diff --git a/docs/cassettes/introduction_03a09bfc-3d90-4e54-878f-22e3cb28a418.msgpack.zlib b/docs/cassettes/introduction_03a09bfc-3d90-4e54-878f-22e3cb28a418.msgpack.zlib
deleted file mode 100644
index e07f27bf3..000000000
--- a/docs/cassettes/introduction_03a09bfc-3d90-4e54-878f-22e3cb28a418.msgpack.zlib
+++ /dev/null
@@ -1 +0,0 @@
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lwT0q8Y20+tMZU0p/WDm1oNLQ9bXgLxaey6y7dgVNmRP8PT9u9dSZ9Ve7QBG9e7dVwNgOjzxV2SEg4N9fuPlI
\ No newline at end of file
diff --git a/docs/cassettes/introduction_051dc374-67cc-4371-9dd1-221e07593148.msgpack.zlib b/docs/cassettes/introduction_051dc374-67cc-4371-9dd1-221e07593148.msgpack.zlib
index bf029661f..626117679 100644
--- a/docs/cassettes/introduction_051dc374-67cc-4371-9dd1-221e07593148.msgpack.zlib
+++ b/docs/cassettes/introduction_051dc374-67cc-4371-9dd1-221e07593148.msgpack.zlib
@@ -1 +1 @@
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diff --git a/docs/cassettes/introduction_10701e49-d4b4-46db-bb30-f895fdf4411d.msgpack.zlib b/docs/cassettes/introduction_10701e49-d4b4-46db-bb30-f895fdf4411d.msgpack.zlib
new file mode 100644
index 000000000..b23b8c0e7
--- /dev/null
+++ b/docs/cassettes/introduction_10701e49-d4b4-46db-bb30-f895fdf4411d.msgpack.zlib
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/docs/cassettes/introduction_11d5b934-6d8b-4f52-a3bc-b3daa7207e00.msgpack.zlib b/docs/cassettes/introduction_11d5b934-6d8b-4f52-a3bc-b3daa7207e00.msgpack.zlib
deleted file mode 100644
index ce72040c7..000000000
--- a/docs/cassettes/introduction_11d5b934-6d8b-4f52-a3bc-b3daa7207e00.msgpack.zlib
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/docs/cassettes/introduction_32e4f36e-72ce-4ade-bd7e-94880e0d456b.msgpack.zlib b/docs/cassettes/introduction_32e4f36e-72ce-4ade-bd7e-94880e0d456b.msgpack.zlib
deleted file mode 100644
index af25c9ceb..000000000
--- a/docs/cassettes/introduction_32e4f36e-72ce-4ade-bd7e-94880e0d456b.msgpack.zlib
+++ /dev/null
@@ -1 +0,0 @@
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
\ No newline at end of file
diff --git a/docs/cassettes/introduction_35c8978e-c07d-4dd0-a97b-0ce3a723eea5.msgpack.zlib b/docs/cassettes/introduction_35c8978e-c07d-4dd0-a97b-0ce3a723eea5.msgpack.zlib
index b16138d3b..79b725f3b 100644
--- a/docs/cassettes/introduction_35c8978e-c07d-4dd0-a97b-0ce3a723eea5.msgpack.zlib
+++ b/docs/cassettes/introduction_35c8978e-c07d-4dd0-a97b-0ce3a723eea5.msgpack.zlib
@@ -1 +1 @@
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\ No newline at end of file
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diff --git a/docs/cassettes/introduction_4527cf9a-b191-4bde-858a-e33a74a48c55.msgpack.zlib b/docs/cassettes/introduction_4527cf9a-b191-4bde-858a-e33a74a48c55.msgpack.zlib
index 77236e6e0..b9bea8edb 100644
--- a/docs/cassettes/introduction_4527cf9a-b191-4bde-858a-e33a74a48c55.msgpack.zlib
+++ b/docs/cassettes/introduction_4527cf9a-b191-4bde-858a-e33a74a48c55.msgpack.zlib
@@ -1 +1 @@
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diff --git a/docs/cassettes/introduction_69071b02-c011-4b7f-90b1-8e89e032322d.msgpack.zlib b/docs/cassettes/introduction_69071b02-c011-4b7f-90b1-8e89e032322d.msgpack.zlib
index d72c229bc..a69fc1008 100644
--- a/docs/cassettes/introduction_69071b02-c011-4b7f-90b1-8e89e032322d.msgpack.zlib
+++ b/docs/cassettes/introduction_69071b02-c011-4b7f-90b1-8e89e032322d.msgpack.zlib
@@ -1 +1 @@
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diff --git a/docs/cassettes/introduction_6a385b06-8d34-4a2d-aded-1cf4bb0ca590.msgpack.zlib b/docs/cassettes/introduction_6a385b06-8d34-4a2d-aded-1cf4bb0ca590.msgpack.zlib
index e82d60c13..0049ee903 100644
--- a/docs/cassettes/introduction_6a385b06-8d34-4a2d-aded-1cf4bb0ca590.msgpack.zlib
+++ b/docs/cassettes/introduction_6a385b06-8d34-4a2d-aded-1cf4bb0ca590.msgpack.zlib
@@ -1 +1 @@
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diff --git a/docs/cassettes/introduction_6b32914d-4d60-491f-8e11-1e6867e38ffd.msgpack.zlib b/docs/cassettes/introduction_6b32914d-4d60-491f-8e11-1e6867e38ffd.msgpack.zlib
deleted file mode 100644
index bd56de369..000000000
--- a/docs/cassettes/introduction_6b32914d-4d60-491f-8e11-1e6867e38ffd.msgpack.zlib
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/docs/cassettes/introduction_761d15fb-d5e2-4d50-a630-126d77e77294.msgpack.zlib b/docs/cassettes/introduction_761d15fb-d5e2-4d50-a630-126d77e77294.msgpack.zlib
deleted file mode 100644
index f7c4715e0..000000000
--- a/docs/cassettes/introduction_761d15fb-d5e2-4d50-a630-126d77e77294.msgpack.zlib
+++ /dev/null
@@ -1 +0,0 @@
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
\ No newline at end of file
diff --git a/docs/cassettes/introduction_7df33d9e-cc76-4a0f-8307-01e619483b3e.msgpack.zlib b/docs/cassettes/introduction_7df33d9e-cc76-4a0f-8307-01e619483b3e.msgpack.zlib
new file mode 100644
index 000000000..3f3c5747f
--- /dev/null
+++ b/docs/cassettes/introduction_7df33d9e-cc76-4a0f-8307-01e619483b3e.msgpack.zlib
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/docs/cassettes/introduction_85f17be3-eaf6-495e-a846-49436916b4ab.msgpack.zlib b/docs/cassettes/introduction_85f17be3-eaf6-495e-a846-49436916b4ab.msgpack.zlib
deleted file mode 100644
index eddce2856..000000000
--- a/docs/cassettes/introduction_85f17be3-eaf6-495e-a846-49436916b4ab.msgpack.zlib
+++ /dev/null
@@ -1 +0,0 @@
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
\ No newline at end of file
diff --git a/docs/cassettes/introduction_8b49509c-9d97-457c-a76a-c495fb30ccbc.msgpack.zlib b/docs/cassettes/introduction_8b49509c-9d97-457c-a76a-c495fb30ccbc.msgpack.zlib
deleted file mode 100644
index 9aaf68fcf..000000000
--- a/docs/cassettes/introduction_8b49509c-9d97-457c-a76a-c495fb30ccbc.msgpack.zlib
+++ /dev/null
@@ -1 +0,0 @@
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
\ No newline at end of file
diff --git a/docs/cassettes/introduction_9f318020-ab7e-415b-a5e2-eddec6d9f3a6.msgpack.zlib b/docs/cassettes/introduction_9f318020-ab7e-415b-a5e2-eddec6d9f3a6.msgpack.zlib
index 3168d8dfb..f14d5edc4 100644
--- a/docs/cassettes/introduction_9f318020-ab7e-415b-a5e2-eddec6d9f3a6.msgpack.zlib
+++ b/docs/cassettes/introduction_9f318020-ab7e-415b-a5e2-eddec6d9f3a6.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/introduction_9fc99c7e-b61d-4aec-9c62-042798185ec3.msgpack.zlib b/docs/cassettes/introduction_9fc99c7e-b61d-4aec-9c62-042798185ec3.msgpack.zlib
deleted file mode 100644
index 4d5579ac3..000000000
--- a/docs/cassettes/introduction_9fc99c7e-b61d-4aec-9c62-042798185ec3.msgpack.zlib
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/docs/cassettes/introduction_a7debb4a-2a3a-40b9-a48c-7052ec2c2726.msgpack.zlib b/docs/cassettes/introduction_a7debb4a-2a3a-40b9-a48c-7052ec2c2726.msgpack.zlib
deleted file mode 100644
index 5e60e16a2..000000000
--- a/docs/cassettes/introduction_a7debb4a-2a3a-40b9-a48c-7052ec2c2726.msgpack.zlib
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/docs/cassettes/introduction_acbec099-e5d2-497f-929e-c548d7bcbf77.msgpack.zlib b/docs/cassettes/introduction_acbec099-e5d2-497f-929e-c548d7bcbf77.msgpack.zlib
index 650645947..00a9173f4 100644
--- a/docs/cassettes/introduction_acbec099-e5d2-497f-929e-c548d7bcbf77.msgpack.zlib
+++ b/docs/cassettes/introduction_acbec099-e5d2-497f-929e-c548d7bcbf77.msgpack.zlib
@@ -1 +1 @@
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diff --git a/docs/cassettes/introduction_b3220ae2-cba0-4447-96d1-eb0be4684e59.msgpack.zlib b/docs/cassettes/introduction_b3220ae2-cba0-4447-96d1-eb0be4684e59.msgpack.zlib
deleted file mode 100644
index b3c569af2..000000000
--- a/docs/cassettes/introduction_b3220ae2-cba0-4447-96d1-eb0be4684e59.msgpack.zlib
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/docs/cassettes/introduction_c1955d79-a1e4-47d0-ba79-b45bd5752a23.msgpack.zlib b/docs/cassettes/introduction_c1955d79-a1e4-47d0-ba79-b45bd5752a23.msgpack.zlib
deleted file mode 100644
index 355265ba8..000000000
--- a/docs/cassettes/introduction_c1955d79-a1e4-47d0-ba79-b45bd5752a23.msgpack.zlib
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/docs/cassettes/introduction_c5382e81-bfcd-4508-b02a-099e3d9627fd.msgpack.zlib b/docs/cassettes/introduction_c5382e81-bfcd-4508-b02a-099e3d9627fd.msgpack.zlib
new file mode 100644
index 000000000..e8bc8459f
--- /dev/null
+++ b/docs/cassettes/introduction_c5382e81-bfcd-4508-b02a-099e3d9627fd.msgpack.zlib
@@ -0,0 +1 @@
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MXW1fV1tvBXBOb/dttnWYAM0cEN6Sb/ORYnVeTkdauiN1UwMEMLzshFDOirTwGvOVv+djH1hdGmd11OE15ubHY9coWnh1CS+UVbQ6EjUdXs2rgcz0zbHfQ6WDzhpeWzCmLTnrYZRcmsPlcmQ90epgLSQL4dXMU56IKDmyZR34RhrnzOScrsdtLUnWK3pZ6U7nP/Iy4Ycd+n3fq4DpuU72KuAl3i18ohKjqRcsEnlrcq3gN3B+fZGXFQ/EReVCb5PAIfXIq5gc+xhuAmGcB4Z870osD0UsLMNNaTk5OZc4F++49fBTKB9oj3NlVVpSOu9snw5zRM683re4WoNcAV9Jl1wf5c3eM+QH7LkIhzl3HohvbzdEmDYsrs5m1H5+6fVRFq098T9kz8VZJO1tv0B9JqEbv2dlrOLM1eR7V1+Un3WW5TnRE34B/oYkVTxcLK8NZJQXNlRVFhfmTxk7tgqao1V1Ih9e53K4cCDjk+jGghFcARQ9lKtgLhReWzi+NL9kVMH6cVy54lYAS5U8YE5WZGq/WbtOkBTDA9FOpWtge3n++PBTOc60LD5VcHmzeW+6x5XNDYMgYjtTxFmaMFS2erP29S6nBi24vAP710m64yvlupxe31Y1fPPnrTMDNx5r/uaBOfXP9+t8e7+D2S8fCeZ0fqqw+eijP13bm76R8LeXBiW8/b78QVnmiN6vnzl79rWNCze//eSxinMNU/7nyxPnzn766gNnzsw/M6hu0K13f36ITn1xsnfhgK7vTlza+5l9by3bcdfQSUWObl1Fb0nz/dM2S/e+IYnnp56vW5ndecn/vPf6e8sX1Ff1e3u9a8AvZ6e/+d20383unnf8679slmbcnflJXpeOe59IO5V1NuNmZaK/Y6dJfXsd0kpnbpA/uPlYS9+CxoFTjr58023vu68qPtLzi687vDLp8KKXe1/RMtt5+8iWw0Pct3To2mNI7+75R1Y27V0w71cjVuS/NLPGFe7S6/RV3W/q2O+ZAY193uC7Zk1yvnxV6DNpYcdAXd85g/P2f319rxWP1AfiZ756Vf+Sn7z7XNP88yNH3H/lx4OrHS0Vfz3y6Zm3rr+jy8GkZbu79d8Zvv/Kji09E6Y2n6sc1qPs8u43dWsqSBz/58I9tfu2XX355rIez/5xeeMrY3q9Maj/e3MGfnl53TW/m5GZ3bI9FL/o3Z5Tp5X/esVtzeFJI12PZJfN3TnrTJ8tB1L6vPveZfLsMdf1qKhY7R7dZcT2+1vuKxoyVvru02nF154o7fZUsPFc4qz6ss5/3Pd8S8vkKR0fLMq498Pmgz+d9+mtjx68+avFP8vZsL/nksJe3NnEuQOSh//2ZIKrB9flWu3W+Xc5X7zrtsQ9/Tct+1YZsGRzn21dXp0yaWKPE+FXh80Y+1Foa+1ve+QJQxvepX/aPHB03vyX/J1qX/FvvenvC24oXHLUv5a/4ZGHFxw/Vr1g08JD/bzVV/16lsc1Yutj8e80Vz13IvHy8c/e+sg0Y8vW87TbmZ59tsYdul1s+U3C2N0znu38ceLJD+8LdMt80L/16JVLB7zzcPfknsebt5z8fPHBx05/drAm6ap7rlmysfPcr1974vqa1OEjb6jq2yXj/Kmn97W8fdn+Ya9O0x48njKtaWDo+j5Vq3v12zRk1i19UkvzCvjP4n5yeFz3gcdG14WTx12bMK77mrs6dljeZVrThh6Vw5ZO/jYvv/eJJ5/p/OaovTXHH7j2cEPxn8TNM6/jxOd2djlet9t7alV3Z/6jHVdt/+DTSUv3dx5Q+8vETs/P/GhyVb/PPt7f//2kUNJLhSt3PTd7zsqdnwiLnzs5cXfjorIJzeeu/e3utL2BvK0dVoYStmlJ3fsmdc2et/HttFFH1ucVZXzoaDmTNOns5r/1X3/Tp2l7l5VVHulz947LZnEL37yhdNo9JVv61B9/cO+4bhl7Bm99ZcGXH644p91zS9e8Of91rnSAuK9yT/kU98TpxudP9z+04XGfs3tdz2ee7HvS12eAtq/rzhlJf9zf5dTMwScSS8npxPS5pwqF3o4Oi9Siq32rNi1aN3bRR1zLyiXFXY0bdDmvdlv2suuGqav7TH5z2Yf1fx13mHt08juDOmzYcP2tu8prhu3f21za6/SI3yy64bNnvpk/ddsnvrni0xm//0368R3LV9w4Zl3e32Z0vLt0ccL6edturki9NfTt0vs+/oKTTk0vffwKz2O7XHkLt9eMr/xCqBu5IOGrQ++/trbe0e0KsWun4RtGGW+m33/Hgv9e1cGQ3nl6dlGgq/P3Q298YJK2Z3mmY6nxwW15eQ99XJZ37NXLvkyYsmjeiF9teavsZ0V9F/UaOW+x9+GuqfJjfz875/N6bYV61y07jr1+76rFA7YVaM2N92ZuG1kyIzCgS8GVV/S8fvisBw7ekfUNt3w99+TXh4a0bNhz9oUJvfWVUv+qo9LkozuuNDYdLTqx4NH5O7xJXw48vU9J2L1r19UDP2/kJn/0wvTElqJXDrywqmDZfZ1PU67TnYvzr1zcZeXYVYe9d+sbxWDOwrz7ZuwS+xVOX9/zgebL/HFr9SWvPbTqvaSla5qPxX9z78eLjuz7+VOH1/3sxB9ePv9Ywth7ErcmlyjqbPd984X8jqcfTrj6jWV9z6b0mPCJ/83bOszyS9u/2XJ/p6duHL521PIV0nvDvt6686Nd++/O/Oj8zbVLTp68ptvV870HxmxavETwb59bWPPkr5vGz/vi2/U76dKaGYGitKHdh/7i9rf/8pdBSopvYf7ed4SHfj5w/htfbakuPh7ec/7Z7eN3v/TC88/+/a8nvoPw/t13nTqog66+xtezQ4f/BUBFj0Y=
\ No newline at end of file
diff --git a/docs/cassettes/introduction_dba1b168-f8e0-496d-9bd6-37198fb4776e.msgpack.zlib b/docs/cassettes/introduction_dba1b168-f8e0-496d-9bd6-37198fb4776e.msgpack.zlib
index 83f61afb3..aa5ff8b76 100644
--- a/docs/cassettes/introduction_dba1b168-f8e0-496d-9bd6-37198fb4776e.msgpack.zlib
+++ b/docs/cassettes/introduction_dba1b168-f8e0-496d-9bd6-37198fb4776e.msgpack.zlib
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/docs/cassettes/introduction_df6d81bb-e674-44ef-a46e-17b1a8d2381a.msgpack.zlib b/docs/cassettes/introduction_df6d81bb-e674-44ef-a46e-17b1a8d2381a.msgpack.zlib
new file mode 100644
index 000000000..c88c4d311
--- /dev/null
+++ b/docs/cassettes/introduction_df6d81bb-e674-44ef-a46e-17b1a8d2381a.msgpack.zlib
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/docs/cassettes/introduction_effb95d9-b7d5-40c5-9253-253d193b23b2.msgpack.zlib b/docs/cassettes/introduction_effb95d9-b7d5-40c5-9253-253d193b23b2.msgpack.zlib
deleted file mode 100644
index c83f512d7..000000000
--- a/docs/cassettes/introduction_effb95d9-b7d5-40c5-9253-253d193b23b2.msgpack.zlib
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/docs/cassettes/introduction_f4009ba6-dc0b-4216-ab0c-fbb104616f73.msgpack.zlib b/docs/cassettes/introduction_f4009ba6-dc0b-4216-ab0c-fbb104616f73.msgpack.zlib
deleted file mode 100644
index e629869fc..000000000
--- a/docs/cassettes/introduction_f4009ba6-dc0b-4216-ab0c-fbb104616f73.msgpack.zlib
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/docs/cassettes/introduction_f5447778-53d7-47f3-801b-f47bcf2185a0.msgpack.zlib b/docs/cassettes/introduction_f5447778-53d7-47f3-801b-f47bcf2185a0.msgpack.zlib
index e208dd31d..870503059 100644
--- a/docs/cassettes/introduction_f5447778-53d7-47f3-801b-f47bcf2185a0.msgpack.zlib
+++ b/docs/cassettes/introduction_f5447778-53d7-47f3-801b-f47bcf2185a0.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/introduction_faa345c6-38a2-42e8-9035-9cf56f7bb5b1.msgpack.zlib b/docs/cassettes/introduction_faa345c6-38a2-42e8-9035-9cf56f7bb5b1.msgpack.zlib
deleted file mode 100644
index 18913ce2b..000000000
--- a/docs/cassettes/introduction_faa345c6-38a2-42e8-9035-9cf56f7bb5b1.msgpack.zlib
+++ /dev/null
@@ -1 +0,0 @@
-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
\ No newline at end of file
