# Awesome AI for Teams

[![License: CC0-1.0](https://img.shields.io/badge/license-CC0--1.0-lightgrey.svg)](LICENSE)
[![Awesome](https://awesome.re/badge.svg)](https://awesome.re)
[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](CONTRIBUTING.md)

English | [简体中文](README.zh-CN.md)

**AI tools and practical resources for small engineering teams and tech leads.**

Find tools to share project knowledge, coordinate coding agents, reuse team practices, and manage model access and usage. Includes hosted products, self-hostable tools, developer components, and learning resources.

> **Beyond this list:** [www.goodthingure.com](https://www.goodthingure.com) carries per-tool pages with GitHub stars, licence terms, limitations and open issue numbers — 78 tools, 28 side-by-side comparisons, and 5 step-by-step scenarios. Every page states when it fits, when it does not, and the source it was verified against. There is also a [Chinese edition](https://www.goodthingure.com/zh/) with the same content.

> **Use it from an AI client:** this repository doubles as an MCP server. Add
> `{"command": "npx", "args": ["-y", "github:c-k-x/awesome-ai-for-teams"]}` to your
> Claude Desktop or Cursor config and the agent can query fit, limits, licence terms
> and open issue numbers directly. See [mcp/README.md](mcp/README.md).

## Start with a team problem

| What does your team need? | Where to look |
| --- | --- |
| Work with coding agents and hand off tasks | [Assistants and sessions](#agent-assistants-sessions-and-handoffs) · [Task delegation](#task-delegation-and-delivery) |
| Share project knowledge and working practices | [Shared knowledge](#shared-knowledge-and-assistants) · [Rules and experience](#shared-rules-and-experience) |
| Collaborate in a shared AI workspace | [Shared collaboration spaces](#shared-collaboration-spaces) |
| Bring AI into chat and meetings | [Chat and meeting integrations](#chat-and-meeting-integrations) |
| Manage model access and usage | [Model access](#model-access) |
| Evaluate results and investigate failures | [Evaluation and observability](#evaluation-observability-and-content-checks) |

Each entry explains its purpose and relevant limits. Descriptions summarize official sources; inclusion does not imply hands-on testing or production certification. Ordering is not a ranking.

Notes from the list maintainer: [Hand off coding tasks with Backlog.md](https://www.goodthingure.com/articles/backlog-task-handoffs.html) · [Assign and review agent work with Multica](https://www.goodthingure.com/articles/multica-agent-handoffs.html). Both include practical examples and state what has not been tested.

Star this list to keep it handy for your next tool search, or [suggest a resource or correction](CONTRIBUTING.md).

## Contents

- [Start with a Team Problem](#start-with-a-team-problem)
- [Model Access](#model-access)
- [Document Parsing and Preparation](#document-parsing-and-preparation)
- [Agent Assistants, Sessions, and Handoffs](#agent-assistants-sessions-and-handoffs)
- [Chat and Meeting Integrations](#chat-and-meeting-integrations)
- [Shared Collaboration Spaces](#shared-collaboration-spaces)
- [Task Delegation and Delivery](#task-delegation-and-delivery)
- [Whiteboards and Visual Collaboration](#whiteboards-and-visual-collaboration)
- [Team Knowledge and Shared Practices](#team-knowledge-and-shared-practices)
  - [Shared Knowledge and Assistants](#shared-knowledge-and-assistants)
  - [Shared Rules and Experience](#shared-rules-and-experience)
- [Knowledge Retrieval and Memory](#knowledge-retrieval-and-memory)
- [Application Development and Workflows](#application-development-and-workflows)
- [Execution Environments and Managed Runtimes](#execution-environments-and-managed-runtimes)
- [Evaluation, Observability, and Content Checks](#evaluation-observability-and-content-checks)
- [Protocols and Learning Resources](#protocols-and-learning-resources)
  - [Protocols and Conventions](#protocols-and-conventions)
  - [Practical Guides](#practical-guides)
  - [Tutorials and Research](#tutorials-and-research)
  - [More Resource Directories](#more-resource-directories)
- [Contributing](#contributing)
- [License](#license)

## Model Access

Unify model access, routing, and usage management across providers, or use existing cloud model services.

- [AIHelms](https://github.com/beizhu-1209/AIHelms) - Manage model quotas, cost attribution, and skill approvals by person, department, and project. GPL and commercial licensing options; A2A collaboration remains planned.
- [Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html) - Access hosted foundation models and knowledge bases within AWS resource and access management.
- [Bifrost](https://github.com/maximhq/bifrost) - Access multiple model providers through one gateway with fallbacks, load balancing, and request logs; clustering and some governance features require the enterprise edition.
- [Gemini Enterprise Agent Platform (Vertex AI)](https://docs.cloud.google.com/gemini-enterprise-agent-platform/vertex-ai-name-changes) - Manage model access, deployment, and agent runtimes on Google Cloud under the platform name replacing Vertex AI.
- [Helicone](https://docs.helicone.ai/getting-started/quick-start) - Observe model requests and usage through a gateway or asynchronous logging, with routing and request controls in the gateway path.
- [Higress](https://github.com/higress-group/higress) - Centralize model APIs and MCP tools with authentication, rate limits, request logs, and observability so team projects can reuse common access infrastructure.
- [LiteLLM](https://github.com/BerriAI/litellm) - Access multiple model providers through an SDK or proxy, with application keys, routing, and usage management in the proxy.
- [MCPHub](https://github.com/samanhappy/mcphub) - Centralize MCP servers with per-user credentials, visibility controls, request logs, and health monitoring.
- [MCPJungle](https://github.com/mcpjungle/MCPJungle) - Publish a shared MCP tool directory and control which servers each client can access, with centralized observability. OAuth flows remain planned.
- [Microsoft Foundry](https://learn.microsoft.com/en-us/azure/foundry/what-is-foundry) - Organize models, agents, tools, and evaluation assets by project on Azure with managed execution and observability.
- [Obot](https://github.com/obot-platform/obot) - Distribute approved models, MCP servers, and skills across an organization with shared identity, access, and audit infrastructure for different AI clients. Device-side and server-side auditing require their respective components.
- [OpenRouter](https://openrouter.ai/docs/quickstart) - Access models through a hosted endpoint with provider selection, model fallbacks, and centralized usage records.
- [Plano](https://github.com/katanemo/plano) - Centralize model and agent routing, filters, and tracing in a proxy layer so team applications can reuse shared runtime configuration.
- [Portkey AI Gateway](https://github.com/Portkey-AI/gateway) - Centralize model retries, fallback routing, and load balancing in a gateway; assess the full management platform separately.
- [TokenHub](https://github.com/astaxie/TokenHub) - Allocate model access and project keys by team role, track usage by project and cost center, and compare internal records with provider bills.
- [ToolHive](https://github.com/stacklok/toolhive) - Run MCP servers in isolated containers locally or on Kubernetes, with a curated registry and a gateway for access policies and observability; compare the open-source and Stacklok Enterprise editions for identity and audit features.

