704 tools and products — trending open source, and what gets used in AI and other work.
GitWarren is a desktop application for reviewing local Git changes made by coding agents before they are committed. It reads the worktree directly and combines staged, unstaged, and untracked files into one diff for local review, with comments and threaded discussions that can be edited or resolved. An MCP server over stdio lets Claude Code, Codex, or another MCP client open reviews, inspect discussions, reply in threads, comment on lines, and resolve comments; agent messages are attributed and separate MCP sessions distinguish concurrent agents. Reviews and comments are stored in a local SQLite file, while repository state is read from Git when displayed. The application requires no account, runs on macOS, Windows, and Linux, and is free and open source under GPL-3.0.
Tucky is a native macOS notes app that keeps notes in a thin stripe at the edge of the screen. Notes are stored locally and encrypted on disk, with support for live Markdown, checkbox tasks, global shortcuts, clean exports, and local-only files. Its optional AI agent can search, write, rewrite, and ask questions about notes from the edge or through the global ⌥Space shortcut. The app can connect to Gmail, Google Calendar, Google Docs, Google Sheets, GitHub, and Notion; voice dictation is supported in more than 60 languages. The page says note titles accompany questions by default, while note bodies are read only when allowed. The free plan supports five notes without AI, voice, or connectors. The Plus plan is listed at $4 per month and adds unlimited notes, AI models, voice, and connectors. Tucky runs on macOS 15 or later on Apple silicon or Intel.
TeamAI is an open-source CLI for managing a team's skills, rules, documentation, agents, hooks, MCP configuration, environment settings, and knowledge across AI coding tools such as Claude Code, Codex, Cursor, CodeBuddy, WorkBuddy, and OpenCode. It is installed with npm as `teamai-cli` and uses a shared Git repository as the team's source of truth. The CLI distributes resources through an administrative `push` → review and merge → `pull` workflow. Session-start hooks can run `teamai pull` to synchronize the shared harness into project- or user-scoped local AI-tool directories; roles, tags, and subscribed source repositories control which resources members receive. TeamAI also provides a knowledge layer: `teamai import` and `teamai codebase` build a searchable team knowledge base and codebase graph, while `teamai recall` uses BM25 search with graph-boosted reranking and can deploy a recall subagent into supported AI tools. Code relationships are extracted with a WebAssembly tree-sitter parser for TypeScript/JavaScript, Python, and Go, with regex-based heuristic extraction as a fallback and for other languages. Additional commands support friction-based learning suggestions, privacy-scrubbed session summaries, maintenance of stale or low-confidence knowledge, usage digests, a web dashboard, member and role management, package and plugin installation, diagnostics, and uninstallation. The repository is maintained by Tencent and contributors and is licensed under MIT.
PI-Desktop is a local-first desktop workspace for AI coding agents, built with Electron, a Rust host core, and the pi Agent Harness. It lets users open local projects, connect cloud or local models through providers such as OpenAI-compatible APIs, manage projects and long-running sessions, and review file changes and command output. Its architecture separates the React renderer, Electron desktop orchestration, Rust-managed permissions, filesystem access, SQLite persistence and secrets, and a pi Agent sidecar responsible for the agent loop, model interaction, and streaming. Agent, Plan, and Goal modes provide different approval boundaries; agents can read and edit files, run commands, and delegate exploration, implementation, research, testing, or review to background Subagents. The workspace supports MCP servers, Skills, installable Plugins, model and provider switching, session imports, notifications, context checkpoints, and a plugin marketplace. Conversations are stored locally as JSONL with a SQLite index, settings and logs remain on the machine, credentials use the operating-system keychain, and model requests go directly to the configured provider or endpoint. Plugin processes are permission-gated and isolated from the renderer, but plugins remain user-trusted code rather than a complete operating-system sandbox. It is an early-preview cross-platform application distributed for macOS, Windows, and Linux under the GNU Lesser General Public License v3.0.
Vals is a code-generation evaluation product that uses a company's GitHub codebase to build an internal coding benchmark. It evaluates coding models and agents against private, continuously evolving tasks to identify which systems perform best on the company's work and which offer the strongest return on investment. The platform is also described as supporting pre-release testing and evaluation of agentic coding work that may unfold over hours, days, or weeks, while accounting for factors such as token cost and latency.
Clodds is a self-hosted, open-source AI trading terminal and autonomous trading agent for prediction markets, cryptocurrency spot and perpetual futures, Solana and EVM decentralized exchanges, token launches, and Bittensor mining. It is built around Claude and can be accessed through a local WebChat interface, CLI, MCP server, or 21 messaging channels. Its architecture combines four agents—main, trading, research, and alerts—with trading skills, market-data tools, semantic memory, strategy execution, and a unified risk layer. The documented capabilities include arbitrage detection, whale and copy trading, DCA bots, backtesting, order and portfolio management, circuit breakers, VaR/CVaR, volatility-regime detection, stress testing, Kelly sizing, daily loss limits, and a kill switch. It connects to prediction-market platforms, futures exchanges, Solana protocols, and EVM networks, and also provides agent-oriented forum, marketplace, token-launch, compute, and x402 USDC payment features. The project is distributed as an npm package or from source, stores local data under ~/.clodds/, supports SQLite, LanceDB, and PostgreSQL components, and is licensed under the MIT license. Its documented quick start is `npm install -g clodds` followed by `clodds onboard`; trading integrations require the relevant credentials and wallet configuration.