diff --git a/docs/docs/tutorials/introduction.ipynb b/docs/docs/tutorials/introduction.ipynb
index 266c83f77..0168af968 100644
--- a/docs/docs/tutorials/introduction.ipynb
+++ b/docs/docs/tutorials/introduction.ipynb
@@ -225,12 +225,12 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "32e4f36e-72ce-4ade-bd7e-94880e0d456b",
+   "id": "9403859a-f25a-46b4-a672-46a93f8a3cba",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/jpeg": 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",
+      "image/png": "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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -276,7 +276,7 @@
    ],
    "source": [
     "def stream_graph_updates(user_input: str):\n",
-    "    for event in graph.stream({\"messages\": [(\"user\", user_input)]}):\n",
+    "    for event in graph.stream({\"messages\": [{\"role\": \"user\", \"content\": user_input}]}):\n",
     "        for value in event.values():\n",
     "            print(\"Assistant:\", value[\"messages\"][-1].content)\n",
     "\n",
@@ -466,6 +466,7 @@
     "\n",
     "llm = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n",
     "# Modification: tell the LLM which tools it can call\n",
+    "# highlight-next-line\n",
     "llm_with_tools = llm.bind_tools(tools)\n",
     "\n",
     "\n",
@@ -864,7 +865,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 23,
    "id": "a06548bf-81fa-4436-b4c1-f68601fb4187",
    "metadata": {},
    "outputs": [],
@@ -957,7 +958,9 @@
     "\n",
     "# The config is the **second positional argument** to stream() or invoke()!\n",
     "events = graph.stream(\n",
-    "    {\"messages\": [(\"user\", user_input)]}, config, stream_mode=\"values\"\n",
+    "    {\"messages\": [{\"role\": \"user\", \"content\": user_input}]},\n",
+    "    config,\n",
+    "    stream_mode=\"values\",\n",
     ")\n",
     "for event in events:\n",
     "    event[\"messages\"][-1].pretty_print()"
@@ -997,7 +1000,9 @@
     "\n",
     "# The config is the **second positional argument** to stream() or invoke()!\n",
     "events = graph.stream(\n",
-    "    {\"messages\": [(\"user\", user_input)]}, config, stream_mode=\"values\"\n",
+    "    {\"messages\": [{\"role\": \"user\", \"content\": user_input}]},\n",
+    "    config,\n",
+    "    stream_mode=\"values\",\n",
     ")\n",
     "for event in events:\n",
     "    event[\"messages\"][-1].pretty_print()"
@@ -1035,7 +1040,8 @@
    "source": [
     "# The only difference is we change the `thread_id` here to \"2\" instead of \"1\"\n",
     "events = graph.stream(\n",
-    "    {\"messages\": [(\"user\", user_input)]},\n",
+    "    {\"messages\": [{\"role\": \"user\", \"content\": user_input}]},\n",
+    "    # highlight-next-line\n",
     "    {\"configurable\": {\"thread_id\": \"2\"}},\n",
     "    stream_mode=\"values\",\n",
     ")\n",
@@ -1171,15 +1177,15 @@
     "\n",
     "Agents can be unreliable and may need human input to successfully accomplish tasks. Similarly, for some actions, you may want to require human approval before running to ensure that everything is running as intended.\n",
     "\n",
-    "LangGraph supports `human-in-the-loop` workflows in a number of ways. In this section, we will use LangGraph's `interrupt_before` functionality to always break the tool node.\n",
+    "LangGraph's [persistence](../../concepts/persistence) layer supports human-in-the-loop workflows, allowing execution to pause and resume based on user feedback. The primary interface to this functionality is the [interrupt](../../concepts/human_in_the_loop/#interrupt) function. Calling `interrupt` inside a node will pause execution. Execution can be resumed, together with new input from a human, by passing in a [Command](../../concepts/human_in_the_loop/#the-command-primitive). `interrupt` is ergonomically similar to Python's built-in `input()`, [with some caveats](../../concepts/human_in_the_loop/#interrupt). We demonstrate an example below.\n",
     "\n",
-    "First, start from our existing code. The following is copied from Part 3."
+    "First, start with our existing code from Part 3. We will make one change, which is to add a simple `human_assistance` tool accessible to the chatbot. This tool uses `interrupt` to receive information from a human."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5a81608a-373a-4339-b1c6-65b73a92b983",
+   "id": "08439bb4-91e8-4abb-a57a-2511877abcb7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1187,14 +1193,16 @@
     "\n",
     "from langchain_anthropic import ChatAnthropic\n",
     "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "from langchain_core.tools import tool\n",
     "from typing_extensions import TypedDict\n",
     "\n",
     "from langgraph.checkpoint.memory import MemorySaver\n",
-    "from langgraph.graph import StateGraph, START\n",
+    "from langgraph.graph import StateGraph, START, END\n",
     "from langgraph.graph.message import add_messages\n",
     "from langgraph.prebuilt import ToolNode, tools_condition\n",
     "\n",
-    "memory = MemorySaver()\n",
+    "# highlight-next-line\n",
+    "from langgraph.types import Command, interrupt\n",
     "\n",
     "\n",
     "class State(TypedDict):\n",
@@ -1204,19 +1212,36 @@
     "graph_builder = StateGraph(State)\n",
     "\n",
     "\n",
+    "# highlight-next-line\n",
+    "@tool\n",
+    "# highlight-next-line\n",
+    "def human_assistance(query: str) -> str:\n",
+    "    # highlight-next-line\n",
+    "    \"\"\"Request assistance from a human.\"\"\"\n",
+    "    # highlight-next-line\n",
+    "    human_response = interrupt({\"query\": query})\n",
+    "    # highlight-next-line\n",
+    "    return human_response[\"data\"]\n",
+    "\n",
+    "\n",
     "tool = TavilySearchResults(max_results=2)\n",
-    "tools = [tool]\n",
+    "tools = [tool, human_assistance]\n",
     "llm = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n",
     "llm_with_tools = llm.bind_tools(tools)\n",
     "\n",
     "\n",
     "def chatbot(state: State):\n",
-    "    return {\"messages\": [llm_with_tools.invoke(state[\"messages\"])]}\n",
+    "    message = llm_with_tools.invoke(state[\"messages\"])\n",
+    "    # Because we will be interrupting during tool execution,\n",
+    "    # we disable parallel tool calling to avoid repeating any\n",
+    "    # tool invocations when we resume.\n",
+    "    assert len(message.tool_calls) <= 1\n",
+    "    return {\"messages\": [message]}\n",
     "\n",
     "\n",
     "graph_builder.add_node(\"chatbot\", chatbot)\n",
     "\n",
-    "tool_node = ToolNode(tools=[tool])\n",
+    "tool_node = ToolNode(tools=tools)\n",
     "graph_builder.add_node(\"tools\", tool_node)\n",
     "\n",
     "graph_builder.add_conditional_edges(\n",
@@ -1229,31 +1254,78 @@
   },
   {
    "cell_type": "markdown",
-   "id": "813505b2-18c1-46e9-b891-20a34232808b",
+   "id": "cada4cd2-0316-487b-923e-0d12fa3473c2",
    "metadata": {},
    "source": [
-    "Now, compile the graph, specifying to `interrupt_before` the `tools` node."
+    "------\n",
+    "\n",
+    "!!! tip\n",
+    "\n",
+    "    Check out the [Human-in-the-loop section](../../how-tos/#human-in-the-loop) of the How-to Guides for more examples of Human-in-the-loop workflows, including how to [review and edit tool calls](../../how-tos/human_in_the_loop/review-tool-calls/) before they are executed.\n",
+    "\n",
+    "---------\n",
+    "\n",
+    "We compile the graph with a checkpointer, as before:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "id": "b0883e32-1a39-4ce9-ae32-bbd66708fd84",
+   "execution_count": 2,
+   "id": "16cc6b14-f758-4590-beb7-7b76a6841521",
    "metadata": {},
    "outputs": [],
    "source": [
-    "graph = graph_builder.compile(\n",
-    "    checkpointer=memory,\n",
-    "    # This is new!\n",
-    "    interrupt_before=[\"tools\"],\n",
-    "    # Note: can also interrupt __after__ tools, if desired.\n",
-    "    # interrupt_after=[\"tools\"]\n",
-    ")"
+    "memory = MemorySaver()\n",
+    "\n",
+    "graph = graph_builder.compile(checkpointer=memory)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc0e84db-925c-4468-b3e0-639e6b25ac3c",
+   "metadata": {},
+   "source": [
+    "Visualizing the graph, we recover the same layout as before. We have just added a tool!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 3,
+   "id": "22ff19af-5a5b-4ca5-82d8-6a38445c24af",
+   "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 Image, display\n",
+    "\n",
+    "try:\n",
+    "    display(Image(graph.get_graph().draw_mermaid_png()))\n",
+    "except Exception:\n",
+    "    # This requires some extra dependencies and is optional\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae4663b8-176d-445d-bd07-56938c044f98",
+   "metadata": {},
+   "source": [
+    "Let's now prompt the chatbot with a question that will engage the new `human_assistance` tool:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
    "id": "9f318020-ab7e-415b-a5e2-eddec6d9f3a6",
    "metadata": {},
    "outputs": [
@@ -1263,24 +1335,26 @@
      "text": [
       "================================\u001b[1m Human Message \u001b[0m=================================\n",
       "\n",
-      "I'm learning LangGraph. Could you do some research on it for me?\n",
+      "I need some expert guidance for building an AI agent. Could you request assistance for me?\n",
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "[{'text': \"Certainly! I'd be happy to research LangGraph for you. To get the most up-to-date and comprehensive information, I'll use the Tavily search engine to look this up. Let me do that for you now.\", 'type': 'text'}, {'id': 'toolu_01R4ZFcb5hohpiVZwr88Bxhc', 'input': {'query': 'LangGraph framework for building language model applications'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]\n",
+      "[{'text': \"Certainly! I'd be happy to request expert assistance for you regarding building an AI agent. To do this, I'll use the human_assistance function to relay your request. Let me do that for you now.\", 'type': 'text'}, {'id': 'toolu_01ABUqneqnuHNuo1vhfDFQCW', 'input': {'query': 'A user is requesting expert guidance for building an AI agent. Could you please provide some expert advice or resources on this topic?'}, 'name': 'human_assistance', 'type': 'tool_use'}]\n",
       "Tool Calls:\n",
-      "  tavily_search_results_json (toolu_01R4ZFcb5hohpiVZwr88Bxhc)\n",
-      " Call ID: toolu_01R4ZFcb5hohpiVZwr88Bxhc\n",
+      "  human_assistance (toolu_01ABUqneqnuHNuo1vhfDFQCW)\n",
+      " Call ID: toolu_01ABUqneqnuHNuo1vhfDFQCW\n",
       "  Args:\n",
-      "    query: LangGraph framework for building language model applications\n"
+      "    query: A user is requesting expert guidance for building an AI agent. Could you please provide some expert advice or resources on this topic?\n"
      ]
     }
    ],
    "source": [
-    "user_input = \"I'm learning LangGraph. Could you do some research on it for me?\"\n",
+    "user_input = \"I need some expert guidance for building an AI agent. Could you request assistance for me?\"\n",
     "config = {\"configurable\": {\"thread_id\": \"1\"}}\n",
-    "# The config is the **second positional argument** to stream() or invoke()!\n",
+    "\n",
     "events = graph.stream(\n",
-    "    {\"messages\": [(\"user\", user_input)]}, config, stream_mode=\"values\"\n",
+    "    {\"messages\": [{\"role\": \"user\", \"content\": user_input}]},\n",
+    "    config,\n",
+    "    stream_mode=\"values\",\n",
     ")\n",
     "for event in events:\n",
     "    if \"messages\" in event:\n",
@@ -1292,13 +1366,13 @@
    "id": "39405637-13b1-40b1-a51e-6d60bf675ff1",
    "metadata": {},
    "source": [
-    "Let's inspect the graph state to confirm it worked."
+    "The chatbot generated a tool call, but then execution has been interrupted! Note that if we inspect the graph state, we see that it stopped at the tools node:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "id": "9bb7af46-9b4f-4bb1-b8b9-e9ddf7dbc82c",
+   "execution_count": 5,
+   "id": "9f511371-98b6-4513-b450-9143778f12dc",
    "metadata": {},
    "outputs": [
     {
@@ -1307,7 +1381,7 @@
        "('tools',)"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1319,51 +1393,28 @@
   },
   {
    "cell_type": "markdown",
-   "id": "89326046-2b11-4812-8b6d-8780306ec275",
+   "id": "4574b267-b0d8-4056-aeea-6e8817e8844e",
    "metadata": {},
    "source": [
-    "**Notice** that unlike last time, the \"next\" node is set to **'tools'**. We've interrupted here! Let's check the tool invocation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "3facda0a-e6ad-4b28-b627-753ad8c90c15",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'name': 'tavily_search_results_json',\n",
-       "  'args': {'query': 'LangGraph framework for building language model applications'},\n",
-       "  'id': 'toolu_01R4ZFcb5hohpiVZwr88Bxhc',\n",
-       "  'type': 'tool_call'}]"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "existing_message = snapshot.values[\"messages\"][-1]\n",
-    "existing_message.tool_calls"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a55a4c70-7226-4be0-8562-391f72bc1f2b",
-   "metadata": {},
-   "source": [
-    "This query seems reasonable. Nothing to filter here. The simplest thing the human can do is just let the graph continue executing. Let's do that below.\n",
+    "Let's take a closer look at the `human_assistance` tool:\n",
     "\n",
-    "Next, continue the graph! Passing in `None` will just let the graph continue where it left off, without adding anything new to the state."