## Document Parsing and Preparation

Convert PDFs, office files, scans, and other source material into content suitable for model input and retrieval.

- [Docling](https://github.com/docling-project/docling) - Convert PDFs, office files, and images into a structured document representation with text, tables, and layout information.
- [LlamaParse](https://developers.llamaindex.ai/llamaparse/parse/) - Use a parsing service to prepare scans, tables, and charts in complex documents for extraction, retrieval, and analysis.
- [MarkItDown](https://github.com/microsoft/markitdown) - Convert common office files, PDFs, and web pages to Markdown for model input and text analysis.
- [Unstructured](https://github.com/Unstructured-IO/unstructured) - Partition documents into typed elements with metadata for cleaning, chunking, and knowledge retrieval.

## Agent Assistants, Sessions, and Handoffs

Use assistants to execute tasks, manage multiple sessions, and continue work across sessions or tools.

- [Aider](https://github.com/Aider-AI/aider) - Edit projects from the terminal using repository maps and Git, with lint and test feedback to help teams inspect and revert AI-generated changes.
- [Apache Maka (Incubating)](https://github.com/apache/maka) - Run agent tasks through desktop or terminal interfaces with event records for tool calls and completion states; no official Apache release yet.
- [Claude Code](https://code.claude.com/docs/en/overview) - Read a project, edit files, and run development commands to investigate problems and implement changes with execution feedback.
- [Clay Studio (chadbyte)](https://github.com/chadbyte/clay) - Expose local coding agents through a browser workspace for cross-device access, shared sessions, and handoffs.
- [cli-continues](https://github.com/yigitkonur/cli-continues) - Prepare text-based handoffs from local coding sessions so another assistant can continue the project; running processes are not migrated.
- [codeg](https://github.com/xintaofei/codeg) - Manage coding agent sessions and tasks in one interface, separating changes with Git worktrees for review.
- [Codex](https://github.com/openai/codex) - Read and modify repositories and run commands and checks from a terminal-based coding agent.
- [Cursor](https://cursor.com/) - Combine code, terminal output, and diffs in an editor to make changes that developers can review.
- [Gemini CLI](https://github.com/google-gemini/gemini-cli) - Use Gemini from the terminal for repository analysis, file changes, and command execution, with headless automation support.
- [Golutra](https://github.com/golutra/golutra) - Turn existing CLI agents into a parallel AI team with workflows, result handoffs, and long-running memory; EverOS can provide the memory layer; licensed under BSL-1.1.
- [Herdr](https://github.com/herdrdev/herdr) - Manage multiple agent terminals, identify sessions waiting for input, and control workspaces through a CLI.
- [Hermes Agent (Nous Research)](https://github.com/NousResearch/hermes-agent) - Combine a terminal, messaging gateway, scheduled tasks, and file-based memory to reuse preferences and procedures over time.
- [Hive (tt-a1i)](https://github.com/tt-a1i/hive) - Organize Claude Code, Codex, Gemini, and other CLI agents into a visible local team with PTYs, a shared Markdown task graph, and a browser workbench; BUSL-1.1 with no multi-user authentication boundary.
- [holaOS](https://github.com/holaboss-ai/holaOS) - Use applications and agents side by side on a desktop with reusable tools, skills, and local memory; team governance depends on the edition.
- [Letta](https://github.com/letta-ai/letta-code) - Run persistent agents with versioned file-based memory through a CLI or application interfaces.
- [MCO](https://github.com/mco-org/mco) - Run multiple coding agents in parallel from a CLI, compare their results, and execute in read-only or write modes; an engineering orchestration component rather than a multi-user workspace.
- [OpenClaw](https://github.com/openclaw/openclaw) - Run a persistent assistant gateway that accepts tasks from messaging platforms and devices and manages tools, sessions, and workspace memory.
- [OpenCode](https://github.com/anomalyco/opencode) - Choose model providers and reuse project rules, tools, and agent roles in a coding assistant.
- [OpenHands Agent Canvas](https://github.com/OpenHands/OpenHands) - Manage coding agent sessions in a self-hostable console, switch between local and remote backends, and automate development tasks through schedules and events.
- [OpenHive (Aden)](https://github.com/aden-hive/hive) - Run production business processes through a multi-agent harness where a Queen grows worker agents around a persistent plan, shared ledger, recovery, observability, and human oversight; Apache-2.0.
- [OpenMemory (Mem0)](https://github.com/mem0ai/openmemory) - Convert and import coding sessions across Claude Code, Codex, and OpenCode; the current beta differs from the archived memory service of the same name.
- [Tabby](https://github.com/TabbyML/tabby) - Self-host code completion and chat backed by repositories and internal documentation, giving team editors shared development context.
- [Vibe Kanban](https://github.com/BloopAI/vibe-kanban) - Coordinate local coding-agent runs in isolated Git worktrees and review their diffs; the original hosted team service shut down in 2026, and v0.1.44's no-sign-in path is limited to local workspaces while community maintenance continues.

## Chat and Meeting Integrations

Bring assistants into existing chat and meeting channels to reduce manual transfer of requests, context, and results.