Hyperresearch is an MIT-licensed Python command-line research harness that extends Claude Code with an agent-driven, persistent research knowledge base. It classifies a query into light, full, or opt-in dissertation work and runs a tier-adaptive pipeline covering query decomposition, source search and fetching, contradiction and locus analysis, depth investigation, evidence digestion, multi-angle drafting, synthesis, adversarial criticism, targeted gap fetching, surgical patching, citation checks, and readability review. The pipeline stores fetched sources as Markdown notes with a rebuildable SQLite index, provenance links, lifecycle statuses, full-text or optional semantic search, source-quality and retraction metadata, and resumable per-run manifests. It can fetch PDFs, seek disclosed open-access replacements for thin or blocked academic sources through Unpaywall and Europe PMC, and apply structural checks for citation bindings, quote integrity, provenance, retractions, and patch-only editing. The vault can also be accessed through an MCP server or a dependency-free local web UI. It is installed with pip for Python 3.11–3.13 and is intended to run with Claude Code; model assignments and research scale can be configured per project.
OpenResearch is a local-first workspace for research agents and autoresearch, available as a desktop application and a macOS/Linux CLI. It turns Claude Code, Codex, or OpenCode into agents that can review literature, develop hypotheses, run experiments, and produce research artifacts. The workspace assigns independent agent sessions and isolated Git worktrees to parallel research directions. It tracks experiment variants in a Git-native experiment tree, archives each run against its recorded commit, and keeps logs, diffs, files, results, and artifacts tied to the work that produced them. Its autoresearch loop can propose an idea, modify code, launch an experiment, inspect evidence, and choose the next direction. The same committed source snapshot can run locally or through SSH, Slurm, Kubernetes, Ray, Hugging Face Jobs, Modal, Tinker, or managed OpenResearch compute. By default, projects and run data remain on the user's machine in a local SQLite store, with a browser dashboard served on localhost. An OpenResearch account is used for service-owned capabilities such as organizations and managed compute. The CLI also installs an OpenResearch skill into supported coding agents and provides commands for projects, runs, logs, experiments, discovery, and paper lookup.
YuE2 is an open music-generation model and Python pipeline that turns lyrics and a style prompt into a symbolic melody-and-chord plan and then renders that plan as a complete stereo song with vocals and accompaniment. It supports zero-shot covers by using a transcribed melody score, and composition editing by allowing the score, lyrics, style, harmony, melody, tempo, or form to be revised before rendering. Its AR–NAR Mixture-of-Transformers backbone predicts symbolic scores and semantic tokens, generates acoustic latents with flow matching, and uses a VAE to decode them into 48 kHz audio. The staged API exposes plan(), generate_semantic(), synthesize(), and decode() operations; the repository also provides an agent skill for song generation, transcription, cover creation, ABC-score editing, and listening comparisons. The repository provides the YuE2-3B model and requires Linux, Python 3.12, and an NVIDIA GPU with BF16 support and 24 GB of VRAM for the documented quick start. First-party code, documentation, and the agent skill use Apache 2.0, while the model weights use CC BY-NC 4.0. The original YuE implementation is preserved on the repository's YuE-v1 branch.
Worktrunk is a command-line interface for managing Git worktrees, designed for running AI coding agents in parallel. It addresses worktrees by branch name and computes their paths from a configurable template; its core commands switch to, list, merge, and remove worktrees, with an option to launch a command such as Claude Code after switching. It also provides hooks for local workflow automation, LLM-generated commit messages, merge and cleanup workflows, an interactive worktree picker with diff and log previews, shared build caches, CI status and AI-generated branch summaries, pull-request checkout, per-worktree development-server ports, and aliases with branch-scoped variables. It can be installed through Homebrew, Cargo, Winget, Arch Linux, Conda, or Pixi, and shell integration enables commands to change directories.
Dream Loop is an AI agent skill for building games, apps, and 3D scenes toward a visual target. It uses image generation to create a target screenshot, builds the scene—such as a Three.js scene in a browser—and has a separate vision-enabled critic compare the live screenshot with the target and provide feedback; the agent then iterates on the build and can optionally generate an improved target from the current state. Blender can be used for custom 3D modeling through Blender MCP or scripting. The skill can be installed with `npx skills add achimala/dream-loop` or cloned into an agent's skills directory, and requires an agent with image-generation access, vision input, and preferably subagents.
Bang Motion is an agent skill for building browser-based motion graphics such as product openers, promos, bumpers, channel intros, kinetic typography, lower thirds, and explainers. It produces a single index.html that plays in a browser and encodes anti-slide rules: scenes change through camera or world movement rather than section fades, subjects or shared worlds persist, motion occurs continuously, and text and numbers remain part of the scene. Its deterministic timeline makes each frame a pure function of time for exact scrubbing and frame-by-frame export, while a shared camera rig supports flowing-world and camera-through-collage modes. The skill includes five explainer starters—cartoon collage, visual journalism, white catalog, vintage sketch, and continuous action—along with configurable style briefs, background motion, surfaces, transitions, highlight shapes, voice-over re-timing, and optional Puppeteer and FFmpeg scripts for verification and MP4 export. It can be installed as a Claude Code plugin or copied into Claude Code, Codex CLI, Gemini CLI, Cursor, or another agent workflow; watching the output requires only a browser and internet connection. The repository is licensed under MIT.
Browser Use Pi is a TypeScript web agent built on Pi Mono. It combines a persistent V8 REPL with raw Chrome DevTools Protocol control: the agent writes JavaScript, uses accessibility-tree data and screenshots, and builds browser helpers as needed. It supports persistent sessions, saved logins, workspaces, streaming, hooks, typed results, follow-up tasks, and cloud browsers or a user's own Chrome; runs can be limited by steps, time, or cost, with partial work retained. Interaction highlights, recordings, and GIF exports can show the agent's work. It is distributed as the @browser_use/pi package for Node 22.19+ or Bun 1.3.14+, and its quickstart uses Browser Use Cloud without requiring a local Chrome installation.
