Tools & resources

Agent Console is an open-source, local-first observability tool by LockedIn Labs for AI coding agents. It reads Claude Code and Codex transcripts to show token usage by cache reads, cache writes, output and uncached input, model and session activity, list-price cost estimates, burn rate and dollars per hour. A self-hosted hub can combine privacy-limited usage metadata from multiple machines over TLS, while keeping prompts, replies, file paths and file contents local. It also provides optional Claude Code OpenTelemetry and Kong or LiteLLM metric ingestion, a Prometheus endpoint, Grafana dashboard, presenting mode, alerts, and policy hooks. It runs locally from source or release downloads on macOS, Linux and Windows, requires Node.js 22 or newer for the source route, and is licensed under the MIT License.

Rungic is an AgentOS product that runs alongside Android on compatible phones, providing a Linux desktop with KDE Plasma and desktop applications such as Firefox, Blender, Krita and VS Code. The Linux environment runs in an LXC container on Android's kernel rather than in a virtual machine or emulator, while an Android app connects its display, touch input, sound and camera to the phone, a floating window or a TV. Users can install software through apt, Flatpak or Discover and use the phone as a touchpad and keyboard when casting. Rungic includes a bundled assistant whose reference integration uses a realtime voice model for conversation and Codex for background tasks. The assistant plans tasks, opens applications, clicks, types and returns created files in the conversation; its work can run in a separate desktop workspace so the user can continue using the phone. The system supports multiple agent workspaces, shared project folders, desktop control, phone controls and proactive system-care suggestions that record evidence, investigation results, repair plans and user decisions. Rungic exposes these capabilities through two MCP servers, command-line tools, D-Bus services and standard Linux interfaces, allowing other agents to be adapted to it. Rungic is under active development and in private preview. Its device adaptation currently requires a compatible, unlockable Android phone with an appropriate GKI kernel, ARM64 support and a Snapdragon processor with an Adreno GPU; the repository documents tested Motorola devices and separate preparation, Linux image-building and installation stages. Device preparation or bootloader unlocking can erase user data, and the repository advises backing up the phone and checking manufacturer and application restrictions.

hypoarena is a fully offline scientific hypothesis-discovery workbench and command-line tool. It generates deterministic synthetic literature containing planted causal chains, competing hypotheses, paraphrase clusters, distractors, and contradictions, then runs a generate–debate–evolve loop through pluggable agent adapters that propose, critique, and revise hypotheses. Its pipeline includes span-level citation and claim-grounding checks, paraphrase deduplication using hashing, n-gram Jaccard, TF-IDF, and MinHash LSH, Bradley–Terry/Elo tournaments for ranking hypotheses or agents, hypothesis evolution operators, and Bayesian evidence accumulation. It produces Markdown and self-contained HTML reports with rankings, grounding flags, deduplication clusters, belief updates, run metadata, and limitations. The core runtime depends on NumPy; an optional CPU-only PyTorch extra demonstrates a trainable ranker. Examples and tests run without network access, real model calls, or downloaded weights, and the project is licensed under the MIT License.

iCode is a lightweight, extensible, fully offline terminal development platform and toolkit for orchestrating AI agents and workflows, developed as an open-source project by openJiuwen-ai. Its terminal interface shows the active agent, model, approval mode and session; renders Markdown and diagrams; and provides workflow execution views with per-node models, tool calls and token spending. Agents are configured rather than coded and can include instructions, tools, sub-agents, skills, MCP servers and memory. Model profiles from different providers can be switched during a conversation, while file diffs, turn-based rollback, token and cache usage, model/tool timelines, and an embedded shell expose and control execution. iCode does not include models, telemetry, analytics or crash reporting; it uses only the providers, MCP servers and hooks configured by the user. The repository describes the project as a work in progress, requires uv to run, and distributes it under the Apache License 2.0 alongside separately licensed third-party components.

Agent Office is a self-hosted 3D workspace for coordinating coding agents across GitHub repositories. It creates a floor for each project, places Claude Code, Codex, OpenCode and other supported agent workers at desks, streams their terminals, and lets users assign issues, queue tasks, provide Git worktrees, inspect changes and open pull requests. Workers can also list, hire, message and dismiss other workers through the agent-office MCP server or command-line interface. The office includes shared chat, voice, screen sharing and a whiteboard, plus a 2D /lite interface for phones and slower computers. It runs locally or can be deployed to AWS, Azure, Railway, Fly.io, Dokploy or an Ubuntu/Debian server, with multi-user accounts and per-user Claude and GitHub sign-ins. The repository describes it as work in progress, subject to breaking changes, and distributes it under the MIT license.

