Every Agent Framework Worth Knowing in 2026, Compared

The market for AI agent frameworks has gone from “a couple of options” to “genuinely confusing” in about six months. Every major AI lab now ships its own SDK for building agents. Independent projects are stacking up stars on GitHub. And the differences between them aren’t always obvious from their landing pages.

This is a practical guide to the frameworks people are actually using to build custom agent applications in early 2026. What each one does, what makes it different, and who it’s built for.

PI

Maker: Mario Zechner (creator of libGDX) Language: TypeScript GitHub: badlogic/pi-mono (26.7k stars) License: Open source Install: npm install -g @mariozechner/pi-coding-agent

PI is the anti-framework. Built by a game engine developer who got frustrated with Claude Code’s complexity, it strips the coding agent concept down to almost nothing. The system prompt is under 1,000 tokens. There are exactly four tools: read, write, edit, and bash. No built-in todos, no plan mode, no MCP, no sub-agents.

The philosophy is blunt: any agent that can write and execute code already has unrestricted access to your system, so layered permission dialogs are theater. PI skips them and gives you full observability instead.

The monorepo (pi-mono) splits into packages. pi-ai is a unified LLM API that talks to Anthropic, OpenAI, Google, xAI, Groq, Cerebras, OpenRouter, and any OpenAI-compatible endpoint. pi-agent-core handles the agent loop. pi-tui renders a terminal UI with differential rendering (only redraws changed lines). pi-coding-agent ties it all together with session management and theming.

Model switching happens mid-session. You can define custom model configs for self-hosted setups like Ollama, llama.cpp, vLLM, or LM Studio. Extensions, skills, and themes are packaged via npm or git, and PI can consume Claude Code and Codex CLI skill formats.

There’s already a notable fork: oh-my-pi by Can Boluk, which adds hash-anchored edits, LSP integration, Python support, browser tools, subagents, and a memory system that extracts durable knowledge from past sessions.

Best for: Developers who want a transparent, hackable coding agent they control completely. If you’ve used Claude Code and wished you could see exactly what it’s doing and strip out everything you don’t need, this is the tool.

Claude Agent SDK (Anthropic)

Maker: Anthropic Language: Python, TypeScript License: Anthropic Commercial Terms of Service

The Claude Agent SDK gives you the same infrastructure that powers Claude Code as a callable library. You get the agent loop, the tool execution model, the context management, and the built-in tools (Read, Write, Edit, Bash, Glob, Grep, WebSearch, WebFetch), all programmable from your own application.

The SDK started life as the “Claude Code SDK” and was renamed in September 2025 to reflect its broader scope. The core pitch: instead of implementing a tool loop yourself, Claude handles tool execution autonomously. You describe what you want done, the SDK manages the rest.

Key capabilities include subagents for parallelization and context isolation, a hooks system for intercepting tool calls (PreToolUse, PostToolUse, Stop, SessionStart), MCP server integration, and permission controls that let you allow, block, or require approval for specific tools. Sessions support resume and fork, and long-running operations get automatic context compaction.

Authentication works through Anthropic API keys, Amazon Bedrock, Google Vertex AI, or Microsoft Azure AI Foundry.

Best for: Teams already invested in Claude who want to build production agent applications using the same battle-tested infrastructure Anthropic uses internally. The hooks system is particularly strong for adding validation, logging, and safety checks.

OpenAI Agents SDK

Maker: OpenAI Language: Python (TypeScript planned) GitHub: openai/openai-agents-python (20.2k stars) License: MIT

OpenAI’s Agents SDK is the production-ready successor to Swarm, the experimental multi-agent project from late 2024. It launched March 2025 alongside the Responses API.

The framework has three core primitives: Agents (an LLM with instructions and tools), Handoffs (delegation between agents), and Guardrails (input/output validation). That’s the whole mental model. OpenAI explicitly positioned it as having “enough features to be worth using, but few enough to make it quick to learn.”

Despite being OpenAI’s SDK, it works with other providers too. Any LLM with a Chat Completions-compatible endpoint will work, which in practice means 100+ models. Other features include function tools with automatic schema generation and Pydantic validation, MCP server integration, tracing and observability, persistent sessions, human-in-the-loop mechanisms, and voice/realtime agent support via gpt-realtime-1.5.

