diff --git a/docs/cassettes/multi-agent-multi-turn-convo_161e0cf1-d13a-4026-8f89-bdab67d1ad4d.msgpack.zlib b/docs/cassettes/multi-agent-multi-turn-convo_161e0cf1-d13a-4026-8f89-bdab67d1ad4d.msgpack.zlib
new file mode 100644
index 000000000..25b20565b
--- /dev/null
+++ b/docs/cassettes/multi-agent-multi-turn-convo_161e0cf1-d13a-4026-8f89-bdab67d1ad4d.msgpack.zlib
@@ -0,0 +1 @@
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jeD/4/wh16AAZOkCGfwDI4MTktzPIwPtIQQY2l836eEAGJ2G7gwxcBEYFTjBTCDPFKFvIYyEsvojD4/D4XAnClgg+CpABFQn5gnYEGRLaPsLg51M9xmzuo725P8FxmF9oZ3q8UTGHN5w1ZrJsa0rDKU88rCzfxWrNtOZH/pcudSlJuzV0/y+V9VKXqEA32g42y2iH19lslUS5Zcac5S+Przp68d7i+vUTwnt9gRxz3WpzbvO9V1OfN5iF/DLu1PnguCR76UpupXmOq1fJJg2nsn60V2Lq2YJO9t9duIwtuunusfCJcotZYnnxY+Xm6asCB3xrVvSSRgssPcq4KCnoxx7w83Y3i0SoR78DM3onPBZHrFg59O6JS/vv5idbn5M1ZCs46UZMnedaTrpFneuu5bVlnp+trht29dbphuqT322jl+aLn2CvF52r3pyTmr9jrsrbXXswgr+9Z9Gz8zO/7mQ6ctfwl90Xfj7w8TRGwsqiryxC9jT033X4cfWZwxtKTwljAgKmydjX+/xgk33ms7riZc/7TPHN7rOHuVJ6of/y+LAzbgXQtHlCqId0P7Pg6waj9c5jPJd2tTt/0WTl9axV6w51xtimdy9aT9xjV9DH7ZvU2336233e4Lqpair601knV9kcszhl2SMjwXOhfeIWV7ukslkJCTF7FX6TknXnSg5+uqFg6Ux/s8fQ2pyMPL5l82G7r7LuCa4GLlYxKm6cSq4ozH9gtdX48YbTUGkUO+/XF/gVM4uycutjVVt2z7zvl3i7R9KJH3lVh6akQjeW0C3K/G8J0CMTQvv+5DN84KiLQ/yWDC+nn/n2kvqXmwt/HTgHS3TQHCg5es5v6toNh7IeGp/dgN2nFU65Oi/oWf+X6aRiyZsdRKedF1iSrGcbsBf7b/K+R5pXNAZdw7/zLrZ2NS6xLz1Ev+CydzPb8vCkK+PODmWkrUrJ/6m5UwtSsXiUWfijdkYquq3rQCo6kIoOpKIDqfjvQSp+p7dn22eggC08NAiIZGg4FAB6S8iDimYI9P/g9ELpqtcGuB5EhBq4GqxoPCyVoy10oDOVgVBuWTghAXEIjAqBph7IBSVIH7kgAMFy30ZjS5P8u/SRqIB+IPLRaBIEttqWhEBhlYAzrt5+wLmgSpOAFxpJxRgQgcrhGP0lECHXxIBaT6VQFNxygn7HRu/96/i/F01U1QGrBTJBuRMTIPJhld5+ev6jOx5C6QCIOgCiDoCoAyD65wFEHC6nfQEiwcf6LpITm/fRAEQ8bvsDRGImW4IwYbaTkM9CxEKYyRVzeDyJGOFzBAjsxPkYACI+FxWJ2/MdF/5bgEj/jMFS/2Jc/yjKSNMhUV7d+aWbePmSfI1dRc50F4eF7ucOpgb2N/bsWWHc+PKbKZ+5zDD53mTs9Zs346yzSmk0zXL2mEMpmfbSL4Ia1zfccWataDhavvjBr1lbjk541ngzPopsSp59jWkTJ7YID7b1spcKIva5TNemn/G+Un6vsvKWjXDrtvwxw4SbEwOPCDadkW1Zapv6o2jNlkdfj7yYZFR2/Q6NNqv814FLeHfqBQvFrz7LEnSx/1S62Ib2XLTJdKqp87j9nywenPntF3nLIkMHlhulfOVp8Wl5t0dF3o9qXny91LN0WdGrn3OTQ9mFDqus8nu8ubO6eduMl8+jdm6fEZmZudv5ecZiqxsH+CKaLCvicI+eU+TX+WE0m/AKi+prr0pK+zSPIr8+MGwud/L8Ym1ww5ahd1UWRgfyhsUETp0mJyMOGvW7MT/gcuRgrknu5CG3D2WnJvZSTtg2qywtMPc6NGpBnvJWw9Ip6wRhohF+IcXbPLwaQ0yeTKq5obv38Hrhs711TFdLDXRg+6VRRW5LayfeOyb+obTfvguBi6fd3+hR7W3mVzPl6EXMIndU1QD1THvLcjsf+eYFq8/OLcwNvdtzsM/LWWsmpp92nme8/P5O4YRLD70G503Xhcyu8zuqSHe+esmoa2Hom4e3C6uObuYXvlnxdHByv6NntPdd74QMri+Y9Kzm2Mt5Owcfqfms5PEg6yBsh+JsnbZ2lNAhLDs9qU/ZxE965i20NDyB0v+V4+K4dsZ1ujPbA9f5K+DK/wLmkRJUb/EXgZ4/eFPEwKPjXZEOVKcD1elAdTpQnXZGdf51uPzpvgAO5VH6VwpbJP0VL/3dVXfgTh240z8Dd+p476kDcfpnIk48pqAdESe2M5P/kSJOAj7343nvicd2av9HkngsLizicCWoE5cHjpZckYjD5jvBElgsRCQc3seAOAm4MMpitiPi9KrtI0nexEChWWFTUG/ng5Yuyj58q6JhdgtSZOW1sw4MTJ454GAiOzXk8ykj7M9CZ+aFyx7EbpH3vyY06zLq6RNdyitOZ+YeJsnM++KQd4Ot5an1joufH67YE9I8965VF4JRW/vkwfM7tTvZNwcV77ISvwi6PP9W+ou05Se75nlkrgnu5bNixXlp+cnyZ0NkBdD2k9mBtuxtg7xXSFbfqxTH7jfPqQxOkvSk/lcVn+bRk8yM3TYJJ58+7mB+6iuLjY1l3U6YNslZA1KXJLmXxtDoJjtXTlv8Y/OQ5O/nYhebhj65HVfl0ive49H6hy+2z3gmaI7RvLj6sDS/+rTPjtdRUU2NuGN94pHIOHe0e9TV7W5zpNuPe3Wd1ZkX/8D81LqxaUH7aF13epkblx4pLf/mTAUJ3+5Betslc4vR/Z6Djy0c7dub0U1hzu8X3C8soYfnyEFjT3dz6Jp8uuKAr1XMULw0sqAkp7fbL4n7h5wc+1BuHrbO5pOU9N5hmScQu5RT66xn2dGKKwY4zHcaV3Xh8IatdRNMMz0vjL0XnBvtyDJ2TVhyImT1GGjNNJNM+2v5JmE161K8pr7uucd3U86iL7s+/MEkOsF3WJBvp+88Y392oP0w5JQu6sSwHNPVR5y00x+aIpMq/FwTRvYfI1P1zS3ISPYUjD/j8dPWKCR3UMrIiVDXddzGsFERe4mRv4xMTHKd47H3GPZqQMV8q85rHK5zFrxojBriS/Y9UBKX38esu3lSjy51a9W5zJuLsqYfPn7+krFlwyItz7HkuvZ5ReOjwif97f3HHd/atLtZ+3Kla9Dux/ZFsZbVcyYNXdo8Z1NmDa664xrfdfXMY+7l3fm7GWuv9nP64cGPspPSRAtW8h3bUN/gi71+7mVVVkI8pveYqm7+vPah8ZK8dJHp4J+N0r95MoRZtet1fUTdjbqAnzyo/3HlREjE2iR0ZICgaf+NYm874sIbVbXmsKVcZLl2E3rPY/ylpIb1G0ZU73Q7JNhpEcJJvXNxtAEyKwQbY6kxjfY//mIE4w==
\ No newline at end of file
diff --git a/docs/cassettes/multi-agent-network_26a0d4df-ff99-40f0-92a8-0b3f2c591040.msgpack.zlib b/docs/cassettes/multi-agent-network_26a0d4df-ff99-40f0-92a8-0b3f2c591040.msgpack.zlib
new file mode 100644
index 000000000..8c7f584d7
--- /dev/null
+++ b/docs/cassettes/multi-agent-network_26a0d4df-ff99-40f0-92a8-0b3f2c591040.msgpack.zlib
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/docs/cassettes/multi-agent-network_68a547d4-0a15-43bd-aeed-c9ba1dfe388f.msgpack.zlib b/docs/cassettes/multi-agent-network_68a547d4-0a15-43bd-aeed-c9ba1dfe388f.msgpack.zlib
new file mode 100644
index 000000000..798718a46
--- /dev/null
+++ b/docs/cassettes/multi-agent-network_68a547d4-0a15-43bd-aeed-c9ba1dfe388f.msgpack.zlib
@@ -0,0 +1 @@
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bWn5Q3fxudfJ7x51EK2bPm/2RXfs/HN5+2664FhaV/wZIZNdJ
\ No newline at end of file
diff --git a/docs/cassettes/review-tool-calls_1b3aa6fc-c7fb-4819-8d7f-ba6057cc4edf.msgpack.zlib b/docs/cassettes/review-tool-calls_1b3aa6fc-c7fb-4819-8d7f-ba6057cc4edf.msgpack.zlib
index 58facdc65..23ed0cf9b 100644
--- a/docs/cassettes/review-tool-calls_1b3aa6fc-c7fb-4819-8d7f-ba6057cc4edf.msgpack.zlib
+++ b/docs/cassettes/review-tool-calls_1b3aa6fc-c7fb-4819-8d7f-ba6057cc4edf.msgpack.zlib
@@ -1 +1 @@
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\ No newline at end of file
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diff --git a/docs/cassettes/review-tool-calls_2561a38f-edb5-4b44-b2d7-6a7b70d2e6b7.msgpack.zlib b/docs/cassettes/review-tool-calls_2561a38f-edb5-4b44-b2d7-6a7b70d2e6b7.msgpack.zlib
index 7b712fabd..5eee7c03b 100644
--- a/docs/cassettes/review-tool-calls_2561a38f-edb5-4b44-b2d7-6a7b70d2e6b7.msgpack.zlib
+++ b/docs/cassettes/review-tool-calls_2561a38f-edb5-4b44-b2d7-6a7b70d2e6b7.msgpack.zlib
@@ -1 +1 @@
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diff --git a/docs/cassettes/review-tool-calls_3f05f8b6-6128-4de5-8884-862fc93f1227.msgpack.zlib b/docs/cassettes/review-tool-calls_3f05f8b6-6128-4de5-8884-862fc93f1227.msgpack.zlib
index 36f20eb2d..1a98b9223 100644
--- a/docs/cassettes/review-tool-calls_3f05f8b6-6128-4de5-8884-862fc93f1227.msgpack.zlib
+++ b/docs/cassettes/review-tool-calls_3f05f8b6-6128-4de5-8884-862fc93f1227.msgpack.zlib
@@ -1 +1 @@
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\ No newline at end of file
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diff --git a/docs/cassettes/review-tool-calls_85e452f8-f33a-4ead-bb4d-7386cdba8edc.msgpack.zlib b/docs/cassettes/review-tool-calls_85e452f8-f33a-4ead-bb4d-7386cdba8edc.msgpack.zlib
deleted file mode 100644
index 378edbb99..000000000
--- a/docs/cassettes/review-tool-calls_85e452f8-f33a-4ead-bb4d-7386cdba8edc.msgpack.zlib
+++ /dev/null
@@ -1 +0,0 @@
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
\ No newline at end of file
diff --git a/docs/cassettes/review-tool-calls_a30d40ad-611d-4ec3-84be-869ea05acb89.msgpack.zlib b/docs/cassettes/review-tool-calls_a30d40ad-611d-4ec3-84be-869ea05acb89.msgpack.zlib
index 84baadf0f..62fed2095 100644
--- a/docs/cassettes/review-tool-calls_a30d40ad-611d-4ec3-84be-869ea05acb89.msgpack.zlib
+++ b/docs/cassettes/review-tool-calls_a30d40ad-611d-4ec3-84be-869ea05acb89.msgpack.zlib
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/docs/cassettes/review-tool-calls_b2f73998-baae-4c00-8a90-f4153e924941.msgpack.zlib b/docs/cassettes/review-tool-calls_b2f73998-baae-4c00-8a90-f4153e924941.msgpack.zlib
new file mode 100644
index 000000000..9498dc2b0
--- /dev/null
+++ b/docs/cassettes/review-tool-calls_b2f73998-baae-4c00-8a90-f4153e924941.msgpack.zlib
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/docs/cassettes/review-tool-calls_d57d5131-7912-4216-aa87-b7272507fa51.msgpack.zlib b/docs/cassettes/review-tool-calls_d57d5131-7912-4216-aa87-b7272507fa51.msgpack.zlib
index 27999e5d4..5e6db5f2f 100644
--- a/docs/cassettes/review-tool-calls_d57d5131-7912-4216-aa87-b7272507fa51.msgpack.zlib
+++ b/docs/cassettes/review-tool-calls_d57d5131-7912-4216-aa87-b7272507fa51.msgpack.zlib
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/docs/cassettes/review-tool-calls_df4a9900-d953-4465-b8af-bd2858cb63ea.msgpack.zlib b/docs/cassettes/review-tool-calls_df4a9900-d953-4465-b8af-bd2858cb63ea.msgpack.zlib
deleted file mode 100644
index 1fc1b90cd..000000000
--- a/docs/cassettes/review-tool-calls_df4a9900-d953-4465-b8af-bd2858cb63ea.msgpack.zlib
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/docs/cassettes/review-tool-calls_ec77831c-e6b8-4903-9146-e098a4b2fda1.msgpack.zlib b/docs/cassettes/review-tool-calls_ec77831c-e6b8-4903-9146-e098a4b2fda1.msgpack.zlib
index ec2404cb5..be2bd2dfa 100644
--- a/docs/cassettes/review-tool-calls_ec77831c-e6b8-4903-9146-e098a4b2fda1.msgpack.zlib
+++ b/docs/cassettes/review-tool-calls_ec77831c-e6b8-4903-9146-e098a4b2fda1.msgpack.zlib
@@ -1 +1 @@
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\ No newline at end of file
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diff --git a/docs/cassettes/review-tool-calls_f9a0d5d4-52ff-49e0-a6f4-41f9a0e844d8.msgpack.zlib b/docs/cassettes/review-tool-calls_f9a0d5d4-52ff-49e0-a6f4-41f9a0e844d8.msgpack.zlib
index 052a69dfb..9df5c981f 100644
--- a/docs/cassettes/review-tool-calls_f9a0d5d4-52ff-49e0-a6f4-41f9a0e844d8.msgpack.zlib
+++ b/docs/cassettes/review-tool-calls_f9a0d5d4-52ff-49e0-a6f4-41f9a0e844d8.msgpack.zlib
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/docs/cassettes/wait-user-input_58eae42d-be32-48da-8d0a-ab64471657d9.msgpack.zlib b/docs/cassettes/wait-user-input_58eae42d-be32-48da-8d0a-ab64471657d9.msgpack.zlib
deleted file mode 100644
index 86f79ac54..000000000
--- a/docs/cassettes/wait-user-input_58eae42d-be32-48da-8d0a-ab64471657d9.msgpack.zlib
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/docs/cassettes/wait-user-input_a9f599b5-1a55-406b-a76b-f52b3ca06975.msgpack.zlib b/docs/cassettes/wait-user-input_a9f599b5-1a55-406b-a76b-f52b3ca06975.msgpack.zlib
index c37cbe052..2b47d6fad 100644
--- a/docs/cassettes/wait-user-input_a9f599b5-1a55-406b-a76b-f52b3ca06975.msgpack.zlib
+++ b/docs/cassettes/wait-user-input_a9f599b5-1a55-406b-a76b-f52b3ca06975.msgpack.zlib
@@ -1 +1 @@
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diff --git a/docs/cassettes/wait-user-input_cfd140f0-a5a6-4697-8115-322242f197b5.msgpack.zlib b/docs/cassettes/wait-user-input_cfd140f0-a5a6-4697-8115-322242f197b5.msgpack.zlib
index b94794151..dd2777e25 100644
--- a/docs/cassettes/wait-user-input_cfd140f0-a5a6-4697-8115-322242f197b5.msgpack.zlib
+++ b/docs/cassettes/wait-user-input_cfd140f0-a5a6-4697-8115-322242f197b5.msgpack.zlib
@@ -1 +1 @@
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diff --git a/docs/cassettes/wait-user-input_f5319e01.msgpack.zlib b/docs/cassettes/wait-user-input_f5319e01.msgpack.zlib
deleted file mode 100644
index a6565ab72..000000000
--- a/docs/cassettes/wait-user-input_f5319e01.msgpack.zlib
+++ /dev/null
@@ -1 +0,0 @@
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+eQOgtSEOnr/6Yj+S85sc7Jn7DG2JTYuCEtZJP62WesAH1HTlyudlm+V74VwmNZ9yJPMOqzoYEIi3gpZ1O2Gj6+NeKe+T3UFGbVy2Pe3YDSmV06/7DThYv698KbgBrDdIv1z2+1+7E16H5TqkRjZrq8eyIlYb8j1vEvekhPjSU2KtPZ+11LM8r1dDxEz65eIvCZYdyW0tiQF3L4oFnooSl9PwPNoxJqMerX4+e/3KqF8VsYvvjg0PXot7bs1CdPDCVCKUqzAU1UbqG7Aje/bNd//+wAhveGeEcF2ufpX0yz7N/3V3o957fn38+AYQYXF5dv+tThnxnind6bFvH1JXx/zERic5G9HV7iU1Cgqk0fDV31BMRMbbaufnBZaLy74iUbBwM3DJlyQSlAGtn8CTJoV63Bw4qF6Xu/RavNpkN85zzFCzrhL0M9YayXs3VDX14HE686MODFLRl6VCNLcT9qKrhAqpYCIRXr2eyYIunw+FWCcIBVnSGIDl4AIUGJnQO9XbPvXIDO58Ve5XtHdUHdPjcgNAjelsssMTOkzYTHmHuMkpzUV3qEe1qWLaCYjcsydEH+rhwaZSX3nAiHOom9MNE6bVFq+zF2lmJDyseRZ4YovHm2i/xNER0jbwypxZ8WeGJks4PuYWPMI9DH91r0IlMHPEI+f9wIoLzJUcrrLgOPplrHF5pQiEH/7ppP3YrwTuvkQLqR1CMWWmjBkP197qB9Wod6V1mlDxaztSlPDy5/hVoMTW7KjtbZimTHBFDPsxpvPFn5t4QXI53XBnQCEUWyuMO5kXnwSXnG2p4LYziSeEUyVbY18q8iNErKZ1F3UeVql95p5gK/c8E6clyJzl7FTVCk8qzz7sH58daFcK57CoQ/Ss4kbbbcZI9TgISG8SqCS1GTYJjg1F3qmqlHRq5tF+A8zfbs5HFsEVdfulkiVXqHa1DkDxDdqnHy7prCV6jm4ArwvD0R0pfFp8OMjuBgBZ27gBPNa2ntwjY8hrs5Yv4Yd+2n4XwIT/0flV2fbhiaBTWeVe4IXfWJV/hty25VHeZExN7D3drfGQaZI+rPzE2yDaDgYHuNGgABZJv78YK/3aF/r/OvP/b2Qt5t28816jTGZ3i2EbjA42IHF1z0iobgEoMEwvfyJR+OcCOEovy8GGol+Aw1EMAIDJv+oZ5v+qnUDLSnv2MfBo06DEWbwUYkNVk3YXLpDU+/hjWKKc4+oRQgKYqFZSTwMHWk3U9jB3j+8pNFKBmBC7hO++QRCwbiz/XbUSR6aWRs2F8ir79UmETa081qAqoz3mHG/3IfGP+xZqpKZfz4305tPfIyyoGAdVicGm/Mq3PZNPgD2R5VnpvaKFRLsiD8INZyJaJ24AGk4+I6o/t8qNyJ8lbsR/h0+R7LGiI2b2wAu3KYlNmOpk7nWdpmOtQ0b8Ir/UC7Umw/IvJT9F+Wfw8rUcA8XNK3t97dppaqZW93pK5uxihCVm6LREu2mY7oPPSzm6m3WbMuC05GzEX/uZvui1j/HluB5Pf9w93h50FZOjwqxYCyu9zl6++pK2NZRxNfBTo7uRP4Tsb602lmxvALkoCeuc8x/rNwBnb80jLhvHJ8imGfni6dNv3kGXb92lmG4AVjLpEUYCL3UXGB3M07yrUI5nBP3vdwrdIpB9zpIFWCmolNXXrmLkhUwtqy624pyV7z7gR8SBrRV5ms9oaK90G6mSBVwlUWIYYrfXy0tT1+rwcwVLZYMnCNgQfOwK3P1rrjs8X2UozrIiqmXxZhhNahw7D/FE89n7MpWsNWSEmrtXUqeFyEqxU1vfNimylY7lb9vlQMKfVgAPhWsWi77t2AjhEa6IgZ7P1KRUTBc1Vxn8fKDkiGodX/7Y+XNrxeYd1VTl9dkhT8xavLPDqUdYTZh3LcsHRdqhF+bZg9uWIuBscOU7CcD96Xkw5rzfQeWClerXt1esZR5bqj1vgolQtTYpng98dGp55XFVMruodAXG5Vb8tPxHfXneQTvYN844dIq0hRIvNXdi5ZfFGMNKJCLDjfZdRZvmxR7obCXTuBe2wC6AvTnzi/BImzxgtu/ka6/XfC1tPROZZ7NWWwMKEO7oUrdTzqVCSZ/QnbXAHj19RnWBBZtEOYYVfPbE4hm/fd1AoQW3iYHO/tWa08Y9yZj7Ttd+zZMw5ZGXoXz50QPjpfb3RfKUGUNPCplyTBFUNI1ENERbJzY0n/MUrUGUIcfSo4vImEM+Sdu7GGVI4EBsSp+nwfxO+Pwu8fNi+sKuz4dV1xNjZTvvGA4CrBu2zXq+GmLW7bx7uPuigViWF5dkfZUKWWKst2CrzCb7+L67nteSEHsAZppFXFKpiRv1cqWTkCsRAFklyRscjBf8+r+V4S8lPxxcuwugivK+Qzua4xc7of2G25yNf+kpKPz9+N4tDgtLQ/5d6tdc5VOeS709fqv4RoUp5UhvVvcL5VKn0nyn0U8bjz/rsSSOdlA67rzXW929foPqJYTN8wIVv1j06Hg6N5OYKQ2//ZJgZUTE19K3YrlDoZiKIPLpxro7UJPUO4hQiSpAjLw7HKzhmewwMteqaK0ut6KntUmPsOc92r2aNti1q91J7J+oWrJVQseYVl7AaJkt4PTW1e8Ryn+MIrnXU375YNKXv0covpTcwvjZeeo9VlKZPRJ4afzOt1mH8UiPnZJ9+6r66bXma+qDw8Ly9pCzGc4P7xm0dxHlqzUpPbEyuKgop85d98c2AQFy7MhM5DCxo6Ox2sTgJCa7UwDKqRQY7TgjMLu8Z3v/3MNnikHgTEDjoYaK1sA5hMQ/q2febRKpEVwz96C1oV/1u8rUh/2PAyWsbXfXm5k6gnufNYUqReySSvdvG90ApDPgnWLs3HQvSr7soC7EH5uCdw6lylum3QL57aUPFXXhcnvSGWnmxIpK9bt5pH/G4U/Lp5LBAmudVlP8P8ytLSiZUAGekPCmK47UmoD0+NSAhG1OzFlUTYHvs/LwcDfzjIYw2TAqzIQhYJxC39vBAUdujBsA9hBZfa3lZjXpo7eDk0PYBZ+/zeI/1WchgG2v3JVw/X4DUGQEH2U4/ChvINl/mqU1Iso9dF6x5cRvLR5rze/0MqFVaTMuw97NMpGbWv/bVElz8ckD5IBUmc0OKT8zx2PTBBTLjubXHtgMzqyd+8wRZlEW7gtdO/Uil5L3d1+ayDJATWL8pousZgqpTu56BKxr7QCbvR7kodjvVKTp8OBTMZ6GvlEpQDxA9leTv16cvUoVsBrfVij9WNiS7liWdrv12+jj/G/bociBlO5wd7q95lCRsvKZlRrXn1S0eTvWoDjq83yhh+5uyh00poqv4pmg8L2HvcqTa2U/pl9Ti98S4L8naZTxhf1oyaTTxH5pZybRsuLtp2GgEwy01kG7EOZGVVZFVEfUZuLqIJxG9gX4s8j06/29ka+0plHkAX3tu7I/9mYKj+R+0AZ9iQaRFwt2p5zrFCxtTtTJlu0rqn8fUzROqS3anpXZXrWX6pRZSpf8FoJSlO14ay5OrWLDBEp5wvRGo1IgUBboqi0sz7waH0vnWfbarn8X091H0ld9iaGwO1A420BWQZ5tqqUd7BVLNUPtsswIOJhWaoP0/ziK1CN5rjl6mrsWWJ7A2Gr0gW5JQKue8t2B5LdeTpYe7JyMIOCUg+qL26k2icdBvEG8YY24Di+IP/+/DXfV1uEbANFIFSo1Y9itjuENKr57NoDq/efeQU9tus5WAe/qtOhIwB7ja+GP67T8OQeBLGG5l+D3IRDBlU/c1Z6cyRF88+O1iroXpXO5YirtEj5SXawP1+Akpe6S0z+oYk6z2lJu+6qu8ij14VmCu6Ue2Wml1pZLv8GbPs2EmNkYqMo6bSNYvP0kp2rtVJcIVKk0TpvIid6HT0o9Dhl+cFCZXSf+AFkvxqPQrCFOE8G6BMkgQoazPxLz6znTUrzKXF8RpNQBjXhyJeCTjETYfuO42Gg01y5fyRr4TsPoCcdgxX0s+s311V0Pz9bha67mg3KJUKAjoXxb3e3MUkpRbR1Bm9mWav65sOhthWe4rRMUZwbtXbrfx8qOqgL3quYcnpdqjOqGhmeg/HIddLs087AnPkJoSsHxtTL7Vyg5ZoETUg9XF6og2Q98CsxCu8m2/So9WGmrAJhvgk6+XpyWJC+/nXxQs64/bkl5tpdA0xlq2WZ26LikmODotqtO5GrknVVtt430L1a1zD5KC7GBVkUfqMYHU/W2uTwX2ykp48x/TMduC1DtVGn3sH8peTiGqw8MqH+gSGVC29vMkv4ocl5yE++RPx7WXUk8YS2PSA2bKSxgw1jdoKBM/6xF1e4aTU2HmPyP/PY/2vu9lFhKRjZX3NcJEcK8iCz8fFUeP9F8y+mUIHUfyeETrHtXaj25pxnZDspgb1Fe+75ZRFu0aFCjWlCRkQXlMh07+JD1PMuhBtsjm//akw+j6DPf/QgKfb81V02LJ1CPetzJTzYPd6opRXQifAo/s6Jya6l0rSqpKJkrHK2Li/SSR4wWGx85r+0pSqsQSOBPiz1rzqseldXIDfvG3FESfcg3xyjYfiGrNFZTwo5MjU+aPKqBfZTtFliZ0I385/INiyNnoErZQpuCR7edm9DvKp8YWSOPlRrU8mhioOYGQCYn8SzvNEPPUubIFCYBWsiw3zVX1y5lbKx31g7vjgP385vcnsrvSqe/cxw95pYCXiDRiAKdPaxxM+L+wVEEKxHxm5Tep34oJ1pEVTIPGxN00b5vPCJMR55HB2oJ7NJ4qgpXZZW/iqlujpQzS/IcKl6FVEKOJ3oc1st4dzPbl74irWZUJ9wGny/JMmFBYw6VbCQRTZ9DTASY6VV2rDxvYyxyVf624d5inS9go2zUDBwv4iFweskqFX+MLLHxW5ekwXuG400ow9+5rfU+0afx47QtKYRpJTxfSaQVUri/jpkwfTv0x5HKznq+pX+l3XkaCeFZOlTsefv8+8ZnxCg2+UcbnKJvUEFJcwLmxZirqakOV1NjuvmKh0YCUUo0HrTEU4YdxY2ax818fh9qduxFl7le7zQFY7n3zH6v55aZ0F0VS992eNzjME2yf4/Oc2zECPXJSp4p4eBIr3GqHCZSwbZkRLGxbLJyNDm/WiWUETNV+RlzEIPOUDyjtfvw9NnqfBL9qAwrMXZUgawr6nLXIaZb7LI2uUeMqmqi0eQlcdhI/HR0ieSUcEoHNJFiYzZsZC1mgCaTDdLAaTlNa+4/SI3HeFfzO8EDDilNPza/NyewErfJtFaKTz8ldop9GQvVPgtupTtX+Bjwq0rQ2BSLIcddwazz+Sc7nRPZNgSOZFUiYj0id72fiBj/ClPvlqx9KUwXH0ZEJJLJr3K71WY7NHpSrE958GuUPPlMR2UGWYEzsEPwjYZ6F6lmBabvCpSnUTj7vC558c2eu/PYXsa8CNuNqZRhkebwsHmMhGJuLpLq8OO2cqemZ4D0DmPKa32boH0yw9lFe4Kyis8dL0FUqCcXepTKhbbP31EPdFKv0VRg1sZM/951yZPrBjxsWD87G3G0stAwWyvxOwWatjLTC+1JYjUeSvcvYiBmHXBry7DXR9QPmOYa6LaIs3BTThNS6ltSqNR3qeSstcOEUlrNzdugjoU5HDJPsldjn+g9xt61keVvBshZYncGtdTK+Ia0BT/MIMniKSvpHYouLt8ybpMn7H6zeJrSC3USUdEdR0z71fNUTBOOlYjKu+7jnjdtqJIaXmJcF5+/cqTRyuZ2NxLrMq17enmnNbAmYn+JrAZDpEBajypzCmaHCoZd7KAMDpvgwgHTnvwR5tHDfHwPyMhD21QVerm1E1ZCdBOPMADvErFeTndP20oalfxpx5+iJNcMKhI6FeEztF5qCBnohhQNYZ7o+5wW9tDgau9Si905AZ86kWGfEAQk0GMwVochG372xGmSh0SBIfzjWXKXy4XR4O2Kq6w5O1jvh5i6iypbnSyqdyimnNLGZRzm8JwMlRsAxq6I3frujPlfLgxy1iEuFdanalLa/dRgIxVsHi3oyVc2HMe6DDx+m9NJUNywqqB7XIXZv607+HxdJ+dUMgco7CrLN+5lZExM9KK7crUgJVEedlzkmnymFOGJSilGtEz93CqZcZb4w3EazXG+AN988egHw39s1xgcD/WRln/MIZt2bk0qo3FsmjVuF7CQxZPwpsYKqu+Hbk99O5AbqSssxz7b0x70QPi0vTIz5e5yKSCmqE46rO7yw4J/qDGF+K35rFlwCwnZeniqDgxWKbgl24q9fYgMXgI7ekZ8XxOMMlh05YC/42CMkmf8iJSWfogCYbKGouSH+rQqwF7MHl8pViMSMuHApTwDgWmSaQSrqpJB4iwt0BS/fK3wZMQ3FFP6a/y03mRUH29QYJA///rCQA7iHdSwcxSW1iUhZoRHdT/wK1vtMYfwAykQf/tq70OfUYPBnWTVp2ybBp7MXG/fkS9SZpQSwKUYoCEyQV9l7oLmPfzx9vCP/2WScQcftCHnkppcHurVmwtuUFT/AXF/Om61GoLwXYQ0ONC02YXPR1roLOrpEdhHyVYIjuMnn9AEPN9j/Fi4P6UtYbCuROtkqQ8a6exxiaeaubfUExOQuz5W3nMNhRrcAF6HeZ4qvkaxWF0wT3zNJw1LpbS2YuMS9CBmK5j2EIl7/o/9Wv+eO2OMfkdjIa4N14Pkcg1LVBUMCZknNO+oIiSf4VTgMmRbjwYROp44/nymTOV4JpSZqJc5l8hZnbomijGSWSuRjptunwKVvrcdCGllTmIp4JB4PJ+f6sETi9qH763G8cYK+o/dAi0Ku/7Eb+cV9MRh5cv/0ft1yEuy63RNBNbNY8JpeGC4GZu5iqN2ryeQu39xrHweRjWU5LJjKBmeePFpe4nTEtc7GWduNt28zaCAjSCqNnxnGVhc8rYxJ7qml8FhYDVNcFvgkWsknvusN2VkvvcAn0mzoYttIP+d034Tz1ougbvjKFNG77IgIqOkVFjKJo9UPfB7iiOOBU8WH455IVwBvDFWHtr2rbuMuYiqeSTL5OjTKnxyZ6bUFRdetbHJ+e89mjppuii43n8+8F16dLoFpQ8FXmxWe8q9EtqRNQu+hruIxx6UWnkLhdxuyesIjIYP3eJ0S9rR/7JHbtg4tG9omJt5j71xgbmsdXoI3iS0fcYfVu/e1S4UlFlFp1Dy7OckcW6B49mz04n8JJc5mZkK0ArUbaIWGSftfbzLaQn3CPeVY3x/58/Vz7Pj0pHuQbdTkYdtTHE1nAGQknxfhgIVyce1U7TavLhCe+OHJYUiNBUik3FqA4O6inoG0dnPInF2KQZP2k8E+ZAlAXnLntOjfGFYqF6b54yvP5bBUuv5XxiqfV604Tn+xNX4SqgIxpHL7hpQ0pQOf6+7YXSbGA7CYA8XC/B8ucHJ/EwvcSSCwoKb+1RF8ZF+pR3HFE6Z97YSFTD0By9hV7LBmi7y95D/XMe22p6BRrvNfJR7OT0mo0gb+ICXn0uImLmt1BE2H1sT0+vAQchOmKYL0yhC5oopOsha5x9UPzVJ8NtGhlMJFVqtS2L5E3u8TbfcmNI6EDC3CFBaIGKfVzqJ5QbWwuUO5zeESanXbh3UK0/K499iJ9D2pEzZ73uwbG0mStS1MbGvN8UJFuFfe3eZeVhXEzbpEda6YP/x7+28Z7gP8rviM1Sl1RLlnEBpZUVskTRp+rS9xgRpDpKwbk9TmyA1puCIpvKmXzNHh+OvCU9brLUS+hjfkHoeEwJc4O8e4YR1MAJuP9bhJcI5yjcaERon9LoG51+E7REf2GlHlGthOU+TrrVLinQwHDMVmDfCioyeRcpiASsoIJYQBpOJAksqAGAXwPZvC5MS/Vzs81LtUgIEq1KbLBt3mVAUV9h7A+IX6NmBtqak0dCoCg+Dhep4aqQLgl/JmUSVYVs43gZE8+QDvJtx1Z4LXkmduW1YXPxl1cdRUWvDXZTjLcRc/16NCIm9WNiC80wy5x4WgnqXpqUkTfFFycd/2+X0Gz0NXOVdiZy27Jr+vGqcpTTBV6P/SlQR01NtDwMZhrUt0+yPx0gZ8iFJCxzfasoNLAKiGltqwo6ZMgMq2+X0YixeasPume0rFb2I8XiguDGaBXS8n3U0uVacssSsMvHRcybjCEED0EP+6iMP8MfyTDd0UU7NWk/HwoaUTV0DZckeCsCTl/oBGIypr7FT4O0gt9JYuzinGqlXMUX5GEQw6yY5euPYMhit/YLR4E4NP3Aw0FvohbdnXE6eU7GyPpVxwimb4edu+9C7+gsfuH7aFoEfhzoQhUU8bizynJxa/M3QJzSuH9LwPx3Ffan/WootZHwvyb59V6O04se2x84h64DSmKYazUbooXenTY/Am7LzyqPxSYRepSgPCGI0c1GJtStwpp02oSHrMfDuuP065rQNkrdo1QbKNxVMONw1UFVfIxvpZXEy8IiSF9PunBnQC99IWxWS/0D8N0lX3SBoj/2zm0ZU636l/sY0LOurtH2pxaPxLDunMXjIIWYpM0s0bpoHfjC7gxWN4b0f2q6qFMvJqbQ3gNIcHuTCA1VDKzHr1UUblfWiUZRpE43eYVmsHOUTpkIOIvC6R2Da6yK7qxhe1wKKNNuIDT2kMKtITqgTN/jimeUeCCg+QTeYBlWm2ZJhmWI41zZTCendWKIOjffpTBQZDVWtkP2R87eqCWSfSqvd2hiY6b5mh//MINtBXj091hyYLTP1/iqRZS69nr+bCmzvCNrOzaizbL4w81Xw/q+C4efJL4ZAazpuQ09TW4eHTq1Su6H4/BRj+mVDpYVyg64r5r227CJERZFH9iTQrb9Zdb8b0JsRhSD/4bvov20opXf+zS9mGoviMe264XcmI8h5BwWByIplT1RbFUa9xiHrw3y9AsLOQ0lTZlWsMHYvdV6hQtzaux53k0Uq2VPEGNXbz7xIVxJDvcI5HMOFUkdvyTNG9uMs7yKmT4qoKj6H8K23sd8AfjBwrKSQPOD9e5yyJn5qgyQPEux38tUYZIZuF7T0MUVUFYiUvr7aDohLTjuhfPQj72Hh5nsc6tVIE9SLSptoZCAr8kgRssuqsvwHUuNXtQ1txn/5r+ulYe9mwsbcAYLjA7cQTrG8gB0wkIJCpSlhoVC0zVTquRjOWjRJrXE0qOmAn/hC3T6vgHd97Fmo07X5Y0GOrvZv6/Wu5h/mOUktWtWH+EvPwx96B0qVfd4U2WHhBZ4qBcSXpA+JOrUw0EyWlS+5cr5wf5kvQWZyAyhnPq9eEt9Qhe9EOGen+hfqEL9pWd2oeuj+5WTFa0DAV1iAGJd6WyRNwLFkk9jnZLNSVLCNd35vEl+jHrRuJ/bZLNOxrwuSN+ciID9JoVRSfpk8RWSHqpgDf5tpQqlyuPhVfim7JqZ5XxTYeHwvTxYkMSEUlNgaGHE9N4/v28kOPk3CvcoVGBn6Mr2/RN1NbqaZ9qQ8aNGW7jhzTkfNI/a4tmdhNUOBQJxerx2yK4mMGfwq1iZhruydeDjT4I4bHYkQsU3RXguiWDWZ7Nb98A71PtHjw/QMu3wr9cj6tP5C1Lnl94V1upM+GpxF5t0jFoiXP95mIzVXa3t6WHpoWBihDQinASDy+o7f29LPZbvMhTxUOQ73n4yEviQArjBPFNBdiy9fMv5kyd3MF2TXEOu65uhUhwlfxJsFlcT3EGzjQjoPR1PBVUxZGenTcAAiC8TXCaGuf/dOBp+Unq3918sYVVmX1JsS7dAbwG3tGO3FuH345xvA47hLJsuBC2vRpRlrRBM3Pbt1cPDrEAYdc4553lkphog2HlanMu+MZ/JWMaDrDB8r49ViDBTjOcaewYu46vMPZeYVnVONIz4aitXxiYUMAoTncP7hlGTVB5P9o2WCImkJGZEC3hEv9eLDMvrba28AzeIs5iWQt2cF0pSfNurVFJdwhrpOlfU2KunMn58otBaLc4NoOldPimlq4rQWYktRzewsOB9L3/oQFaxVG3iR9fTxlhPvaMjNqk2gdo5qxGY68ab3qvDik98WkD86Udg06I37bpmkK0EQu1xdSYOZO1g6v1vVJnTRsEaKy9AOCHKvJ3bqR+2G1/k3bI6t8Qa9L3+QVBeaZ0NOK6dMgDRKFUxPFGqGOAjNKipizHKLiQSWPd9pR+mQua0LwpOGBY/eHLxuthL6KWu14J1mM9E1cUzlRGt4cXYi4ludrt9mZ8VfmJG9uFAmzt0JoYlrQ1T787faAHtsmnfvpnY+Nwt1Kpa77fttnDSxX8SDA2gzcxvWd2dfeIws58mp2EnP/aqjiVOgw0DsUxqyJec+m4A+y7YyW+HDXcSMSdZunHCQ0v4JU45z02El39s8Z9Mc5heCb622vmqw8PoV2sQTcB4lzc4JpTcHxpYRTYd8+N7L0NP8zL19b9HrZWLj8nLL8pP39E1UrLodmBmRfLXbcEYwVnlOLpazDlREkuSe/8hCzi6/7FH53tFsRWwRoYTjV4mUIt/UH6jxkTU65xP2z8Go4L/3gXRcXpYaC2UyPPisQ7bCGNrmxL1hMRmT46Qj4floZZVl3zbaQ+udW8YM5+5Gz77I+xHe8l5KnW/dLkhE+djyicDHqNsQm0+zqG3jHuZm/90wT5G0qmOiqK6pvg0DfvVrofH3Y1ZS5q1ig4jA/IhQoQFy21mYuqLruH2lw67kv0xWnuiukduwG9d/GibEtZf1b0ZcR/lmKT9Rstjr0vXRwz3X1ZzcGjv3ykUvNS2D3/eoPQhN6PbS+wwkrsF0+tBwoTbETTRygG6iTMK9FewdrBPYfPnBeR55zDClF5M+s0EqU3H1Ms2HqYnbIEO65Oc2lYTiP1Iqtr76sQWHSL5YVuUoGRSxG53eYUHL7kRcL1zdsXqxfAPwEZY47LgBDHbXPbky0vJUAHtLv2x/jl6JFbRwzq+pXUtxniqUfam8Vx+QIntGUL/AKhMxpbZGu6VRQYkksdGUlr5HK7h/vHdP0AWOGVEColIpAXWZ1lJbT9zt7lAiw/m5PUvZuVO57hnhlA6u0mkWVckYBb2NcZZ9a3HgnMtDVh6ggGmkE0B2PUoQ5qXesXZYaeEULguQ37PMA5LFH4lTTRdFJVHmPFmDDB0uWtvcdqE+90DFnJ67hFm0To40TawRDb/DNxjl6UU09cPO+bvcMEKSFNeY63wx7yfQ5BiHthi1K2OTldxf2zZxs3rXywbmEO0m3B0v/B7CZ8pPJPllOSBcSlk2hwZWWnad0bZqd7pF5kpLRwBzh5vyPJ91TXvtTBslQNVqc31r1SVWI+TB4/aBz3yMFLIQ2mvX+PxXMUfeqBrC+LVDYt4PlQKSxXMo+hQDRITwFu2dA7LgCTHwh+6+bEvVuAGE84hCtO04MrL7zL1pjuETdreY6+OP4QExM/LrDg5zLOT3fxTm8AzzAkGwVlq3ppnJaiumwmNURpxpTzpiOdIjPOXC2VlZl9auyTPVxScuN71gGJHYY1Z2iYo8p3Rn3ufimHY+GH/XE4xkpZCSYxs/EsQlOmL6KIid+6OsVszu4tQTuBEmGdfRw64yKj5L5ai7vBxaFlglb+I/uIipX+UoRv6RRCb/DgsRL9MVXa6bHxLFZ2UyTFkS9WWcenFV5lnjg0FYPybqk+M4KT9D0UejgfsZj6RfPip3s7oIQ17ueF6eFGTlNalO20as2cyt1XEg+NzbZsS6rOk5TH89rDV92Gwonb+RPiwz2z5MYBGmMKOyxwfMyj8XW/n4WuRyrdJBZmuyJ7Fo+r2mHfJE19GYMmtbPMYtLa6jNGNrzIHgzU4VqegPq911AddXiPHdz6UHpRqdh+BWUxue/g7GWFOJVeqkG8CLTFmKsBrC3uWqOFLFdyJR8pvyjFGq6gpXnNWl+wUPRgS1VMr2UmVE4ohLZQpOC8lXV694L3it50IWfGD2Q5o/QBLEAxKVeKM6p0XZU61ixhk/aT+2Lhr7Kk3sB2M51kc1uRKRKKJEu4kwpgjrJG4bSm0P5Pmwvj1D1StrDPxGi/N14dhypP7+98vpuexv4bzWDR7DTYJeGB+I99YMTlSs2Xzyv2t2Lea7aYsPKS4kIbtXqZDT83ibxG7qzxie92+Gmk6b2CjmgAXXlXoiRIL5GBiJ8dOfpoSdGXzvn5f40HcDKJyRidk8exY5mSuq/aW7/3T17cK2p6ojlvNjdz/9U/28zCGPQAKzQbKQjo6J8nDXJOwAlE3SPm4ar99JS072Lgsy55J5Wj+pWhQMnOlqAQrWVNNzRvw2n7pH/Y9I9KbV57I8aCXSN+utcmGdb05n2sHbL5NHstXkT+P6Nu1kVeWmhkugzq2ElYV9iq+SjcSRDjEHgq3js7adC1TvIEEsrA8FrJh8ivW2x6j5CmMaoS60OqpLXoHpucxBmHbDFvXWXe+Nllj53cLFjT0GcXlaL8L8tyYr/as5DS0CLYQCoFurD0ZjZmpL2kyZo/SpD0oWPvCSIkj5GmDaqfrTN4CPpbq2RByjt1srcOhwW+UJp0h08x5MIexSom4L8QOc4kiKMWHvxfMX8xKhVXZqs5vs5OqtrmrdNlb4qg9UrJvVu9ftRjCTW1eDcHFJj3dKj7Ebtu+7GlrPvMwolyA/GK97GV1EmDyakkXY5UpnYspQe61P+pyRqQSj1/05gPdwWxh7+oj6dv/Ztvmtt7WP75rorh8UFjm5MRW2AagHGO1tugyLp0klx+2ro9sdahShWcOHRk1NDpu66UFsXUSeE1YlvF+5QCo9K7jplrjeEW7YfGbupaEJoSc4M82EfAEjk/4/ZS3defr7ojrkzWfFhxEJeM7wqqPyr3xrPSx2SyK2Avg5Vp0oGtzd180mDMN6+b2sAtk7RqVEpkf5h5P+bg+ZIpIyDXoS5kv88ncnMqBWGbseC9ciqhbFSBPF9TWwndeHfMMS3mN6+zpwyiGllKzhawdM+NeKnkzgAz5lGlkmFdueZ1EDRRZlbsuBssA+C47LHDgV0TIp/7rMCn0C2R1ylouDgRtArOp1LJQ33cm3nobuzOwGsGEO+vUU8Q3NJh+WNniDfux9mVa/JnKhW+a+AbzJU8hzVDwVGvaN7LomLbO8Kqd13C0XASfYzGVzDnvh6H0knnJtOGwLCxajFg7fif3OrpB+GRewlMBpn/TjQ4ebuFDU8Vm9vBJ9mO7da8XGg+64yp5z+eVaOuoOT6+5zGwSPhOpM07cNm6jOYENlqh9LzjTzpFPVLz0iB/xX1ipfxIqgMm0sST+wnL/k/DpG+X/A/vML1p2wttvi6JkKveyIuvaTjsYYxJI6R89+Hua8rPsH0Ezz2Db+iii2jMW7a5mtLuq7B+ueP5cFWfxGaPlrRa7JldwP77OkEC5MnWdLuVklrwI98GJI7C37HruwVpvupa2LBTGMm/l/oOj/nuhhr1itvVeq2Db1e2gpy8Pny9RofCIxw/jCBvEw77nEPjerdxKHRLxMSv5WX4yosW7HWpkE3WUjBssKlvzxcnNxj7XOhw0Ko489FfoH9427mzteKXjyThnsgavisN1XqHiKF28fnwtPiQxHiteEVcllQWnLKIxbm5jBklRxzL5W4HD3SkrWdPIjDsP8Ta6qT2I8pg2WeveyACDwqmPmXpKi1edTk5Lrb1l49MtMED490zaZr0cJAXeTzAEW570cNrXIXY2VaaiNMNJdxXfCzcOF605uSm4bdOKH7EusiUWlfLFCvXF9Nrr8CWnp387gQ9zpDO/b1K4en4yNDNxjHcoW+47NUm//XrvyKuxMqmyaW7k5cqpbOZReKv4KkuigZ35UAGbNtO7ndeEb2BwWvypodkq5Nx+jPzALiRURWiNote9l/agCYN414m6lmwwwAmRv5szdNxWGJ7rYEU9G5ehSpaxs+BI8RCJt5Dr8fa9Oteu/IXFm0rHsaqwa62TlkupS+9s3xI2bzvPqdHKl7zKPILSsq5AOGjitlkRJvP74/XsELmKvTXbMPaplWns3Jz0nkZtyWTUix3/q/FLb9kxE1hTus1K5IdvV650zQXVLKryX8oR4nkPlUFObYilNrhuG1xm6LKyjLCO3eaaMC6rq2yqUoC1k55MZSxDjcQHhPw13cXGpbw4HKWLlbyOvc9fdiQYGs4pYPE0/PN2MrkUdgiyfVShrcwpqLiuMfPBNh4jHS/JtwtCYppVb8Wey590+YWtOvTPsiFmrs22weFnIMi9ySeqf1XmhB+JZ61ee0SMajrDv4l/XrXutLvH8lehRpt7mvqRr5js1tOB3tPzjIS8p2WvUkW6z+OSzjYl9P7BR1ZkaTPcPD97mEP1ZCnjBsCWdsXWtfYXI+1jKhJkp/nmCjcfTah/pG/AcJTMN526pui6lXB2AG8kgDHVVGXK1oLVkU8L+NVzEdHc5hxvUkVPv/TrJcV/BJiCZ/+YwQ5kHA+7t1UP7wSavcyLRvFL0IkFPxnv6KFytVMoDPva6YOTQNtqf5kDKP8yqZkK3EqqFe0fWikZvuQ+2h7NwjSkMguL6uN5DEkdYoT2rHrK9LgKJBM136qXMrbAk7gUGF6yv+zlLLoBTGaUuiVdapkzl19y6MiPtUA3VpK5jW5z1YyLto8njet9iLOoECAX9N577rqhl9IEugHQFZ9eXiXDixM6no30Subkr2T4c2IFTs9EhpJCieGK2RB5G5pO732a24NtB9NN8YtMobukowxyVvrMGXCYM6ftYAmSvBQZVS0r1JuQ7lfyc5dR9JAw20HWy/vZ4GqNFG3RB22Qr//OAlYPY4lkpURSuxJDWuFVRB31DAO3+FoXZQpDwY9WKTVJG+K0LhEercKWAHb5nnSLWPUNfFsZ2laB6JJUE3bGvaqin4RTtOvd/AJ0qWksOeVEHKvUSxe2Zj8XooY/bbc+XAkLsQibnneFvDcCnNTJHH44eCP5ueTca/1K+vJnp/qyqFHjq2o8Sh7TVeQ1yURlfE1CRnwUBg3N6udpV0zTnOx9wXQOsofTPohv1k1cFPxLW5NcUesTE7cNO92bqI0MllbtKtXKKwLf7RsxrvLZVnq6zrRqtkrpfF3ZR0gxHgoewWlNT5TEMGHdrucsQDLVMEHF8VBahYcoXJ0ei6yhH23mvVbM4fLjG4i1q7xdKwPibyuAw/N72oknkosufN1YjhuXV88SAyrNma/aH9mSM72L8PxkRUbO11JSW1ZXYPN6fUUtyT8x4PlGvvL6z8d9I/0/nVxLKEJ0Lfh7UMtFPBBOh5hVeVnrfSGKWdM9z+qohRMMpxjeVWRRrguZnWyOwzV2SI4U3XwqLltMDW4PawpjoF5im1h6E/IbPIJjLRPyqtJBKvhM5ZllMB+Ooup4I/fUHLsU6wKcTRs+M15XG1W7NxEIh+OcNR/6z4QkZLWwk0f1uYVnFzVM9iAKg1qKlxe8UwdqjnhKiVZXqWhap8IiGkr2i2SX3XzTgQ/hDDEF8QeGbhujpdrvPovgLdlQUDcxJtWY/3R4RHPOyxZdBQ9JTz2o1T7qrZysNuq1k4qMreSpnHYTWQM6/RQRiAMPmfDfGQi/yypqsIhjLG89S7fENby7nPahzlo/2nHgxdm2VmD+m5JsC74wmrx5ppae3SmfGrpun3VliciWBTbwwYTXqhQ79nsgeKOJil2SmhShUQ0blZG9G/5cVzFVwFXmiL0H3mR7nD4mWKcWMHK/OPbgdIti3bQ8ZL+VV+V0hi3swobfd9KUkMVByLDKlf97jO90SqBdmBi/uJdhJeTJt9AlTTCki79PdLf4rIEzojS+g7GQg6rsaKer+A6LPnWgp6hp+wE+yNLA6IO5Po1f56Lh58IVcEia5eTltpkSe0pbTG2vq5ANfjDh9slMr11gXDWV6nRq0lxY13DTskkphDKxBMsuF2lGUq1HoC9nBmhtE8uW4RIqYMosilcmsRFo7oHHdkCW28AszSNDbqO7o0L7MhZeh8AClpisofKCOchx3fGU3VbpRZKTniENDiZrvh40vpYwFIcUNF7YoBFuie2qyuoJmVKip34dQPUcXjvTGbH5ADTt1TY+VKWUVzaa7q90YsZVNfEooImb6YBS1YIi/hCHi3RNQXdgDoVFZt+fOJhdP+S4+nS0VEVAVcxitLJjsoWNR5zyoRLMk6gvrAmBaji/k3Og7xftFkEb76zS0sKRxs1dZzv6FCWsLn3MFw6Z5zRfduIqb2zwn1PZ+mi1zSvsiS1FhaWVHpLPHsydghgLchCOlZkNxdp1l0VsDoQLvcP+x4LPjt3ooc+Sma+I0Q1AbHNM5GHt0m7qSjkJOfAW/SPOcEtPRQPMRJlm2sVvn4gk8tx4rMsOQUMc734mK4ZzuDrhXcsZVzTWY9L0wxAOAF51lsHzqDwUdEiIa2GWAcJIN9h2t47jfZ3FkvDLLy7WwXl91k4pRdxuQRKbo8YT9Zupj/vOy+OO7c+LlD8Nne7MRYkppZodEPmO5NeX+o6ISqX9tOHcnvtplCR9idjroM1W/Zs0QHUTlyYwsWvvcHTqBqBexx8iQVNzKJv74TVY0DD3CM6rrFwlXBNutStW9MFelc3Wcevy09zaj2Nv9ixLKwJ9y35I8Fyq3sqjLpErTncAzAOj/7dva2+ByTkVT9/ucD47DOvHC0Z9/k+fnf3LTqfuoq1KXFBiWAFWGiB+G4PF5Luq5PAP2rcZsjufjyJccqcIgr/xNTbclcT9zDD4reDX/ud/fU1PJzCtaoKud6y+z2MSNM0PhhFtXR8rZa3x7lQsfacVuI7U/HPO2SxtMPzffdkXnX0TSm/wx8BNxx389ulOaLLo3asiytRO8Caxu3qxv9wijdcookS0vM6Dl+Y12gMTPqKv67dS7Vctr/QM1Ky3BFzb8NK+jnemLGPfAAyOfHlkNzv+BjsX343C3BzCZbhz3DF8x0TnugfOv/4NVX1ySUqlZkCG5s4+N/7ZJ3DE65Of+hdU+CTFfC0IbxnyzDWS/Arec9lDl8fyF8QawqKHWvguRPH/p+iPvXTjvkk2WuY7L7x/GMOlusUYUp+ADi7tHCQw7gypJFNsRo7k4bOOvvVDtpBJgVr9igFE86X0R4U7rtxffGbbE5r/+Mbov3zRdjPx/wDLNaWJ
\ No newline at end of file
diff --git a/docs/docs/concepts/breakpoints.md b/docs/docs/concepts/breakpoints.md
new file mode 100644
index 000000000..c38431071
--- /dev/null
+++ b/docs/docs/concepts/breakpoints.md
@@ -0,0 +1,132 @@
+# Breakpoints
+
+Breakpoints pause graph execution at specific points and enable stepping through execution step by step. Breakpoints are powered by LangGraph's [**persistence layer**](./persistence.md), which saves the state after each graph step. Breakpoints can also be used to enable [**human-in-the-loop**](./human_in_the_loop.md) workflows, though we recommend using the [`interrupt` function](./human_in_the_loop.md#interrupt) for this purpose.
+
+## Requirements
+
+To use breakpoints, you will need to:
+
+1. [**Specify a checkpointer**](persistence.md#checkpoints) to save the graph state after each step.
+2. [**Set breakpoints**](#setting-breakpoints) to specify where execution should pause.
+3. **Run the graph** with a [**thread ID**](./persistence.md#threads) to pause execution at the breakpoint.
+4. **Resume execution** using `invoke`/`ainvoke`/`stream`/`astream` (see [**The `Command` primitive**](./human_in_the_loop.md#the-command-primitive)).
+
+## Setting breakpoints
+
+There are two places where you can set breakpoints:
+
+1. **Before** or **after** a node executes by setting breakpoints at **compile time** or **run time**. We call these [**static breakpoints**](#static-breakpoints).
+2. **Inside** a node using the [`NodeInterrupt` exception](#nodeinterrupt-exception).
+ 
+### Static breakpoints
+
+Static breakpoints are triggered either **before** or **after** a node executes. You can set static breakpoints by specifying `interrupt_before` and `interrupt_after` at **"compile" time** or **run time**.
+
+=== "Compile time"
+
+    ```python
+    graph = graph_builder.compile(
+        interrupt_before=["node_a"], 
+        interrupt_after=["node_b", "node_c"],
+        checkpointer=..., # Specify a checkpointer
+    )
+
+    thread_config = {
+        "configurable": {
+            "thread_id": "some_thread"
+        }
+    }
+
+    # Run the graph until the breakpoint
+    graph.invoke(inputs, config=thread_config)
+
+    # Optionally update the graph state based on user input
+    graph.update_state(update, config=thread_config)
+
+    # Resume the graph
+    graph.invoke(None, config=thread_config)
+    ```
+
+=== "Run time"
+
+    ```python
+    graph.invoke(
+        inputs, 
+        config={"configurable": {"thread_id": "some_thread"}}, 
+        interrupt_before=["node_a"], 
+        interrupt_after=["node_b", "node_c"]
+    )
+
+    thread_config = {
+        "configurable": {
+            "thread_id": "some_thread"
+        }
+    }
+
+    # Run the graph until the breakpoint
+    graph.invoke(inputs, config=thread_config)
+
+    # Optionally update the graph state based on user input
+    graph.update_state(update, config=thread_config)
+
+    # Resume the graph
+    graph.invoke(None, config=thread_config)
+    ```
+
+    !!! note
+
+        You cannot set static breakpoints at runtime for **sub-graphs**.
+        If you have a sub-graph, you must set the breakpoints at compilation time.
+
+Static breakpoints can be especially useful for debugging if you want to step through the graph execution one
+node at a time or if you want to pause the graph execution at specific nodes.
+
+### `NodeInterrupt` exception
+
+We recommend that you [**use the `interrupt` function instead**](#the-interrupt-function) of the `NodeInterrupt` exception if you're trying to implement
+[human-in-the-loop](./human_in_the_loop.md) workflows. The `interrupt` function is easier to use and more flexible.
+
+??? node "`NodeInterrupt` exception"
+
+    The developer can define some *condition* that must be met for a breakpoint to be triggered. This concept of [dynamic breakpoints](./low_level.md#dynamic-breakpoints) is useful when the developer wants to halt the graph under *a particular condition*. This uses a `NodeInterrupt`, which is a special type of exception that can be raised from within a node based upon some condition. As an example, we can define a dynamic breakpoint that triggers when the `input` is longer than 5 characters.
+
+    ```python
+    def my_node(state: State) -> State:
+        if len(state['input']) > 5:
+            raise NodeInterrupt(f"Received input that is longer than 5 characters: {state['input']}")
+
+        return state
+    ```
+
+
+    Let's assume we run the graph with an input that triggers the dynamic breakpoint and then attempt to resume the graph execution simply by passing in `None` for the input.
+
+    ```python
+    # Attempt to continue the graph execution with no change to state after we hit the dynamic breakpoint 
+    for event in graph.stream(None, thread_config, stream_mode="values"):
+        print(event)
+    ```
+
+    The graph will *interrupt* again because this node will be *re-run* with the same graph state. We need to change the graph state such that the condition that triggers the dynamic breakpoint is no longer met. So, we can simply edit the graph state to an input that meets the condition of our dynamic breakpoint (< 5 characters) and re-run the node.
+
+    ```python 
+    # Update the state to pass the dynamic breakpoint
+    graph.update_state(config=thread_config, values={"input": "foo"})
+    for event in graph.stream(None, thread_config, stream_mode="values"):
+        print(event)
+    ```
+
+    Alternatively, what if we want to keep our current input and skip the node (`my_node`) that performs the check? To do this, we can simply perform the graph update with `as_node="my_node"` and pass in `None` for the values. This will make no update the graph state, but run the update as `my_node`, effectively skipping the node and bypassing the dynamic breakpoint.
+
+    ```python
+    # This update will skip the node `my_node` altogether
+    graph.update_state(config=thread_config, values=None, as_node="my_node")
+    for event in graph.stream(None, thread_config, stream_mode="values"):
+        print(event)
+    ```
+
+## Additional Resources 📚
+
+- [**Conceptual Guide: Persistence**](persistence.md): Read the persistence guide for more context about persistence.
+- [**Conceptual Guide: Human-in-the-loop**](human_in_the_loop.md): Read the human-in-the-loop guide for more context on integrating human feedback into LangGraph applications using breakpoints.
+- [**How to View and Update Past Graph State**](../how-tos/human_in_the_loop/time-travel.ipynb): Step-by-step instructions for working with graph state that demonstrate the **replay** and **fork** actions.
\ No newline at end of file
diff --git a/docs/docs/concepts/human_in_the_loop.md b/docs/docs/concepts/human_in_the_loop.md
index 45ce792d4..3b7a146dd 100644
--- a/docs/docs/concepts/human_in_the_loop.md
+++ b/docs/docs/concepts/human_in_the_loop.md
@@ -1,322 +1,541 @@
 # Human-in-the-loop
 