seiso is a Markdown convention and linter for project documentation written by AI and read by people and coding agents. Its convention assigns each document a kind, such as how-to, reference, or ADR; keeps each fact in one canonical location; requires pointers to named files or symbols; separates different concerns; and avoids rapidly changing values in long-lived pages. The reference implementation checks documents against the convention and reports where each violation occurs and how to fix it, including the decision needed when a judgment call is involved. Stable rules run by default, while experimental preview rules require an explicit option; the CLI also provides commands for initialization, rule explanations, parsing, and checks. It can be installed through Cargo, PyPI, npm, or pipx, and integrates with Claude Code hooks, pre-commit, and CI. The project is licensed under MIT.

Universal Modder is an open-source toolkit by rehan-remade for AI coding agents that create offline, single-player mods for PC games. Its skills and tools identify a game's engine and existing modding route, inspect code and files, back up saves, build a working mod slice, generate art, 3D assets and audio through the fal MCP server, test the result in the running game, package it, and record findings for later agents. The repository supports Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and other agents that read AGENTS.md, and includes the Python `um` CLI for game scanning, asset generation and processing, Windows automation, save management, video capture, publishing checks and a shared knowledge base of game-specific field notes. Its rules restrict use to games owned by the user and offline or single-player work; they prohibit online cheats, anti-cheat or DRM bypasses, and distributing game files or decompiled code. The repository is MIT licensed and requires Python 3.10+ and ffmpeg; Blender is used for 3D-to-sprite rendering.

DoxxNet is an Agentic Defined Network: a private, parallel network for people and software agents to browse, connect devices, call, message, and transfer files. Its P2P communications connect devices directly with end-to-end encryption, while its infrastructure includes private IP space, DNS, and hardware; agents can configure network controls through an API. The service provides encrypted voice and video calls, group and direct messaging, voice memos, on-device translation, and NetDrop transfers for full, unmodified files. It also offers DNS encryption, unwanted-domain blocking, private networking and routing controls, and agent identities with private network permissions. The site states that it does not retain provider-held communication logs and describes the network as zero-log infrastructure.

Fastino Labs is an AI company that builds open-weight language models for agentic AI. Its models are intended for fast type decisions such as intent routing, fact-checking, hallucination checks, and browser-agent tasks, and the video describes them as available for local use and through an inference API. The company’s site identifies GLiNER, GLiGuard, and the Fastino fine-tuning agent among its research and models.

ODS is a self-hosted local AI server stack developed by Osmantic for Linux, Windows with WSL2 and Docker Desktop, and Apple Silicon macOS. Its installers detect the host hardware, select an installable model, generate credentials, and launch a preconfigured stack containing local LLM inference, Open WebUI, a management dashboard, LiteLLM, embeddings, speech-to-text, text-to-speech, agents, n8n workflows, Qdrant-based RAG, search, ComfyUI image generation, and operational and privacy tools. Local mode is the default; cloud and hybrid modes can route through OpenAI, Anthropic, or Together APIs. The stack uses a bootstrap model so chat can begin while the hardware-selected full model downloads in the background. A manifest-based extension system discovers service folders and merges their Docker Compose definitions into the stack, while the `ods` CLI manages services, models, modes, configuration, presets, and lifecycle operations. The repository documents GPU and unified-memory paths for NVIDIA, AMD, Intel Arc, and Apple Silicon hardware, along with model selection, recovery, validation, and operator workflows. ODS is distributed as an Apache-2.0-licensed open-source repository and can be installed with shell or PowerShell installers. The repository identifies version 2.6.0 as its stable release and states that no cloud account or subscription is required for local operation.

video-use is an open-source video-editing tool for coding agents such as Claude Code, Codex, Hermes, and Openclaw. It takes raw footage in a folder and produces an edited MP4, with support for removing filler words and dead space, applying FFmpeg color-grading chains, adding short audio fades at cuts, burning customizable subtitles, and generating animation overlays through HyperFrames, Remotion, Manim, or PIL. Its pipeline transcribes each source with ElevenLabs Scribe, packing word-level timestamps, speaker diarization, and audio events into a Markdown view. The agent reasons over that transcript and requests filmstrip, waveform, and word-label composites from timeline_view only at decision points, rather than processing every video frame as visual context. It then creates an edit decision list, renders the result, evaluates the rendered video at every cut boundary for visual jumps, audio pops, and subtitle problems, and can revise and re-render up to three times. The tool uses an approval-oriented workflow in which the agent inventories sources, proposes an editing strategy, waits for confirmation, executes the edit, evaluates it, and persists session memory in project.md. It requires FFmpeg and an ElevenLabs API key; yt-dlp is optional for downloading online sources. Outputs are placed in an edit directory next to the source footage.

Everything Claude Code is a Claude Code plugin and toolkit containing configurable agents, skills, slash commands, rules, hooks, contexts, MCP server configurations, scripts, and tests for AI-assisted software development. Its agents delegate tasks such as planning, code review, security review, testing, and build-error resolution. Skills define workflows including test-driven development, security review, continuous learning, and verification; hooks run on Claude Code tool or session events to load and save context, suggest compaction, and evaluate sessions; and rules provide persistent guidance for security, coding style, testing, Git workflow, delegation, and performance. Node.js scripts support Windows, macOS, and Linux and detect a project's preferred package manager from environment settings, project configuration, package.json, lock files, or global configuration. The repository can be installed as a Claude Code plugin or copied into Claude configuration directories manually. It includes a Node.js test suite and is distributed under the MIT license.

Marketing Skills is a collection of Markdown-based skills for AI coding agents, built by Corey Haines. It provides specialized workflows for conversion optimization, copywriting, SEO, analytics, advertising, growth engineering, sales, and related marketing tasks, and supports Claude Code, OpenAI Codex, Cursor, Windsurf, and agents implementing the Agent Skills specification. The skills share context and cross-reference one another. The product-marketing skill serves as the foundation, with other skills consulting it for product, audience, and positioning information before handling tasks such as CRO, copywriting, SEO audits, analytics setup, email flows, pricing, or sales enablement. Skills can be installed individually or as a collection through the Skills CLI, Claude Code's plugin system, a repository clone or submodule, or SkillKit. The repository is built and maintained through community contributions, with issues and pull requests welcomed. It is free and MIT-licensed.

Camofox Browser is an open-source anti-detection headless browser server for AI agents, built by the team behind jo and powered by Camoufox, a Firefox fork that applies fingerprint spoofing at the C++ level. It exposes browser automation through a REST API with accessibility snapshots, stable element references for clicking and typing, navigation and search macros, screenshots, downloads, file uploads, structured extraction, YouTube transcript extraction, and OpenAPI documentation. Browser contexts provide isolated cookies and storage, with optional persistence, cookie import, proxy and GeoIP configuration, VNC-based interactive login, and Playwright session tracing. It can run from npm, Docker, Fly.io, or Railway, and supports lazy browser startup and idle shutdown. The repository is licensed under MIT.

DeerFlow is an open-source long-horizon super-agent harness developed in the ByteDance repository. Built on LangGraph and LangChain, it orchestrates a lead agent with sub-agents, progressively loaded skills, memory, tools, and sandboxed execution environments for research, coding, file work, report generation, slide creation, web-page creation, and other multi-step tasks. The runtime provides a filesystem for uploads, workspaces, and outputs; configurable web search, web fetching, browser control, Bash and file tools; MCP servers; reusable Markdown-based skills; long-term memory; scheduled tasks; and messaging-app channels. Sub-agents receive scoped context, tools, and termination conditions, and can run bounded parallel work before returning structured results to the lead agent. Sandbox modes include local execution, isolated Docker containers, Kubernetes-backed execution, and E2B-based environments, depending on configuration. DeerFlow can run locally or with Docker Compose and exposes a Web UI and Gateway at the documented local address. Its setup wizard generates configuration for model providers, search, sandbox, Bash, and file-writing options; the project also documents integrations for LangSmith, Langfuse, and Monocle tracing, personal access tokens, custom agents, MCP background tasks, and extensible Python packages. Version 2.0 is a ground-up rewrite of the earlier 1.x Deep Research framework; active development is on the 2.0 line.

Lightpanda Browser is a headless browser built from scratch in Zig for AI agents and web automation. It is not based on Chromium, Blink, or WebKit; its implementation includes an HTML parser, V8 JavaScript support, a DOM tree and APIs, network loading, cookies, form input, proxy support, network interception, and optional robots.txt compliance. It can dump pages as HTML, Markdown, PNG, or PDF, or expose a Chrome DevTools Protocol and WebDriver BiDi server for clients such as Puppeteer and Playwright. Its native agent mode uses an LLM to navigate pages, click through flows, fill forms, and extract structured data. Sessions can be exported as deterministic, token-free PandaScript JavaScript and replayed without a model at runtime. The browser also provides an MCP server over stdio or HTTP, with isolated or shared browsing sessions for multiple agents. The project distributes nightly binaries for Linux and macOS, Docker images for Linux amd64 and arm64, and packages for Homebrew and the Arch User Repository. It can also be built from source with Zig and its V8, Libcurl, and html5ever dependencies.