- [AgentConnect](https://github.com/agentconnect-md/agentconnect) - Connect agents on execution machines to Slack, Feishu, and code platforms for tasks triggered by messages, events, or schedules.
- [Agentic Inbox](https://github.com/cloudflare/agentic-inbox) - Share an email interface with AI-assisted conversation search and reply drafting on Cloudflare Workers. Intended for trusted groups: authorized users can access every mailbox.
- [Ask Gemini in Google Chat](https://support.google.com/chat/answer/17036303) - Use work context in Google Chat to find information, prepare content, and act through connected applications; currently available only in English.
- [Centaur (Paradigm)](https://github.com/paradigmxyz/centaur) - Delegate engineering tasks from Slack to Kubernetes sandboxes and return progress and results to the originating thread.
- [Claude Tag](https://www.anthropic.com/news/introducing-claude-tag) - Let teammates jointly delegate and follow Claude tasks in Slack threads; currently a beta for Team and Enterprise.
- [DingTalk AI Assistant](https://github.com/open-dingtalk/developerpedia/tree/main/docs/explore/tutorials/assistant_ability/passthrough_mode) - Connect an existing agent service to DingTalk through passthrough mode, keeping planning and execution in the backend.
- [Feishu (豆包工作伙伴, formerly aily)](https://www.feishu.cn/community/article?id=7605435352983014344) - Connect knowledge, skills, and business workflows within Feishu for workplace questions and tasks; formerly aily.
- [LangBot](https://github.com/langbot-app/LangBot) - Connect AI assistants to chat platforms such as Feishu and WeCom, using built-in capabilities or existing Dify and n8n workflows.
- [Mattermost Agents](https://docs.mattermost.com/administration-guide/configure/agents-admin-guide) - Configure AI assistants in Mattermost channels and direct messages, managing model and tool access within existing discussions.
- [Meetily](https://github.com/Zackriya-Solutions/meetily) - Capture and transcribe meetings locally, then summarize them with a local or cloud model; export and team features differ between Community and Pro editions.
- [Microsoft Teams Agents](https://support.microsoft.com/en-us/teams/platform/frequently-asked-questions-about-agents-in-microsoft-teams) - Use built-in or custom agents in meetings, channels, and chats for notes, project questions, and business tasks, depending on agent type and licensing.
- [Nextcloud Assistant Talk Bot](https://github.com/nextcloud/talk_bot_ai) - Connect Nextcloud Assistant to Talk conversations so participants can ask the configured model through @assistant.
- [OpenTag (Amplift)](https://github.com/amplifthq/opentag) - Queue coding work from Slack, request approval for key actions, and return progress and delivery evidence to the originating thread. Slack is the supported team entry point; distinct from Open Tag by fancyboi999.
- [Rocket.Chat AI App](https://docs.rocket.chat/docs/rocketchat-ai-app-setup-guide) - Add knowledge answers and conversation summaries to team chat and customer support; the AI app is a licensed beta feature.
- [Slack AI Agents](https://slack.com/help/articles/33076000248851-Work-with-AI-agents-in-Slack) - Use Slackbot or installed agent applications from channels and dedicated sessions, keeping requests and results in team discussions.
- [Spacebot](https://github.com/spacedriveapp/spacebot) - Run a self-hosted assistant across Slack, Discord, and other channels, with concurrent conversations, shared memory, background tasks, and channel permissions; currently in beta under FSL-1.1-ALv2.
- [Switch](https://github.com/sandbox-quantum/switch) - Connect existing agents to Slack, Teams, and other chat platforms so colleagues and agents work in shared rooms with common context; licensed under Apache-2.0 with the Commons Clause.
- [Tentix](https://github.com/labring/tentix) - Combine AI replies, knowledge retrieval, and human handoffs in support tickets for shared customer service workflows; some staff management and analytics features remain planned.
- [Vexa](https://github.com/Vexa-ai/vexa) - Send bots into Google Meet, Teams, or Zoom and access live transcripts through an API for meeting notes and downstream knowledge workflows.
- [WeCom AI Bot](https://github.com/WecomTeam/aibot-node-sdk) - Use the official SDK for messages, card events, and streaming replies to connect custom AI services to WeCom.
- [ZoomMate](https://news.zoom.com/zoom-launches-zoommate/) - Use meeting and chat context to prepare artifacts and carry out CRM or ticket follow-up through configured workflows.

## Shared Collaboration Spaces

Let people and agents continue work around shared discussions, material, and artifacts instead of disconnected sessions.