Best for: Python developers who want a clean, minimal multi-agent framework. The Handoffs primitive makes it unusually simple to build systems where specialized agents pass work between each other.

Google ADK (Agent Development Kit)

Maker: Google Language: Python, TypeScript, Java, Go GitHub: google/adk-python (18.5k stars) License: Apache-2.0

Google’s ADK launched at Cloud NEXT in April 2025. It’s the same framework powering agents inside Google products like Agentspace and Google Customer Engagement Suite, open-sourced for external developers.

The standout feature is multiple agent types. LLM agents handle reasoning tasks. Workflow agents provide deterministic control with Sequential, Parallel, and Loop patterns. You can mix both in the same system. The tool ecosystem is broad: pre-built tools (Search, Code Execution), custom functions, OpenAPI specs, MCP tools, and an “agent-as-tool” pattern where one agent becomes a tool for another.

ADK integrates deeply with Google’s stack (Gemini models, Vertex AI, Agent Engine) but remains model-agnostic. It includes a built-in evaluation framework, artifact management for file handling, and a development UI for testing. Deployment targets include Vertex AI Agent Engine, Cloud Run, and GKE.

Best for: Teams building on Google Cloud who want tight integration with Gemini and Vertex AI. The workflow agent types are useful when you need deterministic control flow alongside LLM reasoning.

LangGraph (LangChain)

Maker: LangChain Inc. Language: Python, TypeScript GitHub: langchain-ai/langgraph (27.1k stars) License: MIT

LangGraph is LangChain’s dedicated agent orchestration layer. Instead of letting the LLM figure out routing, you model agents as nodes and edges in a directed graph with persistent state. Both LangChain and LangGraph hit 1.0 alpha on September 2, 2025, with a commitment to no breaking changes until 2.0.

The graph-based model gives you explicit control over agent flow, branching, and cycles. Agents are durable: they persist through failures and resume from exactly where they stopped. You can inspect and modify agent state mid-execution (human-in-the-loop), use time-travel debugging to replay past states, and checkpoint progress.

In July 2025, LangChain shipped “Deep Agents” on top of LangGraph, adding planning tools, filesystem backends, and subagent spawning. The framework integrates with LangSmith for observability and evaluation.

LangChain claims 57% of survey respondents have agents in production. Users include Uber, LinkedIn, Klarna, Replit, and Elastic.

Best for: Teams that need fine-grained control over complex agent workflows. The graph abstraction is a good fit when your agent needs to follow specific decision trees with branching logic, rather than free-form reasoning.

CrewAI

Maker: Joao Moura Language: Python GitHub: crewAIInc/crewAI (46.8k stars) License: MIT

CrewAI models agents as team members with distinct roles. You assign a Manager, a Researcher, a Writer, and they delegate, question, and collaborate like colleagues. The framework raised $18M (Series A from Insight Partners) in October 2024 and claims adoption by nearly half of the Fortune 500.

There are two orchestration modes: Crews (autonomous agent teams that work together) and Flows (event-driven, deterministic workflows). Tasks can run sequentially, in parallel, or conditionally. Agents have memory, so they learn from past interactions. CrewAI ships hundreds of built-in tools and supports agent delegation and self-iteration.

CrewAI Enterprise adds a no-code builder, monitoring, alerting, and security controls. The open-source version claims 10M+ agents executed per month.

Best for: Teams building multi-agent systems where the role metaphor fits naturally. If your use case involves distinct specialists (researcher, analyst, writer) collaborating on a deliverable, CrewAI’s abstractions map cleanly onto that pattern.

Mastra

Maker: Gatsby team (Kyle Mathews and team) Language: TypeScript GitHub: mastra-ai/mastra (22.2k stars) License: Apache-2.0

Mastra is the TypeScript-native answer to the Python-dominated agent framework space. Built by the team behind Gatsby, it hit v1.0 in January 2026 and raised $13M from Y Combinator and others. It’s the third-fastest-growing JavaScript framework ever measured by npm, with 1.77M+ monthly downloads.

