Agent Skills & Prompts

Programming — not prompting — LLMs

Declarative framework from Stanford University for programming language models rather than prompting them. DSPy treats LLM interactions as programmable modules with input-output signatures and uses optimization algorithms to automatically compile these modules into effective prompts or fine-tuned weights, replacing brittle prompt strings with structured, modular AI software.

LangChain-powered agent harness with planning and subagents

Deep Agents is a production-ready agent framework built on LangChain and LangGraph for complex agentic workflows. It features a planning system for task decomposition, a filesystem backend for persistent operations, sandboxed shell execution, and isolated subagents with independent context windows. Automatic context summarization keeps agents coherent across long sessions, while smart defaults simplify prompt engineering for multi-step autonomous tasks.

ByteDance's open-source agentic workflow platform

DeerFlow is ByteDance's open-source agentic workflow platform that combines LLM-powered agents with deep research capabilities. It features multi-agent orchestration, web research automation, report generation, and podcast creation from research outputs. Built with LangGraph, it supports configurable LLM backends and provides a modern React-based UI for managing complex AI-driven research and content workflows.

Swiss-army MCP server for local system automation

DesktopCommanderMCP is a general-purpose MCP server that gives AI agents the ability to execute local programs, read and write files, search the filesystem, and edit text files. It acts as a comprehensive local system actuator, enabling coding agents to interact with the development environment beyond just code editing.

Prompt engineering framework treating prompts as versioned Python functions

Ell is a prompt engineering library that treats LLM prompts as versioned, testable Python functions rather than opaque strings. Built by ex-OpenAI researcher William Guss, it provides automatic prompt versioning with content-addressable hashing, a local TensorBoard-like studio for visualizing prompt evolution, and structured output support via Pydantic. 5,800+ GitHub stars, MIT licensed. Designed for teams who want to version-control and systematically improve their prompts over time.

Code management platform for AI coding agents

Entire is an AI-native code management platform designed for teams managing fleets of AI coding agents. Founded by former GitHub CEO Thomas Dohmke, it records the reasoning and instructions behind AI-generated code alongside the code itself, making agent-written software easier to review, audit, and maintain. Launches with Checkpoints, an open-source CLI tool that logs prompts and token usage for Claude Code and Google Gemini CLI, with more agent integrations planned.

Agent harness performance system with 30+ agents and 136 skills

Everything Claude Code is a comprehensive agent harness performance optimization system providing 30 specialized agents, 136 skills, 60 commands, and automated hook workflows for AI-assisted development. Born from an Anthropic hackathon winner and evolved over 10+ months of intensive daily use, it works across Claude Code, Codex, Cursor, and OpenCode with built-in security scanning via AgentShield, continuous learning, and research-first development patterns.

MCP gateway and integration catalog for AI agents

Executor is an MIT-licensed integration layer and MCP gateway for AI agents. It gives Claude Code, Cursor, Codex, and other MCP-speaking clients one endpoint for connected OpenAPI specs, GraphQL APIs, MCP servers, Google Discovery sources, and custom JavaScript tools, with local, cloud, and self-hosted deployment options for teams centralizing tool access.

Modular AI prompt framework for everyday tasks

Fabric is an open-source framework that organizes AI prompts into reusable patterns for solving everyday tasks like summarizing content, explaining code, extracting insights from videos, and generating social media posts. Written in Go with support for 20+ AI providers including OpenAI, Claude, Gemini, and Ollama, it runs from the command line and can serve as a REST API. With 40,000+ GitHub stars, Fabric bridges the gap between AI capabilities and practical workflow automation.

Zero-config conversion of FastAPI endpoints into MCP tools

FastAPI-MCP automatically converts existing FastAPI endpoints into Model Context Protocol tools with zero configuration. With 11,700+ GitHub stars, it is the most popular MCP integration layer, enabling thousands of FastAPI developers to make their APIs accessible to AI agents like Cursor and Claude through the MCP standard.

Turn a Figma design system into a programmable API for AI assistants

Figma Console MCP is open-source design-system infrastructure that exposes Figma to AI assistants through MCP. It supports structured extraction, design creation and editing, debugging, accessibility audits, and bidirectional design-token workflows across local, remote read-only, and paired cloud modes; it is not a generic one-click Figma-to-code exporter.

Visual workflow builder by ByteDance

FlowGram is an open-source visual workflow and flowchart editor framework developed by ByteDance for building node-based workflow applications. It provides a React-based canvas with drag-and-drop node creation, configurable edge routing, zoom and pan controls, and a plugin architecture for extending functionality. FlowGram supports both fixed layout and free-form canvas modes, making it suitable for building AI agent pipelines, data processing flows, and automation builders.