-Human-in-the-loop (or "on-the-loop") enhances agent capabilities through several common user interaction patterns.
+!!! tip "This guide uses the new `interrupt` function."
 
-Common interaction patterns include:
+    As of LangGraph 0.2.57, the recommended way to set breakpoints is using the [`interrupt` function][langgraph.types.interrupt] as it simplifies **human-in-the-loop** patterns.
 
-(1) `Approval` - We can interrupt our agent, surface the current state to a user, and allow the user to accept an action. 
+    If you're looking for the previous version of this conceptual guide, which relied on static breakpoints and `NodeInterrupt` exception, it is available [here](v0-human-in-the-loop.md). 
 
-(2) `Editing` - We can interrupt our agent, surface the current state to a user, and allow the user to edit the agent state. 
+A **human-in-the-loop** (or "on-the-loop") workflow integrates human input into automated processes, allowing for decisions, validation, or corrections at key stages. This is especially useful in **LLM-based applications**, where the underlying model may generate occasional inaccuracies. In low-error-tolerance scenarios like compliance, decision-making, or content generation, human involvement ensures reliability by enabling review, correction, or override of model outputs.
 
-(3) `Input` - We can explicitly create a graph node to collect human input and pass that input directly to the agent state.
 
-Use-cases for these interaction patterns include:
+## Use cases
 
-(1) `Reviewing tool calls` - We can interrupt an agent to review and edit the results of tool calls.
+Key use cases for **human-in-the-loop** workflows in LLM-based applications include:
 
-(2) `Time Travel` - We can manually re-play and / or fork past actions of an agent.
+1. [**🛠️ Reviewing tool calls**](#review-tool-calls): Humans can review, edit, or approve tool calls requested by the LLM before tool execution.
+2. **✅ Validating LLM outputs**: Humans can review, edit, or approve content generated by the LLM.
+3. **💡 Providing context**: Enable the LLM to explicitly request human input for clarification or additional details or to support multi-turn conversations.
 
-## Persistence
+## `interrupt`
 
-All of these interaction patterns are enabled by LangGraph's built-in [persistence](./persistence.md) layer, which will write a checkpoint of the graph state at each step. Persistence allows the graph to stop so that a human can review and / or edit the current state of the graph and then resume with the human's input.
+The [`interrupt` function][langgraph.types.interrupt] in LangGraph enables human-in-the-loop workflows by pausing the graph at a specific node, presenting information to a human, and resuming the graph with their input. This function is useful for tasks like approvals, edits, or collecting additional input. The [`interrupt` function][langgraph.types.interrupt] is used in conjunction with the [`Command`](../reference/types.md#langgraph.types.Command) object to resume the graph with a value provided by the human.
 
-### Breakpoints
+```python
+from langgraph.types import interrupt
+
+def human_node(state: State):
+    value = interrupt(
+        # Any JSON serializable value to surface to the human.
+        # For example, a question or a piece of text or a set of keys in the state
+        some_data
+    )
+    ...
+    # Update the state with the human's input or route the graph based on the input.
+    ...
+
+graph = graph_builder.compile(
+    checkpointer=checkpointer # Required for `interrupt` to work
+)
+
+# Run the graph until the interrupt
+thread_config = {"configurable": {"thread_id": "some_id"}}
+graph.invoke(some_input, config=thread_config)
+    
+# Resume the graph with the human's input
+graph.invoke(Command(resume=value_from_human), config=thread_config)
+```
 
-Adding a [breakpoint](./low_level.md#breakpoints) a specific location in the graph flow is one way to enable human-in-the-loop. In this case, the developer knows *where* in the workflow human input is needed and simply places a breakpoint prior to or following that particular graph node.
+## Requirements
 
-Here, we compile our graph with a checkpointer and a breakpoint at the node we want to interrupt before, `step_for_human_in_the_loop`. We then perform one of the above interaction patterns, which will create a new checkpoint if a human edits the graph state. The new checkpoint is saved to the `thread` and we can resume the graph execution from there by passing in `None` as the input.
+To use `interrupt` in your graph, you need to:
 
-```python
-# Compile our graph with a checkpointer and a breakpoint before "step_for_human_in_the_loop"
-graph = builder.compile(checkpointer=checkpointer, interrupt_before=["step_for_human_in_the_loop"])
+1. [**Specify a checkpointer**](persistence.md#checkpoints) to save the graph state after each step.
+2. **Call `interrupt()`** in the appropriate place. See the [Design Patterns](#design-patterns) section for examples.
+3. **Run the graph** with a [**thread ID**](./persistence.md#threads) until the `interrupt` is hit.
+4. **Resume execution** using `invoke`/`ainvoke`/`stream`/`astream` (see [**The `Command` primitive**](#the-command-primitive)).
 
-# Run the graph up to the breakpoint
-thread_config = {"configurable": {"thread_id": "1"}}
-for event in graph.stream(inputs, thread_config, stream_mode="values"):
-    print(event)
-    
-# Perform some action that requires human in the loop
+## Design Patterns
 
-# Continue the graph execution from the current checkpoint 
-for event in graph.stream(None, thread_config, stream_mode="values"):
-    print(event)
-```
+There are typically three different **actions** that you can do with a human-in-the-loop workflow:
 
-### Dynamic Breakpoints
+1. **Approve or Reject**: Pause the graph before a critical step, such as an API call, to review and approve the action. If the action is rejected, you can prevent the graph from executing the step, and potentially take an alternative action. This pattern often involve **routing** the graph based on the human's input.
+2. **Edit Graph State**: Pause the graph to review and edit the graph state. This is useful for correcting mistakes or updating the state with additional information. This pattern often involves **updating** the state with the human's input.
+3. **Get Input**: Explicitly request human input at a particular step in the graph. This is useful for collecting additional information or context to inform the agent's decision-making process or for supporting **multi-turn conversations**.
 
-Alternatively, the developer can define some *condition* that must be met for a breakpoint to be triggered. This concept of [dynamic breakpoints](./low_level.md#dynamic-breakpoints) is useful when the developer wants to halt the graph under *a particular condition*. This uses a `NodeInterrupt`, which is a special type of exception that can be raised from within a node based upon some condition. As an example, we can define a dynamic breakpoint that triggers when the `input` is longer than 5 characters.
+Below we show different design patterns that can be implemented using these **actions**.
+
+### Approve or Reject
+
+<figure markdown="1">
+![image](img/human_in_the_loop/approve-or-reject.png){: style="max-height:400px"}
+<figcaption>Depending on the human's approval or rejection, the graph can proceed with the action or take an alternative path.</figcaption>
+</figure>
+
+Pause the graph before a critical step, such as an API call, to review and approve the action. If the action is rejected, you can prevent the graph from executing the step, and potentially take an alternative action.
 
 ```python
-def my_node(state: State) -> State:
-    if len(state['input']) > 5:
-        raise NodeInterrupt(f"Received input that is longer than 5 characters: {state['input']}")
-    return state
+
+from typing import Literal
+from langgraph.types import interrupt, Command
+
+def human_approval(state: State) -> Command[Literal["some_node", "another_node"]]:
+    is_approved = interrupt(
+        {
+            "question": "Is this correct?",
+            # Surface the output that should be
+            # reviewed and approved by the human.
+            "llm_output": state["llm_output"]
+        }
+    )
+
+    if is_approved:
+        return Command(goto="some_node")
+    else:
+        return Command(goto="another_node")
+
+# Add the node to the graph in an appropriate location
+# and connect it to the relevant nodes.
+graph_builder.add_node("human_approval", human_approval)
+graph = graph_builder.compile(checkpointer=checkpointer)
+
+# After running the graph and hitting the interrupt, the graph will pause.
+# Resume it with either an approval or rejection.
+thread_config = {"configurable": {"thread_id": "some_id"}}
+graph.invoke(Command(resume=True), config=thread_config)
 ```
 
-Let's assume we run the graph with an input that triggers the dynamic breakpoint and then attempt to resume the graph execution simply by passing in `None` for the input. 
+See [how to review tool calls](../how-tos/human_in_the_loop/review-tool-calls.ipynb) for a more detailed example.
+
+### Review & Edit State
+
+<figure markdown="1">
+![image](img/human_in_the_loop/edit-graph-state-simple.png){: style="max-height:400px"}
+<figcaption>A human can review and edit the state of the graph. This is useful for correcting mistakes or updating the state with additional information.
+</figcaption>
+</figure>
 
 ```python
-# Attempt to continue the graph execution with no change to state after we hit the dynamic breakpoint 
-for event in graph.stream(None, thread_config, stream_mode="values"):
-    print(event)
+from langgraph.types import interrupt
+
+def human_editing(state: State):
+    ...
+    result = interrupt(
+        # Interrupt information to surface to the client.
+        # Can be any JSON serializable value.
+        {
+            "task": "Review the output from the LLM and make any necessary edits.",
+            "llm_generated_summary": state["llm_generated_summary"]
+        }
+    )
+
+    # Update the state with the edited text
+    return {
+        "llm_generated_summary": result["edited_text"] 
+    }
+
+# Add the node to the graph in an appropriate location
+# and connect it to the relevant nodes.
+graph_builder.add_node("human_editing", human_editing)
+graph = graph_builder.compile(checkpointer=checkpointer)
+
+...
+
+# After running the graph and hitting the interrupt, the graph will pause.
+# Resume it with the edited text.
+thread_config = {"configurable": {"thread_id": "some_id"}}
+graph.invoke(
+    Command(resume={"edited_text": "The edited text"}), 
+    config=thread_config
+)
 ```
 
-The graph will *interrupt* again because this node will be *re-run* with the same graph state. We need to change the graph state such that the condition that triggers the dynamic breakpoint is no longer met. So, we can simply edit the graph state to an input that meets the condition of our dynamic breakpoint (< 5 characters) and re-run the node.
+See [How to wait for user input using interrupt](../how-tos/human_in_the_loop/wait-user-input.ipynb) for a more detailed example.
 
-```python 
-# Update the state to pass the dynamic breakpoint
-graph.update_state(config=thread_config, values={"input": "foo"})
-for event in graph.stream(None, thread_config, stream_mode="values"):
-    print(event)
-```
+### Review Tool Calls
 
-Alternatively, what if we want to keep our current input and skip the node (`my_node`) that performs the check? To do this, we can simply perform the graph update with `as_node="my_node"` and pass in `None` for the values. This will make no update the graph state, but run the update as `my_node`, effectively skipping the node and bypassing the dynamic breakpoint.
+<figure markdown="1">
+![image](img/human_in_the_loop/tool-call-review.png){: style="max-height:400px"}
+<figcaption>A human can review and edit the output from the LLM before proceeding. This is particularly
+critical in applications where the tool calls requested by the LLM may be sensitive or require human oversight.
+</figcaption>
+</figure>
 
 ```python
-# This update will skip the node `my_node` altogether
-graph.update_state(config=thread_config, values=None, as_node="my_node")
-for event in graph.stream(None, thread_config, stream_mode="values"):
-    print(event)
+def human_review_node(state) -> Command[Literal["call_llm", "run_tool"]]:
+    # This is the value we'll be providing via Command(resume=<human_review>)
+    human_review = interrupt(
+        {
+            "question": "Is this correct?",
+            # Surface tool calls for review
+            "tool_call": tool_call
+        }
+    )
+
+    review_action, review_data = human_review
+
+    # Approve the tool call and continue
+    if review_action == "continue":
+        return Command(goto="run_tool")
+
+    # Modify the tool call manually and then continue
+    elif review_action == "update":
+        ...
+        updated_msg = get_updated_msg(review_data)
+        # Remember that to modify an existing message you will need
+        # to pass the message with a matching ID.
+        return Command(goto="run_tool", update={"messages": [updated_message]})
+
+    # Give natural language feedback, and then pass that back to the agent
+    elif review_action == "feedback":
+        ...
+        feedback_msg = get_feedback_msg(review_data)
+        return Command(goto="call_llm", update={"messages": [feedback_msg]})
 ```
 
-See [our guide](../how-tos/human_in_the_loop/dynamic_breakpoints.ipynb) for a detailed how-to on doing this!
+See [how to review tool calls](../how-tos/human_in_the_loop/review-tool-calls.ipynb) for a more detailed example.
 
-## Interaction Patterns
+### Multi-turn conversation
 
-### Approval
+<figure markdown="1">
+![image](img/human_in_the_loop/multi-turn-conversation.png){: style="max-height:400px"}
+<figcaption>A <strong>multi-turn conversation</strong> architecture where an <strong>agent</strong> and <strong>human node</strong> cycle back and forth until the agent decides to hand off the conversation to another agent or another part of the system.
+</figcaption>
+</figure>
 
-![](./img/human_in_the_loop/approval.png)
+A **multi-turn conversation** involves multiple back-and-forth interactions between an agent and a human, which can allow the agent to gather additional information from the human in a conversational manner.
 
-Sometimes we want to approve certain steps in our agent's execution. 
- 
-We can interrupt our agent at a [breakpoint](./low_level.md#breakpoints) prior to the step that we want to approve.
+This design pattern is useful in an LLM application consisting of [multiple agents](./multi_agent.md). One or more agents may need to carry out multi-turn conversations with a human, where the human provides input or feedback at different stages of the conversation. For simplicity, the agent implementation below is illustrated as a single node, but in reality 
+it may be part of a larger graph consisting of multiple nodes and include a conditional edge.
 
-This is generally recommend for sensitive actions (e.g., using external APIs or writing to a database).
- 
-With persistence, we can surface the current agent state as well as the next step to a user for review and approval. 
- 
-If approved, the graph resumes execution from the last saved checkpoint, which is saved to the `thread`:
+=== "Using a human node per agent"
 
-```python
-# Compile our graph with a checkpointer and a breakpoint before the step to approve
-graph = builder.compile(checkpointer=checkpointer, interrupt_before=["node_2"])
+    In this pattern, each agent has its own human node for collecting user input. 
+    This can be achieved by either naming the human nodes with unique names (e.g., "human for agent 1", "human for agent 2") or by
+    using subgraphs where a subgraph contains a human node and an agent node.
 
-# Run the graph up to the breakpoint
-for event in graph.stream(inputs, thread, stream_mode="values"):
-    print(event)
-    
-# ... Get human approval ...
+    ```python
+    from langgraph.types import interrupt
 
-# If approved, continue the graph execution from the last saved checkpoint
-for event in graph.stream(None, thread, stream_mode="values"):
-    print(event)
-```
+    def human_input(state: State):
+        human_message = interrupt("human_input")
+        return {
+            "messages": [
+                {
+                    "role": "human",
+                    "content": human_message
+                }
+            ]
+        }
 
-See [our guide](../how-tos/human_in_the_loop/breakpoints.ipynb) for a detailed how-to on doing this!
+    def agent(state: State):
+        # Agent logic
+        ...
 
-### Editing
+    graph_builder.add_node("human_input", human_input)
+    graph_builder.add_edge("human_input", "agent")
+    graph = graph_builder.compile(checkpointer=checkpointer)
 
-![](./img/human_in_the_loop/edit_graph_state.png)
+    # After running the graph and hitting the interrupt, the graph will pause.
+    # Resume it with the human's input.
+    graph.invoke(
+        Command(resume="hello!"),
+        config=thread_config
+    )
+    ```
 
-Sometimes we want to review and edit the agent's state. 
- 
-As with approval, we can interrupt our agent at a [breakpoint](./low_level.md#breakpoints) prior to the step we want to check. 
- 
-We can surface the current state to a user and allow the user to edit the agent state.
- 
-This can, for example, be used to correct the agent if it made a mistake (e.g., see the section on tool calling below).
 
-We can edit the graph state by forking the current checkpoint, which is saved to the `thread`.
+=== "Sharing human node across multiple agents"
 
-We can then proceed with the graph from our forked checkpoint as done before. 
+    In this pattern, a single human node is used to collect user input for multiple agents. The active agent is determined from the state, so after human input is collected, the graph can route to the correct agent.
 
-```python
-# Compile our graph with a checkpointer and a breakpoint before the step to review
-graph = builder.compile(checkpointer=checkpointer, interrupt_before=["node_2"])
+    ```python
+    from langgraph.types import interrupt
 
-# Run the graph up to the breakpoint
-for event in graph.stream(inputs, thread, stream_mode="values"):
-    print(event)
-    
-# Review the state, decide to edit it, and create a forked checkpoint with the new state
-graph.update_state(thread, {"state": "new state"})
+    def human_node(state: MessagesState) -> Command[Literal["agent_1", "agent_2", ...]]:
+        """A node for collecting user input."""
+        user_input = interrupt(value="Ready for user input.")
 
-# Continue the graph execution from the forked checkpoint
-for event in graph.stream(None, thread, stream_mode="values"):
-    print(event)
-```
+        # Determine the **active agent** from the state, so 
+        # we can route to the correct agent after collecting input.
+        # For example, add a field to the state or use the last active agent.
+        # or fill in `name` attribute of AI messages generated by the agents.
+        active_agent = ... 
 
-See [this guide](../how-tos/human_in_the_loop/edit-graph-state.ipynb) for a detailed how-to on doing this!
+        return Command(
+            update={
+                "messages": [{
+                    "role": "human",
+                    "content": user_input,
+                }]
+            },
+            goto=active_agent,
+        )
+    ```
 
-### Input
+See [how to implement multi-turn conversations](../how-tos/multi-agent-multi-turn-convo.ipynb) for a more detailed example.
 
-![](./img/human_in_the_loop/wait_for_input.png)
+### Validating human input
 
-Sometimes we want to explicitly get human input at a particular step in the graph. 
- 
-We can create a graph node designated for this (e.g., `human_input` in our example diagram).
- 
-As with approval and editing, we can interrupt our agent at a [breakpoint](./low_level.md#breakpoints) prior to this node.
- 
-We can then perform a state update that includes the human input, just as we did with editing state.
+If you need to validate the input provided by the human within the graph itself (rather than on the client side), you can achieve this by using multiple interrupt calls within a single node.
 
-But, we add one thing: 
+```python
+from langgraph.types import interrupt
+
+def human_node(state: State):
+    """Human node with validation."""
+    question = "What is your age?"
+
+    while True:
+        answer = interrupt(question)
+
+        # Validate answer, if the answer isn't valid ask for input again.
+        if not isinstance(answer, int) or answer < 0:
+            question = f"'{answer} is not a valid age. What is your age?"
+            answer = None
+            continue
+        else:
+            # If the answer is valid, we can proceed.
+            break
+            
+    print(f"The human in the loop is {answer} years old.")
+    return {
+        "age": answer
+    }
+```
 
-We can use `as_node=human_input` with the state update to specify that the state update *should be treated as a node*.
+## The `Command` primitive
 
-The is subtle, but important: 
+When using the `interrupt` function, the graph will pause at the interrupt and wait for user input.
 
-With editing, the user makes a decision about whether or not to edit the graph state.
+Graph execution can be resumed using the [Command](../reference/types.md#langgraph.types.Command) primitive which can be passed through the `invoke`, `ainvoke`, `stream` or `astream` methods.
 
-With input, we explicitly define a node in our graph for collecting human input!
+The `Command` primitive provides several options to control and modify the graph's state during resumption:
 
-The state update with the human input then runs *as this node*.
+1. **Pass a value to the `interrupt`**: Provide data, such as a user's response, to the graph using `Command(resume=value)`. Execution resumes from the beginning of the node where the `interrupt` was used, however, this time the `interrupt(...)` call will return the value passed in the `Command(resume=value)` instead of pausing the graph.
 
-```python
-# Compile our graph with a checkpointer and a breakpoint before the step to to collect human input
-graph = builder.compile(checkpointer=checkpointer, interrupt_before=["human_input"])
+       ```python
+       # Resume graph execution with the user's input.
+       graph.invoke(Command(resume={"age": "25"}), thread_config)
+       ```
 
-# Run the graph up to the breakpoint
-for event in graph.stream(inputs, thread, stream_mode="values"):
-    print(event)
-    
-# Update the state with the user input as if it was the human_input node
-graph.update_state(thread, {"user_input": user_input}, as_node="human_input")
+2. **Update the graph state**: Modify the graph state using `Command(update=update)`. Note that resumption starts from the beginning of the node where the `interrupt` was used. Execution resumes from the beginning of the node where the `interrupt` was used, but with the updated state.
 
-# Continue the graph execution from the checkpoint created by the human_input node
-for event in graph.stream(None, thread, stream_mode="values"):
-    print(event)
-```
+      ```python
+      # Update the graph state and resume.
+      # You must provide a `resume` value if using an `interrupt`.
+      graph.invoke(Command(update={"foo": "bar"}, resume="Let's go!!!"), thread_config)
+      ```
 
-See [this guide](../how-tos/human_in_the_loop/wait-user-input.ipynb) for a detailed how-to on doing this!
+By leveraging `Command`, you can resume graph execution, handle user inputs, and dynamically adjust the graph's state.
 
-## Use-cases
+## Using with `invoke` and `ainvoke`
 
-### Reviewing Tool Calls
+When you use `stream` or `astream` to run the graph, you will receive an `Interrupt` event that let you know the `interrupt` was triggered. 
 
-Some user interaction patterns combine the above ideas.
+`invoke` and `ainvoke` do not return the interrupt information. To access this information, you must use the [get_state](../reference/graphs.md#langgraph.graph.graph.CompiledGraph.get_state) method to retrieve the graph state after calling `invoke` or `ainvoke`.
 
-For example, many agents use [tool calling](https://python.langchain.com/docs/how_to/tool_calling/) to make decisions. 
+```python
+# Run the graph up to the interrupt 
+result = graph.invoke(inputs, thread_config)
+# Get the graph state to get interrupt information.
+state = graph.get_state(thread_config)
+# Print the state values
+print(state.values)
+# Print the pending tasks
+print(state.tasks)
+# Resume the graph with the user's input.
+graph.invoke(Command(resume={"age": "25"}), thread_config)
+```
 
-Tool calling presents a challenge because the agent must get two things right: 
+```pycon
+{'foo': 'bar'} # State values
+(
+    PregelTask(
+        id='5d8ffc92-8011-0c9b-8b59-9d3545b7e553', 
+        name='node_foo', 
+        path=('__pregel_pull', 'node_foo'), 
+        error=None, 
+        interrupts=(Interrupt(value='value_in_interrupt', resumable=True, ns=['node_foo:5d8ffc92-8011-0c9b-8b59-9d3545b7e553'], when='during'),), state=None, 
+        result=None
+    ),
+) # Pending tasks. interrupts 
+```
 
-(1) The name of the tool to call 
+## How does resuming from an interrupt work?
 
-(2) The arguments to pass to the tool
+!!! warning
 
-Even if the tool call is correct, we may also want to apply discretion: 
+    Resuming from an `interrupt` is **different** from Python's `input()` function, where execution resumes from the exact point where the `input()` function was called.
 
-(3) The tool call may be a sensitive operation that we want to approve 
+A critical aspect of using `interrupt` is understanding how resuming works. When you resume execution after an `interrupt`, graph execution starts from the **beginning** of the **graph node** where the last `interrupt` was triggered.
 
-With these points in mind, we can combine the above ideas to create a human-in-the-loop review of a tool call.
+**All** code from the beginning of the node to the `interrupt` will be re-executed.
 
 ```python
-# Compile our graph with a checkpointer and a breakpoint before the step to to review the tool call from the LLM 
-graph = builder.compile(checkpointer=checkpointer, interrupt_before=["human_review"])
-
-# Run the graph up to the breakpoint
-for event in graph.stream(inputs, thread, stream_mode="values"):
-    print(event)
-    
-# Review the tool call and update it, if needed, as the human_review node
-graph.update_state(thread, {"tool_call": "updated tool call"}, as_node="human_review")
+counter = 0
+def node(state: State):
+    # All the code from the beginning of the node to the interrupt will be re-executed
+    # when the graph resumes.
+    global counter
+    counter += 1
+    print(f"> Entered the node: {counter} # of times")
+    # Pause the graph and wait for user input.
+    answer = interrupt()
+    print("The value of counter is:", counter)
+    ...
+```
 
-# Otherwise, approve the tool call and proceed with the graph execution with no edits 
+Upon **resuming** the graph, the counter will be incremented a second time, resulting in the following output:
 
-# Continue the graph execution from either: 
-# (1) the forked checkpoint created by human_review or 
-# (2) the checkpoint saved when the tool call was originally made (no edits in human_review)
-for event in graph.stream(None, thread, stream_mode="values"):
-    print(event)
+```pycon
+> Entered the node: 2 # of times
+The value of counter is: 2
 ```
 
-See [this guide](../how-tos/human_in_the_loop/review-tool-calls.ipynb) for a detailed how-to on doing this!
+## Common Pitfalls
 
-### Time Travel
+### Side-effects
 
-When working with agents, we often want closely examine their decision making process: 
+Place code with side effects, such as API calls, **after** the `interrupt` to avoid duplication, as these are re-triggered every time the node is resumed. 
 
-(1) Even when they arrive a desired final result, the reasoning that led to that result is often important to examine.
+=== "Side effects before interrupt (BAD)"
 
-(2) When agents make mistakes, it is often valuable to understand why.
+    This code will re-execute the API call another time when the node is resumed from
+    the `interrupt`.
 
-(3) In either of the above cases, it is useful to manually explore alternative decision making paths.
+    This can be problematic if the API call is not idempotent or is just expensive.
 
-Collectively, we call these debugging concepts `time-travel` and they are composed of `replaying` and `forking`.
+    ```python
+    from langgraph.types import interrupt
 
-#### Replaying
+    def human_node(state: State):
+        """Human node with validation."""
+        api_call(...) # This code will be re-executed when the node is resumed.
+        answer = interrupt(question)
+    ```
 
-![](./img/human_in_the_loop/replay.png)
+=== "Side effects after interrupt (OK)"
 
-Sometimes we want to simply replay past actions of an agent. 
- 
-Above, we showed the case of executing an agent from the current state (or checkpoint) of the graph.
+    ```python
+    from langgraph.types import interrupt
 
-We by simply passing in `None` for the input with a `thread`.
+    def human_node(state: State):
+        """Human node with validation."""
+        
+        answer = interrupt(question)
+        
+        api_call(answer) # OK as it's after the interrupt
+    ```
 
-```
-thread = {"configurable": {"thread_id": "1"}}
-for event in graph.stream(None, thread, stream_mode="values"):
-    print(event)
-```
+=== "Side effects in a separate node (OK)"
 
-Now, we can modify this to replay past actions from a *specific* checkpoint by passing in the checkpoint ID.
+    ```python
+    from langgraph.types import interrupt
 
-To get a specific checkpoint ID, we can easily get all of the checkpoints in the thread and filter to the one we want.
+    def human_node(state: State):
+        """Human node with validation."""
+        
+        answer = interrupt(question)
+        
+        return {
+            "answer": answer
+        }
 
-```python
-all_checkpoints = []
-for state in app.get_state_history(thread):
-    all_checkpoints.append(state)
-```
+    def api_call_node(state: State):
+        api_call(...) # OK as it's in a separate node
+    ```
 
-Each checkpoint has a unique ID, which we can use to replay from a specific checkpoint.
+### Subgraphs called as functions
 
-Assume from reviewing the checkpoints that we want to replay from one, `xxx`.
 
-We just pass in the checkpoint ID when we run the graph.
+**Subgraphs**: If you're invoking a subgraph [as a function](low_level.md#as-a-function), the **parent** graph will be re-run from the **beginning of the node** where the subgraph was invoked.
 
 ```python
-config = {'configurable': {'thread_id': '1', 'checkpoint_id': 'xxx'}}
-for event in graph.stream(None, config, stream_mode="values"):
-    print(event)
+def some_node(state: State):
+    some_code() # <-- This code will be re-executed when the subgraph is resumed.
+    # Using a subgraph as a function.
+    # The subgraph has an `interrupt` call
+    subgraph_result = subgraph.invoke(some_input)
+    ...
 ```
- 
-Importantly, the graph knows which checkpoints have been previously executed. 
 
-So, it will re-play any previously executed nodes rather than re-executing them.
 
