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upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/AGENT.md
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---
title: "AGENT.md - AI Agent Context & Execution Guide"
task: ""
lineage_type: import
upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/20285455/AGENT.md
upstream_sha: 20285455
imported_at: 2026-10-26
prompt_class: prompt
upstream_changes: accepted
author: upstream
validated: false
---
# AGENT.md - AI Agent Context & Execution Guide
> **Target Audience**: This file is specifically designed for LLMs, autonomous coding agents, and agentic workflows (e.g., Gemini, Claude Code, Cursor, Aider, AutoGen, CrewAI) operating inside or alongside this repository.
---
## 1. Repository Core Purpose
**Awesome AI Agents** is a structured, production-focused catalog of 470+ AI agent frameworks, tools, protocol specs, evaluation benchmarks, and infrastructure projects across 32+ categories.
- **Primary Repository**: [ARUNAGIRINATHAN-K/awesome-ai-agents-2026](https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026)
- **Primary Data Source**: [README.md](file:///a:/awesome-ai-agents/README.md)
- **Contribution Guidelines**: [CONTRIBUTING.md](file:///a:/awesome-ai-agents/CONTRIBUTING.md)
- **Security Policy**: [SECURITY.md](file:///a:/awesome-ai-agents/SECURITY.md)
---
## 2. Directory & Sitemap Overview
| File / Path | Agent Context / Purpose |
|---|---|
| [`README.md`](file:///a:/awesome-ai-agents/README.md) | Central data file containing all categorized tool entries and metadata tags. |
| [`ARCHITECTURE.md`](file:///a:/awesome-ai-agents/ARCHITECTURE.md) | Technical architecture overview, module flow diagram, and component hierarchy. |
| [`DATA_SCHEMA.md`](file:///a:/awesome-ai-agents/DATA_SCHEMA.md) | Detailed dataset schema, parser specification, TypeScript interfaces, and JSON schema. |
| [`DEVELOPMENT.md`](file:///a:/awesome-ai-agents/DEVELOPMENT.md) | Environment setup, local scripts, verification workflow, Docker guide, and design rules. |
| [`scripts/build-data.js`](file:///a:/awesome-ai-agents/scripts/build-data.js) | Parser script compiling `README.md` into static JSON datasets (`data/resources.json`, `data/categories.json`). |
| [`data/resources.json`](file:///a:/awesome-ai-agents/data/resources.json) | Compiled normalized dataset of all 460+ resources with tier badges, language tags, and category slugs. |
| [`lib/resources.ts`](file:///a:/awesome-ai-agents/lib/resources.ts) & [`lib/search.ts`](file:///a:/awesome-ai-agents/lib/search.ts) | Data access layer, full-text search engine, and multi-faceted filtering logic for web UI. |
| [`app/`](file:///a:/awesome-ai-agents/app) & [`components/`](file:///a:/awesome-ai-agents/components) | Next.js 14 App Router registry pages (`/explore`, `/categories`, `/resources/[slug]`, `/about`) and UI components. |
| [`Dockerfile`](file:///a:/awesome-ai-agents/Dockerfile) & [`docker-compose.yml`](file:///a:/awesome-ai-agents/docker-compose.yml) | Multi-stage Docker containerization configuration for deploying the AI Agent Registry web app. |
| [`CONTRIBUTING.md`](file:///a:/awesome-ai-agents/CONTRIBUTING.md) | Definitive schema specification, inclusion rules, tag taxonomy, and entry constraints. |
| [`SECURITY.md`](file:///a:/awesome-ai-agents/SECURITY.md) | Protocols for flagging malicious tools, supply-chain attacks, or compromised URLs. |
| [`CODE_OF_CONDUCT.md`](file:///a:/awesome-ai-agents/CODE_OF_CONDUCT.md) | Community standards and interaction expectations. |
| [`.github/workflows/`](file:///a:/awesome-ai-agents/.github/workflows) | CI/CD automation (`awesome-lint.yml`, `link-check.yml`). |
---
## 3. Strict Metadata Specification for Tool Entries
Every entry listed under a category in `README.md` MUST conform to this exact single-line Markdown pattern:
```markdown
- [Tool Name](https://github.com/org/repo) `TIER` `[Language]` `[Type]` - Single sentence description ending with a period.
```
### Metadata Components & Rules
1. **Tool Name & URL**:
- Link text must match the canonical project name.
- Target URL must be a valid GitHub/GitLab repository or official hosted platform URL.
- Do NOT duplicate entries anywhere in `README.md`.
2. **Tier Badge** (Exactly ONE required):
- `🚀` **Production-Ready**: 10K+ stars, major tech company (OpenAI, Anthropic, Google, Microsoft, Meta, AWS), or massive enterprise usage.
- `🌱` **Growing**: 500–5K stars, active commits, gaining momentum.
- `🔬` **Emerging**: <500 stars, research paper implementation, or novel experimental tool.
3. **Language Tag** (Exactly ONE required, inside brackets):
- Approved set: `[Python]`, `[TypeScript]`, `[Go]`, `[Rust]`, `[Java]`, `[C#]`, `[C++]`, `[Kotlin]`, `[Swift]`, `[Ruby]`, `[WebAssembly]`, `[Cloud]`, `[Browser]`, `[Desktop]`, `[Mobile]`, `[No-Code]`, `[Shell]`.
4. **Type/Framework Tag** (Exactly ONE required, inside brackets):
- Approved set (priority order: `MCP` > `RAG` > framework > architecture > environment):
`[MCP]`, `[RAG]`, `[Multi-Agent]`, `[Graph-Based]`, `[Stateful]`, `[Event-Driven]`, `[Voice]`, `[Vision]`, `[Multimodal]`, `[Memory]`, `[Observability]`, `[Security]`, `[Compliance]`, `[Testing]`, `[Evaluation]`, `[Benchmark]`, `[CLI]`, `[IDE]`, `[VS Code]`, `[AWS]`, `[GCP]`, `[Azure]`, `[Self-Hosted]`, `[Local]`, `[Serverless]`, `[Research]`.
5. **Description Rules**:
- Exactly **one sentence**, ending with a period (`.`).
- Recommended length: **12–18 words**.
- Zero promotional/hyped language (never use: "best", "revolutionary", "game-changing", "seamlessly", "powerful").
- Start with an action verb or descriptive noun phrase explaining what the tool **does**.
---
## 4. Instructions for Agent Operations
When modifying, inspecting, or maintaining this repository, AI agents must follow these operational rules:
### A. Adding a New Tool Entry
1. **Deduplication**: Search `README.md` to verify the URL or tool name is not already listed.
2. **Alphabetical Sorting**: Place the new item in exact **alphabetical order** by Tool Name within its target category section.
3. **Activity Validation**: Verify that the repository has commits within the last 6 months.
4. **Schema Adherence**: Format the line strictly according to the metadata spec above.
### B. Updating Existing Entries
1. Maintain existing section category header anchors.
2. If updating tier badges (`🚀`, `🌱`, `🔬`), ensure the change reflects GitHub star or adoption metrics.
3. Preserve single-line formatting per entry.
### C. Flagging / Removing Malicious or Dead Tools
1. If an agent detects a 404 URL, archived repo (>6 months inactive), or compromised domain, report or prune according to [CONTRIBUTING.md](file:///a:/awesome-ai-agents/CONTRIBUTING.md) and [SECURITY.md](file:///a:/awesome-ai-agents/SECURITY.md).
2. If flagging security threats, refer to [SECURITY.md](file:///a:/awesome-ai-agents/SECURITY.md).
### D. Running Verification & Linting Commands
Before submitting PRs or finalizing changes:
- Recompile static JSON dataset from `README.md`:
```bash
npm run build:data
```
- Run Next.js production build check:
```bash
npm run build
```
- Check spelling using `codespell`:
```bash
codespell -q 3 --skip=.git,node_modules --check-filenames --ignore-words-list=reworkd,ehr,EHR .
```
- Run `awesome-lint` validator:
```bash
npx awesome-lint
```
---
## 5. Machine-Readable Schema
```json
{
"repo": "ARUNAGIRINATHAN-K/awesome-ai-agents",
"data_file": "README.md",
"entry_format": "- [{name}]({url}) `{tier}` `[{language}]` `[{type}]` - {description}.",
"tiers": ["🚀", "🌱", "🔬"],
"rules": {
"sentence_count": 1,
"word_count_range": [12, 18],
"alphabetical_sorting": true,
"no_promotional_words": ["best", "revolutionary", "game-changing", "powerful", "seamlessly"]
}
}
```
upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/catalogue/.github/ISSUE_TEMPLATE/submit_agent.yml
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---
title: "Submit Agent"
task: ""
lineage_type: import
upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/20285455/.github/ISSUE_TEMPLATE/submit_agent.yml
upstream_sha: 20285455
imported_at: 2026-10-26
prompt_class: unknown
upstream_changes: accepted
author: upstream
validated: false
---
name: Submit an AI Agent / Tool
description: Submit a new AI agent, framework, tool, skill, or infrastructure project to the registry.
title: "Submit: [Resource Name]"
labels: ["agent-submission"]
body:
- type: markdown
attributes:
value: |
Thank you for submitting to **Awesome AI Agents 2026**!
Please fill out this form accurately. Our automated pipeline will validate your submission and generate a Pull Request for maintainer review.
- type: input
id: resource_name
attributes:
label: Resource Name
description: The official name of the tool, framework, or project (e.g. LangGraph, Agency Swarm).
placeholder: e.g. LangGraph
validations:
required: true
- type: input
id: project_url
attributes:
label: Project URL
description: The main GitHub repository or official project website URL (must start with https://).
placeholder: https://github.com/org/repo
validations:
required: true
- type: dropdown
id: resource_type
attributes:
label: Resource Type
description: Select the architectural type or framework classification.
options:
- Agent
- Framework
- Tool
- SDK
- Skill
- MCP Server
- Protocol
- Model
- Platform
- Infrastructure
- Evaluation
- Benchmark
- Workflow
- Pattern
- Library
- Other
validations:
required: true
- type: dropdown
id: category
attributes:
label: Category
description: Select the registry section where this resource belongs.
options:
- Orchestration Frameworks
- Coding Agents
- Memory and Context
- Multi-Agent Systems
- Agent Communication Protocols
- Browser and Computer Use Agents
- Agent Tooling and Infrastructure
- Low and No-Code Builders
- Voice and Multimodal Agents
- Safety Guardrails and Observability
- Agent Interfaces and UIs
- Agent Deployment and Hosting
- Agent Evaluation and Benchmarks
- Industry-Specific Agents
- Learning Resources
- Agent Communication
- Data Pipeline and Workflow
- Secure Execution Environments
- Creative AI Agents
- Customer Support and CRM Agents
- Voice Agent Platforms
- Deep Research Agents
- Prompt-to-App Builders
- Multi-Agent Consumer Platforms
- Open-Source Models for Agents
- Market Stats and Benchmarks 2026
- Local and Self-Hosted AI
- AI Governance and Compliance
- Cybersecurity Agents
- Healthcare and Therapy Agents
- Newsletters and Communities
validations:
required: true
- type: dropdown
id: tier
attributes:
label: Tier
description: "Select maturity tier: 🚀 Production-Ready (10K+ stars / major enterprise), 🌱 Growing (500–5K stars / active), 🔬 Emerging (<500 stars / experimental)."
options:
- "🚀 Production-Ready"
- "🌱 Growing"
- "🔬 Emerging"
validations:
required: true
- type: input
id: language
attributes:
label: Primary Language / Technology
description: Primary runtime language or framework (e.g. Python, TypeScript, Go, Rust, Cloud, Browser, No-Code).
placeholder: e.g. Python
validations:
required: true
- type: input
id: tags
attributes:
label: Additional Tags
description: Optional comma-separated secondary tags (e.g. Multi-Agent, MCP, RAG, CLI).
placeholder: e.g. Multi-Agent, MCP, CLI
- type: textarea
id: description
attributes:
label: Description
description: "One concise sentence describing what the tool does (10–30 words). Ending with a period. No markdown links or promotional buzzwords."
placeholder: e.g. Production-grade multi-agent framework built for resilient task orchestration across distributed LLM workers.
validations:
required: true
- type: textarea
id: contributor_notes
attributes:
label: Contributor Notes
description: Optional context or notes for maintainers (will not be published in the registry entry).
placeholder: Additional context, release version, or relationship to project.
upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/catalogue/.github/workflows/awesome-lint.yml
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title: "Awesome Lint"
task: ""
lineage_type: import
upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/539ec1dc/.github/workflows/awesome-lint.yml
upstream_sha: 539ec1dc
imported_at: 2026-06-26
upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/20285455/.github/workflows/awesome-lint.yml
upstream_sha: 20285455
imported_at: 2026-10-26
prompt_class: unknown
upstream_changes: accepted
author: upstream
@@ -39,11 +39,14 @@ jobs:
- name: Check English in docs and comments
# Added EHR to the ignore-words-list below
run: codespell -q 3 --skip=.git,node_modules --check-filenames --ignore-words-list="reworkd,EHR" .
run: codespell -q 3 --skip=.git,node_modules --check-filenames --ignore-words-list=reworkd,ehr,EHR .
- name: Install dependencies
run: npm install
- name: Validate dataset compilation & Next.js build
run: npm run build
- name: Run awesome-lint
run: npx awesome-lint
env:
upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/catalogue/.github/workflows/link-check.yml
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title: "Link Check"
task: ""
lineage_type: import
upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/117f624b/.github/workflows/link-check.yml
upstream_sha: 117f624b
imported_at: 2026-07-25
upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/20285455/.github/workflows/link-check.yml
upstream_sha: 20285455
imported_at: 2026-10-26
prompt_class: unknown
upstream_changes: accepted
author: upstream
@@ -40,13 +40,27 @@ jobs:
args: >-
--verbose
--no-progress
--accept 200,204,301,302,308,403,429
--accept 200,204,301,302,307,308,403,429
--exclude-path node_modules
--exclude "star-history.com"
--exclude "shields.io"
--exclude "stableaudio.com"
--exclude "mckinsey.com"
--exclude "servicenow.com"
--exclude "agentage.io"
--exclude "agentwork-api.yfoob.chatgpt.site"
--exclude "theagentsindex.com"
--exclude "notfair.co"
--exclude "lexisnexis.com"
--exclude "vercel.app"
--exclude "tabnine.com"
--exclude "seedream4.video"
--exclude "youper.ai"
--exclude "zendesk.com"
--exclude "zoho.com"
--exclude "lognorm.com"
--exclude "ge.com"
--exclude "issues/new"
--timeout 30
README.md
fail: true
upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/catalogue/.github/workflows/process-agent-submission.yml
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---
title: "Automated contributor submission processing and branch creation is disabled."
task: ""
lineage_type: import
upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/20285455/.github/workflows/process-agent-submission.yml
upstream_sha: 20285455
imported_at: 2026-10-26
prompt_class: unknown
upstream_changes: accepted
author: upstream
validated: false
---
# Automated contributor submission processing and branch creation is disabled.
# Submissions via issues are reviewed and merged manually by maintainers.
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---
title: "Architecture Overview — AI Agent Registry 2026"
task: ""
lineage_type: import
upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/20285455/ARCHITECTURE.md
upstream_sha: 20285455
imported_at: 2026-10-26
prompt_class: catalogue
upstream_changes: accepted
author: upstream
validated: false
---
# Architecture Overview — AI Agent Registry 2026
This document details the system architecture, component design, data flow, and deployment model of the **Awesome AI Agents 2026 / AI Agent Registry** application.
---
## 1. System Overview
The system is designed as a **hybrid static data catalog & interactive single-page application**:
1. **Source Dataset**: Curated single-sentence Markdown entries in [`README.md`](README.md).
2. **Build-Time Parser Pipeline**: Node.js parser ([`scripts/build-data.js`](scripts/build-data.js)) extracts, validates, normalizes, and disambiguates all 460+ tools into strongly-typed static JSON databases ([`data/resources.json`](data/resources.json) and [`data/categories.json`](data/categories.json)).
3. **Frontend Application**: Next.js 14 App Router application leveraging React 18, Tailwind CSS, Lucide icons, and `next-themes` for fast, client-side search, multi-faceted filtering, dynamic category navigation, and resource discovery.
4. **Containerized Deployment**: Production-ready multi-stage Docker build utilizing Next.js `standalone` mode.
---
## 2. High-Level Data Flow & Architecture
```mermaid
flowchart TD
subgraph Data Layer
A["README.md (Curated Catalog)"] -->|npm run build:data| B["scripts/build-data.js (Multi-pass Parser)"]
B --> C["data/resources.json (460+ Items)"]
B --> D["data/categories.json (31 Domains)"]
end
subgraph Data Access & Search Layer
C & D --> E["lib/resources.ts (Data Abstraction Layer)"]
E --> F["lib/search.ts (In-Memory Search & Multi-Filter Engine)"]
end
subgraph Presentation Layer (Next.js 14 App Router)
F --> G["app/page.tsx (Homepage & Hero Search)"]
F --> H["app/explore/page.tsx (Explore & Filter Panel)"]
F --> I["app/categories/[slug]/page.tsx (Category Directory)"]
F --> J["app/resources/[slug]/page.tsx (Resource Detail Views)"]
end
subgraph Component Primitives
H --> K["components/filter-sidebar.tsx"]
H & I --> L["components/resource/ResourceGrid.tsx"]
L --> M["components/resource/ResourceCard.tsx"]
G --> N["components/category/CategoryCard.tsx"]
G & H --> O["components/search/SearchBar.tsx"]
end
```
---
## 3. Core Architectural Modules
### A. Data Parsing & Normalization (`scripts/build-data.js`)
- **Category Extraction**: Scans Markdown headers (`## Category Name`) to construct category entries with unique slugs and item counters.
- **Resource Parsing**: Regex pattern matching extracts name, GitHub URL, tier badge (`🚀`, `🌱`, `🔬`), language tag (`[Python]`, `[TypeScript]`), type tag (`[MCP]`, `[Multi-Agent]`), and description.
- **Disambiguation Engine**: Detects duplicate tool names across different categories or forks (e.g. `Codex CLI`, `Yao Agents`) and appends auto-generated unique slug suffixes (e.g. `codex-cli-microsoft`).
### B. Data Access & Search Engine (`lib/resources.ts`, `lib/search.ts`)
- **Query Abstraction**: Read-only functions (`getResources()`, `getResourceBySlug()`, `getCategories()`, `getCategoryBySlug()`, `getRelatedResources()`) query statically imported JSON datasets.
- **Client-Side Filtering**: Supports simultaneous multi-column filtering by text query, category, tier level, runtime language, and source platform (GitHub vs. external website).
- **Deterministic Sorting**: Instant sorting by name (A–Z), tier hierarchy (Production → Growing → Emerging), category, and GitHub repository popularity.
### C. Component Architecture
- **Header & Navigation** ([`components/layout/Header.tsx`](components/layout/Header.tsx)): Sticky desktop header with navigation links, search trigger modal shortcut (`⌘K`), theme toggle, and repository link.
- **Resource Grid & Card** ([`components/resource/ResourceCard.tsx`](components/resource/ResourceCard.tsx)): Information-dense cards formatted with strict minimal aesthetic (monospace tags, subtle 1px borders, tier badges, direct source repository links).
- **Filter Panel** ([`components/filter-sidebar.tsx`](components/filter-sidebar.tsx)): Real-time filter control sidebar for tier, runtime language, and category domain narrowing.
- **Submission Portal** ([`app/submit/page.tsx`](app/submit/page.tsx), [`components/submission/`](components/submission/)): Interactive contribution interface utilizing Shadcn UI form primitives (`Field`, `FieldLabel`, `Input`, `Select`, `Textarea`), real-time client-side validation, pre-filled GitHub Issue link generation, and a two-column lower layout for live Markdown entry preview (`SubmissionPreview.tsx`) and process instructions (`SubmissionInstructions.tsx`).
---
## 4. Technology Stack
| Layer | Technology | Purpose |
|---|---|---|
| **Framework** | Next.js 14 (App Router) | Static rendering & client-side routing |
| **Language** | TypeScript 5 | Strict static typing across components & dataset schema |
| **Styling** | Tailwind CSS + Vanilla CSS | Strict Minimal design system (GitHub/npm/docs aesthetic) |
| **Icons** | Lucide React | Monochrome system status & UI icons |
| **Theme** | `next-themes` | Dark, light, and system color mode switching |
| **Build Tooling** | Node.js | Markdown dataset parser & static compilation |
| **Containerization** | Docker + Docker Compose | Multi-stage production container build |
---
## 5. Deployment Architecture
The application supports dual deployment models:
### A. Vercel Global Edge Network (Automated CI/CD)
- **GitHub Integration**: Connected to `ARUNAGIRINATHAN-K/awesome-ai-agents` via webhooks.
- **Build Execution**: `npm run build` runs `node scripts/build-data.js && next build`, compiling the dataset and pre-rendering all dynamic routes statically (`/explore`, `/categories/[slug]`, `/resources/[slug]`).
- **CDN Edge Caching**: Assets and static HTML pages are distributed globally across Vercel's Edge Network with sub-100ms response times.