Andrej Karpathy Skills is a set of guidelines for Claude Code and Cursor that configures coding agents to avoid common LLM coding pitfalls. Its four principles are Think Before Coding, which requires explicit assumptions, clarification of ambiguity, and stated trade-offs; Simplicity First, which limits speculative features and unnecessary abstractions; Surgical Changes, which restricts edits to the requested scope; and Goal-Driven Execution, which turns tasks into verifiable success criteria and test-driven loops. The guidelines can be installed as a Claude Code plugin, copied into a project's CLAUDE.md file, or applied through the repository's Cursor rule. The repository is licensed under the MIT License.

Browser Use is an AI browser-automation framework and hosted service for assigning web tasks to agents. Its agents operate websites through a browser by opening pages, clicking controls, entering text, filling forms, and extracting structured data. The project provides a Python library for embedding browser agents in applications, a CLI for agent-driven one-off tasks, and a Browser Use Cloud API with hosted agents and managed browser infrastructure. The library supports different LLM providers, custom tools, structured output, reusable browser profiles, and parallel or scheduled automation. The open-source library is free to use and licensed under the MIT License; the hosted service adds scalable browser infrastructure, proxy rotation, stealth browser fingerprinting, CAPTCHA handling, persistent storage, and integrations.

TradingAgents is an open-source, multi-agent LLM financial trading framework developed by TauricResearch for research. It models a trading firm through specialized agents: fundamental, sentiment, news, and technical analysts produce market assessments; bullish and bearish researchers debate those assessments; a Trader Agent proposes the timing and size of a trade; and risk-management and portfolio-management agents evaluate and approve or reject it before execution on a simulated exchange. The framework uses LangGraph and can be run through an interactive CLI, Docker, or as a Python package via the TradingAgentsGraph class and its propagate function. It accepts market and ticker inputs, analysis dates, configurable research depth and debate rounds, and LLM backends including hosted providers, Ollama, and other OpenAI-compatible servers. It gathers financial, market, news, macroeconomic, and social-sentiment data, and supports persistent decision logs and optional LangGraph checkpoint recovery. TradingAgents is intended as a research scaffold rather than financial, investment, or trading advice. Results can vary with the language model, sampling settings, data sources, analysis period, and other nondeterministic factors.

LLM Wiki is a cross-platform desktop application maintained by nash_su that turns imported documents into a persistent, organized, interlinked knowledge base. It uses a three-layer structure of immutable raw sources, LLM-generated wiki pages, and configuration rules, with ingest, query, and lint operations; generated pages use YAML frontmatter, source traceability, an index, an operation log, and [[wikilink]] cross-references. Its ingest pipeline first analyzes each source for entities, concepts, connections, contradictions, and recommended structure, then generates or updates wiki pages, summaries, indexes, research queries, and human-review items. SHA256 caching skips unchanged files, while a persistent queue supports serialized processing, retries, cancellation, and crash recovery. It accepts PDFs, Office documents, EPUB/MOBI, Org mode, images, media, web clips, and URL batches, with optional vision-based image captions and MinerU processing for complex PDFs. Retrieval combines tokenized keyword search, optional LanceDB vector search through OpenAI-compatible embedding endpoints, and graph expansion based on direct links, shared sources, Adamic-Adar similarity, and page-type affinity. The application also provides Louvain community detection, graph insights, deep research through Tavily, SerpApi, or SearXNG, a Rust tool-using chat agent, review workflows, Mermaid and KaTeX rendering, a Chrome web clipper, and project export/import. It includes a token-protected local HTTP API and MCP server for searching, reading files, traversing the graph, rescanning sources, and calling the backend agent. The application is built with Tauri v2, React, TypeScript, and Rust, has macOS, Windows, and Linux builds, and is licensed under GPL-3.0.

Skills is a command-line tool for the open agent skills ecosystem. It installs reusable instruction sets defined in SKILL.md files into supported coding agents such as OpenCode, Claude Code, Codex, Cursor, and others. The `npx skills` CLI resolves skills from GitHub, GitLab, other Git URLs, local paths, or direct file and archive URLs; lists available skills, selects individual skills, and installs them project-wide or globally by symlink or copy. It can also generate a prompt for a skill without installing it, launch a supported agent with that prompt, search for skills, update or remove installations, and create new SKILL.md templates. Skills use YAML frontmatter with a name and description, and the CLI discovers them in standard agent and repository skill directories. The project is licensed under the MIT License.