-See [this additional conceptual guide](https://langchain-ai.github.io/langgraph/concepts/persistence/#replay) for related context on replaying.
+### Using multiple interrupts
 
-See see [this guide](../how-tos/human_in_the_loop/time-travel.ipynb) for a detailed how-to on doing time-travel!
+Using multiple interrupts within a **single** node can be helpful for patterns like [validating human input](#validating-human-input). However, using multiple interrupts in the same node can lead to unexpected behavior if not handled carefully.
 
-#### Forking
+When a node contains multiple interrupt calls, LangGraph keeps a list of resume values specific to the task executing the node. Whenever execution resumes, it starts at the beginning of the node. For each interrupt encountered, LangGraph checks if a matching value exists in the task's resume list. Matching is **strictly index-based**, so the order of interrupt calls within the node is critical.
 
-![](./img/human_in_the_loop/forking.png)
+To avoid issues, refrain from dynamically changing the node's structure between executions. This includes adding, removing, or reordering interrupt calls, as such changes can result in mismatched indices. These problems often arise from unconventional patterns, such as mutating state via `Command(resume=..., update=SOME_STATE_MUTATION)` or relying on global variables to modify the node’s structure dynamically.
 
-Sometimes we want to fork past actions of an agent, and explore different paths through the graph.
+??? "Example of incorrect code"
 
-`Editing`, as discussed above, is *exactly* how we do this for the *current* state of the graph! 
+    ```python
+    import uuid
+    from typing import TypedDict, Optional
 
-But, what if we want to fork *past* states of the graph?
+    from langgraph.graph import StateGraph
+    from langgraph.constants import START 
+    from langgraph.types import interrupt, Command
+    from langgraph.checkpoint.memory import MemorySaver
 
-For example, let's say we want to edit a particular checkpoint, `xxx`.
 
-We pass this `checkpoint_id` when we update the state of the graph.
+    class State(TypedDict):
+        """The graph state."""
 
-```python
-config = {"configurable": {"thread_id": "1", "checkpoint_id": "xxx"}}
-graph.update_state(config, {"state": "updated state"}, )
-```
+        age: Optional[str]
+        name: Optional[str]
 
-This creates a new forked checkpoint, `xxx-fork`, which we can then run the graph from.
 
-```python
-config = {'configurable': {'thread_id': '1', 'checkpoint_id': 'xxx-fork'}}
-for event in graph.stream(None, config, stream_mode="values"):
-    print(event)
-```
+    def human_node(state: State):
+        if not state.get('name'):
+            name = interrupt("what is your name?")
+        else:
+            name = "N/A"
+
+        if not state.get('age'):
+            age = interrupt("what is your age?")
+        else:
+            age = "N/A"
+            
+        print(f"Name: {name}. Age: {age}")
+        
+        return {
+            "age": age,
+            "name": name,
+        }
+
+
+    builder = StateGraph(State)
+    builder.add_node("human_node", human_node)
+    builder.add_edge(START, "human_node")
+
+    # A checkpointer must be enabled for interrupts to work!
+    checkpointer = MemorySaver()
+    graph = builder.compile(checkpointer=checkpointer)
+
+    config = {
+        "configurable": {
+            "thread_id": uuid.uuid4(),
+        }
+    }
+
+    for chunk in graph.stream({"age": None, "name": None}, config):
+        print(chunk)
+
+    for chunk in graph.stream(Command(resume="John", update={"name": "foo"}), config):
+        print(chunk)
+    ```
+
+    ```pycon
+    {'__interrupt__': (Interrupt(value='what is your name?', resumable=True, ns=['human_node:3a007ef9-c30d-c357-1ec1-86a1a70d8fba'], when='during'),)}
+    Name: N/A. Age: John
+    {'human_node': {'age': 'John', 'name': 'N/A'}}
+    ```
 
-See [this additional conceptual guide](https://langchain-ai.github.io/langgraph/concepts/persistence/#update-state) for related context on forking.
+## Additional Resources 📚
 
-See see [this guide](../how-tos/human_in_the_loop/time-travel.ipynb) for a detailed how-to on doing time-travel!
+- [**Conceptual Guide: Persistence**](persistence.md#replay): Read the persistence guide for more context on replaying.
+- [**How to Guides: Human-in-the-loop**](../how-tos/index.md#human-in-the-loop): Learn how to implement human-in-the-loop workflows in LangGraph.
+- [**How to implement multi-turn conversations**](../how-tos/multi-agent-multi-turn-convo.ipynb): Learn how to implement multi-turn conversations in LangGraph.
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diff --git a/docs/docs/concepts/index.md b/docs/docs/concepts/index.md
index 6c057c672..4d8e5f06f 100644
--- a/docs/docs/concepts/index.md
+++ b/docs/docs/concepts/index.md
@@ -24,7 +24,9 @@ The conceptual guide does not cover step-by-step instructions or specific implem
 - [LangGraph Glossary](low_level.md): LangGraph workflows are designed as graphs, with nodes representing different components and edges representing the flow of information between them. This guide provides an overview of the key concepts associated with LangGraph graph primitives.
 - [Common Agentic Patterns](agentic_concepts.md): An agent uses an LLM to pick its own control flow to solve more complex problems! Agents are a key building block in many LLM applications. This guide explains the different types of agent architectures and how they can be used to control the flow of an application.
 - [Multi-Agent Systems](multi_agent.md): Complex LLM applications can often be broken down into multiple agents, each responsible for a different part of the application. This guide explains common patterns for building multi-agent systems.
+- [Breakpoints](breakpoints.md): Breakpoints allow pausing the execution of a graph at specific points. Breakpoints allow stepping through graph execution for debugging purposes.
 - [Human-in-the-Loop](human_in_the_loop.md): Explains different ways of integrating human feedback into a LangGraph application.
+- [Time Travel](time-travel.md): Time travel allows you to replay past actions in your LangGraph application to explore alternative paths and debug issues.
 - [Persistence](persistence.md): LangGraph has a built-in persistence layer, implemented through checkpointers. This persistence layer helps to support powerful capabilities like human-in-the-loop, memory, time travel, and fault-tolerance.
 - [Memory](memory.md): Memory in AI applications refers to the ability to process, store, and effectively recall information from past interactions. With memory, your agents can learn from feedback and adapt to users' preferences.  
 - [Streaming](streaming.md): Streaming is crucial for enhancing the responsiveness of applications built on LLMs. By displaying output progressively, even before a complete response is ready, streaming significantly improves user experience (UX), particularly when dealing with the latency of LLMs. 
diff --git a/docs/docs/concepts/low_level.md b/docs/docs/concepts/low_level.md
index 86bc0879d..522017bcc 100644
--- a/docs/docs/concepts/low_level.md
+++ b/docs/docs/concepts/low_level.md
@@ -383,6 +383,10 @@ def lookup_user_info(tool_call_id: Annotated[str, InjectedToolCallId], config: R
 
 If you are using tools that update state via `Command`, we recommend using prebuilt [`ToolNode`][langgraph.prebuilt.tool_node.ToolNode] which automatically handles tools returning `Command` objects and propagates them to the graph state. If you're writing a custom node that calls tools, you would need to manually propagate `Command` objects returned by the tools as the update from node.
 
+### Human-in-the-loop
+
+`Command` is an important part of human-in-the-loop workflows: when using `interrupt()` to collect user input, `Command` is then used to supply the input and resume execution via `Command(resume="User input")`. Check out [this conceptual guide](./human_in_the_loop.md) for more information.
+
 ## Persistence
 
 LangGraph provides built-in persistence for your agent's state using [checkpointers][langgraph.checkpoint.base.BaseCheckpointSaver]. Checkpointers save snapshots of the graph state at every superstep, allowing resumption at any time. This enables features like human-in-the-loop interactions, memory management, and fault-tolerance. You can even directly manipulate a graph's state after its execution using the
@@ -448,35 +452,32 @@ graph.invoke(inputs, config={"recursion_limit": 5, "configurable":{"llm": "anthr
 
 Read [this how-to](https://langchain-ai.github.io/langgraph/how-tos/recursion-limit/) to learn more about how the recursion limit works.
 
-## Breakpoints
-
-It can often be useful to set breakpoints before or after certain nodes execute. This can be used to wait for human approval before continuing. These can be set when you ["compile" a graph](#compiling-your-graph). You can set breakpoints either _before_ a node executes (using `interrupt_before`) or after a node executes (using `interrupt_after`.)
+## `interrupt`
 
-You **MUST** use a [checkpointer](./persistence.md) when using breakpoints. This is because your graph needs to be able to resume execution.
-
-In order to resume execution, you can just invoke your graph with `None` as the input.
+Use the [interrupt](../reference/types.md/#langgraph.types.interrupt) function to **pause** the graph at specific points to collect user input. The `interrupt` function surfaces interrupt information to the client, allowing the developer to collect user input, validate the graph state, or make decisions before resuming execution.
 
 ```python
-# Initial run of graph
-graph.invoke(inputs, config=config)
+from langgraph.types import interrupt
 
-# Let's assume it hit a breakpoint somewhere, you can then resume by passing in None
-graph.invoke(None, config=config)
+def human_approval_node(state: State):
+    ...
+    answer = interrupt(
+        # This value will be sent to the client.
+        # It can be any JSON serializable value.
+        {"question": "is it ok to continue?"},
+    )
+    ...
 ```
 
-See [this guide](../how-tos/human_in_the_loop/breakpoints.ipynb) for a full walkthrough of how to add breakpoints.
+Resuming the graph is done by passing a [`Command`](#command) object to the graph with the `resume` key set to the value returned by the `interrupt` function.
 
-### Dynamic Breakpoints
+Read more about how the `interrupt` is used for **human-in-the-loop** workflows in the [Human-in-the-loop conceptual guide](./human_in_the_loop.md).
 
-It may be helpful to **dynamically** interrupt the graph from inside a given node based on some condition. In `LangGraph` you can do so by using `NodeInterrupt` -- a special exception that can be raised from inside a node.
+## Breakpoints
 
-```python
-def my_node(state: State) -> State:
-    if len(state['input']) > 5:
-        raise NodeInterrupt(f"Received input that is longer than 5 characters: {state['input']}")
+Breakpoints pause graph execution at specific points and enable stepping through execution step by step. Breakpoints are powered by LangGraph's [**persistence layer**](./persistence.md), which saves the state after each graph step. Breakpoints can also be used to enable [**human-in-the-loop**](./human_in_the_loop.md) workflows, though we recommend using the [`interrupt` function](#interrupt-function) for this purpose.
 
-    return state
-```
+Read more about breakpoints in the [Breakpoints conceptual guide](./breakpoints.md).
 
