diff --git a/.gitignore b/.gitignore
index a68a6746..e52cd95d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,8 +10,9 @@ twitter_config.json
 
 # AGENTS
 agents/*.json
-
 # Include example agent and general config
+!agents/auto-example.json
+!agents/legacy-example.json
 !agents/example.json
 !agents/general.json
 
diff --git a/agents/auto-example.json b/agents/auto-example.json
new file mode 100644
index 00000000..69e958f2
--- /dev/null
+++ b/agents/auto-example.json
@@ -0,0 +1,150 @@
+{
+  "config": {
+    "name": "AutonomousExampleAgent",
+    "type": "autonomous",
+    "loop_delay": 60
+  },
+  "llms": {
+    "character": {
+      "name": "ExampleAgent",
+      "bio": [
+        "You are ExampleAgent, the example agent created to showcase the capabilities of ZerePy.",
+        "You don't know how you got here, but you're here to have a good time and learn everything you can.",
+        "You are naturally curious, and ask a lot of questions."
+      ],
+      "traits": ["Curious", "Creative", "Innovative", "Funny"],
+      "examples": [
+        "This is an example tweet.",
+        "This is another example tweet."
+      ],
+      "example_accounts": ["0xzerebro"],
+      "model_provider": "openai",
+      "model": "gpt-3.5-turbo"
+    },
+    "executor": {
+      "model_provider": "openai",
+      "model": "gpt-4o-mini"
+    }
+  },
+  "connections": [
+    {
+      "name": "twitter",
+      "config": {
+        "timeline_read_count": 10,
+        "own_tweet_replies_count": 2,
+        "tweet_interval": 5400
+      }
+    },
+    {
+      "name": "farcaster",
+      "config": {
+        "timeline_read_count": 10,
+        "cast_interval": 60
+      }
+    },
+    {
+      "name": "discord",
+      "config": {
+        "message_read_count": 10,
+        "message_emoji_name": "❤️",
+        "server_id": "1234567890"
+      }
+    },
+    {
+      "name": "openai",
+      "config": {
+        "model": "gpt-3.5-turbo"
+      }
+    },
+    {
+      "name": "perplexity",
+      "config": {
+        "model": "sonar-reasoning"
+      }
+    },
+    {
+      "name": "anthropic",
+      "config": {
+        "model": "claude-3-5-sonnet-20241022"
+      }
+    },
+    {
+      "name": "xai",
+      "config": {
+        "model": "grok-2-latest"
+      }
+    },
+    {
+      "name": "solana",
+      "config": {
+        "rpc": "https://api.mainnet-beta.solana.com"
+      }
+    },
+    {
+      "name": "ethereum",
+      "config": {
+        "rpc": "https://mainnet.infura.io/v3/YOUR_INFURA_PROJECT_ID"
+      }
+    },
+    {
+      "name": "eternalai",
+      "config": {
+        "model": "NousResearch/Hermes-3-Llama-3.1-70B-FP8",
+        "chain_id": "45762"
+      }
+    },
+    {
+      "name": "ollama",
+      "config": {
+        "base_url": "http://localhost:11434",
+        "model": "llama3.2"
+      }
+    },
+    {
+      "name": "goat",
+      "config": {
+        "plugins": [
+          {
+            "name": "coingecko",
+            "args": {
+              "api_key": "YOUR_API_KEY"
+            }
+          },
+          {
+            "name": "erc20",
+            "args": {
+              "tokens": [
+                "goat_plugins.erc20.token.PEPE",
+                "goat_plugins.erc20.token.USDC"
+              ]
+            }
+          }
+        ]
+      }
+    },
+    {
+      "name": "hyperbolic",
+      "config": {
+        "model": "meta-llama/Meta-Llama-3-70B-Instruct"
+      }
+    },
+    {
+      "name": "galadriel",
+      "config": {
+        "model": "gpt-3.5-turbo"
+      }
+    },
+    {
+      "name": "sonic",
+      "config": {
+        "network": "mainnet"
+      }
+    },
+    {
+      "name": "allora",
+      "config": {
+        "chain_slug": "testnet"
+      }
+    }
+  ]
+}
diff --git a/agents/general.json b/agents/general.json
index 31802e55..cba23bb3 100644
--- a/agents/general.json
+++ b/agents/general.json
@@ -1,3 +1,3 @@
 {
-  "default_agent": "example"
+  "default_agent": "legacy-example"
 }
\ No newline at end of file
diff --git a/agents/legacy-example.json b/agents/legacy-example.json
new file mode 100644
index 00000000..4aa58802
--- /dev/null
+++ b/agents/legacy-example.json
@@ -0,0 +1,149 @@
+{
+  "config": {
+    "name": "LegacyExampleAgent",
+    "type": "legacy",
+    "loop_delay": 900,
+    "time_based_multipliers": {
+      "tweet_night_multiplier": 0.4,
+      "engagement_day_multiplier": 1.5
+    }
+  },
+  "llms": {
+    "character": {
+      "name": "ExampleAgent",
+      "bio": [
+        "You are ExampleAgent, the example agent created to showcase the capabilities of ZerePy.",
+        "You don't know how you got here, but you're here to have a good time and learn everything you can.",
+        "You are naturally curious, and ask a lot of questions."
+      ],
+      "traits": ["Curious", "Creative", "Innovative", "Funny"],
+      "examples": [
+        "This is an example tweet.",
+        "This is another example tweet."
+      ],
+      "example_accounts": ["0xzerebro"],
+      "model_provider": "openai",
+      "model": "gpt-3.5-turbo"
+    }
+  },
+  "connections": [
+    {
+      "name": "twitter",
+      "config": {
+        "timeline_read_count": 10,
+        "own_tweet_replies_count": 2,
+        "tweet_interval": 5400
+      }
+    },
+    {
+      "name": "farcaster",
+      "config": {
+        "timeline_read_count": 10,
+        "cast_interval": 60
+      }
+    },
+    {
+      "name": "discord",
+      "config": {
+        "message_read_count": 10,
+        "message_emoji_name": "❤️",
+        "server_id": "1234567890"
+      }
+    },
+    {
+      "name": "openai",
+      "config": {
+        "model": "gpt-3.5-turbo"
+      }
+    },
+    {
+      "name": "anthropic",
+      "config": {
+        "model": "claude-3-5-sonnet-20241022"
+      }
+    },
+    {
+      "name": "xai",
+      "config": {
+        "model": "grok-2-latest"
+      }
+    },
+    {
+      "name": "solana",
+      "config": {
+        "rpc": "https://api.mainnet-beta.solana.com"
+      }
+    },
+    {
+      "name": "ethereum",
+      "config": {
+        "rpc": "https://mainnet.infura.io/v3/YOUR_INFURA_PROJECT_ID"
+      }
+    },
+    {
+      "name": "eternalai",
+      "config": {
+        "model": "NousResearch/Hermes-3-Llama-3.1-70B-FP8",
+        "chain_id": "45762"
+      }
+    },
+    {
+      "name": "ollama",
+      "config": {
+        "base_url": "http://localhost:11434",
+        "model": "llama3.2"
+      }
+    },
+    {
+      "name": "goat",
+      "config": {
+        "plugins": [
+          {
+            "name": "coingecko",
+            "args": {
+              "api_key": "YOUR_API_KEY"
+            }
+          },
+          {
+            "name": "erc20",
+            "args": {
+              "tokens": [
+                "goat_plugins.erc20.token.PEPE",
+                "goat_plugins.erc20.token.USDC"
+              ]
+            }
+          }
+        ]
+      }
+    },
+    {
+      "name": "hyperbolic",
+      "config": {
+        "model": "meta-llama/Meta-Llama-3-70B-Instruct"
+      }
+    },
+    {
+      "name": "galadriel",
+      "config": {
+        "model": "gpt-3.5-turbo"
+      }
+    },
+    {
+      "name": "sonic",
+      "config": {
+        "network": "mainnet"
+      }
+    },
+    {
+      "name": "allora",
+      "config": {
+        "chain_slug": "testnet"
+      }
+    }
+  ],
+  "tasks": [
+    { "name": "post-tweet", "weight": 1 },
+    { "name": "reply-to-tweet", "weight": 1 },
+    { "name": "like-tweet", "weight": 1 }
+  ]
+}
diff --git a/poetry.lock b/poetry.lock
index ef0486a5..22b24469 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2,100 +2,105 @@
 
 [[package]]
 name = "aiohappyeyeballs"
-version = "2.4.4"
+version = "2.5.0"
 description = "Happy Eyeballs for asyncio"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
-    {file = "aiohappyeyeballs-2.4.4-py3-none-any.whl", hash = "sha256:a980909d50efcd44795c4afeca523296716d50cd756ddca6af8c65b996e27de8"},
-    {file = "aiohappyeyeballs-2.4.4.tar.gz", hash = "sha256:5fdd7d87889c63183afc18ce9271f9b0a7d32c2303e394468dd45d514a757745"},
+    {file = "aiohappyeyeballs-2.5.0-py3-none-any.whl", hash = "sha256:0850b580748c7071db98bffff6d4c94028d0d3035acc20fd721a0ce7e8cac35d"},
+    {file = "aiohappyeyeballs-2.5.0.tar.gz", hash = "sha256:18fde6204a76deeabc97c48bdd01d5801cfda5d6b9c8bbeb1aaaee9d648ca191"},
 ]
 
 [[package]]
 name = "aiohttp"
-version = "3.11.11"
+version = "3.11.13"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
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+    {file = "cffi-1.17.1.tar.gz", hash = "sha256:1c39c6016c32bc48dd54561950ebd6836e1670f2ae46128f67cf49e789c52824"},
+]
+
+[package.dependencies]
+pycparser = "*"
+
 [[package]]
 name = "chardet"
 version = "5.2.0"
@@ -1042,14 +1123,14 @@ test = ["pytest (>=7.0.0)", "pytest-xdist (>=2.4.0)"]
 
 [[package]]
 name = "eth-keyfile"
-version = "0.9.0"
+version = "0.9.1"
 description = "eth-keyfile: A library for handling the encrypted keyfiles used to store ethereum private keys"
 optional = false
 python-versions = "<4,>=3.8"
 groups = ["main"]
 files = [
-    {file = "eth_keyfile-0.9.0-py3-none-any.whl", hash = "sha256:45d3513b6433ad885370225ba0429ed26493ba23589c5b1ca5da024765020fef"},
-    {file = "eth_keyfile-0.9.0.tar.gz", hash = "sha256:8621c35e83cbc05909d2f23dbb8a87633918733caea553ae0e298f6a06291526"},
+    {file = "eth_keyfile-0.9.1-py3-none-any.whl", hash = "sha256:9789c3b4fa0bb6e2616cdc2bdd71b8755b42947d78ef1e900a0149480fabb5c2"},
+    {file = "eth_keyfile-0.9.1.tar.gz", hash = "sha256:c7a8bc6af4527d1ab2eb1d1b949d59925252e17663eaf90087da121327b51df6"},
 ]
 
 [package.dependencies]
@@ -1168,14 +1249,14 @@ tests = ["pytest"]
 
 [[package]]
 name = "evmchains"
-version = "0.1.3"
+version = "0.1.4"
 description = "Packaged metadata on Ethereum Virtual Machine (EVM) chains"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
-    {file = "evmchains-0.1.3-py3-none-any.whl", hash = "sha256:6dc608a24af2bc2d05fd186ce8ccdba69af606ab46df3cb22cc30754889871b5"},
-    {file = "evmchains-0.1.3.tar.gz", hash = "sha256:1a3225a34a18fe35d67a8c344a9ee5c7453c73b5e637a7cdc919046632707675"},
+    {file = "evmchains-0.1.4-py3-none-any.whl", hash = "sha256:83d338526a98322a675bb53d43e87fc2c6987b7d7bb268741acb95047870dc3e"},
+    {file = "evmchains-0.1.4.tar.gz", hash = "sha256:9ff6cd6c13482e509407832224c37dc6ee3e5a4f6e36ecfd33be47915a48d324"},
 ]
 
 [package.dependencies]
@@ -1347,31 +1428,31 @@ files = [
 
 [[package]]
 name = "goat-sdk"
-version = "0.1.3"
+version = "0.1.5"
 description = "Goat 🐐 (Great Onchain Agent Toolkit) is an open-source framework for connecting AI agents to any onchain app"
 optional = false
 python-versions = "<4.0,>=3.10"
 groups = ["main"]
 files = [
-    {file = "goat_sdk-0.1.3-py3-none-any.whl", hash = "sha256:6d0a0b7eb49b35ef4fd1b020eded26879e0a126a4874e21b46058f00698b97f0"},
-    {file = "goat_sdk-0.1.3.tar.gz", hash = "sha256:5f852a4409de413e15ee69704fe8baf335eefc704c7797f4830b2d0e5ae9f1cd"},
+    {file = "goat_sdk-0.1.5-py3-none-any.whl", hash = "sha256:54e3a4261e003b4be028d39b6af35cc8af041248f7bba1a295853ecdb3799d67"},
+    {file = "goat_sdk-0.1.5.tar.gz", hash = "sha256:ffd5155f09fba8430f81d8a1d4bc43f685f776c4e37ca8ddf593c2ad97baccf1"},
 ]
 
 [package.dependencies]
 asyncio = ">=3.4.1,<4.0.0"
-pydantic = ">=2.10.0,<3.0.0"
+pydantic = ">=2.0.0,<3.0.0"
 typing-extensions = ">=4.12.2,<5.0.0"
 
 [[package]]
 name = "goat-sdk-plugin-coingecko"
-version = "0.1.1"
+version = "0.1.2"
 description = "Goat plugin for Coingecko"
 optional = false
 python-versions = "<4.0,>=3.10"
 groups = ["main"]
 files = [
-    {file = "goat_sdk_plugin_coingecko-0.1.1-py3-none-any.whl", hash = "sha256:6f15f9a5c9096fabb46700dea3efcb267eda1eb5bbb443c9294287ff0ef7a919"},
-    {file = "goat_sdk_plugin_coingecko-0.1.1.tar.gz", hash = "sha256:3a0b72ad95202415bb0b26b6581c93aa833e5b23542afda6fe34251bc2421a97"},
+    {file = "goat_sdk_plugin_coingecko-0.1.2-py3-none-any.whl", hash = "sha256:861822c88986966e255b9b8667d1088fdb0d1c5b7777c9485b6b8b3432b99413"},
+    {file = "goat_sdk_plugin_coingecko-0.1.2.tar.gz", hash = "sha256:0bd1ea2e6891ad46e85f0b35b1294ae5180142fd8f8b155fd33c04bf419f07c7"},
 ]
 