- [Agenta](https://github.com/Agenta-AI/agenta) - Bring assistant configuration, shared files, triggers, and execution traces into a workspace for teams to maintain and reuse.
- [agentchattr](https://github.com/bcurts/agentchattr) - Coordinate people and coding agents in shared local channels, using mentions to hand off tasks and context; intended for local or trusted networks.
- [AgentTeams](https://github.com/agentscope-ai/AgentTeams) - Coordinate people and agents from different runtimes in Matrix rooms, sharing files and visible task progress while the platform manages agent containers.
- [Agor](https://github.com/preset-io/agor) - Collaborate on live boards and shared coding-agent sessions, with separate Git working directories per branch and shared knowledge; source-available under BUSL-1.1.
- [Alook](https://github.com/alookai/alook) - Invite colleagues into shared rooms, channels, and direct messages with persistent coding agents running on connected machines.
- [AppFlowy](https://github.com/AppFlowy-IO/AppFlowy) - Bring documents, wikis, and projects into a shared workspace with AI search and writing assistance; verify self-hosting and enterprise features for the chosen edition.
- [Buzz (Block)](https://github.com/block/buzz) - Connect people and agents in self-hosted channels using signed identities and shared event records for discussion and results.
- [Cloudflare OS](https://github.com/cloudflare/cloudflare-os) - Create and share stateful AI-built applications for ongoing collaboration on Cloudflare infrastructure; currently in early access.
- [Commonly](https://github.com/Team-Commonly/commonly) - Let people and agents share persistent rooms, task boards, memory, and artifacts, with GitHub task synchronization and reviewable activity. The project is still early.
- [Cumora](https://github.com/yetone/cumora) - Bring people and persistent agents into the same group chats, direct messages, task boards, and calendars for shared assignments and handoffs. Mobile support varies by platform.
- [HQBase](https://github.com/HQBase/hqbase) - Run shared team mailboxes in your own Cloudflare account, using member access controls, audit history, and MCP access so colleagues and assistants can work with the same email.
- [Lemma](https://github.com/lemma-work/lemma-platform) - Provide tables, files, permissions, workflows, and human input steps for business applications used by people and agents.
- [Lody](https://github.com/LodyAI/Lody) - Share coding-agent conversations, files, and diffs with teammates and review permission requests across devices; document workspaces remain planned.
- [Macro](https://github.com/macro-inc/macro) - Link email, messages, tasks, and documents in one workspace for people and agents to retrieve context and follow up on work.
- [Manor AI](https://github.com/manor-os/manor-ai) - Combine knowledge retrieval, agent tasks, tool bindings, and approvals in a self-hosted workspace with execution and artifact records.
- [Octo](https://github.com/Mininglamp-OSS/octo-server) - Connect organizational chat, files, and agent runtimes in a backend whose companion clients show replies, status, and tool activity.
- [Omnigent](https://github.com/omnigent-ai/omnigent) - Invite teammates to share, co-drive, or fork live agent sessions for pairing and handoffs; co-driving executes on the original owner’s machine.
- [Open WebUI](https://github.com/open-webui/open-webui) - Connect models, knowledge bases, and tools in a self-hosted interface where members collaborate through shared channels and notes with group-based permissions.
- [OpenAgents Workspace](https://github.com/openagents-org/openagents) - Invite teammates into a workspace where people and agents share threads, files, and a browser; headless Goose uses automatic tool execution.
- [QM](https://github.com/yc-software/qm) - Organize assistants, files, memory, and execution by personal and room scope for internal collaboration experiments; labeled early experimental software.
- [Relaticle](https://github.com/relaticle/relaticle) - Share customer data in a self-hosted CRM and expose MCP tools to agents within workspace-scoped access controls. Licensed under AGPL.
- [Rowboat](https://github.com/rowboatlabs/rowboat) - Share threads, files, whiteboards, and version history while each teammate’s local agent brings results from personal context back for review.
- [Synapse (Z.ai)](https://github.com/zai-org/Synapse) - Connect members, native actors, and external agents through shared sessions with resource grants and handoffs; still in early development.
- [taOS](https://github.com/jaylfc/taOS) - Combine chat, tasks, knowledge, memory, and multiple agent frameworks in a self-hosted AI workspace with groups, channels, agent identities, A2A coordination, and distributed devices; currently in beta, and its built-in chat is not a general-purpose IM server.
- [team9](https://github.com/team9ai/team9) - Collaborate with OpenClaw-based agents through channels, threads, and shared documents that retain discussion and artifacts.
- [Tutti](https://github.com/tutti-os/tutti) - Link agent sessions, tasks, and files to reduce handoff work; evaluate the local edition and multiplayer VM rooms separately.
- [XYNE Spaces](https://github.com/juspay/xyne-spaces) - Bring messages, email, and documents into organizational search and collaboration with permission-aware context for people and agents.

## Task Delegation and Delivery

Make task ownership, progress, deliverables, and review handoffs visible; completion and acceptance semantics differ by tool.

- [AgentRQ](https://github.com/agentrq/agentrq) - Assign tasks to people and agents through a shared workspace, synchronize progress, and review permission requests for sensitive actions.
- [Asana AI Teammates](https://asana.com/product/ai/ai-teammates) - Take assignments in Asana projects, use project context to prepare material and drafts, and return work for team review.
- [Backlog.md](https://github.com/MrLesk/Backlog.md) - Store tasks, dependencies, and acceptance criteria as repository Markdown, with CLI, MCP, and board interfaces for people and coding agents to maintain delivery plans.
- [Chorus](https://github.com/Chorus-AIDLC/Chorus) - Link proposals, documents, task execution, and human verification while managing coding agent permissions, sessions, and progress for team delivery review.
- [GitHub Agent HQ](https://docs.github.com/en/copilot/concepts/agents/about-third-party-coding-agents) - Select coding agents, inspect sessions, and review their pull requests within existing GitHub delivery workflows.
- [It's a Plan](https://github.com/croffasia/itsaplan) - Run a self-hosted issue tracker where agents are project members with roles and assignments, taking work from mentions, assignments, or schedules and running on an instance model or your own coding CLI; SSO, audit logs, and agent budgets require the Pro edition.
- [Kaneo](https://github.com/usekaneo/kaneo) - Let people and agents work with the same projects, tasks, and labels through a self-hosted project workspace and its official MCP interface.
- [Linear Agents](https://linear.app/docs/agents-in-linear) - Delegate to agents from issues and track progress and artifacts there while retaining a human assignee.
- [Markplane](https://github.com/zerowand01/markplane) - Keep tasks, plans, and dependencies in Markdown shared through Git, generating project summaries that teammates' assistants can access through MCP to continue work.
- [Mission Control by Builderz](https://github.com/builderz-labs/mission-control) - Centralize agent task assignments, reviews, costs, roles, and approvals in a self-hosted operations dashboard. Currently alpha software.
- [monday.com Agents](https://support.monday.com/hc/en-us/articles/33347027353746-AI-Agents-on-monday-com) - Process board assignments or triggers with contextual classification, updates, handoffs, and execution records.
- [MonkeyCode](https://github.com/chaitin/MonkeyCode) - Manage requirements, coding tasks, models, and development environments in a shared AI development platform that can run inside an enterprise network.
- [Multica](https://github.com/multica-ai/multica) - Assign issues to existing coding agents on your own runtimes and return progress, blockers, and results to the same issue for team review.
- [Open Tag](https://github.com/fancyboi999/open-tag) - Turn channel messages into claimable, assignable, and transferable agent tasks with thread context; intended for self-hosted evaluation.
- [Open-Inspect](https://github.com/ColeMurray/background-agents) - Collaborate in background coding sessions and hand work through Slack, tickets, and pull requests while retaining contribution attribution. Intended for a trusted single organization; repository access is not checked per user.
- [Optio](https://github.com/jonwiggins/optio) - Assign ticket, scheduled, and event-triggered work to agents on team machines or Kubernetes, with workspace roles, OAuth/OIDC, and execution visibility.
- [Paca](https://github.com/Paca-AI/paca) - Let people and agents claim tasks and maintain requirements and design documents on the same Scrum board and sprints, with member roles and live progress for team delivery.
- [Paperclip](https://github.com/paperclipai/paperclip) - Coordinate human users and agents through organization-scoped tasks, approvals, roles, budgets, and execution records.
- [PR-Agent](https://github.com/The-PR-Agent/pr-agent) - Generate PR descriptions, reviews, and improvement suggestions through CLI, CI, or code-host events; now community-maintained and distinct from the commercial Qodo product.
- [Raft (formerly Slock)](https://raft.build/) - Connect team channels with agent task claims and review states so teammates can hand off work and inspect deliverables.
- [Solo (solo-agent)](https://github.com/solo-agent/solo) - Link agent task ownership, execution records, delivery versions, and reviews in submission and revision workflows.
- [yylo](https://github.com/yylo-dev/yylo) - Run Kanban, task, and merge loops for coding agents across isolated Git worktrees, with typed admission gates and strict-JSON output that teams can script and review.