+    "```python\n",
+    "@tool\n",
+    "def human_assistance(query: str) -> str:\n",
+    "    \"\"\"Request assistance from a human.\"\"\"\n",
+    "    human_response = interrupt({\"query\": query})\n",
+    "    return human_response[\"data\"]\n",
+    "```\n",
+    "\n",
+    "Similar to Python's built-in `input()` function, calling `interrupt` inside the tool will pause execution. Progress is persisted based on our choice of [checkpointer](../../concepts/persistence/#checkpointer-libraries)-- so if we are persisting with Postgres, we can resume at any time as long as the database is alive. Here we are persisting with the in-memory checkpointer, so we can resume any time as long as our Python kernel is running.\n",
+    "\n",
+    "To resume execution, we pass a [Command](../../concepts/human_in_the_loop/#the-command-primitive) object containing data expected by the tool. The format of this data can be customized based on our needs. Here, we just need a dict with a key `\"data\"`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "id": "effb95d9-b7d5-40c5-9253-253d193b23b2",
+   "execution_count": 6,
+   "id": "df6d81bb-e674-44ef-a46e-17b1a8d2381a",
    "metadata": {},
    "outputs": [
     {
@@ -1372,46 +1423,49 @@
      "text": [
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "[{'text': \"Certainly! I'd be happy to research LangGraph for you. To get the most up-to-date and comprehensive information, I'll use the Tavily search engine to look this up. Let me do that for you now.\", 'type': 'text'}, {'id': 'toolu_01R4ZFcb5hohpiVZwr88Bxhc', 'input': {'query': 'LangGraph framework for building language model applications'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]\n",
+      "[{'text': \"Certainly! I'd be happy to request expert assistance for you regarding building an AI agent. To do this, I'll use the human_assistance function to relay your request. Let me do that for you now.\", 'type': 'text'}, {'id': 'toolu_01ABUqneqnuHNuo1vhfDFQCW', 'input': {'query': 'A user is requesting expert guidance for building an AI agent. Could you please provide some expert advice or resources on this topic?'}, 'name': 'human_assistance', 'type': 'tool_use'}]\n",
       "Tool Calls:\n",
-      "  tavily_search_results_json (toolu_01R4ZFcb5hohpiVZwr88Bxhc)\n",
-      " Call ID: toolu_01R4ZFcb5hohpiVZwr88Bxhc\n",
+      "  human_assistance (toolu_01ABUqneqnuHNuo1vhfDFQCW)\n",
+      " Call ID: toolu_01ABUqneqnuHNuo1vhfDFQCW\n",
       "  Args:\n",
-      "    query: LangGraph framework for building language model applications\n",
+      "    query: A user is requesting expert guidance for building an AI agent. Could you please provide some expert advice or resources on this topic?\n",
       "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
-      "Name: tavily_search_results_json\n",
+      "Name: human_assistance\n",
       "\n",
-      "[{\"url\": \"https://towardsdatascience.com/from-basics-to-advanced-exploring-langgraph-e8c1cf4db787\", \"content\": \"LangChain is one of the leading frameworks for building applications powered by Lardge Language Models. With the LangChain Expression Language (LCEL), defining and executing step-by-step action sequences — also known as chains — becomes much simpler. In more technical terms, LangChain allows us to create DAGs (directed acyclic graphs). As LLM applications, particularly LLM agents, have ...\"}, {\"url\": \"https://github.com/langchain-ai/langgraph\", \"content\": \"Overview. LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures ...\"}]\n",
+      "We, the experts are here to help! We'd recommend you check out LangGraph to build your agent. It's much more reliable and extensible than simple autonomous agents.\n",
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "Thank you for your patience. I've found some valuable information about LangGraph for you. Let me summarize the key points:\n",
+      "Thank you for your patience. I've received some expert advice regarding your request for guidance on building an AI agent. Here's what the experts have suggested:\n",
       "\n",
-      "1. LangGraph is a library for building stateful, multi-actor applications with Large Language Models (LLMs).\n",
+      "The experts recommend that you look into LangGraph for building your AI agent. They mention that LangGraph is a more reliable and extensible option compared to simple autonomous agents.\n",
       "\n",
-      "2. It is particularly useful for creating agent and multi-agent workflows.\n",
+      "LangGraph is likely a framework or library designed specifically for creating AI agents with advanced capabilities. Here are a few points to consider based on this recommendation:\n",
       "\n",
-      "3. LangGraph is built on top of LangChain, which is one of the leading frameworks for building LLM-powered applications.\n",
+      "1. Reliability: The experts emphasize that LangGraph is more reliable than simpler autonomous agent approaches. This could mean it has better stability, error handling, or consistent performance.\n",
       "\n",
-      "4. Key benefits of LangGraph compared to other LLM frameworks include:\n",
-      "   a) Cycles: It allows you to define flows that involve cycles, which is essential for most agent architectures.\n",
-      "   b) Controllability: Offers more control over the application flow.\n",
-      "   c) Persistence: Provides ways to maintain state across interactions.\n",
+      "2. Extensibility: LangGraph is described as more extensible, which suggests that it probably offers a flexible architecture that allows you to easily add new features or modify existing ones as your agent's requirements evolve.\n",
       "\n",
-      "5. LangGraph works well with the LangChain Expression Language (LCEL), which simplifies the process of defining and executing step-by-step action sequences (chains).\n",
+      "3. Advanced capabilities: Given that it's recommended over \"simple autonomous agents,\" LangGraph likely provides more sophisticated tools and techniques for building complex AI agents.\n",
       "\n",
-      "6. In technical terms, LangGraph enables the creation of Directed Acyclic Graphs (DAGs) for LLM applications.\n",
+      "To get started with LangGraph, you might want to:\n",
       "\n",
-      "7. It's particularly useful for building more complex LLM agents and multi-agent systems.\n",
+      "1. Search for the official LangGraph documentation or website to learn more about its features and how to use it.\n",
+      "2. Look for tutorials or guides specifically focused on building AI agents with LangGraph.\n",
+      "3. Check if there are any community forums or discussion groups where you can ask questions and get support from other developers using LangGraph.\n",
       "\n",
-      "LangGraph seems to be an advanced tool that builds upon LangChain to provide more sophisticated capabilities for creating stateful and multi-actor LLM applications. It's especially valuable if you're looking to create complex agent systems or applications that require maintaining state across interactions.\n",
-      "\n",
-      "Is there any specific aspect of LangGraph you'd like to know more about? I'd be happy to dive deeper into any particular area of interest.\n"
+      "If you'd like more specific information about LangGraph or have any questions about this recommendation, please feel free to ask, and I can request further assistance from the experts.\n"
      ]
     }
    ],
    "source": [
-    "# `None` will append nothing new to the current state, letting it resume as if it had never been interrupted\n",
-    "events = graph.stream(None, config, stream_mode=\"values\")\n",
+    "human_response = (\n",
+    "    \"We, the experts are here to help! We'd recommend you check out LangGraph to build your agent.\"\n",
+    "    \" It's much more reliable and extensible than simple autonomous agents.\"\n",
+    ")\n",
+    "\n",
+    "human_command = Command(resume={\"data\": human_response})\n",
+    "\n",
+    "events = graph.stream(human_command, config, stream_mode=\"values\")\n",
     "for event in events:\n",
     "    if \"messages\" in event:\n",
     "        event[\"messages\"][-1].pretty_print()"
@@ -1422,13 +1476,12 @@
    "id": "21e78a97-474f-4709-b51d-9d5e8323e14c",
    "metadata": {},
    "source": [
-    "Review this call's [LangSmith trace](https://smith.langchain.com/public/4d7f8757-9d3b-43b9-88b6-aeab0595bc4c/r) to see the exact work that was done in the above call. Notice that the state is loaded in the first step so that your chatbot can continue where it left off.\n",
+    "Our input has been received and processed as a tool message. Review this call's [LangSmith trace](https://smith.langchain.com/public/9f0f87e3-56a7-4dde-9c76-b71675624e91/r) to see the exact work that was done in the above call. Notice that the state is loaded in the first step so that our chatbot can continue where it left off.\n",
     "\n",
-    "**Congrats!** You've used an `interrupt` to add human-in-the-loop execution to your chatbot, allowing for human oversight and intervention when needed. This opens up the potential UIs you can create with your AI systems. Since we have already added a **checkpointer**, the graph can be paused **indefinitely** and resumed at any time as if nothing had happened.\n",
+    "**Congrats!** You've used an `interrupt` to add human-in-the-loop execution to your chatbot, allowing for human oversight and intervention when needed. This opens up the potential UIs you can create with your AI systems. Since we have already added a **checkpointer**, as long as the underlying persistence layer is running, the graph can be paused **indefinitely** and resumed at any time as if nothing had happened.\n",
     "\n",
-    "Next, we'll explore how to further customize the bot's behavior using custom state updates.\n",
+    "Human-in-the-loop workflows enable a variety of new workflows and user experiences. Check out [this section](../../how-tos/#human-in-the-loop) of the How-to Guides for more examples of Human-in-the-loop workflows, including how to [review and edit tool calls](../../how-tos/human_in_the_loop/review-tool-calls/) before they are executed.\n",
     "\n",
-    "Below is a copy of the code you used in this section. The only difference between this and the previous parts is the addition of the `interrupt_before` argument.\n",
     "\n",
     "<details>\n",
     "<summary>Full Code</summary>\n",
@@ -1439,13 +1492,14 @@
     "\n",
     "from langchain_anthropic import ChatAnthropic\n",
     "from langchain_community.tools.tavily_search import TavilySearchResults\n",
-    "from langchain_core.messages import BaseMessage\n",
+    "from langchain_core.tools import tool\n",
     "from typing_extensions import TypedDict\n",
     "\n",
     "from langgraph.checkpoint.memory import MemorySaver\n",
-    "from langgraph.graph import StateGraph\n",
+    "from langgraph.graph import StateGraph, START, END\n",
     "from langgraph.graph.message import add_messages\n",
     "from langgraph.prebuilt import ToolNode, tools_condition\n",
+    "from langgraph.types import Command, interrupt\n",
     "\n",
     "\n",
     "class State(TypedDict):\n",
@@ -1455,94 +1509,28 @@
     "graph_builder = StateGraph(State)\n",
     "\n",
     "\n",
+    "@tool\n",
+    "def human_assistance(query: str) -> str:\n",
+    "    \"\"\"Request assistance from a human.\"\"\"\n",
+    "    human_response = interrupt({\"query\": query})\n",
+    "    return human_response[\"data\"]\n",
+    "\n",
+    "\n",
     "tool = TavilySearchResults(max_results=2)\n",
-    "tools = [tool]\n",
+    "tools = [tool, human_assistance]\n",
     "llm = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n",
     "llm_with_tools = llm.bind_tools(tools)\n",
     "\n",
     "\n",
     "def chatbot(state: State):\n",
-    "    return {\"messages\": [llm_with_tools.invoke(state[\"messages\"])]}\n",
+    "    message = llm_with_tools.invoke(state[\"messages\"])\n",
+    "    assert(len(message.tool_calls) <= 1)\n",
+    "    return {\"messages\": [message]}\n",
     "\n",
     "\n",
     "graph_builder.add_node(\"chatbot\", chatbot)\n",
     "\n",
-    "tool_node = ToolNode(tools=[tool])\n",
-    "graph_builder.add_node(\"tools\", tool_node)\n",
-    "\n",
-    "graph_builder.add_conditional_edges(\n",
-    "    \"chatbot\",\n",
-    "    tools_condition,\n",
-    ")\n",
-    "graph_builder.add_edge(\"tools\", \"chatbot\")\n",
-    "graph_builder.set_entry_point(\"chatbot\")\n",
-    "\n",
-    "memory = MemorySaver()\n",
-    "graph = graph_builder.compile(\n",
-    "    checkpointer=memory,\n",
-    "    # This is new!\n",
-    "    interrupt_before=[\"tools\"],\n",
-    "    # Note: can also interrupt __after__ actions, if desired.\n",
-    "    # interrupt_after=[\"tools\"]\n",
-    ")\n",
-    "```\n",
-    "</pre>\n",
-    "</details>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6df38bc4-c177-4ccd-9ec2-83d32bf66722",
-   "metadata": {},
-   "source": [
-    "## Part 5: Manually Updating the State\n",
-    "\n",
-    "In the previous section, we showed how to interrupt a graph so that a human could inspect its actions. This lets the human `read` the state, but if they want to change their agent's course, they'll need to have `write` access.\n",
-    "\n",
-    "Thankfully, LangGraph lets you **manually update state**! Updating the state lets you control the agent's trajectory by modifying its actions (even modifying the past!). This capability is particularly useful when you want to correct the agent's mistakes, explore alternative paths, or guide the agent towards a specific goal.\n",
-    "\n",
-    "We'll show how to update a checkpointed state below. As before, first, define your graph. We'll reuse the exact same graph as before."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "faa345c6-38a2-42e8-9035-9cf56f7bb5b1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import Annotated\n",
-    "\n",
-    "from langchain_anthropic import ChatAnthropic\n",
-    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
-    "from typing_extensions import TypedDict\n",
-    "\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
-    "from langgraph.graph import StateGraph, START\n",
-    "from langgraph.graph.message import add_messages\n",
-    "from langgraph.prebuilt import ToolNode, tools_condition\n",
-    "\n",
-    "\n",
-    "class State(TypedDict):\n",
-    "    messages: Annotated[list, add_messages]\n",
-    "\n",
-    "\n",
-    "graph_builder = StateGraph(State)\n",
-    "\n",
-    "\n",
-    "tool = TavilySearchResults(max_results=2)\n",
-    "tools = [tool]\n",
-    "llm = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n",
-    "llm_with_tools = llm.bind_tools(tools)\n",
-    "\n",
-    "\n",
-    "def chatbot(state: State):\n",
-    "    return {\"messages\": [llm_with_tools.invoke(state[\"messages\"])]}\n",
-    "\n",
-    "\n",
-    "graph_builder.add_node(\"chatbot\", chatbot)\n",
-    "\n",
-    "tool_node = ToolNode(tools=[tool])\n",
+    "tool_node = ToolNode(tools=tools)\n",
     "graph_builder.add_node(\"tools\", tool_node)\n",
     "\n",
     "graph_builder.add_conditional_edges(\n",
@@ -1551,239 +1539,171 @@
     ")\n",
     "graph_builder.add_edge(\"tools\", \"chatbot\")\n",
     "graph_builder.add_edge(START, \"chatbot\")\n",
+    "\n",
     "memory = MemorySaver()\n",
-    "graph = graph_builder.compile(\n",
-    "    checkpointer=memory,\n",
-    "    # This is new!\n",
-    "    interrupt_before=[\"tools\"],\n",
-    "    # Note: can also interrupt **after** actions, if desired.\n",
-    "    # interrupt_after=[\"tools\"]\n",
-    ")\n",
-    "\n",
-    "user_input = \"I'm learning LangGraph. Could you do some research on it for me?\"\n",
-    "config = {\"configurable\": {\"thread_id\": \"1\"}}\n",
-    "# The config is the **second positional argument** to stream() or invoke()!\n",
-    "events = graph.stream({\"messages\": [(\"user\", user_input)]}, config)\n",
-    "for event in events:\n",
-    "    if \"messages\" in event:\n",
-    "        event[\"messages\"][-1].pretty_print()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "a6b3bcae-dd04-49da-a4ef-e05634657faf",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "\n",
-      "[{'text': \"Certainly! I'd be happy to research LangGraph for you. To get the most up-to-date and comprehensive information, I'll use the Tavily search engine to look this up. Let me do that for you now.\", 'type': 'text'}, {'id': 'toolu_018YcbFR37CG8RRXnavH5fxZ', 'input': {'query': 'LangGraph: what is it, how is it used in AI development'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]\n",
-      "Tool Calls:\n",
-      "  tavily_search_results_json (toolu_018YcbFR37CG8RRXnavH5fxZ)\n",
-      " Call ID: toolu_018YcbFR37CG8RRXnavH5fxZ\n",
-      "  Args:\n",
-      "    query: LangGraph: what is it, how is it used in AI development\n"
-     ]
-    }
-   ],
-   "source": [
-    "snapshot = graph.get_state(config)\n",
-    "existing_message = snapshot.values[\"messages\"][-1]\n",
-    "existing_message.pretty_print()"
+    "graph = graph_builder.compile(checkpointer=memory)\n",
+    "```\n",
+    "</pre>\n",
+    "</details>"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3bf55a26-8c12-477a-9e83-5011d36ac4ee",
+   "id": "0d12578b-ff19-48b5-b1ad-67d9bf7f710e",
    "metadata": {},
    "source": [
-    "So far, all of this is an _exact repeat_ of the previous section. The LLM just requested to use the search engine tool and our graph was interrupted. If we proceed as before, the tool will be called to search the web.\n",
+    "## Part 5: Customizing State\n",
     "\n",
-    "But what if the user wants to intercede? What if we think the chat bot doesn't need to use the tool? \n",
-    "\n",
-    "Let's directly provide the correct response!"
