#Open Source (148)

The self-improving agent from Nous Research (248k+ stars). Its learning loop has five mechanisms: periodic memory nudges, autonomous skill creation after tasks, skills that self-improve in use, FTS5 session search with LLM summarization, and Honcho user modeling. One TUI, seven terminal backends (local/Docker/SSH/Singularity/Modal/Daytona/Vercel Sandbox), six chat platforms from a single gateway, no model lock-in. It doubles as a trajectory data-production apparatus. Self-improvement is falsifiable and no controlled experiment has been run, so this is graded as needing reproduction.

Agent HarnessSelf-Improving AgentsNous Research

Python248k52k956

deepseek-ai/deepseek-harnessMIT

DeepSeek Harness: the open-source agent runtime where everything is a plugin

DeepSeek AI's open-source agent harness (CLI dsh, MIT, 233k+ stars). The kernel provides only plugin-system semantics; tools, surfaces and model access all attach as plugins, on top of Cordis' paradigm for spatiotemporal composability (arXiv:2608.25512). It is in developer preview with an explicit warning about compatibility-breaking changes, and SAFETY.md is required reading before you run it. Not benchmarked yet, so this entry is graded as needing reproduction.

Agent HarnessPlugin ArchitectureDeepSeek

TypeScript234k28k1.0k

anomalyco/opencodeMIT

opencode: the open-source coding agent split into client and server

anomalyco/opencode (formerly sst/opencode, 210k stars, MIT) splits the coding agent into a client layer and a server layer: the server exposes OpenAPI 3.1 plus an SDK generated from the spec, and the TUI is just one remote frontend of it, so a single session can be driven from a terminal, an IDE, GitHub Actions and ACP clients such as Zed. More than 75 providers plug in through the AI SDK and Models.dev, OpenCode Zen offers a curated gateway of models tested before listing, the 35+ built-in LSPs ship disabled with docs stating they are not always a net win, skills resolve from .opencode, .claude and .agents roots, and permissions are a three-state allow/ask/deny. It is the most engineering-first branch of open-source coding agents: interface before UI.

AI Coding AgentOpen SourceTUI

TypeScript211k28k816

nextlevelbuilder/ui-ux-pro-max-skillMIT

UI UX Pro Max: a 132k-star AI coding skill that generates complete design systems from 192 industry reasoning rules

An open-source AI skill that injects design intelligence into coding agents: one request generates a complete design system (style + colors + typography + anti-patterns) from 192 industry reasoning rules, 79 searchable UI styles, 192 color palettes, 74 font pairings, 25 chart types and 22 tech stacks (React, Next.js, Astro, SwiftUI, Flutter, Three.js and more). The v2.0 flagship Design System Generator runs five parallel searches (product type, style, palette, landing pattern, typography) through a BM25-ranked reasoning engine to output pattern, style, colors, typography, effects, anti-patterns and a pre-delivery checklist (4.5:1 contrast, no emoji icons, cursor-pointer, prefers-reduced-motion, four responsive breakpoints). MIT licensed, one-line npm CLI install, works with Claude Code and other coding agents.

AI CodingUI/UXOpen Source

Python132k14k530

earendil-works/piMIT

Pi: a layered agent harness monorepo

Pi Agent Harness (formerly badlogic/pi-mono; 108k+ stars, MIT/TypeScript). Not one CLI but layered packages usable on their own: pi-coding-agent (interactive coding agent), pi-agent-core (runtime with tool calling and state), pi-ai (unified multi-provider LLM API), plus chord composition runtime, pi-telemetry contracts, pi-durable persistence and pi-tui differential terminal rendering. The README states plainly that there is no built-in permission system and offers micro-VM / Docker / sandbox paths instead. Not independently benchmarked; graded needs-reproduction.

Agent HarnessCoding AgentTypeScript

TypeScript109k14k334

bytedance/deer-flowMIT

DeerFlow 2.0: ByteDance's super agent harness

ByteDance's open-source super agent harness (82k+ stars). 2.0 is a ground-up rewrite sharing no code with 1.x: sub-agents, extensible skills, sandbox and file system, long-term memory, session goals, plus Manual Context Compaction that hands context management back to the operator. Positioning lifted from a deep-research framework to a runtime for any task. Not independently benchmarked; graded needs-reproduction.