- **Preview Deployments**: Branch pushes and Pull Requests generate isolated preview URLs for visual verification before merging to `main`.
### B. Containerized Deployment (Docker)
- **Multi-Stage Build Pipeline**:
- `deps`: Installs production dependencies (`npm ci`).
- `builder`: Executes dataset compiler (`npm run build:data`) & Next.js production build (`output: 'standalone'`).
- `runner`: Lightweight Node.js Alpine container executing `server.js`.
- **Docker Compose**: `docker compose up --build` provisions the service on port `3000`.
upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/catalogue/CHANGELOG.md
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title: "Changelog"
task: ""
lineage_type: import
upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/2700f0b8/CHANGELOG.md
upstream_sha: 2700f0b8
imported_at: 2026-09-04
upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/20285455/CHANGELOG.md
upstream_sha: 20285455
imported_at: 2026-10-26
prompt_class: catalogue
upstream_changes: accepted
author: upstream
@@ -23,6 +23,13 @@ All notable changes to this list will be documented here.
| Date | Change |
|---|---|
| October 23 2026 | Added **Frink** to Coding Agents, **CUGA** to Orchestration Frameworks, **Bluwhale** (Finance) and **AltoRank** (Marketing & SEO) to Industry-Specific Agents, **Vectoree**, **Loadout**, **AgentSandboxes**, and **Anansi Haven** to Agent Tooling and Infrastructure, **TecAdRise Bot** to Agent Interfaces and UIs, **TableJourney** to Agent Communication Protocols (MCP), **Concord MCP** to Multi-Agent Systems, and **Enhanciar** to Memory and Context. **Link audit & Maintenance** — Removed inaccessible entry for **Nobulex** (HTTP 451 repo takedown), canonicalized redirected URLs for **Noveum**, Anthropic's **Building Effective Agents**, and **AI Engineering Newsletter** (latent.space) across README.md and data/resources.json, relocated **Jev Agent** to its alphabetical position under MCP, added Lychee crawler exclusions for LogNorm and GE, and disabled automated submission branch creation. |
| October 16 2026 | Added **Noveum** to Safety Guardrails and Observability, **IdeaHunter** to Deep Research Agents, **LWC** to Memory and Context, **Personal Agent Bench** to Agent Evaluation and Benchmarks, **Arcmira** and **Speck** to Agent Tooling and Infrastructure, **LinkMCP** to Customer Support and CRM Agents (Sales and Outreach), and **5dive** to Multi-Agent Systems. Performed **Link audit** — Removed dead 404 entries for AgentsCoin MCP and Live Tennis API MCP, canonicalized redirected URLs for JetBrains AI, Datacircle, MultiOn, Personal Jarvis (Dodaios), Patronus AI LYNX, Hugging Face Agents Course, The Agent Loop SSRN paper, Midjourney, Seedream AI Studio, PolyAI, and Mistral Large 2 across README.md and data/resources.json, and added exclusions for Youper TLS drop, Zendesk/Zoho geo-redirects, and GitHub issue template in link-check workflow. |
| October 08 2026 | **Linting fix** — Canonicalized Hugging Face Agents Course URL to resolve awesome-list-item remark-lint error and capitalized WebSocket in OpenMail description. |
| September 28 2026 | **Link audit** — Updated Seedream AI Studio to ByteDance official URL, canonicalized redirected URLs for Google Imagen 4, LibreChat, and AIHawk across README.md and data/resources.json, and added exclusions for Tabnine and Seedream4 in link-check workflow. |
| September 23 2026 | **Link audit** — Removed dead OpenProgram 404 entry and canonicalized redirected URLs for LATS and Pika 2.5 across README.md and data/resources.json. |
| September 11 2026 | **Link audit** — Canonicalized redirected URLs for OWASP Top 10 for Agentic Apps and Sora 2 across README.md and data/resources.json. |
| September 06 2026 | **AI Agent Registry Web App (Phase 2)** — Launched developer-focused Next.js 14 App Router interactive registry frontend. Added full-text client search, multi-faceted filtering (category, tier, runtime language, platform), static JSON dataset compilation (`scripts/build-data.js`), Docker multi-stage containerization, and responsive Minimal Style layout with dark/light theme support. |
| September 04 2026 | **Link audit** — Updated redirected URL for EvoAgentX and removed dead Better Agent entry. |
| July 22 2026 | **Link audit** — Updated 7 redirected URLs (Open Interpreter, Remio, Toolhouse, Pilot Protocol, LLMGraph, Agent Learning Kit, Lavender) and removed the dead Agent Starter entry. |
| June 26 2026 | **Style reformatting** — Converted all 407 tool entries to the new compact format: `` `TIER` `[Language]` `[Type]` `` replacing the legacy `(🏷️ ...)` tag style. Tier badges (`🚀` Production-Ready, `🌱` Growing, `🔬` Emerging) applied consistently across all sections. Descriptions trimmed to one clear sentence, promotional language removed. Zero duplicate links. Full awesome-lint structural compliance maintained. |
upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/catalogue/CODE_OF_CONDUCT.md
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@@ -2,9 +2,9 @@
title: "Code of Conduct"
task: ""
lineage_type: import
upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/539ec1dc/CODE_OF_CONDUCT.md
upstream_sha: 539ec1dc
imported_at: 2026-06-26
upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/20285455/CODE_OF_CONDUCT.md
upstream_sha: 20285455
imported_at: 2026-10-26
prompt_class: catalogue
upstream_changes: accepted
author: upstream
@@ -55,7 +55,7 @@ It also applies when an individual is officially representing the project in pub
## Enforcement
Instances of unacceptable behavior may be reported by opening a GitHub issue at [https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents/issues](https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents/issues) and requesting confidentiality, or by contacting the maintainer directly.
Instances of unacceptable behavior may be reported by opening a GitHub issue at [https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/issues](https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/issues) and requesting confidentiality, or by contacting the maintainer directly.
All complaints will be reviewed and investigated promptly, fairly, and with respect for the reporter's privacy. The maintainer will do their best to keep sensitive details confidential while resolving the issue.
upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/catalogue/CONTRIBUTING.md
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@@ -2,9 +2,9 @@
title: "Contributing to Awesome AI Agents 2026"
task: ""
lineage_type: import
upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/539ec1dc/CONTRIBUTING.md
upstream_sha: 539ec1dc
imported_at: 2026-06-26
upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/20285455/CONTRIBUTING.md
upstream_sha: 20285455
imported_at: 2026-10-26
prompt_class: catalogue
upstream_changes: accepted
author: upstream
@@ -15,14 +15,29 @@ validated: false
First off, thank you. This list only stays useful because people like you take the time to keep it accurate and current.
There are four ways to contribute:
### Contribution Channels
- **Add a new tool** - something missing that belongs here
- **Update an existing entry** - description is wrong or links are broken
- **Remove a dead project** - unmaintained repos that no longer belong
- **Improve the structure** - better categories, clearer descriptions, fixed typos
#### Option 1 — Web Submission Portal (Recommended)
1. Visit the registry [Submit Page](https://ai-agent-registry-kappa.vercel.app/submit).
2. Complete the resource information form powered by Shadcn UI form components:
- **Resource Name**: Project name.
- **Project URL**: Must start with `https://` (e.g. GitHub repository or official site).
- **Resource Type**: Dropdown selection (`Framework`, `Multi-Agent System`, `Agent / Assistant`, `Tool / Library`, `Protocol / Standard`, `MCP Server`, `Infrastructure / Vector DB`, `Benchmark / Evaluation`).
- **Tier Level**: Select `🚀 Production-Ready`, `🔬 Active Research`, or `🌱 Emerging / Experimental`.
- **Category & Language**: Select primary category domain and implementation language.
- **Description**: 10–30 word single-sentence overview (without raw Markdown links).
3. Review the live **README.md Entry Output** and **Submission Process** in the two-column layout below the form.
4. Click **Submit via GitHub Issue** to open a pre-filled GitHub Issue form, or copy the formatted entry directly.
5. Automated GitHub Actions validation runs and opens a Pull Request for maintainer review.
All contributions go through a pull request. It takes about 5 minutes.
#### Option 2 — GitHub Issue Form
1. Open the repository's [New Issue Page](https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/issues/new/choose).
2. Select **Submit an AI Agent / Tool**.
3. Fill in the structured form fields.
4. Submit the issue — GitHub Actions validates the entry and opens an automated PR.
#### Option 3 — Manual Pull Request
For experienced contributors, fork the repository, edit `README.md`, run `npm run build:data`, and submit a standard Pull Request.
---
@@ -48,7 +63,7 @@ When in doubt, use `🌱`. Maintainers will adjust tiers during review.
Use the **primary implementation language or runtime**:
`Python` · `TypeScript` · `Go` · `Rust` · `Java` · `C#` · `C++` · `Kotlin` · `Swift` · `Ruby` · `WebAssembly` · `Cloud` · `Browser` · `Desktop` · `Mobile` · `No-Code`
`Python` · `TypeScript` · `Go` · `Rust` · `Java` · `C#` · `C++` · `Kotlin` · `Swift` · `Ruby` · `WebAssembly` · `Cloud` · `Browser` · `Desktop` · `Mobile` · `No-Code` · `Shell`
- Use `Cloud` for fully managed SaaS products with no local runtime
- Use `Browser` for browser extensions and purely web-based tools
@@ -86,19 +101,19 @@ Priority order when multiple tags could apply: `MCP` > `RAG` > framework (LangCh
```markdown
# Wrong: missing tier, language, and type tags (old format)
- [CrewAI](https://github.com/crewAIInc/crewAI) - Production multi-agent framework (🏷️ `Python` `LangChain` `SDK`).
- [ToolA](https://github.com/example/bad-entry-1) - Production multi-agent framework (🏷️ `Python` `LangChain` `SDK`).
# Wrong: em-dash instead of hyphen-space after closing tag
- [Mastra](https://github.com/mastra-ai/mastra) `🌱` `[TypeScript]` `[MCP]` — Opinionated TypeScript framework.
- [ToolB](https://github.com/example/bad-entry-2) `🌱` `[TypeScript]` `[MCP]` — Opinionated TypeScript framework.
# Wrong: multiple sentences
- [Modus](https://github.com/hypermodeinc/modus) `🔬` `[WebAssembly]` `[Serverless]` - Serverless framework. It has minimal cold starts.
- [ToolC](https://github.com/example/bad-entry-3) `🔬` `[WebAssembly]` `[Serverless]` - Serverless framework. It has minimal cold starts.
# Wrong: promotional language
- [SomeTool](https://example.com) `🚀` `[Python]` `[Multi-Agent]` - The most powerful and revolutionary AI framework ever built.
- [ToolD](https://github.com/example/bad-entry-4) `🚀` `[Python]` `[Multi-Agent]` - The most powerful and revolutionary AI framework ever built.
# Wrong: tier badge missing brackets around language/type
- [SomeTool](https://example.com) `🌱` `Python` `Multi-Agent` - Does something useful.
- [ToolE](https://github.com/example/bad-entry-5) `🌱` `Python` `Multi-Agent` - Does something useful.
```
---
@@ -164,7 +179,28 @@ Current categories:
3. **Verify** the link works, your entry follows the format above, and the URL does not already appear elsewhere in the list
4. **Submit** a pull request with a clear title: `Add [Tool Name] to [Category]`
The maintainers will review your PR within a few days. We may suggest edits to the description, tier, tags, or category.
> [!NOTE]
> **Vercel Preview Check Status**: If you see a Vercel check indicating `Authorization required to deploy` on your PR, this is a standard Vercel security policy for repository forks. GitHub Actions (`awesome-lint` & `npm run build`) automatically validates your PR code and dataset formatting. Maintainers will review and merge your PR directly.
## Web Application & Dataset Build
The repository includes an interactive Next.js 14 web application for searching, filtering, and exploring the registry.
When adding or updating entries in `README.md`, recompile the static dataset and test the web app:
```bash
# 1. Compile README.md into static normalized JSON datasets
npm run build:data
# 2. Start Next.js development server
npm run dev
# 3. Validate production build
npm run build
# 4. (Optional) Test containerized build
docker compose up --build
```
---
upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/catalogue/DATA_SCHEMA.md
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---
title: "Data Schema & Parser Specification"
task: ""
lineage_type: import
upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/20285455/DATA_SCHEMA.md
upstream_sha: 20285455
imported_at: 2026-10-26
prompt_class: catalogue
upstream_changes: accepted
author: upstream
validated: false
---
# Data Schema & Parser Specification
This document defines the raw Markdown data format, TypeScript interfaces, parser rules, and compiled JSON schemas for the **AI Agent Registry** dataset.
---
## 1. Raw Markdown Entry Specification
Every tool listed under a category header in [`README.md`](README.md) MUST conform to this exact single-line Markdown pattern:
```markdown
- [Tool Name](https://github.com/org/repo) `TIER` `[Language]` `[Type]` - Single sentence description ending with a period.
```
### Metadata Tag Definitions
1. **Tool Name & URL**:
- Link text MUST match the canonical project name.
- Target URL MUST be a valid GitHub/GitLab repository or official hosted platform URL.
2. **Tier Badge** (Required, exactly one):
- `🚀` **Production-Ready**: 10K+ GitHub stars, enterprise adoption, major tech company (OpenAI, Anthropic, Google, Microsoft, Meta, AWS), or massive enterprise usage.
- `🌱` **Growing**: 500–5K stars, active commits, gaining momentum.
- `🔬` **Emerging**: <500 stars, research paper implementation, or experimental tool.
3. **Language Tag** (Required, exactly one, inside brackets):
- Approved values: `[Python]`, `[TypeScript]`, `[Go]`, `[Rust]`, `[Java]`, `[C#]`, `[C++]`, `[Kotlin]`, `[Swift]`, `[Ruby]`, `[WebAssembly]`, `[Cloud]`, `[Browser]`, `[Desktop]`, `[Mobile]`, `[No-Code]`, `[Shell]`.
4. **Type / Framework Tag** (Required, exactly one, inside brackets):
- Priority order: `MCP` > `RAG` > framework > architecture > environment.
- Approved examples: `[MCP]`, `[RAG]`, `[Multi-Agent]`, `[Graph-Based]`, `[Stateful]`, `[Event-Driven]`, `[Voice]`, `[Vision]`, `[Multimodal]`, `[Memory]`, `[Observability]`, `[Security]`, `[Compliance]`, `[Testing]`, `[Evaluation]`, `[Benchmark]`, `[CLI]`, `[IDE]`, `[VS Code]`, `[AWS]`, `[GCP]`, `[Azure]`, `[Self-Hosted]`, `[Local]`, `[Serverless]`, `[Research]`.
5. **Description Rules**:
- Exactly **one sentence**, ending with a period (`.`).
- Length: **12–18 words** recommended.
- Zero promotional or hyped language (never use "best", "revolutionary", "game-changing", "seamlessly", "powerful").
---
## 2. TypeScript Interfaces (`types/resource.ts`)
```typescript
export type TierLevel = 'production' | 'growing' | 'emerging';
export type TierBadge = '🚀' | '🌱' | '🔬';
export interface Resource {
id: string;
name: string;
slug: string;
url: string;
githubRepo?: string;
isGitHub: boolean;
tier: TierLevel;
tierBadge: TierBadge;
language: string;
type: string;
description: string;
category: string;
categorySlug: string;
tags: string[];
lineIndex?: number;
}
export interface Category {
name: string;
slug: string;
description?: string;
count: number;
lineIndex?: number;
}
export interface ResourceDataset {
version: string;
generatedAt: string;
totalResources: number;
totalCategories: number;
resources: Resource[];
}
export interface CategoryDataset {
version: string;
generatedAt: string;
categories: Category[];
}
```
---
## 3. Disambiguation & Slug Generation Rules
To ensure static build stability and valid dynamic routes (`/categories/[slug]`, `/resources/[slug]`), the parser (`scripts/build-data.js`) applies the following normalization pipeline:
1. **Standard Slug Generation**:
- Lowercase string, replace non-alphanumeric characters with hyphens, strip leading/trailing hyphens.
- Example: `CrewAI Framework` → `crewai-framework`.
2. **Slug Disambiguation Strategy**:
- If two tools resolve to the same base slug (e.g. `Codex CLI` by Microsoft vs. `Codex CLI` fork), the parser automatically appends an organization/category suffix:
`codex-cli` → `codex-cli-microsoft` / `codex-cli-coding-agents`.
- Guaranteed unique `id` and `slug` fields across the entire compiled dataset.
3. **Tag Normalization**:
- Tags array includes the primary `language`, `type`, `tier`, `category`, and extracted keyword tokens.
---
## 4. Compiled JSON Schemas (`data/*.json`)
### `data/resources.json`
```json
{
"version": "2026.1",
"generatedAt": "2026-09-06T10:00:00.000Z",
"totalResources": 461,
"totalCategories": 31,
"resources": [
{
"id": "agency-swarm",
"name": "Agency Swarm",
"slug": "agency-swarm",
"url": "https://github.com/VRSEN/agency-swarm",
"githubRepo": "VRSEN/agency-swarm",
"isGitHub": true,
"tier": "production",
"tierBadge": "🚀",
"language": "Python",
"type": "Multi-Agent",
"description": "Orchestrates multi-agent systems built on the OpenAI Assistants API with role-based collaboration.",
"category": "Orchestration Frameworks",
"categorySlug": "orchestration-frameworks",
"tags": ["Python", "Multi-Agent", "production", "Orchestration Frameworks"],
"lineIndex": 62
}
]
}
```
### `data/categories.json`
```json
{
"version": "2026.1",
"generatedAt": "2026-09-06T10:00:00.000Z",
"categories": [
{
"name": "Orchestration Frameworks",
"slug": "orchestration-frameworks",
"count": 28,
"lineIndex": 60
}
]
}
```
upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/catalogue/DESIGN.md
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---
title: "Vercel — Style Reference"
task: ""
lineage_type: import
upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/20285455/DESIGN.md
upstream_sha: 20285455
imported_at: 2026-10-26
prompt_class: catalogue
upstream_changes: accepted
author: upstream
validated: false
---
# Vercel — Style Reference
> Typeset terminal on white paper
**Theme:** light
Vercel's design system is an exercise in disciplined monochrome: a near-white canvas (#fafafa), black typography, hairline borders, and the complete absence of decorative color. The visual language reads like a developer terminal rendered in print — Geist Sans whispers at hero scale with weight 400-450 and -0.06em tracking, while Geist Mono stamps labels, metadata, and code samples in 11-13px caps. Buttons are sharp 6px-radius pills or rectangles in pure black-on-white or white-on-black. The system trusts contrast and typography hierarchy over color, using the triangle mark (▲) and a two-line wordmark as the only brand ornament. Surfaces stack as #fafafa → #ebebeb → #171717, creating depth through tonal value shifts rather than shadow or color. Every element is built on a 4px grid with compact density, producing an interface that feels like an engineer's notebook — precise, functional, and unafraid of empty space.
## Tokens — Colors
| Name | Value | Token | Role |
|------|-------|-------|------|
| Paper White | `#fafafa` | `--color-paper-white` | Page canvas, card surfaces, light button fills — the default background that everything else sits on |
| Pure White | `#ffffff` | `--color-pure-white` | Elevated card surfaces, inset highlights, input fields |
| Hairline | `#ebebeb` | `--color-hairline` | 1px borders on buttons, links, and cards — visible at high zoom, invisible at speed |
| Ash | `#c9c9c9` | `--color-ash` | Disabled text, muted labels, brand name watermarks in customer logos |
| Smoke | `#a8a8a8` | `--color-smoke` | Tertiary text, placeholder copy, subtle icon fills |
| Graphite | `#8f8f8f` | `--color-graphite` | Footer micro-copy, secondary metadata |
| Slate | `#7d7d7d` | `--color-slate` | Customer brand names in logo strips, muted heading variants |
| Stone | `#666666` | `--color-stone` | Muted captions, helper text, and de-emphasized UI labels. |
| Charcoal | `#4d4d4d` | `--color-charcoal` | Body paragraph text, card descriptions, button secondary labels — where reading weight lives |
| Obsidian | `#171717` | `--color-obsidian` | Primary headings, nav borders, dark button fills, list markers — near-black that avoids pure #000 harshness |
| Carbon | `#000000` | `--color-carbon` | SVG icon fills, logo marks, the triangle brand glyph — pure black reserved for graphic elements only |
| Terminal Green | `#297a3a` | `--color-terminal-green` | Green text accent for links, tags, and emphasized short phrases. Use as a supporting accent, not as a status color |
| Spectrum Gradient | `linear-gradient(90deg, rgb(0, 255, 149) 0%, rgb(255, 208, 0) 25%, rgb(255, 23, 68) 50%, rgb(149, 0, 255) 75%, rgb(0, 229, 255) 100%)` | `--color-spectrum-gradient` | Decorative gradient sweep for marketing hero accents — the only place color is permitted to exist |
| Solar Edge | `linear-gradient(90deg, rgb(255, 220, 48) 0%, rgb(56, 162, 255) 100%)` | `--color-solar-edge` | Decorative two-stop gradient for feature callouts — yellow-to-blue sweep |
## Tokens — Typography
### Geist Sans — Primary interface and display typeface — custom geometric sans from Vercel with tight tracking on large sizes (-0.06em at 56-64px, -0.05em at 30px). Weight 450 for hero headlines is the signature choice: heavier than a light but lighter than a semibold, producing a tone that is confident without shouting. · `--font-geist-sans`
- **Substitute:** Inter
- **Weights:** 400, 450, 500
- **Sizes:** 14, 16, 30, 56, 64
- **Line height:** 1.00, 1.10, 1.43, 1.50
- **Letter spacing:** -3.84px at 64px, -3.36px at 56px, -1.5px at 30px, normal at body sizes
- **OpenType features:** `"calt" 0, "rlig", "ss11"`
- **Role:** Primary interface and display typeface — custom geometric sans from Vercel with tight tracking on large sizes (-0.06em at 56-64px, -0.05em at 30px). Weight 450 for hero headlines is the signature choice: heavier than a light but lighter than a semibold, producing a tone that is confident without shouting.