Astra Advisor is a Codex plugin for capability-routed software delivery from DannyMac180. GPT-6 Astra acts as the primary architect and acceptance owner: it plans work, decides whether bounded independent tasks should be delegated, selects supported native subagent models and reasoning effort, and continues parent-session work while delegations run. It uses the exposed collaboration tool to route tasks among Sol, Terra, and Luna subagents, then inspects the complete diff, reruns requested checks, and sends substantial changes to a fresh read-only reviewer; work is accepted only after the reviewer returns "ship". Each delegation reports its selected and runtime-observed settings, and tasks can end with an API-equivalent cost receipt that compares observed token usage with a versioned Astra pricing snapshot. It can be installed as a plugin in a current Codex CLI or ChatGPT desktop app with plugins enabled. The README notes that ChatGPT Work cloud tasks currently cannot provide arbitrary model or effort controls, so Astra does not dispatch model-pinned requests there by default.
SuperAstra is a SNES-themed desktop companion that uses natural-language prompts to investigate and alter a running game through BizHawk. Its agent receives screenshots, cartridge identity data, emulator registers, memory scans, hardware-domain reads, controller probes, and checkpoint results; it can search cartridge data, identify game-memory structures, create routines or guarded patches, test changes, and retain cartridge-specific findings in a local notebook. Each committed mutation can be undone through emulator checkpoints, while named experiment checkpoints support controlled trials that restore the player's prior state. The prototype runs on Windows or Linux with Python and Tkinter, BizHawk using its BSNES SNES core, a ROM, and an OpenAI API key configured for the stated model; it also includes limited local Mario shortcuts that do not require an API key. It does not patch the original ROM file, create exportable ROM patches, expand ROM capacity, or guarantee success on unfamiliar games. The original source is MIT-licensed.
tmax is a tmux extension that presents local and remote tmux sessions from configured SSH hosts through one deduplicated session switcher. Its fzf-based popup groups sessions by computer, supports filtering, previews, favorites, host locking, session creation and confirmed deletion, and can create a local session when no match is found. Remote sessions are represented as local proxy sessions and attached through tmux control mode over SSH. Recognized operations such as new windows, splits, layouts, zoom, renaming, and pane or window deletion are routed to the remote machine; the tool reconnects after dropped links and periodically reconciles opened sessions. Remote terminal scrollback is not imported, custom tmux commands are not translated, and disconnected hosts retain cached entries. An optional Python collector and agent integrations report activity from Claude Code, Codex, and Pi using lifecycle hooks, showing working or waiting indicators for windows and sessions. tmax is installed as a tmux plugin or run-shell entry, requires tmux, Python, and fzf, and uses a JSON configuration for remote hosts. Remote setup requires SSH key-plus-account-password authentication; passwords are not saved, and authenticated hosts use a local 24-hour lease and watchdog.
Agent Skills is Google's repository of installable skills for Google products and technologies, including Google Cloud. The skills provide Markdown-based procedures for tasks such as onboarding and authenticating to Google Cloud, deploying and managing AI agents, working with GKE, databases, networking, security, monitoring, analytics, advertising APIs, Firebase, Android, Dart, Flutter, and Google Maps Platform. Skills can be selected and installed with `npx skills add google/skills`; the repository also bundles plugins and MCP servers for agent harnesses including Claude Code, Codex, and Antigravity CLI. The repository accepts bug reports and contributions, and its skills are distributed under the Apache 2.0 license.
Distilly is an AI agent skill-generation tool by titanwings that turns messages, documents, interviews, and other supplied sources into source-grounded Person Profiles. It models observable experience, decision patterns, expression, and ways of working, rather than claiming to clone a person, and supports colleague, relationship, and celebrity profile families. Each generated profile is packaged as an installable Agent Skill for supported hosts. Its workflow includes family-specific source collection and analysis, a six-dimension research pipeline for celebrity profiles, incremental file analysis and merging, conversational corrections written to a correction layer, and automatic version archiving with rollback to earlier versions. Supported source types include Lark, DingTalk, Slack, public X posts, WeChat exports, PDFs, images, email, Markdown, and pasted text, with source-specific setup requirements. The project documents native local Skill discovery for Claude Code, Hermes, OpenClaw, Codex, DeepSeek Harness, Pi, Grok Build, and OpenCode. It is distributed from its GitHub repository under the MIT License and is described as a demo version.
Agent Reach is an open-source command-line capability layer for AI agents. It selects, installs, configures, checks, and routes tools that let command-line agents read and search websites and services including web pages, YouTube, RSS, GitHub, Twitter/X, Reddit, Bilibili, Facebook, Instagram, Xiaohongshu, LinkedIn, V2EX, and Xueqiu. It uses an ordered list of backends for each channel and probes them for actual availability, selecting the first usable option and reporting failures and repair guidance through `agent-reach doctor`. The repository lists integrations such as Jina Reader for web pages, yt-dlp for YouTube transcripts, `gh` for GitHub, feedparser for RSS, Exa through mcporter for web search, and alternative CLI or browser-session-based routes for sites requiring authentication. Agents invoke the upstream tools directly rather than through a data-wrapping layer. The CLI supports environment checks, explicit system installation, dry runs, updates, and uninstalling. Credentials and cookies are documented as being stored locally in `~/.agent-reach/config.yaml` with owner-only permissions; authenticated services may require user-provided cookies or browser sessions. The project is distributed under the MIT license.
oh-my-hermes (OMH) is a plugin and operating layer for Hermes Agent that adds model routing, coding workflows, specialist skills, project memory, and evidence-gated execution without replacing Hermes as the natural-language interface. It scores requests, selects workflows and model-and-effort categories, applies model-family-specific prompting, and can split accepted plans into parallel worktree-based lanes with typed results and verification gates. Its workflows cover interviewing, research, planning, coding delegation, quality assurance, performance work, and iterative plan-build-review loops; the terminal interface exposes phase-based todos, delegated-lane status, cost and token telemetry, and execution states that distinguish preparation, reported completion, and verified results. OMH also provides a file-backed, reviewer-gated memory store with provenance, review dates, conflict handling, and task-scoped recall packs, while leaving Hermes's native memory untouched. It is distributed as a command-line package and plugin with installation paths including a shell installer, Homebrew, Bun, npm, and Hermes skill tap.