CowAgent is an open-source AI assistant and agent harness developed by LinkAI. It plans complex tasks step by step, runs tools and skills, controls computers and external services, maintains long-term memory and a personal knowledge base, and supports multi-agent teams with separate roles, models, skills, knowledge, and workspaces. Its architecture routes messages from web and messaging channels through an agent core that reasons over available models, tools, skills, memory, and knowledge before returning a response. Memory uses a three-tier structure of conversation context, daily memory, and long-term MEMORY.md, with a nightly Deep Dream process that distills memories; the knowledge base is an automatically curated Markdown wiki with cross-references and a browsable knowledge graph. Built-in tools include file and terminal operations, web fetching and search, scheduling, memory retrieval, vision, and browser automation, while MCP servers can be added through configuration. Skills can be installed from the Skill Hub, GitHub, ClawHub, or URLs, or authored through conversation. CowAgent supports multiple LLM providers and routes chat, vision, image generation, speech, and embedding workloads separately. It can run locally, in Docker, on a server, or through its macOS and Windows desktop client, with a web console for configuration and a command-line interface for service and skill management. The project is licensed under the MIT License and was formerly named chatgpt-on-wechat.

Fay is an open-source digital-human agent framework for connecting 2D, 3D, mobile, desktop, and web avatars—or OpenAI-compatible and DeepSeek large language models—to business systems. It provides interfaces for text and voice interaction, avatar control, administration, automatic broadcasting, and intent handling, and can connect interchangeable avatar, ASR, TTS, and language models. The framework supports offline operation, streaming interaction, multi-user concurrency, configurable voice commands, custom knowledge bases and personas, wake-word activation and interruption, scheduled proactive dialogue, agent tool calling, MCP tool management, robot-expression output, and server or standalone deployment. It runs from source with Python on Windows, macOS, and Ubuntu and includes a local management page.

LangBot is an open-source platform for building and operating AI-powered instant-messaging bots. It connects large language models to platforms including Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK, and Matrix, supporting multi-turn conversations, tool calling, multimodal input, streaming output, and agent workflows. The platform includes a built-in retrieval-augmented generation knowledge base, integrations with systems such as Dify, Coze, n8n, Langflow, and Deerflow, and an event-driven plugin system with MCP support. A browser-based management panel configures, manages, and monitors bots, while its multi-pipeline architecture supports separate bot workflows and operational monitoring. LangBot can be run through LangBot Cloud, a one-line uvx launch, Docker Compose, or other deployment methods. It also exposes an MCP endpoint that mirrors its HTTP API for programmatic management of bots, pipelines, plugins, and models.

PentAGI is a self-hosted, AI-powered penetration-testing platform for security professionals, researchers, and ethical hackers. It uses a multi-agent system with specialized researcher, developer, executor, adviser, planner, and reflector roles to investigate targets, plan and execute testing tasks, operate professional security tools, and generate vulnerability reports. The platform runs operations in isolated Docker containers and includes more than 20 security tools, an isolated web scraper, external search integrations, PostgreSQL with pgvector for persistent results and semantic memory, and an optional Graphiti/Neo4j knowledge graph. Its agents use long-term, working, and episodic memory; chain summarization converts conversation chains into a structured ChainAST, summarizes older sections and oversized pairs, and rebuilds the chain when the result is smaller. Optional execution monitoring detects repeated or excessive tool calls and invokes a mentor for alternative strategies, while task planning decomposes a request into actionable subtasks before specialist agents execute it. PentAGI provides a React and TypeScript web interface, Go-based REST and GraphQL APIs with Bearer-token authentication, flow-scoped file management, Markdown and PDF report downloads, and monitoring integrations built around OpenTelemetry, Grafana, VictoriaMetrics, Jaeger, and Loki. It supports multiple hosted and local LLM providers, including OpenAI, Anthropic, Google Gemini, AWS Bedrock, Ollama, DeepSeek, GLM, Kimi, Qwen, and MiniMax, and can be deployed with Docker Compose or Podman. The project states that it is an autonomous and assistant-guided penetration-testing platform rather than a CALDERA-style breach-and-attack-simulation system with predefined campaigns; testing should be limited to systems for which the operator has authorization.