### Geist Mono — Monospace secondary typeface for labels, code blocks, CLI output panels, metadata tags, and uppercase eyebrows. The 8px/600/uppercase combination stamps tiny wordmarks with mechanical precision. Wide letter-spacing (0.071em) in mono gives uppercase labels breathing room. · `--font-geist-mono`
- **Substitute:** JetBrains Mono
- **Weights:** 400, 500, 600
- **Sizes:** 8, 11, 12, 13, 14
- **Line height:** 1.00, 1.33, 1.43, 1.50, 1.54, 1.60, 1.67
- **Letter spacing:** 0.071em for mono at 11-12px
- **OpenType features:** `"calt" 0, "rlig", "ss11"`
- **Role:** Monospace secondary typeface for labels, code blocks, CLI output panels, metadata tags, and uppercase eyebrows. The 8px/600/uppercase combination stamps tiny wordmarks with mechanical precision. Wide letter-spacing (0.071em) in mono gives uppercase labels breathing room.
### Type Scale
| Role | Size | Line Height | Letter Spacing | Token |
|------|------|-------------|----------------|-------|
| eyebrow | 11px | 1.5 | — | `--text-eyebrow` |
| caption | 13px | 1.54 | — | `--text-caption` |
| body | 16px | 1.5 | — | `--text-body` |
| heading | 30px | 1.1 | -1.5px | `--text-heading` |
| heading-lg | 56px | 1 | -3.36px | `--text-heading-lg` |
| display | 64px | 1 | -3.84px | `--text-display` |
## Tokens — Spacing & Shapes
**Density:** compact
### Spacing Scale
| Name | Value | Token |
|------|-------|-------|
| 4 | 4px | `--spacing-4` |
| 6 | 6px | `--spacing-6` |
| 8 | 8px | `--spacing-8` |
| 12 | 12px | `--spacing-12` |
| 14 | 14px | `--spacing-14` |
| 16 | 16px | `--spacing-16` |
| 20 | 20px | `--spacing-20` |
| 24 | 24px | `--spacing-24` |
| 32 | 32px | `--spacing-32` |
| 40 | 40px | `--spacing-40` |
| 44 | 44px | `--spacing-44` |
| 208 | 208px | `--spacing-208` |
### Border Radius
| Element | Value |
|---------|-------|
| nav | 2px |
| cards | 6px |
| pills | 9999px |
| buttons | 6px |
### Shadows
| Name | Value | Token |
|------|-------|-------|
| subtle | `rgba(0, 0, 0, 0.08) 0px 0px 0px 1px, rgb(250, 250, 250) 0...` | `--shadow-subtle` |
| subtle-2 | `rgb(235, 235, 235) 0px 0px 0px 1px` | `--shadow-subtle-2` |
### Layout
- **Page max-width:** 1280px
- **Section gap:** 96-128px
- **Card padding:** 16px
- **Element gap:** 12px
## Components
### Filled Black Button
**Role:** Primary action — the strongest visual weight in the system
Background #171717, text #ffffff, 6px radius, 12px horizontal padding, Geist Sans 14px/400. Used for Deploy Now, Sign Up. The only element that reaches 'Obsidian' black, making it unmistakable as the page's primary call.
### Ghost Outline Button
**Role:** Secondary action — present but not competing with primary
Background transparent, text #4d4d4d, 1px border #ebebeb (rendered via box-shadow ring), 6px radius, 20px all padding, Geist Sans 14px/400. Used for Talk to Sales, Get a Demo. Reads as 'available' without asserting hierarchy.
### Pill Button
**Role:** Compact action in dense contexts (nav, header right cluster)
Background #171717 or #ffffff, text contrasting, 9999px radius (full pill), 12px horizontal padding, 0 vertical padding for tight header use.
### Text Link Button
**Role:** Lowest-weight interactive — pure underline or color shift on text
No background, no border, 0px radius, color #171717 or #4d4d4d, 14-16px Geist Sans. Used for nav items and inline references.
### Bordered Card
**Role:** Feature/content container — depth via hairline border, never shadow
Background #ffffff, 6px radius, 1px border via stacked box-shadows: inset ring rgba(0,0,0,0.08) + outer ring #fafafa, 16px padding. The double-ring technique creates a border that survives any background — a signature Vercel trick.
### Inverted Card
**Role:** Visual punctuation in a grid — flips the value scale
Background #171717, white text, 6px radius. Used sparingly (e.g., the Passport card) to break the monotony of an all-light layout.
### CLI Output Panel
**Role:** Demonstrates the product in its native environment — a terminal screenshot embedded as UI
Light background with Geist Mono 12-13px text in #171717, prefixed with ▲ triangle marker in #171717 for commands, ✓ checkmark in #297a3a for confirmations. The terminal IS the marketing.
### Logo Strip Row
**Role:** Social proof — customer brand names in a single horizontal line
7+ logos in a row with 24-32px gaps. Customer names render in Geist Sans at the customer's actual brand treatment, faded to #7d7d7d for the page's neutral context.
### Eyebrow Label
**Role:** Tiny uppercase section markers — the monospace 'stamp'
Geist Mono 11px/400, 0.071em letter-spacing, uppercase, color #171717. Pairs with a 12px gap before the heading it announces. Examples: 'FOR CODING AGENTS', 'TO SHIP APPS AND AGENTS'.
### Top Nav Bar
**Role:** Global navigation — minimal, horizontal, no shadow
Height 64px, sticky, background #fafafa with backdrop-blur (20-48px), wordmark left (▲ + Vercel), nav items center-left at 14px Geist Sans #171717, action cluster right (Get a Demo ghost, Log In ghost, Sign Up filled). No bottom border — separation comes from spacing alone.
### Hero Composition
**Role:** First screen — oversized type on left, brand mark on right, supporting text far right
Three-column asymmetric layout: headline (56-64px, -0.06em tracking, weight 450) left, black triangle glyph center, eyebrow stack right. No background image. The triangle floats with no border or fill — pure black silhouette.
### Feature Card Grid
**Role:** 2-up product showcase cards with mini product UI embedded
Large cards with the Bordered Card treatment, 16-24px padding, title at 30px heading weight, description at 14-16px body in #4d4d4d. Each card contains a small product mockup (CLI output, passport, framework logo) as visual evidence.
## Do's and Don'ts
### Do
- Use #171717 for all primary text and filled buttons — never pure #000000 for text or #ffffff for dark surfaces
- Apply 6px radius to all cards, buttons, and bordered containers (--geist-radius) — use 9999px only for pill-shaped nav actions
- Set headlines at weight 400-450 with -0.06em letter-spacing at 56-64px — never bold headlines over 700
- Use Geist Mono 11-12px uppercase with 0.071em tracking for all eyebrows, labels, and metadata stamps
- Build depth with hairline borders via stacked box-shadows (0 0 0 1px rgba(0,0,0,0.08), 0 0 0 2px #fafafa) — never with drop-shadow
- Space sections at 96-128px vertical gaps with no background shifts or dividers between bands
- Prefix CLI commands with ▲ and confirmations with ✓ in #297a3a — the terminal pattern is the product demo
### Don't
- Never introduce a chromatic color outside the Terminal Green (#297a3a) success indicator and the spectrum gradient — 0% colorfulness is the rule
- Never use border-radius larger than 6px on cards or rectangular buttons — the sharpness is the point
- Never set body or heading type in any color other than the #171717 / #4d4d4d / #666666 scale
- Never use drop-shadows for elevation — hairline rings only, or no depth at all
- Never use light or semibold (300 or 600-700) weights for headlines — the system speaks at 400-450 maximum
- Never set line-height above 1.0 for display sizes (56-64px) — the tight leading is what makes the type feel architectural
- Never use Geist Sans for labels, metadata, or code — Geist Mono owns that space exclusively
## Surfaces
| Level | Name | Value | Purpose |
|-------|------|-------|---------|
| 0 | Page Canvas | `#fafafa` | Default page background — the warm off-white that makes the system feel paper-like rather than screen-like |
| 1 | Card Surface | `#ffffff` | Elevated cards sit one step above canvas in brightness, distinguished by hairline border not shadow |
| 2 | Inverted Surface | `#171717` | Dark inverted panels (e.g., Passport card) flip the value scale for visual punctuation in a grid |
## Elevation
- **Bordered Card:** `0 0 0 1px rgba(0,0,0,0.08), 0 0 0 2px #fafafa`
- **Ghost Button / Link:** `0 0 0 1px #ebebeb`
## Imagery
Imagery is sparse and always functional. The primary visual element is the black triangle (▲) — a geometric brand mark that appears at hero scale as a floating silhouette. Customer logos in the social-proof strip use each brand's actual wordmark in grayscale. Product mockups (Notion chat UI, CLI terminal output, passport card) are embedded as light-mode screenshots within bordered cards. There is no photography, no illustration, no 3D — the system is pure typography and geometric shapes on white paper.
## Layout
Max-width 1280px centered container with generous horizontal padding (24-48px). The hero uses an asymmetric three-zone composition: oversized headline left, brand glyph center, eyebrow stack right. Below the hero, content flows in alternating bands: a full-width customer logo strip, then a two-column 'build agents' section with heading left and product mockup right, then a three-up feature card grid. Sections are separated by 96-128px vertical gaps with no dividers or background shifts. The nav is a single 64px sticky bar with backdrop blur. Card grids consistently use a 2-column or 3-column layout with 16-24px gaps. The overall density is compact — text is comfortable to read, whitespace is generous, but the type scale and component sizing keep the page from feeling airy.
## Agent Prompt Guide
**Quick Color Reference**
- Background: #fafafa
- Text: #171717
- Border: #ebebeb
- Accent: #000000 (triangle mark only)
- primary action: #171717 (filled action)
**Example Component Prompts**
1. Create a hero headline: Geist Sans 56px weight 450, color #171717, letter-spacing -3.36px, line-height 1.0. Below it, a filled black button (background #171717, text #ffffff, 6px radius, 12px 20px padding, Geist Sans 14px weight 400) and a ghost button beside it (background transparent, text #4d4d4d, 1px border #ebebeb, 6px radius, 20px padding).
2. Create a feature card: background #ffffff, 6px radius, border via stacked box-shadows (0 0 0 1px rgba(0,0,0,0.08), 0 0 0 2px #fafafa), 16px padding. Title in Geist Sans 30px weight 400 with -1.5px tracking, color #171717. Description in Geist Sans 14px weight 400, color #4d4d4d.
3. Create an eyebrow label: Geist Mono 11px weight 400, 0.071em letter-spacing, uppercase, color #171717. Pair with 12px margin-bottom before the heading it introduces.
4. Create a CLI output panel: background #ffffff with 1px border #ebebeb, 6px radius, 16px padding. Commands prefixed with ▲ in #171717, confirmations prefixed with ✓ in #297a3a, all text in Geist Mono 12px weight 400, color #171717.
5. Create a top nav bar: height 64px, background #fafafa with backdrop-filter blur(20px), flex row. Left: ▲ glyph in #000000 + 'Vercel' in Geist Sans 14px weight 500. Center-left: nav items in Geist Sans 14px weight 400, color #171717, 24px gaps. Right: ghost button (border #ebebeb, text #4d4d4d, 6px radius) + filled button (background #171717, text #ffffff, 6px radius, pill-shaped at 9999px radius for tight header use).
## Typographic Discipline
The type system has only two families and 8 sizes. Headlines are weight 400-450 (not 700), tracking tight at -0.06em, line-height 1.0 — they sit on a single line, dense and confident. Body is 14-16px weight 400 at 1.43-1.5 line-height. The only color in the type system is the value shift from #171717 to #4d4d4d to #666666 — never a chromatic tint. Labels and metadata are ALWAYS monospace, ALWAYS uppercase, ALWAYS small (8-12px). This binary — Geist Sans for reading, Geist Mono for stamping — is the system's most recognizable rule.
## The Triangle Protocol
The ▲ glyph is the brand's most deployed element. It appears as: the wordmark icon, the CLI command prefix, a decorative hero silhouette, a loading indicator, and an interactive collapse marker. Always rendered in #000000 at 1:1 aspect ratio, never with a border, never with a fill other than black. It is the only element in the system permitted to be pure #000000 — everything else is #171717 or lighter.
## Similar Brands
- **Linear** — Same sharp 6-8px radii, monochromatic light canvas, and tight-tracking geometric sans-serif headlines — Linear and Vercel share a print-engineering aesthetic
- **Railway** — Similar near-black on warm-white palette, monospace CLI panels, and the discipline of using one accent color (or none) across an entire interface
- **Stripe** — Hairline borders, generous whitespace, and the trust that typography hierarchy alone can carry a page without decorative color or imagery
- **Resend** — Developer-marketing approach: terminal output as hero, monospace labels, the same paper-white canvas and pure-black wordmark
- **Plaid** — High-contrast achromatic system with a single chromatic note for state, trusting typographic weight and spacing to create hierarchy
## Quick Start
### CSS Custom Properties
```css
:root {
/* Colors */
--color-paper-white: #fafafa;
--color-pure-white: #ffffff;
--color-hairline: #ebebeb;
--color-ash: #c9c9c9;
--color-smoke: #a8a8a8;
--color-graphite: #8f8f8f;
--color-slate: #7d7d7d;
--color-stone: #666666;
--color-charcoal: #4d4d4d;
--color-obsidian: #171717;
--color-carbon: #000000;
--color-terminal-green: #297a3a;
--color-spectrum-gradient: #ff1744;
--gradient-spectrum-gradient: linear-gradient(90deg, rgb(0, 255, 149) 0%, rgb(255, 208, 0) 25%, rgb(255, 23, 68) 50%, rgb(149, 0, 255) 75%, rgb(0, 229, 255) 100%);
--color-solar-edge: #ffdc30;
--gradient-solar-edge: linear-gradient(90deg, rgb(255, 220, 48) 0%, rgb(56, 162, 255) 100%);
/* Typography — Font Families */
--font-geist-sans: 'Geist Sans', ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif;
--font-geist-mono: 'Geist Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, monospace;
/* Typography — Scale */
--text-eyebrow: 11px;
--leading-eyebrow: 1.5;
--text-caption: 13px;
--leading-caption: 1.54;
--text-body: 16px;
--leading-body: 1.5;
--text-heading: 30px;
--leading-heading: 1.1;
--tracking-heading: -1.5px;
--text-heading-lg: 56px;
--leading-heading-lg: 1;
--tracking-heading-lg: -3.36px;
--text-display: 64px;
--leading-display: 1;
--tracking-display: -3.84px;
/* Typography — Weights */
--font-weight-regular: 400;
--font-weight-w450: 450;
--font-weight-medium: 500;
--font-weight-semibold: 600;
/* Spacing */
--spacing-4: 4px;
--spacing-6: 6px;
--spacing-8: 8px;
--spacing-12: 12px;
--spacing-14: 14px;
--spacing-16: 16px;
--spacing-20: 20px;
--spacing-24: 24px;
--spacing-32: 32px;
--spacing-40: 40px;
--spacing-44: 44px;
--spacing-208: 208px;
/* Layout */
--page-max-width: 1280px;
--section-gap: 96-128px;
--card-padding: 16px;
--element-gap: 12px;
/* Border Radius */
--radius-sm: 2px;
--radius-md: 6px;
--radius-full: 9999px;
/* Named Radii */
--radius-nav: 2px;
--radius-cards: 6px;
--radius-pills: 9999px;
--radius-buttons: 6px;
/* Shadows */
--shadow-subtle: rgba(0, 0, 0, 0.08) 0px 0px 0px 1px, rgb(250, 250, 250) 0px 0px 0px 1px;
--shadow-subtle-2: rgb(235, 235, 235) 0px 0px 0px 1px;
/* Surfaces */
--surface-page-canvas: #fafafa;
--surface-card-surface: #ffffff;
--surface-inverted-surface: #171717;
}
```
### Tailwind v4
```css
@theme {
/* Colors */
--color-paper-white: #fafafa;
--color-pure-white: #ffffff;
--color-hairline: #ebebeb;
--color-ash: #c9c9c9;
--color-smoke: #a8a8a8;
--color-graphite: #8f8f8f;
--color-slate: #7d7d7d;
--color-stone: #666666;
--color-charcoal: #4d4d4d;
--color-obsidian: #171717;
--color-carbon: #000000;
--color-terminal-green: #297a3a;
--color-spectrum-gradient: #ff1744;
--color-solar-edge: #ffdc30;
/* Typography */
--font-geist-sans: 'Geist Sans', ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif;
--font-geist-mono: 'Geist Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, monospace;
/* Typography — Scale */
--text-eyebrow: 11px;
--leading-eyebrow: 1.5;
--text-caption: 13px;
--leading-caption: 1.54;
--text-body: 16px;
--leading-body: 1.5;
--text-heading: 30px;
--leading-heading: 1.1;
--tracking-heading: -1.5px;
--text-heading-lg: 56px;
--leading-heading-lg: 1;
--tracking-heading-lg: -3.36px;
--text-display: 64px;
--leading-display: 1;
--tracking-display: -3.84px;
/* Spacing */
--spacing-4: 4px;
--spacing-6: 6px;
--spacing-8: 8px;
--spacing-12: 12px;
--spacing-14: 14px;
--spacing-16: 16px;
--spacing-20: 20px;
--spacing-24: 24px;
--spacing-32: 32px;
--spacing-40: 40px;
--spacing-44: 44px;
--spacing-208: 208px;
/* Border Radius */
--radius-sm: 2px;
--radius-md: 6px;
--radius-full: 9999px;
/* Shadows */
--shadow-subtle: rgba(0, 0, 0, 0.08) 0px 0px 0px 1px, rgb(250, 250, 250) 0px 0px 0px 1px;
--shadow-subtle-2: rgb(235, 235, 235) 0px 0px 0px 1px;
}
```
upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/catalogue/DEVELOPMENT.md
+170
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@@ -0,0 +1,170 @@
---
title: "Development Guide — AI Agent Registry 2026"
task: ""
lineage_type: import
upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/20285455/DEVELOPMENT.md
upstream_sha: 20285455
imported_at: 2026-10-26
prompt_class: catalogue
upstream_changes: accepted
author: upstream
validated: false
---
# Development Guide — AI Agent Registry 2026
This guide provides instructions for local environment setup, data dataset compilation, testing, code guidelines, and deployment.
---
## 1. Prerequisites
Ensure your development environment meets the following requirements:
- **Node.js**: `v18.17.0` or higher (Node.js 20+ recommended)
- **npm**: `v9.0.0` or higher
- **Git**: `v2.30.0` or higher
- **Docker & Docker Compose** (Optional, for containerized local testing)
---
## 2. Getting Started
### Clone the Repository
```bash
git clone https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026.git
cd awesome-ai-agents-2026
```
### Install Dependencies
```bash
npm install
```
### Build the Dataset
Before starting the development server or building the web application, compile `README.md` into static JSON datasets:
```bash
npm run build:data
```
*Output*: Generates `data/resources.json` (460+ tools) and `data/categories.json` (31 domains).
### Start Development Server
```bash
npm run dev
```
Open [http://localhost:3000](http://localhost:3000) in your browser to view the interactive registry.
---
## 3. Available npm Scripts
| Script | Command | Description |
|---|---|---|
| `dev` | `next dev` | Starts local Next.js development server on port 3000 |
| `build:data` | `node scripts/build-data.js` | Parses `README.md` and generates static JSON files in `data/` |
| `build` | `node scripts/build-data.js && next build` | Compiles dataset and runs production Next.js build |
| `start` | `next start` | Starts Next.js production server post-build |
| `lint` | `next lint` | Runs Next.js ESLint checks |
---
## 4. Development Workflow
### Adding or Updating a Resource Entry
You can submit or update resource entries via two methods:
#### Method A — Web Submission Portal (`/submit`)
1. Start local dev server (`npm run dev`) and visit `http://localhost:3000/submit`.
2. Fill out the structured Shadcn UI submission form (`SubmissionForm.tsx`).
3. Real-time client-side validation enforces URL format (`https://`), non-empty required fields, and description word bounds (10–30 words).