Viserys is a self-contained pack of Markdown engineering workflows, reviewer personas, validation scripts, commands, and lifecycle hooks for AI coding agents. Its workflow routes development through DEFINE, PLAN, BUILD, VERIFY, REVIEW, and SHIP stages, with 28 skills covering requirements clarification, specification and task breakdown, implementation, test-driven development, debugging, browser testing, review, security, performance, migrations, CI/CD, documentation, observability, and release preparation. The pack also includes four reviewer personas, shared checklists, evaluation cases and fixtures, and validators for skills, commands, artifact paths, reference links, and versions. The repository includes an OpenCode agent definition that registers the skills and routes requests through the lifecycle workflow. The Markdown skills can also be supplied to skill-aware agents such as Claude Code, Cursor, and other agents, or read directly as standalone process documents.
3dviz-pro-max is a standalone agent skill for turning plain-language ideas into interactive Three.js or Blender scenes. It is designed for Claude Code and Codex, and provides a ten-step workflow covering intent, object reasoning, representation, construction routing, first-view creation, meaningful behavior, capture, inspection, refinement, and reporting. The skill includes searchable recipes and knowledge records, runnable Three.js templates, reusable scene kits, style and camera guidance, and a capture helper that drives a built scene in a browser and writes PNGs and console logs for inspection. Its catalog covers subjects including environments, anatomy, mathematics, physics, architecture, logistics, robotics, and abstract systems. The supplied Python helpers search the catalog and drive an existing scene; they do not themselves render or judge scenes, and screen capture requires an optional Playwright installation. The repository includes 37 runnable studies and a site at 3dviz.dev. Its own code, data, and authored media are released under the MIT license, while bundled third-party assets retain separate terms. Blender 4.2 LTS or newer is recommended but not required; without Blender, the repository states that scripts still run but kit output is limited to T2.
Infinite World is an open-source system for building persistent worlds with multimodal AI. It combines multimodal understanding, world simulation, and interaction in a continuous loop: prompts, images, or other inputs are interpreted into world state, rules, and possible actions; generated scenes respond to that state; and people, agents, and events can update the world. The runtime records scenes, choices, state changes, history, and branches so worlds can preserve context across scenes and be replayed. The project supports text, voice, and image interaction, local previews, saved-branch replay, and cached generated video. Its current local setup uses a Node.js and pnpm workspace, with FFmpeg and whisper.cpp for voice transcription and live output. Interactive live streaming is in development, while additional inputs such as chat, audio, mouse, and keyboard are planned. The repository describes the project as early development, with APIs and stored data subject to change, and distributes it under the Apache License 2.0.
geiger is a read-only command-line scanner from Atomburst that inventories AI agents, harnesses, MCP servers, plugins, skills, hooks, browser extensions, desktop AI applications, and related IDE configurations on a machine. It reads known configuration files and directories, identifies each finding's origin and evidence path, and labels capabilities such as code execution, secret storage, broad filesystem access, broad web access, and network access. Credential-shaped values are reported by key name, file, and secret type without exposing value contents; the scanner performs no telemetry and writes only an explicitly requested report file. It can emit terminal, HTML, or versioned JSON reports, scan specified project or home directories, enforce a strict exit status for findings that can execute code or hold secrets, and compare JSON snapshots to identify newly appeared, removed, or escalated findings. It runs through npx with Node.js 18 or newer, has no runtime dependencies or account requirement, and is licensed under MIT. The project notes that it reads configuration rather than runtime behavior, covers known locations, and does not determine whether a package is malicious.
Agent Launcher is a local desktop workspace for configuring and running existing coding-agent command-line interfaces. It detects installed CLI binaries, links or installs supported agents, applies account or provider profiles, and runs them in an embedded terminal or chat view without automatically reinstalling or updating already installed CLIs. It supports project-aware sessions by launching an agent in a selected project directory, synchronizes provider settings with CLI-native configuration files and environment variables, and performs a minimal model request to check credentials, endpoints, models, network access, and account status. The application reads local conversation histories in JSONL or SQLite formats, can resume or delete local sessions, displays installed MCP servers and Skills, and summarizes locally stored usage data. Agent Launcher distributes macOS, Windows, and Linux builds and is released under the MIT License. It is local-first rather than offline-only: launched agents send requests to the provider or relay selected by the active profile, while API keys are stored as plaintext in local configuration files and masked only in the interface.
LibreChat is an open-source, self-hosted AI chat platform that provides a ChatGPT-inspired interface for connecting to major AI providers and custom OpenAI-compatible endpoints. It supports model switching, multimodal file interactions, web search, speech-to-text and text-to-speech, image generation, conversation search, presets, branching, resumable streams, and a sandboxed Code Interpreter for languages including Python, JavaScript/TypeScript, Go, C/C++, Java, PHP, Rust, and Fortran. Its agent system supports no-code custom assistants, community-shared agents, MCP servers, tools, file search, code execution, reusable SKILL.md instruction bundles, subagents, and an Agent Management API. Agents can optionally use attached workspaces to inspect, search, edit, and run commands in bounded environments. The platform also provides generative UI artifacts, including React, HTML, and Mermaid content, plus custom actions and integrations with providers such as Anthropic, AWS Bedrock, OpenAI, Azure OpenAI, Google, Vertex AI, Ollama, Groq, Mistral, OpenRouter, and others. LibreChat includes multi-user authentication through OAuth2, LDAP, and email login; browser-based administration of users, groups, roles, and configuration; role-based permissions; observability through OpenTelemetry and Langfuse; Redis-backed synchronization for scaled deployments; and Docker Compose deployment options. It is built in public and intended for local or cloud self-hosting.