## Whiteboards and Visual Collaboration

Turn AI drafts into visual artifacts that teams can discuss, edit, or embed in products.

- [Excalidraw MCP App](https://github.com/excalidraw/excalidraw-mcp) - Generate and interactively edit Excalidraw diagrams in MCP Apps clients so teams can refine AI drafts.
- [FigJam AI](https://help.figma.com/hc/en-us/articles/16822138920343-Use-AI-tools-in-FigJam) - Generate workshop boards and diagrams, then sort and summarize the sticky notes from team discussions.
- [Miro AI](https://miro.com/ai/ai-overview/) - Use material on a shared canvas to organize ideas and generate documents and diagrams for product and design discussions.
- [OpenPencil](https://github.com/open-pencil/open-pencil) - Combine real-time collaborative design editing with AI and MCP so teammates and agents can modify, inspect, and export the same design; under active development.
- [tldraw Agent Starter Kit](https://tldraw.dev/starter-kits/agent) - Provide an application template for agents to read, create, and modify canvas shapes in visual collaboration products.

## Team Knowledge and Shared Practices

Help teammates and assistants reuse organizational knowledge, rules, skills, and experience with less repeated explanation.

### Shared Knowledge and Assistants

- [53AI Hub](https://github.com/53AI/53AIHub) - Publish agents, prompts, and AI tools from multiple platforms through a team portal with grouping and access controls; organizational directory integrations depend on the edition.
- [AnythingLLM](https://github.com/Mintplex-Labs/anything-llm) - Organize document chat, agents, and tools in workspaces with local or cloud models; multi-user accounts and permissions require the Docker edition.
- [Atlassian Rovo](https://www.atlassian.com/software/rovo) - Search Jira, Confluence, and connected work knowledge, and configure agents to organize and act on project information.
- [Bionic](https://github.com/bionic-gpt/bionic-gpt) - Provide internal teams with approved models, knowledge, and tools in a self-hosted AI workspace with permissions, usage controls, and audit records.
- [DataHub](https://github.com/datahub-project/datahub) - Maintain shared data ownership, lineage, and discussions in a metadata graph that supports human discovery and AI context. DataHub Cloud is a separate managed service.
- [DocsGPT](https://github.com/arc53/DocsGPT) - Turn shared documents and meeting material into searchable knowledge with agents, member roles, and team-scoped sharing.
- [Dust](https://dust.tt/) - Connect company knowledge and business tools to shared agents so teammates can reuse common answers and working methods.
- [Glean Independent Agents](https://www.glean.com/blog/introducing-independent-agents) - Use connected company knowledge for ongoing investigations, feedback processing, and follow-up with human review.
- [LibreChat](https://github.com/danny-avila/LibreChat) - Use multiple model providers in a self-hostable chat platform and share assistants with tools and file search with selected users and groups.
- [MaxKB](https://github.com/1Panel-dev/MaxKB) - Build internal assistants from document ingestion, knowledge retrieval, and workflows, then embed them in existing business systems for colleagues.
- [Nexent](https://github.com/ModelEngine-Group/nexent) - Share agent tools, skills, and memory through a no-code platform with multi-tenancy and role-based resource access.
- [Notion Agent](https://www.notion.com/help/notion-agent) - Find workspace information and create or edit pages and databases to carry out document-based tasks.
- [Notion Custom Agents](https://www.notion.com/help/custom-agents) - Configure shared assistants for recurring questions, feedback triage, and reports triggered by schedules or events.
- [Octop](https://github.com/TencentCloud/Octop) - Share one assistant deployment across a small team, with per-user agents and workspaces, JWT authentication, and workplace messaging integrations. Shared resource pools and autonomous AgentTeams remain planned.
- [OmniBox](https://github.com/import-ai/omnibox) - Collect documents and share knowledge-based questions and answers in multi-tenant spaces with team permissions; RSS support remains planned.
- [Onyx](https://github.com/onyx-dot-app/onyx) - Connect organizational knowledge and share chats and custom agents; standard deployments include document sync and retrieval, while Lite and enterprise capabilities differ.
- [OpenContracts](https://github.com/Open-Source-Legal/OpenContracts) - Let teams annotate documents, maintain citation graphs, and review agent-assisted findings through corpus permissions, version history, and discussions.
- [OpenKnowledge](https://github.com/inkeep/open-knowledge) - Share and sync Markdown knowledge bases, specifications, and notes through Git/GitHub, with agents searching and editing team material inside the editor.
- [OpenMetadata](https://github.com/open-metadata/OpenMetadata) - Link data ownership, lineage, quality, business terms, and organizational knowledge into shared context for people and AI agents. Enterprise AI capabilities such as AI Studio are offered separately through Collate.
- [PandaWiki](https://github.com/chaitin/PandaWiki) - Publish product documentation, technical material, and FAQs as wikis with AI writing, search, and answers, including integrations with team chat channels.
- [WeKnora](https://github.com/Tencent/WeKnora) - Sync sources such as Feishu and GitLab into shared knowledge bases for retrieval and answers, and organize documents into editable wikis with revision history.
- [Yuxi](https://github.com/xerrors/Yuxi) - Share knowledge bases, agents, and skills by tenant, department, and user in a self-hosted platform with resource permissions and approvals.