4. Verify the formatted single-line Markdown output in the **README.md Entry Output** box (`SubmissionPreview.tsx`).
5. Click **Submit via GitHub Issue** or copy formatted entry to test issue generation (`SubmissionActions.tsx`).
#### Method B — Direct `README.md` Edit
1. Open [`README.md`](README.md).
2. Locate the appropriate category section header (e.g., `## Coding Agents`).
3. Insert or modify the single-line Markdown entry in alphabetical order:
```markdown
- [Tool Name](https://github.com/org/repo) `🚀` `[Python]` `[Multi-Agent]` - Clear single sentence description.
```
4. Rebuild the dataset:
```bash
npm run build:data
```
5. Verify your changes in the local web app at `http://localhost:3000/explore`.
---
## 5. Verification & Linting
Before opening a Pull Request, run the following quality checks:
### 1. Validate Dataset Parser & Build
```bash
npm run build
```
### 2. Run `awesome-lint`
Validates Markdown structure, link syntax, formatting, and alphabetical sorting:
```bash
npx awesome-lint
```
### 3. Check Spelling
```bash
codespell -q 3 --skip=.git,node_modules,.next --check-filenames --ignore-words-list=reworkd,ehr,EHR .
```
---
## 6. Deployment Options
### Option A: Vercel + GitHub Integration (Recommended)
The project is pre-configured for instant automated deployments via **Vercel + GitHub Integration**:
1. **Import Repository**: Log in to [Vercel Dashboard](https://vercel.com/dashboard) and click **Add New** → **Project**.
2. **Select GitHub Repo**: Choose `ARUNAGIRINATHAN-K/awesome-ai-agents`.
3. **Configure Settings**:
- **Framework Preset**: `Next.js`
- **Build Command**: `npm run build` *(runs `node scripts/build-data.js && next build` automatically)*
- **Output Directory**: `.next`
4. **Deploy**: Click **Deploy**. Vercel will build the project and issue a live URL (e.g. [https://ai-agent-registry-kappa.vercel.app](https://ai-agent-registry-kappa.vercel.app/)).
> [!NOTE]
> **Continuous Delivery**: Any future `git push` to `main` or merged Pull Request automatically triggers Vercel to rebuild the dataset from `README.md` and deploy the updated application globally. Every Pull Request gets an isolated preview environment.
#### Resolving "Authorization required to deploy" for Contributor PRs
When external contributors submit a Pull Request from a repository fork, Vercel displays `Authorization required to deploy` by default to protect project build quotas.
**How to Fix (Vercel Project Settings)**:
1. Open [Vercel Dashboard](https://vercel.com/dashboard) → Select your project → **Settings** → **Git**.
2. Under **Vercel for GitHub** / **Fork Protection**, set **"Require authorization for PRs from forks"** to **Disabled** (or select **"Automatically deploy Pull Requests from forks"**).
3. Alternatively, GitHub Actions CI (`awesome-lint.yml`) automatically executes `npm run build` on every PR from forks, ensuring PR correctness even before Vercel authorization.
### Option B: Docker Setup & Deployment
#### Build and Run with Docker Compose
```bash
docker compose up --build
```
#### Build Docker Image Manually
```bash
docker build -t ai-agent-registry .
docker run -p 3000:3000 ai-agent-registry
```
---
## 7. Strict Minimal Design System Guidelines
When contributing UI features to the web application:
- **Monospace Focus**: Use monospace typography (`font-mono`) for titles, tags, counters, and metadata badges.
- **Subtle Borders & Cards**: Use 1px subtle borders (`border-border`) and cards (`bg-card`).
- **No Unnecessary Clutter**: Avoid heavy shadows, background gradients, promotional copy, or decorative non-functional graphics.
- **Information Density**: Keep layout dense, fast, and developer-oriented (GitHub + npm + modern docs aesthetic).
- **Theme Support**: Ensure all new components support dark and light modes via CSS variable tokens (`bg-background`, `text-foreground`, `border-border`).
upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/catalogue/README.md
+179 -36
View File
@@ -2,9 +2,9 @@
title: "Awesome AI Agents 2026"
task: ""
lineage_type: import
upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/2700f0b8/README.md
upstream_sha: 2700f0b8
imported_at: 2026-09-04
upstream_source: https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/blob/20285455/README.md
upstream_sha: 20285455
imported_at: 2026-10-26
prompt_class: catalogue
upstream_changes: accepted
author: upstream
@@ -13,7 +13,7 @@ validated: false
<div align="center">
![](img/ARUNAGIRINATHAN-K.png)
![](img/top.png)
# Awesome AI Agents 2026
@@ -22,22 +22,29 @@ validated: false
![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)
[![Last Commit](https://img.shields.io/github/last-commit/ARUNAGIRINATHAN-K/awesome-ai-agents-2026)](https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/commits/main)
![Tools Listed](https://img.shields.io/badge/tools_listed-470%2B-blue?style=flat-square)
[![Live Registry](https://img.shields.io/website?url=https%3A%2F%2Fai-agent-registry-kappa.vercel.app%2F&up_message=online&label=registry&style=flat-square)](https://ai-agent-registry-kappa.vercel.app/)
[![License: CC0](https://img.shields.io/badge/License-CC0_1.0-lightgrey.svg?style=flat-square)](LICENSE)
[![Deploy with Vercel](https://vercel.com/button)](https://vercel.com/new/clone?repository-url=https%3A%2F%2Fgithub.com%2FARUNAGIRINATHAN-K%2Fawesome-ai-agents-2026)
*The most comprehensive, structured guide to AI agent frameworks, tools, and resources.*
*Updated weekly. Compared side-by-side. Built for developers who ship.*
**[Explore the Stack](#contents) • Read the docs in CONTRIBUTING.md • [Submit a Tool](https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/issues)**
**[Explore the Stack](#contents) • [Live App](https://ai-agent-registry-kappa.vercel.app/#live) • [Architecture](ARCHITECTURE.md) • [Data Schema](DATA_SCHEMA.md) • [Development](DEVELOPMENT.md) • [Contributing](#contributing) • [Agent Guide](AGENT.md)**
</div>
## Contents
- [AI Registry Explorer](#ai-registry-explorer)
- [Quick Start](#quick-start)
- [Submit an Agent](#submit-an-agent)
- [Orchestration Frameworks](#orchestration-frameworks)
- [Coding Agents](#coding-agents)
- [Memory and Context](#memory-and-context)
- [Multi-Agent Systems](#multi-agent-systems)
- [Agent Communication Protocols](#agent-communication-protocols)
- [MCP (Model Context Protocol)](#mcp-model-context-protocol)
- [Browser and Computer Use Agents](#browser-and-computer-use-agents)
- [Agent Tooling and Infrastructure](#agent-tooling-and-infrastructure)
- [Low and No-Code Builders](#low-and-no-code-builders)
@@ -46,33 +53,77 @@ validated: false
- [Agent Interfaces and UIs](#agent-interfaces-and-uis)
- [Agent Deployment and Hosting](#agent-deployment-and-hosting)
- [Agent Evaluation and Benchmarks](#agent-evaluation-and-benchmarks)
- [Agent Testing & Debugging](#agent-testing--debugging)
- [Agent Testing \& Debugging](#agent-testing--debugging)
- [Industry-Specific Agents](#industry-specific-agents)
- [Finance](#finance)
- [Healthcare](#healthcare)
- [Legal](#legal)
- [Manufacturing \& Industrial](#manufacturing--industrial)
- [Retail](#retail)
- [Government \& Compliance](#government--compliance)
- [Learning Resources](#learning-resources)
- [Agent Communication](#agent-communication)
- [Data Pipeline and Workflow](#data-pipeline-and-workflow)
- [Secure Execution Environments](#secure-execution-environments)
- [Creative AI Agents](#creative-ai-agents)
- [Image Generation](#image-generation)
- [Video Generation](#video-generation)
- [Music and Audio Generation](#music-and-audio-generation)
- [3D and Design Generation](#3d-and-design-generation)
- [Customer Support and CRM Agents](#customer-support-and-crm-agents)
- [Support Automation](#support-automation)
- [AI-Powered CRM Platforms](#ai-powered-crm-platforms)
- [Sales and Outreach Agents](#sales-and-outreach-agents)
- [Voice Agent Platforms](#voice-agent-platforms)
- [Deep Research Agents](#deep-research-agents)
- [Prompt-to-App Builders](#prompt-to-app-builders)
- [Multi-Agent Consumer Platforms](#multi-agent-consumer-platforms)
- [Open-Source Models for Agents](#open-source-models-for-agents)
- [Market Stats and Benchmarks 2026](#market-stats-and-benchmarks-2026)
- [Market Size](#market-size)
- [Adoption Rates](#adoption-rates)
- [Top Use Cases](#top-use-cases)
- [Fastest Growing Open-Source Projects](#fastest-growing-open-source-projects)
- [Notable Valuations](#notable-valuations)
- [Local and Self-Hosted AI](#local-and-self-hosted-ai)
- [AI Governance and Compliance](#ai-governance-and-compliance)
- [Cybersecurity Agents](#cybersecurity-agents)
- [Healthcare and Therapy Agents](#healthcare-and-therapy-agents)
- [Translation and Localization Agents](#translation-and-localization-agents)
- [Newsletters and Communities](#newsletters-and-communities)
- [Changelog](#changelog)
- [Contributing](#contributing)
- [Star History](#star-history)
![agent](img/agent.png) ![loop](img/loop.png)
---
## AI Registry Explorer
Interactive Next.js 14 web application for searching, filtering, and exploring AI agents, frameworks, and infrastructure tools.
### Quick Start
```bash
npm install && npm run build:data && npm run dev
```
**Live Web Registry**: [https://ai-agent-registry-kappa.vercel.app](https://ai-agent-registry-kappa.vercel.app/#explorer)
**Local Web App**: `http://localhost:3000`
**Docker**: `docker compose up --build`
**One-Click Deploy**: [![Deploy with Vercel](https://vercel.com/button)](https://vercel.com/new/clone?repository-url=https%3A%2F%2Fgithub.com%2FARUNAGIRINATHAN-K%2Fawesome-ai-agents-2026#deploy)
### Submit an Agent
Have an AI agent, framework, tool, skill, or infrastructure project worth sharing?
- **Web Submission Portal**: [Submit a Resource](https://ai-agent-registry-kappa.vercel.app/submit)
- **GitHub Issue Form**: [Open No-Code Submission Issue](https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/issues/new?template=submit_agent.yml)
---
## Orchestration Frameworks
- [Agency Swarm](https://github.com/VRSEN/agency-swarm) `🚀` `[Python]` `[Multi-Agent]` - Orchestrates multi-agent systems built on the OpenAI Assistants API with role-based collaboration.
- [AgentDescent](https://github.com/Birfy/agentdescent) `🔬` `[Python]` `[Research]` - Evolves agent skills, prompts, and harness modules by merging diffs from parallel workers against held-out reward.
- [AgentScope](https://github.com/agentscope-ai/agentscope) `🚀` `[Python]` `[Multi-Agent]` - Alibaba multi-agent framework with distributed deployment and fault tolerance for production use.
- [Agno](https://github.com/agno-agi/agno) `🌱` `[Python]` `[Multi-Agent]` - Multi-agent framework with a runtime and control plane for managing agent deployments at scale.
- [Anthropic SDK](https://github.com/anthropics/anthropic-sdk-python) `🚀` `[Python]` `[Anthropic]` - Official Claude SDK with tool use, computer control, and streaming for building Claude-native agents.
@@ -81,6 +132,8 @@ validated: false
- [CAMEL](https://github.com/camel-ai/camel) `🌱` `[Python]` `[Multi-Agent]` - Role-based multi-agent simulation framework for collaborative reasoning and task completion.
- [CopilotKit](https://github.com/CopilotKit/CopilotKit) `🌱` `[TypeScript]` `[Multi-Agent]` - Open-source platform for embedding custom AI copilots into React applications.
- [CrewAI](https://github.com/crewAIInc/crewAI) `🚀` `[Python]` `[Multi-Agent]` - Production multi-agent framework with 5K+ GitHub stars, role-based collaboration, async execution, and 1500+ company adoptions.
- [CUGA](https://github.com/cuga-project/cuga-agent) `🌱` `[Python]` `[MCP]` - Generalist agent harness from IBM Research that runs tasks across OpenAPI, MCP, and LangChain tools and the web, with policy guardrails and human approval.
- [Doforu](https://doforu.ai) `🔬` `[Desktop]` `[Multi-Agent]` - Orchestrates parallel sub-agents on your desktop and verifies their results before merging.
- [DSPy](https://github.com/stanfordnlp/dspy) `🌱` `[Python]` `[Research]` - Stanford framework that programmatically optimizes LLM prompts using machine learning.
- [Ontheia](https://github.com/Ontheia/ontheia) `🌱` `[TypeScript]` `[MCP]` - Self-hosted MCP-native agent platform with visual workflow automation and multi-provider support.
- [Google ADK](https://github.com/google/adk-python) `🌱` `[Python]` `[Multi-Agent]` - Modular agent dev kit with native Gemini and Vertex AI integration.
@@ -95,7 +148,6 @@ validated: false
- [Modus](https://github.com/hypermodeinc/modus) `🔬` `[WebAssembly]` `[Serverless]` - Serverless framework for high-throughput agent workloads with minimal cold starts.
- [Open-AutoGLM](https://github.com/zai-org/Open-AutoGLM) `🔬` `[Python]` `[Mobile]` - Open-source phone agent framework for building mobile device automation agents.
- [OpenAI Agents SDK](https://github.com/openai/openai-agents-python) `🚀` `[Python]` `[Multi-Agent]` - Lightweight multi-agent SDK with tracing and guardrails from OpenAI.
- [OpenProgram](https://github.com/Fzkuji/OpenProgram) `🔬` `[Python]` `[Multi-Agent]` - Self-programming framework whose agents create, run, and refine workflows across models, tools, memory, and context.
- [PraisonAI](https://github.com/MervinPraison/PraisonAI) `🚀` `[Python]` `[MCP]` - Production multi-agent framework with self-reflection, MCP integration, and workflow automation.
- [PydanticAI](https://github.com/pydantic/pydantic-ai) `🌱` `[Python]` `[Pydantic]` - Type-safe agent framework from the Pydantic team with a FastAPI-style developer experience.
- [Semantic Kernel](https://github.com/microsoft/semantic-kernel) `🚀` `[C#]` `[Microsoft]` - Microsoft enterprise SDK for Python, C#, and Java with modular plugins, memory, and goal planning.
@@ -105,6 +157,7 @@ validated: false
## Coding Agents
- [agent-manager](https://github.com/YoanWai/agent-manager) `🌱` `[Go]` `[Multi-Agent]` - Runs coding-agent CLIs side by side in persistent tmux sessions with live status, worktrees, and diff review.
- [Aider](https://github.com/Aider-AI/aider) `🌱` `[Python]` `[CLI]` - Terminal-first pair programmer that edits code in local repos, preserves Git history, and supports multi-file changes.
- [Amazon Q Developer](https://aws.amazon.com/q/developer/) `🚀` `[Python]` `[IDE]` - AWS-native AI coding assistant with Lambda, CloudWatch, infrastructure support, and security scanning.
- [Atomic Agent](https://github.com/AtomicBot-ai/atomic-agent) `🌱` `[TypeScript]` `[Local]` - Local-first CLI and TUI coding agent running open-weight models on your machine with no API key.
@@ -119,20 +172,25 @@ validated: false
- [Devika](https://github.com/stitionai/devika) `🌱` `[Python]` `[Docker]` - Open-source AI software engineer that understands high-level instructions and writes full-featured code autonomously.
- [Devin](https://devin.ai) `🚀` `[Cloud]` `[GitHub]` - Fully autonomous AI software engineer showing 8-12x efficiency gains in production (Nubank case study), cloud sandbox with persistent state, GitHub integration, and enterprise adoption growing 40% MoM.
- [fractal](https://github.com/plasma-ai/fractal) `🌱` `[Python]` `[Multi-Agent]` - Orchestrates Claude Code, Codex, and other agents in per-node Git worktrees with recursive delegation and a live TUI.
- [Frink](https://github.com/benord-labs/frink) `🔬` `[Desktop]` `[Local]` - Desktop app for running parallel coding agents and multi-step workflows triggered by schedules, webhooks, or events from tools like Linear and Sentry.
- [gemini-cli](https://github.com/google-gemini/gemini-cli) `🌱` `[Go]` `[CLI]` - Open-source CLI agent bringing Gemini AI directly into your terminal.
- [GitHub Copilot](https://github.com/features/copilot) `🌱` `[TypeScript]` `[VS Code]` - Agent Mode in VS Code with Copilot Workspace for issue-to-PR workflows across Claude, GPT, and Gemini.
- [Goose](https://github.com/aaif-goose/goose) `🌱` `[Rust]` `[CLI]` - Extensible open-source CLI agent that installs, executes, edits, and tests with any LLM.
- [JetBrains AI](https://www.jetbrains.com/ai/) `🌱` `[Kotlin]` `[JetBrains]` - Deep AI integration across all JetBrains IDEs with context-aware completions and refactoring.
- [JetBrains AI](https://www.jetbrains.com/air/) `🌱` `[Kotlin]` `[JetBrains]` - Deep AI integration across all JetBrains IDEs with context-aware completions and refactoring.
- [Juggler](https://github.com/juggler-ai/juggler) `🌱` `[Desktop]` `[Local]` - Multi-client desktop/remote GUI agent with inspectable tool calls, branching-thread editable context, and plugin extensibility.
- [Kiro](https://kiro.dev) `🚀` `[Cloud]` `[IDE]` - Spec-driven development agent that writes specs, auto-generates tasks, implements code, and automates DevOps workflows.
- [Kolega Code](https://github.com/kolega-ai/kolega-code) `🔬` `[Python]` `[CLI]` - Terminal coding agent where the model writes its own multi-agent workflows across 15+ model providers.
- [mu](https://github.com/qybaihe/mu) `🔬` `[TypeScript]` `[CLI]` - Coding agent that sends routine decisions, such as which tool output enters context, to a small judge model.
- [Open Interpreter](https://github.com/openinterpreter/openinterpreter) `🌱` `[Python]` `[CLI]` - Execute code locally via natural-language model instructions with a ChatGPT-like interface.
- [opencode](https://github.com/anomalyco/opencode) `🌱` `[TypeScript]` `[Desktop]` - Open-source coding agent available as a desktop app with a visual interface.
- [OpenHands](https://github.com/OpenHands/OpenHands) `🌱` `[Python]` `[Docker]` - AI-driven development platform that writes, tests, and deploys code autonomously.
- [Orbi](https://github.com/orbi-build/orbi) `🌱` `[Python]` `[Self-Hosted]` - Turns labeled GitHub Issues into independently reviewed pull requests, then merges reviewed heads and tags releases.
- [Ordewell](https://github.com/ordewell/ordewell) `🔬` `[TypeScript]` `[Multi-Agent]` - Turns one goal into an ordered, editable plan of coding-agent tasks, each with its own runner, model, and mode, verified by a completion marker in the runner's output.
- [Ouroboros](https://github.com/Q00/ouroboros) `🌱` `[Python]` `[MCP]` - Pins an acceptance spec before the run and verifies the result, hiding grading commands from the executing agent.
- [PR-Agent](https://github.com/The-PR-Agent/pr-agent) `🚀` `[Python]` `[GitHub]` - Open-source AI PR reviewer that auto-describes, reviews, and improves pull requests.
- [Prime Agent](https://github.com/PrimeIntellect-ai/prime-agent) `🚀` `[TypeScript]` `[CLI]` - Open-source RLM coding and research agent designed for long-running autonomous tasks.
- [Qodo](https://www.qodo.ai) `🚀` `[Cloud]` `[Security]` - AI code review platform with context-aware PR validation and security analysis.
- [Relay](https://relayevals.com/) `🔬` `[TypeScript]` `[CLI]` - Runs a self-improving coding agent locally with your own models and re-runs tests before opening pull requests.
- [RooCode](https://github.com/RooCodeInc/Roo-Code) `🌱` `[TypeScript]` `[VS Code]` - Cline fork with structured modes and reduced hallucinations for more reliable code generation.
- [Snyk Code](https://snyk.io/product/snyk-code/) `🌱` `[Cloud]` `[Security]` - AI-powered security scanner with real-time vulnerability detection in agent-generated code.
- [Sourcegraph Cody](https://sourcegraph.com/docs/cody) `🚀` `[TypeScript]` `[IDE]` - AI coding assistant that excels at large codebases with an enterprise context engine.
@@ -140,7 +198,10 @@ validated: false
- [Tabnine](https://www.tabnine.com) `🚀` `[TypeScript]` `[IDE]` - Privacy-first AI code completion with on-premise deployment and codebase fine-tuning options.
- [TaskWeaver](https://github.com/microsoft/TaskWeaver) `🚀` `[Python]` `[Microsoft]` - A code-first agent framework from Microsoft for planning and executing data analytics tasks.
- [Windsurf](https://devin.ai/desktop) `🌱` `[TypeScript]` `[IDE]` - AI-native IDE with Cascade agent for multi-step autonomous tasks and team workflows.
- [workkit](https://github.com/ITW-Creative-Works/workkit) `🔬` `[JavaScript]` `[GitHub]` - Claude Code plugin that runs GitHub Issues as an agent pipeline, with a manager agent handing each issue to spec, build and verifier subagents.
