Pick two or three frameworks and see stars, downloads, capabilities, and trade-offs side by side. Share the link to revisit the comparison anytime.
Type-safe agents with structured outputs from the Pydantic team.
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Comparing agent frameworks is the process of evaluating two or three competing libraries side by side on live community signals, technical capabilities, language, license, and trade-offs so a team can pick the one that fits its stack and roadmap.
Start with the constraints that are not negotiable: the language your team already ships in, the license your legal team will sign off on, and the deployment model you can support. A framework that fails any of those three is not really an option, no matter how popular it is.
Then look at live signals: GitHub stars and contributors show whether the project is gathering momentum, npm and PyPI downloads show whether teams are actually shipping with it, and last-commit date shows whether the maintainers are still around. Capability flags like multi-agent, streaming, tool use, human-in-the-loop, memory, and evaluations narrow the field to frameworks that match your job-to-be-done.
Finish by reading the strengths and trade-offs columns side by side. The smallest framework that covers your real requirements almost always beats the most popular one. Copy the share link once you have a comparison you trust so you can revisit it during planning.
Decisions about agent frameworks rarely happen in isolation. Pair this comparator with the directories and benchmarks that ground the rest of the stack.
Directory
The full directory with live stars, downloads, capability filters, and the find-my-framework quiz.
AI Models
Open and closed model rankings with benchmarks, context windows, modalities, and live API prices.
Start with the language your team already ships in, then narrow by license and deployment model. Use live signals like GitHub stars, contributors, last commit, and npm or PyPI downloads to see which projects are healthy. Finally, line up capability flags such as multi-agent, streaming, tool use, memory, and evaluations to see which framework fits the job you actually need to do.
You can compare up to three frameworks side by side in this tool. Three is the sweet spot: it forces a real decision while still showing meaningful contrast on metrics, capabilities, strengths, and trade-offs without overwhelming the matrix.
GitHub stars and forks measure community interest. Contributors and last-commit date show maintainer health. npm weekly and PyPI monthly downloads show real adoption by teams shipping production code. Capability flags such as multi-agent, streaming, tool use, human-in-the-loop, memory, tracing, and evaluations describe what the framework gives you out of the box.
Stars, downloads, contributors, and commit timestamps refresh on a daily cron against GitHub, npm, and PyPI. Capability flags are editorial and reviewed monthly. Trend arrows show the change since the last sync, so a fast-moving framework looks different from a coasting one even when raw star counts look similar.
Benchmarks
A library of agent and model benchmarks with leaderboards, definitions, and what each test actually measures.
Yes. Every comparison has a permanent, shareable URL. Slugs are sorted alphabetically in the canonical link so the order you click frameworks does not create duplicate URLs. Send the link to your team, drop it into a planning doc, or open it during a stack-decision meeting and the same view will load every time.
No. Stars are a popularity signal, not a fit signal. Many high-star frameworks were built for use cases that do not match yours. The smallest framework that covers your real requirements, in the language your team writes, almost always beats the most popular one. Use stars to filter out abandoned projects, not to pick a winner.
Self-hosted open-source frameworks win on cost, portability, and how deep your team can go on tuning. Managed cloud services win on time to first demo and on built-in observability. Most production teams end up self-hosting an open framework and adding a hosted evaluation or tracing tool on top. The capability filters on the directory page let you split the field by hosting model.
This tool pulls live metrics from GitHub, npm, and PyPI on a daily schedule, lines up capability flags we maintain editorially, and lets you compare up to three frameworks in a single permanent URL. Most other lists are static blog posts that go stale within a quarter. Treat this as a working tool, not a one-time read.