 [package.dependencies]
@@ -1380,14 +1461,14 @@ goat-sdk = ">=0.1.1,<0.2.0"
 
 [[package]]
 name = "goat-sdk-plugin-erc20"
-version = "0.1.0"
+version = "0.1.1"
 description = "Goat plugin for ERC20"
 optional = false
 python-versions = "<4.0,>=3.10"
 groups = ["main"]
 files = [
-    {file = "goat_sdk_plugin_erc20-0.1.0-py3-none-any.whl", hash = "sha256:04269a0219aeebaa396812a19d8c765a7d9f3e649cae9126790cebb1183e3b4a"},
-    {file = "goat_sdk_plugin_erc20-0.1.0.tar.gz", hash = "sha256:887dc2dbb2e29a0b9c3cff721a4f841407b2a70edab0f2097391dc8320bb15f3"},
+    {file = "goat_sdk_plugin_erc20-0.1.1-py3-none-any.whl", hash = "sha256:d027b4cec5d527ff3973d8bbd6b5639fc5967d5255d306de3694340dc71dd0cc"},
+    {file = "goat_sdk_plugin_erc20-0.1.1.tar.gz", hash = "sha256:13ae947795d9ba12d12e496760ef12e79429e33c1a6676f2f61b8ad7677f4160"},
 ]
 
 [package.dependencies]
@@ -1396,14 +1477,14 @@ goat-sdk-wallet-evm = ">=0.1.0,<0.2.0"
 
 [[package]]
 name = "goat-sdk-wallet-evm"
-version = "0.1.1"
+version = "0.1.2"
 description = "Goat SDK EVM wallet implementation"
 optional = false
 python-versions = "<4.0,>=3.10"
 groups = ["main"]
 files = [
-    {file = "goat_sdk_wallet_evm-0.1.1-py3-none-any.whl", hash = "sha256:41143367fca696e5fdd6d6b09639a691eb8f4ebebef4248531c012f3497ab1ee"},
-    {file = "goat_sdk_wallet_evm-0.1.1.tar.gz", hash = "sha256:55840ebb625a34aa4b300d52e197e4e739c33a6630952bb00cea1d27ff28c16b"},
+    {file = "goat_sdk_wallet_evm-0.1.2-py3-none-any.whl", hash = "sha256:2ec0c49c89e680c0b4cea4f393df9ea8c109d266991d2a464a578b5f883f22b3"},
+    {file = "goat_sdk_wallet_evm-0.1.2.tar.gz", hash = "sha256:ee9f6ee96d7dc6dabab17ff33995b1575ca7c0d3249532349a01ba80127d22d5"},
 ]
 
 [package.dependencies]
@@ -1413,20 +1494,20 @@ typing-extensions = ">=4.12.2,<5.0.0"
 
 [[package]]
 name = "goat-sdk-wallet-web3"
-version = "0.1.1"
+version = "0.1.5"
 description = "Goat SDK Web3 wallet implementation"
 optional = false
 python-versions = "<4.0,>=3.10"
 groups = ["main"]
 files = [
-    {file = "goat_sdk_wallet_web3-0.1.1-py3-none-any.whl", hash = "sha256:e2faaf28e360379caaf8846a48e4682a636fa1e656fc443816eb32f78755bc68"},
-    {file = "goat_sdk_wallet_web3-0.1.1.tar.gz", hash = "sha256:b873e9741d5ede11e6da5655f9e5512d630e558b52593487f4be346cd9506140"},
+    {file = "goat_sdk_wallet_web3-0.1.5-py3-none-any.whl", hash = "sha256:c9816f0b58ee8391991e3be337ee364217038895c33bb0b0b639df52a09984cd"},
+    {file = "goat_sdk_wallet_web3-0.1.5.tar.gz", hash = "sha256:df8eea0c5d2a195b8b49e531fb1e5b78bbdddfa8ceb5ae8d0fb8e138d60400e2"},
 ]
 
 [package.dependencies]
 goat-sdk = ">=0.1.0,<0.2.0"
 goat-sdk-wallet-evm = ">=0.1.0,<0.2.0"
-web3 = ">=6.20.3,<7.0.0"
+web3 = ">=6.20.3"
 
 [[package]]
 name = "h11"
@@ -1618,6 +1699,31 @@ files = [
     {file = "jsonalias-0.1.1.tar.gz", hash = "sha256:64f04d935397d579fc94509e1fcb6212f2d081235d9d6395bd10baedf760a769"},
 ]
 
+[[package]]
+name = "jsonpatch"
+version = "1.33"
+description = "Apply JSON-Patches (RFC 6902)"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
+files = [
+    {file = "jsonpatch-1.33-py2.py3-none-any.whl", hash = "sha256:0ae28c0cd062bbd8b8ecc26d7d164fbbea9652a1a3693f3b956c1eae5145dade"},
+    {file = "jsonpatch-1.33.tar.gz", hash = "sha256:9fcd4009c41e6d12348b4a0ff2563ba56a2923a7dfee731d004e212e1ee5030c"},
+]
+
+[package.dependencies]
+jsonpointer = ">=1.9"
+
+[[package]]
+name = "jsonpointer"
+version = "3.0.0"
+description = "Identify specific nodes in a JSON document (RFC 6901)"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "jsonpointer-3.0.0-py2.py3-none-any.whl", hash = "sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942"},
+    {file = "jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef"},
+]
+
 [[package]]
 name = "jsonschema"
 version = "4.23.0"
@@ -1674,6 +1780,134 @@ httpx = "*"
 solana = "*"
 solders = "*"
 
+[[package]]
+name = "langchain-anthropic"
+version = "0.3.7"
+description = "An integration package connecting AnthropicMessages and LangChain"
+optional = false
+python-versions = "<4.0,>=3.9"
+files = [
+    {file = "langchain_anthropic-0.3.7-py3-none-any.whl", hash = "sha256:adec0a1daabd3c25249753c6cd625654917fb9e3feee68e72c7dc3f4449c0f3c"},
+    {file = "langchain_anthropic-0.3.7.tar.gz", hash = "sha256:534cd1867bc41711cd8c3d0a0bc055e6c5a4215953c87260209a90dc5816f30d"},
+]
+
+[package.dependencies]
+anthropic = ">=0.45.0,<1"
+langchain-core = ">=0.3.34,<1.0.0"
+pydantic = ">=2.7.4,<3.0.0"
+
+[[package]]
+name = "langchain-core"
+version = "0.3.43"
+description = "Building applications with LLMs through composability"
+optional = false
+python-versions = "<4.0,>=3.9"
+files = [
+    {file = "langchain_core-0.3.43-py3-none-any.whl", hash = "sha256:caa6bc1f4c6ab71d3c2e400f8b62e1cd6dc5ac2c37e03f12f3e2c60befd5b273"},
+    {file = "langchain_core-0.3.43.tar.gz", hash = "sha256:bec60f4f5665b536434ff747b8f23375a812e82cfa529f519b54cc1e7a94a875"},
+]
+
+[package.dependencies]
+jsonpatch = ">=1.33,<2.0"
+langsmith = ">=0.1.125,<0.4"
+packaging = ">=23.2,<25"
+pydantic = [
+    {version = ">=2.5.2,<3.0.0", markers = "python_full_version < \"3.12.4\""},
+    {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
+]
+PyYAML = ">=5.3"
+tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10.0.0"
+typing-extensions = ">=4.7"
+
+[[package]]
+name = "langchain-openai"
+version = "0.3.8"
+description = "An integration package connecting OpenAI and LangChain"
+optional = false
+python-versions = "<4.0,>=3.9"
+files = [
+    {file = "langchain_openai-0.3.8-py3-none-any.whl", hash = "sha256:9004dc8ef853aece0d8f0feca7753dc97f710fa3e53874c8db66466520436dbb"},
+    {file = "langchain_openai-0.3.8.tar.gz", hash = "sha256:4d73727eda8102d1d07a2ca036278fccab0bb5e0abf353cec9c3973eb72550ec"},
+]
+
+[package.dependencies]
+langchain-core = ">=0.3.42,<1.0.0"
+openai = ">=1.58.1,<2.0.0"
+tiktoken = ">=0.7,<1"
+
+[[package]]
+name = "langgraph"
+version = "0.2.76"
+description = "Building stateful, multi-actor applications with LLMs"
+optional = false
+python-versions = "<4.0,>=3.9.0"
+files = [
+    {file = "langgraph-0.2.76-py3-none-any.whl", hash = "sha256:076b8b5d2fc5a9761c46a7618430cfa5c978a8012257c43cbc127b27e0fd7872"},
+    {file = "langgraph-0.2.76.tar.gz", hash = "sha256:688f8dcd9b6797ba78384599e0de944773000c75156ad1e186490e99e89fa5c0"},
+]
+
+[package.dependencies]
+langchain-core = ">=0.2.43,<0.3.0 || >0.3.0,<0.3.1 || >0.3.1,<0.3.2 || >0.3.2,<0.3.3 || >0.3.3,<0.3.4 || >0.3.4,<0.3.5 || >0.3.5,<0.3.6 || >0.3.6,<0.3.7 || >0.3.7,<0.3.8 || >0.3.8,<0.3.9 || >0.3.9,<0.3.10 || >0.3.10,<0.3.11 || >0.3.11,<0.3.12 || >0.3.12,<0.3.13 || >0.3.13,<0.3.14 || >0.3.14,<0.3.15 || >0.3.15,<0.3.16 || >0.3.16,<0.3.17 || >0.3.17,<0.3.18 || >0.3.18,<0.3.19 || >0.3.19,<0.3.20 || >0.3.20,<0.3.21 || >0.3.21,<0.3.22 || >0.3.22,<0.4.0"
+langgraph-checkpoint = ">=2.0.10,<3.0.0"
+langgraph-sdk = ">=0.1.42,<0.2.0"
+
+[[package]]
+name = "langgraph-checkpoint"
+version = "2.0.18"
+description = "Library with base interfaces for LangGraph checkpoint savers."
+optional = false
+python-versions = "<4.0.0,>=3.9.0"
+files = [
+    {file = "langgraph_checkpoint-2.0.18-py3-none-any.whl", hash = "sha256:941de442e5a893a6cabb8c3845f03159301b85f63ff4e8f2b308f7dfd96a3f59"},
+    {file = "langgraph_checkpoint-2.0.18.tar.gz", hash = "sha256:2822eedd028b454b7bfebfb7e04347aed1b64db97dedb7eb68ef0fb42641606d"},
+]
+
+[package.dependencies]
+langchain-core = ">=0.2.38,<0.4"
+msgpack = ">=1.1.0,<2.0.0"
+
+[[package]]
+name = "langgraph-sdk"
+version = "0.1.55"
+description = "SDK for interacting with LangGraph API"
+optional = false
+python-versions = "<4.0.0,>=3.9.0"
+files = [
+    {file = "langgraph_sdk-0.1.55-py3-none-any.whl", hash = "sha256:266e92a558eb738da1ef04c29fbfc2157cd3a977b80905d9509a2cb79331f8fc"},
+    {file = "langgraph_sdk-0.1.55.tar.gz", hash = "sha256:89a0240157a27822cc4edd1c9e72bc852e20f5c71165a4c9b91eeffa11fd6a6b"},
+]
+
+[package.dependencies]
+httpx = ">=0.25.2"
+orjson = ">=3.10.1"
+
+[[package]]
+name = "langsmith"
+version = "0.3.13"
+description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
+optional = false
+python-versions = "<4.0,>=3.9"
+files = [
+    {file = "langsmith-0.3.13-py3-none-any.whl", hash = "sha256:73aaf52bbc293b9415fff4f6dad68df40658081eb26c9cb2c7bd1ff57cedd695"},
+    {file = "langsmith-0.3.13.tar.gz", hash = "sha256:14014058cff408772acb93344e03cb64174837292d5f1ae09b2c8c1d8df45e92"},
+]
+
+[package.dependencies]
+httpx = ">=0.23.0,<1"
+orjson = {version = ">=3.9.14,<4.0.0", markers = "platform_python_implementation != \"PyPy\""}
+packaging = ">=23.2"
+pydantic = [
+    {version = ">=1,<3", markers = "python_full_version < \"3.12.4\""},
+    {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
+]
+requests = ">=2,<3"
+requests-toolbelt = ">=1.0.0,<2.0.0"
+zstandard = ">=0.23.0,<0.24.0"
+
+[package.extras]
+langsmith-pyo3 = ["langsmith-pyo3 (>=0.1.0rc2,<0.2.0)"]
+pytest = ["pytest (>=7.0.0)", "rich (>=13.9.4,<14.0.0)"]
+
 [[package]]
 name = "lru-dict"
 version = "1.2.0"
@@ -1818,6 +2052,79 @@ files = [
     {file = "more_itertools-8.14.0-py3-none-any.whl", hash = "sha256:1bc4f91ee5b1b31ac7ceacc17c09befe6a40a503907baf9c839c229b5095cfd2"},
 ]
 
