Every AI Agent Framework.
One Page. Our Honest Take.
We build AI agents for a living. This is the reference we wish existed when we started — no hype, no affiliate links, just what works and when to use it.
Filter by your language or use case. Each entry has our take on when it shines and when to pick something else.
9 frameworks
LangGraph
by LangChainGraph-based agent workflows with state management, streaming, and human-in-the-loop support. Pairs with LangSmith for observability.
Rock-solid for complex multi-step workflows. The learning curve pays off at scale.
OpenAI Agents SDK
by OpenAIThe production successor to Swarm. Minimal abstractions — agents, tools, and handoffs. Fastest path to shipping if you're already on OpenAI.
Great starting point. We often prototype here, then move to LangGraph or CrewAI as complexity grows.
CrewAI
by CrewAI IncRole-based agent teams. Define agents by role (researcher, writer, analyst), give them tools, and let them collaborate.
Fastest time-to-demo for clients. The role metaphor clicks immediately in business conversations.
Google ADK
by GoogleGoogle's agent framework with native Vertex AI integration. Supports Gemini and 20+ other models via LiteLLM. Rich tool ecosystem including MCP.
Still new (Apr 2025), but the tool interop is impressive. You can use LangChain tools, LlamaIndex, even other agents as tools.
Microsoft Agent Framework
by MicrosoftAutoGen + Semantic Kernel unified into one framework. Azure-native with the enterprise governance features large orgs actually need. GA Q1 2026.
The conversational agent pattern — agents talking to each other — is genuinely useful for complex reasoning tasks.
Dify
by LangGeniusFull visual platform for AI workflows. Drag-and-drop pipeline editor, built-in RAG, prompt management, and agent orchestration. Self-hostable.
Unbeatable for demos and MVPs. We use it to prototype before building custom.
smolagents
by HuggingFaceAgents that think in code. Minimal abstractions, very Pythonic. If your team lives in notebooks and prefers writing code over configuring YAML, this is it.
Delightfully simple. The 'agents write code' paradigm feels natural for technical users.
Mastra
by Mastra AIThe TypeScript-native agent framework. First-class option if your backend is Node.js and you don't want to maintain a Python service just for agents.
Refreshing to see a first-class TS framework. The ecosystem is smaller but growing fast.
OpenClaw
by Open SourceFull agent runtime with workspace management, multi-channel messaging (Telegram, Discord, WhatsApp, Slack), skill system, and sub-agent orchestration.
We run our entire operation on it. It's what powers this page being written.
Persona & Skill Libraries
Pre-built agent personas and design skills. Don't start from scratch.
Agency Agents
51 personas51 pre-built agent personas — frontend dev, backend dev, security engineer, growth hacker, community manager, and more. Drop-in SOUL.md files compatible with OpenClaw, Claude Code, and similar workspace-based tools.
Great starting point for persona design. Browse even if you don't use them directly.GitHub →
Impeccable
18 commandsFrontend design skill for AI coding tools. Commands like /distill (simplify UI), /colorize (brand colors), /animate, and /delight. Makes vibe-coded UIs look intentional instead of generic.
Install this if you're using Claude Code or Cursor for frontend work. The difference is noticeable.GitHub →
Google ADK Skills
GoogleDevelopment skills covering APIs, coding patterns, deployment, and evaluation. Works with Gemini CLI, Claude Code, and Cursor.
Useful reference for structuring your own dev skills.GitHub →
Testing, Security & Memory
The tooling that keeps agents from going off the rails.
PromptFoo
Unit testing + red teaming for LLM prompts and agents. Compare prompts across models, run automated security scans, and integrate with CI/CD. Acquired by OpenAI in Mar 2026 — stays open source.
npx promptfoo@latest initEvery production AI app should run PromptFoo before shipping. We use it for our own agents.
OpenViking
ByteDance's context database for agents. Tiered loading (L0/L1/L2) dramatically reduces token consumption. Filesystem-based with auto-compression and self-evolving memory.
Watch this one. The tiered loading concept is the right direction for cost-conscious agent deployments.
Mem0
Memory layer for AI agents. Persistent memory across sessions, user preference tracking, and conversation history management.
Useful if your agent needs to remember users across conversations without building your own memory system.
Quick Picks
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Maintained by GTA Labs · Toronto · Updated March 2026
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