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TOPIC GUIDE

Agent frameworks

Compare starting points for agents in Python and TypeScript, with explicit tool and output boundaries.

products
7
things to compare
3
updated
2026-09-28

An agent framework coordinates a model with tools and application state. A useful evaluation starts with one bounded workflow and clear permissions. Framework features only matter when they support that workflow and its failure cases.

What to compare

  1. Define what the agent may read, change and delegate.
  2. Test invalid tool inputs and unavailable tools, not only a successful demo.
  3. Separate application state, model output and durable execution requirements.

This is a focused selection, not an exhaustive ranking. See each profile for evidence and review scope.

Projects to explore

LangChain

A Python framework for composing model calls, tools, and agent behavior through a configurable agent harness.

Read the project profile

Pydantic AI

A Python agent framework that uses typed inputs and outputs to connect model behavior with application code.

Read the project profile

LangChain.js

A JavaScript and TypeScript framework for building agents with model integrations, tools, and configurable behavior.

Read the project profile

Eliza

An open-source TypeScript agent framework and product stack with a runtime, applications, and plugins.

Read the project profile

Agno

An open-source agent SDK plus AgentOS, a runtime for building, deploying and improving agents in your own cloud.

Read the project profile

smolagents

Hugging Face's minimal open-source Python library for building agents, with first-class code agents that write their actions as code.

Read the project profile

Stagehand

Browserbase's open-source SDK for browser agents, adding natural-language act, extract and observe to a Playwright-style API in TypeScript, Python and Go.

Read the project profile

Go a little deeper

Pydantic AI vs LangChain for structured output

A documentation-based comparison for Python developers choosing a typed agent interface or a configurable integration framework.

Read the guide

How to evaluate an AI tool before adding it to your stack

A practical checklist for comparing AI tools by task, evidence, failure behavior and maintenance cost.

Read the guide