Agent Skills is a curated skill registry and npm-based CLI for extending AI coding agents such as Claude Code, Cursor, Copilot, and Antigravity with packaged instructions, templates, and reference files. The CLI fetches a catalog, lets users search and select skills, caches downloads locally, and installs them to selected agents by copying or symlinking them at global or project scope; it also supports updating, removing, auditing, and managing cached skills. The repository describes the catalog as an open-source, human-curated library with CI static analysis, content hashing, lockfiles, path isolation, symlink guards, sanitization, audit logging, and Snyk Agent Scan checks. It also includes an MCP server that exposes progressive-disclosure tools for listing, searching, reading, and fetching skill files. The CLI source is MIT-licensed; maintainer-authored skills are generally CC BY 4.0, while third-party skills retain their own licenses.

Atlas is a desktop application for source control and shared context across coding-agent sessions, developed by Pacifio. It runs Claude Code, Codex, its native agent, and agents from the ACP registry against a codebase, recording prompts, tool calls, file changes, and patches in local session storage. When a commit is created, Atlas links it to the session that produced it, preserving those checkpoint relationships through rebases and amends and allowing the session to be queried later. Before sending a prompt, Atlas resolves local @ mentions, combines project notes and agent memory, performs on-device semantic retrieval, and can add a handoff from a previous session. Markdown knowledge files, CLAUDE.md, AGENTS.md, plans, decisions, failures, file changes, skills, papers, and past sessions can therefore be shared between agents. The application also provides a code editor, Git operations and diffs, a terminal, a project knowledge base, research tools, a browser, spatial boards, and agent activity history. Atlas is local by default: code, notes, and sessions remain on the machine, secrets are scrubbed before persistence, and local mode works without an account or network. Optional organisations can sync across devices and teammates. The repository states that macOS is the supported platform; Linux and Windows builds are untested. It is distributed under the MIT license.

Voicebox is a local-first, open-source AI voice studio developed in the jamiepine/voicebox repository. It combines voice cloning, text-to-speech, speech-to-text dictation, voice profiles, audio effects, and agent voice output in a desktop application. It generates speech through seven switchable TTS engines, including Qwen3-TTS, Qwen CustomVoice, LuxTTS, Chatterbox, HumeAI TADA, and Kokoro, and supports voice cloning from reference audio, preset voices, multilingual generation, automatic text chunking, crossfading, and multi-track story editing. Whisper-based transcription powers global dictation, in-app recording, and the transcription API; an optional local Qwen3 LLM can refine dictation or rewrite text according to a voice profile's persona. The application exposes REST endpoints for speech generation, agent speech, transcription, and profile management, plus a built-in MCP server with tools for speaking, transcribing, and browsing captures and profiles. It runs models and stores voice data locally, using Tauri with a React/TypeScript frontend and FastAPI backend; platform backends include MLX on Apple Silicon and PyTorch on CUDA, ROCm, XPU, DirectML, or CPU. The repository is licensed under the MIT License and provides macOS, Windows, and Docker distribution, with Linux build-from-source instructions.

AstrBot is an open-source all-in-one AI agent chatbot platform and development framework that connects instant-messaging platforms with LLM services, plugins, multimodal features, MCP, skills, knowledge bases, persona settings, and automatic context compression. It supports platforms including QQ, Telegram, WeCom, Feishu, DingTalk, Slack, Discord, LINE, and others, and can connect to services such as OpenAI-compatible APIs, Anthropic, Gemini, Ollama, LM Studio, Dify, Coze, and Alibaba Cloud Bailian. Its plugin system offers one-click installation of more than 1,000 plugins, while its agent sandbox isolates code and shell execution and supports session-level resource reuse. AstrBot provides a WebUI and web ChatUI with agent sandbox and web-search support, and can be deployed with uv, Docker or Docker Compose, desktop applications, or other documented deployment methods.

WeChat Bot is an open-source IM agent and command-line tool based on Wechaty. It connects WeChat QR-code logins, Lark events, Telegram's Bot API, and WhatsApp Cloud API to AI services including ChatGPT, DeepSeek, Ollama, Claude, Pi, and other configured providers for message replies and analysis. The project routes received messages through a pipeline that captures WeChat messages locally in JSONL, sends them to a selected model or agent, and posts the response back to the IM channel. Its `wb wx` commands use OpenCLI wx-cli to access local WeChat chats, contacts, group members, favorites, and cached Moments; `wb stats` and `wb analyze` provide local statistics or AI-assisted group and friend analysis. Replies can be restricted with contact and group allowlists, prefixes, and group-mention rules, and non-text messages are not automatically sent to the reply pipeline. The repository is a Node.js project requiring Node.js 18 or later and can be installed with npm or run in Docker. It is licensed under the MIT License. The README warns that WeChat Web protocols can trigger account warnings or bans and recommends narrow allowlists and usage scopes.