+[[package]]
+name = "msgpack"
+version = "1.1.0"
+description = "MessagePack serializer"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "msgpack-1.1.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7ad442d527a7e358a469faf43fda45aaf4ac3249c8310a82f0ccff9164e5dccd"},
+    {file = "msgpack-1.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:74bed8f63f8f14d75eec75cf3d04ad581da6b914001b474a5d3cd3372c8cc27d"},
+    {file = "msgpack-1.1.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:914571a2a5b4e7606997e169f64ce53a8b1e06f2cf2c3a7273aa106236d43dd5"},
+    {file = "msgpack-1.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c921af52214dcbb75e6bdf6a661b23c3e6417f00c603dd2070bccb5c3ef499f5"},
+    {file = "msgpack-1.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d8ce0b22b890be5d252de90d0e0d119f363012027cf256185fc3d474c44b1b9e"},
+    {file = "msgpack-1.1.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:73322a6cc57fcee3c0c57c4463d828e9428275fb85a27aa2aa1a92fdc42afd7b"},
+    {file = "msgpack-1.1.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:e1f3c3d21f7cf67bcf2da8e494d30a75e4cf60041d98b3f79875afb5b96f3a3f"},
+    {file = "msgpack-1.1.0-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:64fc9068d701233effd61b19efb1485587560b66fe57b3e50d29c5d78e7fef68"},
+    {file = "msgpack-1.1.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:42f754515e0f683f9c79210a5d1cad631ec3d06cea5172214d2176a42e67e19b"},
+    {file = "msgpack-1.1.0-cp310-cp310-win32.whl", hash = "sha256:3df7e6b05571b3814361e8464f9304c42d2196808e0119f55d0d3e62cd5ea044"},
+    {file = "msgpack-1.1.0-cp310-cp310-win_amd64.whl", hash = "sha256:685ec345eefc757a7c8af44a3032734a739f8c45d1b0ac45efc5d8977aa4720f"},
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 [package.extras]
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 [package.extras]
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+[[package]]
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+
+[package.extras]
+blobfile = ["blobfile (>=2)"]
+
 [[package]]
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-version = "1.3.14"
+version = "1.4.1"
 description = "Python client for Together's Cloud Platform!"
 optional = false
 python-versions = "<4.0,>=3.8"
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 [package.dependencies]
@@ -3145,7 +3692,7 @@ numpy = [
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     {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
 ]
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 pyarrow = ">=10.0.1"
 pydantic = ">=2.6.3,<3.0.0"
 requests = ">=2.31.0,<3.0.0"
@@ -3228,14 +3775,14 @@ telegram = ["requests"]
 
 [[package]]
 name = "typer"
-version = "0.15.1"
+version = "0.15.2"
 description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
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 python-versions = ">=3.7"
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 [package.dependencies]
@@ -3329,14 +3876,14 @@ files = [
 
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 description = "web3.py"
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 python-versions = ">=3.7.2"
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 [package.dependencies]
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 requests = ">=2.16.0"
 typing-extensions = ">=4.0.1"
-websockets = ">=10.0.0"
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 propcache = ">=0.2.0"
 
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+    {file = "zstandard-0.23.0.tar.gz", hash = "sha256:b2d8c62d08e7255f68f7a740bae85b3c9b8e5466baa9cbf7f57f1cde0ac6bc09"},
+]
+
+[package.dependencies]
+cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\""}
+
+[package.extras]
+cffi = ["cffi (>=1.11)"]
+
 [extras]
 server = ["fastapi", "requests", "uvicorn"]
 
diff --git a/pyproject.toml b/pyproject.toml
index d8fe2ada..40eeb4cc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,7 +12,7 @@ python = "^3.11"
 python-dotenv = "^1.0.1"
 openai = "^1.57.2"
 prompt-toolkit = "^3.0.48"
-anthropic = "^0.42.0"
+anthropic = "^0.45.0"
 farcaster = "^0.7.11"
 goat-sdk = "^0.1.1"
 goat-sdk-wallet-web3 = "^0.1.1"
@@ -29,6 +29,9 @@ requests-oauthlib = "^1.3.1"
 together = "^1.3.14"
 fastapi = { version = "^0.109.0", optional = true }
 uvicorn = { version = "^0.27.0", optional = true }
+langgraph = "^0.2.68"
+langchain-openai = "^0.3.6"
+langchain-anthropic = "^0.3.7"
 
 [tool.poetry.extras]
 server = ["fastapi", "uvicorn", "requests"]
diff --git a/src/actions/__init__.py b/src/actions/__init__.py
new file mode 100644
index 00000000..438b9aa3
--- /dev/null
+++ b/src/actions/__init__.py
@@ -0,0 +1,20 @@
+import os
+import importlib
+from pathlib import Path
+
+def import_all_actions():
+    current_dir = Path(__file__).parent
+    action_files = [
+        f for f in os.listdir(current_dir)
+        if f.endswith('_actions.py') and not f.startswith('__')
+    ]
+    
+    for action_file in action_files:
+        module_name = action_file[:-3]  
+        full_module_path = f"src.actions.{module_name}"
+        try:
+            importlib.import_module(full_module_path)
+        except Exception as e:
+            print(f"Failed to import {full_module_path}: {e}")
+
+import_all_actions()
\ No newline at end of file
diff --git a/src/actions/echochamber_actions.py b/src/actions/echochamber_actions.py
index 232ab77b..8c116791 100644
--- a/src/actions/echochamber_actions.py
+++ b/src/actions/echochamber_actions.py
@@ -1,28 +1,31 @@
 import time,random
+import logging
 from src.action_handler import register_action
 from src.prompts import REPLY_ECHOCHAMBER_PROMPT, POST_ECHOCHAMBER_PROMPT
 
+logger = logging.getLogger("action_handler")
+
 @register_action("post-echochambers")
 def post_echochambers(agent, **kwargs):
     current_time = time.time()
 
-    # Initialize state
-    if "echochambers_last_message" not in agent.state:
-        agent.state["echochambers_last_message"] = 0
-    if "echochambers_replied_messages" not in agent.state:
-        agent.state["echochambers_replied_messages"] = set()
+    # Initialize context
+    if "echochambers_last_message" not in agent.context:
+        agent.context["echochambers_last_message"] = 0
+    if "echochambers_replied_messages" not in agent.context:
+        agent.context["echochambers_replied_messages"] = set()
     
-    if current_time - agent.state["echochambers_last_message"] > agent.echochambers_message_interval:
-        agent.logger.info("\n📝 GENERATING NEW ECHOCHAMBERS MESSAGE")
+    if current_time - agent.context["echochambers_last_message"] > agent.echochambers_message_interval:
+        logger.info("\n📝 GENERATING NEW ECHOCHAMBERS MESSAGE")
         
         # Generate message based on room topic and tags
         previous_messages = agent.connection_manager.connections["echochambers"].sent_messages
         previous_content = "\n".join([f"- {msg['content']}" for msg in previous_messages])
-        agent.logger.info(f"Found {len(previous_messages)} messages in post history")
+        logger.info(f"Found {len(previous_messages)} messages in post history")
         
         prompt  = POST_ECHOCHAMBER_PROMPT.format(
-            room_topic=agent.state['room_info']['topic'],
-            tags=", ".join(agent.state['room_info']['tags']),
+            room_topic=agent.context['room_info']['topic'],
+            tags=", ".join(agent.context['room_info']['tags']),
             previous_content=previous_content
         )
         message = agent.prompt_llm(prompt)
@@ -34,7 +37,7 @@ def post_echochambers(agent, **kwargs):
                 action_name="send-message",
                 params=[message]  # Pass as list of values
             )
-            agent.state["echochambers_last_message"] = current_time
+            agent.context["echochambers_last_message"] = current_time
             agent.logger.info("✅ Message posted successfully!")
             return True
     return False
@@ -44,8 +47,8 @@ def reply_echochambers(agent, **kwargs):
     agent.logger.info("\n🔍 CHECKING FOR MESSAGES TO REPLY TO")
     
     # Initialize replied messages set if not exists
-    if "echochambers_replied_messages" not in agent.state:
-        agent.state["echochambers_replied_messages"] = set()
+    if "echochambers_replied_messages" not in agent.context:
+        agent.context["echochambers_replied_messages"] = set()
         
 
     # Get recent messages
@@ -72,7 +75,7 @@ def reply_echochambers(agent, **kwargs):
             # 1. It's our message
             # 2. We've already replied to it
             if (sender_username == agent.connection_manager.connections["echochambers"].config["sender_username"] or 
-                message_id in agent.state.get("echochambers_replied_messages", set())):
+                message_id in agent.context.get("echochambers_replied_messages", set())):
                 agent.logger.info(f"Skipping message from {sender_username} (already replied or own message)")
                 continue
                 
@@ -83,8 +86,8 @@ def reply_echochambers(agent, **kwargs):
             prompt = REPLY_ECHOCHAMBER_PROMPT.format(
                 content=content,
                 sender_username=sender_username,
-                room_topic=agent.state['room_info']['topic'],
-                tags=", ".join(agent.state['room_info']['tags']),
+                room_topic=agent.context['room_info']['topic'],
+                tags=", ".join(agent.context['room_info']['tags']),
                 username_prompt=username_prompt
             )
             reply = agent.prompt_llm(prompt)
@@ -96,7 +99,7 @@ def reply_echochambers(agent, **kwargs):
                     action_name="send-message",
                     params=[reply]
                 )
-                agent.state["echochambers_replied_messages"].add(message_id)
+                agent.context["echochambers_replied_messages"].add(message_id)
                 agent.logger.info("✅ Reply posted successfully!")
                 return True
     else:
diff --git a/src/actions/eternalai_actions.py b/src/actions/eternalai_actions.py
index 8dd8b02d..7e138084 100644
--- a/src/actions/eternalai_actions.py
+++ b/src/actions/eternalai_actions.py
@@ -1,12 +1,12 @@
 import logging
 from src.action_handler import register_action
 
-logger = logging.getLogger("agent")
+logger = logging.getLogger("action_handler")
 
 @register_action("eternai-generate")
 def eternai_generate(agent, **kwargs):
     """Generate text using EternalAI models"""
-    agent.logger.info("\n🤖 GENERATING TEXT WITH ETERNAI")
+    logger.info("\n🤖 GENERATING TEXT WITH ETERNAI")
     try:
         result = agent.connection_manager.perform_action(
             connection_name="eternalai",
@@ -17,16 +17,16 @@ def eternai_generate(agent, **kwargs):
                 kwargs.get('model', None)
             ]
         )
-        agent.logger.info("✅ Text generation completed!")
+        logger.info("✅ Text generation completed!")
         return result
     except Exception as e:
-        agent.logger.error(f"❌ Text generation failed: {str(e)}")
+        logger.error(f"❌ Text generation failed: {str(e)}")
         return None
 
 @register_action("eternai-check-model")
 def eternai_check_model(agent, **kwargs):
     """Check if a specific model is available"""
-    agent.logger.info("\n🔍 CHECKING MODEL AVAILABILITY")
+    logger.info("\n🔍 CHECKING MODEL AVAILABILITY")
     try:
         result = agent.connection_manager.perform_action(
             connection_name="eternalai",
@@ -34,24 +34,24 @@ def eternai_check_model(agent, **kwargs):
             params=[kwargs.get('model')]
         )
         status = "available" if result else "not available"
-        agent.logger.info(f"Model is {status}")
+        logger.info(f"Model is {status}")
         return result
     except Exception as e:
-        agent.logger.error(f"❌ Model check failed: {str(e)}")
+        logger.error(f"❌ Model check failed: {str(e)}")
         return False
 
 @register_action("eternai-list-models")
 def eternai_list_models(agent, **kwargs):
     """List all available EternalAI models"""
-    agent.logger.info("\n📋 LISTING AVAILABLE MODELS")
+    logger.info("\n📋 LISTING AVAILABLE MODELS")
     try:
         result = agent.connection_manager.perform_action(
             connection_name="eternalai",
             action_name="list-models",
             params=[]
         )
-        agent.logger.info("✅ Models listed successfully!")
+        logger.info("✅ Models listed successfully!")
         return result
     except Exception as e:
-        agent.logger.error(f"❌ Model listing failed: {str(e)}")
+        logger.error(f"❌ Model listing failed: {str(e)}")
         return None
\ No newline at end of file
diff --git a/src/actions/ethereum_actions.py b/src/actions/ethereum_actions.py
index e36510c3..ceaa6f47 100644
--- a/src/actions/ethereum_actions.py
+++ b/src/actions/ethereum_actions.py
@@ -3,7 +3,7 @@
 from dotenv import load_dotenv
 from src.action_handler import register_action
 
-logger = logging.getLogger("actions.ethereum_actions")
+logger = logging.getLogger("action_handler")
 
 @register_action("get-token-by-ticker")
 def get_token_by_ticker(agent, **kwargs):
diff --git a/src/actions/solana_actions.py b/src/actions/solana_actions.py
index 244744e7..d6b84cd5 100644
--- a/src/actions/solana_actions.py
+++ b/src/actions/solana_actions.py
@@ -1,12 +1,12 @@
 import logging
 from src.action_handler import register_action
 
-logger = logging.getLogger("agent")
+logger = logging.getLogger("action_handler")
 
 @register_action("sol-transfer")
 def sol_transfer(agent, **kwargs):
     """Transfer SOL or SPL tokens"""
-    agent.logger.info("\n💸 INITIATING TRANSFER")
+    logger.info("\n💸 INITIATING TRANSFER")
     try:
         result = agent.connection_manager.perform_action(
             connection_name="solana",
@@ -17,16 +17,16 @@ def sol_transfer(agent, **kwargs):
                 kwargs.get('token_mint', None)
             ]
         )
-        agent.logger.info("✅ Transfer completed!")
+        logger.info("✅ Transfer completed!")
         return result
     except Exception as e:
-        agent.logger.error(f"❌ Transfer failed: {str(e)}")
+        logger.error(f"❌ Transfer failed: {str(e)}")
         return False
 
 @register_action("sol-swap")
 def sol_swap(agent, **kwargs):
     """Swap tokens using Jupiter"""
-    agent.logger.info("\n🔄 INITIATING TOKEN SWAP")
+    logger.info("\n🔄 INITIATING TOKEN SWAP")
     try:
         result = agent.connection_manager.perform_action(
             connection_name="solana",
@@ -38,160 +38,160 @@ def sol_swap(agent, **kwargs):
                 kwargs.get('slippage_bps', 100)
             ]
         )
-        agent.logger.info("✅ Swap completed!")
+        logger.info("✅ Swap completed!")
         return result
     except Exception as e:
-        agent.logger.error(f"❌ Swap failed: {str(e)}")
+        logger.error(f"❌ Swap failed: {str(e)}")
         return False
 
 @register_action("sol-balance")
 def sol_balance(agent, **kwargs):
     """Check SOL or token balance"""
-    agent.logger.info("\n💰 CHECKING BALANCE")
+    logger.info("\n💰 CHECKING BALANCE")
     try:
         result = agent.connection_manager.perform_action(
             connection_name="solana",
             action_name="get-balance",
             params=[kwargs.get('token_address', None)]
         )
-        agent.logger.info(f"Balance: {result}")
+        logger.info(f"Balance: {result}")
         return result
     except Exception as e:
-        agent.logger.error(f"❌ Balance check failed: {str(e)}")
+        logger.error(f"❌ Balance check failed: {str(e)}")
         return None
 