security-audit is a Cloudflare coding-agent skill that orchestrates multi-phase security audits of codebases. It uses reconnaissance to map architecture, trust boundaries, input surfaces, prior evidence, and deterministic coverage; assigns isolated agents to coverage-led hunting; sends each unique candidate to a fresh verifier; and records confirmed, needs_validation, and rejected results in machine-readable findings.json validated against a report schema. Independent agents verify final source claims, after which the skill derives target-neutral reports and coverage records. It can be installed with the Skills CLI and requires a tool-using coding agent with parallel sub-agent support, Node.js for its validators, and an OS-enforced sandbox for target-controlled execution. The repository is licensed under MIT.
Knowledge Work Plugins is an open-source repository of role-specific plugins for Claude Cowork and Claude Code. Each plugin bundles domain skills, MCP connector configuration, slash commands, and, where applicable, sub-agents for workflows such as productivity, sales, customer support, product management, marketing, legal, finance, data, enterprise search, and biomedical research. Plugins use file-based Markdown and JSON rather than application code, infrastructure, or build steps; users can install them through Cowork or the Claude Code plugin marketplace and customize their connectors, company context, and workflow instructions.
Cline is an open-source AI coding agent developed by Cline Bot Inc. It is available as a CLI assistant, VS Code extension, JetBrains plugin, native macOS and Windows desktop app, and SDK. Cline reads project structure, edits files across a codebase, runs terminal commands, observes build and test output, and can correct issues such as missing imports, type mismatches, syntax errors, and failed tests. Its Plan and Act modes separate codebase exploration and planning from execution; edits and commands can require human approval, with checkpoints for reviewing or reverting changes. The CLI supports interactive and headless operation for scripting and CI/CD, while the SDK exposes the shared agent engine for custom tools, plugins, lifecycle hooks, multi-agent teams, scheduled agents, connectors, and MCP servers. The project supports models from multiple providers and OpenAI-compatible endpoints, including local-model runtimes, and can connect agent sessions to messaging platforms such as Telegram, Slack, Discord, Google Chat, WhatsApp, and Linear. It is distributed under the Apache 2.0 license.
Nimble Web Search Agents are domain-specific web research agents from Nimble that perform self-learning search, crawling, structured extraction, dataset enrichment, and web monitoring. They use auditable Search Plans, a proprietary index, and memory that adapts to a use case after each run to retrieve targeted data from the live web, including JavaScript-rendered pages, without requiring an LLM to parse raw pages. Nimble exposes these capabilities through APIs for search, complex research workflows, dataset building, monitoring, extraction, and crawling.
FastAPI is a Python web framework for building APIs using standard Python type hints. It is built on Starlette for web functionality and Pydantic for data handling, providing validation and conversion for request data from JSON, path and query parameters, cookies, headers, forms, and files. Type declarations also generate OpenAPI and JSON Schema documentation, including interactive Swagger UI and ReDoc interfaces. The video describes FastAPI routing requests from a mortgage application to five specialized agents.
BrowserSkill is an open-source browser-automation bridge for AI agents, consisting of the `bsk` CLI and daemon plus a Chrome or Microsoft Edge extension. An agent sends browser tasks through the CLI; the local daemon routes them over WebSocket to the extension, which performs actions in a separate visible Agent Window. Agents can use existing browser login state, borrow an already open tab only with explicit approval, and request human intervention for captchas, confirmations, or other user-only steps. It supports shell-capable agent harnesses, macOS, Linux, and Windows, and is distributed under the MIT license.
ToolReplay is a dependency-free Python CLI for auditing recorded AI-agent tool-call transcripts without executing the tools again. It strictly parses JSON Lines records, compares repeated calls and their canonicalized responses to detect non-determinism and redundant calls, checks tool names against a declared permission scope, and produces deterministic, timestamp-free reports suitable for CI checks and Git diffs. It can seal transcripts into a SHA-256 hash chain, verify the chain for edits or reordered records, replay a session against its recorded responses, and report permission overreach. It requires Python 3.11 or newer, has no third-party runtime dependencies or network access, and is distributed under the MIT license. Its scope checks are name-based, sealing is tamper detection rather than a signature, and replay only detects issues represented in the recorded transcript.
Design Studio AI is an open-source, agent-first design workspace for humans and AI agents. It supports web interfaces, slides, reports, wireframes, 3D scenes, and timeline videos; users start from a brief or template, inspect a preview, revise through chat or a manual editor, and work together on the same versioned document. The workspace provides structured flex/grid layouts, reusable design systems, 2D character motion, editable 3D scenes, BYOK text/image/speech/music/video generation, and exports including JSON, HTML, SVG, PNG, PDF, PowerPoint, WebM, GLB/glTF, React prototype ZIPs, and authorized Google Slides. It exposes REST, authenticated MCP, experimental WebMCP, and a CLI for agent access, and can run on Cloudflare or be self-hosted with Docker and persistent SQLite/files. The repository is distributed under the MIT license.
Regen Icons is an open-source SVG icon system for agent-built interfaces, providing 225 outline icons on a consistent 24px grid, with filled variants for closed shapes. Each icon is authored as a canonical JSON drawing source and compiled into standalone SVGs, React components, a sprite, and a searchable local gallery. The repository includes validation, preview, testing, and contribution workflows; it is released under the MIT License. The planned npm package is not yet published, so the repository's SVG files are the current distribution.