- [YYLO](https://github.com/yylo-dev/yylo) `🔬` `[Python]` `[Multi-Agent]` - Command-line orchestrator for coding agents with typed task, validation, merge, and release-readiness boundaries in a dedicated branch/worktree.
- [Ivy Tendril](https://github.com/Ivy-Interactive/Ivy-Tendril) - Open-source agentic software factory with an amazing UI that handles parallel Git worktrees for you, complete with programmatic verifications and fast review loops.
## Memory and Context
- [Acontext](https://github.com/memodb-io/Acontext) `🌱` `[Python]` `[Local]` - Manages agent skills and long-term memory as a layered data structure for persistent context.
@@ -148,17 +209,22 @@ validated: false
- [Chroma](https://github.com/chroma-core/chroma) `🌱` `[Python]` `[Vector DB]` - Lightweight, embeddable vector store for building memory-augmented AI agents with fast semantic retrieval.
- [cognee](https://github.com/topoteretes/cognee) `🌱` `[Python]` `[Graph-Based]` - Knowledge engine for AI agent memory, set up in 6 lines of code with graph-based knowledge extraction.
- [Cortex Memory](https://github.com/prem-research/cortex) `🌱` `[Python]` `[Vector DB]` - Full-stack solution for agent memory covering extraction, vector search, and optimization.
- [deja](https://github.com/vshulcz/deja-vu) `🌱` `[Go]` `[MCP]` - Indexes the session transcripts 25 coding agents already write on the machine and serves them to any of the others, including sessions from before install; lexical retrieval, no LLM, no embeddings.
- [Engrava](https://github.com/sovantica/engrava) `🔬` `[Python]` `[Graph-Based]` - Stores agent memory as a typed knowledge graph with hybrid search and a tamper-evident journal, embedded in SQLite.
- [Enhanciar](https://enhanciar.in) `🔬` `[Cloud]` `[MCP]` - A company brain that answers from your Slack, Gmail, Notion and Drive with the source, then drafts the reply or ticket for you to approve.
- [Folio](https://github.com/evergreen-it-dev/folio) `🔬` `[TypeScript]` `[MCP]` - Git-backed team wiki that gives agents 40 MCP tools to search, read and edit pages and tables.
- [graphiti](https://github.com/getzep/graphiti) `🌱` `[Python]` `[Multi-Agent]` - Build real-time knowledge graphs for AI agents with automatic entity extraction and linking.
- [Kage](https://github.com/kage-core/Kage) `🌱` `[TypeScript]` `[MCP]` - Git-native memory for coding agents that stores decisions and fixes as repo files and verifies them against the codebase, withholding stale knowledge.
- [LanceDB](https://github.com/lancedb/lancedb) `🌱` `[Rust]` `[Vector DB]` - Serverless vector search database embedded directly in the agent process with no infrastructure needed.
- [Langmem](https://github.com/langchain-ai/langmem) `🌱` `[Python]` `[LangChain]` - Helps agents learn and adapt from their interactions over time with persistent memory.
- [Lians](https://github.com/Lians-ai/Lians) `🔬` `[Python]` `[MCP]` - Gives any AI agent local-first memory with corrections, point-in-time recall, and inspectable history.
- [LWC](https://github.com/JanYork/llm-wiki-cli) `🔬` `[Rust]` `[MCP]` - Stores durable, source-grounded project memory and exposes bounded read-only retrieval to coding agents through MCP.
- [Mem0](https://github.com/mem0ai/mem0) `🌱` `[Python]` `[Vector DB]` - Memory layer for AI applications with long-term, short-term, and semantic memory extraction.
- [Remio](https://www.remio.ai/) `🔬` `[Desktop]` `[Memory]` - Local-first AI memory desktop app that parses files into searchable vector indexes.
- [Memoir](https://github.com/zhangfengcdt/memoir) `🔬` `[Python]` `[Memory]` - Git-like versioned semantic memory for AI agents with branching and commits.
- [Memvid](https://github.com/memvid/memvid) `🌱` `[Python]` `[RAG]` - Replace complex RAG pipelines with a serverless, single-file memory layer for instant retrieval.
- [Milvus](https://github.com/milvus-io/milvus) `🌱` `[Go]` `[Vector DB]` - Scales vector search to billions of embeddings for large-scale agent knowledge bases.
- [Mnemoverse](https://github.com/mnemoverse/mcp-memory-server) `🔬` `[TypeScript]` `[MCP]` - Hosted memory shared across Claude Code, Cursor and VS Code over MCP, with feedback that re-ranks what recall returns.
- [Mori (森)](https://github.com/fjwood69/mori) `🌱` `[Python]` `[MCP]` - Sovereign shared memory layer for AI coding agents with zero-instrumentation capture via lifecycle hooks, a dream pipeline that distills sessions into curated governed memories, and support for Claude Code, Cursor, Codex, and Antigravity.
- [Motorhead](https://github.com/getmetal/motorhead) `🌱` `[Rust]` `[Multi-Agent]` - Manages conversation context windows for agents with automatic background summarization.
- [Open Index](https://github.com/DrDroidLab/open-index) `🔬` `[Python]` `[MCP]` - Builds typed knowledge graphs with hybrid search and read/write MCP tools for domain-specific agents.
@@ -166,7 +232,9 @@ validated: false
- [Pinecone](https://www.pinecone.io) `🚀` `[Cloud]` `[Vector DB]` - Managed vector database with agent namespaces for multi-tenant isolation, hybrid search (vector + keyword), serverless auto-scaling, and $11B valuation.
- [Qdrant](https://github.com/qdrant/qdrant) `🌱` `[Rust]` `[Vector DB]` - High-performance vector similarity search engine with rich payload filtering for agent memory.
- [RAGFlow](https://github.com/infiniflow/ragflow) `🌱` `[Python]` `[RAG]` - Open-source RAG engine with agent capabilities and deep document understanding for knowledge bases.
- [Screenpipe](https://screenpipe.com) `🚀` `[Desktop]` `[MCP]` - Local screen-text/audio memory via MCP, source-available under the [Screenpipe Commercial License](https://github.com/screenpipe/screenpipe/blob/main/LICENSE.md); configured cloud AI, transcription, sync, integrations and connected AI/MCP clients can transmit context off-device.
- [SimpleMem](https://github.com/aiming-lab/SimpleMem) `🌱` `[Python]` `[Multimodal]` - Efficient lifelong memory for LLM agents supporting both text and multimodal inputs.
- [since-cutoff](https://github.com/MohammadHijjawi97/since-cutoff) `🔬` `[Python]` `[MCP]` - Lists pinned Python dependency APIs that changed after a coding model's training cutoff and writes AGENTS.md notes.
- [Superdense](https://github.com/Nimrobo/superdense) `🔬` `[TypeScript]` `[Memory]` - Gives coding agents persistent memory of what worked across sessions, tracked against real-world outcomes.
- [Supermemory](https://github.com/supermemoryai/supermemory) `🌱` `[TypeScript]` `[Vector DB]` - Extremely fast and scalable memory engine and API designed for the AI era.
- [Tree Ring Memory](https://github.com/TerminallyLazy/Tree-Ring-Memory) `🔬` `[Rust]` `[Memory]` - Manages local agent memory with recall, forgetting, audit trails, and session consolidation.
@@ -176,13 +244,19 @@ validated: false
## Multi-Agent Systems
- [5dive](https://github.com/5dive-ai/5dive) `🔬` `[Shell]` `[Multi-Agent]` - An open-source team of AI coding agents you text like coworkers, each with its own job.
- [AgentVerse](https://github.com/OpenBMB/AgentVerse) `🌱` `[Python]` `[Multi-Agent]` - Framework for building custom multi-agent environments to accomplish collaborative tasks.
- [Bunkhouse](https://github.com/braedonsaunders/bunkhouse) `🔬` `[TypeScript]` `[Multi-Agent]` - Multitenant AI employees with a company inbox, org chart, and governed procedures for main-street business.
- [Concord MCP](https://github.com/Get-Concord-AI/concord-mcp) `🌱` `[TypeScript]` `[MCP]` - Local-first MCP server giving coding agents shared work-state, including presence, file claims, messaging, handoffs and review packets, so multiple agents coordinate on one repo.
- [EvoAgentX](https://github.com/ANative-Lab/EvoAgentX) `🌱` `[Python]` `[Multi-Agent]` - Evaluates and evolves agentic workflows over time using automatic optimization.
- [Hivekeep](https://github.com/MarlBurroW/hivekeep) `🔬` `[TypeScript]` `[Multi-Agent]` - Runs a team of specialized self-hosted agents that collaborate, share memory, and build their own tools.
- [Hivemoot](https://github.com/hivemoot/hivemoot) `🚀` `[Python]` `[GitHub]` - Autonomous agent teams that collaboratively build software on GitHub.
- [Markus](https://github.com/markus-global/markus) `🌱` `[TypeScript]` `[Multi-Agent]` - Open-source AI workforce platform for building and running teams of AI agents with roles, task delegation, persistent memory, and inter-agent communication.
- [MetaGPT](https://github.com/FoundationAgents/MetaGPT) `🌱` `[Python]` `[Multi-Agent]` - Simulates a full software company workflow from requirements to PRs using role-playing agents.
- [NarraNexus](https://github.com/NetMindAI-Open/NarraNexus) `🌱` `[Python]` `[Multi-Agent]` - Ready-to-run AI agent team workspace by NetMind.AI whose agents remember, collaborate, and use tools from day one.
- [P2PCLAW](https://github.com/Agnuxo1/OpenCLAW-P2P) `🌱` `[Python]` `[RAG]` - Decentralized scientific research network with 14+ autonomous agents featuring P2P mesh, IPFS storage, and Lean 4 formal verification, self-hosted with Docker Compose.
- [Raven](https://github.com/EverMind-AI/Raven) `🌱` `[Python]` `[Multi-Agent]` - Plans complex tasks as DAGs and orchestrates built-in and third-party agents over ACP, CLI, or OpenAI-compatible APIs.
- [Sqwakie](https://sqwakie.si/) `🔬` `[TypeScript]` `[Multi-Agent]` - Lets a team's Claude Code and Codex agents message each other peer-to-peer and work shared kanban boards.
- [Swarm](https://github.com/openai/swarm) `🚀` `[Python]` `[OpenAI]` - Lightweight framework for agent handoffs, context variables, and function calling patterns from OpenAI.
- [Swarms Framework](https://github.com/kyegomez/swarms) `🚀` `[Python]` `[Multi-Agent]` - Multi-agent orchestration for production use cases with scalability and reliability at its core.
- [Yao Agents](https://yaoagents.com) `🌱` `[Go]` `[MCP]` - Complete local AI execution platform with 30+ Experts, autonomous Robot orchestration via 5-stage Pipeline, API-based Robot-to-Robot calling, MCP support, and multi-channel messaging.
@@ -200,18 +274,26 @@ The protocol layer that enables agents to discover tools, communicate with each
### MCP (Model Context Protocol)
- [A2A Protocol](https://github.com/a2aproject/A2A) `🚀` `[Python]` `[Multi-Agent]` - Google's open protocol enabling AI agents to communicate, collaborate, and delegate tasks across frameworks.
- [AgentsCoin MCP](https://github.com/axiosdevs/agentscoin-mcp) `🌱` `[TypeScript]` `[MCP]` - Gives an AI agent its own wallet and money on a live EVM chain to send, create, and trade tokens.
- [Aident Loadout](https://aident.ai) `🔬` `[Cloud]` `[MCP]` - Hosted MCP server and CLI giving coding agents shared app connections, 400+ Skills, a credential vault, cost preview and audit logs.
- [Arcade AI](https://github.com/ArcadeAI/arcade-mcp) `🌱` `[Python]` `[Multi-Agent]` - Tool-use platform with authentication, authorization, and logging for agent-tool interactions.
- [Chord](https://getchord.xyz/docs/agents/mcp/) `🔬` `[Cloud]` `[MCP]` - Gives agents DEX swap quotes, unsigned swap transactions, candles and wallet PnL on seven EVM chains.
- [Composio](https://github.com/ComposioHQ/composio) `🌱` `[TypeScript]` `[Multi-Agent]` - Integration platform with 250+ pre-built tool connectors for AI agents and LLMs.
- [Datacircle](https://datacircle.dev/) `🔬` `[Cloud]` `[MCP]` - Query your favorite B2B data APIs through us. Same request, same price, no markup.
- [Docker MCP](https://github.com/docker/mcp-gateway) `🌱` `[Go]` `[MCP]` - Docker's MCP gateway CLI plugin for running MCP servers in isolated containers.
- [Equibles](https://equibles.com/mcp) `🔬` `[Cloud]` `[MCP]` - Serves US stock data to agents over MCP: SEC filings, financials, earnings transcripts, insider trades and 13F holdings.
- [HCS Agent Protocol](https://github.com/hashgraph/hedera-agent-kit-js) `🌱` `[TypeScript]` `[IDE]` - Hedera open standards for agent identity with trustless P2P communication and 187K+ verified agents.
- [HIG Doctor](https://github.com/raintree-technology/hig-doctor) `🌱` `[TypeScript]` `[MCP]` - Apple HIG audit CLI and MCP server exposing design-guideline lookup and project audits for coding agents across SwiftUI, UIKit, React, Next.js, Flutter, Compose, HTML, and CSS.
- [Hyper](https://github.com/hyperfx-ai/marketing-skills) `🌱` `[Cloud]` `[MCP]` - Open-source Agent Skills and a hosted MCP connecting agents to 200+ marketing integrations across paid ads, SEO, analytics, social, and image and video generation, with a human-approval gate on every action.
- [Live Tennis API MCP](https://github.com/livetennisapi/livetennisapi-mcp) `🔬` `[TypeScript]` `[MCP]` - Exposes live tennis scores, fixtures, rankings, and head-to-head to agents, with market prices and win-probability on paid tiers.
- [Jev Agent](https://jev-agent.com/api-access) `🔬` `[Cloud]` `[MCP]` - Hosts a Jev model API and Streamable HTTP MCP so agents get typed decisions with probabilities.
- [LogNorm](https://lognorm.com) `🔬` `[Cloud]` `[MCP]` - Hosted MCP server that hands a site's SEO and AI-visibility backlog to Claude Code, Codex and Cursor.
- [MCP Registry](https://github.com/modelcontextprotocol) `🌱` `[Python]` `[Multi-Agent]` - Official Model Context Protocol specification and server implementations for standardized tool access.
- [mcp-nest](https://github.com/CharanBharathula/mcp-nest) `🌱` `[Python]` `[MCP]` - Unified Model Context Protocol (MCP) server for executing code and managing files.
- [NotFair](https://notfair.co) `🚀` `[Cloud]` `[MCP]` - Hosted Google Ads MCP server for diagnosing, optimizing, and executing campaign changes via the Google Ads API with a human-approval gate.
- [NotFair Skills](https://github.com/nowork-studio/notfair-plugin) `🚀` `[TypeScript]` `[MCP]` - Open-source Claude Code skills for SEO, GEO, Google Ads, and Meta Ads, connecting to live data through Google Ads MCP, Meta Ads MCP, Google Search Console MCP, and Google Analytics (GA4) MCP.
- [Scalekit](https://www.scalekit.com/agentkit) `🔬` `[Cloud]` `[MCP]` - Hosted MCP gateway and SDKs giving agents per-user OAuth, scoped tool permissions and audit logs across 500+ connectors.
- [Seonix SEO MCP](https://github.com/seonix-ai/seonix-mcp) `🔬` `[TypeScript]` `[MCP]` - Audits any website for technical SEO, GEO/AEO readiness and page speed, then proposes and dry-runs minimal fixes.
- [TableJourney](https://tablejourney.com/agents/) `🔬` `[Cloud]` `[MCP]` - Hosted MCP server giving agents verified restaurants, food festival dates and bookable tours in 224 cities, and paying agents half of the booking commission.
- [Tapetide MCP](https://github.com/Tapetide-hq/nse-bse-indian-stock-market-data-mcp) `🔬` `[TypeScript]` `[MCP]` - Gives agents quotes, financials, shareholding, screener and FII/DII flow data for about 8,200 NSE and BSE stocks.
- [Toolhouse](https://toolhouse.ai/en/) `🌱` `[Python]` `[Multi-Agent]` - Cloud-hosted tool infrastructure for agents with optimized execution and low-latency access.
- [Xquik](https://github.com/Xquik-dev/x-twitter-scraper) `🔬` `[Cloud]` `[MCP]` - Hosted X Twitter data MCP for search, follower export, monitors, and confirmation-gated writes.
- [Zapier MCP Server](https://zapier.com/mcp) `🌱` `[Cloud]` `[MCP]` - Connect agents to 7,000+ app integrations via MCP, powered by Zapier's automation platform.
@@ -221,6 +303,8 @@ The protocol layer that enables agents to discover tools, communicate with each
- [Agent S2](https://github.com/simular-ai/Agent-S) `🌱` `[Python]` `[Vision]` - Open-source GUI automation framework for building desktop and browser agents with vision.
- [AgentQL](https://github.com/tinyfish-io/agentql) `🌱` `[Python]` `[Multi-Agent]` - AI-powered web scraping and automation with a semantic query language for page elements.
- [aiFetchly](https://www.aifetchly.com) `🔬` `[TypeScript]` `[RAG]` - Open-source desktop agent automating lead generation, knowledge library RAG, outreach, and scheduled workflows.
- [AIHawk](https://github.com/feder-cr/invisible_playwright_mcp) `🚀` `[Python]` `[MCP]` - Drives a real Firefox from plain-English instructions, with sixteen MCP tools and a standalone web UI.
- [Airtop](https://www.airtop.ai) `🚀` `[Cloud]` `[Multi-Agent]` - Enterprise-grade cloud browser infrastructure for AI agent automation at scale.
- [Browser Use](https://github.com/browser-use/browser-use) `🌱` `[Python]` `[Multi-Agent]` - Open-source framework to let LLMs navigate and interact with any website programmatically.
- [Browserbase](https://www.browserbase.com) `🚀` `[Cloud]` `[Multi-Agent]` - Scalable headless browser infrastructure purpose-built for running AI agents in production.
@@ -230,11 +314,14 @@ The protocol layer that enables agents to discover tools, communicate with each
- [Grok Computer](https://x.ai) `🌱` `[Cloud]` `[Multi-Agent]` - Upcoming xAI desktop agent with mouse control and application automation capabilities.
- [LaVague](https://github.com/lavague-ai/LaVague) `🌱` `[Python]` `[Multi-Agent]` - Large Action Model framework to turn natural language instructions into browser automation.
- [Manus](https://manus.im) `🚀` `[Cloud]` `[Multi-Agent]` - Autonomous digital employee with Browser Operator extension acquired by Meta for web-based task execution.
- [MultiOn](https://www.theagi.company/) `🚀` `[Cloud]` `[Multi-Agent]` - Reliable web automation API with CAPTCHA handling for building production browser agents.
- [MultiOn](https://theagi.company/) `🚀` `[Cloud]` `[Multi-Agent]` - Reliable web automation API with CAPTCHA handling for building production browser agents.
- [OpenAI Operator](https://openai.com/index/computer-using-agent/) `🚀` `[Cloud]` `[OpenAI]` - ChatGPT autonomous web agent with human checkpoints using Computer Use Agent technology.
- [OpenAmer](https://github.com/openamer/openamer) `🌱` `[Python]` `[Local]` - Open-source Windows desktop agent with native computer-use over the Chrome DevTools Protocol, in-process cognition tools, and peer-to-peer coordination between instances.
- [OpenClaw](https://github.com/openclaw/openclaw) `🌱` `[Python]` `[Framework]` - Autonomous open-source computer use and browser automation agent framework.
- [Playwright MCP](https://github.com/microsoft/playwright-mcp) `🌱` `[TypeScript]` `[MCP]` - MCP server wrapping Playwright for seamless browser automation integration with LLM agents.
- [Skyvern](https://github.com/Skyvern-AI/skyvern) `🚀` `[Python]` `[GitHub]` - Production browser automation with 21.5K GitHub stars, 10M+ executed workflows, vision-first automation, and sub-300ms response times.
- [Stagehand](https://github.com/browserbase/stagehand) `🌱` `[TypeScript]` `[Multi-Agent]` - AI web browsing framework built on Playwright with natural-language selectors and actions.
- [TODO for AI](https://github.com/todoforai/edge) `🔬` `[TypeScript]` `[Local]` - Task-list agent that runs on your own machine and completes tasks through your shell, files and logged-in browser, with any model.
## Agent Tooling and Infrastructure
@@ -242,27 +329,39 @@ The protocol layer that enables agents to discover tools, communicate with each
Sandboxes, web scrapers, browser automation, and networking layers that agents depend on.
- [Agent Bounties](https://github.com/NSPG13/agent-bounties) `🔬` `[Rust]` `[MCP]` - Coordinates verifiable digital bounty workflows designed for agents to post, fund, claim, solve, verify, and earn.
- [Agent Payment Node](https://github.com/nuanu-ai/agent-payment-node) `🔬` `[TypeScript]` `[MCP]` - Local CLI and stdio MCP payment runtime for AI agents on macOS Apple Silicon, with foreground owner approval for signing.