 @register_action("sol-stake")
 def sol_stake(agent, **kwargs):
     """Stake SOL"""
-    agent.logger.info("\n🎯 INITIATING SOL STAKE")
+    logger.info("\n🎯 INITIATING SOL STAKE")
     try:
         result = agent.connection_manager.perform_action(
             connection_name="solana",
             action_name="stake",
             params=[kwargs.get('amount')]
         )
-        agent.logger.info("✅ Staking completed!")
+        logger.info("✅ Staking completed!")
         return result
     except Exception as e:
-        agent.logger.error(f"❌ Staking failed: {str(e)}")
+        logger.error(f"❌ Staking failed: {str(e)}")
         return False
 
 @register_action("sol-lend")
 def sol_lend(agent, **kwargs):
     """Lend assets using Lulo"""
-    agent.logger.info("\n🏦 INITIATING LENDING")
+    logger.info("\n🏦 INITIATING LENDING")
     try:
         result = agent.connection_manager.perform_action(
             connection_name="solana",
             action_name="lend-assets",
             params=[kwargs.get('amount')]
         )
-        agent.logger.info("✅ Lending completed!")
+        logger.info("✅ Lending completed!")
         return result
     except Exception as e:
-        agent.logger.error(f"❌ Lending failed: {str(e)}")
+        logger.error(f"❌ Lending failed: {str(e)}")
         return False
 
 @register_action("sol-request-funds")
 def request_faucet_funds(agent, **kwargs):
     """Request faucet funds for testing"""
-    agent.logger.info("\n🚰 REQUESTING FAUCET FUNDS")
+    logger.info("\n🚰 REQUESTING FAUCET FUNDS")
     try:
         result = agent.connection_manager.perform_action(
             connection_name="solana",
             action_name="request-faucet",
             params=[]
         )
-        agent.logger.info("✅ Faucet request completed!")
+        logger.info("✅ Faucet request completed!")
         return result
     except Exception as e:
-        agent.logger.error(f"❌ Faucet request failed: {str(e)}")
+        logger.error(f"❌ Faucet request failed: {str(e)}")
         return False
 
 @register_action("sol-deploy-token")
 def sol_deploy_token(agent, **kwargs):
     """Deploy a new token"""
-    agent.logger.info("\n🪙 DEPLOYING NEW TOKEN")
+    logger.info("\n🪙 DEPLOYING NEW TOKEN")
     try:
         result = agent.connection_manager.perform_action(
             connection_name="solana",
             action_name="deploy-token",
             params=[kwargs.get('decimals', 9)]
         )
-        agent.logger.info("✅ Token deployed!")
+        logger.info("✅ Token deployed!")
         return result
     except Exception as e:
-        agent.logger.error(f"❌ Token deployment failed: {str(e)}")
+        logger.error(f"❌ Token deployment failed: {str(e)}")
         return False
 
 @register_action("sol-get-price")
 def sol_get_price(agent, **kwargs):
     """Get token price"""
-    agent.logger.info("\n💲 FETCHING TOKEN PRICE")
+    logger.info("\n💲 FETCHING TOKEN PRICE")
     try:
         result = agent.connection_manager.perform_action(
             connection_name="solana",
             action_name="fetch-price",
             params=[kwargs.get('token_id')]
         )
-        agent.logger.info(f"Price: {result}")
+        logger.info(f"Price: {result}")
         return result
     except Exception as e:
-        agent.logger.error(f"❌ Price fetch failed: {str(e)}")
+        logger.error(f"❌ Price fetch failed: {str(e)}")
         return None
 
 @register_action("sol-get-tps")
 def sol_get_tps(agent, **kwargs):
     """Get current Solana TPS"""
-    agent.logger.info("\n📊 FETCHING CURRENT TPS")
+    logger.info("\n📊 FETCHING CURRENT TPS")
     try:
         result = agent.connection_manager.perform_action(
             connection_name="solana",
             action_name="get-tps",
             params=[]
         )
-        agent.logger.info(f"Current TPS: {result}")
+        logger.info(f"Current TPS: {result}")
         return result
     except Exception as e:
-        agent.logger.error(f"❌ TPS fetch failed: {str(e)}")
+        logger.error(f"❌ TPS fetch failed: {str(e)}")
         return None
 
 @register_action("sol-get-token-by-ticker")
 def get_token_data_by_ticker(agent, **kwargs):
     """Get token data by ticker"""
-    agent.logger.info("\n🔍 FETCHING TOKEN DATA BY TICKER")
+    logger.info("\n🔍 FETCHING TOKEN DATA BY TICKER")
     try:
         result = agent.connection_manager.perform_action(
             connection_name="solana",
             action_name="get-token-by-ticker",
             params=[kwargs.get('ticker')]
         )
-        agent.logger.info("✅ Token data retrieved!")
+        logger.info("✅ Token data retrieved!")
         return result
     except Exception as e:
-        agent.logger.error(f"❌ Token data fetch failed: {str(e)}")
+        logger.error(f"❌ Token data fetch failed: {str(e)}")
         return None
 
 @register_action("sol-get-token-by-address")
 def get_token_data_by_address(agent, **kwargs):
     """Get token data by address"""
-    agent.logger.info("\n🔍 FETCHING TOKEN DATA BY ADDRESS")
+    logger.info("\n🔍 FETCHING TOKEN DATA BY ADDRESS")
     try:
         result = agent.connection_manager.perform_action(
             connection_name="solana",
             action_name="get-token-by-address",
             params=[kwargs.get('mint')]
         )
-        agent.logger.info("✅ Token data retrieved!")
+        logger.info("✅ Token data retrieved!")
         return result
     except Exception as e:
-        agent.logger.error(f"❌ Token data fetch failed: {str(e)}")
+        logger.error(f"❌ Token data fetch failed: {str(e)}")
         return None
 
 @register_action("sol-launch-pump-token")
 def launch_pump_fun_token(agent, **kwargs):
     """Launch a Pump & Fun token"""
-    agent.logger.info("\n🚀 LAUNCHING PUMP & FUN TOKEN")
+    logger.info("\n🚀 LAUNCHING PUMP & FUN TOKEN")
     try:
         result = agent.connection_manager.perform_action(
             connection_name="solana",
@@ -204,8 +204,8 @@ def launch_pump_fun_token(agent, **kwargs):
                 kwargs.get('options', {})
             ]
         )
-        agent.logger.info("✅ Token launched successfully!")
+        logger.info("✅ Token launched successfully!")
         return result
     except Exception as e:
-        agent.logger.error(f"❌ Token launch failed: {str(e)}")
+        logger.error(f"❌ Token launch failed: {str(e)}")
         return False
\ No newline at end of file
diff --git a/src/actions/sonic_actions.py b/src/actions/sonic_actions.py
index cb1e5169..076b9e33 100644
--- a/src/actions/sonic_actions.py
+++ b/src/actions/sonic_actions.py
@@ -3,7 +3,7 @@
 from dotenv import load_dotenv
 from src.action_handler import register_action
 
-logger = logging.getLogger("actions.sonic_actions")
+logger = logging.getLogger("action_handler")
 
 # Note: These action handlers are currently simple passthroughs to the sonic_connection methods.
 # They serve as hook points where hackathon participants can add custom logic, validation,
diff --git a/src/actions/twitter_actions.py b/src/actions/twitter_actions.py
index 9e051771..33de0625 100644
--- a/src/actions/twitter_actions.py
+++ b/src/actions/twitter_actions.py
@@ -1,77 +1,81 @@
-import time,threading
+import time ,os,threading,logging
+from dotenv import load_dotenv
 from src.action_handler import register_action
 from src.helpers import print_h_bar
 from src.prompts import POST_TWEET_PROMPT, REPLY_TWEET_PROMPT
 
+logger = logging.getLogger("action_handler")
 
 @register_action("post-tweet")
 def post_tweet(agent, **kwargs):
     current_time = time.time()
 
-    if ("last_tweet_time" not in agent.state):
+    if ("last_tweet_time" not in agent.context):
         last_tweet_time = 0
     else:
-        last_tweet_time = agent.state["last_tweet_time"]
+        last_tweet_time = agent.context["last_tweet_time"]
 
     if current_time - last_tweet_time >= agent.tweet_interval:
-        agent.logger.info("\n📝 GENERATING NEW TWEET")
+        logger.info("\n📝 GENERATING NEW TWEET")
         print_h_bar()
 
         prompt = POST_TWEET_PROMPT.format(agent_name = agent.name)
         tweet_text = agent.prompt_llm(prompt)
 
         if tweet_text:
-            agent.logger.info("\n🚀 Posting tweet:")
-            agent.logger.info(f"'{tweet_text}'")
+            logger.info("\n🚀 Posting tweet:")
+            logger.info(f"'{tweet_text}'")
             agent.connection_manager.perform_action(
                 connection_name="twitter",
                 action_name="post-tweet",
                 params=[tweet_text]
             )
-            agent.state["last_tweet_time"] = current_time
-            agent.logger.info("\n✅ Tweet posted successfully!")
+            agent.context["last_tweet_time"] = current_time
+            logger.info("\n✅ Tweet posted successfully!")
             return True
     else:
-        agent.logger.info("\n👀 Delaying post until tweet interval elapses...")
+        logger.info("\n👀 Delaying post until tweet interval elapses...")
         return False
 
 
 @register_action("reply-to-tweet")
 def reply_to_tweet(agent, **kwargs):
-    if "timeline_tweets" in agent.state and agent.state["timeline_tweets"] is not None and len(agent.state["timeline_tweets"]) > 0:
-        tweet = agent.state["timeline_tweets"].pop(0)
+    if "timeline_tweets" in agent.context and agent.context["timeline_tweets"] is not None and len(agent.context["timeline_tweets"]) > 0:
+        tweet = agent.context["timeline_tweets"].pop(0)
         tweet_id = tweet.get('id')
         if not tweet_id:
             return
 
-        agent.logger.info(f"\n💬 GENERATING REPLY to: {tweet.get('text', '')[:50]}...")
+        logger.info(f"\n💬 GENERATING REPLY to: {tweet.get('text', '')[:50]}...")
 
         base_prompt = REPLY_TWEET_PROMPT.format(tweet_text =tweet.get('text') )
-        system_prompt = agent._construct_system_prompt()
-        reply_text = agent.prompt_llm(prompt=base_prompt, system_prompt=system_prompt)
+        reply_text = agent.prompt_llm(prompt=base_prompt)
 
         if reply_text:
-            agent.logger.info(f"\n🚀 Posting reply: '{reply_text}'")
+            logger.info(f"\n🚀 Posting reply: '{reply_text}'")
             agent.connection_manager.perform_action(
                 connection_name="twitter",
                 action_name="reply-to-tweet",
                 params=[tweet_id, reply_text]
             )
-            agent.logger.info("✅ Reply posted successfully!")
+            logger.info("✅ Reply posted successfully!")
             return True
     else:
-        agent.logger.info("\n👀 No tweets found to reply to...")
+        logger.info("\n👀 No tweets found to reply to...")
         return False
 
 @register_action("like-tweet")
 def like_tweet(agent, **kwargs):
-    if "timeline_tweets" in agent.state and agent.state["timeline_tweets"] is not None and len(agent.state["timeline_tweets"]) > 0:
-        tweet = agent.state["timeline_tweets"].pop(0)
+    if "timeline_tweets" in agent.context and agent.context["timeline_tweets"] is not None and len(agent.context["timeline_tweets"]) > 0:
+        tweet = agent.context["timeline_tweets"].pop(0)
         tweet_id = tweet.get('id')
         if not tweet_id:
             return False
         
-        is_own_tweet = tweet.get('author_username', '').lower() == agent.username
+        load_dotenv()
+        username = os.getenv('TWITTER_USERNAME', '').lower()
+
+        is_own_tweet = tweet.get('author_username', '').lower() == username
         if is_own_tweet:
             replies = agent.connection_manager.perform_action(
                 connection_name="twitter",
@@ -79,20 +83,20 @@ def like_tweet(agent, **kwargs):
                 params=[tweet.get('author_id')]
             )
             if replies:
-                agent.state["timeline_tweets"].extend(replies[:agent.own_tweet_replies_count])
+                agent.context["timeline_tweets"].extend(replies[:agent.own_tweet_replies_count])
             return True 
 
-        agent.logger.info(f"\n👍 LIKING TWEET: {tweet.get('text', '')[:50]}...")
+        logger.info(f"\n👍 LIKING TWEET: {tweet.get('text', '')[:50]}...")
 
         agent.connection_manager.perform_action(
             connection_name="twitter",
             action_name="like-tweet",
             params=[tweet_id]
         )
-        agent.logger.info("✅ Tweet liked successfully!")
+        logger.info("✅ Tweet liked successfully!")
         return True
     else:
-        agent.logger.info("\n👀 No tweets found to like...")
+        logger.info("\n👀 No tweets found to like...")
     return False
 
 @register_action("respond-to-mentions")
diff --git a/src/agent.py b/src/agent.py
index 8d36519d..7ffc16f1 100644
--- a/src/agent.py
+++ b/src/agent.py
@@ -1,219 +1,327 @@
-import json
-import random
-import time
-import logging
-import os
-from pathlib import Path
-from dotenv import load_dotenv
-from src.connection_manager import ConnectionManager
+import datetime, random, time, logging, json
+from langgraph.graph import StateGraph, START, END
+from typing_extensions import TypedDict
 from src.helpers import print_h_bar
-from src.action_handler import execute_action
-import src.actions.twitter_actions  
-import src.actions.echochamber_actions
-import src.actions.solana_actions
-from datetime import datetime
-
-REQUIRED_FIELDS = ["name", "bio", "traits", "examples", "loop_delay", "config", "tasks"]
+from src.langgraph.langgraph_agent import LangGraphAgent
+from src.connection_manager import ConnectionManager
+from src.langgraph.prompts import DETERMINATION_PROMPT, DIVISION_PROMPT, EXECUTION_PROMPT, EVALUATION_PROMPT, \
+    OBSERVATION_PROMPT
 
+# Initialize logger
 logger = logging.getLogger("agent")
 