The framework covers agents, graph-based workflow orchestration, RAG (with built-in web scraping and vector database support), short-term and long-term memory, MCP server authoring, human-in-the-loop with state persistence, and evaluation tools. It works with 81 LLM providers and 2,436+ models through Vercel AI SDK integration.

Production users include Replit, SoftBank, PayPal, Adobe, and Docker.

Best for: TypeScript/JavaScript developers who don’t want to context-switch into Python. If your stack is Node, Next.js, or React, Mastra fits without friction.

Microsoft Agent Framework

Maker: Microsoft Language: Python, .NET/C# GitHub: microsoft/autogen (56k+ stars) License: MIT

This is the successor to AutoGen, which is now in maintenance mode (bug fixes only, no new features). Microsoft merged AutoGen’s simple agent abstractions with Semantic Kernel’s enterprise features into a unified framework. Public preview launched October 2025, targeting 1.0 GA by end of Q1 2026.

Two orchestration modes: Agent Orchestration (LLM-driven, creative reasoning) and Workflow Orchestration (deterministic business logic). Features include graph-based multi-agent workflows, checkpointing and durability, MCP support, and AutoGen Studio (a no-code GUI for building agents).

Best for: Enterprise teams on the Microsoft stack, particularly those using Azure and .NET. The Semantic Kernel integration gives you session-based state management, type safety, middleware, and telemetry out of the box.

More Frameworks Worth Watching

Smolagents (Hugging Face)

GitHub (26.2k stars, Apache-2.0)

Smolagents takes a different approach: instead of tool-calling via JSON, agents write their actions as executable Python code. The core logic fits in about 1,000 lines. Sandboxed execution runs through E2B, Docker, or WebAssembly. It’s model-agnostic and modality-agnostic (text, vision, video, audio). You can share and pull tools via Hugging Face Hub.

Agno (formerly Phidata)

GitHub (38.8k stars, Apache-2.0)

Agno rebranded from Phidata and positions itself on raw performance, claiming 5,000x faster agent instantiation and 50x lower memory consumption than LangGraph. It’s a Python framework with a FastAPI runtime for horizontal scaling, 100+ integrations, and an AgentOS control plane. Good for teams where latency and throughput are primary concerns.

Pydantic AI

GitHub (15.7k stars, MIT)

Built by the Pydantic team (whose validators power the Python SDKs for OpenAI, Anthropic, and Google). Type safety is the headline feature: the framework is designed to give IDEs and AI coding tools maximum context for autocomplete and type checking. Supports 15+ providers and integrates with Pydantic Logfire for OpenTelemetry observability. Also has a CLI called clai with an optional web UI.

Vercel AI SDK

GitHub (22.9k stars)

The most-downloaded TypeScript AI toolkit (20M+ monthly downloads). AI SDK 6, shipped in 2026, added first-class Agent abstractions on top of the existing tool execution and streaming infrastructure. Includes DevTools for inspecting LLM calls, full MCP support, and AI Elements (pre-built UI components for agent interfaces). Works with React, Next.js, Svelte, Vue, and Angular.

How to Choose

The decision usually comes down to three questions:

What language does your team write? If TypeScript, your realistic options are Mastra, Vercel AI SDK, PI, and the Claude Agent SDK’s TypeScript variant. If Python, you have everything. If .NET, Microsoft Agent Framework is the clear pick.

Do you need multi-agent coordination? CrewAI, LangGraph, and Microsoft Agent Framework are built for systems where multiple specialized agents collaborate. The others are primarily single-agent frameworks with varying levels of orchestration support.

How much control do you want? PI gives you complete transparency and four tools. The official SDKs from Anthropic, OpenAI, and Google give you managed agent loops with safety rails. LangGraph gives you explicit graph-based control. CrewAI and Mastra give you higher-level abstractions. There’s no right answer, just tradeoffs between simplicity and convenience.

Pick based on your existing stack and the complexity of what you’re building. For a single coding agent, PI or Claude Agent SDK. For multi-agent workflows, CrewAI or LangGraph. For TypeScript-native development, Mastra. For Google Cloud integration, ADK. For minimal Python agents, OpenAI Agents SDK or Smolagents.

Sources:

PI