Supermemory is an AI memory and context engine with a hosted API, application, plugins, MCP server, and local runtime. It extracts facts from conversations, tracks temporal changes and contradictions, automatically forgets expired information, and maintains user profiles containing stable facts and recent activity. Its API combines memory retrieval with hybrid RAG search, allowing applications and AI agents to query personalized context and knowledge-base documents together. It also processes uploaded PDFs, images, videos, and code, and can synchronize data from services such as Google Drive, Gmail, Notion, OneDrive, GitHub, and web pages. SDKs are available for JavaScript and Python, with integrations for AI frameworks including the Vercel AI SDK, LangChain, LangGraph, OpenAI Agents SDK, Mastra, Agno, and n8n. Supermemory can provide persistent memory to compatible assistants through plugins or its MCP server. The local distribution runs as a single binary or through npx, uses local embeddings by default, supports OpenAI-compatible and Ollama-backed models, and exposes the same Memory API on localhost for offline or self-hosted use.

Codex-X is a cross-platform desktop management tool for OpenAI Codex Desktop and Codex CLI. It provides a visual interface for managing prompt templates, switching between official login profiles and third-party API providers, organizing and repairing local sessions, managing Skills and MCP servers, and inspecting or editing Codex TOML and JSON configuration files. The application can import or edit Markdown prompts, append or replace existing prompt content with automatic backups, test provider endpoints and discover models, import providers from cc-switch, group sessions by project, detect or repair provider inconsistencies, and permanently delete selected sessions from Codex storage. It also previews MCP servers, installs Skills from ZIP files, enables or disables individual extensions, tracks local token usage by date and model, and attributes subagent usage to its main conversation. Codex-X is built as a Tauri 2 desktop application with a React, TypeScript, and Vite frontend, a Rust backend, and SQLite local storage. It provides macOS, Windows, and Linux packages, including Windows portable distribution, and is released under the MIT License.

Agent-Native is an open-source TypeScript framework for building agentic applications with purpose-built user interfaces. Developers define each capability once as an action; the same action is used as an agent tool and called by the UI, with shared validation, permissions, implementation, data, and application state. Agents work through this shared action layer rather than clicking through the interface. Actions can also be exposed through HTTP, MCP, A2A, and a CLI. The framework includes agent chat, authentication and permissions, reusable skills and memory, scheduled or event-driven automations, multi-agent teams, and PostgreSQL support with PGlite for local development. It is distributed under the MIT license.

AX is Google's open agentic orchestration runtime for declaring and running autonomous agent workloads in Kubernetes clusters. It defines Tasks, Workspaces, Gateways, and Models as ax.io/v1alpha1 manifests: Tasks run agent code in isolated sandboxes, Workspaces preconfigure Git repositories, MCP servers, and skill packages, Gateways restrict outbound traffic with host allowlists, and Models configure the platform's LLM access through Kubernetes secrets. The control plane runs on Agent Substrate and is operated through a kubectl-shaped CLI that applies manifests, watches task status, opens SSH sessions into sandboxes, and suspends or resumes checkpointed agents. AX is described as being designed for high-throughput cluster operation and is still subject to breaking changes before a stable release. It is distributed under the Apache License 2.0.

CLI-Anything is an open-source toolkit and agent plugin for generating command-line harnesses that make existing software accessible to AI agents. It includes integrations for agent platforms such as Claude Code, Cursor, Pi, OpenCode, Codex, and others, plus CLI-Hub for browsing, installing, updating, and launching published harnesses. Its generator follows a seven-phase workflow: it analyzes a target codebase or software, designs command groups and state handling, implements a Click-based CLI with a REPL and JSON output, plans and writes unit and end-to-end tests, documents the result, and packages it for installation. Generated harnesses are intended to call the real application's backend, create or modify its native project files, and invoke the application for operations such as rendering or export rather than replacing it with a simplified implementation. They provide stateful sessions with undo/redo, command-line help for agent discovery, structured JSON responses, and human-readable output. CLI-Anything is distributed under the Apache License 2.0. Its repository also contains agent skill definitions, platform-specific plugins and installers, generated harnesses for numerous applications, and the CLI-Hub package manager installable with pip.