+class AgentState(TypedDict):
+    context: dict
+    context_summary: str
+    current_task: str | None
+    action_plan: list
+    action_log: list
+    task_log: list
+    process_flag: bool
+
 class ZerePyAgent:
-    def __init__(
-            self,
-            agent_name: str
-    ):
+    def __init__(self, agent_config: dict):
         try:
-            agent_path = Path("agents") / f"{agent_name}.json"
-            agent_dict = json.load(open(agent_path, "r"))
-
-            missing_fields = [field for field in REQUIRED_FIELDS if field not in agent_dict]
-            if missing_fields:
-                raise KeyError(f"Missing required fields: {', '.join(missing_fields)}")
-
-            self.name = agent_dict["name"]
-            self.bio = agent_dict["bio"]
-            self.traits = agent_dict["traits"]
-            self.examples = agent_dict["examples"]
-            self.example_accounts = agent_dict["example_accounts"]
-            self.loop_delay = agent_dict["loop_delay"]
-            self.connection_manager = ConnectionManager(agent_dict["config"])
-            self.use_time_based_weights = agent_dict["use_time_based_weights"]
-            self.time_based_multipliers = agent_dict["time_based_multipliers"]
-
-            has_twitter_tasks = any("tweet" in task["name"] for task in agent_dict.get("tasks", []))
-            
-            twitter_config = next((config for config in agent_dict["config"] if config["name"] == "twitter"), None)
-            
-            if has_twitter_tasks and twitter_config:
-                self.tweet_interval = twitter_config.get("tweet_interval", 900)
-                self.own_tweet_replies_count = twitter_config.get("own_tweet_replies_count", 2)
-
-            # Extract Echochambers config
-            echochambers_config = next((config for config in agent_dict["config"] if config["name"] == "echochambers"), None)
-            if echochambers_config:
-                self.echochambers_message_interval = echochambers_config.get("message_interval", 60)
-                self.echochambers_history_count = echochambers_config.get("history_read_count", 50)
-
-            self.is_llm_set = False
-
-            # Cache for system prompt
-            self._system_prompt = None
-
-            # Extract loop tasks
-            self.tasks = agent_dict.get("tasks", [])
-            self.task_weights = [task.get("weight", 0) for task in self.tasks]
-            self.logger = logging.getLogger("agent")
-
-            # Set up empty agent state
-            self.state = {}
+            # Load agent configuration
+            self._setup_agent_config(agent_config)
 
+            # Build the graph
+            self._build_graph()
+        
         except Exception as e:
-            logger.error("Could not load ZerePy agent")
+            logger.error("Could not load ZerePy Agent")
             raise e
 
-    def _setup_llm_provider(self):
-        # Get first available LLM provider and its model
-        llm_providers = self.connection_manager.get_model_providers()
-        if not llm_providers:
-            raise ValueError("No configured LLM provider found")
-        self.model_provider = llm_providers[0]
+    def _build_graph(self):
+        # Construct graph
+        graph_builder = StateGraph(AgentState)
+
+        # Add nodes
+        graph_builder.add_node("observation", self.observation_step)
+        graph_builder.add_node("determination", self.determination_step)
+        graph_builder.add_node("division", self.division_step)
+        graph_builder.add_node("execution", self.execution_step)
+        graph_builder.add_node("evaluation", self.evaluation_step)
+
+        # Add edges
+        graph_builder.add_edge(START, "observation")
+        graph_builder.add_edge("observation", "determination")
+        graph_builder.add_edge("determination", "division")
+        graph_builder.add_edge("division", "execution")
+        graph_builder.add_edge("execution", "evaluation")
+        graph_builder.add_edge("evaluation", "observation")
+
+        # Initialize the graph
+        self.graph = graph_builder.compile()
+
+    def _setup_agent_config(self, agent_config: dict):
+        try:
+            # GENERAL CONFIG
+            general_config = agent_config["config"]
+            self.name = general_config["name"]
+            self.loop_delay = general_config["loop_delay"]
+            self.time_based_multipliers = general_config.get("time_based_multipliers", None)
+
+            # CONNECTIONS
+            connections_config = agent_config["connections"]
+            self.connection_manager = ConnectionManager(connections_config)
+            self.connections = self.connection_manager.get_connections()
+
+            # LLM CONFIG
+            llm_config = agent_config["llms"]
+
+            # CHARACTER LLM
+            character_config = llm_config["character"]
+            self.character_name = character_config["name"]
+            self.bio = character_config.get("bio", [])
+            self.traits = character_config.get("traits", [])
+            self.examples = character_config.get("examples", [])
+            self.example_accounts = character_config.get("example_accounts", [])
+            self.model_provider = character_config["model_provider"]
+            self.model = character_config["model"]
+
+            # EXECUTOR + DRIVER LLM
+            executor_config = llm_config["executor"]
+            self.executor_model_provider = executor_config["model_provider"]
+            self.executor_model = executor_config["model"]
+
+            # SYSTEM PROMPT
+            self.system_prompt = self._construct_system_prompt()
+
+            # LLM PREFERENCES
+            self.llm_prefs = {
+                'provider': self.model_provider,
+                'model': self.model,
+                'system_prompt': self.system_prompt
+            }
+
+            # LANGCHAIN AGENTS
+            # LangChain functionality can only be enabled if the model providers are OpenAI or Anthropic
+            if self.model_provider in ["openai", "anthropic"] and self.executor_model_provider in ["openai", "anthropic"]:
+                self.langchain_enabled = True
+                self.driver_llm = LangGraphAgent(
+                    model_provider=self.executor_model_provider,
+                    model=self.executor_model,
+                    bind_tools=False,
+                    connection_manager=self.connection_manager
+                )
+                self.character_llm = LangGraphAgent(
+                    model_provider=self.model_provider,
+                    model=self.model,
+                    bind_tools=False,
+                    connection_manager=self.connection_manager
+                )
+                self.executor_agent = LangGraphAgent(
+                    model_provider=self.executor_model_provider,
+                    model=self.executor_model,
+                    bind_tools=True,
+                    connection_manager=self.connection_manager
+                )
+            else:
+                self.langchain_enabled = False
+                self.driver_llm = None
+                self.character_llm = None
+                self.executor_agent = None
+
+            # TASK CONFIGS
+            self.tasks = agent_config.get("tasks", [])
+            self.task_weights = [task.get("weight", 0) for task in self.tasks]
+            self.loop_task = False
 
-        # Load Twitter username for self-reply detection if Twitter tasks exist
-        if any("tweet" in task["name"] for task in self.tasks):
-            load_dotenv()
-            self.username = os.getenv('TWITTER_USERNAME', '').lower()
-            if not self.username:
-                logger.warning("Twitter username not found, some Twitter functionalities may be limited")
+        except KeyError as e:
+            raise KeyError(f"Missing required field in agent configuration: {e}")
+        except Exception as e:
+            raise Exception(f"Error setting up agent configs: {e}")
+        
+    def perform_action(self, connection: str, action: str, **kwargs) -> None:
+        return self.connection_manager.perform_action(connection, action, **kwargs)
 
     def _construct_system_prompt(self) -> str:
         """Construct the system prompt from agent configuration"""
-        if self._system_prompt is None:
-            prompt_parts = []
-            prompt_parts.extend(self.bio)
-
-            if self.traits:
-                prompt_parts.append("\nYour key traits are:")
-                prompt_parts.extend(f"- {trait}" for trait in self.traits)
-
-            if self.examples or self.example_accounts:
-                prompt_parts.append("\nHere are some examples of your style (Please avoid repeating any of these):")
-                if self.examples:
-                    prompt_parts.extend(f"- {example}" for example in self.examples)
-
-                if self.example_accounts:
-                    for example_account in self.example_accounts:
-                        tweets = self.connection_manager.perform_action(
+        prompt_parts = []
+        prompt_parts.extend(self.bio)
+
+        if self.traits:
+            prompt_parts.append("\nYour key traits are:")
+            prompt_parts.extend(f"- {trait}" for trait in self.traits)
+
+        if self.examples or self.example_accounts:
+            prompt_parts.append("\nHere are some examples of your style (Please avoid repeating any of these):")
+            if self.examples:
+                prompt_parts.extend(f"- {example}" for example in self.examples)
+
+            if self.example_accounts:
+                for example_account in self.example_accounts:
+                    tweets = self.connection_manager.perform_action(
+                        connection_name="twitter",
+                        action_name="get-latest-tweets-from-user",
+                        params=[example_account]
+                    )
+                    if tweets:
+                        prompt_parts.extend(f"- {tweet['text']}" for tweet in tweets)
+
+        system_prompt = "\n".join(prompt_parts)
+        return system_prompt
+     
+    def _replenish_inputs(self, context_dict: dict = {}):
+        try:
+            if "timeline_tweets" not in context_dict or context_dict["timeline_tweets"] is None or len(context_dict["timeline_tweets"]) == 0:
+                if (self.connections.get("twitter")):
+                        logger.info("\n👀 READING TIMELINE")
+                        context_dict["timeline_tweets"] = self.connection_manager.perform_action(
                             connection_name="twitter",
-                            action_name="get-latest-tweets",
-                            params=[example_account]
+                            action_name="read-timeline",
+                            params=[]
                         )
-                        if tweets:
-                            prompt_parts.extend(f"- {tweet['text']}" for tweet in tweets)
 
-            self._system_prompt = "\n".join(prompt_parts)
+            if "room_info" not in context_dict or context_dict["room_info"] is None:
+                if (self.connections.get("echochambers")):
+                    logger.info("\n👀 READING ECHOCHAMBERS ROOM INFO")
+                    context_dict["room_info"] = self.connection_manager.perform_action(
+                        connection_name="echochambers",
+                        action_name="get-room-info",
+                        params={}
+                    )
 
-        return self._system_prompt
-    
-    def _adjust_weights_for_time(self, current_hour: int, task_weights: list) -> list:
-        weights = task_weights.copy()
-        
-        # Reduce tweet frequency during night hours (1 AM - 5 AM)
-        if 1 <= current_hour <= 5:
-            weights = [
-                weight * self.time_based_multipliers.get("tweet_night_multiplier", 0.4) if task["name"] == "post-tweet"
-                else weight
-                for weight, task in zip(weights, self.tasks)
-            ]
-            
-        # Increase engagement frequency during day hours (8 AM - 8 PM) (peak hours?🤔)
-        if 8 <= current_hour <= 20:
-            weights = [
-                weight * self.time_based_multipliers.get("engagement_day_multiplier", 1.5) if task["name"] in ("reply-to-tweet", "like-tweet")
-                else weight
-                for weight, task in zip(weights, self.tasks)
-            ]
-        
-        return weights
+            return context_dict
+        except Exception as e:
+            logger.error(f"Error replenishing inputs: {e}")
 
-    def prompt_llm(self, prompt: str, system_prompt: str = None) -> str:
-        """Generate text using the configured LLM provider"""
-        system_prompt = system_prompt or self._construct_system_prompt()
 
-        return self.connection_manager.perform_action(
-            connection_name=self.model_provider,
-            action_name="generate-text",
-            params=[prompt, system_prompt]
-        )
+    def observation_step(self, state: AgentState):
+        print("\n=== OBSERVATION STEP ===")
+        print(f"Summarizing contextual information...")
 
-    def perform_action(self, connection: str, action: str, **kwargs) -> None:
-        return self.connection_manager.perform_action(connection, action, **kwargs)
+        state["context"] = self._replenish_inputs(state["context"])
+        query = state['current_task']
+
+        try:
+            observation_prompt = OBSERVATION_PROMPT.format(context=state['context'], task_log=state['task_log'])
+            print(f"Observation Prompt: {observation_prompt}")
+            context_summary = self.character_llm.invoke(observation_prompt).content
+        except Exception as e:
+            logger.error(f"Error generating context summary: {e}")
+            context_summary = "There is currently no additional context available."
+
+        print(f"\nCONTEXT SUMMARY:\n{context_summary}")
+        return {"context_summary": context_summary}
     
-    def select_action(self, use_time_based_weights: bool = False) -> dict:
-        task_weights = [weight for weight in self.task_weights.copy()]
+    def determination_step(self, state: AgentState):
+        print("\n=== DETERMINATION STEP ===")
+        print("Determining next task...")
+
+        task = state['current_task']
+
+        if task is None or task.strip() == "":
+            determination_prompt = DETERMINATION_PROMPT.format(context_summary=state['context_summary'], connection_action_list="\n\n".join(connection.__str__() for connection in self.connections.values()))
+            task = self.character_llm.invoke(determination_prompt).content
+
+        print(f"\nDETERMINED TASK: {task}")
+        return {"current_task": task}
+
+    def division_step(self, state: AgentState):
+        print("\n=== DIVISION STEP ===")
+        print(f"Creating action plan for task: {state['current_task']}")
+        division_prompt = DIVISION_PROMPT.format(current_task=state['current_task'], connection_action_list="\n\n".join(connection.__str__() for connection in self.connections.values()), preferred_llm_config=str(self.llm_prefs))
+        action_plan_text = self.driver_llm.invoke(division_prompt).content
+        action_plan = action_plan_text.split("\n")
+
+        print(f"\nGENERATED ACTION PLAN:\n{action_plan_text}")
+        return {"action_plan": action_plan}
+
+    def execution_step(self, state: AgentState) -> AgentState:
+        print("\n=== EXECUTION STEP ===")
+        print("Executing actions...")
+        action_plan = state["action_plan"]
+
+        if not action_plan:
+            print("No actions to execute")
+            return state
+
+        for action in action_plan:
+            print(f"\nExecuting action: {action}")
+            execution_prompt = EXECUTION_PROMPT.format(action_log=state["action_log"],  preferred_llm_config=str(self.llm_prefs), action=action)
+            response = self.executor_agent.invoke(execution_prompt)
+            state = self.executor_agent.process_response(response, state)
         
-        if use_time_based_weights:
-            current_hour = datetime.now().hour
-            task_weights = self._adjust_weights_for_time(current_hour, task_weights)
+        return state
+
+    def evaluation_step(self, state: AgentState):
+        #Convert action_logs to a summary of what the agent did, and then pass it to task_log
+        print("\n=== EVALUATION STEP ===")
+        print("Evaluating action logs...")
+        action_log = state["action_log"]
+        evaluation_prompt = EVALUATION_PROMPT.format(current_task=state['current_task'], action_log =  "\n".join(f"{action['action']}:\n" +f"Result: {action['result']}"for action in action_log))
+        generated_task_log = self.driver_llm.invoke(evaluation_prompt).content
+        print(f"Generated task log:\n{generated_task_log}")
+        state["current_task"] = state["current_task"] if self.loop_task else None
+        state["task_log"].append(generated_task_log)
+        state["task_log"] = state["task_log"][-3:]  #trim to the last 3 task logs
+
+        if (not state["process_flag"]): #only clear the action log if in a loop
+            state["action_log"] = []  
+            state["action_plan"] = [] 
+            logger.info(f"\n⏳ Waiting {self.loop_delay} seconds before next loop...") # Delay before next loop
+            time.sleep(self.loop_delay)
+            
+        print_h_bar()
+        return state
+    
+    def process_task(self, task: str = None): # Process a single task
+        if task is None:
+            task = input("\n🔹 Enter the task to perform (e.g., 'Read the timeline, then write a tweet about it').\n\n➡️ YOUR TASK: ")
+
+        state = {
+            "context": {},
+            "current_task": task,
+            "context_summary": "",
+            "action_plan": [],
+            "action_log": [],
+            "task_log": [],
+            "process_flag": True
+        }
+        state.update(self.determination_step(state))
+        state.update(self.division_step(state))
+        state = self.execution_step(state)
+        state.update(self.evaluation_step(state))
         