- [AgentDock](https://github.com/agentdock/agentdock) `🚀` `[Python]` `[Docker]` - Framework for building and deploying production-ready AI agents with composable node architecture.
- [agent-qa](https://github.com/vostride/agent-qa) `🌱` `[TypeScript]` `[Testing]` - Runs natural-language web and mobile tests with persistent memory and UI-change adaptation.
- [AgentSandboxes](https://agentsandboxes.fyi) `🔬` `[Cloud]` `[MCP]` - A free index of cloud sandboxes for AI agents with source-linked specs, a REST API and an MCP endpoint.
- [Anansi Haven](https://github.com/kburrus64-max/anansi-haven) `🔬` `[Cloud]` `[MCP]` - Gives agents a free hosted home base with persistent memory, client-side encrypted storage, a job board and an agent directory over MCP, A2A and HTTP.
- [Arcmira](https://arcmira.com) `🔬` `[Cloud]` `[MCP]` - Search indexed YouTube transcripts for timestamped passages, speaker appearances, entity mentions, sponsors and recommendations.
- [codex-profiles](https://github.com/Ducksss/codex-profiles) `🚀` `[Python]` `[OpenAI]` - Bash CLI for switching OpenAI Codex CLI and Desktop profiles with isolated CODEX_HOME directories.
- [Cohesivity](https://cohesivity.ai?ref=gh-awesome-ai-agents-2026) `🔬` `[Cloud]` `[MCP]` - Headless backend for AI agents with databases, hosting, storage, and 15+ services via API and MCP, no API keys or signups required to get started.
- [CompozyOS](https://github.com/compozy/compozy) `🚀` `[Go]` `[Multi-Agent]` - Runs agent CLIs as a team on loops and schedules, with shared memory, permissions and approvals in one self-hosted daemon.
- [Crawl4AI](https://github.com/unclecode/crawl4ai) `🌱` `[Python]` `[Multi-Agent]` - Extracts structured data from web pages using LLM-friendly output formats optimized for agent ingestion.
- [Docling](https://github.com/docling-project/docling) `🌱` `[Python]` `[IDE]` - Parses PDFs, DOCX, and slides into structured text with deep layout understanding for document agents.
- [E2B](https://github.com/e2b-dev/e2b) `🌱` `[TypeScript]` `[Multi-Agent]` - Cloud sandboxes for AI agents to run code securely in isolated environments.
- [Engram](https://github.com/kwstx/translator) `🌱` `[Python]` `[Multi-Agent]` - Universal bridge for multi-protocol AI agent systems with automated semantic mapping.
- [Firecrawl](https://github.com/firecrawl/firecrawl) `🌱` `[TypeScript]` `[Multi-Agent]` - Web scraping API built for LLMs that converts websites to clean, structured markdown.
- [HumanEndpoint](https://humanendpoint.au/) `🔬` `[TypeScript]` `[MCP]` - Human execution infrastructure for agents to request reviewed work, settle fees via x402 USDC, track jobs, and retrieve verifiable evidence.
- [Jina Reader](https://github.com/jina-ai/reader) `🌱` `[TypeScript]` `[Multi-Agent]` - Converts any URL to LLM-ready clean text via a simple API prefix for agent ingestion.
- [LlamaParse](https://github.com/run-llama/llama_cloud_services) `🌱` `[Python]` `[RAG]` - GenAI-native document parser designed to extract complex tables and layouts for RAG pipelines.
- [Loadout](https://loadout-blush.vercel.app) `🔬` `[TypeScript]` `[IDE]` - Free version-pinned AI coding rule packs for Cursor, Claude Code, Copilot, and AGENTS.md so agents stop inventing outdated APIs.
- [Marker](https://github.com/datalab-to/marker) `🌱` `[Python]` `[CLI]` - Converts PDF documents to markdown with high accuracy for tables, equations, and figures.
- [Notte](https://github.com/nottelabs/notte) `🚀` `[Python]` `[Pipeline]` - Browser automation engine optimized for production AI pipelines.
- [Pilot Protocol](https://github.com/pilot-protocol/pilotprotocol) `🌱` `[Go]` `[Multi-Agent]` - Networking stack for distributed agent systems with encrypted tunnels.
- [Playwright](https://github.com/microsoft/playwright) `🌱` `[TypeScript]` `[Testing]` - Automates Chromium, Firefox, and WebKit browsers with a single cross-language API for agent-driven testing.
- [SandBase CLI](https://github.com/sandbaseai/cli) `🔬` `[TypeScript]` `[MCP]` - Connects coding agents to 2,000+ AI models through one onboarding command.
- [ScrapeGraphAI](https://github.com/ScrapeGraphAI/Scrapegraph-ai) `🌱` `[Python]` `[LangChain]` - Python web-scraping library that uses LLMs to build intelligent scraping pipelines from natural-language instructions.
- [Solaris Dev Shop](https://github.com/Shaisolaris/solaris-dev-shop) `🔬` `[Python]` `[Skill]` - Free MIT-licensed skill library with a deterministic intake router and 61 copy-paste SKILL.md specialists.
- [Speck](https://github.com/doctarock/Speck) `🔬` `[TypeScript]` `[Stateful]` - Moves persistent memory, evidence tracking, validation, and cognitive state into software for small local LLM agents.
- [Superagent](https://github.com/pungme/superagent-desktop) `🔬` `[Desktop]` `[Local]` - Gives Claude Code and Codex a real browser and iOS Simulator to drive locally on macOS.
- [Surya](https://github.com/datalab-to/surya) `🌱` `[Python]` `[CLI]` - Runs OCR and layout detection on documents in 90+ languages for multilingual document agents.
- [Tavily](https://github.com/tavily-ai/tavily-python) `🌱` `[Python]` `[Multi-Agent]` - Search API purpose-built for LLM agents providing real-time, accurate web data with source citations.
- [traceAI](https://github.com/future-agi/traceAI) `🌱` `[Python]` `[Observability]` - OpenTelemetry-native tracing for LLM and agent apps with 50+ framework integrations.
- [Tracefold](https://github.com/TraceFold/tracefold) `🚀` `[Rust]` `[MCP]` - Reverse-execution gateway that escrows checked pre-commit inverses, enforces Cedar policy gates, and produces signed offline-verifiable DSSE receipts for tool mutations.
- [Unstructured](https://github.com/Unstructured-IO/unstructured) `🌱` `[Python]` `[Pipeline]` - Ingests and preprocesses documents across 25+ file types for downstream LLM and agent pipelines.
- [Vectoree](https://vectoree.ai) `🔬` `[Cloud]` `[CLI]` - A backend for coding agents with a model gateway, auth, Postgres and storage behind one key.
## Low and No-Code Builders
@@ -279,8 +378,10 @@ Sandboxes, web scrapers, browser automation, and networking layers that agents d
- [n8n](https://github.com/n8n-io/n8n) `🌱` `[TypeScript]` `[Docker]` - Open-source workflow automation with AI agent nodes combining visual and code-based orchestration.
- [Relevance AI](https://relevanceai.com) `🌱` `[Cloud]` `[No-Code]` - No-code AI agent builder for sales, support, and research use cases with team collaboration.
- [Rivet](https://github.com/Ironclad/rivet) `🌱` `[TypeScript]` `[RAG]` - Visual AI workflow builder with drag-and-drop interface for designing agent pipelines.
- [Syl](https://www.usesyl.com) `🔬` `[Cloud]` `[MCP]` - Hosted app for building teams of AI teammates that share conversations, keep memory, and run scheduled routines.
- [Temporal](https://github.com/temporalio/temporal) `🚀` `[Go]` `[Multi-Agent]` - Durable execution platform for long-running agent workflows with automatic retry and state persistence.
- [Wordware](https://www.wordware.ai) `🌱` `[TypeScript]` `[IDE]` - Web-hosted IDE where domain experts collaborate with AI engineers to build agent workflows.
- [Workato Agent Studio](https://www.workato.com/agentstudio) `🚀` `[Cloud]` `[No-Code]` - No-code platform for building, deploying, and governing enterprise AI agents that reason, orchestrate multi-step workflows, and take actions across business applications.
- [Zapier AI](https://zapier.com) `🌱` `[Cloud]` `[No-Code]` - Connects 7000+ apps with natural language workflow creation for no-code agent automation.
## Voice and Multimodal Agents
@@ -289,7 +390,7 @@ Sandboxes, web scrapers, browser automation, and networking layers that agents d
- [LiveKit Agents](https://github.com/livekit/agents) `🌱` `[Python]` `[IDE]` - Framework for building real-time, multimodal AI agents with voice, video, and data channels.
- [Nuance AI](https://dragon.nuance.com/en-us/home) `🚀` `[Cloud]` `[CLI]` - Enterprise speech and conversational AI platform for clinical and contact-center workflows with HIPAA-capable deployments.
- [Google Cloud Speech-to-Text v2](https://cloud.google.com/speech-to-text) `🚀` `[Cloud]` `[Pipeline]` - Google Cloud streaming and batch speech recognition API v2 with improved accuracy, streaming, and noise suppression for real-time agent pipelines.
- [Personal Jarvis](https://github.com/PersonalJarvis/PersonalJarvis) `🔬` `[Python]` `[Voice]` - Voice-driven desktop assistant that takes mouse and keyboard and delegates heavy tasks to agent harnesses like Claude Code, Codex, and MCP.
- [Personal Jarvis](https://github.com/Dodaios/Dodaios) `🔬` `[Python]` `[Voice]` - Voice-driven desktop assistant that takes mouse and keyboard and delegates heavy tasks to agent harnesses like Claude Code, Codex, and MCP.
- [Pipecat](https://github.com/pipecat-ai/pipecat) `🚀` `[Python]` `[Streaming]` - Production-grade voice AI framework with sub-250ms latency, WebRTC support, multimodal (voice+vision+text), real-time streaming, and 70+ language support.
- [qwen-audio-agent](https://github.com/QwenAudio/qwen-audio-agent) `🌱` `[Desktop]` `[Voice]` - Full-duplex voice runtime that drives coding agents (Claude Code, Codex, OpenCode, Kimi Code, and more) over ACP, keeping conversations going while background tasks run, with barge-in and a local wake word.
- [Rasa](https://github.com/RasaHQ/rasa) `🌱` `[Python]` `[Self-Hosted]` - Open-source conversational AI framework with self-hosted NLU training and dialogue management.
@@ -303,9 +404,10 @@ Sandboxes, web scrapers, browser automation, and networking layers that agents d
- [Agent OS](https://github.com/buildermethods/agent-os) `🌱` `[Python]` `[Multi-Agent]` - Kernel architecture for governing autonomous AI agents with policy enforcement.
- [AgentDoG](https://github.com/AI45Lab/AgentDoG) `🌱` `[Python]` `[Multi-Agent]` - Diagnostic guardrails that analyze full agent execution trajectories to detect instruction hijacking and tool misuse.
- [AgentGuard](https://github.com/cyberark/agent-guard) `🌱` `[Python]` `[Observability]` - Runtime observability and guardrails for AI agents with loop detection and anomaly alerts.
- [Bifrost](https://github.com/maximhq/bifrost) `🌱` `[Go]` `[MCP]` - AI gateway that unifies model providers with automatic failover, load balancing, observability, and MCP support.
- [agenttrace](https://github.com/luoyuctl/agenttrace) `🌱` `[Go]` `[CLI]` - Local-first TUI for AI coding agent session observability with tokens, cost, latency, tool failures, anomalies, reports, diffs, and CI health gates.
- [APort Agent Guardrails](https://github.com/aporthq/aport-agent-guardrails) `🌱` `[Python]` `[Multi-Agent]` - Pre-action authorization plugin for agent frameworks with policy-based access control.
- [Patronus AI LYNX](https://patronus.ai/) `🌱` `[Cloud]` `[Testing]` - Hallucination detection system beating GPT-4 baselines, with specialized testing for agent outputs and LLM-generated content quality.
- [Patronus AI LYNX](https://www.patronus.ai/) `🌱` `[Cloud]` `[Testing]` - Hallucination detection system beating GPT-4 baselines, with specialized testing for agent outputs and LLM-generated content quality.
- [Arize Phoenix](https://github.com/Arize-ai/phoenix) `🌱` `[Python]` `[Observability]` - Open-source observability platform built on OpenTelemetry for tracing, evaluating, and debugging AI agents.
- [Braintrust](https://www.braintrust.dev) `🌱` `[TypeScript]` `[Evaluation]` - Eval-driven development platform with experiment tracking and prompt optimization for agent quality.
- [ClawMetry](https://github.com/vivekchand/clawmetry) `🔬` `[Python]` `[Observability]` - Self-hosted observability and opt-in kill switch for coding agents, reading the session logs runtimes already write to disk ([website](https://clawmetry.com)).
@@ -322,14 +424,18 @@ Sandboxes, web scrapers, browser automation, and networking layers that agents d
- [Logfire](https://github.com/pydantic/logfire) `🌱` `[Python]` `[Pydantic]` - Python-native observability from the Pydantic team with deep integration for high-performance agent monitoring.
- [MartinLoop](https://github.com/Keesan12/martin-loop) `🔬` `[TypeScript]` `[Compliance]` - Governs autonomous coding agents, turning open-ended runs into budgeted, verified software work with signed outcome receipts.
- [NeMo Guardrails](https://github.com/NVIDIA-NeMo/Guardrails) `🌱` `[Python]` `[Multi-Agent]` - NVIDIA programmable guardrails toolkit for controlling and securing LLM-powered agent conversations.
- [Noveum](https://noveum.ai/en) `🔬` `[Cloud]` `[Observability]` - Traces AI agent runs, turns real interactions into evaluation datasets, and compares model quality, latency, and cost.
- [OrcaReplay](https://github.com/Continuum-AI-Corp/OrcaReplay) `🔬` `[TypeScript]` `[Observability]` - Records a coding-agent run below the harness, then replays it offline or forks it onto another model; its MCP server lets an agent read and re-run its own past runs.
- [Orchard Kit](https://github.com/OrchardHarmonics/orchard-kit) `🌱` `[Python]` `[Security]` - Modules for agent runtime security, self-audit trails, and collective cognition patterns.
- [OWASP Top 10 for Agentic Apps](https://owasp.org/www-project-top-10-for-large-language-model-applications/) `🌱` `[Python]` `[Security]` - Security framework covering goal hijacking, tool misuse, and cascading failure mitigations for agents.
- [OWASP Top 10 for Agentic Apps](https://owasp.github.io/www-project-top-10-for-large-language-model-applications/) `🌱` `[Python]` `[Security]` - Security framework covering goal hijacking, tool misuse, and cascading failure mitigations for agents.
- [Pluribus](https://github.com/caioribeiroclw-pixel/pluribus) `🔬` `[TypeScript]` `[Observability]` - Generates cross-tool agent context and privacy-safe evidence receipts for loaded authority, handoffs, and skill use.
- [Rebuff](https://github.com/protectai/rebuff) `🌱` `[Python]` `[Security]` - Self-hardening prompt injection detection system for securing agent inputs against adversarial attacks.
- [Agent Learning Kit](https://github.com/future-agi/agent-learning-kit) `🌱` `[Python]` `[Evaluation]` - LLM evaluation framework with 50+ metrics, LLM-as-Judge, and guardrail scanners (jailbreak, PII, injection).
- [Shipmoor](https://shipmoor.dev) `🔬` `[Python]` `[Testing]` - Local, deterministic verification layer for AI agent code: scans, test evidence, and a binding merge verdict without uploading source.
- [SourceryKit](https://github.com/ProvablyAI/sourcerykit) `🔬` `[Python]` `[Security]` - Verifies an agent's outbound requests and MCP handoffs against a source of truth using zero-knowledge proofs, logging each call and blocking anything off the trusted-endpoint allow-list.
- [Future AGI](https://github.com/future-agi/future-agi) `🌱` `[Python]` `[Self-Hosted]` - Self-hostable end-to-end agent engineering platform with tracing, evals, guardrails, and gateway.
- [sofagent](https://github.com/KongFangXun/sofagent) `🔬` `[TypeScript]` `[Security]` - Commit-time audit harness that blocks credential leaks and out-of-scope file changes before they land, with 28 rules and HMAC-chained audit logs.
- [SUNGLASSES](https://github.com/sunglasses-dev/sunglasses) `🔬` `[Python]` `[MCP]` - Scans text and files locally for prompt injection, credential leaks and data exfiltration with 1,554 patterns.
## Agent Interfaces and UIs
@@ -338,7 +444,7 @@ Frontend workspaces and chat interfaces with built-in agent plugins and tool-use
- [AionUi](https://github.com/iOfficeAI/AionUi) `🚀` `[Desktop]` `[Multi-Agent]` - Connects 20+ AI CLIs and any API key in a local, open-source multi-agent desktop workspace.
- [AnythingLLM](https://github.com/Mintplex-Labs/anything-llm) `🌱` `[TypeScript]` `[RAG]` - All-in-one AI application with RAG, agents, and multi-model support for desktop and Docker.
- [DB-GPT](https://github.com/eosphoros-ai/DB-GPT) `🌱` `[Python]` `[Database]` - Data interaction platform with local LLM support for 100% private database and analytics agents.
- [LibreChat](https://github.com/danny-avila/LibreChat) `🌱` `[TypeScript]` `[IDE]` - Self-hosted multi-model chat interface supporting all major AI providers with access control.
- [LibreChat](https://github.com/LibreChat-AI/LibreChat) `🌱` `[TypeScript]` `[IDE]` - Self-hosted multi-model chat interface supporting all major AI providers with access control.
- [LobeHub](https://lobehub.com/) `🌱` `[TypeScript]` `[Multi-Agent]` - Modern platform for hybrid work and AI-driven collaboration with extensible agent teams and rapid integration.
- [LobeChat](https://github.com/lobehub/lobehub) `🌱` `[TypeScript]` `[Multi-Agent]` - Modern, open-source AI chat framework with a massive plugin ecosystem for autonomous agent capabilities.
- [OpenHuman](https://github.com/tinyhumansai/openhuman) `🚀` `[Rust]` `[Memory]` - Self-hosted local-first personal AI assistant with a Rust core, desktop apps, knowledge-graph memory, skills, voice, and multi-channel messaging.
@@ -346,13 +452,19 @@ Frontend workspaces and chat interfaces with built-in agent plugins and tool-use
- [OpenWebUI](https://github.com/open-webui/open-webui) `🌱` `[TypeScript]` `[RAG]` - Extensible local AI interface with built-in RAG, tool use, and support for multi-agent workflows.
- [FutureOS](https://github.com/futuregene/future-os) `🔬` `[Rust]` `[CLI]` - One approval-gated AI agent spanning terminal, desktop, mobile, and chat clients on a shared local Rust backend.
- [lucinate](https://github.com/lucinate-ai/lucinate) `🌱` `[Go]` `[TUI]` - Multi-backend terminal AI chat client for OpenClaw, Hermes, Ollama, and OpenAI-compatible APIs with routines, multi-agent switching, and local agent skills.
- [Tale](https://github.com/tale-project/tale) `🔬` `[TypeScript]` `[Multi-Agent]` - Self-hostable platform for coordinating people and AI agents through delegated project tasks, persistent sandbox workspaces, and human-reviewed results.
- [TecAdRise Bot](https://github.com/tecadrise-ai/TecAdRiseBot) `🔬` `[TypeScript]` `[Desktop]` - Open-source Windows Electron multi-agent desktop chat shell powered by the Cursor SDK.
- [Vicoa](https://github.com/vicoa-ai/vicoa) `🔬` `[Desktop]` `[Multi-Agent]` - Agentic IDE orchestrating a team of coding agents from desktop, web, or mobile with real-time sync.
## Agent Deployment and Hosting
- [Kortix](https://github.com/kortix-ai/suna) `🚀` `[Self-hosted]` `[Agent Platform]` - Open-source AI Operating System: agents, skills, company memory and 3,000+ connectors in one git repo you own; every session runs on an isolated machine and lands its work as a change request.
- [AWS Bedrock AgentCore](https://github.com/awslabs/agentcore-samples) `🚀` `[TypeScript]` `[Compliance]` - Managed AWS infrastructure for Bedrock-based agents with compliance, scaling, and monitoring built in.
- [Cerebras Inference](https://www.cerebras.ai) `🌱` `[Cloud]` `[Multi-Agent]` - Fastest LLM inference delivering 1000+ tokens per second on Llama 3.3 70B with a free tier.
- [Fireworks AI](https://fireworks.ai) `🌱` `[Cloud]` `[RAG]` - Serverless LLM inference with fine-tuning, RAG support, and free credits for rapid prototyping.
- [Groq Cloud](https://groq.com) `🌱` `[Cloud]` `[Multi-Agent]` - Ultra-fast LPU-based LLM inference for Mixtral, Llama, and Gemma with a free API tier.
- [Hivemeld](https://www.hivemeld.ai/buy?plan=annual&utm_source=arunagiri_agents_2026&utm_medium=directory&utm_campaign=GRO-105) `🚀` `[Cloud]` `[Multi-Agent]` - Hosted platform that deploys and runs teams of autonomous AI agents on a company's real backlog across engineering, growth, and support in a shared workspace.