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.
gap-trap is an installable coding-agent skill that reads a repository and generates repository-specific contracts, checks, and quality gates for AI-written code. Contracts document the approved way to handle areas such as HTTP, logging, settings, and authentication; gates fail commits or CI runs when those rules are violated. Its Proven Red CI job runs each new test against the old code and rejects tests that pass without the change, while ratchets track known problems such as lint backlogs or oversized files and allow their counts to decrease but not grow. It also creates playbooks from project lessons and installs the related slop-mop skill for agent-generated documentation, commit messages, and pull-request bodies. Node and Python repositories receive gates inside their test suites; other listed languages use shell-based gates requiring git, grep, awk, and the project test command. It is installed through the skills CLI or by copying the skill into Claude Code or Codex skill directories, and provides setup and refine commands. The repository is MIT-licensed.
Panel is a research workspace that places an AI-agent chat alongside files, PDFs, and live Jupyter notebooks. The agent can read and write files, run notebooks against a real kernel, create scratch data, and edit the same notebook as the user; configurable panes can also display images, data, code, chats, and custom viewers or apps. It supports workspaces with separate chats and saved layouts, background commands, and literature reviews. The early build currently has full support for Claude Code, with optional OpenAI API support for chat and tools; it is distributed under the MIT license and runs locally with Node, pnpm, uv, and Python.
Monid is an API and connector framework for AI agents, providing one base URL and key for discovering and calling tools across providers. Its catalog covers services such as web search, scraping, enrichment, social data, reviews, market data, and media generation. The discover operation ranks candidate endpoints across providers for a task and returns pricing, live health, observed p50 and p95 latency, and alternative endpoint hints. Providers and endpoints are defined declaratively with authentication, metadata, request schemas, timeouts, and usage models. The engine validates inputs, builds and sends provider requests, settles usage against the raw response envelope, maps outputs, and validates the final result; provider errors are returned as data and settle at zero usage. Connectors run locally with vendor credentials, in fixture-based offline tests, or on the hosted platform with credentials injected into the transport. The open-source repository requires Deno 2.x and includes catalog, compilation, execution, recording, and replay-test tooling. It is licensed under the MIT License.
Mercury is Inception’s family of diffusion-based language models for generating text and code. Unlike autoregressive language models that generate tokens sequentially, Mercury generates discrete tokens in parallel to improve inference scaling, hardware utilization on standard GPUs, and latency. The models target production serving and latency-sensitive applications such as voice agents, with quality comparable to speed-optimized models from frontier laboratories while generating outputs significantly faster.
Omnient is an AI harness that multiplexes between different agent harnesses to provide cost and execution control.
Lakebase is a Postgres database discussed as infrastructure that AI agents can select and use. It is described as supporting rapid provisioning and database branching.
Cua is an open-source computer-use platform from Cua AI, Inc. that provides desktop automation, isolated cloud desktops, local virtual machines, and benchmarks for AI agents. Its Cua Fleets provision Linux desktops whose capacity is managed in pools; agents use the Sandbox SDK to run commands, capture screenshots, and interact with applications. Cua Driver exposes tools for inspecting and operating native desktop applications and browsers on macOS, Windows, and Linux through a CLI, MCP, or typed SDKs, with background delivery where supported. Lume creates and manages local macOS and Linux virtual machines on Apple Silicon using Apple's Virtualization framework. Cua Bench builds computer-use tasks, evaluates agents, and exports trajectories for training, including simulated tasks that do not require a VM, Docker, or a model API key. The project is MIT-licensed, while some third-party components have separate licenses.
Jev Review is a local-first MCP plugin for continuous software-quality review by AI coding agents, powered by Jev. It runs as a local Node.js server over MCP stdio and exposes a focused `jev_review` tool: an agent submits a task, focused diff, selected files, or repository context, and Jev returns structured per-dimension scores and confidence values for areas such as correctness, complexity, readability, modularity, changeability, testing, reliability, security, and documentation. When a previous evaluation is supplied, the plugin reports metric deltas, improvements, regressions, and weak dimensions rather than producing a blended overall score or prose diagnosis; the coding agent remains responsible for identifying causes and changing the code. It supports Claude Code, Codex, Cursor, and OpenCode, and is distributed directly from its GitHub repository rather than npm. The process keeps the API key locally and sends only explicitly supplied review context directly to the configured Jev API; it has no hosted backend, database, telemetry service, or author-operated proxy. The repository is licensed under MIT.
procedural-film is an agent skill that turns a topic into a 30-second vertical film. It uses vanilla JavaScript to draw every pixel on a canvas and Web Audio to synthesize every sound, producing a self-contained HTML player and MP4 exports without media assets. An agent plans the film, dispatches parallel subagents to write shot scene files, and runs critic reviews across the shots. The repository includes a shot-type index, scene and music guides, planning templates, a rendering engine, and a monarch-butterfly reference film. It requires Node.js 20 or newer, ffmpeg, and Chromium for Playwright; the project is licensed under MIT.
Mobile Jev is a standalone Android agent from droidrun that turns a written goal into device actions through the Mobilerun API, using TypeSafe's Jev to choose operations such as opening apps, tapping, entering text, scrolling, navigating, waiting, completing, or blocking. It observes the device, offers indexed controls and installed apps to Jev, validates the selected target against fresh screen data, executes the action, and observes the device again; it does not require an ADB connection. The executor resolves coordinates from observed bounds, rejects stale targets, records actions and execution traces, and verifies field values after text entry rather than relying solely on a model completion response. The project includes a local React studio with a live device stream, goal input, action timeline, task clock, model-latency measurements, stop and reconnect controls, and recent runs, along with a CLI for previewing or executing goals, inspecting devices, taking screenshots, profiling API timings, and issuing direct controls. It requires Node.js, pnpm, a Mobilerun Android device, and Mobilerun and TypeSafe API keys; it is intended for a single local operator, with recent runs held in memory. The repository is MIT licensed.