Agent HarnessMulti-agentSkill System

Python83k11k346

paperclipai/paperclipMIT

Paperclip: run a company with a team of AI agents

Open-source orchestration for teams of AI agents: a Node.js server and React UI where you bring your own agents, set goals, and track work and spend from one dashboard. It looks like a task manager; underneath sit org charts, budgets, governance, goal alignment and agent coordination. It manages business goals, not pull requests. MIT, self-hostable.

Agent orchestrationMulti-agentGovernance

TypeScript81k15k395

stablyai/orcaMIT

Orca: the ADE that grounds parallel agents in git worktrees

Orca calls itself an ADE (Agentic Development Environment), the AI orchestrator for 100x builders (75k+ stars, MIT/TypeScript, YC-backed). Central mechanism: fan one prompt across Codex, Claude Code, OpenCode and Pi, each isolated in its own git worktree, then compare results and merge the winner. Also WebGL terminals, Design Mode (click a UI element to send its HTML/CSS/screenshot into the prompt), native GitHub and Linear integration, diff annotations routed back to the agent, SSH worktrees and mobile notifications. Not independently benchmarked; graded needs-reproduction.

Agent orchestrationAgent HarnessCoding Agent

TypeScript76k5.0k131

multica-ai/multicaNOASSERTION

Multica: AI coding agents that show up on the board

An open-source, self-hostable workspace where you assign issues to AI coding agents the way you would to a teammate: they pick up the work, run on a runtime you control, comment as they go, and hand results back for review. Works with 26 agent CLIs and keeps intent, runs, decisions and diffs on one issue, with code never leaving your machine.

Agent orchestrationCoding AgentMulti-agent

Go51k6.6k184

abhigyanpatwari/GitNexusPolyForm Noncommercial

GitNexus: The Zero-Server Code Intelligence Engine

Indexes any codebase into a knowledge graph — dependencies, call chains, clusters and execution flows precomputed — then serves it to coding agents through 17 smart MCP tools so Cursor, Claude Code and Codex never miss code.

Code IntelligenceMCPGraph RAG

TypeScript47k5.1k160

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

Genesis-Embodied-AI/genesis-worldApache-2.0

Genesis World: The Unified Multi-Physics Simulation Platform for Physical AI

Genesis AI's open simulation platform for physical AI: one Pythonic API over a multi-physics engine (rigid, FEM, MPM, PBD, SPH, Stable Fluid, IPC), the Nyx path tracer, and Quadrants, a cross-platform Python-to-GPU compiler. It is built to be the evaluation engine for robot foundation models rather than a data factory: 30,000 parallel environments at 43M FPS, and under a zero-shot real-to-sim protocol, sim-vs-real evaluation agreement of Pearson 0.8996 with mean maximum rank violation 0.0166. Apache 2.0, 29.9k stars.

SimulationPhysics EngineEmbodied AI

Python30k2.9k224

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

GLips/Figma-Context-MCPMIT

Framelink MCP for Figma: design files compressed into usable context, not screenshots

Also known as Figma-Context-MCP, npm package figma-developer-mcp, TypeScript, MIT, and a third-party implementation rather than the Dev Mode MCP server Figma ships itself. It exposes only two tools: get_figma_data (read-only) and download_figma_images (removable with –skip-image-downloads, and confined to writing inside –image-dir). The narrow surface is deliberate; the engineering sits in the fetch → simplify → serialize pipeline behind it. Simplify uses composable extractors (layout, text, visuals, component, plus the allExtractors, layoutAndText, contentOnly, visualsOnly and layoutOnly bundles) to compress the raw Figma response into semantic layout and styling in a single tree walk, with maxDepth and nodeFilter, and outputs tree (default, cheapest in tokens), yaml or json. The part worth copying is that compression effectiveness is a metric computed on every call: rawSizeKb against simplifiedSizeKb, rawNodeCount against simplifiedNodeCount, component/instance/text/image node counts, namedStyleCount (the source comment states a high count is a design-system maturity signal), hasVariables (whether Figma Variables are used at all), and separate fetch/simplify/serialize timings; the same hooks drive MCP progress notifications and heartbeats so a large file taking tens of seconds does not look hung. Judgement shows in the details: node IDs accept both the plain and the deeply nested instance shape, the depth parameter description says do not use it unless the user explicitly asks, the proxy default deliberately avoids installing EnvHttpProxyAgent when no proxy variables exist so a stale variable cannot route traffic through an intermediary returning 403, and stdio mode warns at startup when –image-dir is unset. Credentials come from FIGMA_API_KEY or FIGMA_OAUTH_TOKEN; telemetry can be disabled with –no-telemetry or DO_NOT_TRACK=1 and secrets are redacted from reports. 15.9k stars. It generates no frontend code - the output is design facts, and translating them into React, Vue or SwiftUI is your coding agent job. Not yet run against a Figma file of our own, and no screenshot-versus-structured-data accuracy comparison, so this is graded as pending reproduction.