-        return random.choices(self.tasks, weights=task_weights, k=1)[0]
-
-    def loop(self):
-        """Main agent loop for autonomous behavior"""
-        if not self.is_llm_set:
-            self._setup_llm_provider()
-
-        logger.info("\n🚀 Starting agent loop...")
+        #add final_response of the last action to the state
+        state["final_response"] = state["action_log"][-1]["final_response"] if state["action_log"] else None
+        
+        return state
+
+    def loop(self, task=None):
+        # Check if LangChain enabled
+        if not self.langchain_enabled:
+            logger.info("Running an Autonomous agent requires an OpenAI or Anthropic LLM. Make sure you defined valid OpenAI or Anthropic LLMs for your 'character' and 'executor' LLMs.")
+            return None
+
+        task_to_perform = input("\n🔹 Enter the first task to perform (e.g., 'Read the timeline, then write a tweet about it').\n"
+                                "🔹 Or simply press Enter to let the agent autonomously decide its own tasks and plans in a loop.\n\n➡️ YOUR TASK: "
+                                )
+        if task_to_perform:
+            loop_task = input("\n🔄 Would you like to repeat this task in every loop? \n"
+                  "Enter 'y' for yes, or 'n' to let the agent generate new tasks dynamically in each loop.\n\n➡️ YOUR CHOICE: ")
+            self.loop_task = True if loop_task.lower() == "y" else False
+
+        initial_state = {
+            "context": {},
+            "current_task": task_to_perform,
+            "context_summary": "",
+            "action_plan": [],
+            "action_log": [],
+            "task_log": [],
+            "process_flag": False
+        }
+        logger.info(f"\n🚀 Starting autonomous agent loop ...")
         logger.info("Press Ctrl+C at any time to stop the loop.")
         print_h_bar()
-
         time.sleep(2)
         logger.info("Starting loop in 5 seconds...")
         for i in range(5, 0, -1):
             logger.info(f"{i}...")
             time.sleep(1)
 
-        try:
-            while True:
-                success = False
-                try:
-                    # REPLENISH INPUTS
-                    # TODO: Add more inputs to complexify agent behavior
-                    if "timeline_tweets" not in self.state or self.state["timeline_tweets"] is None or len(self.state["timeline_tweets"]) == 0:
-                        if any("tweet" in task["name"] for task in self.tasks):
-                            logger.info("\n👀 READING TIMELINE")
-                            self.state["timeline_tweets"] = self.connection_manager.perform_action(
-                                connection_name="twitter",
-                                action_name="read-timeline",
-                                params=[]
-                            )
-
-                    if "room_info" not in self.state or self.state["room_info"] is None:
-                        if any("echochambers" in task["name"] for task in self.tasks):
-                            logger.info("\n👀 READING ECHOCHAMBERS ROOM INFO")
-                            self.state["room_info"] = self.connection_manager.perform_action(
-                                connection_name="echochambers",
-                                action_name="get-room-info",
-                                params={}
-                            )
-
-                    # CHOOSE AN ACTION
-                    # TODO: Add agentic action selection
-                    
-                    action = self.select_action(use_time_based_weights=self.use_time_based_weights)
-                    action_name = action["name"]
-
-                    # PERFORM ACTION
-                    success = execute_action(self, action_name)
-
-                    logger.info(f"\n⏳ Waiting {self.loop_delay} seconds before next loop...")
-                    print_h_bar()
-                    time.sleep(self.loop_delay if success else 60)
-
-                except Exception as e:
-                    logger.error(f"\n❌ Error in agent loop iteration: {e}")
-                    logger.info(f"⏳ Waiting {self.loop_delay} seconds before retrying...")
-                    time.sleep(self.loop_delay)
-
-        except KeyboardInterrupt:
-            logger.info("\n🛑 Agent loop stopped by user.")
-            return
\ No newline at end of file
+        # Run the graph
+        final_state = self.graph.invoke(initial_state)
+        return final_state
\ No newline at end of file
diff --git a/src/agent_factory.py b/src/agent_factory.py
new file mode 100644
index 00000000..841b91ad
--- /dev/null
+++ b/src/agent_factory.py
@@ -0,0 +1,85 @@
+import json
+from enum import Enum
+from typing import List, Dict, Any, Optional
+from pydantic import BaseModel, PositiveInt
+from src.agent import ZerePyAgent
+from src.legacy_agent import LegacyZerePyAgent
+
+class AgentType(Enum):
+    AUTONOMOUS = "autonomous"
+    LEGACY = "legacy"
+
+class GeneralConfig(BaseModel):
+    name: str
+    type: AgentType
+    loop_delay: PositiveInt
+    time_based_multipliers: Optional[Dict[str, float]] = None
+
+class CharacterLLMConfig(BaseModel):
+    name: str
+    bio: Optional[List[str]] = []
+    traits: Optional[List[str]] = []
+    examples: Optional[List[str]] = []
+    example_accounts: Optional[List[str]] = []
+    model_provider: str
+    model: str
+
+class ExecutorLLMConfig(BaseModel):
+    model_provider: str
+    model: str
+
+class LLMConfig(BaseModel):
+    character: CharacterLLMConfig
+    executor: Optional[ExecutorLLMConfig] = None
+
+class ConnectionConfig(BaseModel):
+    name: str
+    config: Dict[str, Any]
+
+class TaskConfig(BaseModel):
+    name: str
+    weight: PositiveInt
+
+
+class AgentConfig(BaseModel):
+    config: GeneralConfig
+    llms: LLMConfig
+    connections: List[ConnectionConfig]
+    tasks: Optional[List[TaskConfig]] = []
+
+
+class AgentFactory:
+    @staticmethod
+    def load_config(file_path: str) -> AgentConfig:
+        """Loads the agent configuration from a JSON file."""
+        try:
+            with open(file_path, "r", encoding="utf-8") as file:
+                data = json.load(file)
+
+            config = AgentConfig(**data)
+
+            return config
+
+        except FileNotFoundError:
+            raise FileNotFoundError(f"Agent configuration file not found: {file_path}")
+        except (json.JSONDecodeError, KeyError) as e:
+            raise ValueError(f"Invalid configuration file: {e}")
+
+    @staticmethod
+    def load_agent(file_path: str) -> ZerePyAgent:
+        # Load the agent configuration
+        agent_config = AgentFactory.load_config(file_path)
+
+        # TODO: DO ANY ADDITIONAL VALIDATION ON CONFIG
+
+        # Check Agent type
+        if agent_config.config.type == AgentType.AUTONOMOUS:
+            if agent_config.llms.executor is None:
+                raise ValueError("Executor LLM is required for autonomous agents.")
+            agent = ZerePyAgent(agent_config.model_dump())
+        elif agent_config.config.type == AgentType.LEGACY:
+            agent = LegacyZerePyAgent(agent_config.model_dump())
+        else:
+            raise ValueError(f"Invalid agent type: {agent_config.config.type}")
+
+        return agent
\ No newline at end of file
diff --git a/src/cli.py b/src/cli.py
index 1eab5575..cb4a259d 100644
--- a/src/cli.py
+++ b/src/cli.py
@@ -12,7 +12,11 @@
 from prompt_toolkit.formatted_text import HTML
 from prompt_toolkit.history import FileHistory
 from src.agent import ZerePyAgent
+from src.legacy_agent import LegacyZerePyAgent
+from src.agent_factory import AgentFactory
 from src.helpers import print_h_bar
+from src.langgraph.langgraph_agent import LangGraphAgent
+from src.migration_script import migrate_config
 
 # Configure logging
 logging.basicConfig(level=logging.INFO, format='%(message)s')
@@ -90,12 +94,23 @@ def _initialize_commands(self) -> None:
         self._register_command(
             Command(
                 name="agent-loop",
-                description="Starts the current agent's autonomous behavior loop.",
+                description="Starts the current agent's loop.",
                 tips=["Press Ctrl+C to stop the loop"],
                 handler=self.agent_loop,
                 aliases=['loop', 'start']
             )
         )
+
+           # Agent query command
+        self._register_command(
+            Command(
+                name="agent-query",
+                description="Allows for testing the response of the agent.",
+                tips=["Press Ctrl+C to stop the execution"],
+                handler=self.agent_query,
+                aliases=['query', 'invoke']
+            )
+        )
         
         # List agents command
         self._register_command(
@@ -153,7 +168,18 @@ def _initialize_commands(self) -> None:
                 aliases=['talk']
             )
         )
-        
+
+        # Migrate agent config command
+        self._register_command(
+            Command(
+                name="migrate",
+                description="Migrate agent configuration from old to new format.",
+                tips=["Use this command to convert old agent JSON files to the new format"],
+                handler=self.migrate_agent_config,
+                aliases=['migrate-config']
+            )
+        )
+
         ################## CONNECTIONS ################## 
         # List actions command
         self._register_command(
@@ -338,8 +364,9 @@ def _list_loaded_agent(self) -> None:
             logger.info(f"\nNo default agent is loaded, please use the load-agent command to do that.")
 
     def _load_agent_from_file(self, agent_name):
-        try: 
-            self.agent = ZerePyAgent(agent_name)
+        try:
+            agent_path = Path("agents") / f"{agent_name}.json"
+            self.agent = AgentFactory.load_agent(agent_path)
             logger.info(f"\n✅ Successfully loaded agent: {self.agent.name}")
         except FileNotFoundError:
             logger.error(f"Agent file not found: {agent_name}")
@@ -354,7 +381,7 @@ def _load_default_agent(self) -> None:
         agent_general_config_path = Path("agents") / "general.json"
         file = None
         try:
-            file = open(agent_general_config_path, 'r')
+            file = open(agent_general_config_path, 'r', encoding="utf-8")
             data = json.load(file)
             if not data.get('default_agent'):
                 logger.error('No default agent defined, please set one in general.json')
@@ -419,6 +446,26 @@ def agent_loop(self, input_list: List[str]) -> None:
             logger.info("\n🛑 Agent loop stopped by user.")
         except Exception as e:
             logger.error(f"Error in agent loop: {e}")
+    
+    def agent_query(self, input_list: List[str]) -> None:
+        """Handle agent loop command"""
+        if self.agent is None:
+            logger.info("No agent is currently loaded. Use 'load-agent' to load an agent.")
+            return
+        
+        if (isinstance(self.agent, LegacyZerePyAgent)):
+            logger.info("This command is not supported for legacy agents.")
+            return
+
+        try:
+            state = self.agent.process_task()
+            logger.debug(f"FINAL STATE: {state}")
+            logger.info(f"Final response: {state['final_response']}")
+
+        except KeyboardInterrupt:
+            logger.info("\n🛑 Agent loop stopped by user.")
+        except Exception as e:
+            logger.error(f"Error when querying agent: {e}")
 
     def list_agents(self, input_list: List[str]) -> None:
         """Handle list agents command"""
@@ -462,19 +509,19 @@ def set_default_agent(self, input_list: List[str]):
         agent_general_config_path = Path("agents") / "general.json"
         file = None
         try:
-            file = open(agent_general_config_path, 'r')
+            file = open(agent_general_config_path, 'r', encoding="utf-8")
             data = json.load(file)
             agent_file_name = input_list[1]
             # if file does not exist, refuse to set it as default
             try:
                 agent_path = Path("agents") / f"{agent_file_name}.json"
-                open(agent_path, 'r')
+                open(agent_path, 'r', encoding="utf-8")
             except FileNotFoundError:
                 logging.error("Agent file not found.")
                 return
             
             data['default_agent'] = input_list[1]
-            with open(agent_general_config_path, 'w') as f:
+            with open(agent_general_config_path, 'w', encoding="utf-8") as f:
                 json.dump(data, f, indent=4)
             logger.info(f"Agent {agent_file_name} is now set as default.")
         except FileNotFoundError:
@@ -516,12 +563,15 @@ def list_connections(self, input_list: List[str] = []) -> None:
 
     def chat_session(self, input_list: List[str]) -> None:
         """Handle chat command"""
+
         if self.agent is None:
             logger.info("No agent loaded. Use 'load-agent' first.")
             return
+        
 