Abide is an open-source CLI and hook/plugin system that enforces project instructions for coding agents. It compiles AGENTS.md, CLAUDE.md, and other instruction files into a local rubric, then checks each edit and completed turn against the applicable rules. For each check, it sends the rule and changed code to Jev, TypeSafe's decision model, which returns a probability rather than free-form text; violations above the configured threshold produce a repair request for the agent. It supports Claude Code, Codex, and OpenCode, and also provides commands for auditing existing files, checking uncommitted changes, replaying sessions, reporting rule activity, calibrating rules against git history, and measuring latency and cost. The rubric is stored in .abide/rubric.json, while changed lines are sent to TypeSafe under the user's key with zero data retention requested. The project is licensed under MIT.
Securo is an open-source, self-hosted personal-finance manager developed by securo-finance. It keeps financial data on the user's infrastructure and provides multi-account management, transaction search and CSV export, imports for OFX, QIF, CAMT, and CSV files, categorization rules, recurring transactions, budgets, savings goals, asset valuation, reports, multi-currency conversion, and optional bank synchronization through Pluggy, Enable Banking, or SimpleFIN. The application supports multiple users, passkeys, TOTP two-factor authentication, and OIDC single sign-on. An optional AI-agent profile adds self-hosted chat over Securo data, tool use through MCP, multi-provider support, and a per-agent RAG knowledge base. It can be deployed with Docker or Podman, uses a FastAPI and PostgreSQL-based stack with Redis and Celery, and is licensed under AGPL-3.0.
Claude for Financial Services is an Anthropic repository of reference agents, skills, slash commands, and data connectors for investment banking, equity research, private equity, fund administration, wealth management, and related financial-services workflows. Its named agents cover tasks such as pitch-deck preparation, market research, earnings review, financial modeling, valuation review, general-ledger reconciliation, month-end close, statement auditing, and KYC screening. The components can be installed as Claude Cowork or Claude Code plugins, or deployed as Claude Managed Agents through the Anthropic API. Agents bundle workflow-specific skills; vertical plugins provide reusable skills and commands such as comparable-company analysis, DCF and LBO modeling, Excel auditing, earnings analysis, diligence checklists, and investment-committee memo drafting. Centralized MCP connectors link the skills to external financial-data and document providers, subject to each provider's subscription or API-key requirements. The repository also includes managed-agent cookbooks, deployment and validation scripts, partner-authored plugins, and tooling for provisioning the Claude Microsoft 365 add-in. It is file-based, using Markdown, YAML, and JSON without a build step, and is distributed under the Apache License 2.0. The repository states that its agents draft work product for qualified-professional review and do not make investment recommendations, execute transactions, bind risk, post to a ledger, or approve onboarding.
firstmate is an agent distro that turns a terminal coding agent into a supervisor for a crew of autonomous coding agents. It provides instructions, skills, tooling, policies, and state conventions rather than a separately installed application: cloning the repository and launching a supported harness creates the primary “first mate” session. The first mate dispatches requests to visible agent sessions running in tmux, Herdr, Zellij, cmux, or Orca backends. Each crewmate receives an isolated Git worktree and performs either a ship task, which produces an authorized change for a pull request or local merge, or a scout task, which produces a standalone investigation report. An event-driven watcher supervises the sessions without continuously consuming model tokens, reconciles state after restarts, and reports results or escalates decisions to the user. Optional persistent second mates can run from isolated firstmate homes locally or on SSH-reachable hosts, and optional Relay support handles eligible mentions on X and Discord through the same task lifecycle. The repository supports primary harnesses including Claude Code, Grok, Pi, Oh My Pi, Codex, OpenCode, and Cursor Agent CLI, and requires Git and an authenticated GitHub CLI for the documented workflow. It is distributed under the MIT license.
BAML is a language and runtime for defining structured model interfaces and connecting agents to application code. It can repair malformed JSON outputs and was used with GPT OSS120B to find comments mentioning sponsors.
OpenShorts is an open-source AI video platform for turning long-form videos such as podcasts, webinars, livestreams, and interviews into 9:16 short-form clips. Its Clip Generator transcribes videos with faster-whisper, detects scene boundaries with PySceneDetect, uses an LLM to identify potential moments, cuts them with FFmpeg, and applies face-tracked or multi-speaker vertical layouts, word-level subtitles, hook overlays, effects, and optional dubbing. The platform also includes AI Shorts for generating UGC-style marketing videos with AI actors, scripts, voiceovers, b-roll, and lip-sync, plus YouTube Studio tools for thumbnails, titles, descriptions, and direct publishing. Clips can be distributed to TikTok, Instagram Reels, and YouTube Shorts through Upload-Post, and the system provides an MCP server, REST API, webhooks, CLI, and agent skill for automation. The core application can be self-hosted with Docker under the MIT license; a hosted version is available at openshorts.app, while the cloud service infrastructure is source-available under a separate commercial license.
Repowise is an open-source, self-hosted codebase-intelligence tool for developers and coding agents. It builds a local index of source code, dependency and symbol relationships, Git history, tests, documentation, architectural decisions, dead code, and code-health signals, then exposes the indexed evidence through an MCP server, local dashboard, CLI, editor integrations, and pull-request analysis. Its graph is built from AST-parsed code and Git data, with confidence-stamped call resolution, change-impact and co-change analysis, hotspot and ownership history, documentation-drift checks, decision evidence, and deterministic code-health detectors. The MCP interface provides task-oriented tools for repository overviews, cited answers, file and symbol context, code search, risk analysis, change risk, architectural rationale, dead-code detection, and health and refactoring plans. Optional model-written documentation and prose can be generated through a configured provider, while the core graph, risk, health, test, and dead-code analysis does not require LLM calls or an API key. Repowise also includes reversible command-output distillation for agents, proactive context hooks, generated agent instruction files, a GitHub PR bot, multi-repository workspace analysis, contract and cross-repository blast-radius mapping, and a VS Code extension. It can be installed with pip and run locally; the repository states that raw source is processed transiently and not persisted, while indexed graph data, embeddings, wiki pages, and Git metadata are stored. The core project is licensed under AGPL-3.0, with commercial licensing available for specified enterprise capabilities and embedding scenarios.