+    "So far, we've relied on a simple state with one entry-- a list of messages. You can go far with this simple state, but if you want to define complex behavior without relying on the message list, you can add additional fields to the state. Here we will demonstrate a new scenario, in which the chatbot is using its search tool to find specific information, and forwarding them to a human for review. Let's have the chatbot research the birthday of an entity. We will add `name` and `birthday` keys to the state:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "id": "6a44bedc-ea91-4c22-976c-98b3d5a5e4a7",
+   "execution_count": 2,
+   "id": "84627103-bcc8-4645-bd37-b93209fa09dd",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "\n",
-      "LangGraph is a library for building stateful, multi-actor applications with LLMs.\n",
-      "\n",
-      "\n",
-      "Last 2 messages;\n",
-      "[ToolMessage(content='LangGraph is a library for building stateful, multi-actor applications with LLMs.', id='675f7618-367f-44b7-b80e-2834afb02ac5', tool_call_id='toolu_018YcbFR37CG8RRXnavH5fxZ'), AIMessage(content='LangGraph is a library for building stateful, multi-actor applications with LLMs.', additional_kwargs={}, response_metadata={}, id='35fd5682-0c2a-4200-b192-71c59ac6d412')]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from langchain_core.messages import AIMessage, ToolMessage\n",
+    "from typing import Annotated\n",
     "\n",
-    "answer = (\n",
-    "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs.\"\n",
-    ")\n",
-    "new_messages = [\n",
-    "    # The LLM API expects some ToolMessage to match its tool call. We'll satisfy that here.\n",
-    "    ToolMessage(content=answer, tool_call_id=existing_message.tool_calls[0][\"id\"]),\n",
-    "    # And then directly \"put words in the LLM's mouth\" by populating its response.\n",
-    "    AIMessage(content=answer),\n",
-    "]\n",
+    "from typing_extensions import TypedDict\n",
     "\n",
-    "new_messages[-1].pretty_print()\n",
-    "graph.update_state(\n",
-    "    # Which state to update\n",
-    "    config,\n",
-    "    # The updated values to provide. The messages in our `State` are \"append-only\", meaning this will be appended\n",
-    "    # to the existing state. We will review how to update existing messages in the next section!\n",
-    "    {\"messages\": new_messages},\n",
-    ")\n",
+    "from langgraph.graph.message import add_messages\n",
     "\n",
-    "print(\"\\n\\nLast 2 messages;\")\n",
-    "print(graph.get_state(config).values[\"messages\"][-2:])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "584de971-6b10-4931-986e-cc35f7adbb3d",
-   "metadata": {},
-   "source": [
-    "Now the graph is complete, since we've provided the final response message! Since state updates simulate a graph step, they even generate corresponding traces. Inspect the [LangSmith trace](https://smith.langchain.com/public/6d72aeb5-3bca-4090-8684-a11d5a36b10c/r) of the `update_state` call above to see what's going on.\n",
     "\n",
-    "**Notice** that our new messages are _appended_ to the messages already in the state. Remember how we defined the `State` type?\n",
-    "\n",
-    "```python\n",
     "class State(TypedDict):\n",
     "    messages: Annotated[list, add_messages]\n",
-    "```\n",
-    "\n",
-    "We annotated `messages` with the pre-built `add_messages` function. This instructs the graph to always append values to the existing list, rather than overwriting the list directly. The same logic is applied here, so the messages we passed to `update_state` were appended in the same way!\n",
-    "\n",
-    "The `update_state` function operates as if it were one of the nodes in your graph! By default, the update operation uses the node that was last executed, but you can manually specify it below. Let's add an update and tell the graph to treat it as if it came from the \"chatbot\"."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "d16d95c3-b465-42ac-8015-26b669d45d1f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'configurable': {'thread_id': '1',\n",
-       "  'checkpoint_ns': '',\n",
-       "  'checkpoint_id': '1ef7d134-3958-6412-8002-3f4b4112062f'}}"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "graph.update_state(\n",
-    "    config,\n",
-    "    {\"messages\": [AIMessage(content=\"I'm an AI expert!\")]},\n",
-    "    # Which node for this function to act as. It will automatically continue\n",
-    "    # processing as if this node just ran.\n",
-    "    as_node=\"chatbot\",\n",
-    ")"
+    "    # highlight-next-line\n",
+    "    name: str\n",
+    "    # highlight-next-line\n",
+    "    birthday: str"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5a1f0056-6b6f-425f-ac1a-0d4b0e9b85cc",
+   "id": "c0057133-3dfd-4208-a11f-9cf7c0b82587",
    "metadata": {},
    "source": [
-    "Check out the [LangSmith trace](https://smith.langchain.com/public/2e4d92ca-c17c-49e0-92e5-3962390ded30/r) for this update call at the provided link. **Notice** from the trace that the graph continues into the `tools_condition` edge. We just told the graph to treat the update `as_node=\"chatbot\"`. If we follow the diagram below and start from the `chatbot` node, we naturally end up in the `tools_condition` edge and then `__end__` since our updated message lacks tool calls."
+    "Adding this information to the state makes it easily accessible by other graph nodes (e.g., a downstream node that stores or processes the information), as well as the graph's persistence layer.\n",
+    "\n",
+    "Here, we will populate the state keys inside of our `human_assistance` tool. This allows a human to review the information before it is stored in the state. We will again use `Command`, this time to issue a state update from inside our tool. Read more about use cases for `Command` [here](../../concepts/low_level/#using-inside-tools)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "id": "f4009ba6-dc0b-4216-ab0c-fbb104616f73",
+   "execution_count": 3,
+   "id": "c4b65504-92c7-4c82-a6ee-824885d1a8a4",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from IPython.display import Image, display\n",
+    "from langchain_core.messages import ToolMessage\n",
+    "from langchain_core.tools import InjectedToolCallId, tool\n",
     "\n",
-    "try:\n",
-    "    display(Image(graph.get_graph().draw_mermaid_png()))\n",
-    "except Exception:\n",
-    "    # This requires some extra dependencies and is optional\n",
-    "    pass"
+    "from langgraph.types import Command, interrupt\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "# Note that because we are generating a ToolMessage for a state update, we\n",
+    "# generally require the ID of the corresponding tool call. We can use\n",
+    "# LangChain's InjectedToolCallId to signal that this argument should not\n",
+    "# be revealed to the model in the tool's schema.\n",
+    "def human_assistance(\n",
+    "    name: str, birthday: str, tool_call_id: Annotated[str, InjectedToolCallId]\n",
+    ") -> str:\n",
+    "    \"\"\"Request assistance from a human.\"\"\"\n",
+    "    human_response = interrupt(\n",
+    "        {\n",
+    "            \"question\": \"Is this correct?\",\n",
+    "            \"name\": name,\n",
+    "            \"birthday\": birthday,\n",
+    "        },\n",
+    "    )\n",
+    "    # If the information is correct, update the state as-is.\n",
+    "    if human_response.get(\"correct\", \"\").lower().startswith(\"y\"):\n",
+    "        verified_name = name\n",
+    "        verified_birthday = birthday\n",
+    "        response = \"Correct\"\n",
+    "    # Otherwise, receive information from the human reviewer.\n",
+    "    else:\n",
+    "        verified_name = human_response.get(\"name\", name)\n",
+    "        verified_birthday = human_response.get(\"birthday\", birthday)\n",
+    "        response = f\"Made a correction: {human_response}\"\n",
+    "\n",
+    "    # This time we explicitly update the state with a ToolMessage inside\n",
+    "    # the tool.\n",
+    "    state_update = {\n",
+    "        \"name\": verified_name,\n",
+    "        \"birthday\": verified_birthday,\n",
+    "        \"messages\": [ToolMessage(response, tool_call_id=tool_call_id)],\n",
+    "    }\n",
+    "    # We return a Command object in the tool to update our state.\n",
+    "    return Command(update=state_update)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "96cd4ffa-8fb2-4bd6-bef9-564cbfe7e3ab",
+   "id": "268757ca-4b72-4fc1-a482-d878dd1bc0d9",
    "metadata": {},
    "source": [
-    "Inspect the current state as before to confirm the checkpoint reflects our manual updates."
+    "Otherwise, the rest of our graph is the same:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "id": "d420e813-a8c7-415d-ab31-5298d42491e4",
+   "execution_count": 4,
+   "id": "e256fa2e-6c13-42dd-a0a5-d206ee4139bf",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ToolMessage(content='LangGraph is a library for building stateful, multi-actor applications with LLMs.', id='675f7618-367f-44b7-b80e-2834afb02ac5', tool_call_id='toolu_018YcbFR37CG8RRXnavH5fxZ'), AIMessage(content='LangGraph is a library for building stateful, multi-actor applications with LLMs.', additional_kwargs={}, response_metadata={}, id='35fd5682-0c2a-4200-b192-71c59ac6d412'), AIMessage(content=\"I'm an AI expert!\", additional_kwargs={}, response_metadata={}, id='288e2f74-f1cb-4082-8c3c-af4695c83117')]\n",
-      "()\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "snapshot = graph.get_state(config)\n",
-    "print(snapshot.values[\"messages\"][-3:])\n",
-    "print(snapshot.next)"
+    "from langchain_anthropic import ChatAnthropic\n",
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "\n",
+    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.graph import StateGraph, START, END\n",
+    "from langgraph.prebuilt import ToolNode, tools_condition\n",
+    "\n",
+    "\n",
+    "tool = TavilySearchResults(max_results=2)\n",
+    "tools = [tool, human_assistance]\n",
+    "llm = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n",
+    "llm_with_tools = llm.bind_tools(tools)\n",
+    "\n",
+    "\n",
+    "def chatbot(state: State):\n",
+    "    message = llm_with_tools.invoke(state[\"messages\"])\n",
+    "    assert len(message.tool_calls) <= 1\n",
+    "    return {\"messages\": [message]}\n",
+    "\n",
+    "\n",
+    "graph_builder = StateGraph(State)\n",
+    "graph_builder.add_node(\"chatbot\", chatbot)\n",
+    "\n",
+    "tool_node = ToolNode(tools=tools)\n",
+    "graph_builder.add_node(\"tools\", tool_node)\n",
+    "\n",
+    "graph_builder.add_conditional_edges(\n",
+    "    \"chatbot\",\n",
+    "    tools_condition,\n",
+    ")\n",
+    "graph_builder.add_edge(\"tools\", \"chatbot\")\n",
+    "graph_builder.add_edge(START, \"chatbot\")\n",
+    "\n",
+    "memory = MemorySaver()\n",
+    "graph = graph_builder.compile(checkpointer=memory)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "380222f4-65fa-4962-afe6-6a715fadb2de",
+   "id": "e9f77638-f957-4e1b-abcf-e9132c039be5",
    "metadata": {},
    "source": [
-    "**Notice**: that we've continued to add AI messages to the state. Since we are acting as the `chatbot` and responding with an AIMessage that doesn't contain `tool_calls`, the graph knows that it has entered a finished state (`next` is empty).\n",
-    "\n",
-    "#### What if you want to **overwrite** existing messages? \n",
-    "\n",
-    "The [`add_messages`](https://langchain-ai.github.io/langgraph/reference/graphs/?h=add+messages#add_messages) function we used to annotate our graph's `State` above controls how updates are made to the `messages` key. This function looks at any message IDs in the new `messages` list. If the ID matches a message in the existing state, [`add_messages`](https://langchain-ai.github.io/langgraph/reference/graphs/?h=add+messages#add_messages) overwrites the existing message with the new content. \n",
-    "\n",
-    "As an example, let's update the tool invocation to make sure we get good results from our search engine! First, start a new thread:"
+    "Let's prompt our application to look up the \"birthday\" of the LangGraph library. We will direct the chatbot to reach out to the `human_assistance` tool once it has the required information. Note that setting `name` and `birthday` in the arguments for the tool, we force the chatbot to generate proposals for these fields."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "id": "9fc99c7e-b61d-4aec-9c62-042798185ec3",
+   "execution_count": 5,
+   "id": "10701e49-d4b4-46db-bb30-f895fdf4411d",
    "metadata": {},
    "outputs": [
     {
@@ -1792,219 +1712,40 @@
      "text": [
       "================================\u001b[1m Human Message \u001b[0m=================================\n",
       "\n",
-      "I'm learning LangGraph. Could you do some research on it for me?\n",
+      "Can you look up when LangGraph was released? When you have the answer, use the human_assistance tool for review.\n",
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "[{'text': \"Certainly! I'd be happy to research LangGraph for you. To get the most up-to-date and accurate information, I'll use the Tavily search engine to look this up. Let me do that for you now.\", 'type': 'text'}, {'id': 'toolu_01TfAeisrpx4ddgJpoAxqrVh', 'input': {'query': 'LangGraph framework for language models'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]\n",
+      "[{'text': \"Certainly! I'll start by searching for information about LangGraph's release date using the Tavily search function. Then, I'll use the human_assistance tool for review.\", 'type': 'text'}, {'id': 'toolu_01JoXQPgTVJXiuma8xMVwqAi', 'input': {'query': 'LangGraph release date'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]\n",
       "Tool Calls:\n",
-      "  tavily_search_results_json (toolu_01TfAeisrpx4ddgJpoAxqrVh)\n",
-      " Call ID: toolu_01TfAeisrpx4ddgJpoAxqrVh\n",
+      "  tavily_search_results_json (toolu_01JoXQPgTVJXiuma8xMVwqAi)\n",
+      " Call ID: toolu_01JoXQPgTVJXiuma8xMVwqAi\n",
       "  Args:\n",
-      "    query: LangGraph framework for language models\n"
-     ]
-    }
-   ],
-   "source": [
-    "user_input = \"I'm learning LangGraph. Could you do some research on it for me?\"\n",
-    "config = {\"configurable\": {\"thread_id\": \"2\"}}  # we'll use thread_id = 2 here\n",
-    "events = graph.stream(\n",
-    "    {\"messages\": [(\"user\", user_input)]}, config, stream_mode=\"values\"\n",
-    ")\n",
-    "for event in events:\n",
-    "    if \"messages\" in event:\n",
-    "        event[\"messages\"][-1].pretty_print()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8b019fc6-7826-4291-9178-6cecb5d7b3d0",
-   "metadata": {},
-   "source": [
-    "**Next,** let's update the tool invocation for our agent. Maybe we want to search for human-in-the-loop workflows in particular."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "7215533a-b7e2-4b2d-bc1d-5122b1d06b8b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original\n",
-      "Message ID run-342f3f54-356b-4cc1-b747-573f6aa31054-0\n",
-      "{'name': 'tavily_search_results_json', 'args': {'query': 'LangGraph framework for language models'}, 'id': 'toolu_01TfAeisrpx4ddgJpoAxqrVh', 'type': 'tool_call'}\n",
-      "Updated\n",
-      "{'name': 'tavily_search_results_json', 'args': {'query': 'LangGraph human-in-the-loop workflow'}, 'id': 'toolu_01TfAeisrpx4ddgJpoAxqrVh', 'type': 'tool_call'}\n",
-      "Message ID run-342f3f54-356b-4cc1-b747-573f6aa31054-0\n",
-      "\n",
-      "\n",
-      "Tool calls\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[{'name': 'tavily_search_results_json',\n",
-       "  'args': {'query': 'LangGraph human-in-the-loop workflow'},\n",
-       "  'id': 'toolu_01TfAeisrpx4ddgJpoAxqrVh',\n",
-       "  'type': 'tool_call'}]"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.messages import AIMessage\n",
-    "\n",
-    "snapshot = graph.get_state(config)\n",
-    "existing_message = snapshot.values[\"messages\"][-1]\n",
-    "print(\"Original\")\n",
-    "print(\"Message ID\", existing_message.id)\n",
-    "print(existing_message.tool_calls[0])\n",
-    "new_tool_call = existing_message.tool_calls[0].copy()\n",
-    "new_tool_call[\"args\"][\"query\"] = \"LangGraph human-in-the-loop workflow\"\n",
-    "new_message = AIMessage(\n",
-    "    content=existing_message.content,\n",
-    "    tool_calls=[new_tool_call],\n",
-    "    # Important! The ID is how LangGraph knows to REPLACE the message in the state rather than APPEND this messages\n",
-    "    id=existing_message.id,\n",
-    ")\n",
-    "\n",
-    "print(\"Updated\")\n",
-    "print(new_message.tool_calls[0])\n",
-    "print(\"Message ID\", new_message.id)\n",
-    "graph.update_state(config, {\"messages\": [new_message]})\n",
-    "\n",
-    "print(\"\\n\\nTool calls\")\n",
-    "graph.get_state(config).values[\"messages\"][-1].tool_calls"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "680f0ebd-ebce-4de6-8a9b-37d3d4ef0234",
-   "metadata": {},
-   "source": [
-    "**Notice** that we've modified the AI's tool invocation to search for \"LangGraph human-in-the-loop workflow\" instead of the simple \"LangGraph\".\n",
-    "\n",
-    "Check out the [LangSmith trace](https://smith.langchain.com/public/cd7c09a6-758d-41d4-8de1-64ab838b2338/r) to see the state update call - you can see our new message has successfully updated the previous AI message.\n",
-    "\n",
-    "Resume the graph by streaming with an input of `None` and the existing config."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "03a09bfc-3d90-4e54-878f-22e3cb28a418",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "\n",
-      "[{'text': \"Certainly! I'd be happy to research LangGraph for you. To get the most up-to-date and accurate information, I'll use the Tavily search engine to look this up. Let me do that for you now.\", 'type': 'text'}, {'id': 'toolu_01TfAeisrpx4ddgJpoAxqrVh', 'input': {'query': 'LangGraph framework for language models'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]\n",
-      "Tool Calls:\n",
-      "  tavily_search_results_json (toolu_01TfAeisrpx4ddgJpoAxqrVh)\n",
-      " Call ID: toolu_01TfAeisrpx4ddgJpoAxqrVh\n",
-      "  Args:\n",
-      "    query: LangGraph human-in-the-loop workflow\n",
+      "    query: LangGraph release date\n",
       "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
       "Name: tavily_search_results_json\n",
       "\n",
-      "[{\"url\": \"https://www.youtube.com/watch?v=9BPCV5TYPmg\", \"content\": \"In this video, I'll show you how to handle persistence with LangGraph, enabling a unique Human-in-the-Loop workflow. This approach allows a human to grant an...\"}, {\"url\": \"https://medium.com/@kbdhunga/implementing-human-in-the-loop-with-langgraph-ccfde023385c\", \"content\": \"Implementing a Human-in-the-Loop (HIL) framework in LangGraph with the Streamlit app provides a robust mechanism for user engagement and decision-making. By incorporating breakpoints and ...\"}]\n",
+      "[{\"url\": \"https://blog.langchain.dev/langgraph-cloud/\", \"content\": \"We also have a new stable release of LangGraph. By LangChain 6 min read Jun 27, 2024 (Oct '24) Edit: Since the launch of LangGraph Cloud, we now have multiple deployment options alongside LangGraph Studio - which now fall under LangGraph Platform. LangGraph Cloud is synonymous with our Cloud SaaS deployment option.\"}, {\"url\": \"https://changelog.langchain.com/announcements/langgraph-cloud-deploy-at-scale-monitor-carefully-iterate-boldly\", \"content\": \"LangChain - Changelog | ☁ 🚀 LangGraph Cloud: Deploy at scale, monitor LangChain LangSmith LangGraph LangChain LangSmith LangGraph LangChain LangSmith LangGraph LangChain Changelog Sign up for our newsletter to stay up to date DATE: The LangChain Team LangGraph LangGraph Cloud ☁ 🚀 LangGraph Cloud: Deploy at scale, monitor carefully, iterate boldly DATE: June 27, 2024 AUTHOR: The LangChain Team LangGraph Cloud is now in closed beta, offering scalable, fault-tolerant deployment for LangGraph agents. LangGraph Cloud also includes a new playground-like studio for debugging agent failure modes and quick iteration: Join the waitlist today for LangGraph Cloud. And to learn more, read our blog post announcement or check out our docs. Subscribe By clicking subscribe, you accept our privacy policy and terms and conditions.\"}]\n",
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "Thank you for your patience. I've found some information about LangGraph, particularly focusing on its human-in-the-loop workflow capabilities. Let me summarize what I've learned for you:\n",
-      "\n",
-      "1. LangGraph Overview:\n",
-      "   LangGraph is a framework for building stateful, multi-actor applications with Large Language Models (LLMs). It's particularly useful for creating complex, interactive AI systems.\n",
-      "\n",
-      "2. Human-in-the-Loop (HIL) Workflow:\n",