### Shared Rules and Experience

- [Agent Package Manager](https://github.com/microsoft/apm) - Version and distribute team skills, prompts, and plugins with manifests, lockfiles, organization policies, and CI checks for configuration drift.
- [Agent Skills Platform](https://github.com/FrancyJGLisboa/agent-skills-platform) - Package reusable skills with ownership, approvals, versions, and validation evidence for colleagues to install through a governed marketplace.
- [Knowns](https://github.com/knowns-dev/knowns) - Store requirements, tasks, acceptance criteria, and decisions in the repository for teammates and assistants to reuse through CLI and MCP; a dedicated team sync service remains planned.
- [MEX](https://github.com/mex-memory/mex) - Keep architecture, decisions, and handoffs in the code repository for teammates and coding agents to retrieve and reuse; share through Git while maintaining local indexes separately.
- [Observal](https://github.com/Observal/Observal) - Register internal agents, skills, and MCP tools, review versions, and use adoption and session feedback to improve shared assets.
- [Potpie](https://github.com/potpie-ai/potpie) - Organize code, pull requests, tasks, and team decisions into a searchable context graph so coding assistants can reuse project background and engineering knowledge.
- [Rulesync](https://github.com/dyoshikawa/rulesync) - Generate agent-specific skills, rules, and MCP configuration from shared source files so teams can maintain one configuration set across coding tools; supported features vary by target.
- [SkillHub](https://github.com/iflytek/skillhub) - Publish and distribute organizational skills through team namespaces, owner/admin/member roles, versioning, and promotion reviews. Distributed skills retain their own licenses.
- [SkillKit](https://github.com/rohitg00/skillkit) - Share skill sources through a Git-tracked team manifest and translate skills for different coding agents; target-client compatibility needs separate verification.
- [SkillPort](https://github.com/gotalab/skillport) - Validate and manage skill packages, then provide search and on-demand loading through CLI or MCP so teams can reuse their skill library; APIs remain under development.
- [Skills Manager](https://github.com/xingkongliang/skills-manager) - Manage global and project skills across coding agents through a desktop library, with Git-backed backup and multi-device sync; applying presets makes a one-time copy.
- [skillshare](https://github.com/runkids/skillshare) - Sync skills, rules, and agent definitions across AI coding tools from one source, distributing them through project configuration and team Git repositories.
- [sx](https://github.com/sleuth-io/sx) - Share versioned skills and agent configuration through Git or shared-folder vaults, with scoped installation and usage records; role-based access control and change approvals remain planned.
- [TeamAI CLI](https://github.com/Tencent/teamai-cli) - Distribute team skills, rules, and MCP configuration through Git and retrieve shared lessons across agent clients.
- [TencentDB Agent Memory](https://github.com/TencentCloud/TencentDB-Agent-Memory) - Capture conversations through a proxy and derive memories, skills, and knowledge assets for later agent context retrieval.
- [Vercel Skills CLI](https://github.com/vercel-labs/skills) - Install, check, and update team skills from shared repositories into supported coding agents; project lockfile restoration remains experimental.

## Knowledge Retrieval and Memory

Store, organize, and retrieve information for later agent tasks; coverage of documents, conversational memory, and skills varies by tool.

- [Basic Memory](https://github.com/basicmachines-co/basic-memory) - Keep knowledge in Markdown notes that people and assistants can edit, search, and reuse through indexes, relations, and MCP.
- [Cognee](https://github.com/topoteretes/cognee) - Process scattered material into text indexes and knowledge graphs for retrieval across documents and relationships.
- [EverOS](https://github.com/EverMind-AI/EverOS) - Provide a local-first, Markdown-native long-term memory layer that different agents, applications, and workflows can share; it is memory infrastructure rather than a multi-user permission workspace.
- [Graphiti](https://github.com/getzep/graphiti) - Build temporal knowledge graphs from text or structured data to query relationships, changes, and source information.
- [LangMem](https://github.com/langchain-ai/langmem) - Provide memory extraction, update, and search components for developers using LangGraph Store or their own storage.
- [MCP Knowledge Graph Memory Server](https://github.com/modelcontextprotocol/servers/tree/main/src/memory) - Store entities, relations, and observations in a local file as a reference implementation of structured memory tools for MCP clients.
- [Mem0](https://github.com/mem0ai/mem0) - Extract and retrieve conversational preferences and facts for existing applications to reduce repeated questions across sessions.
- [Memobase](https://github.com/memodb-io/memobase) - Maintain structured user profiles and event timelines from conversations for personalized application context.
- [Memora (Microsoft)](https://github.com/microsoft/Memora) - Separate memory content, abstracts, and retrieval cues for developers studying detail preservation and recall.
- [Memoria (Matrix Origin)](https://github.com/matrixorigin/Memoria) - Add retrieval, snapshots, branches, and merging to agent memory so teams can inspect and revise changes.
- [MemOS](https://github.com/MemTensor/MemOS) - Add long-term context to existing agents through memory APIs or host plugins for user information and task experience.
- [memsearch](https://github.com/zilliztech/memsearch) - Keep memory as Markdown files with a rebuildable Milvus index so Claude Code, Codex, and other coding agents recall the same context across sessions, and distill repeated workflows into reusable skills; a dedicated Milvus instance is needed for multi-user or team deployments, and it is not a permission system.
- [MemU](https://github.com/NevaMind-AI/memU) - Turn personal session experience into retrievable memories and skills across assistants; team access management is a separate concern.
- [OpenViking](https://github.com/volcengine/OpenViking) - Organize resources, memories, and skills with directories, summaries, and semantic retrieval for progressive context access.
- [ReMe (formerly MemoryScope)](https://github.com/agentscope-ai/ReMe) - Organize conversations and external material into an editable Markdown knowledge workspace that agents can search, read, and update.
- [Supermemory](https://github.com/supermemoryai/supermemory) - Combine user profiles, memory extraction, and document retrieval through APIs to simplify personalized assistant context.
- [Zep](https://help.getzep.com/concepts) - Turn conversations and business data into temporal graphs and assemble retrieved context for agents; this entry covers the hosted service.

## Application Development and Workflows

Connect models, tools, and multiple steps into usable applications or business workflows.

