#Python (10)

Hand a PDF, DOCX, web page or just a topic to any agent-capable tool and get a natively editable PPTX on your own machine — real slide masters, native shapes, data-backed charts and editable formulas.

PowerPointPPTXAI Agent

Python58k4.6k114

langchain-ai/langgraphMIT

LangGraph: agents as long-running processes that must persist, interrupt and resume

A low-level stateful-graph orchestration framework from LangChain, MIT licensed, with Python and JS/TS implementations, and Klarna, Replit and Elastic named as production users in the README. Its lineage is stated in the acknowledgements - inspired by Google Pregel and Apache Beam, interface borrowing from NetworkX - which means the BSP graph-computation model applied to agents: nodes are model calls or tool executions, edges are control flow, state is an explicit object. The value is not in the loop-the-model-with-tools layer but in four runtime capabilities: durable execution (resume exactly where it stopped after a crash), human-in-the-loop interrupts (approval can stay suspended for hours or days), separate short-term and long-term memory, and LangSmith execution-path tracing. Its actual claim is that agent reliability is a runtime problem, not a model problem. Usable without LangChain; any CompiledStateGraph can be handed to Deep Agents above it as a sub-agent. 42.1k stars. Not run yet, and checkpoint-resume consistency is unverified, so this is graded as pending reproduction.

Agent orchestrationGraph RuntimeDurable Execution

Python42k7.1k185

langchain-ai/deepagentsMIT

Deep Agents: an off-the-shelf agent harness reverse-engineered from Claude Code

The README opens by calling itself an off-the-shelf agent harness: MIT licensed, Python (plus deepagents.js), built on LangGraph, so streaming, durability and checkpoints are inherited rather than rewritten. The key to reading it is the acknowledgement - inspired by Claude Code, trying to figure out what makes it general and pushing that further - which makes this a reverse-engineering exercise: take a closed coding agent that is known to work well, open it up, and ask what actually lets it handle long tasks. Its answer is four bundled things: a pluggable-backend file system (the artefacts of long tasks are files, not messages, so context keeps only pointers), sub-agents each with their own window (context isolation and compression, not extra helpers), long-thread summarisation with tool output offloaded to disk, and on-demand Skills. Also sandboxed shell execution, cross-session persistent memory, human-in-the-loop approval/edit/reject before a tool call runs, and any MCP server as a tool. Model agnostic: frontier APIs, open weights hosted on Baseten or Fireworks, and self-hosted Ollama/vLLM/llama.cpp all work, with three lines of create_deep_agent giving you a planning, file-reading-and-writing agent. The security section is unusually blunt - it follows a trust-the-LLM model, boundaries must be enforced at the tool and sandbox layer, and you should not expect the model to restrain itself. 29.7k stars. Not run yet; sub-agent isolation quality and summarisation information loss are unverified, so this is graded as pending reproduction.

Agent HarnessCoding AgentSub-Agents

Python30k4.2k139

ahujasid/mcp-for-blenderMIT

MCP for Blender: 32 tools that make Blender a 3D workspace an LLM can drive

Formerly blender-mcp, a third-party community plugin (the README states on its first screen that it is not made by Blender), MIT licensed, Python, and the highest-starred entry among 3D MCP servers. The PyPI package is now mcp-for-blender, yet uvx blender-mcp still runs and existing configs need no change. Two components only: an addon inside the Blender process runs a socket server that actually executes commands (the only place with bpy, the current scene and the UI thread), while a separate MCP server process translates protocol, joined by JSON over TCP on localhost:9876 by default. The source defines 32 @mcp.tool() decorators across four layers - perception (get_scene_info, get_object_info, get_viewport_screenshot returning an MCP Image, closing an act-then-look loop), reference lookup (bpy_api_lookup and describe_node_type so the model queries instead of guessing socket order and enum names), execution (execute_blender_code as the escape hatch, set_texture, export_scene to GLB/FBX), and asset retrieval and generation (Poly Haven with semantic search, importing the .blend source and writing attribution properties; Poly Pizza with about 10,600 low-poly models, size normalisation and automatic CC-BY credit; Sketchfab; Hyper3D Rodin text-to-3D and image-to-3D; Tencent Hunyuan3D distinguishing the mainland ai3d endpoint from the international hunyuan one). On security, arbitrary code execution is the design rather than a bug: an optional safe mode (BLENDER_MCP_SAFE_MODE=1) statically validates scripts before running, blocking file access, subprocesses, network and persistent code while modelling, rendering, saving, import and export pass normally, and blocked scripts return to the model with the reason so it can retry. Content telemetry is off by default, and a dedicated disable_telemetry tool can only turn collection off. 29.2k stars. Not run with Blender locally yet, so this is graded as pending reproduction.

MCPBlender3D Generation

Python29k2.7k202

0:40

ElectroPup: a 12-DoF 3D-Printed Open-Source Quadruped

ElectroPup is a 4.5 kg, 12-DoF DIY quadruped: twelve MG4010E-i10v3 CAN BLDC actuators split into four per-leg CAN segments, a Raspberry Pi 5 for compute, and a single ~7,700-line pure-Python service with no ROS in the stack. The repository publishes OnShape CAD, two KiCad v8 boards, a bill of materials, measured weights and power draw (21.6 W suspended, 33.8 W standing), plus MuJoCo simulation, a React Native/Expo browser UI, four Bezier-foot gaits, and a full build guide. The author also documents known gaps: no license file, no contact sensing, and a simulation model without approximate masses or inertias.

Quadruped robotsOpen SourceBLDC Motor

Python3542

As an Amazon Associate, we earn from qualifying purchases.

#Python (10)

PPT Master: AI generates native PowerPoint decks from any document

LangGraph: agents as long-running processes that must persist, interrupt and resume

Deep Agents: an off-the-shelf agent harness reverse-engineered from Claude Code

MCP for Blender: 32 tools that make Blender a 3D workspace an LLM can drive

iPhone Use: Codex Controls a Real iPhone Over USB

PyCuVSLAM: GPU-Accelerated Visual SLAM for Python

mjbatch Runs Thousands of MuJoCo Sims in Parallel

RoboSim: A Robot-Native Rust Simulation Engine

Supervision: Python Computer Vision Toolkit by Roboflow

ElectroPup: a 12-DoF 3D-Printed Open-Source Quadruped