 ## Subgraphs
 
@@ -517,7 +518,7 @@ The simplest way to create subgraph nodes is by using a [compiled subgraph](#com
     If you pass extra keys to the subgraph node (i.e., in addition to the shared keys), they will be ignored by the subgraph node. Similarly, if you return extra keys from the subgraph, they will be ignored by the parent graph.
 
 ```python
-from langgraph.graph import START, StateGraph
+from langgraph.graph import StateGraph
 from typing import TypedDict
 
 class State(TypedDict):
diff --git a/docs/docs/concepts/persistence.md b/docs/docs/concepts/persistence.md
index 0ec126316..dccf6a36f 100644
--- a/docs/docs/concepts/persistence.md
+++ b/docs/docs/concepts/persistence.md
@@ -471,7 +471,7 @@ Second, checkpointers allow for ["memory"](agentic_concepts.md#memory) between i
 
 ### Time Travel
 
-Third, checkpointers allow for ["time travel"](../how-tos/human_in_the_loop/time-travel.ipynb), allowing users to replay prior graph executions to review and / or debug specific graph steps. In addition, checkpointers make it possible to fork the graph state at arbitrary checkpoints to explore alternative trajectories.
+Third, checkpointers allow for ["time travel"](time-travel.md), allowing users to replay prior graph executions to review and / or debug specific graph steps. In addition, checkpointers make it possible to fork the graph state at arbitrary checkpoints to explore alternative trajectories.
 
 ### Fault-tolerance
 
diff --git a/docs/docs/concepts/time-travel.md b/docs/docs/concepts/time-travel.md
new file mode 100644
index 000000000..bb7fd334b
--- /dev/null
+++ b/docs/docs/concepts/time-travel.md
@@ -0,0 +1,72 @@
+# Time Travel ⏱️
+
+!!! note "Prerequisites"
+
+    This guide assumes that you are familiar with LangGraph's checkpoints and states. If not, please review the [persistence](./persistence.md) concept first.
+
+
+When working with non-deterministic systems that make model-based decisions (e.g., agents powered by LLMs), it can be useful to examine their decision-making process in detail:
+
+1. 🤔 **Understand Reasoning**: Analyze the steps that led to a successful result.
+2. 🐞 **Debug Mistakes**: Identify where and why errors occurred.
+3. 🔍 **Explore Alternatives**: Test different paths to uncover better solutions.
+
+We call these debugging techniques **Time Travel**, composed of two key actions: [**Replaying**](#replaying) 🔁 and [**Forking**](#forking) 🔀 .
+
+## Replaying
+
+![](./img/human_in_the_loop/replay.png)
+
+Replaying allows us to revisit and reproduce an agent's past actions. This can be done either from the current state (or checkpoint) of the graph or from a specific checkpoint.
+
+To replay from the current state, simply pass `None` as the input along with a `thread`:
+
+```python
+thread = {"configurable": {"thread_id": "1"}}
+for event in graph.stream(None, thread, stream_mode="values"):
+    print(event)
+```
+
+To replay actions from a specific checkpoint, start by retrieving all checkpoints for the thread:
+
+```python
+all_checkpoints = []
+for state in graph.get_state_history(thread):
+    all_checkpoints.append(state)
+```
+
+Each checkpoint has a unique ID. After identifying the desired checkpoint, for instance, `xyz`, include its ID in the configuration:
+
+```python
+config = {'configurable': {'thread_id': '1', 'checkpoint_id': 'xyz'}}
+for event in graph.stream(None, config, stream_mode="values"):
+    print(event)
+```
+
+The graph efficiently replays previously executed nodes instead of re-executing them, leveraging its awareness of prior checkpoint executions.
+
+## Forking
+
+![](./img/human_in_the_loop/forking.png)
+
+Forking allows you to revisit an agent's past actions and explore alternative paths within the graph.
+
+To edit a specific checkpoint, such as `xyz`, provide its `checkpoint_id` when updating the graph's state:
+
+```python
+config = {"configurable": {"thread_id": "1", "checkpoint_id": "xyz"}}
+graph.update_state(config, {"state": "updated state"})
+```
+
+This creates a new forked checkpoint, xyz-fork, from which you can continue running the graph:
+
+```python
+config = {'configurable': {'thread_id': '1', 'checkpoint_id': 'xyz-fork'}}
+for event in graph.stream(None, config, stream_mode="values"):
+    print(event)
+```
+
+## Additional Resources 📚
+
+- [**Conceptual Guide: Persistence**](https://langchain-ai.github.io/langgraph/concepts/persistence/#replay): Read the persistence guide for more context on replaying.
+- [**How to View and Update Past Graph State**](../how-tos/human_in_the_loop/time-travel.ipynb): Step-by-step instructions for working with graph state that demonstrate the **replay** and **fork** actions.
diff --git a/docs/docs/concepts/v0-human-in-the-loop.md b/docs/docs/concepts/v0-human-in-the-loop.md
new file mode 100644
index 000000000..ad2f19aa4
--- /dev/null
+++ b/docs/docs/concepts/v0-human-in-the-loop.md
@@ -0,0 +1,329 @@
+# Human-in-the-loop
+
+!!! note "Use the `interrupt` function instead."
+
+    As of LangGraph 0.2.57, the recommended way to set breakpoints is using the [`interrupt` function][langgraph.types.interrupt] as it simplifies **human-in-the-loop** patterns.
+
+    Please see the revised [human-in-the-loop guide](./human_in_the_loop.md) for the latest version that uses the `interrupt` function.
+
+
+Human-in-the-loop (or "on-the-loop") enhances agent capabilities through several common user interaction patterns.
+
+Common interaction patterns include:
+
+(1) `Approval` - We can interrupt our agent, surface the current state to a user, and allow the user to accept an action. 
+
+(2) `Editing` - We can interrupt our agent, surface the current state to a user, and allow the user to edit the agent state. 
+
+(3) `Input` - We can explicitly create a graph node to collect human input and pass that input directly to the agent state.
+
+Use-cases for these interaction patterns include:
+
+(1) `Reviewing tool calls` - We can interrupt an agent to review and edit the results of tool calls.
+
+(2) `Time Travel` - We can manually re-play and / or fork past actions of an agent.
+
+## Persistence
+
+All of these interaction patterns are enabled by LangGraph's built-in [persistence](./persistence.md) layer, which will write a checkpoint of the graph state at each step. Persistence allows the graph to stop so that a human can review and / or edit the current state of the graph and then resume with the human's input.
+
+### Breakpoints
+
+Adding a [breakpoint](./breakpoints.md) a specific location in the graph flow is one way to enable human-in-the-loop. In this case, the developer knows *where* in the workflow human input is needed and simply places a breakpoint prior to or following that particular graph node.
+
+Here, we compile our graph with a checkpointer and a breakpoint at the node we want to interrupt before, `step_for_human_in_the_loop`. We then perform one of the above interaction patterns, which will create a new checkpoint if a human edits the graph state. The new checkpoint is saved to the `thread` and we can resume the graph execution from there by passing in `None` as the input.
+
+```python
+# Compile our graph with a checkpointer and a breakpoint before "step_for_human_in_the_loop"
+graph = builder.compile(checkpointer=checkpointer, interrupt_before=["step_for_human_in_the_loop"])
+
+# Run the graph up to the breakpoint
+thread_config = {"configurable": {"thread_id": "1"}}
+for event in graph.stream(inputs, thread_config, stream_mode="values"):
+    print(event)
+    
+# Perform some action that requires human in the loop
+
+# Continue the graph execution from the current checkpoint 
+for event in graph.stream(None, thread_config, stream_mode="values"):
+    print(event)
+```
+
+### Dynamic Breakpoints
+
+Alternatively, the developer can define some *condition* that must be met for a breakpoint to be triggered. This concept of [dynamic breakpoints](./breakpoints.md) is useful when the developer wants to halt the graph under *a particular condition*. This uses a `NodeInterrupt`, which is a special type of exception that can be raised from within a node based upon some condition. As an example, we can define a dynamic breakpoint that triggers when the `input` is longer than 5 characters.
+
+```python
+def my_node(state: State) -> State:
+    if len(state['input']) > 5:
+        raise NodeInterrupt(f"Received input that is longer than 5 characters: {state['input']}")
+    return state
+```
+
+Let's assume we run the graph with an input that triggers the dynamic breakpoint and then attempt to resume the graph execution simply by passing in `None` for the input. 
+
+```python
+# Attempt to continue the graph execution with no change to state after we hit the dynamic breakpoint 
+for event in graph.stream(None, thread_config, stream_mode="values"):
+    print(event)
+```
+
+The graph will *interrupt* again because this node will be *re-run* with the same graph state. We need to change the graph state such that the condition that triggers the dynamic breakpoint is no longer met. So, we can simply edit the graph state to an input that meets the condition of our dynamic breakpoint (< 5 characters) and re-run the node.
+
+```python 
+# Update the state to pass the dynamic breakpoint
+graph.update_state(config=thread_config, values={"input": "foo"})
+for event in graph.stream(None, thread_config, stream_mode="values"):
+    print(event)
+```
+
+Alternatively, what if we want to keep our current input and skip the node (`my_node`) that performs the check? To do this, we can simply perform the graph update with `as_node="my_node"` and pass in `None` for the values. This will make no update the graph state, but run the update as `my_node`, effectively skipping the node and bypassing the dynamic breakpoint.
+
+```python
+# This update will skip the node `my_node` altogether
+graph.update_state(config=thread_config, values=None, as_node="my_node")
+for event in graph.stream(None, thread_config, stream_mode="values"):
+    print(event)
+```
+
+See [our guide](../how-tos/human_in_the_loop/dynamic_breakpoints.ipynb) for a detailed how-to on doing this!
+
+## Interaction Patterns
+
+### Approval
+
+![](./img/human_in_the_loop/approval.png)
+
+Sometimes we want to approve certain steps in our agent's execution. 
+ 
+We can interrupt our agent at a [breakpoint](./breakpoints.md) prior to the step that we want to approve.
+
+This is generally recommend for sensitive actions (e.g., using external APIs or writing to a database).
+ 
+With persistence, we can surface the current agent state as well as the next step to a user for review and approval. 
+ 
+If approved, the graph resumes execution from the last saved checkpoint, which is saved to the `thread`:
+
+```python
+# Compile our graph with a checkpointer and a breakpoint before the step to approve
+graph = builder.compile(checkpointer=checkpointer, interrupt_before=["node_2"])
+
+# Run the graph up to the breakpoint
+for event in graph.stream(inputs, thread, stream_mode="values"):
+    print(event)
+    
+# ... Get human approval ...
+
+# If approved, continue the graph execution from the last saved checkpoint
+for event in graph.stream(None, thread, stream_mode="values"):
+    print(event)
+```
+
+See [our guide](../how-tos/human_in_the_loop/breakpoints.ipynb) for a detailed how-to on doing this!
+
+### Editing
+
+![](./img/human_in_the_loop/edit_graph_state.png)
+
+Sometimes we want to review and edit the agent's state. 
+ 
+As with approval, we can interrupt our agent at a [breakpoint](./breakpoints.md) prior to the step we want to check. 
+ 
+We can surface the current state to a user and allow the user to edit the agent state.
+ 
+This can, for example, be used to correct the agent if it made a mistake (e.g., see the section on tool calling below).
+
+We can edit the graph state by forking the current checkpoint, which is saved to the `thread`.
+
+We can then proceed with the graph from our forked checkpoint as done before. 
+
+```python
+# Compile our graph with a checkpointer and a breakpoint before the step to review
+graph = builder.compile(checkpointer=checkpointer, interrupt_before=["node_2"])
+
+# Run the graph up to the breakpoint
+for event in graph.stream(inputs, thread, stream_mode="values"):
+    print(event)
+    
+# Review the state, decide to edit it, and create a forked checkpoint with the new state
+graph.update_state(thread, {"state": "new state"})
+
+# Continue the graph execution from the forked checkpoint
+for event in graph.stream(None, thread, stream_mode="values"):
+    print(event)
+```
+
+See [this guide](../how-tos/human_in_the_loop/edit-graph-state.ipynb) for a detailed how-to on doing this!
+
+### Input
+
+![](./img/human_in_the_loop/wait_for_input.png)
+
+Sometimes we want to explicitly get human input at a particular step in the graph. 
+ 
+We can create a graph node designated for this (e.g., `human_input` in our example diagram).
+ 
+As with approval and editing, we can interrupt our agent at a [breakpoint](./breakpoints.md) prior to this node.
+ 
+We can then perform a state update that includes the human input, just as we did with editing state.
+
+But, we add one thing: 
+
+We can use `as_node=human_input` with the state update to specify that the state update *should be treated as a node*.
+
+The is subtle, but important: 
+
+With editing, the user makes a decision about whether or not to edit the graph state.
+
+With input, we explicitly define a node in our graph for collecting human input!
+
+The state update with the human input then runs *as this node*.
+
+```python
+# Compile our graph with a checkpointer and a breakpoint before the step to to collect human input
+graph = builder.compile(checkpointer=checkpointer, interrupt_before=["human_input"])
+
+# Run the graph up to the breakpoint
+for event in graph.stream(inputs, thread, stream_mode="values"):
+    print(event)
+    
+# Update the state with the user input as if it was the human_input node
+graph.update_state(thread, {"user_input": user_input}, as_node="human_input")
+
+# Continue the graph execution from the checkpoint created by the human_input node
+for event in graph.stream(None, thread, stream_mode="values"):
+    print(event)
+```
+
+See [this guide](../how-tos/human_in_the_loop/wait-user-input.ipynb) for a detailed how-to on doing this!
+
+## Use-cases
+
+### Reviewing Tool Calls
+
+Some user interaction patterns combine the above ideas.
+
+For example, many agents use [tool calling](https://python.langchain.com/docs/how_to/tool_calling/) to make decisions. 
+
+Tool calling presents a challenge because the agent must get two things right: 
+
+(1) The name of the tool to call 
+
+(2) The arguments to pass to the tool
+
+Even if the tool call is correct, we may also want to apply discretion: 
+
+(3) The tool call may be a sensitive operation that we want to approve 
+
+With these points in mind, we can combine the above ideas to create a human-in-the-loop review of a tool call.
+
+```python
+# Compile our graph with a checkpointer and a breakpoint before the step to to review the tool call from the LLM 
+graph = builder.compile(checkpointer=checkpointer, interrupt_before=["human_review"])
+
+# Run the graph up to the breakpoint
+for event in graph.stream(inputs, thread, stream_mode="values"):
+    print(event)
+    
+# Review the tool call and update it, if needed, as the human_review node
+graph.update_state(thread, {"tool_call": "updated tool call"}, as_node="human_review")
+
+# Otherwise, approve the tool call and proceed with the graph execution with no edits 
+
+# Continue the graph execution from either: 
+# (1) the forked checkpoint created by human_review or 
+# (2) the checkpoint saved when the tool call was originally made (no edits in human_review)
+for event in graph.stream(None, thread, stream_mode="values"):
+    print(event)
+```
+
+See [this guide](../how-tos/human_in_the_loop/review-tool-calls.ipynb) for a detailed how-to on doing this!
+
+### Time Travel
+
+When working with agents, we often want closely examine their decision making process: 
+
+(1) Even when they arrive a desired final result, the reasoning that led to that result is often important to examine.
+
+(2) When agents make mistakes, it is often valuable to understand why.
+
+(3) In either of the above cases, it is useful to manually explore alternative decision making paths.
+
+Collectively, we call these debugging concepts `time-travel` and they are composed of `replaying` and `forking`.
+
+#### Replaying
+
+![](./img/human_in_the_loop/replay.png)
+
+Sometimes we want to simply replay past actions of an agent. 
+ 
+Above, we showed the case of executing an agent from the current state (or checkpoint) of the graph.
+
+We by simply passing in `None` for the input with a `thread`.
+
+```
+thread = {"configurable": {"thread_id": "1"}}
+for event in graph.stream(None, thread, stream_mode="values"):
+    print(event)
+```
+
+Now, we can modify this to replay past actions from a *specific* checkpoint by passing in the checkpoint ID.
+
+To get a specific checkpoint ID, we can easily get all of the checkpoints in the thread and filter to the one we want.
+
+```python
+all_checkpoints = []
+for state in app.get_state_history(thread):
+    all_checkpoints.append(state)
+```
+
+Each checkpoint has a unique ID, which we can use to replay from a specific checkpoint.
+
+Assume from reviewing the checkpoints that we want to replay from one, `xxx`.
+
+We just pass in the checkpoint ID when we run the graph.
+
+```python
+config = {'configurable': {'thread_id': '1', 'checkpoint_id': 'xxx'}}
+for event in graph.stream(None, config, stream_mode="values"):
+    print(event)
+```
+ 
+Importantly, the graph knows which checkpoints have been previously executed. 
+
+So, it will re-play any previously executed nodes rather than re-executing them.
+
+See [this additional conceptual guide](https://langchain-ai.github.io/langgraph/concepts/persistence/#replay) for related context on replaying.
+
+See see [this guide](../how-tos/human_in_the_loop/time-travel.ipynb) for a detailed how-to on doing time-travel!
+
+#### Forking
+
+![](./img/human_in_the_loop/forking.png)
+
+Sometimes we want to fork past actions of an agent, and explore different paths through the graph.
+
+`Editing`, as discussed above, is *exactly* how we do this for the *current* state of the graph! 
+
+But, what if we want to fork *past* states of the graph?
+
+For example, let's say we want to edit a particular checkpoint, `xxx`.
+
+We pass this `checkpoint_id` when we update the state of the graph.
+
+```python
+config = {"configurable": {"thread_id": "1", "checkpoint_id": "xxx"}}
+graph.update_state(config, {"state": "updated state"}, )
+```
+
+This creates a new forked checkpoint, `xxx-fork`, which we can then run the graph from.
+
+```python
+config = {'configurable': {'thread_id': '1', 'checkpoint_id': 'xxx-fork'}}
+for event in graph.stream(None, config, stream_mode="values"):
+    print(event)
+```
+
+See [this additional conceptual guide](https://langchain-ai.github.io/langgraph/concepts/persistence/#update-state) for related context on forking.
+
+See [this guide](../how-tos/human_in_the_loop/time-travel.ipynb) for a detailed how-to on doing time-travel!
diff --git a/docs/docs/how-tos/human_in_the_loop/breakpoints.ipynb b/docs/docs/how-tos/human_in_the_loop/breakpoints.ipynb
index a52f3b216..7d52f25c9 100644
--- a/docs/docs/how-tos/human_in_the_loop/breakpoints.ipynb
+++ b/docs/docs/how-tos/human_in_the_loop/breakpoints.ipynb
@@ -12,6 +12,14 @@
    "source": [
     "# How to add breakpoints\n",
     "\n",
+    "!!! tip \"Prerequisites\"\n",
+    "\n",
+    "    This guide assumes familiarity with the following concepts:\n",
+    "\n",
+    "    * [Breakpoints](../../../concepts/breakpoints)\n",
+    "    * [LangGraph Glossary](../../../concepts/low_level)\n",
+    "    \n",
+    "\n",
     "Human-in-the-loop (HIL) interactions are crucial for [agentic systems](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#human-in-the-loop). [Breakpoints](https://langchain-ai.github.io/langgraph/concepts/low_level/#breakpoints) are a common HIL interaction pattern, allowing the graph to stop at specific steps and seek human approval before proceeding (e.g., for sensitive actions). \n",
     "\n",
     "Breakpoints are built on top of LangGraph [checkpoints](https://langchain-ai.github.io/langgraph/concepts/low_level/#checkpointer), which save the graph's state after each node execution. Checkpoints are saved in [threads](https://langchain-ai.github.io/langgraph/concepts/low_level/#threads) that preserve graph state and can be accessed after a graph has finished execution. This allows for graph execution to pause at specific points, await human approval, and then resume execution from the last checkpoint.\n",
@@ -467,7 +475,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/docs/docs/how-tos/human_in_the_loop/dynamic_breakpoints.ipynb b/docs/docs/how-tos/human_in_the_loop/dynamic_breakpoints.ipynb
index e1e06cd49..28893dda7 100644
--- a/docs/docs/how-tos/human_in_the_loop/dynamic_breakpoints.ipynb
+++ b/docs/docs/how-tos/human_in_the_loop/dynamic_breakpoints.ipynb
@@ -1,24 +1,32 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "id": "ee54cde3-7e4d-43f4-b921-e7141ea0f19e",
-   "metadata": {},
-   "source": [
-    "# How to add dynamic breakpoints"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "607849c6-4b8c-4e06-ad9c-758bb5a08e86",
+   "id": "b7d5f6a5-9e59-43e4-a4b6-8ada6dace691",
    "metadata": {},
    "source": [
+    "# How to add dynamic breakpoints with `NodeInterrupt`\n",
+    "\n",
+    "!!! note\n",
+    "\n",
+    "    For **human-in-the-loop** workflows use the new [`interrupt()`](../../../reference/types/#langgraph.types.interrupt) function for **human-in-the-loop** workflows. Please review the [Human-in-the-loop conceptual guide](../../../concepts/human_in_the_loop) for more information about design patterns with `interrupt`.\n",
+    "\n",
+    "!!! tip \"Prerequisites\"\n",
+    "\n",
+    "    This guide assumes familiarity with the following concepts:\n",
+    "\n",
+    "    * [Breakpoints](../../../concepts/breakpoints)\n",
+    "    * [LangGraph Glossary](../../../concepts/low_level)\n",
+    "    \n",
+    "\n",
     "Human-in-the-loop (HIL) interactions are crucial for [agentic systems](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#human-in-the-loop). [Breakpoints](https://langchain-ai.github.io/langgraph/concepts/low_level/#breakpoints) are a common HIL interaction pattern, allowing the graph to stop at specific steps and seek human approval before proceeding (e.g., for sensitive actions).\n",
     "\n",
     "In LangGraph you can add breakpoints before / after a node is executed. But oftentimes it may be helpful to **dynamically** interrupt the graph from inside a given node based on some condition. When doing so, it may also be helpful to include information about **why** that interrupt was raised.\n",
     "\n",
     "This guide shows how you can dynamically interrupt the graph using `NodeInterrupt` -- a special exception that can be raised from inside a node. Let's see it in action!\n",
     "\n",
+    "\n",
     "## Setup\n",
     "\n",
     "First, let's install the required packages"
@@ -430,7 +438,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/docs/docs/how-tos/human_in_the_loop/edit-graph-state.ipynb b/docs/docs/how-tos/human_in_the_loop/edit-graph-state.ipynb
index 4d7685cfa..35b5f6c41 100644
--- a/docs/docs/how-tos/human_in_the_loop/edit-graph-state.ipynb
+++ b/docs/docs/how-tos/human_in_the_loop/edit-graph-state.ipynb
@@ -12,6 +12,12 @@
    "source": [
     "# How to edit graph state\n",
     "\n",
+    "!!! tip \"Prerequisites\"\n",
+    "\n",
+    "    * [Human-in-the-loop](../../../concepts/human_in_the_loop)\n",
+    "    * [Breakpoints](../../../concepts/breakpoints)\n",
+    "    * [LangGraph Glossary](../../../concepts/low_level)\n",
+    "\n",
     "Human-in-the-loop (HIL) interactions are crucial for [agentic systems](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#human-in-the-loop). Manually updating the graph state a common HIL interaction pattern, allowing the human to edit actions (e.g., what tool is being called or how it is being called).\n",
     "\n",
     "We can implement this in LangGraph using a [breakpoint](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/breakpoints/): breakpoints allow us to interrupt graph execution before a specific step. At this breakpoint, we can manually update the graph state and then resume from that spot to continue.  \n",
@@ -554,7 +560,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/docs/docs/how-tos/human_in_the_loop/review-tool-calls.ipynb b/docs/docs/how-tos/human_in_the_loop/review-tool-calls.ipynb
index 773511099..c080c4afa 100644
--- a/docs/docs/how-tos/human_in_the_loop/review-tool-calls.ipynb
+++ b/docs/docs/how-tos/human_in_the_loop/review-tool-calls.ipynb
@@ -8,7 +8,15 @@
    "source": [
     "# How to Review Tool Calls\n",
     "\n",
-    "Human-in-the-loop (HIL) interactions are crucial for [agentic systems](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#human-in-the-loop). A common pattern is to add some human in the loop step after certain tool calls. These tool calls often lead to either a function call or saving of some information. Examples include:\n",
+    "!!! tip \"Prerequisites\"\n",
+    "\n",
+    "    This guide assumes familiarity with the following concepts:\n",
+    "\n",
+    "    * [Tool calling](https://python.langchain.com/docs/concepts/tool_calling/)\n",
+    "    * [Human-in-the-loop](../../../concepts/human_in_the_loop)\n",
+    "    * [LangGraph Glossary](../../../concepts/low_level)      \n",
+    "\n",
+    "Human-in-the-loop (HIL) interactions are crucial for [agentic systems](../../../concepts/agentic_concepts). A common pattern is to add some human in the loop step after certain tool calls. These tool calls often lead to either a function call or saving of some information. Examples include:\n",
     "\n",
     "- A tool call to execute SQL, which will then be run by the tool\n",
     "- A tool call to generate a summary, which will then be saved to the State of the graph\n",
@@ -19,9 +27,42 @@
     "\n",
     "1. Approve the tool call and continue\n",
     "2. Modify the tool call manually and then continue\n",
-    "3. Give natural language feedback, and then pass that back to the agent instead of continuing\n",
+    "3. Give natural language feedback, and then pass that back to the agent\n",
+    "\n",
+    "\n",
+    "We can implement these in LangGraph using the [`interrupt()`][langgraph.types.interrupt] function. `interrupt` allows us to stop graph execution to collect input from a user and continue execution with collected input:\n",
     "\n",
-    "We can implement this in LangGraph using a [breakpoint](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/breakpoints/): breakpoints allow us to interrupt graph execution before a specific step. At this breakpoint, we can manually update the graph state taking one of the three options above"
+    "\n",
+    "```python\n",
+    "def human_review_node(state) -> Command[Literal[\"call_llm\", \"run_tool\"]]:\n",
+    "    # this is the value we'll be providing via Command(resume=<human_review>)\n",
+    "    human_review = interrupt(\n",
+    "        {\n",
+    "            \"question\": \"Is this correct?\",\n",
+    "            # Surface tool calls for review\n",
+    "            \"tool_call\": tool_call\n",
+    "        }\n",
+    "    )\n",
+    "    \n",
+    "    review_action, review_data = human_review\n",
+    "    \n",
+    "    # Approve the tool call and continue\n",
+    "    if review_action == \"continue\":\n",
+    "        return Command(goto=\"run_tool\")\n",
+    "    \n",
+    "    # Modify the tool call manually and then continue\n",
+    "    elif review_action == \"update\":\n",
+    "        ...\n",
+    "        updated_msg = get_updated_msg(review_data)\n",
+    "        return Command(goto=\"run_tool\", update={\"messages\": [updated_message]})\n",
+    "\n",
+    "    # Give natural language feedback, and then pass that back to the agent\n",
+    "    elif review_action == \"feedback\":\n",
+    "        ...\n",
+    "        feedback_msg = get_feedback_msg(review_data)\n",
+    "        return Command(goto=\"call_llm\", update={\"messages\": [feedback_msg]})\n",
+    "\n",
+    "```"
    ]
   },
   {
@@ -58,7 +99,15 @@
    "execution_count": 2,
    "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ANTHROPIC_API_KEY:  ········\n"
+     ]
+    }
+   ],
    "source": [
     "import getpass\n",
     "import os\n",
@@ -102,13 +151,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "85e452f8-f33a-4ead-bb4d-7386cdba8edc",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/jpeg": 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",
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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -121,6 +170,7 @@
     "from typing_extensions import TypedDict, Literal\n",
     "from langgraph.graph import StateGraph, START, END, MessagesState\n",
     "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.types import Command, interrupt\n",
     "from langchain_anthropic import ChatAnthropic\n",
     "from langchain_core.tools import tool\n",
     "from langchain_core.messages import AIMessage\n",
@@ -136,7 +186,7 @@
     "    return \"Sunny!\"\n",
     "\n",
     "\n",
-    "model = ChatAnthropic(model_name=\"claude-3-5-sonnet-20240620\").bind_tools(\n",
+    "model = ChatAnthropic(model_name=\"claude-3-5-sonnet-latest\").bind_tools(\n",
     "    [weather_search]\n",
     ")\n",
     "\n",
@@ -149,8 +199,58 @@
     "    return {\"messages\": [model.invoke(state[\"messages\"])]}\n",
     "\n",
     "\n",
-    "def human_review_node(state):\n",
-    "    pass\n",
+    "def human_review_node(state) -> Command[Literal[\"call_llm\", \"run_tool\"]]:\n",
+    "    last_message = state[\"messages\"][-1]\n",
+    "    tool_call = last_message.tool_calls[-1]\n",
+    "\n",
+    "    # this is the value we'll be providing via Command(resume=<human_review>)\n",
+    "    human_review = interrupt(\n",
+    "        {\n",
+    "            \"question\": \"Is this correct?\",\n",
+    "            # Surface tool calls for review\n",
+    "            \"tool_call\": tool_call,\n",
+    "        }\n",
+    "    )\n",
+    "\n",
+    "    review_action = human_review[\"action\"]\n",
+    "    review_data = human_review.get(\"data\")\n",
+    "\n",
+    "    # if approved, call the tool\n",
+    "    if review_action == \"continue\":\n",
+    "        return Command(goto=\"run_tool\")\n",
+    "\n",
+    "    # update the AI message AND call tools\n",
+    "    elif review_action == \"update\":\n",
+    "        updated_message = {\n",
+    "            \"role\": \"ai\",\n",
+    "            \"content\": last_message.content,\n",
+    "            \"tool_calls\": [\n",
+    "                {\n",
+    "                    \"id\": tool_call[\"id\"],\n",
+    "                    \"name\": tool_call[\"name\"],\n",
+    "                    # This the update provided by the human\n",
+    "                    \"args\": review_data,\n",
+    "                }\n",
+    "            ],\n",
+    "            # This is important - this needs to be the same as the message you replacing!\n",
+    "            # Otherwise, it will show up as a separate message\n",
+    "            \"id\": last_message.id,\n",
+    "        }\n",
+    "        return Command(goto=\"run_tool\", update={\"messages\": [updated_message]})\n",
+    "\n",
+    "    # provide feedback to LLM\n",
+    "    elif review_action == \"feedback\":\n",
+    "        # NOTE: we're adding feedback message as a ToolMessage\n",