MCPFigmaDesign-to-Code

TypeScript16k1.3k65

XiaomiMiMo/MiMo-CodeMIT

MiMo Code: Xiaomi terminal coding agent betting on memory and self-evolution

Xiaomi open-sourced terminal-native AI coding assistant, TypeScript built with bun. The source is MIT but usage is additionally bound by USE_RESTRICTIONS.md, the MiMo terms of service and the trademark policy - read those before treating it as plain MIT. The README states it is a fork of OpenCode: it keeps the multi-provider, TUI, LSP, MCP and plugin core and adds persistent memory (SQLite FTS5 full-text search across four kinds - project MEMORY.md, session checkpoints, scratch notes and task progress - injected automatically on session resume), intelligent context management (near the limit it rebuilds from the latest checkpoint plus project memory plus task progress plus retained recent messages, ranked by importance against a token budget), goals and stop conditions (/goal sets the condition, and when the agent wants to stop a separate judge model assesses whether it was truly met, which targets optimistic early quitting), and deterministic JS workflows in a sandbox (compose splits independent tasks into isolated git worktrees with per-task TDD; also deep-research, fact-check with three-reviewer adversarial voting, and research-experiment with an anti-metric-gaming audit). Twenty built-in skills (arxiv, claude-code, codex, docx, pdf, pptx, xlsx, html-to-video, product-design and more), compatible with four skill roots - .agents/skills, .claude/skills, .codex/skills, .opencode/skills - where a user skill of the same name overrides the built-in. /dream and /distill are its signature: the first distils recent session trajectories into project memory and prunes stale entries, the second finds your repeated manual routines and packages high-confidence candidates into reusable skills. Model agnostic - the Xiaomi platform, Codex/ChatGPT OAuth, or any OpenAI-compatible endpoint. 13.4k stars. Not installed or run yet; memory-restore accuracy, judge-model effectiveness and the vendor-stated cache hit rates are unverified, so this is graded as pending reproduction.

Coding AgentAgent HarnessPersistent Memory

TypeScript13k1.4k76

genspark-ai/genofficeApache-2.0

GenOffice: Open-Source AI Office Suite

GenOffice is the first full-featured open-source AI office suite (Apache-2.0): real .docx/.xlsx/.pptx/PDF/Markdown editing with byte-preserving Word compatibility, block-level AI editing with document-aware agents, BYOK model support, fully local.

AI OfficeOpen SourceOffice Suite

TypeScript7.9k1.0k36

Neroued/ninferApache-2.0

NInfer: A From-Scratch C++/CUDA Inference Engine for a Single RTX 5090

A from-scratch C++/CUDA inference engine for five explicitly registered Qwen checkpoints on one NVIDIA GeForce RTX 5090. Startup-frozen residency picks MTP or DFlash speculative decoding, Vision, and one of five KV storage formats; a shared Device KV pool plus pinned Host State/KV checkpoints reuse exact prompt prefixes across 240k-token contexts. Measured aggregate decode reaches 1,146.9 tok/s at concurrency 8 and 15,544.3 tok/s on a 7,680-token prefill.

LLM InferenceCUDASpeculative Decoding

C++1.4k26113

dexmal/dexboticMIT

Dexbotic: A One-Stop VLA Toolbox for Pretraining, Fine-Tuning, Inference, and Evaluation

A one-stop VLA toolbox from Dexmal: the unified DexData format spans pretraining, fine-tuning (full/LoRA/RL), inference, and evaluation, supporting mainstream models (π0, CogACT, OFT, MemVLA, GR00T N1) plus the in-house dual-expert DM0; DB-pretraining brings consistent gains across five simulation benchmarks, with 62% average success on RoboChallenge Table30 real-robot evaluation.

