agents OSS
Open-source desktop app for running AI agents across 32+ integrations
75%
Panel ship
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Community
Free
Entry
Craft Agents OSS is a free, Apache-licensed desktop app and CLI framework for building and running AI agents against real-world workflows. Built by the team behind the Craft.do document editor, it connects to 32+ integrations out of the box — MCP servers, REST APIs, Google Workspace, Slack, GitHub, and local filesystems — with no manual configuration required. It supports Anthropic, OpenAI, Google AI, and any OpenAI-compatible backend in a single unified UI. The core idea is an "agent canvas" where users drag tools onto a timeline, set up triggers, and watch agents execute multi-step workflows in real time. It also ships a headless server mode, making it usable as a remote agent runner in CI/CD pipelines or staging environments. The project hit 4,200+ stars on GitHub within 24 hours of launch. What distinguishes Craft Agents from similar tools like Dify or n8n is its desktop-first UX and tight integration with Claude's computer-use and agent loop capabilities. The Craft team has deep product experience — this isn't a weekend hack but a polished tool with well-documented agent primitives, error handling, and rate limiting built in from day one.
Developer Tools
RLM
Run recursive self-calling LLMs with sandboxed execution environments
75%
Panel ship
—
Community
Paid
Entry
RLM (Recursive Language Model) is a plug-and-play Python inference library that lets you run models that call themselves recursively within configurable sandboxed execution environments. Rather than a fixed inference pipeline, RLM exposes the recursive call graph as a first-class primitive — models can iterate, self-correct, and re-invoke themselves across different environments without special orchestration glue. The library was first published in December 2025 and has accumulated 3,498 stars on GitHub. It targets researchers and engineers exploring architectures where the model itself controls how many times it reasons before committing to an output — a capability becoming central to advanced reasoning systems but usually buried in proprietary labs. Why it matters: most open-source inference tools treat the model as a stateless function. RLM bets that the next wave of reasoning breakthroughs comes from architectures where inference depth is dynamic and model-controlled. Early adopters are using it to reproduce recursive reasoning experiments without access to frontier-model APIs.
Reviewer scorecard
“This is the missing middle layer between raw SDK calls and fully managed platforms. 32 integrations with zero config and a headless mode means you can drop it into an existing workflow in under an hour. Apache 2.0 license is the cherry on top.”
“Finally a clean abstraction for recursive inference without building the scaffolding yourself. The sandbox configurability means you can experiment with different execution environments without rewriting your harness each time. For researchers reproducing chain-of-recursive-thought papers, this cuts setup time dramatically.”
“The 4k stars in 24 hours is impressive but hype-fueled. We've seen a dozen 'universal agent frameworks' launch in the last year — most get abandoned once the novelty wears off. Wait to see if the integration library is actively maintained before betting your workflows on it.”
“3,500 stars is respectable but the library is still at v0.x with no production deployments publicly documented. Recursive self-calling can blow up token costs exponentially if you're not careful about termination conditions. Until there's clearer documentation on guardrails and cost controls, treat this as a research toy, not production infra.”
“Desktop-native agent runners are the 2026 equivalent of the browser as the universal platform. The Craft team's product pedigree and the open-source architecture mean this could become the go-to scaffolding for agent apps the way Electron became the default for desktop apps.”
“Recursive inference is one of the key unlock mechanisms for models that self-improve their reasoning at test time. RLM democratizes this capability at a moment when OpenAI and Anthropic are building proprietary versions internally. The researcher who masters this abstraction today has a significant head start.”
“Finally, an agent tool designed by people who actually care about UX. The drag-and-drop canvas is the first agent builder I've used that didn't feel like configuring XML. Non-engineers on my team were running their own agents in about 20 minutes.”
“For creative applications — iterative story refinement, self-critiquing copy — recursive inference is genuinely useful and RLM makes it accessible. The open sandbox model means you can wire it to any content generation pipeline without vendor lock-in.”
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