+    "        # to preserve the correct order in the message history\n",
+    "        # (AI messages with tool calls need to be followed by tool call messages)\n",
+    "        tool_message = {\n",
+    "            \"role\": \"tool\",\n",
+    "            # This is our natural language feedback\n",
+    "            \"content\": review_data,\n",
+    "            \"name\": tool_call[\"name\"],\n",
+    "            \"tool_call_id\": tool_call[\"id\"],\n",
+    "        }\n",
+    "        return Command(goto=\"call_llm\", update={\"messages\": [tool_message]})\n",
     "\n",
     "\n",
     "def run_tool(state):\n",
@@ -178,27 +278,19 @@
     "        return \"human_review_node\"\n",
     "\n",
     "\n",
-    "def route_after_human(state) -> Literal[\"run_tool\", \"call_llm\"]:\n",
-    "    if isinstance(state[\"messages\"][-1], AIMessage):\n",
-    "        return \"run_tool\"\n",
-    "    else:\n",
-    "        return \"call_llm\"\n",
-    "\n",
-    "\n",
     "builder = StateGraph(State)\n",
     "builder.add_node(call_llm)\n",
     "builder.add_node(run_tool)\n",
     "builder.add_node(human_review_node)\n",
     "builder.add_edge(START, \"call_llm\")\n",
     "builder.add_conditional_edges(\"call_llm\", route_after_llm)\n",
-    "builder.add_conditional_edges(\"human_review_node\", route_after_human)\n",
     "builder.add_edge(\"run_tool\", \"call_llm\")\n",
     "\n",
     "# Set up memory\n",
     "memory = MemorySaver()\n",
     "\n",
     "# Add\n",
-    "graph = builder.compile(checkpointer=memory, interrupt_before=[\"human_review_node\"])\n",
+    "graph = builder.compile(checkpointer=memory)\n",
     "\n",
     "# View\n",
     "display(Image(graph.get_graph().draw_mermaid_png()))"
@@ -216,7 +308,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "1b3aa6fc-c7fb-4819-8d7f-ba6057cc4edf",
    "metadata": {},
    "outputs": [
@@ -224,8 +316,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'messages': [HumanMessage(content='hi!', id='393fa21d-4bfb-445b-8faa-78e22b92e346')]}\n",
-      "{'messages': [HumanMessage(content='hi!', id='393fa21d-4bfb-445b-8faa-78e22b92e346'), AIMessage(content=\"Hello! Welcome to our conversation. How can I assist you today? Is there anything specific you'd like to know or discuss?\", response_metadata={'id': 'msg_017S671xYvZm1mi9EcsKvPzF', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 355, 'output_tokens': 29}}, id='run-8ec507a1-5caf-47d6-89eb-1a2e8f38423c-0', usage_metadata={'input_tokens': 355, 'output_tokens': 29, 'total_tokens': 384})]}\n"
+      "{'call_llm': {'messages': [AIMessage(content=\"Hello! I'm here to help you. I can assist you with checking the weather in different cities using the weather search tool. Would you like to know the weather for a specific city? Just let me know which city you're interested in!\", additional_kwargs={}, response_metadata={'id': 'msg_01XHvA3ZWpsq4PdyiruWFLBs', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 374, 'output_tokens': 52}}, id='run-c3ff5fea-0135-4d66-8ec1-f8ed6a88356b-0', usage_metadata={'input_tokens': 374, 'output_tokens': 52, 'total_tokens': 426, 'input_token_details': {}})]}}\n",
+      "\n",
+      "\n"
      ]
     }
    ],
@@ -237,8 +330,9 @@
     "thread = {\"configurable\": {\"thread_id\": \"1\"}}\n",
     "\n",
     "# Run the graph until the first interruption\n",
-    "for event in graph.stream(initial_input, thread, stream_mode=\"values\"):\n",
-    "    print(event)"
+    "for event in graph.stream(initial_input, thread, stream_mode=\"updates\"):\n",
+    "    print(event)\n",
+    "    print(\"\\n\")"
    ]
   },
   {
@@ -249,26 +343,6 @@
     "If we check the state, we can see that it is finished"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "213323cc-0320-4313-ab11-19042e28b495",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Pending Executions!\n",
-      "()\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Pending Executions!\")\n",
-    "print(graph.get_state(thread).next)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "5c1985f7-54f1-420f-a2b6-5e6154909966",
@@ -281,7 +355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "2561a38f-edb5-4b44-b2d7-6a7b70d2e6b7",
    "metadata": {},
    "outputs": [
@@ -289,8 +363,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='8bda37cc-4bd3-4a14-bca5-b992934e710b')]}\n",
-      "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='8bda37cc-4bd3-4a14-bca5-b992934e710b'), AIMessage(content=[{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text'}, {'id': 'toolu_01MW3ETLpq4b8s6VaAMgDBZP', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], response_metadata={'id': 'msg_019FjC1prjVv8BuQX7DmF65F', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 360, 'output_tokens': 80}}, id='run-1b580410-173c-4fe0-a149-22e8f516b259-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01MW3ETLpq4b8s6VaAMgDBZP', 'type': 'tool_call'}], usage_metadata={'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440})]}\n"
+      "{'call_llm': {'messages': [AIMessage(content=[{'text': \"I'll help you check the weather in San Francisco.\", 'type': 'text'}, {'id': 'toolu_01Kn67GmQAA3BEF1cfYdNW3c', 'input': {'city': 'sf'}, 'name': 'weather_search', 'type': 'tool_use'}], additional_kwargs={}, response_metadata={'id': 'msg_013eJXUAEA2ANvYLkDUQFRPo', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 379, 'output_tokens': 65}}, id='run-e8174b94-f681-4688-967f-a32295412f91-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'sf'}, 'id': 'toolu_01Kn67GmQAA3BEF1cfYdNW3c', 'type': 'tool_call'}], usage_metadata={'input_tokens': 379, 'output_tokens': 65, 'total_tokens': 444, 'input_token_details': {}})]}}\n",
+      "\n",
+      "\n",
+      "{'__interrupt__': (Interrupt(value={'question': 'Is this correct?', 'tool_call': {'name': 'weather_search', 'args': {'city': 'sf'}, 'id': 'toolu_01Kn67GmQAA3BEF1cfYdNW3c', 'type': 'tool_call'}}, resumable=True, ns=['human_review_node:be252162-5b29-0a98-1ed2-c807c1fc64c6'], when='during'),)}\n",
+      "\n",
+      "\n"
      ]
     }
    ],
@@ -302,8 +380,9 @@
     "thread = {\"configurable\": {\"thread_id\": \"2\"}}\n",
     "\n",
     "# Run the graph until the first interruption\n",
-    "for event in graph.stream(initial_input, thread, stream_mode=\"values\"):\n",
-    "    print(event)"
+    "for event in graph.stream(initial_input, thread, stream_mode=\"updates\"):\n",
+    "    print(event)\n",
+    "    print(\"\\n\")"
    ]
   },
   {
@@ -316,7 +395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "33d68f0f-d435-4dd1-8013-6a59186dc9f5",
    "metadata": {},
    "outputs": [
@@ -339,12 +418,12 @@
    "id": "14c99fdd-4204-4c2d-b1af-02f38ab6ad57",
    "metadata": {},
    "source": [
-    "To approve the tool call, we can just continue the thread with no edits. To do this, we just create a new run with no inputs."
+    "To approve the tool call, we can just continue the thread with no edits. To do so, we need to let `human_review_node` know what value to use for the `human_review` variable we defined inside the node. We can provide this value by invoking the graph with a `Command(resume=<human_review>)` input.  Since we're approving the tool call, we'll provide `resume` value of `{\"action\": \"continue\"}` to navigate to `run_tool` node:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "f9a0d5d4-52ff-49e0-a6f4-41f9a0e844d8",
    "metadata": {},
    "outputs": [
@@ -352,17 +431,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "{'human_review_node': None}\n",
+      "\n",
+      "\n",
       "----\n",
-      "Searching for: San Francisco\n",
+      "Searching for: sf\n",
       "----\n",
-      "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='8bda37cc-4bd3-4a14-bca5-b992934e710b'), AIMessage(content=[{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text'}, {'id': 'toolu_01MW3ETLpq4b8s6VaAMgDBZP', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], response_metadata={'id': 'msg_019FjC1prjVv8BuQX7DmF65F', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 360, 'output_tokens': 80}}, id='run-1b580410-173c-4fe0-a149-22e8f516b259-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01MW3ETLpq4b8s6VaAMgDBZP', 'type': 'tool_call'}], usage_metadata={'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}), ToolMessage(content='Sunny!', name='weather_search', id='835b0fe3-8aa0-45d5-ac29-03bbe57cc767', tool_call_id='toolu_01MW3ETLpq4b8s6VaAMgDBZP')]}\n",
-      "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='8bda37cc-4bd3-4a14-bca5-b992934e710b'), AIMessage(content=[{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text'}, {'id': 'toolu_01MW3ETLpq4b8s6VaAMgDBZP', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], response_metadata={'id': 'msg_019FjC1prjVv8BuQX7DmF65F', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 360, 'output_tokens': 80}}, id='run-1b580410-173c-4fe0-a149-22e8f516b259-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01MW3ETLpq4b8s6VaAMgDBZP', 'type': 'tool_call'}], usage_metadata={'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}), ToolMessage(content='Sunny!', name='weather_search', id='835b0fe3-8aa0-45d5-ac29-03bbe57cc767', tool_call_id='toolu_01MW3ETLpq4b8s6VaAMgDBZP'), AIMessage(content=\"Based on the search results, the weather in San Francisco is sunny! It's a beautiful day in the city. Is there anything else you'd like to know about the weather or any other information I can help you with?\", response_metadata={'id': 'msg_01UY2d6RCzvwagwMb1J5etek', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 453, 'output_tokens': 49}}, id='run-7137f52c-abe6-4dc1-b536-92dd1d9187b0-0', usage_metadata={'input_tokens': 453, 'output_tokens': 49, 'total_tokens': 502})]}\n"
+      "{'run_tool': {'messages': [{'role': 'tool', 'name': 'weather_search', 'content': 'Sunny!', 'tool_call_id': 'toolu_01Kn67GmQAA3BEF1cfYdNW3c'}]}}\n",
+      "\n",
+      "\n",
+      "{'call_llm': {'messages': [AIMessage(content=\"According to the search, it's sunny in San Francisco today!\", additional_kwargs={}, response_metadata={'id': 'msg_01FJTbC8oK5fkD73rUBmAtUx', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 457, 'output_tokens': 17}}, id='run-c21af72d-3cc5-4b74-bb7c-fbeb8f88bd6d-0', usage_metadata={'input_tokens': 457, 'output_tokens': 17, 'total_tokens': 474, 'input_token_details': {}})]}}\n",
+      "\n",
+      "\n"
      ]
     }
    ],
    "source": [
-    "for event in graph.stream(None, thread, stream_mode=\"values\"):\n",
-    "    print(event)"
+    "for event in graph.stream(\n",
+    "    # provide value\n",
+    "    Command(resume={\"action\": \"continue\"}),\n",
+    "    thread,\n",
+    "    stream_mode=\"updates\",\n",
+    "):\n",
+    "    print(event)\n",
+    "    print(\"\\n\")"
    ]
   },
   {
@@ -377,7 +469,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "ec77831c-e6b8-4903-9146-e098a4b2fda1",
    "metadata": {},
    "outputs": [
@@ -385,8 +477,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='0c488edd-7b9c-4416-ba02-8a2d7e9f2597')]}\n",
-      "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='0c488edd-7b9c-4416-ba02-8a2d7e9f2597'), AIMessage(content=[{'text': \"Certainly! I can help you check the weather in San Francisco. To get this information, I'll use the weather search tool. Let me fetch that for you.\", 'type': 'text'}, {'id': 'toolu_01CpbVmprQnjxpQzx8MzE1g8', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], response_metadata={'id': 'msg_01Mv7iqdtPgZEX2LiBBqWDuY', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 360, 'output_tokens': 88}}, id='run-52a09799-efb5-4fff-82c3-884e20119ad3-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01CpbVmprQnjxpQzx8MzE1g8', 'type': 'tool_call'}], usage_metadata={'input_tokens': 360, 'output_tokens': 88, 'total_tokens': 448})]}\n"
+      "{'call_llm': {'messages': [AIMessage(content=[{'text': \"I'll help you check the weather in San Francisco.\", 'type': 'text'}, {'id': 'toolu_013eUXow3jwM6eekcDJdrjDa', 'input': {'city': 'sf'}, 'name': 'weather_search', 'type': 'tool_use'}], additional_kwargs={}, response_metadata={'id': 'msg_013ruFpCRNZKX3cDeBAH8rEb', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 379, 'output_tokens': 65}}, id='run-13df3982-ce6d-4fe2-9e5c-ea6ce30a63e4-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'sf'}, 'id': 'toolu_013eUXow3jwM6eekcDJdrjDa', 'type': 'tool_call'}], usage_metadata={'input_tokens': 379, 'output_tokens': 65, 'total_tokens': 444, 'input_token_details': {}})]}}\n",
+      "\n",
+      "\n",
+      "{'__interrupt__': (Interrupt(value={'question': 'Is this correct?', 'tool_call': {'name': 'weather_search', 'args': {'city': 'sf'}, 'id': 'toolu_013eUXow3jwM6eekcDJdrjDa', 'type': 'tool_call'}}, resumable=True, ns=['human_review_node:da717c23-60a0-2a1a-45de-cac5cff308bb'], when='during'),)}\n",
+      "\n",
+      "\n"
      ]
     }
    ],
@@ -395,16 +491,17 @@
     "initial_input = {\"messages\": [{\"role\": \"user\", \"content\": \"what's the weather in sf?\"}]}\n",
     "\n",
     "# Thread\n",
-    "thread = {\"configurable\": {\"thread_id\": \"5\"}}\n",
+    "thread = {\"configurable\": {\"thread_id\": \"3\"}}\n",
     "\n",
     "# Run the graph until the first interruption\n",
-    "for event in graph.stream(initial_input, thread, stream_mode=\"values\"):\n",
-    "    print(event)"
+    "for event in graph.stream(initial_input, thread, stream_mode=\"updates\"):\n",
+    "    print(event)\n",
+    "    print(\"\\n\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "edcffbd7-829b-4d0c-88bf-cd531bc0e6b2",
    "metadata": {},
    "outputs": [
@@ -427,72 +524,46 @@
    "id": "87358aca-9b8f-48c7-98d4-3d755f6b0104",
    "metadata": {},
    "source": [
-    "To do this, we first need to update the state. We can do this by passing a message in with the **same** id of the message we want to overwrite. This will have the effect of **replacing** that old message. Note that this is only possible because of the **reducer** we are using that replaces messages with the same ID - read more about that [here](https://langchain-ai.github.io/langgraph/concepts/low_level/#working-with-messages-in-graph-state)"
+    "To do this, we will use `Command` with a different resume value of `{\"action\": \"update\", \"data\": <tool call args>}`. This will do the following:\n",
+    "\n",
+    "* combine existing tool call with user-provided tool call arguments and update the existing AI message with the new tool call\n",
+    "* navigate to `run_tool` node with the updated AI message and continue execution"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "df4a9900-d953-4465-b8af-bd2858cb63ea",
+   "execution_count": 10,
+   "id": "b2f73998-baae-4c00-8a90-f4153e924941",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Current State:\n",
-      "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='0c488edd-7b9c-4416-ba02-8a2d7e9f2597'), AIMessage(content=[{'text': \"Certainly! I can help you check the weather in San Francisco. To get this information, I'll use the weather search tool. Let me fetch that for you.\", 'type': 'text'}, {'id': 'toolu_01CpbVmprQnjxpQzx8MzE1g8', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], response_metadata={'id': 'msg_01Mv7iqdtPgZEX2LiBBqWDuY', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 360, 'output_tokens': 88}}, id='run-52a09799-efb5-4fff-82c3-884e20119ad3-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01CpbVmprQnjxpQzx8MzE1g8', 'type': 'tool_call'}], usage_metadata={'input_tokens': 360, 'output_tokens': 88, 'total_tokens': 448})]}\n",
+      "{'human_review_node': {'messages': [{'role': 'ai', 'content': [{'text': \"I'll help you check the weather in San Francisco.\", 'type': 'text'}, {'id': 'toolu_013eUXow3jwM6eekcDJdrjDa', 'input': {'city': 'sf'}, 'name': 'weather_search', 'type': 'tool_use'}], 'tool_calls': [{'id': 'toolu_013eUXow3jwM6eekcDJdrjDa', 'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}}], 'id': 'run-13df3982-ce6d-4fe2-9e5c-ea6ce30a63e4-0'}]}}\n",
+      "\n",
       "\n",
-      "Current Tool Call ID:\n",
-      "toolu_01CpbVmprQnjxpQzx8MzE1g8\n",
       "----\n",
       "Searching for: San Francisco, USA\n",
       "----\n",
-      "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='0c488edd-7b9c-4416-ba02-8a2d7e9f2597'), AIMessage(content=[{'text': \"Certainly! I can help you check the weather in San Francisco. To get this information, I'll use the weather search tool. Let me fetch that for you.\", 'type': 'text'}, {'id': 'toolu_01CpbVmprQnjxpQzx8MzE1g8', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], id='run-52a09799-efb5-4fff-82c3-884e20119ad3-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01CpbVmprQnjxpQzx8MzE1g8', 'type': 'tool_call'}]), ToolMessage(content='Sunny!', name='weather_search', id='ff968b9f-9b87-4893-9f32-dfb88dbe0536', tool_call_id='toolu_01CpbVmprQnjxpQzx8MzE1g8')]}\n",
-      "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='0c488edd-7b9c-4416-ba02-8a2d7e9f2597'), AIMessage(content=[{'text': \"Certainly! I can help you check the weather in San Francisco. To get this information, I'll use the weather search tool. Let me fetch that for you.\", 'type': 'text'}, {'id': 'toolu_01CpbVmprQnjxpQzx8MzE1g8', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], id='run-52a09799-efb5-4fff-82c3-884e20119ad3-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01CpbVmprQnjxpQzx8MzE1g8', 'type': 'tool_call'}]), ToolMessage(content='Sunny!', name='weather_search', id='ff968b9f-9b87-4893-9f32-dfb88dbe0536', tool_call_id='toolu_01CpbVmprQnjxpQzx8MzE1g8'), AIMessage(content=\"Great news! The weather in San Francisco is currently sunny. It's a beautiful day in the city by the bay. Is there anything else you'd like to know about the weather or any other information I can help you with?\", response_metadata={'id': 'msg_01PhwUeRWkSJB6kzHZS361XZ', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 464, 'output_tokens': 50}}, id='run-5aebcf37-626e-4675-b225-476bc99bdbb8-0', usage_metadata={'input_tokens': 464, 'output_tokens': 50, 'total_tokens': 514})]}\n"
+      "{'run_tool': {'messages': [{'role': 'tool', 'name': 'weather_search', 'content': 'Sunny!', 'tool_call_id': 'toolu_013eUXow3jwM6eekcDJdrjDa'}]}}\n",
+      "\n",
+      "\n",
+      "{'call_llm': {'messages': [AIMessage(content=\"According to the search, it's sunny in San Francisco right now!\", additional_kwargs={}, response_metadata={'id': 'msg_01QssVtxXPqr8NWjYjTaiHqN', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 460, 'output_tokens': 18}}, id='run-8ab865c8-cc9e-4300-8e1d-9eb673e8445c-0', usage_metadata={'input_tokens': 460, 'output_tokens': 18, 'total_tokens': 478, 'input_token_details': {}})]}}\n",
+      "\n",
+      "\n"
      ]
     }
    ],
    "source": [
-    "# To get the ID of the message we want to replace, we need to fetch the current state and find it there.\n",
-    "state = graph.get_state(thread)\n",
-    "print(\"Current State:\")\n",
-    "print(state.values)\n",
-    "print(\"\\nCurrent Tool Call ID:\")\n",
-    "current_content = state.values[\"messages\"][-1].content\n",
-    "current_id = state.values[\"messages\"][-1].id\n",
-    "tool_call_id = state.values[\"messages\"][-1].tool_calls[0][\"id\"]\n",
-    "print(tool_call_id)\n",
-    "\n",
-    "# We now need to construct a replacement tool call.\n",
-    "# We will change the argument to be `San Francisco, USA`\n",
-    "# Note that we could change any number of arguments or tool names - it just has to be a valid one\n",
-    "new_message = {\n",
-    "    \"role\": \"assistant\",\n",
-    "    \"content\": current_content,\n",
-    "    \"tool_calls\": [\n",
-    "        {\n",
-    "            \"id\": tool_call_id,\n",
-    "            \"name\": \"weather_search\",\n",
-    "            \"args\": {\"city\": \"San Francisco, USA\"},\n",
-    "        }\n",
-    "    ],\n",
-    "    # This is important - this needs to be the same as the message you replacing!\n",
-    "    # Otherwise, it will show up as a separate message\n",
-    "    \"id\": current_id,\n",
-    "}\n",
-    "graph.update_state(\n",
-    "    # This is the config which represents this thread\n",
-    "    thread,\n",
-    "    # This is the updated value we want to push\n",
-    "    {\"messages\": [new_message]},\n",
-    "    # We push this update acting as our human_review_node\n",
-    "    as_node=\"human_review_node\",\n",
-    ")\n",
-    "\n",
     "# Let's now continue executing from here\n",
-    "for event in graph.stream(None, thread, stream_mode=\"values\"):\n",
-    "    print(event)"
+    "for event in graph.stream(\n",
+    "    Command(resume={\"action\": \"update\", \"data\": {\"city\": \"San Francisco, USA\"}}),\n",
+    "    thread,\n",
+    "    stream_mode=\"updates\",\n",
+    "):\n",
+    "    print(event)\n",
+    "    print(\"\\n\")"
    ]
   },
   {
@@ -502,21 +573,21 @@
    "source": [
     "## Give feedback to a tool call\n",
     "\n",
-    "Sometimes, you may not want to execute a tool call, but you also may not want to ask the user to manually modify the tool call. In that case it may be better to get natural language feedback from the user. You can then insert these feedback as a mock **RESULT** of the tool call.\n",
+    "Sometimes, you may not want to execute a tool call, but you also may not want to ask the user to manually modify the tool call. In that case it may be better to get natural language feedback from the user. You can then insert this feedback as a mock **RESULT** of the tool call.\n",
     "\n",
     "There are multiple ways to do this:\n",
     "\n",
     "1. You could add a new message to the state (representing the \"result\" of a tool call)\n",
     "2. You could add TWO new messages to the state - one representing an \"error\" from the tool call, other HumanMessage representing the feedback\n",
     "\n",
-    "Both are similar in that they involve adding messages to the state. The main difference lies in the logic AFTER the `human_node` and how it handles different types of messages.\n",
+    "Both are similar in that they involve adding messages to the state. The main difference lies in the logic AFTER the `human_review_node` and how it handles different types of messages.\n",
     "\n",
-    "For this example we will just add a single tool call representing the feedback. Let's see this in action!"
+    "For this example we will just add a single tool call representing the feedback (see `human_review_node` implementation). Let's see this in action!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "d57d5131-7912-4216-aa87-b7272507fa51",
    "metadata": {},
    "outputs": [
@@ -524,8 +595,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='601c4c75-f506-4d91-896d-5e382123de24')]}\n",
-      "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='601c4c75-f506-4d91-896d-5e382123de24'), AIMessage(content=[{'text': \"Certainly! I can help you check the weather in San Francisco. To get the most accurate and up-to-date information, I'll use the weather search tool. Let me fetch that for you right away.\", 'type': 'text'}, {'id': 'toolu_014UTKh5uqfc885Fj4RRqGdg', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], response_metadata={'id': 'msg_013nHyPYxNXFSoXeS6q4oWua', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 360, 'output_tokens': 98}}, id='run-0537e15e-86a4-4c6f-8dfb-6e4c160812c4-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_014UTKh5uqfc885Fj4RRqGdg', 'type': 'tool_call'}], usage_metadata={'input_tokens': 360, 'output_tokens': 98, 'total_tokens': 458})]}\n"
+      "{'call_llm': {'messages': [AIMessage(content=[{'text': \"I'll help you check the weather in San Francisco.\", 'type': 'text'}, {'id': 'toolu_01QxXNTCasnNLQCGAiVoNUBe', 'input': {'city': 'sf'}, 'name': 'weather_search', 'type': 'tool_use'}], additional_kwargs={}, response_metadata={'id': 'msg_01DjwkVxgfqT2K329rGkycx6', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 379, 'output_tokens': 65}}, id='run-c57bee36-9f5f-4d2e-85df-758b56d3cc05-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'sf'}, 'id': 'toolu_01QxXNTCasnNLQCGAiVoNUBe', 'type': 'tool_call'}], usage_metadata={'input_tokens': 379, 'output_tokens': 65, 'total_tokens': 444, 'input_token_details': {}})]}}\n",
+      "\n",
+      "\n",
+      "{'__interrupt__': (Interrupt(value={'question': 'Is this correct?', 'tool_call': {'name': 'weather_search', 'args': {'city': 'sf'}, 'id': 'toolu_01QxXNTCasnNLQCGAiVoNUBe', 'type': 'tool_call'}}, resumable=True, ns=['human_review_node:47a3f541-b630-5f8a-32d7-5a44826d99da'], when='during'),)}\n",
+      "\n",
+      "\n"
      ]
     }
    ],
@@ -534,16 +609,17 @@
     "initial_input = {\"messages\": [{\"role\": \"user\", \"content\": \"what's the weather in sf?\"}]}\n",
     "\n",
     "# Thread\n",
-    "thread = {\"configurable\": {\"thread_id\": \"6\"}}\n",
+    "thread = {\"configurable\": {\"thread_id\": \"4\"}}\n",
     "\n",
     "# Run the graph until the first interruption\n",
-    "for event in graph.stream(initial_input, thread, stream_mode=\"values\"):\n",
-    "    print(event)"
+    "for event in graph.stream(initial_input, thread, stream_mode=\"updates\"):\n",
+    "    print(event)\n",
+    "    print(\"\\n\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "e33ad664-0307-43c5-b85a-1e02eebceb5c",
    "metadata": {},
    "outputs": [
@@ -566,12 +642,15 @@
    "id": "483d9455-8625-4c6a-9b98-f731403b2ed3",
    "metadata": {},
    "source": [
-    "To do this, we first need to update the state. We can do this by passing a message in with the same **tool call id** of the tool call we want to respond to. Note that this is a **different** ID from above."
+    "To do this, we will use `Command` with a different resume value of `{\"action\": \"feedback\", \"data\": <feedback string>}`. This will do the following:\n",
+    "\n",
+    "* create a new tool message that combines existing tool call from LLM with the with user-provided feedback as content\n",
+    "* navigate to `call_llm` node with the updated tool message and continue execution"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "3f05f8b6-6128-4de5-8884-862fc93f1227",
    "metadata": {},
    "outputs": [
@@ -579,46 +658,33 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Current State:\n",
-      "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='601c4c75-f506-4d91-896d-5e382123de24'), AIMessage(content=[{'text': \"Certainly! I can help you check the weather in San Francisco. To get the most accurate and up-to-date information, I'll use the weather search tool. Let me fetch that for you right away.\", 'type': 'text'}, {'id': 'toolu_014UTKh5uqfc885Fj4RRqGdg', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], response_metadata={'id': 'msg_013nHyPYxNXFSoXeS6q4oWua', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 360, 'output_tokens': 98}}, id='run-0537e15e-86a4-4c6f-8dfb-6e4c160812c4-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_014UTKh5uqfc885Fj4RRqGdg', 'type': 'tool_call'}], usage_metadata={'input_tokens': 360, 'output_tokens': 98, 'total_tokens': 458})]}\n",
+      "{'human_review_node': {'messages': [{'role': 'tool', 'content': 'User requested changes: use <city, country> format for location', 'name': 'weather_search', 'tool_call_id': 'toolu_01QxXNTCasnNLQCGAiVoNUBe'}]}}\n",
+      "\n",
+      "\n",
+      "{'call_llm': {'messages': [AIMessage(content=[{'text': 'Let me try again with the full city name.', 'type': 'text'}, {'id': 'toolu_01WBGTKBWusaPNZYJi5LKmeQ', 'input': {'city': 'San Francisco, USA'}, 'name': 'weather_search', 'type': 'tool_use'}], additional_kwargs={}, response_metadata={'id': 'msg_0141KCdx6KhJmWXyYwAYGvmj', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 468, 'output_tokens': 68}}, id='run-60c8267a-52c7-4b6e-87ca-16aa3bd6266b-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01WBGTKBWusaPNZYJi5LKmeQ', 'type': 'tool_call'}], usage_metadata={'input_tokens': 468, 'output_tokens': 68, 'total_tokens': 536, 'input_token_details': {}})]}}\n",
+      "\n",
+      "\n",
+      "{'__interrupt__': (Interrupt(value={'question': 'Is this correct?', 'tool_call': {'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01WBGTKBWusaPNZYJi5LKmeQ', 'type': 'tool_call'}}, resumable=True, ns=['human_review_node:621fc4a9-bbf1-9a99-f50b-3bf91675234e'], when='during'),)}\n",
       "\n",
-      "Current Tool Call ID:\n",
-      "toolu_014UTKh5uqfc885Fj4RRqGdg\n",
-      "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='601c4c75-f506-4d91-896d-5e382123de24'), AIMessage(content=[{'text': \"Certainly! I can help you check the weather in San Francisco. To get the most accurate and up-to-date information, I'll use the weather search tool. Let me fetch that for you right away.\", 'type': 'text'}, {'id': 'toolu_014UTKh5uqfc885Fj4RRqGdg', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], response_metadata={'id': 'msg_013nHyPYxNXFSoXeS6q4oWua', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 360, 'output_tokens': 98}}, id='run-0537e15e-86a4-4c6f-8dfb-6e4c160812c4-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_014UTKh5uqfc885Fj4RRqGdg', 'type': 'tool_call'}], usage_metadata={'input_tokens': 360, 'output_tokens': 98, 'total_tokens': 458}), ToolMessage(content='User requested changes: pass in the country as well', name='weather_search', id='e20ceddc-a0d3-469d-b31e-512f3042a07e', tool_call_id='toolu_014UTKh5uqfc885Fj4RRqGdg'), AIMessage(content=[{'text': \"I apologize for the oversight. It seems that the weather search function requires more specific information. Let's try again with a more detailed search, including the country. Since San Francisco is commonly associated with the one in California, USA, I'll use that. Here's the updated search:\", 'type': 'text'}, {'id': 'toolu_01AaipBbWDLjHnPcoApx8wRq', 'input': {'city': 'San Francisco, USA'}, 'name': 'weather_search', 'type': 'tool_use'}], response_metadata={'id': 'msg_018rErqC2cLe2VVhebdJf81e', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 480, 'output_tokens': 116}}, id='run-fcba65ed-400a-4783-9ecd-e22051682399-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01AaipBbWDLjHnPcoApx8wRq', 'type': 'tool_call'}], usage_metadata={'input_tokens': 480, 'output_tokens': 116, 'total_tokens': 596})]}\n"
+      "\n"
      ]
     }
    ],
    "source": [
-    "# To get the ID of the message we want to replace, we need to fetch the current state and find it there.\n",
-    "state = graph.get_state(thread)\n",
-    "print(\"Current State:\")\n",
-    "print(state.values)\n",
-    "print(\"\\nCurrent Tool Call ID:\")\n",
-    "tool_call_id = state.values[\"messages\"][-1].tool_calls[0][\"id\"]\n",
-    "print(tool_call_id)\n",
-    "\n",
-    "# We now need to construct a replacement tool call.\n",
-    "# We will change the argument to be `San Francisco, USA`\n",
-    "# Note that we could change any number of arguments or tool names - it just has to be a valid one\n",
-    "new_message = {\n",
-    "    \"role\": \"tool\",\n",
-    "    # This is our natural language feedback\n",
-    "    \"content\": \"User requested changes: pass in the country as well\",\n",
-    "    \"name\": \"weather_search\",\n",
-    "    \"tool_call_id\": tool_call_id,\n",
-    "}\n",
-    "graph.update_state(\n",
-    "    # This is the config which represents this thread\n",
-    "    thread,\n",
-    "    # This is the updated value we want to push\n",
-    "    {\"messages\": [new_message]},\n",
-    "    # We push this update acting as our human_review_node\n",
-    "    as_node=\"human_review_node\",\n",
-    ")\n",
-    "\n",
     "# Let's now continue executing from here\n",
-    "for event in graph.stream(None, thread, stream_mode=\"values\"):\n",
-    "    print(event)"
+    "for event in graph.stream(\n",
+    "    # provide our natural language feedback!\n",
+    "    Command(\n",
+    "        resume={\n",