Natural language scripting for LLM-system interaction

GPTScript is an Apache 2.0 licensed framework with 3,300+ GitHub stars that enables natural language scripting where LLMs interact with local systems, APIs, and tools through simple prompt definitions. It supports multiple model providers including OpenAI-compatible APIs and local models, providing a lightweight approach to building AI agents that can execute CLI commands, call APIs, and process files.

Meta-prompting and context engineering system for Claude Code agents

GSD is a meta-prompting, context engineering, and spec-driven development system designed for Claude Code and compatible AI coding agents. With over 46,000 GitHub stars, it implements a structured four-phase workflow of Discuss, Plan, Execute, and Verify to combat context rot in long AI coding sessions. The system uses multi-agent orchestration with persistent file-based memory.

MCP server for AI-powered reverse engineering

GhidraMCP is an MCP server that enables LLMs to autonomously perform reverse engineering tasks through NSA's Ghidra disassembly framework. It exposes binary analysis capabilities like decompilation, function listing, cross-references, and symbol analysis as MCP tools, letting AI assistants generate malware reports and analyze compiled binaries.

Instant MCP server for any GitHub repository

GitMCP is a free, open-source remote MCP server that transforms any GitHub repository or GitHub Pages site into an AI-accessible documentation hub. Just replace github.com with gitmcp.io in any repo URL to give AI assistants grounded context about that project — eliminating code hallucinations with zero configuration required.

Pull Requests as a Service with AI + developers

GitStart is a YC-backed platform that delivers merge-ready pull requests by combining AI coding agents with human developer oversight. Teams assign sprint-sized tickets and the AI Ticket Studio converts vague requirements into well-scoped specs, then hybrid agents generate production-ready code through a five-stage quality process with a 98% merge rate reported across customer teams.

MCP server directory and gateway

AI gateway and model management platform that provides a unified API for accessing multiple LLM providers (OpenAI, Anthropic, Google, Mistral, and more) through a single endpoint. Features model routing, cost tracking, usage analytics, rate limiting, and API key management across providers. Simplifies multi-provider AI integration by handling authentication, billing, and failover in one place. Useful for teams evaluating multiple models or building provider-agnostic AI applications.

Agent Development Kit by Google

Google's open-source framework for building AI agents with Gemini models. Supports multi-agent orchestration, tool use, and deployment to Vertex AI or Cloud Run. Provides a structured approach to agent development with built-in evaluation, testing, and monitoring capabilities, making it the official path for teams building agent systems within the Google Cloud ecosystem.

MCP server for secure database tooling with AI agents

Google GenAI Toolbox is an open-source MCP server from Google that specializes in easy, fast, and secure database tools for AI agents. It provides structured database access through the Model Context Protocol, enabling agents to query, inspect schemas, and manage data across supported databases with built-in security controls.

First-party MCP server for querying and managing Grafana

Grafana's official Model Context Protocol server, letting AI assistants search dashboards, query datasources, and manage alerts/incidents on your Grafana instance through a standard MCP interface.

Build real-time temporal knowledge graphs for AI agents

Graphiti is an open-source Python framework by Zep for building temporally-aware knowledge graphs for AI agents. It continuously integrates conversations, business data, and external information into queryable graphs with bi-temporal tracking. The hybrid retrieval combines semantic search, BM25 keywords, and graph traversal for sub-300ms queries without LLM calls at retrieval time.

Modular AI agent framework with off-prompt data

Griptape is an open-source Python framework for building AI agents and workflows with a focus on modularity and enterprise-grade off-prompt data handling. It separates predictable pipeline logic from unpredictable LLM interactions, providing structures for sequential and parallel task execution with built-in memory management and tool integration.

NLP and RAG pipeline framework by deepset

Haystack is an open-source AI orchestration framework by deepset for building production-ready LLM applications with explicit control over retrieval, routing, memory, and generation pipelines. Its component-based architecture lets developers chain specialized pieces into branching, looping pipelines for semantic search, RAG, QA, and autonomous agents. Integrates with OpenAI, Anthropic, Mistral, Cohere, Hugging Face, Azure, AWS Bedrock, and major vector stores.

AI-native API gateway by Alibaba with MCP server hosting and LLM routing

Higress is an open-source AI-native API gateway developed by Alibaba that combines traditional API management with LLM-specific capabilities like token-based rate limiting, model routing, prompt caching, and MCP server hosting. Built on Envoy and Istio, it provides enterprise-grade traffic management while natively understanding AI workload patterns including streaming responses, long-lived connections, and multi-model fallback chains.