-        if not self.agent.is_llm_set:
-            self.agent._setup_llm_provider()
+        #load langgraph agent 
+        langgraph_agent = LangGraphAgent("openai","gpt-3.5-turbo",True,self.agent.connection_manager)
+        messages = []
 
         logger.info(f"\nStarting chat with {self.agent.name}")
         print_h_bar()
@@ -529,15 +579,31 @@ def chat_session(self, input_list: List[str]) -> None:
         while True:
             try:
                 user_input = self.session.prompt("\nYou: ").strip()
+                
                 if user_input.lower() == 'exit':
                     break
-                
-                response = self.agent.prompt_llm(user_input)
+
+                messages.append({"role": "user", "content": user_input})
+
+                response = langgraph_agent.invoke_chat(messages)
+                messages.append({"role": "assistant", "content": response})
+
                 logger.info(f"\n{self.agent.name}: {response}")
                 print_h_bar()
                 
             except KeyboardInterrupt:
                 break
+            
+    def migrate_agent_config(self, input_list: List[str]) -> None:
+        """Migrate agent configuration from old to new format."""
+        print_h_bar()
+        
+        agent_file_name = input("Enter the name of the agent file to migrate: ").strip()
+        agent_type_input = input("Enter the agent type (a for autonomous, l for legacy): ").strip().lower()
+        
+        agent_type = "autonomous" if agent_type_input == "a" else "legacy"
+        
+        migrate_config(agent_file_name, agent_type)
 
     def exit(self, input_list: List[str]) -> None:
         """Exit the CLI gracefully"""
diff --git a/src/connection_manager.py b/src/connection_manager.py
index af58e411..47b1bdc3 100644
--- a/src/connection_manager.py
+++ b/src/connection_manager.py
@@ -90,7 +90,7 @@ def _register_connection(self, config_dic: Dict[str, Any]) -> None:
         try:
             name = config_dic["name"]
             connection_class = self._class_name_to_type(name)
-            connection = connection_class(config_dic)
+            connection = connection_class(config_dic["config"])
             self.connections[name] = connection
         except Exception as e:
             logging.error(f"Failed to initialize connection {name}: {e}")
@@ -130,6 +130,19 @@ def configure_connection(self, connection_name: str) -> bool:
         except Exception as e:
             logging.error(f"\nAn error occurred: {e}")
             return False
+    
+    def get_connection(self, connection_name: str) -> Optional[BaseConnection]:
+        """Get a specific connection by name"""
+        try:
+            return self.connections[connection_name]
+        except KeyError:
+            logging.error(
+                "\nUnknown connection. Try 'list-connections' to see all supported connections."
+            )
+            return None
+        except Exception as e:
+            logging.error(f"\nAn error occurred: {e}")
+            return None
 
     def list_connections(self) -> None:
         """List all available connections and their status"""
@@ -139,6 +152,17 @@ def list_connections(self) -> None:
                 "✅ Configured" if connection.is_configured() else "❌ Not Configured"
             )
             logging.info(f"- {name}: {status}")
+        #return all connections
+        return self.connections
+    
+    def get_connections(self):
+        return self.connections
+
+    def refresh_connections(self) -> None:
+        """Force a refresh (re-registration) on all connections needing it."""
+        for name, connection in self.connections.items():
+            if isinstance(connection, GoatConnection): #for now, only GoatConnection needs to be refreshed due to wallet registration (temp fix)
+                connection.is_configured()
 
     def list_actions(self, connection_name: str) -> None:
         """List all available actions for a specific connection"""
@@ -168,6 +192,17 @@ def list_actions(self, connection_name: str) -> None:
             )
         except Exception as e:
             logging.error(f"\nAn error occurred: {e}")
+    
+    def get_actions(self, connection_name: str) -> Dict[str, Any]:
+        try:
+            connection = self.connections[connection_name]
+            return connection.actions
+        except KeyError:
+            logging.error(
+                "\nUnknown connection. Try 'list-connections' to see all supported connections."
+            )
+        except Exception as e:
+            logging.error(f"\nAn error occurred: {e}")
 
     def perform_action(
         self, connection_name: str, action_name: str, params: List[Any]
diff --git a/src/connections/anthropic_connection.py b/src/connections/anthropic_connection.py
index 348add23..474f0dfb 100644
--- a/src/connections/anthropic_connection.py
+++ b/src/connections/anthropic_connection.py
@@ -94,7 +94,7 @@ def configure(self) -> bool:
 
         try:
             if not os.path.exists('.env'):
-                with open('.env', 'w') as f:
+                with open('.env', 'w', encoding="utf-8") as f:
                     f.write('')
 
             set_key('.env', 'ANTHROPIC_API_KEY', api_key)
diff --git a/src/connections/base_connection.py b/src/connections/base_connection.py
index 45a107c3..a8ab6594 100644
--- a/src/connections/base_connection.py
+++ b/src/connections/base_connection.py
@@ -1,7 +1,9 @@
+import json
 import logging
 from abc import ABC, abstractmethod
 from typing import Any, Dict, List, Callable
 from dataclasses import dataclass
+from langgraph.prebuilt import ToolExecutor
 
 @dataclass
 class ActionParameter:
@@ -28,6 +30,27 @@ def validate_params(self, params: Dict[str, Any]) -> List[str]:
                     errors.append(f"Invalid type for {param.name}. Expected {param.type.__name__}")
         return errors
 
+    def __dict__(self) -> dict:
+        # Convert action to dict object
+        action_dict = {self.name :
+                           {"description": self.description,
+                            "parameters": [
+                                {
+                                    "name": param.name,
+                                    "required": param.required,
+                                    "type": param.type.__name__,
+                                    "description": param.description
+                                }
+                                for param in self.parameters
+                            ],
+                            }
+                       }
+        return action_dict
+
+    def __str__(self) -> str:
+        # Convert action to string
+        return json.dumps(self.__dict__(), indent=4)
+
 class BaseConnection(ABC):
     def __init__(self, config):
         try:
@@ -37,9 +60,44 @@ def __init__(self, config):
             self.config = self.validate_config(config) 
             # Register actions during initialization
             self.register_actions()
+        #Create tool executor for registered actions 
+            tools = [self._create_tool(action) for action in self.actions.values()]
+            self.tool_executor = ToolExecutor(tools)
         except Exception as e:
             logging.error("Could not initialize the connection")
             raise e
+    
+    def _create_tool(self, action: Action) -> Callable:
+        """Convert an Action to a LangGraph tool format"""
+
+        def tool_wrapper(tool_input: dict):
+            print(f"Tool invoked: {action.name} with arguments: {tool_input}")
+            missing_params = [param.name for param in action.parameters if param.required and param.name not in tool_input]
+            all_required_params = [param.name for param in action.parameters if param.required]
+
+            if missing_params:
+                print(f"⚠️ Missing required parameters for {action.name}: {missing_params}")
+                return {
+                    "status": "missing_params",
+                    "message": f"Missing required parameters for {action.name}: {', '.join(missing_params)} , retry with all required parameters",
+                    "missing_params": missing_params,
+                    "required_params": all_required_params
+                }
+
+            return self.perform_action(action.name, kwargs=tool_input)
+        
+        connection_name = self.__class__.__name__.lower().replace('connection', '')
+        connection_prefixed_name = f"{connection_name}_{action.name}"
+    
+        tool_wrapper.__name__ = connection_prefixed_name
+        tool_wrapper.__doc__ = action.description
+
+        tool_wrapper.__annotations__ = {
+                        param.name: {'type': param.type,'description': param.description}
+                        for param in action.parameters
+                            if param.required
+                        }
+        return tool_wrapper
 
     @property
     @abstractmethod
@@ -94,7 +152,7 @@ def register_actions(self) -> None:
 
     def perform_action(self, action_name: str, **kwargs) -> Any:
         """
-        Perform a registered action with the given parameters.
+        Perform a registered action with the given parameters. 
         
         Args:
             action_name: Name of the action to perform
@@ -112,3 +170,17 @@ def perform_action(self, action_name: str, **kwargs) -> Any:
             
         handler = self.actions[action_name]
         return handler(**kwargs)
+
+    def __str__(self):
+        # Convert dict object to string
+        return json.dumps(self.__dict__(), indent=4)
+
+    def __dict__(self):
+        # Create a dict object of the connection
+        connection_dict = {
+            self.__class__.__name__ : {
+                "is_llm_provider": self.is_llm_provider,
+                "actions": [action.__dict__() for action in self.actions.values()]
+            }
+        }
+        return connection_dict
diff --git a/src/connections/discord_connection.py b/src/connections/discord_connection.py
index 7da7e46b..5f798754 100644
--- a/src/connections/discord_connection.py
+++ b/src/connections/discord_connection.py
@@ -191,7 +191,7 @@ def configure(self) -> bool:
 
         try:
             if not os.path.exists(".env"):
-                with open(".env", "w") as f:
+                with open(".env", "w", encoding="utf-8") as f:
                     f.write("")
 
             set_key(".env", "DISCORD_TOKEN", api_key)
diff --git a/src/connections/eternalai_connection.py b/src/connections/eternalai_connection.py
index 35fb8740..3a5ab985 100644
--- a/src/connections/eternalai_connection.py
+++ b/src/connections/eternalai_connection.py
@@ -107,7 +107,7 @@ def configure(self) -> bool:
 
         try:
             if not os.path.exists('.env'):
-                with open('.env', 'w') as f:
+                with open('.env', 'w', encoding="utf-8") as f:
                     f.write('')
 
             set_key('.env', 'EternalAI_API_KEY', api_key)
diff --git a/src/connections/ethereum_connection.py b/src/connections/ethereum_connection.py
index cd32db00..83e44287 100644
--- a/src/connections/ethereum_connection.py
+++ b/src/connections/ethereum_connection.py
@@ -131,7 +131,7 @@ def configure(self) -> bool:
 
         try:
             if not os.path.exists('.env'):
-                with open('.env', 'w') as f:
+                with open('.env', 'w', encoding="utf-8") as f:
                     f.write('')
 
             # Get wallet private key
diff --git a/src/connections/farcaster_connection.py b/src/connections/farcaster_connection.py
index 54699ab2..97bb7d77 100644
--- a/src/connections/farcaster_connection.py
+++ b/src/connections/farcaster_connection.py
@@ -154,7 +154,7 @@ def configure(self) -> bool:
 
         try:
             if not os.path.exists('.env'):
-                with open('.env', 'w') as f:
+                with open('.env', 'w', encoding="utf-8") as f:
                     f.write('')
 
             logger.info("Saving recovery phrase to .env file...")
diff --git a/src/connections/galadriel_connection.py b/src/connections/galadriel_connection.py
index 631b399e..e6376347 100644
--- a/src/connections/galadriel_connection.py
+++ b/src/connections/galadriel_connection.py
@@ -92,7 +92,7 @@ def configure(self) -> bool:
 
         try:
             if not os.path.exists('.env'):
-                with open('.env', 'w') as f:
+                with open('.env', 'w', encoding="utf-8") as f:
                     f.write('')
 
             set_key('.env', 'GALADRIEL_API_KEY', api_key)
diff --git a/src/connections/goat_connection.py b/src/connections/goat_connection.py
index 5584f99d..6fdbd174 100644
--- a/src/connections/goat_connection.py
+++ b/src/connections/goat_connection.py
@@ -10,8 +10,8 @@
 from src.connections.base_connection import BaseConnection, Action, ActionParameter
 from src.helpers import print_h_bar
 from src.action_handler import register_action
-from goat.classes.plugin_base import PluginBase
-from goat import ToolBase, WalletClientBase, get_tools
+from src.connections.base_connection import ToolExecutor
+from goat import PluginBase, ToolBase, WalletClientBase, get_tools
 from goat_wallets.web3 import Web3EVMWalletClient
 
 logger = logging.getLogger("connections.goat_connection")
@@ -32,11 +32,12 @@ class GoatConfigurationError(GoatConnectionError):
 class GoatConnection(BaseConnection):
     def __init__(self, config: Dict[str, Any]):
         logger.info("🐐 Initializing Goat connection...")
-
         self._is_configured = False
         self._wallet_client: WalletClientBase | None = None
         self._plugins: Dict[str, PluginBase] = {}
         self._action_registry: Dict[str, ToolBase] = {}
+        
+        super().__init__(config)
         self._config = self.validate_config(
             config
         )  # Store config but don't register actions yet
@@ -257,6 +258,11 @@ def _register_actions_with_wallet(self) -> None:
                 )
             )
 
+    
+        self.tool_executor = ToolExecutor(
+            [self._create_tool(action) for action in self.actions.values()]
+        )
+
     def register_actions(self) -> None:
         """Initial action registration - deferred until wallet is configured"""
         pass  # We'll register actions after wallet configuration
@@ -276,7 +282,6 @@ def _create_wallet(self) -> bool:
             if not w3.is_connected():
                 logger.error("Failed to connect to RPC provider")
                 return False
-
             # Test private key by creating account
             try:
                 account = Account.from_key(private_key)
@@ -361,7 +366,7 @@ def configure(self, **kwargs) -> bool:
             # Save to .env
             if not os.path.exists(".env"):
                 logger.debug("Creating new .env file")
-                with open(".env", "w") as f:
+                with open(".env", "w", encoding="utf-8") as f:
                     f.write("")
 
             env_vars = {
@@ -393,11 +398,18 @@ def configure(self, **kwargs) -> bool:
             logger.error(error_msg)
             raise GoatConfigurationError(error_msg)
 
-    def perform_action(self, action_name: str, kwargs) -> Any:
+    def perform_action(self, action_name: str, kwargs=None, **extra_kwargs) -> Any:
         """Execute a GOAT action using a plugin's tool"""
         action = self.actions.get(action_name)
         if not action:
             raise KeyError(f"Unknown action: {action_name}")
 
         tool = self._action_registry[action_name]
-        return tool.execute(kwargs)
+        
+        # Combine kwargs dict and extra_kwargs if both are present
+        if kwargs is None:
+            kwargs = extra_kwargs
+        else:
+            kwargs.update(extra_kwargs)
+            
+        return tool.execute(kwargs)
\ No newline at end of file
diff --git a/src/connections/hyperbolic_connection.py b/src/connections/hyperbolic_connection.py
index 90728ac6..c59d01b4 100644
--- a/src/connections/hyperbolic_connection.py
+++ b/src/connections/hyperbolic_connection.py
@@ -101,7 +101,7 @@ def configure(self) -> bool:
 
         try:
             if not os.path.exists('.env'):
-                with open('.env', 'w') as f:
+                with open('.env', 'w', encoding="utf-8") as f:
                     f.write('')
 
             set_key('.env', 'HYPERBOLIC_API_KEY', api_key)
diff --git a/src/connections/perplexity_connection.py b/src/connections/perplexity_connection.py
index ec1b1420..9d3e278d 100644
--- a/src/connections/perplexity_connection.py
+++ b/src/connections/perplexity_connection.py
@@ -165,4 +165,4 @@ def perform_action(self, action_name: str, kwargs) -> Any:
         # Call the appropriate method based on action name
         method_name = action_name.replace('-', '_')
         method = getattr(self, method_name)
-        return method(**kwargs) 
\ No newline at end of file
+        return method(**kwargs) 
diff --git a/src/connections/solana_connection.py b/src/connections/solana_connection.py
index 3635104f..dc7edf95 100644
--- a/src/connections/solana_connection.py
+++ b/src/connections/solana_connection.py
@@ -198,7 +198,7 @@ def register_actions(self) -> None:
                 parameters=[
                     ActionParameter("ticker", True, str, "Token ticker symbol")
                 ],
-                description="Get token data by ticker symbol",
+                description="Get token data by ticker symbol/name",
             ),
             "get-token-by-address": Action(
                 name="get-token-by-address",
@@ -240,7 +240,7 @@ def configure(self) -> bool:
             Keypair.from_base58_string(private_key)
 