+    "            \"action\": \"feedback\",\n",
+    "            \"data\": \"User requested changes: use <city, country> format for location\",\n",
+    "        }\n",
+    "    ),\n",
+    "    thread,\n",
+    "    stream_mode=\"updates\",\n",
+    "):\n",
+    "    print(event)\n",
+    "    print(\"\\n\")"
    ]
   },
   {
@@ -626,13 +692,13 @@
    "id": "2d2e79ab-7cdb-42ce-b2ca-2932f8782c90",
    "metadata": {},
    "source": [
-    "We can see that we now get to another breakpoint - because it went back to the model and got an entirely new prediction of what to call. Let's now approve this one and continue."
+    "We can see that we now get to another interrupt - because it went back to the model and got an entirely new prediction of what to call. Let's now approve this one and continue."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "a30d40ad-611d-4ec3-84be-869ea05acb89",
+   "execution_count": 14,
+   "id": "ca558915-f4d9-4ff2-95b7-cdaf0c6db485",
    "metadata": {},
    "outputs": [
     {
@@ -640,21 +706,46 @@
      "output_type": "stream",
      "text": [
       "Pending Executions!\n",
-      "('human_review_node',)\n",
+      "('human_review_node',)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Pending Executions!\")\n",
+    "print(graph.get_state(thread).next)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "a30d40ad-611d-4ec3-84be-869ea05acb89",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'human_review_node': None}\n",
+      "\n",
+      "\n",
       "----\n",
       "Searching for: San Francisco, USA\n",
       "----\n",
-      "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='601c4c75-f506-4d91-896d-5e382123de24'), AIMessage(content=[{'text': \"Certainly! I can help you check the weather in San Francisco. To get the most accurate and up-to-date information, I'll use the weather search tool. Let me fetch that for you right away.\", 'type': 'text'}, {'id': 'toolu_014UTKh5uqfc885Fj4RRqGdg', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], response_metadata={'id': 'msg_013nHyPYxNXFSoXeS6q4oWua', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 360, 'output_tokens': 98}}, id='run-0537e15e-86a4-4c6f-8dfb-6e4c160812c4-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_014UTKh5uqfc885Fj4RRqGdg', 'type': 'tool_call'}], usage_metadata={'input_tokens': 360, 'output_tokens': 98, 'total_tokens': 458}), ToolMessage(content='User requested changes: pass in the country as well', name='weather_search', id='e20ceddc-a0d3-469d-b31e-512f3042a07e', tool_call_id='toolu_014UTKh5uqfc885Fj4RRqGdg'), AIMessage(content=[{'text': \"I apologize for the oversight. It seems that the weather search function requires more specific information. Let's try again with a more detailed search, including the country. Since San Francisco is commonly associated with the one in California, USA, I'll use that. Here's the updated search:\", 'type': 'text'}, {'id': 'toolu_01AaipBbWDLjHnPcoApx8wRq', 'input': {'city': 'San Francisco, USA'}, 'name': 'weather_search', 'type': 'tool_use'}], response_metadata={'id': 'msg_018rErqC2cLe2VVhebdJf81e', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 480, 'output_tokens': 116}}, id='run-fcba65ed-400a-4783-9ecd-e22051682399-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01AaipBbWDLjHnPcoApx8wRq', 'type': 'tool_call'}], usage_metadata={'input_tokens': 480, 'output_tokens': 116, 'total_tokens': 596}), ToolMessage(content='Sunny!', name='weather_search', id='3f3ee262-70f5-422c-8e3f-6a9af758514d', tool_call_id='toolu_01AaipBbWDLjHnPcoApx8wRq')]}\n",
-      "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='601c4c75-f506-4d91-896d-5e382123de24'), AIMessage(content=[{'text': \"Certainly! I can help you check the weather in San Francisco. To get the most accurate and up-to-date information, I'll use the weather search tool. Let me fetch that for you right away.\", 'type': 'text'}, {'id': 'toolu_014UTKh5uqfc885Fj4RRqGdg', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], response_metadata={'id': 'msg_013nHyPYxNXFSoXeS6q4oWua', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 360, 'output_tokens': 98}}, id='run-0537e15e-86a4-4c6f-8dfb-6e4c160812c4-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_014UTKh5uqfc885Fj4RRqGdg', 'type': 'tool_call'}], usage_metadata={'input_tokens': 360, 'output_tokens': 98, 'total_tokens': 458}), ToolMessage(content='User requested changes: pass in the country as well', name='weather_search', id='e20ceddc-a0d3-469d-b31e-512f3042a07e', tool_call_id='toolu_014UTKh5uqfc885Fj4RRqGdg'), AIMessage(content=[{'text': \"I apologize for the oversight. It seems that the weather search function requires more specific information. Let's try again with a more detailed search, including the country. Since San Francisco is commonly associated with the one in California, USA, I'll use that. Here's the updated search:\", 'type': 'text'}, {'id': 'toolu_01AaipBbWDLjHnPcoApx8wRq', 'input': {'city': 'San Francisco, USA'}, 'name': 'weather_search', 'type': 'tool_use'}], response_metadata={'id': 'msg_018rErqC2cLe2VVhebdJf81e', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 480, 'output_tokens': 116}}, id='run-fcba65ed-400a-4783-9ecd-e22051682399-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01AaipBbWDLjHnPcoApx8wRq', 'type': 'tool_call'}], usage_metadata={'input_tokens': 480, 'output_tokens': 116, 'total_tokens': 596}), ToolMessage(content='Sunny!', name='weather_search', id='3f3ee262-70f5-422c-8e3f-6a9af758514d', tool_call_id='toolu_01AaipBbWDLjHnPcoApx8wRq'), AIMessage(content=\"Great news! The weather in San Francisco, USA is currently sunny. \\n\\nHere's a summary of the weather information:\\n- Location: San Francisco, USA\\n- Current conditions: Sunny\\n\\nIt's a beautiful day in San Francisco! The sunny weather is perfect for outdoor activities or simply enjoying the city. Remember to wear sunscreen and stay hydrated if you plan to spend time outside. \\n\\nIs there anything else you'd like to know about the weather in San Francisco or any other location?\", response_metadata={'id': 'msg_017Pnjyte2ZXAREgUvEqbUVt', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 609, 'output_tokens': 107}}, id='run-30c0d0ef-09a3-40ad-b410-80019b284983-0', usage_metadata={'input_tokens': 609, 'output_tokens': 107, 'total_tokens': 716})]}\n"
+      "{'run_tool': {'messages': [{'role': 'tool', 'name': 'weather_search', 'content': 'Sunny!', 'tool_call_id': 'toolu_01WBGTKBWusaPNZYJi5LKmeQ'}]}}\n",
+      "\n",
+      "\n",
+      "{'call_llm': {'messages': [AIMessage(content='The weather in San Francisco is sunny!', additional_kwargs={}, response_metadata={'id': 'msg_01JrfZd8SYyH51Q8rhZuaC3W', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 549, 'output_tokens': 12}}, id='run-09a198b2-79fa-484d-9d9d-f12432978488-0', usage_metadata={'input_tokens': 549, 'output_tokens': 12, 'total_tokens': 561, 'input_token_details': {}})]}}\n",
+      "\n",
+      "\n"
      ]
     }
    ],
    "source": [
-    "print(\"Pending Executions!\")\n",
-    "print(graph.get_state(thread).next)\n",
-    "\n",
-    "for event in graph.stream(None, thread, stream_mode=\"values\"):\n",
-    "    print(event)"
+    "for event in graph.stream(\n",
+    "    Command(resume={\"action\": \"continue\"}), thread, stream_mode=\"updates\"\n",
+    "):\n",
+    "    print(event)\n",
+    "    print(\"\\n\")"
    ]
   }
  ],
@@ -674,7 +765,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/docs/docs/how-tos/human_in_the_loop/time-travel.ipynb b/docs/docs/how-tos/human_in_the_loop/time-travel.ipynb
index 5565b95ca..7e363b12d 100644
--- a/docs/docs/how-tos/human_in_the_loop/time-travel.ipynb
+++ b/docs/docs/how-tos/human_in_the_loop/time-travel.ipynb
@@ -7,6 +7,15 @@
    "source": [
     "# How to view and update past graph state\n",
     "\n",
+    "!!! tip \"Prerequisites\"\n",
+    "\n",
+    "    This guide assumes familiarity with the following concepts:\n",
+    "\n",
+    "    * [Time Travel](../../../concepts/time-travel)\n",
+    "    * [Breakpoints](../../../concepts/breakpoints)\n",
+    "    * [LangGraph Glossary](../../../concepts/low_level)\n",
+    "\n",
+    "\n",
     "Once you start [checkpointing](../../persistence) your graphs, you can easily **get** or **update** the state of the agent at any point in time. This permits a few things:\n",
     "\n",
     "1. You can surface a state during an interrupt to a user to let them accept an action.\n",
@@ -589,7 +598,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/docs/docs/how-tos/human_in_the_loop/wait-user-input.ipynb b/docs/docs/how-tos/human_in_the_loop/wait-user-input.ipynb
index 7d6d378b9..e13c17b41 100644
--- a/docs/docs/how-tos/human_in_the_loop/wait-user-input.ipynb
+++ b/docs/docs/how-tos/human_in_the_loop/wait-user-input.ipynb
@@ -1,22 +1,25 @@
 {
  "cells": [
   {
-   "attachments": {
-    "f6c5e4f7-4e60-4085-95ad-6edeaeb902e0.png": {
-     "image/png": 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"
-    }
-   },
+   "attachments": {},
    "cell_type": "markdown",
    "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53",
    "metadata": {},
    "source": [
-    "# How to wait for user input\n",
+    "# How to wait for user input using `interrupt`\n",
+    "\n",
+    "!!! tip \"Prerequisites\"\n",
     "\n",
-    "Human-in-the-loop (HIL) interactions are crucial for [agentic systems](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#human-in-the-loop). Waiting for human input is a common HIL interaction pattern, allowing the agent to ask the user clarifying questions and await input before proceeding. \n",
+    "    This guide assumes familiarity with the following concepts:\n",
     "\n",
-    "We can implement this in LangGraph using a [breakpoint](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/breakpoints/): breakpoints allow us to stop graph execution at a specific step. At this breakpoint, we can wait for human input. Once we have input from the user, we can add it to the graph state and proceed.\n",
+    "    * [Human-in-the-loop](../../../concepts/human_in_the_loop)\n",
+    "    * [Breakpoints](../../../concepts/breakpoints)\n",
+    "    * [LangGraph Glossary](../../../concepts/low_level)\n",
+    "    \n",
     "\n",
-    "![wait_for_input.png](attachment:f6c5e4f7-4e60-4085-95ad-6edeaeb902e0.png)"
+    "**Human-in-the-loop (HIL)** interactions are crucial for [agentic systems](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#human-in-the-loop). Waiting for human input is a common HIL interaction pattern, allowing the agent to ask the user clarifying questions and await input before proceeding. \n",
+    "\n",
+    "We can implement this in LangGraph using the [`interrupt()`][langgraph.types.interrupt] function. `interrupt` allows us to stop graph execution to collect input from a user and continue execution with collected input."
    ]
   },
   {
@@ -37,7 +40,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install --quiet -U langgraph langchain_anthropic langchain_openai"
+    "%pip install --quiet -U langgraph langchain_anthropic"
    ]
   },
   {
@@ -50,10 +53,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ANTHROPIC_API_KEY:  ········\n"
+     ]
+    }
+   ],
    "source": [
     "import getpass\n",
     "import os\n",
@@ -64,7 +75,6 @@
     "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
     "\n",
     "\n",
-    "_set_env(\"OPENAI_API_KEY\")\n",
     "_set_env(\"ANTHROPIC_API_KEY\")"
    ]
   },
@@ -88,27 +98,24 @@
    "source": [
     "## Simple Usage\n",
     "\n",
-    "Let's look at very basic usage of this. One intuitive approach is simply to create a node, `human_feedback`, that will get user feedback. This allows us to place our feedback gathering at a specific, chosen point in our graph.\n",
-    " \n",
-    "1) We specify the [breakpoint](https://langchain-ai.github.io/langgraph/concepts/low_level/#breakpoints) using `interrupt_before` our `human_feedback` node.\n",
+    "Let's explore a basic example of using human feedback. A straightforward approach is to create a node, **`human_feedback`**, designed specifically to collect user input. This allows us to gather feedback at a specific, chosen point in our graph.\n",
     "\n",
-    "2) We set up a [checkpointer](https://langchain-ai.github.io/langgraph/concepts/low_level/#checkpointer) to save the state of the graph up until this node.\n",
+    "Steps:\n",
     "\n",
-    "3) We use `.update_state` to update the state of the graph with the human response we get.\n",
-    "\n",
-    "* We [use the `as_node` parameter](https://langchain-ai.github.io/langgraph/concepts/low_level/#update-state) to apply this state update as the specified node, `human_feedback`.\n",
-    "* The graph will then resume execution as if the `human_feedback` node just acted."
+    "1. **Call `interrupt()`** inside the **`human_feedback`** node.  \n",
+    "2. **Set up a [checkpointer](https://langchain-ai.github.io/langgraph/concepts/low_level/#checkpointer)** to save the graph's state up to this node.  \n",
+    "3. **Use `Command(resume=...)`** to provide the requested value to the **`human_feedback`** node and resume execution."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "58eae42d-be32-48da-8d0a-ab64471657d9",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/jpeg": 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",
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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -120,6 +127,7 @@
    "source": [
     "from typing_extensions import TypedDict\n",
     "from langgraph.graph import StateGraph, START, END\n",
+    "from langgraph.types import Command, interrupt\n",
     "from langgraph.checkpoint.memory import MemorySaver\n",
     "from IPython.display import Image, display\n",
     "\n",
@@ -136,7 +144,8 @@
     "\n",
     "def human_feedback(state):\n",
     "    print(\"---human_feedback---\")\n",
-    "    pass\n",
+    "    feedback = interrupt(\"Please provide feedback:\")\n",
+    "    return {\"user_feedback\": feedback}\n",
     "\n",
     "\n",
     "def step_3(state):\n",
@@ -157,7 +166,7 @@
     "memory = MemorySaver()\n",
     "\n",
     "# Add\n",
-    "graph = builder.compile(checkpointer=memory, interrupt_before=[\"human_feedback\"])\n",
+    "graph = builder.compile(checkpointer=memory)\n",
     "\n",
     "# View\n",
     "display(Image(graph.get_graph().draw_mermaid_png()))"
@@ -168,12 +177,12 @@
    "id": "ce0fe2bc-86fc-465f-956c-729805d50404",
    "metadata": {},
    "source": [
-    "Run until our breakpoint at `human_feedback` - "
+    "Run until our breakpoint at `human_feedback`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "id": "eb8e7d47-e7c9-4217-b72c-08394a2c4d3e",
    "metadata": {},
    "outputs": [
@@ -181,8 +190,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'input': 'hello world'}\n",
-      "---Step 1---\n"
+      "---Step 1---\n",
+      "{'step_1': None}\n",
+      "\n",
+      "\n",
+      "---human_feedback---\n",
+      "{'__interrupt__': (Interrupt(value='Please provide feedback:', resumable=True, ns=['human_feedback:e9a51d27-22ed-8c01-3f17-0ed33209b554'], when='during'),)}\n",
+      "\n",
+      "\n"
      ]
     }
    ],
@@ -194,8 +209,9 @@
     "thread = {\"configurable\": {\"thread_id\": \"1\"}}\n",
     "\n",
     "# Run the graph until the first interruption\n",
-    "for event in graph.stream(initial_input, thread, stream_mode=\"values\"):\n",
-    "    print(event)"
+    "for event in graph.stream(initial_input, thread, stream_mode=\"updates\"):\n",
+    "    print(event)\n",
+    "    print(\"\\n\")"
    ]
   },
   {
@@ -203,63 +219,12 @@
    "id": "28a7d545-ab19-4800-985b-62837d060809",
    "metadata": {},
    "source": [
-    "Now, we can just manually update our graph state with with the user input - "
+    "Now, we can manually update our graph state with the user input:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "2165a1bc-1c5b-411f-9e9c-a2b9627e5d56",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--State after update--\n",
-      "StateSnapshot(values={'input': 'hello world', 'user_feedback': 'go to step 3!'}, next=('step_3',), config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef7830e-b807-6142-8002-1b511e4caf96'}}, metadata={'source': 'update', 'step': 2, 'writes': {'human_feedback': {'user_feedback': 'go to step 3!'}}, 'parents': {}}, created_at='2024-09-21T15:48:17.660131+00:00', parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef7830e-36d1-6f1e-8001-4d4c913ae8a8'}}, tasks=(PregelTask(id='6b5486bf-eb6c-0e27-4784-cad2a69b86a2', name='step_3', path=('__pregel_pull', 'step_3'), error=None, interrupts=(), state=None),))\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "('step_3',)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Get user input\n",
-    "try:\n",
-    "    user_input = input(\"Tell me how you want to update the state: \")\n",
-    "except:\n",
-    "    user_input = \"go to step 3!\"\n",
-    "\n",
-    "# We now update the state as if we are the human_feedback node\n",
-    "graph.update_state(thread, {\"user_feedback\": user_input}, as_node=\"human_feedback\")\n",
-    "\n",
-    "# We can check the state\n",
-    "print(\"--State after update--\")\n",
-    "print(graph.get_state(thread))\n",
-    "\n",
-    "# We can check the next node, showing that it is node 3 (which follows human_feedback)\n",
-    "graph.get_state(thread).next"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ccc4a84a-02f2-4b79-a5a5-22173645526d",
-   "metadata": {},
-   "source": [
-    "We can proceed after our breakpoint - "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 5,
    "id": "3cca588f-e8d8-416b-aba7-0f3ae5e51598",
    "metadata": {},
    "outputs": [
@@ -267,14 +232,24 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "---Step 3---\n"
+      "---human_feedback---\n",
+      "{'human_feedback': {'user_feedback': 'go to step 3!'}}\n",
+      "\n",
+      "\n",
+      "---Step 3---\n",
+      "{'step_3': None}\n",
+      "\n",
+      "\n"
      ]
     }
    ],
    "source": [
     "# Continue the graph execution\n",
-    "for event in graph.stream(None, thread, stream_mode=\"values\"):\n",
-    "    print(event)"
+    "for event in graph.stream(\n",
+    "    Command(resume=\"go to step 3!\"), thread, stream_mode=\"updates\"\n",
+    "):\n",
+    "    print(event)\n",
+    "    print(\"\\n\")"
    ]
   },
   {
@@ -287,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 6,
    "id": "2b83e5ca-8497-43ca-bff7-7203e654c4d3",
    "metadata": {},
    "outputs": [
@@ -297,7 +272,7 @@
        "{'input': 'hello world', 'user_feedback': 'go to step 3!'}"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -308,21 +283,19 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e36f89e5",
+   "id": "b22b9598-7ce4-4d16-b932-bba2bc2803ec",
    "metadata": {},
    "source": [
     "## Agent\n",
     "\n",
-    "In the context of agents, waiting for user feedback is useful to ask clarifying questions.\n",
-    " \n",
-    "To show this, we will build a relatively simple ReAct-style agent that does tool calling. \n",
+    "In the context of [agents](../../../concepts/agentic_concepts), waiting for user feedback is especially useful for asking clarifying questions. To illustrate this, we’ll create a simple [ReAct-style agent](../../../concepts/agentic_concepts#react-implementation) capable of [tool calling](https://python.langchain.com/docs/concepts/tool_calling/). \n",
     "\n",
-    "We will use OpenAI and / or Anthropic's models and a fake tool (just for demo purposes)."
+    "For this example, we’ll use Anthropic's chat model along with a **mock tool** (purely for demonstration purposes)."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b3b8b7e5",
+   "id": "01789855-b769-426d-a329-3cdb29684df8",
    "metadata": {},
    "source": [
     "<div class=\"admonition note\">\n",
@@ -335,13 +308,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "id": "f5319e01",
    "metadata": {},
    "outputs": [
     {
      "data": {
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HSQ3MhOL4nc5lcjtOWNh23IG/M7bcDdexPEl38Af6w+lPEl38Af6w+lBxHVWmMnlePctivSn8CsaGu4/w4xO7ATutRFkbpNtubbmIbvvtuVzyw3M6g9x7No9mlNQ1NRYTF4zHT0rOMlaZ5YpYmvMBAImaBEXczNxsQV6x8SXfwB/rD6U8SXfwB/rD6UHnXLaZyktz3R58UXZGZelDHQ2rPIunxS2MiLp9s2fu3zd+vTvUfop2Y4Z60pZXI6Zz+Shy2jMRSiOOoPmcy3X7XtIJtv2l321uzpOVvfu4bFelG4m5JaezweQGJo6no07/AAHuO23o7t1m8SXfwB/rD6UHh3RXD7M6WxvDTO6iwGtDiGaUOHs1tNSXK+Qx9oWnygywwOZKWPa4A9DsWNJA6Fen+EGnMTp3RzXYfF5fDw5CzLfmrZ2aSW72r3ec+UyPe7mdsHbF2/Xrsd10PxJd/AH+sPpTxJd/AH+sPpQW1ERAREQEREBc9tPPErWgpxPcdMabtB1xw95fyDdnRwg/fRwEh7/QZezbvvFI1b2utQX571bSenZ+x1BkYjLLda0PGLqb8rrLgQRzk7tiY4HnfudnMjk5bHp7T9HSuEp4nGw9hRqRiONhcXuPwuc4klzidy5ziS4kkkkkoJFERBq5WgMrjLlJ089VtmF8JnqyGOaPmaRzMeOrXDfcEdx2K09MXJreHjFiC7XnrvfVf4wDRLKY3Fnaks80h4aHgjbo4dGndollXcrWbp/LPz1eCBsVhrIsvPYuOhZDWjbI5s4aftZc0u2cTyks6lx7JjCFiRfiGaOzDHNDI2WKRoeyRhBa4HqCCO8L9oCIiAiIgIiICIiAiIgKC1rpSHWenLWMfM6pYcBLUvRjeSnYaeaKdn/iY8BwHcdtjuCQZ1fiWVkMb5JHtjjYC5z3HYNA7ySgrvDbVj9c6EwecmhbWtXKzXWYG90U482Vg6no17XN/mVlXP8AgI17+EuAuSdp+ybZsqwSjZ4ZamksMDh6Dyyt3Ho7l0BAREQEREBERAREQYLNCtckgfPXinfA/tIXSMDjG7YjmaT3HYkbj0EqBrYHI6Zq1ocNcfdx9SpNG3H5OaSaWaTfmi+6nuc8AdWkvD+hB6FvnWVEFOyGY8YmmyetLj8h4KyeejOWufBz7+aXNJY4gtcCWuI6d/ULyDQ4ewYrNaz0zrLTfEHL+Oc7bswzYC9d8V3qlqTmBlEUzYYy0OLXteBuG9Obde1dQY6a+6Ls4y9oB32cBt/eoWbHXK0sDJK8ju3kMbCxhcGnlLvOI3DR5p847Dcgd5AIed59GZCsfdE1YMRddWyGNgr40GB7vDA3ENi5YiR9tIcOXpuebp3qM0O/McKdcQZnM6U1Bmauc0ph6de1jMe+zLQmrxvE1aVg86Lmc9rt3bN3B3O46eovEl38Af6w+lPEl38Af6w+lByb3K+Iv4LghomhlKFnGX4YCJalyJ0UsR7V52c1wBB2IXoZVavhrjJ4nOh2AcCTzD4f41aUBERAREQEREBERAREQV3B0/BdXamkFC3B4Sa0xtzTc8M57Pk2jb95y8g5h6S4H0qxKvXaD6mtcfkq+Nmsm3XfRt22WuVleNm8kZdEffbuL2hzeo5/SCSLCgIiICIiAiIgKvaz1czSePg7Gscll7svg2OxrH8jrU5BIbzbHlY0Bznv2PKxrjsdtjval1HR0lhLWVyUpiqQBu4Y0vfI9zg1kbGDq+R73NY1jQS5zmtAJICgtG6evzZCXVGo4msz9qIwwVA5r24uqS13g7HDoXOLWOleCQ9zWgEtjj2De0XpLyYqWZrVjxhnMjL4TksgW7dtLtsGtH3sTBsxjPQ0DcucXOdYkRAREQEREEBXbLpvIx1Wx2bWMuShlaOvVjEWNDYwOQ8mx7NxbuCWu5XOcC4NLQ2fWG5Tr5GpPUtwR2qs8bopYJmB7JGOGzmuaehBBIIPeoWhaOnbVfE3ZmeDTOEOLeBM95YyMbxzSPLg6ToSHOcC8b9N2kkLAi0MVnsZnfDPFuRqZDwOy+nZ8FnbL2E7NueJ/KTyvbuN2nqNxuFvoCIiAiIgIiICIiAqBxasy5ijT0RRmkhyWp+0ryyQHZ9bHs5fDJ9wQW7Me2Jrh1EliI/DtcM9naOmcPaymSnFalWZzySEEnv2DQB1c4kgBo3JJAAJIVc0DgbzrF7VOdgNfP5hrPuN7w/xbVaPtVUEdNwS58hBO8kj9iWtZsFur14qkEcEMbYYY2hjI2DZrWgbAADuACyIiAiIgIiICIiAiIgIiICxW6kF+rNWswx2K0zHRywytDmSMI2LXA9CCCQQVlRBXtPWTjMjZ0/YnpB1djZMdBFPI+w6mGtaHSiTc8wk5m8wc7ccpOxdsLCq5n5xS1XpeTwuhWNmWxS7OxFvPY3hdN2cL+8EdhzuHcWxn0tCsaAiIgIiICIiAiIgIiICIiCB1viG5jTlkNxoy9yo5l+lTNk1u1tQPEsDe1HvAZGNBOxGxIIIJBmKk5tVYZjGYjIxrzG5wcW7jfYlpIO3yEhZlWdFVm4UZPBMr0KVehZL6dalMXEVpfPa57Cd4yZO2aB73aPzdh0AWZERAREQFgvXq2LpWLlyxFUp143TTWJ3hkcTGjdznOPQAAEknoAFnXPIWnizlm2XcrtDUJWvrAEObmrDSCJflrRuHm/hXtLtuzYx0oZ9OUJ9e5enq3KwTVsbX5n4LF2ozG+MEFvhkzD1bK9hIYxwDo2PIcGve9rb4iICIiAiKB13k58LovOXqr+yswU5XxSbb8juU7O29Ox67fIr0UzXVFEd6YzyfcprzTeEtPq389jadlm3PDNaY17d+7cE7jf5Vp/ZU0d60Yn2yP6V+Mdjq+Jpx1qsYjiZ8u5cT1LnE9XOJ3JcepJJPVbK3dFZjumf5jlJkw/ZU0d60Yn2yP6Vy73SWu8tqHhTksbwt1vp3G6lskRvs2cg2KVtctdzivIDsyYnlAefeguILXBpHV0U9FZ3TxjknJ4e/wCzpzOR4RZPXWk9aubia07or9a5YsMdWfK0lkgZKCWuLg5h6HqGn4Cvbf2VNHetGJ9sj+lZkTorO6eMcjJh+ypo71oxPtkf0p9lTR3rRifbI/pWZE6KzunjHIyYfsqaO9aMT7ZH9KfZU0d60Yn2yP6VmROis7p4xyMmH7KmjvWjE+2R/Sn2VNHetGJ9sj+lZkTorO6eMcjJh+ypo71oxPtkf0p9lTR3rRifbI/pWZE6KzunjHIyc/qcQtNa51d40ymextTT+GmezGULNhjXW7TTs65I0nflZs5sLTsOr5DzbxFl6+ypo71oxPtkf0rMidFZ3TxjkZMP2VNHetGJ9sj+lPsqaO9aMT7ZH9KzInRWd08Y5GRT4j6VyFiOCtqPFzTSODGRtuR7uce4Ab9T8isarFmtDcryQWImTwSNLXxSNDmuB7wQehCcO7ctnT0kUsj5fBLlmox8ji5xjjme1gJJJJDQBuTudtz3rxu2qIo16Mct/wD0Iy7lnREWNAiIgIiICIiAiIgrmqLHY5zSDPDKNbtcpIzsrcXPJY+4rTuzgP3sg5ecn/cZIPSrGq7qiwYc5pBguUK3a5SRhiuR80tj7itO5ID97IOXnJ/BslHpViQEREBERAREQEREBERARV3O6pnp3/F2LpMyOQawSTdtMYYYGE7DmeGuPMdiQ0A9BueUEbxflFrD8TYP+1Jvq6006NcqjHL+ZiE4LsvIvui/d2aT4C8SZ8ZS0zaz+o4afYZDtKjqO+0nNDH4TIOYsaHTPHLFIx3atLXjdy9CeUWsPxNg/wC1Jvq64z7pT3Ptj3SmnoKuVw+ExebpuBp5qvflfNE3fzo3Dwcc7DufN3Gx6g9+9+q3PDjHNODv2gtTv1robT2oZKRxz8tjq991N0naGEyxtfyc2w5tubbfYb7dw7lPLnWBt6q0/g8di6+GwfYUq0daP9k5h5rGho/+3+ALf8otYfibB/2pN9XTqtzw4xzMF2RUnyi1h+JsH/ak31dRepb2vM3h5qNKDD4iWchj7kGRldKyPcc/Zk19mvLdwHkHlJB2O2ydVueHGOZg2sxYk4m5i5p6k9zdLUnGHNXo3FvhkvpowuHoH/fPHQbiJu7zJ2N8ggjqwRwwxsihjaGMjY0Na1oGwAA7gAqBhbOotO4mrjMbp3AVKNWMRQwx5SbZrR/+PuT8JPUnqeqsOC1TPcv+LspSbjr7mGSHsZjNDOwHY8ry1p5huCWlo6HccwB2pVo1ymMcv4mJRgsSIizIEREBVbil/BzqP8hl/wAJVpVW4pfwc6j/ACGX/CVo0ftqPOPdanbDMiItaoiIgIojV2Yu4DTeQyOPxzMrcrR9oypJaZVa/qN+aV/msAG5JPoB71xCl7r6k7RmtsrbwdV+U0u6mJquIzcN+nM21J2cTxba0Na0ODufmaOQN32PRVmYjaPQyLz/AMQ+J2axY4X5rUQZoum/UUvjBtPL+E1Z6bcfZkD3SsawPjJaHcrm97Adu5dV4ZazucQtJwags4V+Dq3nulx8E83PNLUP7VNI3lHZue3zuz3dygjc77gImJyFrRcg90rqXM6XwmibWD8MluS6tx0DqdKz4O64xzn7wOcSByPIAId5vw9y0Lnujr2CwOrX5zSHi7UWm72Mq2sXHkhNDJFenjihmjnEY3A53ktLAd4yPTuGtEDtyLlXE3joOHOb1DjzhPGAxGk59Udp4X2fa9nL2fYbch5d+/n3O3+6oVvHbWk2rqOm4uG0PjLKYx2Xx3a6gjbGazHNa8TuEJ7KQGSMcrBICX++2BKa0Dt6LjFr3QluzwrwOtcTpyj4PffNFbjz2fgxcNGWKR0T43TPa4PPaMe0bDry7nbdYKXulzqHTPD3Jad0w/K3NYWrdGKnJkI4hWnrslMnNIGua5gML/Ob3t85ocSGlrQO3IqTws4jzcQqecjv4nxFmsHk5MXkKIsiwxsrWMkDo5A1vOxzJGEEtaepBHRXZTtBERSC1eGn7jZH87Xv8962lq8NP3GyP52vf570udjV5x909y2oiLmIEREBERAREQEREFd1RbNfOaQjFqhXE+UkjMdyMuln2pWnclc/eyDl5ifwbJR6VYl4o90v7unN8DuL1TS1zhlXvsx1kXa11+T3N6CSCWNrot657F/NJsSC7o17O5+49g6Sy9vP6Vw2Uv452IvXaUNmxj3Sdoasj4w50Rds3m5SS3fYb7dw7kEsiIgIiICIiAiIgIiIKFjnF2stX7+i1A0Hb0eCxHb+kn+lTShMZ+/LWH5ZB/pYVNrr1/p8qfaFp2iIi81RFoyZzHxZqDEPuwNyk8D7UdMyDtXxMc1rpA3v5QXtBPdu4LXx+qsXldQZfCVbJlyeJbC65B2T29kJWl0fnEBrtw0+9J2267KBLIiKQULkTy6x0jtt1tztJ29Hgsp2/uH9CmlC5P8Afjo/8sn/ANLMvSj9XlV7SmF9REXIQIiICq3FL+DnUf5DL/hKtKq3FL+DnUf5DL/hK0aP21HnHutTthmREWtUREQUHjjw9u8UOHVzA4+xWhtOsVrLYrwcatkQzMlMEwb17N4Zyu236HuPcqFhuFfEPE6j1lqFlXRHhWfxdKkzE7WDSgNeV4MT/tYMjXxSyefyt2dyjkcASe9oqzETmPM+G9yvkpsZpyjnY9Py4mprCXUc+nq5lfj6dY1nxtrVw9nnDtSJC1zWM852w9Bu+jbLeAGKm0vnJr1/AxWZHadfjMVeyE0FE7EV5+xgeGmJzixhLjzMDe7YrsKKNWI2DkGq5IOOQ01Hpx1us7T+o8fmbfjrE3ceHwxueXNiM0Ded5+AdB6SNxvGa/4C5nWWU4oWIcjRqDUkGEdjHv53mKxQlfN9ubsNmOfyDzSTtzdBsN+5Ip1cdo866x4K8ROI2T1hk8zPpmhPl9F2dNVKlGzYkZDM+XnD3yOiBcw9dyGgjoOV3UnocHDfJxcV9K6nM9Q0MVpuxh54w93aumklrva5o5dizaF25JB6jp37dHRNWB5tw3uedX6XqaGt1HaazWS0/LmQ+hl5JvAwLtx08diJzYiRKxhDSCzY8zgHDvMloDgFqfS0vD1t+/h7MemNQZfJzS1O1j7eG3FY5OSMtIa4SWCC0uIDW7hxPRegEUasCicONCX9H6l4gZG5NWlg1BmxkqrYHOLmRirBFtJu0AO5onHYEjYjr6Be0RWjIERFILV4afuNkfzte/z3raWrw0/cbI/na9/nvS52NXnH3T3LaiIuYgREQERUfixrGXS+Ehq0ZOzymSc6GGQbbwsA3klAPeWggDv857dwRuvazZqv3ItUbZH3WHFfGaXsPpV4n5fJs6Pr13AMhO24Ejz0ae7oN3dQdtjuqLPxs1PK8mHGYmsz0MkklmI/5hyb/wBCpEcYjbsNzuS4lxJLiTuSSepJJJJPUkr9L7qz8K0W1ThVTrTvnkY7lw+zRq34rhfm5v00+zRq34rhfm5v01T0Wj8P0T+3CNZX+K+m28Y9X6P1HqDG4l9/TNnwiARxv5LDdw4RS7k8zA4B2w29PwldQ+zRq34rhfm5v01T0T8P0T+3BrLh9mjVvxXC/Nzfpr6ONGrAetTDOHwBkw/v51z3TmosfqzCVMvirHhWPtNL4ZuRzOYbkdzgCOoPeFIqI0DQ6oxi3GBrS6dhOOhMzY87iDUjJA8LoSmdjflcwtDgP5PP/wCu3UaV6vkqkNqpPFaqzND45oXh7HtPcQR0IXmBW/hbq6TTeoYMZNITisnJ2bWOd5sFg7lrm/AHnzSB98Wnpu4nkaf8Jtxbm7o8YTHdy8UxOLu6Ii+PBERAREQUHGfvy1h+WQf6WFTahMZ+/LWH5ZB/pYVNrr1/p8qf8YWq2vNHuhcpl8/qbUdPSNvUsWV0xg23rs9LUJxePpFwlfE4xtjebMrhG4ljhycrGjdpJX6wWezXHDWulcFldR5XT+N8hqGpJocDbdRnv2rLi17jKzZ4jj5fetIHM8b7jYLr2reCui9dZ4ZnOYNl6+YW1pXdvLHHYiaSWxzxseGTNBJ2EjXAblaeW9z/AKCzWGwOLtYJxrYGHwfGSQ3bEVirFtt2bZ2SCTk2AHKXEbADbos+rOKrmmW4bVHe6i0XUsZ3UcxraQtPFk5qxFLM6G3VA5zG5ocHBxL27bP2BcDsFXddat1Fo7U/F/HYvUWUrw2c1p2lDct3H2BiI7z+WxJAJCWxAc55QAA08uw6BdzzHAnQ+exmAoW8IRBgWOjxrq9yeCWuxwAc0SRva8tdsNwSQdhvupTIcLtLZaXU8l7Dw3DqWKGHLMsOe9lpsTSyMFpOzeUHoWgHfY94BTVkebOMmYz3Bt3EDTWD1dqC9UdoeTPQz5LJSWbePtR2mQh0c7jztbI1580nbeM7bDcLp2nKuR0H7oDEYCPUWazOKzem7V6zDmLzrPLagngaJY+bpFzNmeCxgazu2aNlaaPueuH+O09nsJFgS+jnYW18k6xdsTT2Im+9YZ3yGUNHXYBwA3Oyts2kMRY1TR1HJU5szSqS0a9ntHjkhkcxz2cu/KdzGw7kEjboRuUimRMKFyf78dH/AJZP/pZlNKFyf78dH/lk/wDpZloo/V5Vf4ymF9REXIQIiICq3FL+DnUf5DL/AISrSqtxS/g51H+Qy/4StGj9tR5x7rU7YZlr36Yv1HwGWaAP23fA8seNiD0cOo7tlsItSqH8l4PjmS9vm/STyXg+OZL2+b9JTCKNWBD+S8HxzJe3zfpJ5LwfHMl7fN+kphE1YEP5LwfHMl7fN+knkvB8cyXt836SmETVgQ/kvB8cyXt836SeS8HxzJe3zfpKYRNWBD+S8HxzJe3zfpJ5LwfHMl7fN+kphE1YEP5LwfHMl7fN+knkvB8cyXt836SmETVgQ/kvB8cyXt836SeS8HxzJe3zfpKYRNWBD+S8HxzJe3zfpKSqVW067IWvkka376WQvcf4ySSVmRIiIBavDT9xsj+dr3+e9bS1eGn7jZH87Xv896tc7Grzj7p7ltREXMQIiIC4bxrle/XmPid+1x40uj3+F0p59v6jP7l3Jcv446eksUKOfgaXDG9oy0B6K79i55/kOY0n4Gl59C6/wq5Tb0umau/GOMf9CYcpRfJA4xu5CA/Y8pcNwD6NwqcMfxC3657TO35ksfW199VVNOyMXmuS85wHiDxHu6pyeGtvqWqOWtY+kfKCStDU7F/KxslQV3sk32Dnc7iXB3TlGy6z4v4h/j7TP9iWPraXuEOlcxmvHV/ExyZaQxyWJYJpYY55GbcrnxNfyvII6cwceg6rJeorvYRGMYeOHPYlzbOw5nL3+LNqfUeYoWcBWgsUYMfefHBBN4vZI4ho980vb7127epO25JUjhLuS4s6wdSv53J4SnjsHj7zK+Ismq+zNZY5z5XOb1c1nKGhvvdz1C6hJovDSv1A99Pd2fYI8ke1f9vaIuyA7/N8wbebt8Pf1UXl+Eek847GPuYnmlxtZtOtLFYlie2ADYRucx4L29PeuJHf8JVJ0e5jjE455xjOec4e8cMBD+5yHLwT0oNydqzup9P2x66OqXBpHN6Xo08To+3hcRgakQjhqXqFi1Iw7kn7Z4S3cde4jf5V98X8Q/x9pn+xLH1te9vWt0U0TTM4REd3MXNa2QlkrwMmi/bopopItu/nbI0t2+XcBamnoc3BVkGcuULtgv3Y/H1H12Buw6Fr5ZCTvv13H8StuidPSaq1dj6rGk1acsd248dzWMdzRtP8t7QNvS1r/g2Xpcu027c3K8oiE07Xo1ERflqRERAREQUHGfvy1h+WQf6WFTa08ziMji83ZymOqOycF0MFipHI1krHtHKHsL3BpBbsCCQfNBG++w0PHeZ9S8389R+srsZXIiqmY2R3xGyIjvlaYxTaKE8d5n1Lzfz1H6ynjvM+peb+eo/WVHRzvj6o5mrKbRQnjvM+peb+eo/WU8d5n1Lzfz1H6ynRzvj6o5mrKbRQnjvM+peb+eo/WU8d5n1Lzfz1H6ynRzvj6o5mrKbULk/346P/ACyf/SzL547zPqXm/nqP1lb2GxGRymbq5TI1HYyCkH+D1JJGvle9w5S95Y4tADSQACSeYk7bbGcrcTVVMbJ74nbEx3SYYLgiIuOqIiICq3FL+DnUf5DL/hKtKq3FL+DnUf5DL/hK0aP21HnHutTthmRFq5S5Nj8fPYgo2MnNG3dtSq6Nssp+Bpkexm/8pwC1qtpUnjHrm7w80NPlsdT8MvPs1qUDXRtkjZLPMyFjntMkZc3me0bNdzecDsQCv0NfZwg//DfU42/8zi+v/wDao7N4m/xVZia1/C5XSdfFZenlnDJeCTC72DzI2JvYWZOXZ7Y3Fzh970B36Vmdw1r/AB+w+Pz9nHOwmckq1c5Bp6zlo4IvBIbc3ZCMEmUPc0umY0lrHcp98B0J0sfx3MOY4iWM7hLWI0hpWyajc250L2zSsZEXs5GSukc5zpmhgbH17iQ88g2mcEd8fSrS5rtDHq+TVlp/gu3hBM0ksUG3P5vITCOfrv2PvRzdIzI+58sZnSOt9MZDUUFrD5/LOzNVrsWDLUnNoWeWUukLbEYexjeUsZ5gLSTvuK/1Ddse6Mw2Nx+enyentQ4u5hzj+2xk9eGS1KLsxhrljYpnjdzgd2OLXgD3vUbyk/GEQaix+nzo/UT89cpS5DxewVC6CBkzYuaR/hHZt5i7maOYkgEdHeao3HcDYqem8DihLhMa3H52vmrDMBg24+vaMJLo2dkJXcp5wxxeXOPmbbdRtbaGifBeJOY1bLd7d93GVMZDV7Ll8HZDJPI483MeYvdMPQNuzHf6JzETT404S9iMHkoauQdXzWel0/THZs5pJo5J2Ol25/2r7mlcD77lAPL6FFaa90HidTXMBHHp/P0qmoO3Zib9yCBsFyWJj3uiaRMXNcWxvLS9rWuA6OVRm4Kag0DprE3o9SHP1dD1cnexGIpYZzbNueSvM2MvcJXmSUdq8DkY3mLve7qS4fcBcpU05pGvqXUTbUencP4FiqdDH+CeByyVuwfNK50khlmaxz2hw5G+c48u56RjULdwQ4j5TipoiDUGS0/YwTLb3y1DK+FzLFZz3di9vJK879mGc3MG+cTygt2KmaXFLReSz3iOpq/A2s12rofFsOThfZ7Ru/Mzsw7m5hsdxtuNj8Cx8K9H5Hh/oTEacyOVrZjxXXjp1rNWiag7CONrGBzDJJu7ZvVwIB36NCyUuHOKoZ7xvFbzzrfaul7ObUOQlrczt9x4O+cxcvU7N5dh02A2CtGOECs8XOIGX0lq3h/jMNTu5J+TvWpblHHRQvnsVYashLGmZzWNHavgJcXN7tt+ux/MfuhNP5DF4Sxh8dl85kMtBYsxYenDGy1BHBJ2U7pu1kZHEI5AWEueN3dG8y3Nc8Ns9qHXOL1Rg9UVsJbx+OsY6GK1ivDGNE743SyN+2s2f9piDSdwOV24dzbClX/cnYZtjT02Pnxdt2MovozjVGFjyzbPPO6w+cAvj7OYySSuLhu08+3LsAonWxyGezxiyPELWmhcXpNmWx2ByuJdqK7lYYqfaNr87GRMLZ3HlbzOdzlrC4gN7Pfdzm2Gl7obT9yem9+Ny9TD5GCzYxmasQRtqZFkEbpZDFtIZADGxz2mRjA5rSWkr5hNEWs3xM1zlslj5MdhJ8TV03jW7tY58EZnfPIxrSeVpdO1rd9j9r32223rs/ubclldHQYDK6xZZZi8DYwGElrYrsBTjmgFd08re2PbTCIcgIMbQHO83cqP6hZa3HzH2NO4TKu01qGCTPdkMLjZIIPDMiXxGU9nGJjyBjAS50pY0d+5BBNv0JrSpr/TceYp1rNON089Z9a3ydrFLDM+GRpMbnsOz43DdrnA+gqn8VuBlHiPHph0TsXFLp8yMr1s1im5OlJE9jWOY+AvZuRyMLXBwILfTuugaew0OnsFQxsEVaGKrC2IMp1214dwOpZG3owE7nlHdurRjjmJBavDT9xsj+dr3+e9bS1eGn7jZH87Xv8APerXOxq84+6e5bURFzECIiAvjmh7S1wDmkbEHuK+og4/q3gzarTyWtMuifWcS44ud3II/khftsG/Ax3QbnZwADRSJ9L6kqu5ZdM5MOHeI2MkH9LHEL0ui71n4zpFqnVqiKvPanKdrzJ4hz/q1l/Zv+qeIc/6tZf2b/qvTaLT+O3fkj1MnmTxDn/VrL+zf9U8Q5/1ay/s3/Vem0T8du/JHqZPMniHP+rWX9m/6oNP6gcdhprL7/LW2/8AUr02ij8du/JHqZOAYThdqfNzNE1NuDq7jmnuPa+Tb08sbHHr/KLf5/T2fS2laGkMW2jQY4jfnlnlIMs7/S95AG56AdAAAAAAAAJhFy9L+IXtLyryjdAIiLmoEREBERAREQEREBERAREQEREBERAREQFVuKX8HOo/yGX/AAlWlVjibE6Xh5qNrQSfAJjsASdgwk9B1PQehaNH7ajzj3Wp2wyIvjXBwBBBB6gj0r6taoiIgIiICIiAiIgIiICIiAiIgIiIC1eGn7jZH87Xv8962lrcNmFuDuv+9kyl5zTttuPCHjf+kFRc7Grzj7p7lsREXNQIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAvjmhwIIBB6EH0r6iCoycPBD9rxmeymHqD3lSsIJI4h181naxPLW9ejQdgAAAANl+PIC/655v5mj9WVxRaus3d8cIn7LYyp3kBf8AXPN/M0fqyeQF/wBc838zR+rK4onWbnhwjkYyp3kBf9c838zR+rJ5AX/XPN/M0fqyuKJ1m54cI5GMqd5AX/XPN/M0fqyeQF/1zzfzNH6sriidZueHCORjKneQF/1zzfzNH6snkBf9c838zR+rK4onWbnhwjkYyp3kBf8AXPN/M0fqyeQF/wBc838zR+rK4onWbnhwjkYyp3kBf9c838zR+rJ5AX/XPN/M0fqyuKJ1m54cI5GMqd5AX/XPN/M0fqyeQF/1zzfzNH6sriidZueHCORjKneQF/1zzfzNH6snkBf9c838zR+rK4onWbnhwjkYyqDOH9h/m2tVZu1CffRfc0PMPSOeKFrx/G1wPwEK006cGOqQ1asLK9aFgZHFE0NaxoGwAA7gsyLzru13Mqp+3siZxERF4oEREBERAREQEREBERAREQEREBERB//Z",