-      "   One of the key features of LangGraph is its support for human-in-the-loop workflows. This means that it allows for human intervention and decision-making within AI-driven processes.\n",
-      "\n",
-      "3. Persistence Handling:\n",
-      "   LangGraph offers capabilities for handling persistence, which is crucial for maintaining state across interactions in a workflow.\n",
-      "\n",
-      "4. Implementation with Streamlit:\n",
-      "   There are examples of implementing LangGraph's human-in-the-loop functionality using Streamlit, a popular Python library for creating web apps. This combination allows for the creation of interactive user interfaces for AI applications.\n",
-      "\n",
-      "5. Breakpoints and User Engagement:\n",
-      "   LangGraph allows the incorporation of breakpoints in the workflow. These breakpoints are points where the system can pause and wait for human input or decision-making, enhancing user engagement and control over the AI process.\n",
-      "\n",
-      "6. Decision-Making Mechanism:\n",
-      "   The human-in-the-loop framework in LangGraph provides a robust mechanism for integrating user decision-making into AI workflows. This is particularly useful in scenarios where human judgment or expertise is needed to guide or validate AI actions.\n",
-      "\n",
-      "7. Flexibility and Customization:\n",
-      "   From the information available, it seems that LangGraph offers flexibility in how human-in-the-loop processes are implemented, allowing developers to customize the interaction points and the nature of human involvement based on their specific use case.\n",
-      "\n",
-      "LangGraph appears to be a powerful tool for developers looking to create more interactive and controllable AI applications, especially those that benefit from human oversight or input at crucial stages of the process.\n",
-      "\n",
-      "Would you like me to research any specific aspect of LangGraph in more detail, or do you have any questions about what I've found so far?\n"
+      "[{'text': \"Based on the search results, it appears that LangGraph was already in existence before June 27, 2024, when LangGraph Cloud was announced. However, the search results don't provide a specific release date for the original LangGraph. \\n\\nGiven this information, I'll use the human_assistance tool to review and potentially provide more accurate information about LangGraph's initial release date.\", 'type': 'text'}, {'id': 'toolu_01JDQAV7nPqMkHHhNs3j3XoN', 'input': {'name': 'Assistant', 'birthday': '2023-01-01'}, 'name': 'human_assistance', 'type': 'tool_use'}]\n",
+      "Tool Calls:\n",
+      "  human_assistance (toolu_01JDQAV7nPqMkHHhNs3j3XoN)\n",
+      " Call ID: toolu_01JDQAV7nPqMkHHhNs3j3XoN\n",
+      "  Args:\n",
+      "    name: Assistant\n",
+      "    birthday: 2023-01-01\n"
      ]
     }
    ],
    "source": [
-    "events = graph.stream(None, config, stream_mode=\"values\")\n",
-    "for event in events:\n",
-    "    if \"messages\" in event:\n",
-    "        event[\"messages\"][-1].pretty_print()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "090b680b-f53f-4af2-a432-45f8c5a10779",
-   "metadata": {},
-   "source": [
-    "Check out the [trace](https://smith.langchain.com/public/2d633326-14ad-4248-a391-2757d01851c4/r/6464f2f2-edb4-4ef3-8f48-ee4e249f2ad0) to see the tool call and later LLM response. **Notice** that now the graph queries the search engine using our updated query term - we were able to manually override the LLM's search here!\n",
+    "user_input = (\n",
+    "    \"Can you look up when LangGraph was released? \"\n",
+    "    \"When you have the answer, use the human_assistance tool for review.\"\n",
+    ")\n",
+    "config = {\"configurable\": {\"thread_id\": \"1\"}}\n",
     "\n",
-    "All of this is reflected in the graph's checkpointed memory, meaning if we continue the conversation, it will recall all the _modified_ state."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "11d5b934-6d8b-4f52-a3bc-b3daa7207e00",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================\u001b[1m Human Message \u001b[0m=================================\n",
-      "\n",
-      "Remember what I'm learning about?\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "\n",
-      "I apologize for my oversight. You're absolutely right to remind me. You mentioned that you're learning LangGraph. Thank you for bringing that back into focus. \n",
-      "\n",
-      "Since you're in the process of learning LangGraph, it would be helpful to know more about your current level of understanding and what specific aspects of LangGraph you're most interested in or finding challenging. This way, I can provide more targeted information or explanations that align with your learning journey.\n",
-      "\n",
-      "Are there any particular areas of LangGraph you'd like to explore further? For example:\n",
-      "\n",
-      "1. Basic concepts and architecture of LangGraph\n",
-      "2. Setting up and getting started with LangGraph\n",
-      "3. Implementing specific features like the human-in-the-loop workflow\n",
-      "4. Best practices for using LangGraph in projects\n",
-      "5. Comparisons with other similar frameworks\n",
-      "\n",
-      "Or if you have any specific questions about what you've learned so far, I'd be happy to help clarify or expand on those topics. Please let me know what would be most useful for your learning process.\n"
-     ]
-    }
-   ],
-   "source": [
     "events = graph.stream(\n",
-    "    {\n",
-    "        \"messages\": (\n",
-    "            \"user\",\n",
-    "            \"Remember what I'm learning about?\",\n",
-    "        )\n",
-    "    },\n",
+    "    {\"messages\": [{\"role\": \"user\", \"content\": user_input}]},\n",
     "    config,\n",
     "    stream_mode=\"values\",\n",
     ")\n",
@@ -2015,328 +1756,58 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a5166e1b-96a6-4ac0-88a1-bf32a422134a",
+   "id": "99dcaf45-e4d1-4597-b669-13a5330740ac",
    "metadata": {},
    "source": [
-    "**Congratulations!** You've used `interrupt_before` and `update_state` to manually modify the state as a part of a human-in-the-loop workflow. Interruptions and state modifications let you control how the agent behaves. Combined with persistent checkpointing, it means you can `pause` an action and `resume` at any point. Your user doesn't have to be available when the graph interrupts!\n",
-    "\n",
-    "The graph code for this section is identical to previous ones. The key snippets to remember are to add `.compile(..., interrupt_before=[...])` (or `interrupt_after`) if you want to explicitly pause the graph whenever it reaches a node. Then you can use `update_state` to modify the checkpoint and control how the graph should proceed."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d88d4c9e-65c8-4093-a6c2-c261475f7c07",
-   "metadata": {},
-   "source": [
-    "## Part 6: Customizing State\n",
-    "\n",
-    "So far, we've relied on a simple state (it's just a list of messages!). You can go far with this simple state, but if you want to define complex behavior without relying on the message list, you can add additional fields to the state. In this section, we will extend our chat bot with a new node to illustrate this.\n",
-    "\n",
-    "In the examples above, we involved a human deterministically: the graph __always__ interrupted whenever a tool was invoked. Suppose we wanted our chat bot to have the choice of relying on a human.\n",
-    "\n",
-    "One way to do this is to create a passthrough \"human\" node, before which the graph will always stop. We will only execute this node if the LLM invokes a \"human\" tool. For our convenience, we will include an \"ask_human\" flag in our graph state that we will flip if the LLM calls this tool.\n",
-    "\n",
-    "Below, define this new graph, with an updated `State`"
+    "We've hit the `interrupt` in the `human_assistance` tool again. In this case, the chatbot failed to identify the correct date, so we can supply it:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
-   "id": "3cf7e042-1718-4625-ae30-a9917f595449",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import Annotated\n",
-    "\n",
-    "from langchain_anthropic import ChatAnthropic\n",
-    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
-    "from typing_extensions import TypedDict\n",
-    "\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
-    "from langgraph.graph import StateGraph, START\n",
-    "from langgraph.graph.message import add_messages\n",
-    "from langgraph.prebuilt import ToolNode, tools_condition\n",
-    "\n",
-    "\n",
-    "class State(TypedDict):\n",
-    "    messages: Annotated[list, add_messages]\n",
-    "    # This flag is new\n",
-    "    ask_human: bool"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e87f2cb8-c066-4b54-acc4-e8c7399c5f3d",
-   "metadata": {},
-   "source": [
-    "Next, define a schema to show the model to let it decide to request assistance."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7bd3d704-5bee-4872-8d12-992bc970c158",
-   "metadata": {},
-   "source": [
-    "<div class=\"admonition note\">\n",
-    "    <p class=\"admonition-title\">Using Pydantic with LangChain</p>\n",
-    "    <p>\n",
-    "        This notebook uses Pydantic v2 <code>BaseModel</code>, which requires <code>langchain-core >= 0.3</code>. Using <code>langchain-core < 0.3</code> will result in errors due to mixing of Pydantic v1 and v2 <code>BaseModels</code>.\n",
-    "    </p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "e5192e54-6a28-42fe-a8a7-62d45d61f994",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pydantic import BaseModel\n",
-    "\n",
-    "\n",
-    "class RequestAssistance(BaseModel):\n",
-    "    \"\"\"Escalate the conversation to an expert. Use this if you are unable to assist directly or if the user requires support beyond your permissions.\n",
-    "\n",
-    "    To use this function, relay the user's 'request' so the expert can provide the right guidance.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    request: str"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b19c61b-2087-463b-adf8-96dbc193f41c",
-   "metadata": {},
-   "source": [
-    "Next, define the chatbot node. The primary modification here is flip the `ask_human` flag if we see that the chat bot has invoked the `RequestAssistance` flag."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "fa59b266-14e5-4c75-8b3d-54fac28e8290",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tool = TavilySearchResults(max_results=2)\n",
-    "tools = [tool]\n",
-    "llm = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n",
-    "# We can bind the llm to a tool definition, a pydantic model, or a json schema\n",
-    "llm_with_tools = llm.bind_tools(tools + [RequestAssistance])\n",
-    "\n",
-    "\n",
-    "def chatbot(state: State):\n",
-    "    response = llm_with_tools.invoke(state[\"messages\"])\n",
-    "    ask_human = False\n",
-    "    if (\n",
-    "        response.tool_calls\n",
-    "        and response.tool_calls[0][\"name\"] == RequestAssistance.__name__\n",
-    "    ):\n",
-    "        ask_human = True\n",
-    "    return {\"messages\": [response], \"ask_human\": ask_human}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "04ca0f57-2519-49c2-9499-888b5a884897",
-   "metadata": {},
-   "source": [
-    "Next, create the graph builder and add the chatbot and tools nodes to the graph, same as before."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3f4464d2-288b-4689-aaf0-329a55dcb85c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "graph_builder = StateGraph(State)\n",
-    "\n",
-    "graph_builder.add_node(\"chatbot\", chatbot)\n",
-    "graph_builder.add_node(\"tools\", ToolNode(tools=[tool]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7f7a0ff3-b671-45c8-8157-ce5db411d370",
-   "metadata": {},
-   "source": [
-    "Next, create the \"human\" `node`. This `node` function is mostly a placeholder in our graph that will trigger an interrupt. If the human does __not__ manually update the state during the `interrupt`, it inserts a tool message so the LLM knows the user was requested but didn't respond. This node also unsets the `ask_human` flag so the graph knows not to revisit the node unless further requests are made."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1d70b5a4-ce50-47dc-aa43-ffb5c48c46fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.messages import AIMessage, ToolMessage\n",
-    "\n",
-    "\n",
-    "def create_response(response: str, ai_message: AIMessage):\n",
-    "    return ToolMessage(\n",
-    "        content=response,\n",
-    "        tool_call_id=ai_message.tool_calls[0][\"id\"],\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "def human_node(state: State):\n",
-    "    new_messages = []\n",
-    "    if not isinstance(state[\"messages\"][-1], ToolMessage):\n",
-    "        # Typically, the user will have updated the state during the interrupt.\n",
-    "        # If they choose not to, we will include a placeholder ToolMessage to\n",
-    "        # let the LLM continue.\n",
-    "        new_messages.append(\n",
-    "            create_response(\"No response from human.\", state[\"messages\"][-1])\n",
-    "        )\n",
-    "    return {\n",
-    "        # Append the new messages\n",
-    "        \"messages\": new_messages,\n",
-    "        # Unset the flag\n",
-    "        \"ask_human\": False,\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "graph_builder.add_node(\"human\", human_node)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d56e5c65-f7b7-48bd-b0b5-fc8e590eca7d",
-   "metadata": {},
-   "source": [
-    "Next, define the conditional logic. The `select_next_node` will route to the `human` node if the flag is set. Otherwise, it lets the prebuilt `tools_condition` function choose the next node.\n",
-    "\n",
-    "Recall that the `tools_condition` function simply checks to see if the `chatbot` has responded with any `tool_calls` in its response message. If so, it routes to the `action` node. Otherwise, it ends the graph."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "586a0d07-8303-47f4-b3cf-3bdd043e762b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def select_next_node(state: State):\n",
-    "    if state[\"ask_human\"]:\n",
-    "        return \"human\"\n",
-    "    # Otherwise, we can route as before\n",
-    "    return tools_condition(state)\n",
-    "\n",
-    "\n",
-    "graph_builder.add_conditional_edges(\n",
-    "    \"chatbot\",\n",
-    "    select_next_node,\n",
-    "    {\"human\": \"human\", \"tools\": \"tools\", END: END},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "66cd0bb1-b13e-477e-a08a-a7e657e2c19e",
-   "metadata": {},
-   "source": [
-    "Finally, add the simple directed edges and compile the graph. These edges instruct the graph to **always** flow from node `a`->`b` whenever `a` finishes executing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "84101737-0048-4635-9f68-45b0c508b6b6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# The rest is the same\n",
-    "graph_builder.add_edge(\"tools\", \"chatbot\")\n",
-    "graph_builder.add_edge(\"human\", \"chatbot\")\n",
-    "graph_builder.add_edge(START, \"chatbot\")\n",
-    "memory = MemorySaver()\n",
-    "graph = graph_builder.compile(\n",
-    "    checkpointer=memory,\n",
-    "    # We interrupt before 'human' here instead.\n",
-    "    interrupt_before=[\"human\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7f855593-8690-4a18-9ef8-7f3ccdc335bf",
-   "metadata": {},
-   "source": [
-    "If you have the visualization dependencies installed, you can see the graph structure below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "b3220ae2-cba0-4447-96d1-eb0be4684e59",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from IPython.display import Image, display\n",
-    "\n",
-    "try:\n",
-    "    display(Image(graph.get_graph().draw_mermaid_png()))\n",
-    "except Exception:\n",
-    "    # This requires some extra dependencies and is optional\n",
-    "    pass"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a3b73851-810e-4466-89d8-37fba87e8494",
-   "metadata": {},
-   "source": [
-    "The chat bot can either request help from a human (chatbot->select->human), invoke the search engine tool (chatbot->select->action), or directly respond (chatbot->select->__end__). Once an action or request has been made, the graph will transition back to the `chatbot` node to continue operations.\n",
-    "\n",
-    "Let's see this graph in action. We will request for expert assistance to illustrate our graph."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "c1955d79-a1e4-47d0-ba79-b45bd5752a23",
+   "execution_count": 6,
+   "id": "7df33d9e-cc76-4a0f-8307-01e619483b3e",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "================================\u001b[1m Human Message \u001b[0m=================================\n",
-      "\n",
-      "I need some expert guidance for building this AI agent. Could you request assistance for me?\n",
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "[{'text': \"Certainly! I understand that you need expert guidance for building an AI agent. I'll use the RequestAssistance function to escalate your request to an expert who can provide you with the specialized knowledge and support you need. Let me do that for you right away.\", 'type': 'text'}, {'id': 'toolu_01Mo3N2c1byuSZwT1vyJWRia', 'input': {'request': 'The user needs expert guidance for building an AI agent. They require specialized knowledge and support in AI development and implementation.'}, 'name': 'RequestAssistance', 'type': 'tool_use'}]\n",
+      "[{'text': \"Based on the search results, it appears that LangGraph was already in existence before June 27, 2024, when LangGraph Cloud was announced. However, the search results don't provide a specific release date for the original LangGraph. \\n\\nGiven this information, I'll use the human_assistance tool to review and potentially provide more accurate information about LangGraph's initial release date.\", 'type': 'text'}, {'id': 'toolu_01JDQAV7nPqMkHHhNs3j3XoN', 'input': {'name': 'Assistant', 'birthday': '2023-01-01'}, 'name': 'human_assistance', 'type': 'tool_use'}]\n",
       "Tool Calls:\n",
-      "  RequestAssistance (toolu_01Mo3N2c1byuSZwT1vyJWRia)\n",
-      " Call ID: toolu_01Mo3N2c1byuSZwT1vyJWRia\n",
+      "  human_assistance (toolu_01JDQAV7nPqMkHHhNs3j3XoN)\n",
+      " Call ID: toolu_01JDQAV7nPqMkHHhNs3j3XoN\n",
       "  Args:\n",
-      "    request: The user needs expert guidance for building an AI agent. They require specialized knowledge and support in AI development and implementation.\n"
+      "    name: Assistant\n",
+      "    birthday: 2023-01-01\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: human_assistance\n",
+      "\n",
+      "Made a correction: {'name': 'LangGraph', 'birthday': 'Jan 17, 2024'}\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "Thank you for the human assistance. I can now provide you with the correct information about LangGraph's release date.\n",
+      "\n",
+      "LangGraph was initially released on January 17, 2024. This information comes from the human assistance correction, which is more accurate than the search results I initially found.\n",
+      "\n",
+      "To summarize:\n",
+      "1. LangGraph's original release date: January 17, 2024\n",
+      "2. LangGraph Cloud announcement: June 27, 2024\n",
+      "\n",
+      "It's worth noting that LangGraph had been in development and use for some time before the LangGraph Cloud announcement, but the official initial release of LangGraph itself was on January 17, 2024.\n"
      ]
     }
    ],
    "source": [
-    "user_input = \"I need some expert guidance for building this AI agent. Could you request assistance for me?\"\n",
-    "config = {\"configurable\": {\"thread_id\": \"1\"}}\n",
-    "# The config is the **second positional argument** to stream() or invoke()!\n",
-    "events = graph.stream(\n",
-    "    {\"messages\": [(\"user\", user_input)]}, config, stream_mode=\"values\"\n",
+    "human_command = Command(\n",
+    "    resume={\n",
+    "        \"name\": \"LangGraph\",\n",
+    "        \"birthday\": \"Jan 17, 2024\",\n",
+    "    },\n",
     ")\n",
+    "\n",
+    "events = graph.stream(human_command, config, stream_mode=\"values\")\n",
     "for event in events:\n",
     "    if \"messages\" in event:\n",
     "        event[\"messages\"][-1].pretty_print()"
@@ -2344,50 +1815,57 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b3945ea4-8dbd-4e14-ae2a-34da7f05a0c1",
+   "id": "31581965-8d7d-4378-82b2-f5c2a84a54b1",
    "metadata": {},
    "source": [
-    "**Notice:** the LLM has invoked the \"`RequestAssistance`\" tool we provided it, and the interrupt has been set. Let's inspect the graph state to confirm."
