Alternatives
Products that do what Blender for AI Agents does
Creating 3D is hard. LLMs seem to be getting better at tool use and spatial understanding. While MCPs have proved to be a good way to use these tools- the current methods have these challenges: - Access to scene graph and core C modules of Blender - Lack of parallelism, only way is to run blender headless - Lack of deterministic and fast verification layer - Inference stack- only way to use inference is to hook another MCP We're building Mixar, think Cursor for 3D. One access point to all generative inference, an agent to build scenes/blockouts, do boring stuff like UVs and export…
- 1
- 2
- 3
- 4
- 5
Design automation in Figma using AI and natural language
Jan 2026
- 6
- 7
- 8
- 9
- 10
- 11
- 12
- 13
- 14LF
We built a no/low-code tool that lets you spin up MCPs from a single prompt. MCPs give LLMs access to tools, data, and actions—but they’re hard to build and deploy. Our tool abstracts that: describe what you want, and it auto-generates and hosts the necessary components. No UI flows, no manual chaining—just prompt and go. Examples: • Pull email, parse a DocSend, check Reddit, draft reply • Extract data from a niche site + send a Slack alert • Combine tools without writing glue code Live demo: https://www.youtube.com/watch?v=4uCiaQrgfoE Built over a weekend after getting…
2025 · generatemcp.com
- 15MM
I built MCPlexor to solve a token waste problem I kept running into with MCP-based agents. The Problem: MCP (Model Context Protocol) is great for giving LLMs access to external tools. But if you connect multiple servers (GitHub, Linear, Postgres, Slack), you end up with 40-50k tokens of tool definitions injected into every request – before the agent even does anything. On a 200k context model, that's 25% gone. On smaller models, it's worse. And most runs only use 1-2 tools. The Solution: MCPlexor sits between your agent and your MCP servers. Instead of loading all tool definitions upfront:…
Feb 2026 · mcplexor.com
- 16MT
I've noticed all 3D AI generators create monlithic blobs that are impossible to edit. So, alongwith a friend, I built this project where you can generate 3D objects with separate, editable parts. I'm looking for community feedback. The tool is free, but you'll have to BYOK. It uses models like Gemini, Claude or chatGPT to generate a Blender construction script. Basically the output is not merely geometry. It's the procedure that built it, plus the asset itself. This means you can actually go in and edit the "kit of parts" afterwards, instead of starting over.
May 2026 · github.com
- 17RA
Hi HN folks, I have been building AI agents for quite some time now. The shift has gone from LLM + Tools → LLM Workflows → Agent + Tools + Memory, and now we are finally seeing true agency emerge: agents as systems composed of tools, command-line access, fine-grained system capabilities, and memory. This way of building agents is powerful, and I believe it is here to stay. But the real question is: are the systems powering these agents ready for that future? I do not think so. Using Docker for a single agent is not going to scale well, because agents need to be lightweight and fast. LLMs…
Mar 2026 · github.com
- 18
- 19JA
Hey HN, I'm one of the creators of joinly.ai, an open-source solution that makes browser-based video conferences accessible to AI agents, allowing you to interact with them in real time. Think of it as a connector layer that brings the functionality of your AI agents to your meetings, allowing you to essentially build your own custom meeting assistant. We didn't want to build yet another Python framework for creating agents with its own syntactic sugar that you have to learn. That's why we opted for a different approach, utilizing an MCP server. Our MCP server provides essential meeting…
2025 · github.com
- 20IM
Every time I wanted to use LLMs in my existing pipelines the integration was very bloated, complex, and too slow. This is why I created a lightweight library that works just like scikit-learn, the flow generally follows a pipeline-like structure where you “fit” (learn) a skill from sample data or an instruction set, then “predict” (apply the skill) to new data, returning structured results. High-Level Concept Flow Your Data --> Load Skill / Learn Skill --> Create Tasks --> Run Tasks --> Structured Results --> Downstream Steps And the bast part: Every step can be saved and reused as…
2025 · github.com
- 21AF
I’ve always been looking for a solution to communicate larger technical systems faster. This includes a lot of known details and a technical de-risking plan. It is clear that LLMs need that structure and are capable of digesting it well. I’ve built MCP server for guiding the Cursor IDE through the plan that comes from the LLM assisted pipeline: PRD → TRD → Project files tree → Implementation Plan 1. The whole chain comes from one end-to-end solution 2. Codebase produced is easy to navigate by the automatically managed tracebility 3. Specification updates get reflected in tasks, so the flow…
2025 · github.com
- 22CM
Hey HN, I've been building AutoAgents, an AI agent framework in Rust. Today I'm sharing a feature I haven't seen done well elsewhere: composable middleware layers for LLM inference pipelines. The problem Every agent framework lets you swap LLM providers. Almost none of them give you a structured way to enforce safety, caching, or data sanitization in the inference path itself. You end up with guardrails as application-level if-statements, caching bolted on as a separate service, and PII handling as a "we'll add it later" TODO that never ships. This gets worse with local models. Cloud APIs…
Mar 2026 · github.com
- 23LA
Hi HN, I'm excited to share Latitude Agents—the first autonomous agent platform built for the Model Context Protocol (MCP). With Latitude Agents, you can design, evaluate, and deploy self-improving AI agents that integrate directly with your tools and data. We've been working on agents for a while, and continue to be impressed by the things they can do. When we learned about the Model Context Protocol, we knew it was the missing piece to enable truly autonomous agents. MCP servers were first thought out as an extension for local AI tools (i.e Claude Desktop) so they aren't easily hostable in…
2025 · latitude.so
- 24MO
Why we built it: • Claude Code: great for coding, but no video/audio support, localhost only • OpenAI SDK: single-model, no native multimedia tools • Both: no integrated DevTools for debugging agent reasoning So, we built Mix as an alternative for multimodal applications. • Native video/audio/PDF analysis tools (via Gemini for vision, Claude for reasoning) • Multi-model routing instead of single-provider lock-in • One-command Supabase setup for cloud deployment (vs localhost-only) • HTTP architecture that enables visual DevTools alongside agent workflows • Go backend: 50-80%…
Oct 2025 · github.com
Ranked by how close each launch is in meaning, then by votes. Refine with a description →