PanWatch is a self-hosted AI stock-monitoring assistant for A-shares, Hong Kong stocks, and U.S. stocks. It provides multi-account portfolio management, market and opportunity views, simulated trading, technical indicators, price alerts, stock research, and notifications through Telegram, WeCom, DingTalk, Lark, Bark, or custom webhooks. Its deep-analysis workflow integrates the TradingAgents framework: technical, sentiment, news, and fundamental analyst agents produce analyses, which proceed through bullish/bearish debate, risk review, and portfolio-manager decision synthesis. The system also includes scheduled pre-market, intraday, and post-market agents, plus AI-filtered financial-news collection. Alerts can combine price, percentage-change, turnover, and volume-ratio conditions with AND/OR rules and scheduling limits. PanWatch runs through Docker or local Python and supports a progressive web app for mobile use. It uses FastAPI, SQLAlchemy, APScheduler, the OpenAI SDK, React, TypeScript, and Tailwind CSS; it supports OpenAI-compatible providers including OpenAI, Zhipu, DeepSeek, and Ollama. Optional OpenTelemetry export covers agent runs, LLM calls, and TradingAgents nodes. The project is released under the MIT license.

StarNet is a local-first desktop harness for creating and running AI agents inside a pixel-art space station. The station layout represents the workflow: rooms define capability-scoped teams, hallways authorize handoffs, and placed objects grant capabilities. Each agent run uses its own workspace, transcript, memory, and bounded permissions, while the station reflects runtime state rather than simulating activity. The harness streams model calls through a local Node sidecar and applies explicit capability and consent checks to tool use. It supports OpenRouter and provider sign-ins for Anthropic, OpenAI, and Google, as well as local models through Ollama. Agents can run concurrently, connect to Telegram, Discord, Slack, Signal, and Matrix, use MCP servers and other connectors, launch recipes and reusable skills, run on schedules, and deliver files through an OUTBOX. Spend, budgets, tasks, schedules, transcripts, and memory persist in the local StarNet workspace; provider requests leave the machine when an agent runs, while secrets are held by the sidecar or operating-system keychain rather than the frontend. StarNet is distributed as a desktop application for Windows and macOS, with Windows identified as the most-tested target. It is also runnable from source with Node.js, and its code is available under the MIT License. The StarNet name, logo, artwork, sprites, and other brand identity are not covered by that license.

OpenShell is an open-source runtime from NVIDIA for running fleets of autonomous AI agents in isolated sandboxes. It lets agents read files, install packages, call APIs, and use credentials while enforcing declared filesystem, process, and network policies. Each agent runs in a sandbox where kernel-level controls govern file access, system calls, and network connections. OpenShell also uses formal verification to review policy changes before approval, flagging newly permitted access such as connections to additional hosts with credentials or calls to new API methods. Credentials are withheld from agents and added only to requests bound for approved endpoints. The project provides a CLI, local gateway, sandbox lifecycle management, policy tooling, inference-provider routing, Kubernetes deployment, extensibility APIs, and Python, TypeScript, Go, and Rust SDKs. OpenShell supports Linux, macOS on Apple Silicon, and experimentally Windows through WSL 2, with Docker, Podman, or host virtualization. It is licensed under the Apache License 2.0 and collects configurable anonymous operational telemetry.

PageIndex is a vectorless, reasoning-based retrieval-augmented generation platform from VectifyAI. It replaces vector indexes and chunking with a hierarchical tree index for each document, then uses an LLM to search that tree agentically, following document structure and context to retrieve relevant sections with traceable references. The PageIndex SDK supports local indexing, retrieval, and chat with a user's own LLM key, as well as PageIndex Cloud for managed parsing, OCR, image understanding, tree-index construction, and storage. It can process text-based PDFs locally and supports cloud-hosted indexes for scanned and image-rich documents; the platform also provides an application for long professional documents, an agent integration interface, and a cloud-only file-level tree index for reasoning across document collections.

CodeGraph is a local code-intelligence tool for AI coding agents, distributed as a CLI, MCP server, and embeddable npm library by Colby McHenry. It parses source code into a SQLite knowledge graph of symbols, files, calls, imports, inheritance, dependencies, framework routes, and cross-language bridges, then resolves references and provides full-text search, source context, call paths, callers, callees, impact analysis, and affected-test discovery. Its primary MCP operation, codegraph_explore, returns relevant verbatim source together with relationships and blast-radius information so agents can query structure without file-by-file exploration. A native Rust parsing kernel supports more than 20 languages, with a portable fallback for unsupported platforms or files that fail native parsing. A native file watcher incrementally updates the local graph after changes, while connect-time reconciliation catches edits made between agent sessions. The tool installs and configures MCP integrations for agents including Claude Code, Cursor, Codex CLI, OpenCode, Gemini CLI, and GitHub Copilot; projects are initialized with codegraph init and stored locally under .codegraph. The repository states that the software is MIT-licensed and that telemetry can be disabled.