This　paper　proposes　an　analyzable　agent-based　route　choice　modeling　framework　with　good　theoretical　properties.　This　modeling　framework　allows　heterogeneous　individual　learning　rules　and　learning　rates.　As　long　as　travelers＇　route　choice　behaviors　conform　to　the　framework,　even　though　their　learning　rules　and　learning　rates　are　heterogeneous,　the　network　flows　can　be　proven　to　be　with　asymptotically　stable　fixed　points.　An　approximation　for　network　flow　distribution　is　proposed　from　the　perspective　of　the　stochastic　process.　Some　phenomena　observed　in　laboratory　experiments　are　well　captured　by　the　agent-based　framework.　Many　existing　network-level　day-to-day　dynamic　models　can　be　regarded　as　special　cases　of　the　framework　by　setting　the　concrete　learning　rules　and　learning　rates　of　the　agents.　Numerical　simulations　are　used　to　show　model　properties.　This　study　can　deepen　our　understanding　of　the　behavioral　mechanism　of　individual-level　day-to-day　route　choice　and　network-level　day-to-day　traffic　flow　dynamics.