VLAManipulationNavigation

Python1.4k1814

AI CodingDevelopmentTopC

Trae: ByteDance two-form-factor coding line - TraeCode (IDE + SOLO) and TraeWork, plus a stalled MIT-open shell

Trae is the AI coding line from ByteDance, and the official docs split it into two clearly different things. TraeCode is a development tool with AI deeply integrated, in two modes: IDE mode keeps the editor, terminal, debugger, extensions and source control for scenarios that need fine-grained control over code changes and execution, while SOLO mode hands the lead to AI - describe the requirement in natural language, by voice or by uploading local files, and it decomposes the task itself and runs code generation, testing, preview, change summary and deployment, with a task panel, an AI conversation panel and a tool panel (built-in editor, doc viewer, browser) from left to right. TraeWork is the AI-native workspace grown out of the SOLO mode of TraeCode, on web, desktop and mobile, in Work / Code / Design modes, aimed at product managers, data analysts, operations and designers rather than developers only. The long-task ceilings are stated concretely: Max mode extends the context window to 1M, allows up to 200 tool-call rounds per task and reads up to 750 lines per file read to cut down on chunking. bytedance/trae-agent is MIT-licensed but has not been updated for about eight months and cannot stand for the current engineering of Trae. Boundaries: no third-party benchmark readings are published (no SWE-bench or Terminal-Bench self-evidence), so every capability claim comes from feature docs and vendor descriptions; clients are closed source; 1M / 200 rounds / 750 lines are ceilings rather than typical experience and the vendor itself says they raise cost substantially; model availability is doubly limited by region (Seed, MiniMax and GLM unavailable to US users) and by plan tier, so teams across regions do not share one model set; concurrent cloud tasks are a hard tier difference and Free excludes SOLO, so evaluation cannot look at monthly price alone. Confidence C (vendor-claim).

1M · 200 roundsMax mode limits (context / tool rounds per task)Vendor Claim · 2026-09

ProductByteDanceSite Repo

aliyun/ai-agent-handbookApache-2.0

AI Agent HandBook: Alibaba Cloud Enterprise Agent White Paper

Alibaba Cloud open-source (Apache-2.0) white paper distilling enterprise agent deployment experience across the agent lifecycle — architecture, building, runtime, governance and optimization — in 30 Markdown chapters. Core thesis: understand the Model/Harness responsibility boundary instead of attributing everything to model capability. Covers agent loops, task state machines, context builders, action planes, MCP/A2A, sandbox runtimes, AI-gateway governance and evaluation flywheels, plus a 2026 agent developer survey and real cases (ABACI kernel-patch testing, Kitta code review, Bilibili content insights).

AI Agent HandBookAgent HarnessAgentic Application

7041148

dreamers-laboratory/image-to-3d-pipelineApache-2.0

image-to-3d-pipeline: A Fair Benchmark and Browser Explorer for Image-to-3D Models

A fixed-protocol harness for comparing image-to-3D models. One set of AI renders of a fictional submersible goes into TRELLIS V1/V2 and TripoSR; each mesh is inspected through an identical Blender 4.3 EEVEE orbit and scored on a 0-2 five-axis rubric, then the winner ships into a Three.js + three-mesh-bvh explorer with real collision. TRELLIS V2 stochastic ranks first at 1.56 MB / 11,866 faces.

TrellisTripoSRHunyuan3D

JavaScript258200

mekion/the-bimo-projectApache-2.0

Bimo: a 45 cm Open-Source Hip-Head Biped Trained in Isaac Lab and Distilled onto an RP2040

Bimo is a 45 cm, roughly 1.6 kg hip-head biped kit: eight STS-3215 bus servos, a BNO08x 9-DoF IMU, four VL53L0X rangefinders, two 180-degree cameras, and a custom RP2040 board closing a 20 Hz control loop. The repository publishes the Python control API (1023 lines), three MCU firmware builds (1349 lines), a ROS2 wrapper (1431 lines) and an Isaac Lab training environment (874 lines: six reward terms, dense domain randomization, and a system-identified STS3215Actuator with 3.113 Hz bandwidth, directional gear backlash and a 5 ms bus delay), plus a 26MB Bimo.usd model. The CPG gait's 104 Fourier coefficients and per-joint amplitude gains are hardcoded in both Python and C, and a [256,128,64] PPO teacher distills into a [64,32] student that goes through onnx2c into the firmware, so the robot walks untethered on the MCU. All code is Apache-2.0, but the CAD and electronics the README promises are still marked coming soon: no STL, STEP, schematic, gerbers or BOM exist in the working tree or in git history, and neither release ships pretrained weights (zero assets) or a DIY assembly manual. Currently v1.1.0 with 198 stars, in pre-order status.

Bipedal robotsHumanoidOpen Source

Python19887

5:56

BaoCut: Open-Source AI Bilingual Subtitle Translation

@dotey · 27 views · 2026-10-14

BaoCutSubtitle TranslationASR

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