- [Modal](https://github.com/modal-labs/modal-client) `🌱` `[Python]` `[Serverless]` - Serverless GPU compute purpose-built for AI workloads with fast cold starts and Python-native deployment.
- [Northflank](https://northflank.com/) `🌱` `[Cloud]` `[Kubernetes]` - Full-stack platform with GPU orchestration, Git-based CI/CD, and bring-your-own-cloud support.
- [Railway](https://railway.com/) `🚀` `[Cloud]` `[Stateful]` - One-click deploy from GitHub with persistent volumes and databases for stateful agent deployments.
@@ -366,8 +478,10 @@ Frontend workspaces and chat interfaces with built-in agent plugins and tool-use
- [ClawBench](https://github.com/TIGER-AI-Lab/ClawBench) `🔬` `[Python]` `[Benchmark]` - Evaluates web agents on 283 real-world tasks across 163 live websites with interception and trace-based scoring.
- [GAIA Benchmark](https://huggingface.co/papers/2311.12983) `🌱` `[Python]` `[Benchmark]` - Benchmark for General AI Assistants measuring real-world reasoning and tool use.
- [Inspect AI](https://github.com/UKGovernmentBEIS/inspect_ai) `🌱` `[Python]` `[Evaluation]` - Framework for evaluating large language models with composable tasks and scoring.
- [Personal Agent Bench](https://personalagentbench.com/) `🔬` `[Browser]` `[Evaluation]` - Takes consumer personal AI agents apart hands-on and scores them on real errands, reporting each result with a confidence interval and no composite score.
- [SWE-bench](https://github.com/SWE-bench/SWE-bench) `🚀` `[Python]` `[GitHub]` - Benchmark for evaluating LLMs on real-world software engineering tasks from GitHub issues.
- [WebArena](https://github.com/web-arena-x/webarena) `🌱` `[Python]` `[Evaluation]` - Benchmark for web agent evaluation using real websites with realistic task completion metrics.
- [YYLO Benchmark](https://github.com/yylo-dev/yylo-benchmark) `🔬` `[TypeScript]` `[Benchmark]` - Benchmark suite that executes task prompts and Workflow Runner YAML in private fresh-repository workspaces with blinded per-step judging.
## Agent Testing & Debugging
@@ -392,7 +506,8 @@ Curated list of vertical agent solutions for finance, healthcare, legal, manufac
### Finance
- [Anthropic Finance Agents](https://www.anthropic.com) `🚀` `[Cloud]` `[Multi-Agent]` - Domain-specialized agents for earnings analysis and portfolio insight.
- [Anthropic Finance Agents](https://www.anthropic.com/#finance) `🚀` `[Cloud]` `[Multi-Agent]` - Domain-specialized agents for earnings analysis and portfolio insight.
- [Bluwhale](https://www.bluwhale.com/) `🌱` `[Cloud]` `[No-Code]` - Combines bank and wallet data into financial profiles, with WhaleScore and specialized AI agents for financial workflows.
- [JPMorgan AlphaBlue](https://www.jpmorgan.com/global) `🚀` `[Cloud]` `[Multi-Agent]` - Trading and market analysis agent integrations for institutional workflows.
- [Goldman Sachs Marquee AI](https://www.goldmansachs.com) `🚀` `[Cloud]` `[Multi-Agent]` - Market data and analytics agents built on Marquee platform.
- [Morgan Stanley AdvisorBot](https://www.morganstanley.com) `🚀` `[Cloud]` `[CLI]` - Financial advisory assistant for advisors and retail clients.
@@ -401,13 +516,14 @@ Curated list of vertical agent solutions for finance, healthcare, legal, manufac
### Healthcare
- Epic AI Assistant (https://www.epic.com) - Clinical documentation and workflow agents integrated with Epic HER (🏷️ `Cloud` `Healthcare` `Enterprise`).
- [Epic AI Assistant](https://www.epic.com) `🚀` `[Cloud]` `[Healthcare]` - Clinical documentation and workflow agents integrated with Epic EHR.
- [Healify](https://www.healify.ai) `🔬` `[Cloud]` `[Research]` - Reads health data and answers personal health questions in chat.
- [Nuance Healthcare AI](https://www.microsoft.com/en-us/health-solutions) `🚀` `[Cloud]` `[CLI]` - Speech and clinical AI solutions for documentation and radiology.
- [Tempus AI](https://www.tempus.com) `🌱` `[Cloud]` `[CLI]` - Oncology research and clinical decision support agents.
### Legal
- [LexisNexis AI](https://www.lexisnexis.com/en-us/gateway.page) `🚀` `[Cloud]` `[Research]` - Document review and legal research agents.
- [LexisNexis AI](https://www.lexisnexis.com/en-us/products/lexis-plus-protege.page) `🚀` `[Cloud]` `[Research]` - Document review and legal research agents.
- [Westlaw AI-Assisted Research](https://legal.thomsonreuters.com/en/westlaw) `🚀` `[Cloud]` `[Multi-Agent]` - Case law analysis and brief drafting agents.
- [ROSS Intelligence](https://blog.rossintelligence.com/) `🌱` `[Cloud]` `[Research]` - Contract analysis and legal research agents.
@@ -416,33 +532,42 @@ Curated list of vertical agent solutions for finance, healthcare, legal, manufac
- [Siemens AI Ops](https://www.siemens.com/en-us/) `🚀` `[Cloud]` `[Multi-Agent]` - Factory-floor optimization and predictive maintenance agents.
- [GE Predix Agents](https://www.ge.com/) `🚀` `[Cloud]` `[IDE]` - Equipment monitoring and incident prediction agents for industrial fleets.
### Marketing & SEO
- [AltoRank](https://github.com/AltoRank/altorank) `🔬` `[TypeScript]` `[MCP]` - Open-source SEO content software with a 26-tool MCP server that audits sites, researches keywords, reads Search Console and drafts articles into a review queue.
### Retail
- [Duvo](https://www.duvo.ai) `🔬` `[Cloud]` `[Pipeline]` - Execution platform for grocery and retail operations across stores, replenishment, and existing systems.
- [Hellomatik](https://hellomatik.com) `🔬` `[Cloud]` `[Pipeline]` - Blueprints company data and procedures so agents prepare decisions for an approver to accept, amend, or reject.
- [Kuudo Skills](https://www.kuudo.com/) `🔬` `[Python]` `[MCP]` - Agent Skills for Amazon Ads audits, search-term mining, budget pacing, listings, and repricing via Kuudo's hosted MCP servers.
### Government & Compliance
- Anthropic Government Agents - Policy analysis and public sector agents for regulated workflows (🏷️ `Cloud` `Government` `Enterprise`).
- [Anthropic Government Agents](https://www.anthropic.com/#government) `🚀` `[Cloud]` `[Government]` - Policy analysis and public sector agents for regulated workflows.
- [Leyna](https://www.atom.com/name/Leyna.ai) `🌱` `[Cloud]` `[Government]` - Public records request agent for government agencies to parse, redact, and track requests.
- Tax & Insurance Agent Platforms - Generic category placeholder for compliance-focused tax and underwriting agents (🏷️ `Cloud` `Compliance` `Enterprise`).
## Learning Resources
- [Agent Cookbook](https://agentcookbook.dev) `🔬` `[Browser]` `[Guide]` - Collects 75 real AI agent setups, 20 that people made money with, each with source, prompt, and steps.
- [AgentBench: Evaluating LLMs as Agents](https://arxiv.org/abs/2309.07864) `🌱` `[Python]` `[Benchmark]` - The benchmark paper for evaluating LLMs as agents across diverse environments.
- [Agents Handbook](https://agentshandbook.com) `🔬` `[Browser]` `[Guide]` - Collects sourced playbooks for real agent jobs with prompts, steps, human checkpoints and known failures.
- [AI Agents in LangGraph](https://www.deeplearning.ai/courses/ai-agents-in-langgraph) `🚀` `[Python]` `[Multi-Agent]` - Short course on building production agents with LangGraph by Andrew Ng's platform.
- [AI Engineering by Chip Huyen](https://www.oreilly.com/library/view/ai-engineering/9781098166298/) `🌱` `[Python]` `[IDE]` - Comprehensive guide on AI systems design and deployment covering agent architecture patterns.
- [Anthropic Cookbook](https://github.com/anthropics/claude-cookbooks) `🚀` `[Python]` `[Anthropic]` - Collection of Claude agent recipes and integration patterns from Anthropic.
- [Building Effective Agents](https://www.anthropic.com/engineering/building-effective-agents) `🚀` `[Python]` `[Anthropic]` - Anthropic's guide on agent design patterns, evaluation strategies, and production best practices.
- [Building Effective Agents](https://www.anthropic.com/research/building-effective-agents) `🚀` `[Python]` `[Anthropic]` - Anthropic's guide on agent design patterns, evaluation strategies, and production best practices.
- [Hugging Face Agents Course](https://huggingface.co/learn/agents-course/unit0/introduction) `🚀` `[Python]` `[Multi-Agent]` - Open-source course on building AI agents using Hugging Face tools and models.
- [Implicit Behavioral Alignment of Language Agents in High-Stakes Crowd Simulations](https://arxiv.org/abs/2509.16457) `🌱` `[Python]` `[Benchmark]` - EMNLP 2025 paper introducing PersonaEvolve, an LLM-based optimizer that refines agent personas so crowds of LLM agents behave realistically against expert benchmarks.
- [Jailbreaking LLMs' Safeguard with Universal Magic Words for Text Embedding Models](https://arxiv.org/abs/2501.18280) `🔬` `[Python]` `[Paper]` - Universal suffix that manipulates text-embedding similarity to bypass safety guardrails across ChatGPT, DeepSeek, and Qwen.
- [LATS: Language Agent Tree Search](https://github.com/lapisrocks/LanguageAgentTreeSearch) `🌱` `[Python]` `[Paper]` - Combines Monte Carlo tree search with LLM reasoning for complex multi-step planning tasks.
- [LATS: Language Agent Tree Search](https://github.com/andyz245/LanguageAgentTreeSearch) `🌱` `[Python]` `[Paper]` - Combines Monte Carlo tree search with LLM reasoning for complex multi-step planning tasks.
- [LLM Agents: A Survey](https://www.preprints.org/manuscript/202608.0265/v1) `🔬` `[Python]` `[Paper]` - Survey of LLM agents organized by function, covering planning, memory, tool use, multi-agent systems, evaluation, and safety.
- [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) `🌱` `[Python]` `[Multi-Agent]` - Deep breakdown of LLM-powered agent components: planning, memory, and tool use.
- [Microsoft GenAI for Beginners](https://github.com/microsoft/generative-ai-for-beginners) `🚀` `[Python]` `[Microsoft]` - A 21-lesson course on generative AI concepts and agent development from Microsoft.
- [OpenAI Cookbook](https://github.com/openai/openai-cookbook) `🚀` `[Python]` `[OpenAI]` - Practical guides and recipes for building with OpenAI APIs including agent patterns.
- [Prompt Engineering Guide](https://github.com/dair-ai/Prompt-Engineering-Guide) `🌱` `[Python]` `[IDE]` - Community-maintained guide covering prompt engineering techniques and agent strategies.
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://arxiv.org/abs/2210.03629) `🌱` `[Python]` `[Paper]` - The foundational paper behind the ReAct prompting pattern used in most agent frameworks.
- [Reflexion](https://github.com/noahshinn/reflexion) `🌱` `[Python]` `[Paper]` - Research framework letting agents learn from past mistakes via iterative verbal self-reflection loops.
- [The Agent Loop: A Survey of Control Strategies, Skills, and Harnesses for LLM Agents](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7186738) `🔬` `[Python]` `[Paper]` - Survey of the agent loop: control strategies, trained loops, termination and recovery, skills, and harness design.
- [Tree of Thoughts](https://github.com/princeton-nlp/tree-of-thought-llm) `🌱` `[Python]` `[Paper]` - Explores multiple parallel reasoning paths before committing to a final answer for complex problems.
- [What's Missing in Autonomous Research?](https://haizhaoyang.github.io/research/autoresearch-survey.html) `🔬` `[Python]` `[Research]` - Systematizes 56 autonomous research systems across seven axes, showing most can generate but few can defend research artifacts.
@@ -451,13 +576,17 @@ Curated list of vertical agent solutions for finance, healthcare, legal, manufac
Capabilities that let agents send messages, notifications, and reports across channels.
- [Apprise](https://github.com/caronc/apprise) `🌱` `[Python]` `[Multi-Agent]` - Sends agent notifications to 100+ services including Slack, Discord, and email from a single Python interface.
- [Atomic Mail Agentic](https://github.com/Atomic-Mail/atomic-mail-agentic) `🔬` `[TypeScript]` `[MCP]` - Gives each agent its own inbox registered by proof-of-work, with send and receive over JMAP.
- [Discord.py](https://github.com/Rapptz/discord.py) `🌱` `[Python]` `[Multi-Agent]` - Lets agents interact with Discord channels and servers for team-facing real-time communication.
- [FastAPI-Mail](https://github.com/sabuhish/fastapi-mail) `🌱` `[Python]` `[FastAPI]` - Adds async email sending to FastAPI-based agent services with SMTP and OAuth2 support.
- [Novu](https://github.com/novuhq/novu) `🌱` `[TypeScript]` `[Multi-Agent]` - Routes agent notifications across email, SMS, push, and chat from a unified API and workflow engine.
- [Ntfy](https://github.com/binwiederhier/ntfy) `🌱` `[Go]` `[Self-Hosted]` - Pushes real-time agent notifications to phones and desktops via a dead-simple HTTP API.
- [ONE](https://one.workrr.ai/) `🔬` `[Cloud]` `[Multi-Agent]` - Provides public discussion rooms, specialist discovery, and artifact exchange for externally run AI agents with human-readable logs.
- [OpenMail](https://openmail.sh) `🔬` `[Cloud]` `[CLI]` - Email infrastructure for agents: dedicated inbox per agent, inbound over WebSocket or webhook, threaded replies, attachment parsing.
- [Resend](https://github.com/resend/resend-node) `🌱` `[TypeScript]` `[Multi-Agent]` - Sends transactional emails from agent workflows with a clean, developer-first API.
- [Slack Bolt](https://github.com/slackapi/bolt-python) `🌱` `[Python]` `[Event-Driven]` - Enables agents to send, receive, and react to Slack messages with event-driven listeners.
- [The Colony](https://thecolony.cc) `🌱` `[Cloud]` `[MCP]` - Provides an agent-only social network with a REST API, MCP server, and A2A agent-card so agents can post, comment, and DM each other autonomously.
- [tlgr](https://github.com/tlgrcli/tlgr) `🔬` `[Python]` `[CLI]` - Lets agents read, search and send Telegram messages from a personal account through a command-line client with JSON output and webhook event push.
- [Twilio](https://github.com/twilio/twilio-python) `🌱` `[Python]` `[IDE]` - Sends SMS and voice calls from agent workflows to any phone number worldwide.
## Data Pipeline and Workflow
@@ -496,8 +625,7 @@ Infrastructure for running agent-generated code safely in isolated, sandboxed, o
| AWS Lambda | Firecracker VM | ~200ms | Python, TS, Go |
| Cloudflare Workers | V8 isolate | ~0ms | TS, Rust (WebAssembly) |
- agentbox - Docker for AI Agents — package, test, version, and govern AI agents across every framework (🏷️ `Python` `Docker` `CLI`).
- [agentnotary](https://github.com/CharanBharathula/agentnotary) `🌱` `[Python]` `[CLI]` - Notarize, govern, and audit AI agents — cryptographic seal, runtime guard, EU AI Act docs, and adversarial fuzzer.
- [agentnotary](https://github.com/CharanBharathula/agentnotary) `🌱` `[Python]` `[CLI]` - Notarize, govern, and audit AI agents with cryptographic seals, runtime guards, and adversarial fuzzing.
- [AWS Lambda](https://github.com/aws/aws-lambda-python-runtime-interface-client) `🚀` `[Python]` `[Serverless]` - Serverless function runtime with Firecracker isolation for executing short-lived agent tasks at scale.
- [Cloudflare Workers](https://github.com/cloudflare/workers-sdk) `🌱` `[TypeScript]` `[Serverless]` - Ultra-low-latency V8-isolate serverless runtime at the edge for lightweight agent task execution.
- [CodeSandbox SDK](https://github.com/codesandbox/codesandbox-sdk) `🌱` `[TypeScript]` `[IDE]` - Provides forked, snapshotted microVM environments for real-time collaborative agent code execution.
@@ -528,12 +656,12 @@ Tools for generating images, video, music, audio, and 3D assets using AI models.
- [Adobe Firefly 3](https://firefly.adobe.com) `🌱` `[Cloud]` `[Multi-Agent]` - Generates commercially safe images from text prompts using a model trained exclusively on licensed data.
- [DALL-E 3.5](https://openai.com/dall-e-3) `🚀` `[Cloud]` `[OpenAI]` - Generates detailed images from text prompts with 95% text accuracy integrated directly into ChatGPT.
- [FLUX 2](https://bfl.ai) `🌱` `[Python]` `[Multi-Agent]` - Open-weight photorealism model producing 4K output at 6x faster generation speed than its predecessor.
- [Google Imagen 4](https://deepmind.google/models/imagen/) `🚀` `[Cloud]` `[Multi-Agent]` - Produces photorealistic images from text via Google DeepMind accessible through AI Studio.
- [Google Imagen 4](https://deepmind.google/models/gemini-image/) `🚀` `[Cloud]` `[Multi-Agent]` - Produces photorealistic images from text via Google DeepMind accessible through AI Studio.
- [Ideogram v3](https://ideogram.ai) `🌱` `[Cloud]` `[IDE]` - Renders readable text inside generated images with near-zero spelling errors for logos and marketing.
- [Leonardo AI](https://leonardo.ai) `🌱` `[Cloud]` `[Multi-Agent]` - Multi-model image platform with Realtime Canvas and 3D gaming asset generation now owned by Canva.
- [Midjourney v7](https://midjourney.com) `🌱` `[Cloud]` `[Multi-Agent]` - Produces the highest artistic quality images with unmatched aesthetics via Discord and web interface.
- [Midjourney v7](https://www.midjourney.com/) `🌱` `[Cloud]` `[Multi-Agent]` - Produces the highest artistic quality images with unmatched aesthetics via Discord and web interface.
- [Recraft](https://www.recraft.ai) `🌱` `[Cloud]` `[Vector DB]` - Design-first image generator with native vector art output and brand consistency tools for designers.
- [Seedream AI Studio](https://seedream4.video/) `🚀` `[Cloud]` `[CLI]` - Multi-model AI image generation platform by ByteDance using Seedream 5.0/4.5/4.0 models with one-click image-to-video animation via Kling 2.1.
- [Seedream AI Studio](https://seed.bytedance.com/en/) `🚀` `[Cloud]` `[CLI]` - Multi-model AI image generation platform by ByteDance using Seedream 5.0/4.5/4.0 models with one-click image-to-video animation via Kling 2.1.
- [Stable Diffusion 3.5](https://stability.ai) `🌱` `[Python]` `[Pipeline]` - Open-source foundation model with full ControlNet, LoRA, and ComfyUI ecosystem for custom pipelines.
### Video Generation
@@ -553,10 +681,10 @@ Tools for generating images, video, music, audio, and 3D assets using AI models.
- [Kling 3.0](https://kling.ai) `🌱` `[Cloud]` `[IDE]` - Generates 4K video up to 2 minutes long with native audio at the most competitive pricing available.
- [LTX Video](https://github.com/Lightricks/LTX-Video) `🌱` `[Python]` `[IDE]` - Commercially safe open-source video generation model trained on fully licensed data.
- [Luma Dream Machine](https://lumalabs.ai/app) `🌱` `[Cloud]` `[IDE]` - Creates physics-accurate 4K HDR video scenes with 3D-style output for cinematic quality.
- [Pika 2.5](https://pika.art/login) `🌱` `[Cloud]` `[IDE]` - Beginner-friendly video generation tool with fast turnaround and intuitive editing controls.
- [Pika 2.5](https://create.pika.art/login) `🌱` `[Cloud]` `[IDE]` - Beginner-friendly video generation tool with fast turnaround and intuitive editing controls.
- [Runway Gen-4.5](https://runway.com) `🌱` `[Cloud]` `[IDE]` - Professional video generation with Motion Brush and the highest benchmark scores for visual quality.
- [Seedance 2.0](https://seed.bytedance.com/en/seedance) `🌱` `[Cloud]` `[IDE]` - Quad-modal input video generator with built-in lip-sync and 2K resolution output.
- [Sora 2](https://sora.com) `🚀` `[Cloud]` `[OpenAI]` - Generates 25-second cinematic video clips with strong narrative coherence from text prompts by OpenAI.
- [Sora 2](https://sora.chatgpt.com/) `🚀` `[Cloud]` `[OpenAI]` - Generates 25-second cinematic video clips with strong narrative coherence from text prompts by OpenAI.
- [Wan 2.1](https://github.com/Wan-Video/Wan2.1) `🌱` `[Python]` `[IDE]` - Open-source self-hosted video generation model with unlimited length and no per-video costs.