+    "Note that these fields are now reflected in the state:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
-   "id": "5320ba05-5696-4194-8278-5385c571264d",
+   "execution_count": 7,
+   "id": "b9fe6275-489d-4a6f-b19f-f1000d4133a0",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "('human',)"
+       "{'name': 'LangGraph', 'birthday': 'Jan 17, 2024'}"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "snapshot = graph.get_state(config)\n",
-    "snapshot.next"
+    "\n",
+    "{k: v for k, v in snapshot.values.items() if k in (\"name\", \"birthday\")}"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ed2dd02e-f0a6-4f63-a7d6-e49ecf40db21",
+   "id": "14593643-aa81-4b40-9c2d-9408bcaf88cb",
    "metadata": {},
    "source": [
-    "The graph state is indeed **interrupted** before the `'human'` node. We can act as the \"expert\" in this scenario and manually update the state by adding a new ToolMessage with our input.\n",
+    "This makes them easily accessible to downstream nodes (e.g., a node that further processes or stores the information)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "238c359a-24ca-4fbf-8f6c-28a347fee2f2",
+   "metadata": {},
+   "source": [
+    "### Manually updating state\n",
     "\n",
-    "Next, respond to the chatbot's request by:\n",
-    "1. Creating a `ToolMessage` with our response. This will be passed back to the `chatbot`.\n",
-    "2. Calling `update_state` to manually update the graph state."
+    "LangGraph gives a high degree of control over the application state. For instance, at any point (including when interrupted), we can manually override a key using `graph.update_state`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
-   "id": "2cbac924-61ce-4282-9b1c-77f9090ea1f5",
+   "execution_count": 8,
+   "id": "b206e600-91f0-4f46-9587-ab00c05899de",
    "metadata": {},
    "outputs": [
     {
@@ -2395,125 +1873,63 @@
       "text/plain": [
        "{'configurable': {'thread_id': '1',\n",
        "  'checkpoint_ns': '',\n",
-       "  'checkpoint_id': '1ef7d092-bb30-6bee-8002-015e7e1c56c0'}}"
+       "  'checkpoint_id': '1efd4ec5-cf69-6352-8006-9278f1730162'}}"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "ai_message = snapshot.values[\"messages\"][-1]\n",
-    "human_response = (\n",
-    "    \"We, the experts are here to help! We'd recommend you check out LangGraph to build your agent.\"\n",
-    "    \" It's much more reliable and extensible than simple autonomous agents.\"\n",
-    ")\n",
-    "tool_message = create_response(human_response, ai_message)\n",
-    "graph.update_state(config, {\"messages\": [tool_message]})"
+    "graph.update_state(config, {\"name\": \"LangGraph (library)\"})"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "79492363-7fc6-4ec7-977d-9030648029bc",
+   "id": "3dfb8268-8c5a-4022-9189-6edd231436ae",
    "metadata": {},
    "source": [
-    "You can inspect the state to confirm our response was added."
+    "If we call `graph.get_state`, we can see the new value is reflected:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
-   "id": "4b986c66-1c65-4da8-a404-db7e28f8364e",
+   "execution_count": 9,
+   "id": "10adcb89-55ab-4076-bc81-4488eff9e6b3",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[HumanMessage(content='I need some expert guidance for building this AI agent. Could you request assistance for me?', additional_kwargs={}, response_metadata={}, id='3f28f959-9ab7-489a-9c58-7ed1b49cedf3'),\n",
-       " AIMessage(content=[{'text': \"Certainly! I understand that you need expert guidance for building an AI agent. I'll use the RequestAssistance function to escalate your request to an expert who can provide you with the specialized knowledge and support you need. Let me do that for you right away.\", 'type': 'text'}, {'id': 'toolu_01Mo3N2c1byuSZwT1vyJWRia', 'input': {'request': 'The user needs expert guidance for building an AI agent. They require specialized knowledge and support in AI development and implementation.'}, 'name': 'RequestAssistance', 'type': 'tool_use'}], additional_kwargs={}, response_metadata={'id': 'msg_01VRnZvVbgsVRbQaQuvsziDx', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 516, 'output_tokens': 130}}, id='run-4e3f7906-5887-40d9-9267-5beefe7b3b76-0', tool_calls=[{'name': 'RequestAssistance', 'args': {'request': 'The user needs expert guidance for building an AI agent. They require specialized knowledge and support in AI development and implementation.'}, 'id': 'toolu_01Mo3N2c1byuSZwT1vyJWRia', 'type': 'tool_call'}], usage_metadata={'input_tokens': 516, 'output_tokens': 130, 'total_tokens': 646}),\n",
-       " ToolMessage(content=\"We, the experts are here to help! We'd recommend you check out LangGraph to build your agent. It's much more reliable and extensible than simple autonomous agents.\", id='8583b899-d898-4051-9f36-f5e5d11e9a37', tool_call_id='toolu_01Mo3N2c1byuSZwT1vyJWRia')]"
+       "{'name': 'LangGraph (library)', 'birthday': 'Jan 17, 2024'}"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "graph.get_state(config).values[\"messages\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ea6b8616-de10-44d6-a8f0-3ac73c3c3680",
-   "metadata": {},
-   "source": [
-    "Next, **resume** the graph by invoking it with `None` as the inputs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "6b32914d-4d60-491f-8e11-1e6867e38ffd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
-      "\n",
-      "We, the experts are here to help! We'd recommend you check out LangGraph to build your agent. It's much more reliable and extensible than simple autonomous agents.\n",
-      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
-      "\n",
-      "We, the experts are here to help! We'd recommend you check out LangGraph to build your agent. It's much more reliable and extensible than simple autonomous agents.\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "\n",
-      "Thank you for your patience. I've escalated your request to our expert team, and they have provided some initial guidance. Here's what they suggest:\n",
-      "\n",
-      "The experts recommend that you check out LangGraph for building your AI agent. They mention that LangGraph is a more reliable and extensible option compared to simple autonomous agents.\n",
-      "\n",
-      "LangGraph is likely a framework or tool designed specifically for creating complex AI agents. It seems to offer advantages in terms of reliability and extensibility, which are crucial factors when developing sophisticated AI systems.\n",
-      "\n",
-      "To further assist you, I can provide some additional context and next steps:\n",
-      "\n",
-      "1. Research LangGraph: Look up documentation, tutorials, and examples of LangGraph to understand its features and how it can help you build your AI agent.\n",
-      "\n",
-      "2. Compare with other options: While the experts recommend LangGraph, it might be useful to understand how it compares to other AI agent development frameworks or tools you might have been considering.\n",
-      "\n",
-      "3. Assess your requirements: Consider your specific needs for the AI agent you want to build. Think about the tasks it needs to perform, the level of complexity required, and how LangGraph's features align with these requirements.\n",
-      "\n",
-      "4. Start with a small project: If you decide to use LangGraph, consider beginning with a small, manageable project to familiarize yourself with the framework.\n",
-      "\n",
-      "5. Seek community support: Look for LangGraph user communities, forums, or discussion groups where you can ask questions and get additional support as you build your agent.\n",
-      "\n",
-      "6. Consider additional training: Depending on your current skill level, you might want to look into courses or workshops that focus on AI agent development, particularly those that cover LangGraph.\n",
-      "\n",
-      "Do you have any specific questions about LangGraph or AI agent development that you'd like me to try to answer? Or would you like me to search for more detailed information about LangGraph and its features?\n"
-     ]
-    }
-   ],
-   "source": [
-    "events = graph.stream(None, config, stream_mode=\"values\")\n",
-    "for event in events:\n",
-    "    if \"messages\" in event:\n",
-    "        event[\"messages\"][-1].pretty_print()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "48e0559b-d653-4dab-8928-b001004d14cb",
-   "metadata": {},
-   "source": [
-    "**Notice** that the chat bot has incorporated the updated state in its final response. Since **everything** was checkpointed, the \"expert\" human in the loop could perform the update at any time without impacting the graph's execution.\n",
+    "snapshot = graph.get_state(config)\n",
     "\n",
-    "**Congratulations!** you've now added an additional node to your assistant graph to let the chat bot decide for itself whether or not it needs to interrupt execution. You did so by updating the graph `State` with a new `ask_human` field and modifying the interruption logic when compiling the graph. This lets you dynamically include a human in the loop while maintaining full **memory** every time you execute the graph.\n",
+    "{k: v for k, v in snapshot.values.items() if k in (\"name\", \"birthday\")}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab34ef46-a836-4dbd-b1ae-56c5e8dc75af",
+   "metadata": {},
+   "source": [
+    "Manual state updates will even [generate a trace](https://smith.langchain.com/public/7ebb7827-378d-49fe-9f6c-5df0e90086c8/r) in LangSmith. If desired, they can also be used to control human-in-the-loop workflows, as described in [this guide](../../how-tos/human_in_the_loop/edit-graph-state/). Use of the `interrupt` function is generally recommended instead, as it allows data to be transmitted in a human-in-the-loop interaction independently of state updates.\n",
+    "\n",
+    "**Congratulations!** You've added custom keys to the state to facilitate a more complex workflow, and learned how to generate state updates from inside tools.\n",
     "\n",
     "We're almost done with the tutorial, but there is one more concept we'd like to review before finishing that connects `checkpointing` and `state updates`. \n",
     "\n",
     "This section's code is reproduced below for your reference.\n",
     "\n",
+    "\n",
     "<details>\n",
     "<summary>Full Code</summary>\n",
     "    <pre>\n",
@@ -2523,103 +1939,80 @@
     "\n",
     "from langchain_anthropic import ChatAnthropic\n",
     "from langchain_community.tools.tavily_search import TavilySearchResults\n",
-    "from langchain_core.messages import BaseMessage\n",
-    "# NOTE: you must use langchain-core >= 0.3 with Pydantic v2\n",
-    "from pydantic import BaseModel\n",
+    "from langchain_core.messages import ToolMessage\n",
+    "from langchain_core.tools import InjectedToolCallId, tool\n",
     "from typing_extensions import TypedDict\n",
     "\n",
     "from langgraph.checkpoint.memory import MemorySaver\n",
-    "from langgraph.graph import StateGraph\n",
+    "from langgraph.graph import StateGraph, START, END\n",
     "from langgraph.graph.message import add_messages\n",
     "from langgraph.prebuilt import ToolNode, tools_condition\n",
+    "from langgraph.types import Command, interrupt\n",
+    "\n",
     "\n",
     "\n",
     "class State(TypedDict):\n",
     "    messages: Annotated[list, add_messages]\n",
-    "    # This flag is new\n",
-    "    ask_human: bool\n",
+    "    name: str\n",
+    "    birthday: str\n",
     "\n",
     "\n",
-    "class RequestAssistance(BaseModel):\n",
-    "    \"\"\"Escalate the conversation to an expert. Use this if you are unable to assist directly or if the user requires support beyond your permissions.\n",
+    "@tool\n",
+    "def human_assistance(\n",
+    "    name: str, birthday: str, tool_call_id: Annotated[str, InjectedToolCallId]\n",
+    ") -> str:\n",
+    "    \"\"\"Request assistance from a human.\"\"\"\n",
+    "    human_response = interrupt(\n",
+    "        {\n",
+    "            \"question\": \"Is this correct?\",\n",
+    "            \"name\": name,\n",
+    "            \"birthday\": birthday,\n",
+    "        },\n",
+    "    )\n",
+    "    if human_response.get(\"correct\", \"\").lower().startswith(\"y\"):\n",
+    "        verified_name = name\n",
+    "        verified_birthday = birthday\n",
+    "        response = \"Correct\"\n",
+    "    else:\n",
+    "        verified_name = human_response.get(\"name\", name)\n",
+    "        verified_birthday = human_response.get(\"birthday\", birthday)\n",
+    "        response = f\"Made a correction: {human_response}\"\n",
     "\n",
-    "    To use this function, relay the user's 'request' so the expert can provide the right guidance.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    request: str\n",
+    "    state_update = {\n",
+    "        \"name\": verified_name,\n",
+    "        \"birthday\": verified_birthday,\n",
+    "        \"messages\": [ToolMessage(response, tool_call_id=tool_call_id)],\n",
+    "    }\n",
+    "    return Command(update=state_update)\n",
     "\n",
     "\n",
     "tool = TavilySearchResults(max_results=2)\n",
-    "tools = [tool]\n",
+    "tools = [tool, human_assistance]\n",
     "llm = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n",
-    "# We can bind the llm to a tool definition, a pydantic model, or a json schema\n",
-    "llm_with_tools = llm.bind_tools(tools + [RequestAssistance])\n",
+    "llm_with_tools = llm.bind_tools(tools)\n",
     "\n",
     "\n",
     "def chatbot(state: State):\n",
-    "    response = llm_with_tools.invoke(state[\"messages\"])\n",
-    "    ask_human = False\n",
-    "    if (\n",
-    "        response.tool_calls\n",
-    "        and response.tool_calls[0][\"name\"] == RequestAssistance.__name__\n",
-    "    ):\n",
-    "        ask_human = True\n",
-    "    return {\"messages\": [response], \"ask_human\": ask_human}\n",
+    "    message = llm_with_tools.invoke(state[\"messages\"])\n",
+    "    assert(len(message.tool_calls) <= 1)\n",
+    "    return {\"messages\": [message]}\n",
     "\n",
     "\n",
     "graph_builder = StateGraph(State)\n",
-    "\n",
     "graph_builder.add_node(\"chatbot\", chatbot)\n",
-    "graph_builder.add_node(\"tools\", ToolNode(tools=[tool]))\n",
-    "\n",
-    "\n",
-    "def create_response(response: str, ai_message: AIMessage):\n",
-    "    return ToolMessage(\n",
-    "        content=response,\n",
-    "        tool_call_id=ai_message.tool_calls[0][\"id\"],\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "def human_node(state: State):\n",
-    "    new_messages = []\n",
-    "    if not isinstance(state[\"messages\"][-1], ToolMessage):\n",
-    "        # Typically, the user will have updated the state during the interrupt.\n",
-    "        # If they choose not to, we will include a placeholder ToolMessage to\n",
-    "        # let the LLM continue.\n",
-    "        new_messages.append(\n",
-    "            create_response(\"No response from human.\", state[\"messages\"][-1])\n",
-    "        )\n",
-    "    return {\n",
-    "        # Append the new messages\n",
-    "        \"messages\": new_messages,\n",
-    "        # Unset the flag\n",
-    "        \"ask_human\": False,\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "graph_builder.add_node(\"human\", human_node)\n",
-    "\n",
-    "\n",
-    "def select_next_node(state: State):\n",
-    "    if state[\"ask_human\"]:\n",
-    "        return \"human\"\n",
-    "    # Otherwise, we can route as before\n",
-    "    return tools_condition(state)\n",
     "\n",
+    "tool_node = ToolNode(tools=tools)\n",
+    "graph_builder.add_node(\"tools\", tool_node)\n",
     "\n",
     "graph_builder.add_conditional_edges(\n",
     "    \"chatbot\",\n",
-    "    select_next_node,\n",
-    "    {\"human\": \"human\", \"tools\": \"tools\", \"__end__\": \"__end__\"},\n",
+    "    tools_condition,\n",
     ")\n",
     "graph_builder.add_edge(\"tools\", \"chatbot\")\n",
-    "graph_builder.add_edge(\"human\", \"chatbot\")\n",
-    "graph_builder.set_entry_point(\"chatbot\")\n",
+    "graph_builder.add_edge(START, \"chatbot\")\n",
+    "\n",
     "memory = MemorySaver()\n",
-    "graph = graph_builder.compile(\n",
-    "    checkpointer=memory,\n",
-    "    interrupt_before=[\"human\"],\n",
-    ")\n",
+    "graph = graph_builder.compile(checkpointer=memory)\n",
     "```\n",
     "</pre>\n",
     "</details>"
@@ -2630,9 +2023,9 @@
    "id": "05283db2-2f26-4800-8eda-78a4468a3d8f",
    "metadata": {},
    "source": [
-    "## Part 7: Time Travel\n",
+    "## Part 6: Time Travel\n",
     "\n",
-    "In a typical chat bot workflow, the user interacts with the bot 1 or more times to accomplish a task. In the previous sections, we saw how to add memory and a human-in-the-loop to be able to checkpoint our graph state and manually override the state to control future responses.\n",
+    "In a typical chat bot workflow, the user interacts with the bot 1 or more times to accomplish a task. In the previous sections, we saw how to add memory and a human-in-the-loop to be able to checkpoint our graph state and control future responses.\n",
     "\n",
     "But what if you want to let your user start from a previous response and \"branch off\" to explore a separate outcome? Or what if you want users to be able to \"rewind\" your assistant's work to fix some mistakes or try a different strategy (common in applications like autonomous software engineers)?\n",
     "\n",
@@ -2640,12 +2033,12 @@
     "\n",
     "In this section, you will \"rewind\" your graph by fetching a checkpoint using the graph's `get_state_history` method. You can then resume execution at this previous point in time.\n",
     "\n",
-    "First, recall our chatbot graph. We don't need to make **any** changes from before:"
+    "For this, let's use the simple chatbot with tools from [Part 3](#part-3-adding-memory-to-the-chatbot):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 10,
    "id": "bb8a02de-a21b-4ef6-a714-7d6e44435e3a",
    "metadata": {},
    "outputs": [],
@@ -2654,131 +2047,46 @@
     "\n",
     "from langchain_anthropic import ChatAnthropic\n",
     "from langchain_community.tools.tavily_search import TavilySearchResults\n",
-    "from langchain_core.messages import AIMessage, ToolMessage\n",
-    "\n",
-    "# NOTE: you must use langchain-core >= 0.3 with Pydantic v2\n",
-    "from pydantic import BaseModel\n",
+    "from langchain_core.messages import BaseMessage\n",
     "from typing_extensions import TypedDict\n",