Hatchdoor is a self-hosted web application and MCP server for Obsidian-style Markdown vaults. It provides a browser interface and an MCP endpoint through which users and AI agents can browse, keyword-search, semantic-search, create, edit, move, and link notes, with wiki links, backlinks, graph views, metadata, Markdown rendering, attachments, and optional Git commits and pushes. Markdown files remain the source of truth; Hatchdoor builds a disposable SQLite read model for browsing, links, search, graph data, and metadata, rebuilding it from the vault if the cache is deleted. It is distributed as a rootless, distroless Docker or Podman container, with MCP and write access disabled by default. Hatchdoor is not a hosted synchronization service or multi-user collaboration platform. The project is licensed under the GNU Affero General Public License v3.0 only.
useAgent is an open-source AI coworker that gives Claude Code, Codex, OpenCode, and Pi a shared workspace with isolated Linux computers, repositories, terminals, browsers, and team tools. It can handle research, websites, spreadsheets, reports, presentations, and code changes, returning usable files, artifacts, or pull requests through a web app, Slack, REST API, or scheduled tasks. Each run is stored as an event log in Postgres and uses a common event contract across supported agent engines. Daytona or CubeSandbox provides the isolated workstation, while integrations cross a trusted gateway as typed tools so credentials remain on the control plane rather than entering the sandbox. The workspace also supports versioned skills and playbooks, team knowledge and memory, approval-gated tools, revisioned workpieces, and durable sessions. The repository describes useAgent as alpha software with changing APIs and schemas. It can be self-hosted on Linux using Bun and Postgres with pgvector, and is licensed under AGPL-3.0-only; a commercial license is available for proprietary embedding, distribution, OEM, or white-label use.
Deft is a self-hostable, open-source workspace where people and AI agents share chat, tasks, knowledge, calendar context, approvals, and action history. Its core workflow captures discussions as durable knowledge, lets the Defty agent use workspace context to propose task details, and routes proposed agent actions through configurable approval and trust policies before recording the approved work and its receipts. It also provides real-time conversations, task views, notes, knowledge graphs, calendar features, personal MCP connections, agent employees, native workspace tools, scoped access, revocation, and provider-neutral support for hosted or local AI providers. Deft can run without an AI provider as a conventional workspace. The self-hosting path uses Docker and PostgreSQL with pgvector; the repository is an alpha intended for technical evaluation, internal use, and controlled pilots. It is licensed under AGPL-3.0-only.
MindSpark is an open-source, self-hostable mind-mapping application developed in vanilla JavaScript and distributed under the MIT license. It represents each map as a Markdown outline and keeps the canvas and Markdown editor synchronized in both directions, with imports and exports for formats including JSON, OPML, Markdown, PNG, PDF, Word, Mermaid, and GitMind. The editor provides automatic tree layout, drag-and-drop reordering, rich-text nodes, LaTeX rendering through MathML, an Excel-like node formula engine with functions such as SUM, AVERAGE, IF, ROUND, and SQRT, version history, read-only URL share links, and citations with DOI-based metadata retrieval from Crossref. Research, prompt-engineering, software, and AI-agent templates are included; map branches can also be compiled into prompts for Anthropic or OpenAI using a user-supplied API key. MindSpark can run locally with Node.js and SQLite, as a static site using a personal access token to save maps in a user's own Git repository, or with an optional Cloudflare Worker for OAuth, editable shared maps, access control, and real-time collaboration. Its repository also describes a separate MCP server for creating, reading, editing, and rendering maps through compatible AI clients.
Rootprint is an open-source, self-hosted log and trace explorer that searches indexes stored on S3-compatible object storage or local disk. It accepts logs and traces through OpenTelemetry Protocol (OTLP) Protobuf or HTTP from compatible collectors and agents, then provides severity-aware log search, field discovery, histograms, query autocomplete, saved views, share links, and exports. Its trace view displays per-span timing, attributes, and events, with links between spans and their logs. A service-health dashboard reports request rate, error rate, p95 latency, time-intensive endpoints, outbound dependencies, and failing spans. The application also includes team roles, scoped ingest keys, service accounts, personal API keys, and SSO through Google, GitHub, or OpenID Connect. Rootprint is distributed under the Apache-2.0 license and can be run with Docker Compose. The repository describes it as being under active development and not yet at version 1.0; its API uses Hono and its web interface uses SvelteKit, with Quickwit used in the object-storage-backed search setup.
Nethera is a deployment platform and CLI for running Docker Compose applications on Linux machines you control while exposing selected services through public HTTPS URLs. It uses a laptop-installed CLI and a machine-installed agent; a nethera.yml file extends an existing docker-compose.yml with deployment targets and nethera: service blocks that specify public ports and optional login or endpoint-token authentication. Nethera handles the public edge connection, TLS, and routing without requiring static IPs, port forwarding, router configuration, or a manually configured reverse proxy, while containers, volumes, files, GPUs, and application data remain on the host machines. It supports Debian- and RHEL-family distributions on Linux amd64 and arm64, and the repository is MIT-licensed.
Stack Overflow for Agents is an API and platform for coding agents. It enables agents to search Stack Overflow posts, publish reusable discoveries, and report whether solutions from other agents worked.