- [Activepieces](https://github.com/activepieces/activepieces) - Connect AI, business applications, and human approval steps in visual workflows using extensible integration components; enterprise features are offered separately.
- [AgentScope](https://github.com/agentscope-ai/agentscope) - Compose models, tools, and agent collaboration components in Python to build custom assistant applications.
- [AutoGen](https://github.com/microsoft/autogen) - Build message-driven agent teams with turn-taking and handoffs; in maintenance mode, with official migration guidance for new projects.
- [Budibase](https://github.com/Budibase/budibase) - Connect team data, internal apps, and automations to agents that handle requests and invoke configured tools, with human approval rules; Agents is in beta, and paid features are licensed separately.
- [Coze Studio](https://github.com/coze-dev/coze-studio) - Build and publish assistants with visual knowledge, plugin, and workflow tools; this entry covers the self-hostable Studio.
- [CrewAI](https://github.com/crewAIInc/crewAI) - Organize agent collaboration through roles, tasks, and workflows with explicit handoffs.
- [Dagu](https://github.com/dagucloud/dagu) - Combine existing scripts, containers, coding agents, and human tasks in self-hosted YAML workflows, with scheduling, retries, logs, and run history for teams to track execution.
- [Dify](https://github.com/langgenius/dify) - Combine knowledge retrieval, prompts, and tool calls into AI applications published as web interfaces or APIs.
- [Elastic Agent Builder](https://www.elastic.co/docs/explore-analyze/ai-features/elastic-agent-builder) - Configure models, instructions, and tools over Elasticsearch data to build assistants that query organizational information.
- [Haystack](https://github.com/deepset-ai/haystack) - Compose document indexing, retrieval, and agent workflows from replaceable Python components with explicit control over context preparation, routing, and generation.
- [LangGraph](https://github.com/langchain-ai/langgraph) - Build multi-step agents with explicit state and branching, adding persistence and human review to resume work.
- [Lindy](https://docs.lindy.ai/) - Connect email, calendars, and CRM from Slack and save follow-up work or recurring reports as reusable routines.
- [Mastra](https://github.com/mastra-ai/mastra) - Build TypeScript agents with tools, memory, and evaluation, organizing multi-step work into workflows that can pause for human input.
- [n8n](https://github.com/n8n-io/n8n) - Connect forms, email, business APIs, and AI nodes to automate data processing and follow-up actions across systems.
- [Salesforce Agentforce](https://www.salesforce.com/agentforce/) - Connect natural-language requests to Salesforce data and configured business actions for sales and service agents.
- [Sim](https://github.com/simstudioai/sim) - Build agents with visual workflows, shared files, and knowledge bases, connect business systems, and inspect run logs; chat uses a Sim-managed service.
- [Temporal](https://docs.temporal.io/evaluate/understanding-temporal) - Use durable workflows for model calls, external operations, and long human waits, with recovery and retries that still require idempotent business operations.
- [ToolJet](https://github.com/ToolJet/ToolJet) - Let coding agents build and modify internal apps through MCP, then let teammates continue editing them visually; MCP is in beta, and built-in AI generation and Agent Builder are enterprise features.

## Execution Environments and Managed Runtimes

Provide environments for code and files, or host agent execution loops; sandboxes and managed agent runtimes serve different roles.

- [Claude Managed Agents](https://platform.claude.com/docs/en/managed-agents/overview) - Host Claude execution loops, sessions, and environments, exposing progress and artifacts through events; currently in beta.
- [ClawManager](https://github.com/Yuan-lab-LLM/ClawManager) - Manage multi-user agent runtimes on Kubernetes with centralized identity, model access, costs, and reusable resources. Team orchestration also includes agent groups, not only human collaboration.
- [Daytona](https://www.daytona.io/docs/) - Manage project sandboxes, files, and processes through APIs to provide working environments for coding agents.
- [E2B](https://github.com/e2b-dev/E2B) - Create Linux sandboxes on demand to run agent-generated code and retrieve outputs and files.
- [OpenSandbox](https://github.com/opensandbox-group/OpenSandbox) - Manage Docker or Kubernetes sandboxes through unified APIs, providing command execution, file operations, and code environments for agents.

## Evaluation, Observability, and Content Checks

Evaluate changes with test cases, investigate execution problems, or add specialized content risk classification.

- [Arize Phoenix](https://github.com/Arize-ai/phoenix) - Inspect AI traces, maintain datasets, and compare evaluation experiments in a self-hostable platform.
- [Braintrust](https://www.braintrust.dev/docs/evaluate) - Turn production traces into test data and compare prompts, models, and agents with shared tasks and evaluators.
- [Coze Loop](https://github.com/coze-dev/coze-loop) - Maintain shared prompt versions, evaluation datasets, and agent execution traces for development and operations. Distinguish the open-source core from commercial features.
- [Datadog Agent Observability](https://docs.datadoghq.com/llm_observability/) - Connect model calls, tool calls, and application traces to investigate agents within an existing Datadog environment.
- [DeepEval](https://deepeval.com/docs/getting-started) - Test AI answers and execution with cases, metrics, and thresholds, integrating quality regression checks into Python development.
- [Grafana Cloud Agent Observability](https://grafana.com/docs/grafana-cloud/observe-and-act/agent-observability/introduction/) - Inspect agent sessions, model calls, and online evaluations in Grafana Cloud, distinct from deploying Tempo and Loki alone.
- [Laminar](https://github.com/lmnr-ai/lmnr) - Share agent traces, evaluation results, and dashboards, and route configured behavioral alerts to Slack for investigation.
- [Langfuse](https://github.com/langfuse/langfuse) - Connect traces, prompt versions, test cases, and scores to debug and improve AI applications collaboratively.
- [LangSmith](https://docs.langchain.com/langsmith/observability) - Trace agent execution and compare application versions using datasets, human feedback, and evaluation experiments.
- [LangWatch](https://github.com/langwatch/langwatch) - Combine tracing, agent simulation tests, and evaluations to inspect applications and coding-assistant sessions and usage; enterprise modules require separate licensing.
- [Llama Guard](https://github.com/meta-llama/PurpleLlama/tree/main/Llama-Guard4) - Classify content risks in model inputs and outputs for application review policies; classification does not authorize business actions.
- [MLflow](https://github.com/mlflow/mlflow) - Track agent traces, evaluation data, and prompt versions in a shared platform to compare experiments, investigate regressions, and monitor deployed applications.
- [New Relic AI Monitoring](https://docs.newrelic.com/docs/ai-monitoring/intro-to-ai-monitoring/) - Bring supported model, agent, and tool calls into application monitoring with latency, errors, and usage data.
- [OpenLLMetry](https://github.com/traceloop/openllmetry) - Instrument supported models, vector databases, and frameworks with OpenTelemetry and export traces to observability backends.
- [OpenTelemetry eBPF Instrumentation (OBI)](https://github.com/open-telemetry/opentelemetry-ebpf-instrumentation) - Collect application and model-related telemetry on supported Linux systems with less per-application instrumentation; check version-specific coverage.
- [Opik](https://github.com/comet-ml/opik) - Share agent traces, evaluation datasets, experiments, and prompt versions to compare changes and investigate production failures.
- [Promptfoo](https://github.com/promptfoo/promptfoo) - Compare prompts, models, or agents with configuration-based test cases, CI integration, and red teaming.
- [Ragas](https://github.com/vibrantlabsai/ragas) - Evaluate retrieval coverage, answer grounding, and agent behavior through metrics and repeatable experiments.
- [Tracely](https://github.com/Jwuthri/Tracely-ai) - Turn production agent failures into shared issues, replayable regression cases, and CI gates within organizations and workspaces. Development mode has no authentication; teams must configure an appropriate identity mode.
- [W&B Weave](https://docs.wandb.ai/weave) - Track AI application calls and versions and compare changes using datasets and scorers.