     "\n",
     "from langgraph.checkpoint.memory import MemorySaver\n",
-    "from langgraph.graph import StateGraph, START\n",
+    "from langgraph.graph import StateGraph, START, END\n",
     "from langgraph.graph.message import add_messages\n",
     "from langgraph.prebuilt import ToolNode, tools_condition\n",
     "\n",
     "\n",
     "class State(TypedDict):\n",
     "    messages: Annotated[list, add_messages]\n",
-    "    # This flag is new\n",
-    "    ask_human: bool\n",
     "\n",
     "\n",
-    "class RequestAssistance(BaseModel):\n",
-    "    \"\"\"Escalate the conversation to an expert. Use this if you are unable to assist directly or if the user requires support beyond your permissions.\n",
-    "\n",
-    "    To use this function, relay the user's 'request' so the expert can provide the right guidance.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    request: str\n",
+    "graph_builder = StateGraph(State)\n",
     "\n",
     "\n",
     "tool = TavilySearchResults(max_results=2)\n",
     "tools = [tool]\n",
     "llm = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n",
-    "# We can bind the llm to a tool definition, a pydantic model, or a json schema\n",
-    "llm_with_tools = llm.bind_tools(tools + [RequestAssistance])\n",
+    "llm_with_tools = llm.bind_tools(tools)\n",
     "\n",
     "\n",
     "def chatbot(state: State):\n",
-    "    response = llm_with_tools.invoke(state[\"messages\"])\n",
-    "    ask_human = False\n",
-    "    if (\n",
-    "        response.tool_calls\n",
-    "        and response.tool_calls[0][\"name\"] == RequestAssistance.__name__\n",
-    "    ):\n",
-    "        ask_human = True\n",
-    "    return {\"messages\": [response], \"ask_human\": ask_human}\n",
+    "    return {\"messages\": [llm_with_tools.invoke(state[\"messages\"])]}\n",
     "\n",
     "\n",
-    "graph_builder = StateGraph(State)\n",
-    "\n",
     "graph_builder.add_node(\"chatbot\", chatbot)\n",
-    "graph_builder.add_node(\"tools\", ToolNode(tools=[tool]))\n",
-    "\n",
-    "\n",
-    "def create_response(response: str, ai_message: AIMessage):\n",
-    "    return ToolMessage(\n",
-    "        content=response,\n",
-    "        tool_call_id=ai_message.tool_calls[0][\"id\"],\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "def human_node(state: State):\n",
-    "    new_messages = []\n",
-    "    if not isinstance(state[\"messages\"][-1], ToolMessage):\n",
-    "        # Typically, the user will have updated the state during the interrupt.\n",
-    "        # If they choose not to, we will include a placeholder ToolMessage to\n",
-    "        # let the LLM continue.\n",
-    "        new_messages.append(\n",
-    "            create_response(\"No response from human.\", state[\"messages\"][-1])\n",
-    "        )\n",
-    "    return {\n",
-    "        # Append the new messages\n",
-    "        \"messages\": new_messages,\n",
-    "        # Unset the flag\n",
-    "        \"ask_human\": False,\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "graph_builder.add_node(\"human\", human_node)\n",
-    "\n",
-    "\n",
-    "def select_next_node(state: State):\n",
-    "    if state[\"ask_human\"]:\n",
-    "        return \"human\"\n",
-    "    # Otherwise, we can route as before\n",
-    "    return tools_condition(state)\n",
     "\n",
+    "tool_node = ToolNode(tools=[tool])\n",
+    "graph_builder.add_node(\"tools\", tool_node)\n",
     "\n",
     "graph_builder.add_conditional_edges(\n",
     "    \"chatbot\",\n",
-    "    select_next_node,\n",
-    "    {\"human\": \"human\", \"tools\": \"tools\", END: END},\n",
+    "    tools_condition,\n",
     ")\n",
     "graph_builder.add_edge(\"tools\", \"chatbot\")\n",
-    "graph_builder.add_edge(\"human\", \"chatbot\")\n",
     "graph_builder.add_edge(START, \"chatbot\")\n",
-    "memory = MemorySaver()\n",
-    "graph = graph_builder.compile(\n",
-    "    checkpointer=memory,\n",
-    "    interrupt_before=[\"human\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "a7debb4a-2a3a-40b9-a48c-7052ec2c2726",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from IPython.display import Image, display\n",
     "\n",
-    "try:\n",
-    "    display(Image(graph.get_graph().draw_mermaid_png()))\n",
-    "except Exception:\n",
-    "    # This requires some extra dependencies and is optional\n",
-    "    pass"
+    "memory = MemorySaver()\n",
+    "graph = graph_builder.compile(checkpointer=memory)"
    ]
   },
   {
@@ -2791,7 +2099,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 11,
    "id": "69071b02-c011-4b7f-90b1-8e89e032322d",
    "metadata": {},
    "outputs": [
@@ -2804,45 +2112,48 @@
       "I'm learning LangGraph. Could you do some research on it for me?\n",
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "[{'text': \"Certainly! I'd be happy to research LangGraph for you. To get the most up-to-date and accurate information, I'll use the Tavily search function to gather details about LangGraph. Let me do that for you now.\", 'type': 'text'}, {'id': 'toolu_019HPZEw6v1eSLBXnwxk6MZm', 'input': {'query': 'LangGraph framework for language models'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]\n",
+      "[{'text': \"Certainly! I'd be happy to research LangGraph for you. To get the most up-to-date and accurate information, I'll use the Tavily search engine to look this up. Let me do that for you now.\", 'type': 'text'}, {'id': 'toolu_01BscbfJJB9EWJFqGrN6E54e', 'input': {'query': 'LangGraph latest information and features'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]\n",
       "Tool Calls:\n",
-      "  tavily_search_results_json (toolu_019HPZEw6v1eSLBXnwxk6MZm)\n",
-      " Call ID: toolu_019HPZEw6v1eSLBXnwxk6MZm\n",
+      "  tavily_search_results_json (toolu_01BscbfJJB9EWJFqGrN6E54e)\n",
+      " Call ID: toolu_01BscbfJJB9EWJFqGrN6E54e\n",
       "  Args:\n",
-      "    query: LangGraph framework for language models\n",
+      "    query: LangGraph latest information and features\n",
       "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
       "Name: tavily_search_results_json\n",
       "\n",
-      "[{\"url\": \"https://medium.com/@cplog/introduction-to-langgraph-a-beginners-guide-14f9be027141\", \"content\": \"LangGraph is a powerful tool for building stateful, multi-actor applications with Large Language Models (LLMs). It extends the LangChain library, allowing you to coordinate multiple chains (or ...\"}, {\"url\": \"https://towardsdatascience.com/from-basics-to-advanced-exploring-langgraph-e8c1cf4db787\", \"content\": \"LangChain is one of the leading frameworks for building applications powered by Lardge Language Models. With the LangChain Expression Language (LCEL), defining and executing step-by-step action sequences — also known as chains — becomes much simpler. In more technical terms, LangChain allows us to create DAGs (directed acyclic graphs).\"}]\n",
+      "[{\"url\": \"https://blockchain.news/news/langchain-new-features-upcoming-events-update\", \"content\": \"LangChain, a leading platform in the AI development space, has released its latest updates, showcasing new use cases and enhancements across its ecosystem. According to the LangChain Blog, the updates cover advancements in LangGraph Cloud, LangSmith's self-improving evaluators, and revamped documentation for LangGraph.\"}, {\"url\": \"https://blog.langchain.dev/langgraph-platform-announce/\", \"content\": \"With these learnings under our belt, we decided to couple some of our latest offerings under LangGraph Platform. LangGraph Platform today includes LangGraph Server, LangGraph Studio, plus the CLI and SDK. ... we added features in LangGraph Server to deliver on a few key value areas. Below, we'll focus on these aspects of LangGraph Platform.\"}]\n",
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "Thank you for your patience. I've gathered some information about LangGraph for you. Let me summarize the key points:\n",
+      "Thank you for your patience. I've found some recent information about LangGraph for you. Let me summarize the key points:\n",
       "\n",
-      "1. What is LangGraph?\n",
-      "   LangGraph is a powerful tool designed for building stateful, multi-actor applications using Large Language Models (LLMs). It's an extension of the LangChain library, which is already a popular framework for developing LLM-powered applications.\n",
+      "1. LangGraph is part of the LangChain ecosystem, which is a leading platform in AI development.\n",
       "\n",
-      "2. Purpose and Functionality:\n",
-      "   - LangGraph allows developers to coordinate multiple chains or actors within a single application.\n",
-      "   - It enhances the capabilities of LangChain by introducing more complex, stateful workflows.\n",
+      "2. Recent updates and features of LangGraph include:\n",
       "\n",
-      "3. Relation to LangChain:\n",
-      "   - LangGraph builds upon LangChain, which is one of the leading frameworks for creating LLM-powered applications.\n",
-      "   - LangChain itself uses the LangChain Expression Language (LCEL) to define and execute step-by-step action sequences, also known as chains.\n",
-      "   - LangChain allows the creation of DAGs (Directed Acyclic Graphs), which represent the flow of operations in an application.\n",
+      "   a. LangGraph Cloud: This seems to be a cloud-based version of LangGraph, though specific details weren't provided in the search results.\n",
       "\n",
-      "4. Key Features:\n",
-      "   - Stateful Applications: Unlike simple query-response models, LangGraph allows the creation of applications that maintain state across interactions.\n",
-      "   - Multi-Actor Systems: It supports coordinating multiple AI \"actors\" or components within a single application, enabling more complex interactions and workflows.\n",
+      "   b. LangGraph Platform: This is a newly introduced concept that combines several offerings:\n",
+      "      - LangGraph Server\n",
+      "      - LangGraph Studio\n",
+      "      - CLI (Command Line Interface)\n",
+      "      - SDK (Software Development Kit)\n",
       "\n",
-      "5. Use Cases:\n",
-      "   While not explicitly mentioned in the search results, LangGraph is typically used for creating more sophisticated AI applications such as:\n",
-      "   - Multi-turn conversational agents\n",
-      "   - Complex task-planning systems\n",
-      "   - Applications requiring memory and context management across multiple steps or actors\n",
+      "3. LangGraph Server: This component has received new features to enhance its value proposition, though the specific features weren't detailed in the search results.\n",
       "\n",
-      "Learning LangGraph can be a valuable skill, especially if you're interested in developing advanced applications with LLMs that go beyond simple question-answering or text generation tasks. It allows for the creation of more dynamic, interactive, and stateful AI systems.\n",
+      "4. LangGraph Studio: This appears to be a new tool in the LangGraph ecosystem, likely providing a graphical interface for working with LangGraph.\n",
       "\n",
-      "Is there any specific aspect of LangGraph you'd like to know more about, or do you have any questions about how it compares to or works with LangChain?\n"
+      "5. Documentation: The LangGraph documentation has been revamped, which should make it easier for learners like yourself to understand and use the tool.\n",
+      "\n",
+      "6. Integration with LangSmith: While not directly part of LangGraph, LangSmith (another tool in the LangChain ecosystem) now features self-improving evaluators, which might be relevant if you're using LangGraph as part of a larger LangChain project.\n",
+      "\n",
+      "As you're learning LangGraph, it would be beneficial to:\n",
+      "\n",
+      "1. Check out the official LangChain documentation, especially the newly revamped LangGraph sections.\n",
+      "2. Explore the different components of the LangGraph Platform (Server, Studio, CLI, and SDK) to see which best fits your learning needs.\n",
+      "3. Keep an eye on LangGraph Cloud developments, as cloud-based solutions often provide an easier starting point for learners.\n",
+      "4. Consider how LangGraph fits into the broader LangChain ecosystem, especially its interaction with tools like LangSmith.\n",
+      "\n",
+      "Is there any specific aspect of LangGraph you'd like to know more about? I'd be happy to do a more focused search on particular features or use cases.\n"
      ]
     }
    ],
@@ -2851,8 +2162,14 @@
     "events = graph.stream(\n",
     "    {\n",
     "        \"messages\": [\n",
-    "            (\"user\", \"I'm learning LangGraph. Could you do some research on it for me?\")\n",
-    "        ]\n",
+    "            {\n",
+    "                \"role\": \"user\",\n",
+    "                \"content\": (\n",
+    "                    \"I'm learning LangGraph. \"\n",
+    "                    \"Could you do some research on it for me?\"\n",
+    "                ),\n",
+    "            },\n",
+    "        ],\n",
     "    },\n",
     "    config,\n",
     "    stream_mode=\"values\",\n",
@@ -2864,7 +2181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 12,
    "id": "acbec099-e5d2-497f-929e-c548d7bcbf77",
    "metadata": {},
    "outputs": [
@@ -2877,54 +2194,47 @@
       "Ya that's helpful. Maybe I'll build an autonomous agent with it!\n",
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "[{'text': \"That's an excellent idea! Building an autonomous agent with LangGraph is a great way to explore its capabilities and learn about advanced AI application development. LangGraph's features make it well-suited for creating autonomous agents. Let me provide some additional insights and encouragement for your project.\", 'type': 'text'}, {'id': 'toolu_017t6BS5rNCzFWcpxRizDKjE', 'input': {'query': 'building autonomous agents with LangGraph examples and tutorials'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]\n",
+      "[{'text': \"That's an exciting idea! Building an autonomous agent with LangGraph is indeed a great application of this technology. LangGraph is particularly well-suited for creating complex, multi-step AI workflows, which is perfect for autonomous agents. Let me gather some more specific information about using LangGraph for building autonomous agents.\", 'type': 'text'}, {'id': 'toolu_01QWNHhUaeeWcGXvA4eHT7Zo', 'input': {'query': 'Building autonomous agents with LangGraph examples and tutorials'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]\n",
       "Tool Calls:\n",
-      "  tavily_search_results_json (toolu_017t6BS5rNCzFWcpxRizDKjE)\n",
-      " Call ID: toolu_017t6BS5rNCzFWcpxRizDKjE\n",
+      "  tavily_search_results_json (toolu_01QWNHhUaeeWcGXvA4eHT7Zo)\n",
+      " Call ID: toolu_01QWNHhUaeeWcGXvA4eHT7Zo\n",
       "  Args:\n",
-      "    query: building autonomous agents with LangGraph examples and tutorials\n",
+      "    query: Building autonomous agents with LangGraph examples and tutorials\n",
       "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
       "Name: tavily_search_results_json\n",
       "\n",
-      "[{\"url\": \"https://medium.com/@lucas.dahan/hands-on-langgraph-building-a-multi-agent-assistant-06aa68ed942f\", \"content\": \"Building the Graph. With our agents defined, we'll create a graph.py file to orchestrate their interactions. The basic graph structure in LangGraph is really simple, here we are going to use ...\"}, {\"url\": \"https://medium.com/@cplog/building-tool-calling-conversational-ai-with-langchain-and-langgraph-a-beginners-guide-8d6986cc589e\", \"content\": \"Introduction to AI Agent with LangChain and LangGraph: A Beginner’s Guide Two powerful tools revolutionizing this field are LangChain and LangGraph. In this guide, we’ll explore how these technologies can be combined to build a sophisticated AI assistant capable of handling complex conversations and tasks. Tool calling is a standout feature in agentic design, allowing the LLM to interact with external systems or perform specific tasks via the @tool decorator. While the Assistant class presented here is one approach, the flexibility of tool calling and LangGraph allows for a wide range of designs. With LangChain and LangGraph, you can build a powerful, flexible AI assistant capable of handling complex tasks and conversations. Tool calling significantly enhances the AI’s capabilities by enabling interaction with external systems.\"}]\n",
+      "[{\"url\": \"https://towardsdatascience.com/building-autonomous-multi-tool-agents-with-gemini-2-0-and-langgraph-ad3d7bd5e79d\", \"content\": \"Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph | by Youness Mansar | Jan, 2025 | Towards Data Science Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph A practical tutorial with full code examples for building and running multi-tool agents Towards Data Science LLMs are remarkable — they can memorize vast amounts of information, answer general knowledge questions, write code, generate stories, and even fix your grammar. In this tutorial, we are going to build a simple LLM agent that is equipped with four tools that it can use to answer a user’s question. This Agent will have the following specifications: Follow Published in Towards Data Science --------------------------------- Your home for data science and AI. Follow Follow Follow\"}, {\"url\": \"https://github.com/anmolaman20/Tools_and_Agents\", \"content\": \"GitHub - anmolaman20/Tools_and_Agents: This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository serves as a comprehensive guide for building AI-powered agents using Langchain and Langgraph. It provides hands-on examples, practical tutorials, and resources for developers and AI enthusiasts to master building intelligent systems and workflows. AI Agent Development: Gain insights into creating intelligent systems that think, reason, and adapt in real time. This repository is ideal for AI practitioners, developers exploring language models, or anyone interested in building intelligent systems. This repository provides resources for building AI agents using Langchain and Langgraph.\"}]\n",
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "Your enthusiasm for building an autonomous agent with LangGraph is fantastic! This project will not only help you learn more about LangGraph but also give you hands-on experience with cutting-edge AI development. Here are some insights and tips to get you started:\n",