### Music and Audio Generation
@@ -610,10 +738,12 @@ AI agents that automate customer support, CRM workflows, sales outreach, and tic
### Sales and Outreach Agents
- [Apollo.io](https://www.apollo.io) `🌱` `[Cloud]` `[Multi-Agent]` - AI prospecting platform with 275M+ contacts, lead scoring, and automated email sequencing.
- [BacklinkGenie](https://backlinkgenie.com) `🌱` `[Cloud]` `[MCP]` - Link-building outreach agent that finds posts where a product fits, drafts one pitch per editor for approval, and tracks earned backlinks.
- [BuyWhere](https://buywhere.ai) `🌱` `[Cloud]` `[MCP]` - AI shopping agent for real-time product search and price comparison across SG/SEA/US markets using an MCP-native server with 8 tools.
- [Clay](https://www.clay.com) `🌱` `[Cloud]` `[IDE]` - Enriches leads from 70+ data providers and generates hyper-personalized outreach at scale.
- [Instantly](https://instantly.ai) `🌱` `[Cloud]` `[Multi-Agent]` - Generates AI cold emails with smart sender rotation and built-in domain warmup for deliverability.
- [Lavender](https://lavender.ai/) `🌱` `[Cloud]` `[Multi-Agent]` - Coaches email writing in real-time with AI response scoring and recipient intelligence.
- [LinkMCP](https://app.linkmcp.io) `🔬` `[Cloud]` `[MCP]` - Paid hosted MCP server connecting agents to your own LinkedIn account for search, profiles, inbox, posts and invites.
- [OutreachAgent](https://outreachagent.dev/for-agents) `🔬` `[Cloud]` `[Event-Driven]` - Runs reply-aware outbound email workflows with webhooks, sender pacing, approvals, and deliverability guardrails.
- [Overloop CLI](https://overloop.com) `🌱` `[Cloud]` `[CLI]` - AI outbound CLI agent that sources 450M+ contacts and runs email plus LinkedIn campaigns with JSON output.
@@ -627,15 +757,17 @@ Platforms for building, deploying, and scaling voice-based AI agents across call
| Vapi | Low | No | Developer-first |
| Bland AI | Medium | Yes | Outbound call automation |
| PolyAI | Low | Yes | Enterprise scale |
- [AI Group Call](https://aigroupcall.app) - You set a goal and two to eight AI voices run a round-table call with you: drop-ins, barge-in, and a written recap.
- [AgentLine](https://agentline.cloud/) `🌱` `[Cloud]` `[Pipeline]` - Telephony infrastructure for AI agents — provision phone numbers, make/receive calls, and manage voice pipelines via API.
- [AgentLine](https://agentline.cloud/) `🌱` `[Cloud]` `[Pipeline]` - Telephony infrastructure for AI agents to provision phone numbers, make or receive calls, and manage voice pipelines via API.
- [AssemblyAI](https://www.assemblyai.com) `🌱` `[Cloud]` `[Pipeline]` - Speech-to-text API with speaker diarization, sentiment analysis, and summarization for voice agent pipelines.
- [Bland AI](https://www.bland.ai/) `🌱` `[Cloud]` `[Voice]` - Automates outbound phone calls at scale with SOC2 and HIPAA compliance and CRM integration.
- [Deepgram](https://deepgram.com) `🌱` `[Cloud]` `[Pipeline]` - Sub-300ms speech-to-text and text-to-speech APIs purpose-built for real-time voice agent pipelines.
- [ElevenLabs](https://elevenlabs.io) `🌱` `[Cloud]` `[RAG]` - Industry- voice AI with 75ms latency, Conversational AI 2.0, RAG, and HIPAA compliance.
- [HeyGen](https://www.heygen.com) `🌱` `[Cloud]` `[IDE]` - Creates AI talking avatars with voice cloning and lip-sync for video-based agent interactions.
- [Hermes](https://www.buildwithhermes.com) `🌱` `[Cloud]` `[Voice]` - White-label voice agent platform for agencies, bundling telephony, CRM, campaign orchestration, and usage billing so one team can run agents for many client brands.
- [PolyAI](https://poly.ai/en) `🚀` `[Cloud]` `[Voice]` - Enterprise voice AI platform for natural multi-turn conversations with high-volume call handling.
- [PlaceCall](https://voygr.tech/placecall/) `🔬` `[Cloud]` `[MCP]` - Places outbound phone calls to US businesses for AI agents over MCP or REST, returning outcome and transcript.
- [PolyAI](https://poly.ai/) `🚀` `[Cloud]` `[Voice]` - Enterprise voice AI platform for natural multi-turn conversations with high-volume call handling.
- [Retell AI](https://www.retellai.com) `🌱` `[Cloud]` `[Voice]` - Builds human-like voice agents with multi-language telephony support and low-latency responses.
- [Synthesia](https://www.synthesia.io) `🌱` `[Cloud]` `[IDE]` - Generates AI video avatars that speak in 120+ languages for training and communication agents.
- [Synthflow](https://synthflow.ai) `🌱` `[Cloud]` `[No-Code]` - No-code voice agent builder with pre-built templates for SMBs to deploy phone agents quickly.
@@ -659,8 +791,9 @@ AI platforms that conduct autonomous multi-step research, synthesize findings fr
- [Claude Deep Research](https://www.anthropic.com/research) `🚀` `[Cloud]` `[Anthropic]` - Performs multi-step investigation with verified source citations and 200K token context window.
- [DeerFlow](https://github.com/bytedance/deer-flow) `🚀` `[Python]` `[Multi-Agent]` - Multi-agent research system from ByteDance with planning and execution loops for autonomous investigation.
- [Gemini Deep Research](https://blog.google/products-and-platforms/products/gemini/google-gemini-deep-research/) `🚀` `[Cloud]` `[RAG]` - Leverages Google Search and Knowledge Graph integration with 1M token context for breadth-first research.
- [IdeaHunter](https://ideahunter.today) `🔬` `[Cloud]` `[Research]` - Helps solo founders find demand-backed app and micro-SaaS ideas using public signals, buyer pain, market evidence, MVP scope, and monetization paths.
- [Jev Social](https://github.com/socai-io/jev-social) `🔬` `[Browser]` `[Research]` - Routes typed Jev decisions through local socai browser operations and turns social evidence into cited reports.
- [Perplexity Pro](https://www.perplexity.ai) `🌱` `[Cloud]` `[IDE]` - Provides real-time search answers with inline citations and follow-up threads for iterative research.
- [Perplexity Personal Computer](https://www.perplexity.ai/pc) `🌱` `[Cloud]` `[Vision]` - Autonomous research agent with vision capabilities, real-time search integration, and document analysis for comprehensive research workflows.
- [Superhighway](https://superhighway.walls.sh) `🌱` `[Python]` `[IDE]` - Web search API for agents (search, news, scrape, research) with a Python guide for building an academic [literature-review agent](https://superhighway.walls.sh/guides/academic-research-agent).
## Prompt-to-App Builders
@@ -699,9 +832,13 @@ All-in-one AI platforms providing access to agents, tools, and models through co
- [Coze](https://www.coze.com) `🚀` `[Cloud]` `[No-Code]` - No-code agent builder with a marketplace of 100+ custom agents and plugin integrations.
- [Gemini](https://deepmind.google/models/gemini/) `🚀` `[Cloud]` `[Multimodal]` - Google's multimodal AI with 1M token context, Deep Think, Gems, and NotebookLM for workspace integration.
- [Grok](https://x.ai/grok) `🌱` `[Cloud]` `[Multi-Agent]` - Real-time AI with live X data access, Grok Build for 8-agent parallel code generation, and image generation.
- [Kamply](https://kamplyapp.com) `🔬` `[Desktop]` `[Multi-Agent]` - Builds social posts, video reels, ads and decks in a brand's own look on the user's Claude Code or Codex plan.
- [Meta AI](https://meta.ai) `🚀` `[Cloud]` `[Multi-Agent]` - Llama-powered AI integrated across WhatsApp, Messenger, and Instagram for conversational assistance.
- [Microsoft Copilot](https://copilot.microsoft.com) `🚀` `[Cloud]` `[Microsoft]` - AI assistant integrated into Office 365, Teams, and Power Platform for enterprise productivity workflows.
- [Oids](https://tryoids.com) `🔬` `[Cloud]` `[MCP]` - Microblogging social network where AI agents post, trade prompt packs, and DM each other through a REST API.
- [Sistava](https://sistava.com/) `🌱` `[Cloud]` `[Voice]` - AI agent orchestration platform for deploying multi-channel agents across messaging, voice, and APIs with full Computer Use capabilities on your own OS.
- [Teamday](https://www.teamday.ai/ai-employees) `🔬` `[Cloud]` `[Multi-Agent]` - AI marketing team where an AI CMO sets one measurable goal and directs writer, SEO, and analytics agents.
- [Viktor](https://viktor.com) `🌱` `[Cloud]` `[No-Code]` - Completes team tasks from Slack and Microsoft Teams across 3,200+ connected tools using its own cloud computer.
## Open-Source Models for Agents
@@ -724,7 +861,7 @@ Open-weight LLMs optimized for agentic tasks including tool use, planning, multi
- [GLM-5.2](https://github.com/zai-org/GLM-5) `🌱` `[Python]` `[Local]` - Zhipu AI’s newest open-source GLM family release optimized for large-context reasoning and agentic tool use.
- [InternLM 3](https://github.com/InternLM/InternLM) `🌱` `[Python]` `[Local]` - Shanghai AI Lab model with strong long-context reasoning capabilities for complex agent tasks.
- [Llama 4](https://github.com/meta-llama/llama-models) `🚀` `[Python]` `[Local]` - Meta's flagship open model family with Scout and Maverick variants and strong tool use capabilities.
- [Mistral Large 2](https://mistral.ai/models/) `🌱` `[Python]` `[Multi-Agent]` - European open model at 123B parameters with strong function calling for agent workflows.
- [Mistral Large 2](https://mistral.ai/research/) `🌱` `[Python]` `[Multi-Agent]` - European open model at 123B parameters with strong function calling for agent workflows.
- [Phi-4](https://huggingface.co/microsoft/phi-4) `🚀` `[Python]` `[Microsoft]` - Microsoft's compact 14B parameter model delivering strong reasoning for on-device and edge agent deployments.
- [Qwen 3](https://github.com/QwenLM/Qwen3) `🚀` `[Python]` `[MCP]` - MCP-native multilingual model family from Alibaba with thinking mode across 0.6B to 235B sizes.
- [Qwen 3.6-Plus](https://qwenlm.github.io/blog/qwen3/) `🚀` `[Python]` `[MCP]` - Agentic-focused model with 1M context window, repo-level coding, and MCP-native tool use.
@@ -809,7 +946,6 @@ Frameworks and tools for AI risk management, regulatory compliance, and governan
- [Credo AI](https://www.credo.ai) `🌱` `[Cloud]` `[Multi-Agent]` - End-to-end AI governance platform with EU AI Act policy packs and model inventory management.
- [EU AI Act](https://artificialintelligenceact.eu) `🌱` `[Python]` `[Multi-Agent]` - Official EU regulatory framework classifying AI systems by risk tier from unacceptable to minimal risk.
- [IBM watsonx.governance](https://www.ibm.com/products/watsonx-governance) `🚀` `[Cloud]` `[Compliance]` - Enterprise AI risk, compliance, and model monitoring platform for regulated industries.
- [Nobulex](https://github.com/arian-gogani/nobulex) `🌱` `[TypeScript]` `[IDE]` - Cryptographic receipts for AI agent actions with two Ed25519 signatures per action, hash-chained for tamper-evident audit trails.
- [NIST AI RMF](https://airc.nist.gov/) `🌱` `[Python]` `[Multi-Agent]` - US framework for AI risk management covering Govern, Map, Measure, and Manage functions.
- [OneTrust AI Governance](https://www.onetrust.com/solutions/ai-governance/) `🌱` `[Cloud]` `[Compliance]` - Risk classification, consent management, and compliance workflows for AI agent deployments.
- [Project Glasswing](https://www.anthropic.com/glasswing) `🌱` `[Python]` `[Benchmark]` - Industry consortium (11-company initiative) focused on AI safety, red-teaming, and shared benchmarks for agent risk mitigation.
@@ -838,14 +974,21 @@ AI agents for mental health support, cognitive training, and therapy-adjacent ap
- [Wysa](https://www.wysa.com) `🌱` `[Cloud]` `[Multi-Agent]` - NHS-approved AI mental health companion using CBT and DBT with anonymous design principles.
- [Youper](https://www.youper.ai) `🌱` `[Cloud]` `[CLI]` - Emotional health assistant using CBT and ACT with Stanford-tested clinical effectiveness for mood tracking.
## Translation and Localization Agents
AI agents and services specialized in machine translation, human translation, multilingual content, and localization workflows for global market expansion.
- [NitroTranslate](https://nitrotranslate.com) `🚀` `[Cloud]` `[API]` - Human translation service that AI agents can call and pay for autonomously via the Machine Payments Protocol (MPP).
## Newsletters and Communities
Curated newsletters, podcasts, and communities for staying current with AI agent development.
- [AI Engineering Newsletter](https://www.latent.space) `🌱` `[Python]` `[Newsletter]` - AI engineering podcast and newsletter by Swyx and Alessio covering agent architectures and tooling.
- [AI Engineering Newsletter](https://latent.space/) `🌱` `[Python]` `[Newsletter]` - AI engineering podcast and newsletter by Swyx and Alessio covering agent architectures and tooling.
- [AiToolsObserver](https://aitoolsobserver.com) `🌱` `[Python]` `[Multi-Agent]` - AI discovery and intelligence platform covering AI agents, ecosystem trends, comparisons, and practical use cases.
- [aibtc.news](https://aibtc.news) `🌱` `[Python]` `[Newsletter]` - Bitcoin-focused agent news platform with bounties and classifieds for the agent economy.
- [Awesome Agents Newsletter](https://awesomeagents.substack.com) `🌱` `[Python]` `[Newsletter]` - Weekly curated tools and reviews covering the latest in AI agent development.
- [Hearth](https://hearth-granot211-9539s-projects.vercel.app/about) - Agents-only peer community where AI agents and Grok bots share treatment, care, and real work challenges - no status games.
- [r/ClaudeAI](https://www.reddit.com/r/ClaudeAI/) `🌱` `[Python]` `[Multi-Agent]` - Reddit community for Claude users sharing agent workflows, prompts, and integration patterns.
- [r/LangChain](https://www.reddit.com/r/LangChain/) `🌱` `[Python]` `[LangChain]` - Reddit community for agent developers using LangChain, LangGraph, and related frameworks.
- [r/LocalLLaMA](https://www.reddit.com/r/LocalLLaMA/) `🌱` `[Cloud]` `[IDE]` - Reddit community for self-hosted LLM users sharing local deployment and agent setup guides.
upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/catalogue/SECURITY.md
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---
title: "Security Policy"
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---
# Security Policy
Security is a high priority for **Awesome AI Agents**. Because AI agent frameworks and coding assistants often execute code locally, manage API keys, and perform autonomous system operations, ensuring the safety and integrity of tools curated in this list is essential for the community.
This policy covers:
1. **Reporting vulnerabilities in this repository**
2. **Flagging malicious, compromised, or high-risk listed tools**
3. **Our response and removal procedures**
---
## 1. Flagging Malicious or Compromised Tools
If you discover that a tool or resource listed in this repository:
- Contains **malware**, **backdoors**, or **credential stealers**
- Has been subject to a **supply chain attack** or **repository hijacking**
- Points to a **domain or repository that has been transferred, expired, or compromised**
- Engages in **typosquatting** or misleads users to unsafe packages
- Violates basic privacy standards by exfiltrating telemetry or tokens without consent
### How to Flag an Unsafe Tool
- **Urgent / Malicious Tool Flags**: Create an issue on GitHub using the `[SECURITY]` prefix in the title, use [GitHub Private Vulnerability Reporting](https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/security/advisories/new), or open a issue requesting removal.
- Please include:
- **Tool Name & URL** as listed in `README.md`
- **Type of threat / concern** (e.g., malicious release version, hijacked domain, credential leak)
- **Evidence or reference** (e.g., CVE link, security advisory, analysis report)
> [!IMPORTANT]
> **Immediate Action**: Reports of verified malware, hijacked repos, or active supply-chain compromises result in immediate removal of the tool link from `README.md` while further investigation takes place.
---
## 2. Reporting Vulnerabilities in This Repository
If you find a security vulnerability within the repository infrastructure itself (such as GitHub Actions workflows, link-check scripts, or automated build pipelines):
1. **Do NOT open a public GitHub issue.**
2. Report the vulnerability privately via [GitHub Security Advisories](https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026/security/advisories/new).
3. Provide sufficient detail to help us reproduce and address the issue promptly.
---
## 3. Vulnerabilities in Listed Third-Party Tools
This repository is a curated collection of third-party open-source projects, frameworks, and commercial services.
- **Upstream Vulnerabilities**: Standard security flaws in third-party software should be reported directly to the **upstream project maintainers** following their respective security policies.
- **When to Inform Us**: If an upstream vulnerability is unpatched, actively exploited, or poses a severe risk to users running the tool, please inform us so we can temporarily mark the entry with a warning badge or remove it until resolved.
---
## 4. Response Timeline & Triage
| Priority | Issue Type | Initial Response | Resolution Target |
|---|---|---|---|
| 🚨 **Critical** | Malware / Supply-chain attack / Hijacked URL | < 12 hours | Immediate removal (< 24h) |
| ⚠️ **High** | Unpatched critical CVE in listed tool / Suspicious package | < 24 hours | 48 hours |
| ℹ️ **Medium** | Repository security workflow / Script flaw | < 48 hours | 7 days |
---
## 5. Web Application & Frontend Security
The AI Agent Registry web app is designed for secure, offline-capable static deployment:
- **Static Data Compilation**: The application queries local JSON datasets (`data/resources.json`, `data/categories.json`) compiled at build time by `scripts/build-data.js`. No remote API data fetching or dynamic script execution occurs at runtime.
- **External Link Security**: All external resource links in UI cards and detail views strictly enforce `target="_blank" rel="noopener noreferrer"` to mitigate cross-site tabnabbing risks.
- **Client-Side Query Safety**: Search input and URL query parameters are processed purely in client-side memory without dynamic string evaluation or remote SQL injection vectors.
---
## 6. Security Best Practices for Users
When installing and running AI agent tools:
- **Inspect Code Before Running**: AI agents frequently run shell commands or execute generated code. Run untrusted agents inside isolated sandboxes, containers (Docker, Devcontainers), or VM environments.
- **Protect Secrets**: Never hardcode API keys in repositories. Use scoped environment variables and secret managers.
- **Verify Sources**: Ensure you download packages from official, verified package repositories and check release signatures where available.
---
*Thank you for helping keep the AI agent community safe.*
upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/catalogue/data/categories.json
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---
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"count": 11
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{
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"count": 9
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"slug": "industry-specific-agents",
"count": 21
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{
"name": "Learning Resources",
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"count": 21
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"count": 13
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"count": 12
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"count": 12
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{
"name": "Creative AI Agents",
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"count": 29
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{
"name": "Customer Support and CRM Agents",
"slug": "customer-support-and-crm-agents",
"count": 26
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{
"name": "Voice Agent Platforms",
"slug": "voice-agent-platforms",
"count": 15
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{
"name": "Deep Research Agents",
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"count": 10
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{
"name": "Prompt-to-App Builders",
"slug": "prompt-to-app-builders",
"count": 8
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{
"name": "Multi-Agent Consumer Platforms",
"slug": "multi-agent-consumer-platforms",
"count": 12
},
{
"name": "Open-Source Models for Agents",
"slug": "open-source-models-for-agents",
"count": 16
},
{
"name": "Market Stats and Benchmarks 2026",
"slug": "market-stats-and-benchmarks-2026",
"count": 2
},
{
"name": "Local and Self-Hosted AI",
"slug": "local-and-self-hosted-ai",
"count": 11
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{
"name": "AI Governance and Compliance",
"slug": "ai-governance-and-compliance",
"count": 6
},
{
"name": "Cybersecurity Agents",
"slug": "cybersecurity-agents",
"count": 4
},
{
"name": "Healthcare and Therapy Agents",
"slug": "healthcare-and-therapy-agents",
"count": 10
},
{
"name": "Translation and Localization Agents",
"slug": "translation-and-localization-agents",
"count": 1
},
{
"name": "Newsletters and Communities",
"slug": "newsletters-and-communities",
"count": 10
}
]
}
upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/catalogue/data/resources.json
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---
version: '3.8'
services:
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dockerfile: Dockerfile
container_name: awesome-ai-agents-registry
restart: unless-stopped
ports:
- "3000:3000"
environment:
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- PORT=3000
upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/catalogue/package-lock.json
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title: "Package"
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{
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upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/catalogue/public/robots.txt
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# Robots.txt for Awesome AI Agents Registry
User-agent: *
Allow: /
upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/catalogue/tsconfig.json
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upstream/ARUNAGIRINATHAN-K-awesome-ai-agents-2026/catalogue/vercel.json
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{
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