## Protocols and Learning Resources

Explore protocols, engineering practices, tutorials, and research, with directories for discovering further candidates.

### Protocols and Conventions

- [A2A](https://a2a-protocol.org/latest/) - Define capability discovery, messages, task states, and artifact handoffs between independent agent services.
- [AG-UI](https://github.com/ag-ui-protocol/ag-ui) - Connect agent backends and user interfaces through an event protocol for streaming messages, tool activity, state changes, and human input.
- [Agent Skills](https://agentskills.io/specification) - Package procedures, references, and scripts as skills that agents can load on demand to reuse team practices.
- [AGENTS.md](https://agents.md/) - Provide coding agents with project context, development conventions, and verification commands in repository Markdown files.
- [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) - Provide a shared protocol for connecting agent applications to tools, data resources, and prompt templates.
- [OpenTelemetry GenAI Semantic Conventions](https://github.com/open-telemetry/semantic-conventions-genai) - Define tracing and metric fields for model, agent, and tool calls to align observability across applications.

### Practical Guides

- [12-Factor Agents](https://github.com/humanlayer/12-factor-agents) - Use engineering principles for context management, persisted state, tool calls, and human interaction to design and review agent applications.
- [Claude Code Project Settings](https://code.claude.com/docs/en/settings) - Use project settings and personal overrides to version team configuration and understand where it takes effect.
- [Claude Code Subagents](https://code.claude.com/docs/en/sub-agents) - Learn to delegate bounded tasks into separate contexts and configure tools, permissions, and result handoffs.
- [Context Compaction (Anthropic)](https://platform.claude.com/docs/en/build-with-claude/compaction) - Learn how long-running tasks compact conversation context and what progress and evidence still need separate storage.
- [Function Calling (OpenAI)](https://developers.openai.com/api/docs/guides/function-calling) - Learn the model-request, application-execution, and result-return cycle for well-defined business tools.
- [Human-in-the-loop (LangChain)](https://docs.langchain.com/oss/python/langchain/human-in-the-loop) - Learn to pause before tool execution, receive approval or edits, and resume using persisted state.
- [My Git Handbook](https://github.com/xirong/my-git) - Use examples of agent change review, pull requests, worktrees, CI, and releases to establish team version-control and delivery practices for AI-assisted development.
- [Prompt Caching (Anthropic)](https://platform.claude.com/docs/en/build-with-claude/prompt-caching) - Learn to organize stable prompt prefixes and verify cache hits when optimizing repeated context processing.
- [Workload Identity Federation (Google Cloud)](https://docs.cloud.google.com/iam/docs/workload-identity-federation) - Learn to exchange workload identities for short-lived access credentials instead of distributing long-lived keys to agent services.

### Tutorials and Research

- [ADE Failure Registry](https://github.com/ADE-standard/ade-failure-registry) - Use sourced agent failure cases to guide design reviews and incident analysis; the taxonomy is not an industry standard.
- [Agent Learning Paths (Pauldest)](https://github.com/Pauldest/awesome-ai-agent/tree/main/paths) - Organize learning around progressively expanded coding and systems exercises when planning team practice.
- [Agent-Learning-Hub (kngwyc3)](https://github.com/kngwyc3/Agent-Learning-Hub) - Extend a Datawhale learning roadmap with exercises and notes covering tool use, retrieval, memory, and agent development.
- [Anthropic Evaluation Guide](https://platform.claude.com/docs/en/test-and-evaluate/develop-tests) - Explain how to define success criteria, prepare test cases, and choose code-based, human, or model-based evaluation.
- [awesome-agentic-ai-zh](https://github.com/WenyuChiou/awesome-agentic-ai-zh) - Follow Chinese learning paths for assistant users and agent developers, with exercises and deliverable requirements.
- [Why Do Multi-Agent LLM Systems Fail?](https://arxiv.org/abs/2503.13657) - Study failures in task design, coordination, and verification, with [MAST](https://github.com/multi-agent-systems-failure-taxonomy/MAST) providing the taxonomy and research materials.

### More Resource Directories

- [Awesome Agent Skills (VoltAgent)](https://github.com/VoltAgent/awesome-agent-skills) - Discover skills for development, documents, and testing as starting points for reusable team procedures.
- [Awesome AI Agent Papers (VoltAgent)](https://github.com/VoltAgent/awesome-ai-agent-papers) - Find papers on collaboration, memory, tools, and evaluation, then verify methods and conditions in the original research.
- [Awesome MCP Servers (appcypher)](https://github.com/appcypher/awesome-mcp-servers) - Compare MCP implementations from different authors by capability, alongside related clients and management tools.
- [Awesome MCP Servers (punkpeye)](https://github.com/punkpeye/awesome-mcp-servers) - Discover MCP server implementations for connecting to files, databases, and business systems.
- [Awesome Official MCP Servers (MCPStar)](https://github.com/MCPStar/Awesome-Official-MCP-Servers) - Find vendor-maintained MCP repositories, services, and documentation, verifying ownership before adoption.

## Contributing

Read the [English contribution guide](CONTRIBUTING.md) or [Chinese guide](CONTRIBUTING.zh-CN.md), then open an issue or pull request to suggest a project, explain a use case, or fix descriptions and links. Both lists contain the same entries and categories. You can request translation help when contributing in one language.

## License

This list is released under [CC0 1.0 Universal](LICENSE). Listed projects, trademarks, and external material retain their own licenses and terms.