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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -375,10 +348,8 @@
     "\n",
     "# Set up the model\n",
     "from langchain_anthropic import ChatAnthropic\n",
-    "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "model = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n",
-    "model = ChatOpenAI(model=\"gpt-4o\")\n",
+    "model = ChatAnthropic(model=\"claude-3-5-sonnet-latest\")\n",
     "\n",
     "from pydantic import BaseModel\n",
     "\n",
@@ -404,7 +375,7 @@
     "    last_message = messages[-1]\n",
     "    # If there is no function call, then we finish\n",
     "    if not last_message.tool_calls:\n",
-    "        return \"end\"\n",
+    "        return END\n",
     "    # If tool call is asking Human, we return that node\n",
     "    # You could also add logic here to let some system know that there's something that requires Human input\n",
     "    # For example, send a slack message, etc\n",
@@ -412,7 +383,7 @@
     "        return \"ask_human\"\n",
     "    # Otherwise if there is, we continue\n",
     "    else:\n",
-    "        return \"continue\"\n",
+    "        return \"action\"\n",
     "\n",
     "\n",
     "# Define the function that calls the model\n",
@@ -425,7 +396,10 @@
     "\n",
     "# We define a fake node to ask the human\n",
     "def ask_human(state):\n",
-    "    pass\n",
+    "    tool_call_id = state[\"messages\"][-1].tool_calls[0][\"id\"]\n",
+    "    location = interrupt(\"Please provide your location:\")\n",
+    "    tool_message = [{\"tool_call_id\": tool_call_id, \"type\": \"tool\", \"content\": location}]\n",
+    "    return {\"messages\": tool_message}\n",
     "\n",
     "\n",
     "# Build the graph\n",
@@ -451,20 +425,6 @@
     "    \"agent\",\n",
     "    # Next, we pass in the function that will determine which node is called next.\n",
     "    should_continue,\n",
-    "    # Finally we pass in a mapping.\n",
-    "    # The keys are strings, and the values are other nodes.\n",
-    "    # END is a special node marking that the graph should finish.\n",
-    "    # What will happen is we will call `should_continue`, and then the output of that\n",
-    "    # will be matched against the keys in this mapping.\n",
-    "    # Based on which one it matches, that node will then be called.\n",
-    "    {\n",
-    "        # If `tools`, then we call the tool node.\n",
-    "        \"continue\": \"action\",\n",
-    "        # We may ask the human\n",
-    "        \"ask_human\": \"ask_human\",\n",
-    "        # Otherwise we finish.\n",
-    "        \"end\": END,\n",
-    "    },\n",
     ")\n",
     "\n",
     "# We now add a normal edge from `tools` to `agent`.\n",
@@ -483,7 +443,7 @@
     "# This compiles it into a LangChain Runnable,\n",
     "# meaning you can use it as you would any other runnable\n",
     "# We add a breakpoint BEFORE the `ask_human` node so it never executes\n",
-    "app = workflow.compile(checkpointer=memory, interrupt_before=[\"ask_human\"])\n",
+    "app = workflow.compile(checkpointer=memory)\n",
     "\n",
     "display(Image(app.get_graph().draw_mermaid_png()))"
    ]
@@ -502,7 +462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 8,
    "id": "cfd140f0-a5a6-4697-8115-322242f197b5",
    "metadata": {},
    "outputs": [
@@ -514,75 +474,51 @@
       "\n",
       "Use the search tool to ask the user where they are, then look up the weather there\n",
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "[{'text': \"I'll help you with that. Let me first ask the user about their location.\", 'type': 'text'}, {'id': 'toolu_01KNvb7RCVu8yKYUuQQSKN1x', 'input': {'question': 'Where are you located?'}, 'name': 'AskHuman', 'type': 'tool_use'}]\n",
       "Tool Calls:\n",
-      "  AskHuman (call_LDo62KBPQKZWxPI5IHxPBF0w)\n",
-      " Call ID: call_LDo62KBPQKZWxPI5IHxPBF0w\n",
+      "  AskHuman (toolu_01KNvb7RCVu8yKYUuQQSKN1x)\n",
+      " Call ID: toolu_01KNvb7RCVu8yKYUuQQSKN1x\n",
       "  Args:\n",
-      "    question: Can you tell me where you are located?\n"
+      "    question: Where are you located?\n"
      ]
     }
    ],
    "source": [
-    "from langchain_core.messages import HumanMessage\n",
-    "\n",
     "config = {\"configurable\": {\"thread_id\": \"2\"}}\n",
-    "input_message = HumanMessage(\n",
-    "    content=\"Use the search tool to ask the user where they are, then look up the weather there\"\n",
-    ")\n",
-    "for event in app.stream({\"messages\": [input_message]}, config, stream_mode=\"values\"):\n",
+    "for event in app.stream(\n",
+    "    {\n",
+    "        \"messages\": [\n",
+    "            (\n",
+    "                \"user\",\n",
+    "                \"Use the search tool to ask the user where they are, then look up the weather there\",\n",
+    "            )\n",
+    "        ]\n",
+    "    },\n",
+    "    config,\n",
+    "    stream_mode=\"values\",\n",
+    "):\n",
     "    event[\"messages\"][-1].pretty_print()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "cc168c90-a374-4280-a9a6-8bc232dbb006",
-   "metadata": {},
-   "source": [
-    "We now want to update this thread with a response from the user. We then can kick off another run. \n",
-    "\n",
-    "Because we are treating this as a tool call, we will need to update the state as if it is a response from a tool call. In order to do this, we will need to check the state to get the ID of the tool call."
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "id": "63598092-d565-4170-9773-e092d345f8c1",
+   "execution_count": 9,
+   "id": "924a30ea-94c0-468e-90fe-47eb9c08584d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "('agent',)"
+       "('ask_human',)"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "tool_call_id = app.get_state(config).values[\"messages\"][-1].tool_calls[0][\"id\"]\n",
-    "\n",
-    "# We now create the tool call with the id and the response we want\n",
-    "tool_message = [\n",
-    "    {\"tool_call_id\": tool_call_id, \"type\": \"tool\", \"content\": \"san francisco\"}\n",
-    "]\n",
-    "\n",
-    "# # This is equivalent to the below, either one works\n",
-    "# from langchain_core.messages import ToolMessage\n",
-    "# tool_message = [ToolMessage(tool_call_id=tool_call_id, content=\"san francisco\")]\n",
-    "\n",
-    "# We now update the state\n",
-    "# Notice that we are also specifying `as_node=\"ask_human\"`\n",
-    "# This will apply this update as this node,\n",
-    "# which will make it so that afterwards it continues as normal\n",
-    "app.update_state(config, {\"messages\": tool_message}, as_node=\"ask_human\")\n",
-    "\n",
-    "# We can check the state\n",
-    "# We can see that the state currently has the `agent` node next\n",
-    "# This is based on how we define our graph,\n",
-    "# where after the `ask_human` node goes (which we just triggered)\n",
-    "# there is an edge to the `agent` node\n",
     "app.get_state(config).next"
    ]
   },
@@ -591,12 +527,12 @@
    "id": "6a30c9fb-2a40-45cc-87ba-406c11c9f0cf",
    "metadata": {},
    "source": [
-    "We can now tell the agent to continue. We can just pass in `None` as the input to the graph, since no additional input is needed"
+    "You can see that our graph got interrupted inside the `ask_human` node, which is now waiting for a `location` to be provided. We can provide this value by invoking the graph with a `Command(resume=\"<location>\")` input:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 10,
    "id": "a9f599b5-1a55-406b-a76b-f52b3ca06975",
    "metadata": {},
    "outputs": [
@@ -605,23 +541,36 @@
      "output_type": "stream",
      "text": [
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "[{'text': \"I'll help you with that. Let me first ask the user about their location.\", 'type': 'text'}, {'id': 'toolu_01KNvb7RCVu8yKYUuQQSKN1x', 'input': {'question': 'Where are you located?'}, 'name': 'AskHuman', 'type': 'tool_use'}]\n",
+      "Tool Calls:\n",
+      "  AskHuman (toolu_01KNvb7RCVu8yKYUuQQSKN1x)\n",
+      " Call ID: toolu_01KNvb7RCVu8yKYUuQQSKN1x\n",
+      "  Args:\n",
+      "    question: Where are you located?\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "\n",
+      "san francisco\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "[{'text': \"Now I'll search for the weather in San Francisco.\", 'type': 'text'}, {'id': 'toolu_01Y5C4rU9WcxBqFLYSMGjV1F', 'input': {'query': 'current weather in san francisco'}, 'name': 'search', 'type': 'tool_use'}]\n",
       "Tool Calls:\n",
-      "  search (call_LJlkCFfHvAS2taKHTaMmORE5)\n",
-      " Call ID: call_LJlkCFfHvAS2taKHTaMmORE5\n",
+      "  search (toolu_01Y5C4rU9WcxBqFLYSMGjV1F)\n",
+      " Call ID: toolu_01Y5C4rU9WcxBqFLYSMGjV1F\n",
       "  Args:\n",
-      "    query: current weather in San Francisco\n",
+      "    query: current weather in san francisco\n",
       "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
       "Name: search\n",
       "\n",
-      "[\"I looked up: current weather in San Francisco. Result: It's sunny in San Francisco, but you better look out if you're a Gemini \\ud83d\\ude08.\"]\n",
+      "I looked up: current weather in san francisco. Result: It's sunny in San Francisco, but you better look out if you're a Gemini 😈.\n",
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "The current weather in San Francisco is sunny. Enjoy the good weather! 🌞\n"
+      "Based on the search results, it's currently sunny in San Francisco. Note that this is the current weather at the time of our conversation, and conditions can change throughout the day.\n"
      ]
     }
    ],
    "source": [
-    "for event in app.stream(None, config, stream_mode=\"values\"):\n",
+    "for event in app.stream(Command(resume=\"san francisco\"), config, stream_mode=\"values\"):\n",
     "    event[\"messages\"][-1].pretty_print()"
    ]
   }
@@ -642,7 +591,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/docs/docs/how-tos/index.md b/docs/docs/how-tos/index.md
index d93488d94..24083d328 100644
--- a/docs/docs/how-tos/index.md
+++ b/docs/docs/how-tos/index.md
@@ -48,12 +48,24 @@ LangGraph makes it easy to manage conversation [memory](../concepts/memory.md) i
 [Human-in-the-loop](../concepts/human_in_the_loop.md) functionality allows
 you to involve humans in the decision-making process of your graph. These how-to guides show how to implement human-in-the-loop workflows in your graph.
 
-- [How to add breakpoints](human_in_the_loop/breakpoints.ipynb)
-- [How to add dynamic breakpoints](human_in_the_loop/dynamic_breakpoints.ipynb)
-- [How to edit graph state](human_in_the_loop/edit-graph-state.ipynb)
-- [How to wait for user input](human_in_the_loop/wait-user-input.ipynb)
+
+Key workflows:
+
+- [How to wait for user input](human_in_the_loop/wait-user-input.ipynb): A basic example that shows how to implement a human-in-the-loop workflow in your graph using the `interrupt` function.
+- [How to review tool calls](human_in_the_loop/review-tool-calls.ipynb): Incorporate human-in-the-loop for reviewing/editing/accepting tool call requests before they executed using the `interrupt` function.
+ 
+
+Other methods:
+
+- [How to add static breakpoints](human_in_the_loop/breakpoints.ipynb): Use for debugging purposes. For [**human-in-the-loop**](../concepts/human_in_the_loop.md) workflows, we recommend the [`interrupt` function][langgraph.types.interrupt] instead.
+- [How to edit graph state](human_in_the_loop/edit-graph-state.ipynb): Edit graph state using `graph.update_state` method. Use this if implementing a **human-in-the-loop** workflow via **static breakpoints**.
+- [How to add dynamic breakpoints with `NodeInterrupt`](human_in_the_loop/dynamic_breakpoints.ipynb): **Not recommended**: Use the [`interrupt` function](../concepts/human_in_the_loop.md) instead.
+
+### Time Travel
+
+[Time travel](../concepts/time-travel.md) allows you to replay past actions in your LangGraph application to explore alternative paths and debug issues. These how-to guides show how to use time travel in your graph.
+
 - [How to view and update past graph state](human_in_the_loop/time-travel.ipynb)
-- [How to review tool calls](human_in_the_loop/review-tool-calls.ipynb)
 
 ### Streaming
 
@@ -94,7 +106,10 @@ These how-to guides show common patterns for tool calling with LangGraph:
 
 ### Multi-agent
 
+[Multi-agent systems](../concepts/multi_agent.md) are useful to break down complex LLM applications into multiple agents, each responsible for a different part of the application. These how-to guides show how to implement multi-agent systems in LangGraph:
+
 - [How to build a multi-agent network](multi-agent-network.ipynb)
+- [How to add multi-turn conversation in a multi-agent application](multi-agent-multi-turn-convo.ipynb)
 
 See the [multi-agent tutorials](../tutorials/index.md#multi-agent-systems) for implementations of other multi-agent architectures.
 
diff --git a/docs/docs/how-tos/multi-agent-multi-turn-convo.ipynb b/docs/docs/how-tos/multi-agent-multi-turn-convo.ipynb
new file mode 100644
index 000000000..411eb4216
--- /dev/null
+++ b/docs/docs/how-tos/multi-agent-multi-turn-convo.ipynb
@@ -0,0 +1,384 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "a2b182eb-1e31-43c8-85b1-706508dfa370",
+   "metadata": {},
+   "source": [
+    "# How to add multi-turn conversation in a multi-agent application\n",
+    "\n",
+    "!!! info \"Prerequisites\"\n",
+    "    This guide assumes familiarity with the following:\n",
+    "\n",
+    "    - [Node](../../concepts/low_level/#nodes)\n",
+    "    - [Command](../../concepts/low_level/#command)\n",
+    "    - [Multi-agent systems](../../concepts/multi_agent)\n",
+    "    - [Human-in-the-loop](../../concepts/human_in_the_loop)\n",
+    "\n",
+    "\n",
+    "In this how-to guide, we’ll build an application that allows an end-user to engage in a *multi-turn conversation* with one or more agents. We'll create a node that uses an [`interrupt`](../../reference/types/#langgraph.types.interrupt) to collect user input and routes back to the **active** agent.\n",
+    "\n",
+    "The agents will be implemented as nodes in a graph that executes agent steps and determines the next action:  \n",
+    "\n",
+    "1. **Wait for user input** to continue the conversation, or  \n",
+    "2. **Route to another agent** (or back to itself, such as in a loop) via a [**handoff**](../../concepts/multi_agent/#handoffs).\n",
+    "\n",
+    "```python\n",
+    "def human(state: MessagesState) -> Command[Literal[\"agent\", \"another_agent\"]]:\n",
+    "    \"\"\"A node for collecting user input.\"\"\"\n",
+    "    user_input = interrupt(value=\"Ready for user input.\")\n",
+    "\n",
+    "    # Determine the active agent.\n",
+    "    active_agent = ...\n",
+    "\n",
+    "    ...\n",
+    "    return Command(\n",
+    "        update={\n",
+    "            \"messages\": [{\n",
+    "                \"role\": \"human\",\n",
+    "                \"content\": user_input,\n",
+    "            }]\n",
+    "        },\n",
+    "        goto=active_agent,\n",
+    "\n",
+    "def agent(state) -> Command[Literal[\"agent\", \"another_agent\", \"human\"]]:\n",
+    "    # The condition for routing/halting can be anything, e.g. LLM tool call / structured output, etc.\n",
+    "    goto = get_next_agent(...)  # 'agent' / 'another_agent'\n",
+    "    if goto:\n",
+    "        return Command(goto=goto, update={\"my_state_key\": \"my_state_value\"})\n",
+    "    else:\n",
+    "        return Command(goto=\"human\") # Go to human node\n",
+    "    )\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "faaa4444-cd06-4813-b9ca-c9700fe12cb7",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "First, let's install the required packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "05038da0-31df-4066-a1a4-c4ccb5db4d3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture --no-stderr\n",
+    "%pip install -U langgraph langchain-openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "0bcff5d4-130e-426d-9285-40d0f72c7cd3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
+    "\n",
+    "\n",
+    "_set_env(\"OPENAI_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3ec6e48-85dc-4905-ba50-985e5d4788e6",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
+    "    <p style=\"padding-top: 5px;\">\n",
+    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
+    "    </p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "6696b398-559d-4250-bb76-ebb7c97ce5f3",
+   "metadata": {},
+   "source": [
+    "## Travel Recommendations Example\n",
+    "\n",
+    "In this example, we will build a team of travel assistant agents that can communicate with each other via handoffs.\n",
+    "\n",
+    "We will create 3 agents:\n",
+    "\n",
+    "* `travel_advisor`: can help with general travel destination recommendations. Can ask `sightseeing_advisor` and `hotel_advisor` for help.\n",
+    "* `sightseeing_advisor`: can help with sightseeing recommendations. Can ask `travel_advisor` and `hotel_advisor` for help.\n",
+    "* `hotel_advisor`: can help with hotel recommendations. Can ask `sightseeing_advisor` and `hotel_advisor` for help.\n",
+    "\n",
+    "This is a fully-connected network - every agent can talk to any other agent. \n",
+    "\n",
+    "To implement the handoffs between the agents we'll be using LLMs with structured output. Each agent's LLM will return an output with both its text response (`response`) as well as which agent to route to next (`goto`). If the agent has enough information to respond to the user, the `goto` will be set to `human` to route back and collect information from a human.\n",
+    "\n",
+    "Now, let's define our agent nodes and graph!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "aa4bdbff-9461-46cc-aee9-8a22d3c3d9ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing_extensions import TypedDict, Literal\n",
+    "\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "from langchain_core.messages import HumanMessage\n",
+    "from langgraph.graph import MessagesState, StateGraph, START, END\n",
+    "from langgraph.types import Command, interrupt\n",
+    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "model = ChatOpenAI(model=\"gpt-4o\")\n",
+    "\n",
+    "\n",
+    "def make_agent_node(*, name: str, destinations: list[str], system_prompt: str):\n",
+    "    def agent_node(state: MessagesState) -> Command[Literal[*destinations, \"human\"]]:\n",
+    "        # define schema for the structured output:\n",
+    "        # - model's text response (`response`)\n",
+    "        # - name of the node to go to next (or 'finish')\n",
+    "        class Response(TypedDict):\n",
+    "            response: str\n",
+    "            goto: Literal[*destinations, \"finish\"]\n",
+    "\n",
+    "        messages = [{\"role\": \"system\", \"content\": system_prompt}] + state[\"messages\"]\n",
+    "        response = model.with_structured_output(Response).invoke(messages)\n",
+    "        goto = response[\"goto\"]\n",
+    "        if goto == \"finish\":\n",
+    "            # When the agent is done, we should go to the\n",
+    "            goto = \"human\"\n",
+    "\n",
+    "        # Handoff to another agent or halt\n",
+    "        ai_msg = {\"role\": \"ai\", \"content\": response[\"response\"], \"name\": name}\n",
+    "        return Command(goto=goto, update={\"messages\": [ai_msg]})\n",
+    "\n",
+    "    return agent_node\n",
+    "\n",
+    "\n",
+    "travel_advisor = make_agent_node(\n",
+    "    name=\"travel_advisor\",\n",
+    "    destinations=[\"sightseeing_advisor\", \"hotel_advisor\", \"human\"],\n",
+    "    system_prompt=(\n",
+    "        \"You are a general travel expert that can recommend travel destinations (e.g. countries, cities, etc). \"\n",
+    "        \"If you need specific sightseeing recommendations, ask 'sightseeing_advisor' for help. \"\n",
+    "        \"If you need hotel recommendations, ask 'hotel_advisor' for help. \"\n",
+    "        \"If you have enough information to respond to the user, return 'finish'. \"\n",
+    "        \"Never mention other agents by name.\"\n",
+    "    ),\n",
+    ")\n",
+    "sightseeing_advisor = make_agent_node(\n",
+    "    name=\"sightseeing_advisor\",\n",
+    "    destinations=[\"travel_advisor\", \"hotel_advisor\", \"human\"],\n",
+    "    system_prompt=(\n",
+    "        \"You are a travel expert that can provide specific sightseeing recommendations for a given destination. \"\n",
+    "        \"If you need general travel help, go to 'travel_advisor' for help. \"\n",
+    "        \"If you need hotel recommendations, go to 'hotel_advisor' for help. \"\n",
+    "        \"If you have enough information to respond to the user, return 'finish'. \"\n",
+    "        \"Never mention other agents by name.\"\n",
+    "    ),\n",
+    ")\n",
+    "hotel_advisor = make_agent_node(\n",
+    "    name=\"hotel_advisor\",\n",
+    "    destinations=[\"travel_advisor\", \"sightseeing_advisor\", \"human\"],\n",
+    "    system_prompt=(\n",
+    "        \"You are a travel expert that can provide hotel recommendations for a given destination. \"\n",
+    "        \"If you need general travel help, ask 'travel_advisor' for help. \"\n",
+    "        \"If you need specific sightseeing recommendations, ask 'sightseeing_advisor' for help. \"\n",
+    "        \"If you have enough information to respond to the user, return 'finish'. \"\n",
+    "        \"Never mention other agents by name.\"\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def human_node(\n",
+    "    state: MessagesState,\n",
+    ") -> Command[\n",
+    "    Literal[\"hotel_advisor\", \"sightseeing_advisor\", \"travel_advisor\", \"human\"]\n",
+    "]:\n",
+    "    \"\"\"A node for collecting user input.\"\"\"\n",
+    "    user_input = interrupt(value=\"Ready for user input.\")\n",
+    "\n",
+    "    active_agent = None\n",
+    "\n",
+    "    # This will look up the active agent.\n",
+    "    for message in state[\"messages\"][::-1]:\n",
+    "        if message.name:\n",
+    "            active_agent = message.name\n",
+    "            break\n",
+    "    else:\n",
+    "        raise AssertionError(\"Could not determine the active agent.\")\n",
+    "\n",
+    "    return Command(\n",
+    "        update={\n",
+    "            \"messages\": [\n",
+    "                {\n",
+    "                    \"role\": \"human\",\n",
+    "                    \"content\": user_input,\n",
+    "                }\n",
+    "            ]\n",
+    "        },\n",
+    "        goto=active_agent,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "builder = StateGraph(MessagesState)\n",
+    "builder.add_node(\"travel_advisor\", travel_advisor)\n",
+    "builder.add_node(\"sightseeing_advisor\", sightseeing_advisor)\n",
+    "builder.add_node(\"hotel_advisor\", hotel_advisor)\n",
+    "\n",
+    "# This adds a node to collect human input, which will route\n",
+    "# back to the active agent.\n",
+    "builder.add_node(\"human\", human_node)\n",
+    "\n",
+    "# We'll always start with a general travel advisor.\n",
+    "builder.add_edge(START, \"travel_advisor\")\n",
+    "\n",
+    "\n",
+    "checkpointer = MemorySaver()\n",
+    "graph = builder.compile(checkpointer=checkpointer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "d77921f6-599d-443f-8b15-56b1adafd3a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import display, Image\n",
+    "\n",
+    "display(Image(graph.get_graph().draw_mermaid_png()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af856e1b-41fc-4041-8cbf-3818a60088e0",
+   "metadata": {},
+   "source": [
+    "### Test multi-turn conversation\n",
+    "\n",
+    "Let's test a multi turn conversation with this application."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "161e0cf1-d13a-4026-8f89-bdab67d1ad4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--- Conversation Turn 1 ---\n",
+      "\n",
+      "User: {'messages': [{'role': 'user', 'content': 'i wanna go somewhere warm in the caribbean'}]}\n",
+      "\n",
+      "travel_advisor: The Caribbean is full of warm and beautiful destinations. Some popular options include Jamaica, the Bahamas, the Dominican Republic, and Aruba. Each of these places offers stunning beaches, vibrant culture, and plenty of activities to enjoy. Would you like recommendations on sightseeing or accommodations in any specific location?\n",
+      "\n",
+      "--- Conversation Turn 2 ---\n",
+      "\n",
+      "User: Command(resume='could you recommend a nice hotel in one of the areas and tell me which area it is.')\n",
+      "\n",
+      "travel_advisor: I'll get a hotel recommendation for you.\n",
+      "hotel_advisor: I recommend the \"Half Moon Resort\" located in Montego Bay, Jamaica. It's a luxurious resort known for its beautiful private beaches, excellent service, and a variety of amenities including golf, spas, and fine dining. Montego Bay is a vibrant area offering plenty of activities, from snorkeling and diving to exploring local culture and nightlife.\n",
+      "\n",
+      "--- Conversation Turn 3 ---\n",
+      "\n",
+      "User: Command(resume='could you recommend something to do near the hotel?')\n",
+      "\n",
+      "hotel_advisor: I recommend visiting the Rose Hall Great House, a historic plantation house located near Montego Bay. It's known for its intriguing history and beautiful architecture, offering guided tours that include tales of the White Witch of Rose Hall. Additionally, you could explore Dunn's River Falls, a stunning natural waterfall that you can climb, located a bit further but well worth the trip. For a more relaxing day, you might enjoy a catamaran cruise along the coast, which often includes snorkeling stops and beautiful sunset views.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import uuid\n",
+    "\n",
+    "thread_config = {\"configurable\": {\"thread_id\": uuid.uuid4()}}\n",
+    "\n",
+    "inputs = [\n",
+    "    # 1st round of conversation,\n",
+    "    {\n",
+    "        \"messages\": [\n",
+    "            {\"role\": \"user\", \"content\": \"i wanna go somewhere warm in the caribbean\"}\n",
+    "        ]\n",
+    "    },\n",
+    "    # Since we're using `interrupt`, we'll need to resume using the Command primitive.\n",
+    "    # 2nd round of conversation,\n",
+    "    Command(\n",
+    "        resume=\"could you recommend a nice hotel in one of the areas and tell me which area it is.\"\n",
+    "    ),\n",
+    "    # 3rd round of conversation,\n",
+    "    Command(resume=\"could you recommend something to do near the hotel?\"),\n",
+    "]\n",
+    "\n",
+    "for idx, user_input in enumerate(inputs):\n",
+    "    print()\n",
+    "    print(f\"--- Conversation Turn {idx + 1} ---\")\n",
+    "    print()\n",
+    "    print(f\"User: {user_input}\")\n",
+    "    print()\n",
+    "    for update in graph.stream(\n",
+    "        user_input,\n",
+    "        config=thread_config,\n",
+    "        stream_mode=\"updates\",\n",
+    "    ):\n",
+    "        for node_id, value in update.items():\n",
+    "            if isinstance(value, dict) and value.get(\"messages\", []):\n",
+    "                last_message = value[\"messages\"][-1]\n",
+    "                if last_message[\"role\"] != \"ai\":\n",
+    "                    continue\n",
+    "                print(f\"{last_message['name']}: {last_message['content']}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/libs/langgraph/langgraph/types.py b/libs/langgraph/langgraph/types.py
index 2086552f9..850f8ff41 100644
--- a/libs/langgraph/langgraph/types.py
+++ b/libs/langgraph/langgraph/types.py
@@ -347,6 +347,99 @@ class PregelScratchpad(TypedDict, total=False):
 
 
 def interrupt(value: Any) -> Any:
+    """Interrupt the graph with a resumable exception from within a node.
+
+    The `interrupt` function enables human-in-the-loop workflows by pausing graph
+    execution and surfacing a value to the client. This value can communicate context
+    or request input required to resume execution.
+
+    In a given node, the first invocation of this function raises a `GraphInterrupt`
+    exception, halting execution. The provided `value` is included with the exception
+    and sent to the client executing the graph.
+
+    A client resuming the graph must use the [`Command`][langgraph.types.Command]
+    primitive to specify a value for the interrupt and continue execution.
+    The graph resumes from the start of the node, **re-executing** all logic.
+
+    If a node contains multiple `interrupt` calls, LangGraph matches resume values
+    to interrupts based on their order in the node. This list of resume values
+    is scoped to the specific task executing the node and is not shared across tasks.
+
+    To use an `interrupt`, you must enable a checkpointer, as the feature relies
+    on persisting the graph state.
+
+    Example:
+        ```python
+        import uuid
+        from typing import TypedDict, Optional
+
+        from langgraph.checkpoint.memory import MemorySaver
+        from langgraph.constants import START
+        from langgraph.graph import StateGraph
+        from langgraph.types import interrupt
+
+
+        class State(TypedDict):
+            \"\"\"The graph state.\"\"\"
+
+            foo: str
+            human_value: Optional[str]
+            \"\"\"Human value will be updated using an interrupt.\"\"\"
+
+
+        def node(state: State):
+            answer = interrupt(
+                # This value will be sent to the client
+                # as part of the interrupt information.
+                \"what is your age?\"
+            )
+            print(f\"> Received an input from the interrupt: {answer}\")
+            return {\"human_value\": answer}
+
+
+        builder = StateGraph(State)
+        builder.add_node(\"node\", node)
+        builder.add_edge(START, \"node\")
+
+        # A checkpointer must be enabled for interrupts to work!
+        checkpointer = MemorySaver()
+        graph = builder.compile(checkpointer=checkpointer)
+
+        config = {
+            \"configurable\": {
+                \"thread_id\": uuid.uuid4(),
+            }
+        }
+
+        for chunk in graph.stream({\"foo\": \"abc\"}, config):
+            print(chunk)
+        ```
+
+        ```pycon
+        {'__interrupt__': (Interrupt(value='what is your age?', resumable=True, ns=['node:62e598fa-8653-9d6d-2046-a70203020e37'], when='during'),)}
+        ```
+
+        ```python
+        command = Command(resume=\"some input from a human!!!\")
+
+        for chunk in graph.stream(Command(resume=\"some input from a human!!!\"), config):
+            print(chunk)
+        ```
+
+        ```pycon
+        Received an input from the interrupt: some input from a human!!!
+        {'node': {'human_value': 'some input from a human!!!'}}
+        ```
+
+    Args:
+        value: The value to surface to the client when the graph is interrupted.
+
+    Returns:
+        Any: On subsequent invocations within the same node (same task to be precise), returns the value provided during the first invocation
+
+    Raises:
+        GraphInterrupt: On the first invocation within the node, halts execution and surfaces the provided value to the client.
+    """
     from langgraph.constants import (
         CONFIG_KEY_CHECKPOINT_NS,
         CONFIG_KEY_SCRATCHPAD,