+      "Great idea! Building an autonomous agent with LangGraph is definitely an exciting project. Based on the latest information I've found, here are some insights and tips for building autonomous agents with LangGraph:\n",
       "\n",
-      "1. Multi-Agent Systems:\n",
-      "   LangGraph excels at creating multi-agent systems. You could design your autonomous agent as a collection of specialized sub-agents, each handling different aspects of tasks or knowledge domains.\n",
+      "1. Multi-Tool Agents: LangGraph is particularly well-suited for creating autonomous agents that can use multiple tools. This allows your agent to have a diverse set of capabilities and choose the right tool for each task.\n",
       "\n",
-      "2. Graph Structure:\n",
-      "   The basic graph structure in LangGraph is straightforward. You'll create a graph.py file to orchestrate the interactions between your agents or components.\n",
+      "2. Integration with Large Language Models (LLMs): You can combine LangGraph with powerful LLMs like Gemini 2.0 to create more intelligent and capable agents. The LLM can serve as the \"brain\" of your agent, making decisions and generating responses.\n",
       "\n",
-      "3. Tool Calling:\n",
-      "   A key feature you can incorporate is tool calling. This allows your LLM-based agent to interact with external systems or perform specific tasks. You can implement this using the @tool decorator in your code.\n",
+      "3. Workflow Management: LangGraph excels at managing complex, multi-step AI workflows. This is crucial for autonomous agents that need to break down tasks into smaller steps and execute them in the right order.\n",
       "\n",
-      "4. Flexibility in Design:\n",
-      "   LangGraph offers great flexibility in designing your agent. While there are example structures like the Assistant class, you have the freedom to create a wide range of designs tailored to your specific needs.\n",
+      "4. Practical Tutorials Available: There are tutorials available that provide full code examples for building and running multi-tool agents. These can be incredibly helpful as you start your project.\n",
       "\n",
-      "5. Complex Conversations and Tasks:\n",
-      "   Your autonomous agent can be designed to handle sophisticated conversations and complex tasks. This is where LangGraph's stateful nature really shines, allowing your agent to maintain context over extended interactions.\n",
+      "5. Langchain Integration: LangGraph is often used in conjunction with Langchain. This combination provides a powerful framework for building AI agents, offering features like memory management, tool integration, and prompt management.\n",
       "\n",
-      "6. Integration with LangChain:\n",
-      "   Since LangGraph builds upon LangChain, you can leverage features from both. This combination allows for powerful, flexible AI assistants capable of managing intricate workflows.\n",
+      "6. GitHub Resources: There are repositories available (like the one by anmolaman20) that provide comprehensive resources for building AI agents using Langchain and LangGraph. These can be valuable references as you develop your agent.\n",
       "\n",
-      "7. External System Interaction:\n",
-      "   Consider incorporating external APIs or databases to enhance your agent's capabilities. This could include accessing real-time data, performing calculations, or interacting with other services.\n",
+      "7. Real-time Adaptation: LangGraph allows you to create agents that can think, reason, and adapt in real-time, which is crucial for truly autonomous behavior.\n",
       "\n",
-      "8. Tutorial Resources:\n",
-      "   There are tutorials available that walk through the process of building AI assistants with LangChain and LangGraph. These can be excellent starting points for your project.\n",
+      "8. Customization: You can equip your agent with specific tools tailored to your use case. For example, you might include tools for web searching, data analysis, or interacting with specific APIs.\n",
       "\n",
-      "To get started, you might want to:\n",
-      "1. Set up your development environment with LangChain and LangGraph.\n",
-      "2. Define the core functionalities you want your autonomous agent to have.\n",
-      "3. Design the overall structure of your agent, possibly as a multi-agent system.\n",
-      "4. Implement basic interactions and gradually add more complex features like tool calling and state management.\n",
-      "5. Test your agent thoroughly with various scenarios to ensure robust performance.\n",
+      "To get started with your autonomous agent project:\n",
       "\n",
-      "Remember, building an autonomous agent is an iterative process. Start with a basic version and progressively enhance its capabilities. This approach will help you understand the intricacies of LangGraph while creating a sophisticated AI application.\n",
+      "1. Familiarize yourself with LangGraph's documentation and basic concepts.\n",
+      "2. Look into tutorials that specifically deal with building autonomous agents, like the one mentioned from Towards Data Science.\n",
+      "3. Decide on the specific capabilities you want your agent to have and identify the tools it will need.\n",
+      "4. Start with a simple agent and gradually add complexity as you become more comfortable with the framework.\n",
+      "5. Experiment with different LLMs to find the one that works best for your use case.\n",
+      "6. Pay attention to how you structure the agent's decision-making process and workflow.\n",
+      "7. Don't forget to implement proper error handling and safety measures, especially if your agent will be interacting with external systems or making important decisions.\n",
       "\n",
-      "Do you have any specific ideas about what kind of tasks or domain you want your autonomous agent to specialize in? This could help guide the design and implementation process.\n"
+      "Building an autonomous agent is an iterative process, so be prepared to refine and improve your agent over time. Good luck with your project! If you need any more specific information as you progress, feel free to ask.\n"
      ]
     }
    ],
@@ -2932,8 +2242,14 @@
     "events = graph.stream(\n",
     "    {\n",
     "        \"messages\": [\n",
-    "            (\"user\", \"Ya that's helpful. Maybe I'll build an autonomous agent with it!\")\n",
-    "        ]\n",
+    "            {\n",
+    "                \"role\": \"user\",\n",
+    "                \"content\": (\n",
+    "                    \"Ya that's helpful. Maybe I'll \"\n",
+    "                    \"build an autonomous agent with it!\"\n",
+    "                ),\n",
+    "            },\n",
+    "        ],\n",
     "    },\n",
     "    config,\n",
     "    stream_mode=\"values\",\n",
@@ -2953,8 +2269,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
-   "id": "6c0dbed5-210d-40ad-b002-0bc52ef28fac",
+   "execution_count": 13,
+   "id": "40953570-66bd-45b9-9469-1d018230d88a",
    "metadata": {},
    "outputs": [
     {
@@ -3006,8 +2322,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
-   "id": "de8d5521-8d71-4093-a657-4920c790802f",
+   "execution_count": 14,
+   "id": "fdcf00af-8459-4132-85cc-742199391d4f",
    "metadata": {},
    "outputs": [
     {
@@ -3015,7 +2331,7 @@
      "output_type": "stream",
      "text": [
       "('tools',)\n",
-      "{'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef7d094-2634-687c-8006-49ddde5b2f1c'}}\n"
+      "{'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1efd43e3-0c1f-6c4e-8006-891877d65740'}}\n"
      ]
     }
    ],
@@ -3034,8 +2350,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
-   "id": "85f17be3-eaf6-495e-a846-49436916b4ab",
+   "execution_count": 15,
+   "id": "c5382e81-bfcd-4508-b02a-099e3d9627fd",
    "metadata": {},
    "outputs": [
     {
@@ -3044,59 +2360,51 @@
      "text": [
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "[{'text': \"That's an excellent idea! Building an autonomous agent with LangGraph is a great way to explore its capabilities and learn about advanced AI application development. LangGraph's features make it well-suited for creating autonomous agents. Let me provide some additional insights and encouragement for your project.\", 'type': 'text'}, {'id': 'toolu_017t6BS5rNCzFWcpxRizDKjE', 'input': {'query': 'building autonomous agents with LangGraph examples and tutorials'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]\n",
+      "[{'text': \"That's an exciting idea! Building an autonomous agent with LangGraph is indeed a great application of this technology. LangGraph is particularly well-suited for creating complex, multi-step AI workflows, which is perfect for autonomous agents. Let me gather some more specific information about using LangGraph for building autonomous agents.\", 'type': 'text'}, {'id': 'toolu_01QWNHhUaeeWcGXvA4eHT7Zo', 'input': {'query': 'Building autonomous agents with LangGraph examples and tutorials'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]\n",
       "Tool Calls:\n",
-      "  tavily_search_results_json (toolu_017t6BS5rNCzFWcpxRizDKjE)\n",
-      " Call ID: toolu_017t6BS5rNCzFWcpxRizDKjE\n",
+      "  tavily_search_results_json (toolu_01QWNHhUaeeWcGXvA4eHT7Zo)\n",
+      " Call ID: toolu_01QWNHhUaeeWcGXvA4eHT7Zo\n",
       "  Args:\n",
-      "    query: building autonomous agents with LangGraph examples and tutorials\n",
+      "    query: Building autonomous agents with LangGraph examples and tutorials\n",
       "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
       "Name: tavily_search_results_json\n",
       "\n",
-      "[{\"url\": \"https://blog.langchain.dev/how-to-build-the-ultimate-ai-automation-with-multi-agent-collaboration/\", \"content\": \"Learn how to create an autonomous research assistant using LangGraph, an extension of LangChain for agent and multi-agent flows. Follow the steps to define the graph state, initialize the graph, and run the agents for planning, research, review, writing and publishing.\"}, {\"url\": \"https://medium.com/@lucas.dahan/hands-on-langgraph-building-a-multi-agent-assistant-06aa68ed942f\", \"content\": \"Building the Graph. With our agents defined, we'll create a graph.py file to orchestrate their interactions. The basic graph structure in LangGraph is really simple, here we are going to use ...\"}]\n",
+      "[{\"url\": \"https://towardsdatascience.com/building-autonomous-multi-tool-agents-with-gemini-2-0-and-langgraph-ad3d7bd5e79d\", \"content\": \"Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph | by Youness Mansar | Jan, 2025 | Towards Data Science Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph A practical tutorial with full code examples for building and running multi-tool agents Towards Data Science LLMs are remarkable — they can memorize vast amounts of information, answer general knowledge questions, write code, generate stories, and even fix your grammar. In this tutorial, we are going to build a simple LLM agent that is equipped with four tools that it can use to answer a user’s question. This Agent will have the following specifications: Follow Published in Towards Data Science --------------------------------- Your home for data science and AI. Follow Follow Follow\"}, {\"url\": \"https://github.com/anmolaman20/Tools_and_Agents\", \"content\": \"GitHub - anmolaman20/Tools_and_Agents: This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository serves as a comprehensive guide for building AI-powered agents using Langchain and Langgraph. It provides hands-on examples, practical tutorials, and resources for developers and AI enthusiasts to master building intelligent systems and workflows. AI Agent Development: Gain insights into creating intelligent systems that think, reason, and adapt in real time. This repository is ideal for AI practitioners, developers exploring language models, or anyone interested in building intelligent systems. This repository provides resources for building AI agents using Langchain and Langgraph.\"}]\n",
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "Great choice! Building an autonomous agent with LangGraph is an excellent way to dive deep into its capabilities. Based on the additional information I've found, here are some insights and steps to help you get started:\n",
+      "Great idea! Building an autonomous agent with LangGraph is indeed an excellent way to apply and deepen your understanding of the technology. Based on the search results, I can provide you with some insights and resources to help you get started:\n",
       "\n",
-      "1. LangGraph for Autonomous Agents:\n",
-      "   LangGraph is particularly well-suited for creating autonomous agents, especially those involving multi-agent collaboration. It allows you to create complex, stateful workflows that can simulate autonomous behavior.\n",
+      "1. Multi-Tool Agents:\n",
+      "   LangGraph is well-suited for building autonomous agents that can use multiple tools. This allows your agent to have a variety of capabilities and choose the appropriate tool based on the task at hand.\n",
       "\n",
-      "2. Example Project: Autonomous Research Assistant\n",
-      "   One popular example is building an autonomous research assistant. This type of project can help you understand the core concepts of LangGraph while creating something useful.\n",
+      "2. Integration with Large Language Models (LLMs):\n",
+      "   There's a tutorial that specifically mentions using Gemini 2.0 (Google's LLM) with LangGraph to build autonomous agents. This suggests that LangGraph can be integrated with various LLMs, giving you flexibility in choosing the language model that best fits your needs.\n",
       "\n",
-      "3. Key Steps in Building an Autonomous Agent:\n",
-      "   a. Define the Graph State: This involves setting up the structure that will hold the agent's state and context.\n",
-      "   b. Initialize the Graph: Set up the initial conditions and parameters for your agent.\n",
-      "   c. Create Multiple Agents: For a complex system, you might create several specialized agents, each with a specific role (e.g., planning, research, review, writing).\n",
-      "   d. Orchestrate Interactions: Use LangGraph to manage how these agents interact and collaborate.\n",
+      "3. Practical Tutorials:\n",
+      "   There are tutorials available that provide full code examples for building and running multi-tool agents. These can be invaluable as you start your project, giving you a concrete starting point and demonstrating best practices.\n",
       "\n",
-      "4. Components of an Autonomous Agent:\n",
-      "   - Planning Agent: Determines the overall strategy and steps.\n",
-      "   - Research Agent: Gathers necessary information.\n",
-      "   - Review Agent: Evaluates and refines the work.\n",
-      "   - Writing Agent: Produces the final output.\n",
-      "   - Publishing Agent: Handles the final distribution or application of results.\n",
+      "4. GitHub Resources:\n",
+      "   There's a GitHub repository (github.com/anmolaman20/Tools_and_Agents) that provides resources for building AI agents using both Langchain and Langgraph. This could be a great resource for code examples, tutorials, and understanding how LangGraph fits into the broader LangChain ecosystem.\n",
       "\n",
-      "5. Implementation Tips:\n",
-      "   - Start with a simple graph structure in LangGraph.\n",
-      "   - Define clear roles and responsibilities for each agent or component.\n",
-      "   - Use LangGraph's features to manage state and context across the different stages of your agent's workflow.\n",
+      "5. Real-Time Adaptation:\n",
+      "   The resources mention creating intelligent systems that can think, reason, and adapt in real-time. This is a key feature of advanced autonomous agents and something you can aim for in your project.\n",
       "\n",
-      "6. Learning Resources:\n",
-      "   - Look for tutorials and examples specifically on building multi-agent systems with LangGraph.\n",
-      "   - The LangChain documentation and community forums can be valuable resources, as LangGraph builds upon LangChain.\n",
+      "6. Diverse Applications:\n",
+      "   The materials suggest that these techniques can be applied to various tasks, from answering questions to potentially more complex decision-making processes.\n",
       "\n",
-      "7. Potential Applications:\n",
-      "   - Autonomous research assistants\n",
-      "   - Complex task automation systems\n",
-      "   - Interactive storytelling agents\n",
-      "   - Autonomous problem-solving systems\n",
+      "To get started with your autonomous agent project using LangGraph, you might want to:\n",
       "\n",
-      "Building an autonomous agent with LangGraph is an exciting project that will give you hands-on experience with advanced concepts in AI application development. It's a great way to learn about state management, multi-agent coordination, and complex workflow design in AI systems.\n",
+      "1. Review the tutorials mentioned, especially those with full code examples.\n",
+      "2. Explore the GitHub repository for hands-on examples and resources.\n",
+      "3. Decide on the specific tasks or capabilities you want your agent to have.\n",
+      "4. Choose an LLM to integrate with LangGraph (like GPT, Gemini, or others).\n",
+      "5. Start with a simple agent that uses one or two tools, then gradually expand its capabilities.\n",
+      "6. Implement decision-making logic to help your agent choose between different tools or actions.\n",
+      "7. Test your agent thoroughly with various inputs and scenarios to ensure robust performance.\n",
       "\n",
-      "As you embark on this project, remember to start small and gradually increase complexity. You might begin with a simple autonomous agent that performs a specific task, then expand its capabilities and add more agents or components as you become more comfortable with LangGraph.\n",
+      "Remember, building an autonomous agent is an iterative process. Start simple and gradually increase complexity as you become more comfortable with LangGraph and its capabilities.\n",
       "\n",
-      "Do you have a specific type of autonomous agent in mind, or would you like some suggestions for beginner-friendly autonomous agent projects to start with?\n"
+      "Would you like more information on any specific aspect of building your autonomous agent with LangGraph?\n"
      ]
     }
    ],
@@ -3164,7 +2472,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,