Agent memory system that learns, not just remembers

Hindsight is an agent memory system that enables AI agents to learn from experience rather than just store conversations. It organizes memories into three biomimetic categories: World knowledge for facts, Experiences for agent events, and Mental Models for learned understanding. The system provides retain, recall, and reflect operations backed by a temporal knowledge graph with parallel retrieval strategies including semantic, keyword, graph traversal, and temporal search.

HubSpot's hosted MCP server for CRM data and workflows.

HubSpot MCP Server is HubSpot's hosted remote MCP server for connecting compatible AI tools to CRM data. It uses OAuth 2.1 with PKCE through MCP auth apps, then exposes HubSpot context such as contacts, companies, deals, tickets, activities, and marketing or content objects according to user permissions, granted scopes, and sensitive-data restrictions in the account.

ACP skill definitions giving coding agents HuggingFace ML superpowers

Hugging Face Skills is the official collection of ACP skill definitions that give AI coding agents access to HuggingFace ML capabilities. The 13 skills cover LLM fine-tuning with TRL, vision model training, dataset management, model evaluation, and cloud job submission on HF infrastructure. Compatible with Claude Code, Codex, Gemini CLI, and Cursor via a single npx command.

Open-source natural-language browser automation framework for AI agents

An AGPL-3.0 TypeScript framework that drives a real browser from natural-language tasks — the self-hostable OSS framework from Hyperbrowser, distinct from the vendor's paid cloud browser product.

Structured LLM outputs with validation

Instructor is the most popular Python library for extracting structured, validated data from large language models, with over 3 million monthly downloads and ports across Python, TypeScript, Go, Ruby, Elixir, and Rust. It uses Pydantic models to define output schemas and automatically handles validation, retries, and error correction when the LLM output does not match. Instructor patches existing client libraries instead of replacing them, preserving full access to the underlying API.

Intercom's remote MCP server for support and Help Center context.

Intercom MCP Server is Intercom's hosted remote MCP server for connecting AI tools to Intercom workspace data. It supports the recommended Streamable HTTP endpoint, OAuth or bearer-token authentication, and tools for conversations, contacts, companies, and Help Center article workflows, with access limited by token permissions and Intercom workspace policy.

BEAM/Elixir-native framework for durable multi-agent systems

Jido is an Elixir-native AI agent framework that leverages the BEAM virtual machine's concurrency and fault-tolerance for building durable, distributed multi-agent systems. It provides primitives for agent lifecycle management, skill composition, and message-based coordination. Designed for teams running Elixir in production who need agent capabilities. Apache-2.0 with 1,600+ GitHub stars.

Open-source post-building layer for agents — tracing, evals, and online monitoring

Judgeval is the open-source post-building layer for AI agents from Judgment Labs, providing OpenTelemetry-based tracing, hosted and custom evaluation scorers, and online behavior monitoring for LLM-powered applications. Instrument any function with a single decorator, score live production traffic against faithfulness and instruction-adherence checks, and feed real-world failures back into reinforcement learning or supervised fine-tuning loops.

JavaScript framework for building and visualizing multi-agent workflows on a Kanban board

KaibanJS is an MIT-licensed JavaScript framework for defining AI agents, tasks, tools, and teams, then orchestrating their work through a Kanban-inspired runtime and visual board. It can run inside Node.js, React, or Next.js projects, supports custom UIs and headless workflows, and provides real-time task-state visibility for multi-agent applications.

Control plane for autonomous AI agents

Keycard is the control plane for autonomous agents, providing identity verification, policy enforcement, and scoped access management. Resolves agent identity, enforces security policies, and issues time-limited resource-specific access tokens. Provides full visibility into every agent action with drift detection, automatic remediation, and integrations with Datadog, Linear, GitHub, and other services for agent-driven incident response and security operations.

MCP integration platform for agent tool use at scale

Klavis AI is an Apache-2.0 MCP integration platform for teams connecting AI agents to external SaaS tools and APIs. The public repo and official docs position it as infrastructure for reliable tool access at scale, so it fits teams that want reusable MCP connectors without treating every integration as a one-off script or custom OAuth maintenance project.

Kotlin-native AI agent framework by JetBrains with MCP support

Koog is JetBrains official Kotlin-native framework for building predictable, fault-tolerant AI agents. It provides structured agent workflows with MCP protocol support, type-safe tool definitions, and deterministic execution patterns designed for JVM production environments. As the first production-grade Kotlin agent framework, it fills the gap in a JVM ecosystem dominated by Python-based alternatives and integrates naturally with existing Kotlin and Java backend infrastructure.

RAG-based document QA with multi-user support and agent reasoning

Kotaemon is an open-source RAG-powered document question-answering interface backed by Cinnamon AI. It supports multi-user workspaces with access controls, advanced retrieval pipelines including hybrid search and knowledge graph extraction, and agentic reasoning for complex multi-step queries. The web UI handles PDFs, Office documents, and images with citations pointing to exact source passages, making it suitable for both individual research and team knowledge management.

Polyglot document intelligence framework with Rust core

Kreuzberg is a polyglot document intelligence framework with a high-performance Rust core that extracts text, metadata, images, and structured data from 91+ file formats. Available for Python, Ruby, Java, Go, PHP, C#, TypeScript, plus CLI, REST API, and MCP server. Features multiple OCR backends (Tesseract, EasyOCR, PaddleOCR), table extraction with structure preservation, and native async support.

Visual framework for building multi-agent AI apps

LangFlow is an open-source visual framework for building multi-agent AI apps with drag-and-drop. Built on LangChain, it lets developers compose chains, agents, and RAG pipelines by connecting modular components visually. Features real-time interaction, Python customization, one-click deployment, and export to LangChain code. Supports all major LLM providers, vector stores, and tools. With 146K+ GitHub stars, it bridges visual prototyping and production deployment.

Multi-agent programming framework inspired by the Actor model

Langroid is a lightweight Python framework from CMU and UW-Madison researchers for building LLM applications using a multi-agent programming paradigm inspired by the Actor Framework. Agents are first-class citizens that encapsulate LLM state, vector stores, and tools, then collaborate via message passing through hierarchical task delegation. With 3,900+ GitHub stars, Langroid works with any LLM provider and does not depend on LangChain or other frameworks.

Sentry-style observability for AI agent conversations

Latitude is an agent observability platform for teams that need to inspect LLM traces, conversations, issues, and evaluation feedback in one workflow. Its public repo and docs position it as a Sentry-style monitor for AI agents, with semantic search, issue detection, annotations, MCP-assisted fixes, and cloud or self-hosted deployment paths for production debugging.

Twelve-lesson curriculum for building AI coding agents from scratch

Learn Claude Code is a comprehensive educational framework with 12 structured lessons teaching AI coding agent construction from first principles. With over 46,000 GitHub stars and 7,000 forks, it provides complete runnable Python implementations progressing from a 50-line bash agent to a 550-line skills agent with modular extensibility and subagent orchestration.

Memory-first coding agent that learns over time

Letta Code is a memory-first CLI coding agent that persists across sessions and improves over time. Unlike stateless tools like Claude Code or Codex, it works with long-lived agents remembering your codebase, preferences, and past interactions. Model-agnostic supporting Claude, GPT, Gemini, and more. Features git-backed context repositories, skill learning from experience, sleep-time reflection, and subagent orchestration. #1 model-agnostic OSS harness on Terminal-Bench.

Open-source real-time voice, video, and AI agent infrastructure

LiveKit is an open-source WebRTC infrastructure platform for building real-time voice, video, and data applications. It powers ChatGPT Advanced Voice Mode and serves as the foundation for AI agent voice interactions. LiveKit provides server-side SDKs for Go, Python, Node.js, and Rust alongside client SDKs for every major platform. The Agents framework lets developers build voice-enabled AI assistants with speech-to-text, LLM processing, and text-to-speech pipelines in Python or Node.js.

Community self-hosted MCP server for Jira and Confluence (Python)

MCP Atlassian is a community-built, self-hosted Python MCP server (sooperset/mcp-atlassian) that connects AI coding agents to Jira and Confluence using API tokens or personal access tokens. It runs on your own infrastructure and supports both Cloud and Server/Data Center deployments — a flexible alternative to Atlassian's official remote MCP server when you need on-prem control, custom auth, or self-managed configuration.

IBM-backed ContextForge gateway for federating MCP, A2A, REST, and gRPC APIs

MCP Context Forge is IBM’s Apache-2.0 ContextForge project for operating a gateway, registry, and proxy across MCP servers, A2A agents, REST APIs, and gRPC services. It centralizes discovery, authentication, policy controls, federation, and observability, with deployment paths through PyPI, Docker, and Kubernetes.

Official debugger for MCP server development

MCP Inspector is the official interactive developer tool from the Model Context Protocol team for testing, debugging, and validating MCP servers. It provides a visual interface to inspect available tools, test transport configurations, export configs for different clients, and verify protocol compliance during MCP server development.