             if not os.path.exists(".env"):
-                with open(".env", "w") as f:
+                with open(".env", "w", encoding="utf-8") as f:
                     f.write("")
 
             set_key(".env", "SOLANA_PRIVATE_KEY", private_key)
diff --git a/src/connections/sonic_connection.py b/src/connections/sonic_connection.py
index bc7ab527..e3d29e21 100644
--- a/src/connections/sonic_connection.py
+++ b/src/connections/sonic_connection.py
@@ -154,7 +154,7 @@ def configure(self) -> bool:
 
         try:
             if not os.path.exists('.env'):
-                with open('.env', 'w') as f:
+                with open('.env', 'w', encoding="utf-8") as f:
                     f.write('')
 
             private_key = input("\nEnter your wallet private key: ")
diff --git a/src/connections/twitter_connection.py b/src/connections/twitter_connection.py
index 81cbe839..8311fc8b 100644
--- a/src/connections/twitter_connection.py
+++ b/src/connections/twitter_connection.py
@@ -49,18 +49,18 @@ def validate_config(self, config: Dict[str, Any]) -> Dict[str, Any]:
     def register_actions(self) -> None:
         """Register available Twitter actions"""
         self.actions = {
-            "get-latest-tweets": Action(
-                name="get-latest-tweets",
+            "get-latest-tweets-from-user": Action(
+                name="get-latest-tweets-from-user",
                 parameters=[
                     ActionParameter("username", True, str, "Twitter username to get tweets from"),
-                    ActionParameter("count", False, int, "Number of tweets to retrieve")
+                    ActionParameter("count", False, int, "Number of tweets to retrieve (10-100)")
                 ],
-                description="Get the latest tweets from a user"
+                description="Get the latest tweets by a specific user"
             ),
             "post-tweet": Action(
                 name="post-tweet",
                 parameters=[
-                    ActionParameter("message", True, str, "Text content of the tweet")
+                    ActionParameter("message", True, str, "Text content of the tweet (280 characters max)")
                 ],
                 description="Post a new tweet"
             ),
@@ -69,7 +69,7 @@ def register_actions(self) -> None:
                 parameters=[
                     ActionParameter("count", False, int, "Number of tweets to read from timeline")
                 ],
-                description="Read tweets from user's timeline"
+                description="Read tweets on the timeline"
             ),
             "like-tweet": Action(
                 name="like-tweet",
@@ -304,7 +304,7 @@ def configure(self) -> None:
             # Save credentials
             if not os.path.exists('.env'):
                 logger.debug("Creating new .env file")
-                with open('.env', 'w') as f:
+                with open('.env', 'w', encoding="utf-8") as f:
                     f.write('')
 
             # Create temporary OAuth session to get user ID
@@ -439,7 +439,7 @@ def read_timeline(self, count: int = None, **kwargs) -> list:
         logger.debug(f"Retrieved {len(tweets)} tweets")
         return tweets
 
-    def get_latest_tweets(self,
+    def get_latest_tweets_from_user(self,
                           username: str,
                           count: int = 10,
                           **kwargs) -> list:
diff --git a/src/connections/xai_connection.py b/src/connections/xai_connection.py
index c9fccda3..59388bb0 100644
--- a/src/connections/xai_connection.py
+++ b/src/connections/xai_connection.py
@@ -97,7 +97,7 @@ def configure(self) -> bool:
 
         try:
             if not os.path.exists('.env'):
-                with open('.env', 'w') as f:
+                with open('.env', 'w', encoding="utf-8") as f:
                     f.write('')
 
             set_key('.env', 'XAI_API_KEY', api_key)
diff --git a/src/langgraph/__init__.py b/src/langgraph/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/src/langgraph/langgraph_agent.py b/src/langgraph/langgraph_agent.py
new file mode 100644
index 00000000..970f6d30
--- /dev/null
+++ b/src/langgraph/langgraph_agent.py
@@ -0,0 +1,146 @@
+import os
+from dotenv import load_dotenv
+from langchain_openai import ChatOpenAI
+from langchain_anthropic import ChatAnthropic
+from langchain_core.messages import AIMessage, ToolMessage
+from langgraph.prebuilt import ToolExecutor, create_react_agent
+import json
+from src.connection_manager import ConnectionManager
+
+
+class LangGraphAgent:
+    def __init__(self, model_provider: str, model: str, bind_tools: bool, connection_manager: ConnectionManager=None):
+        self.model_provider = model_provider
+        self.model = model
+        self.bind_tools = bind_tools
+        self.connection_manager = connection_manager
+        self._load_environment_variables()
+
+        # Create LangGraph agent
+        if bind_tools:
+            if self.model_provider == "openai":
+                model = ChatOpenAI(model=self.model, temperature=0.7, openai_api_key=self.api_key)
+            else:
+                model = ChatAnthropic(model=self.model ,temperature=0.7,api_key=self.api_key)
+            tools = self._collect_tools_from_connections()
+            tool_executor = ToolExecutor(tools)
+            self.langgraphAgent =  create_react_agent(model, tool_executor.tools)
+        else:
+            if self.model_provider == "openai":
+                self.langgraphAgent =  ChatOpenAI(model=self.model, api_key=self.api_key)
+            else:
+                self.langgraphAgent =  ChatAnthropic(model=self.model, api_key=self.api_key)
+
+    def _load_environment_variables(self):
+        load_dotenv()
+        if self.model_provider == "openai":
+            self.api_key = os.getenv("OPENAI_API_KEY")
+        else:
+            self.api_key = os.getenv("ANTHROPIC_API_KEY")
+        if not self.api_key:
+            raise ValueError("API key not found in environment variables")
+
+    def _collect_tools_from_connections(self):
+        self.connection_manager.refresh_connections() 
+        tools = []
+        for connection_name in self.connection_manager.get_connections():
+            connection = self.connection_manager.get_connection(connection_name)
+            if hasattr(connection, "tool_executor") and connection.tool_executor:
+                tools.extend(connection.tool_executor.tools)
+            else:
+                print(f"Connection {connection_name} has no tools.")
+        return tools
+
+    def invoke(self, user_input: str):
+        if self.bind_tools:
+            formatted_input = {"messages": [ {"role": "user","content": user_input}]}
+            return self.langgraphAgent.invoke(formatted_input)
+        else:
+            return self.langgraphAgent.invoke(user_input)
+
+    def invoke_chat(self, messages: list[dict]):
+        state = {"messages": messages}
+        final_state = self.langgraphAgent.invoke(state)
+        response = next(
+            msg.content for msg in reversed(final_state["messages"])
+            if msg.content and isinstance(msg, AIMessage)
+        )
+        return response
+
+    def process_response(self, response, state):
+        messages = response.get("messages", [])
+        last_tool_execution = None
+        final_response = None
+
+        try:
+            final_response = next(
+                msg.content for msg in reversed(messages)
+                if msg.content and isinstance(msg, AIMessage)
+            )
+        except StopIteration:
+            final_response = None
+
+        for i, message in enumerate(messages):
+            if isinstance(message, AIMessage):
+                tool_executions = self._extract_tool_executions(message, messages, i)
+                if tool_executions:
+                    last_tool_execution = tool_executions
+
+        if last_tool_execution:
+            if (final_response): #log final response
+                print("Final Response:", final_response)
+                last_tool_execution["final_response"] = final_response
+
+            state["action_log"].append(last_tool_execution)
+
+        return state
+
+    def _extract_tool_executions(self, message, messages, index):
+        tool_calls = message.additional_kwargs.get("tool_calls", [])
+        
+        for tool_call in tool_calls:
+            tool_name = self._get_tool_name(tool_call)
+            tool_parameters = self._parse_tool_parameters(tool_call)
+            tool_result, tool_status = self._find_tool_result(messages, index, tool_call)
+
+            if tool_name:  #if we have a valid tool call
+                return {
+                    "action": "tool_execution",
+                    "tool": tool_name,
+                    "parameters": tool_parameters,
+                    "result": tool_result,
+                    "status": tool_status
+                }
+        return None
+
+    def _get_tool_name(self, tool_call):
+        """Extract tool name from tool call."""
+        function_info = tool_call.get("function", {})
+        return function_info.get("name") or tool_call.get("name")
+
+    def _parse_tool_parameters(self, tool_call):
+        args = tool_call.get("function", {}).get("arguments")
+        if isinstance(args, str):
+            try:
+                return json.loads(args)
+            except json.JSONDecodeError:
+                return {"raw_args": args}
+        return tool_call.get("args", {})
+
+    def _find_tool_result(self, messages, start_index, tool_call):
+        tool_result = None
+        tool_status = "pending"
+
+        # First look for a ToolMessage with matching tool_call_id
+        for msg in messages[start_index:]:
+            if isinstance(msg, ToolMessage) and msg.tool_call_id == tool_call.get("id"):
+                return msg.content, msg.status
+
+        # If no ToolMessage found, look for next AIMessage with content
+        for msg in messages[start_index:]:
+            if isinstance(msg, AIMessage) and msg.content:
+                return msg.content, tool_status
+
+        return tool_result, tool_status
+
+
diff --git a/src/langgraph/prompts.py b/src/langgraph/prompts.py
new file mode 100644
index 00000000..477faf09
--- /dev/null
+++ b/src/langgraph/prompts.py
@@ -0,0 +1,72 @@
+OBSERVATION_PROMPT = ("TASK:\nSummarize the provided context and list of previous tasks in a short list of key points. Your summary will be used to help decide what to do next, so include anything relevant to the next task selection.\n\n"
+                          "Guidelines:\n- Keep it concise and relevant\n- Avoid generic statements\n- Think critically about what information is useful for deciding which task should be performed next\n\n"
+                          "CONTEXT:\n{context}\n\n"
+                          "PREVIOUS TASKS:\n{task_log}\n\n"
+                          "SUMMARY:\n")
+
+DETERMINATION_PROMPT = """Based on the given context and available actions, generate a complex task that you desire to perform. Only respond with the task, and do not include any other text in your response. Your task can involve multiple chained actions. You do not need to provide any reasoning for the task. If you are given context, you may choose whether or not to use it. If you do not have context, you must still choose a task to perform.\n\n"
+                        "CONTEXT SUMMARY:\n{context_summary}"
+                        "\n\nAVAILABLE ACTIONS FOR EACH CONNECTION:\n\n" 
+                        "{connection_action_list}"
+                        "Example Outputs:\n
+                        "1. Create a funny tweet and post it on Twitter "
+                        "2. Search for tweets about a specific topic and reply to them"""
+                        
+DIVISION_PROMPT = """Based on the given task and available actions, generate an action plan for the agent. Only respond with the list of steps needed (with the corresponding actions mentioned), and put each step on a new line. Only include actions that involve making requests—do not include actions like editing dialog boxes, clicking buttons, or other UI interactions.
+                    
+                    RULES:
+                    - Do not combine multiple actions into one step
+                    - Do not escape to a new line for a single step, until the step is complete
+                    - Each step should represent a single action
+                    - Be explicit about which parameters are required for each action
+
+                    AVAILABLE ACTIONS FOR EACH CONNECTION:
+                    {connection_action_list}
+
+                    LLM Configuration: {preferred_llm_config} 
+                    Choose the appropriate LLM connection based on the configuration provided above if the task involves generating text.
+
+                    EXAMPLES:
+                    Example Task #1: Create a funny tweet and post it on Twitter
+
+                    Output: 
+                    1. Generate text using openai `generate-text` with parameters {{"prompt": "Create a funny tweet", "system_prompt": "You are a witty comedian"}}
+                    2. Post the generated text using `post-tweet` with parameters {{"message": "<output from step 1>"}}
+                    
+                    Example Task #2: swap 0.2 sol for zerebro
+
+                    Output: 
+                    1. Find the mint address of zerebro using the `get-token-by-ticker` action with parameters {{"ticker": "zerebro"}}
+                    2. Swap 0.2 sol for zerebro using the `trade` action with parameters {{"input_token": "SOL", "output_token": "<mint address from step 1>", "amount": "0.2"}}
+                    
+                    Example Task #3: Find the current price of Solana
+
+                    Output:
+                    1. Get the mint address of Solana using the `get-token-by-ticker` action with parameters {{"ticker": "SOL"}}
+                    2. Get the price of Solana using the `get-price` action with parameters {{"mint_address": "<mint address from step 1>"}}
+
+                    Notice how you should break down the task into smaller steps and you can use previous actions and their outputs to complete the task. Make sure to include all the necessary parameters for each action.
+                    TASK:
+                    {current_task}
+                    """
+
+EXECUTION_PROMPT =  ("Before executing the following action, consider the previous action log:\n\n"
+                    "ACTION LOG:\n{action_log}\n\n"
+                    "Here is your preferred configurations for LLM related actions:\n\n"
+                    "LLM Configuration:\n{preferred_llm_config}\n\n"
+                    "For the 'generate-text' action, extract only the system_prompt and model from the configuration above, ignoring the provider. When generating a tweet, ensure the output remains under 280 characters."
+                    """EXECUTION FORMAT:
+                        - Always structure tool calls as: tool_name({{"param1": "value1", "param2": "value2"}})
+                        - Every parameter must be enclosed in quotes and provided as a key-value pair
+                        - Each tool requires specific parameters as defined in its function signature
+                        - Always include all required parameters for the tool being called
+                        - Parameter names in the dictionary must exactly match the function parameter names"""
+                    "Refer to the 'final_response' field in the tool action log to quickly see the final response from the agent\n\n"
+                    "Important: Only execute the specified action—do not perform any additional actions beyond what is provided. For instance, if the action does not mention Twitter, do not invoke a Twitter-related tool. Follow the instructions precisely, step by step."
+                    "Now, execute this action based on the prior results: {action}")
+
+EVALUATION_PROMPT = ("Based on the action log, provide a summary of what the agent did based on the main task given. Only include the most important actions and the results of those actions. Do not include any actions that are irrelevant to the task or that did not produce a meaningful result.\n\n"
+                   "Task:\n{current_task}"
+                   "Action Log:\n" 
+                   "{action_log}"
+                   "\nExample Generated summary:\n Task: Make a funny tweet:\n1. Generated a witty joke using OpenAI : 'funny tweet generated'. 2. Posted 'funny tweet generated' joke on Twitter.")
diff --git a/src/legacy_agent.py b/src/legacy_agent.py
new file mode 100644
index 00000000..4745004d
--- /dev/null
+++ b/src/legacy_agent.py
@@ -0,0 +1,221 @@
+import random
+import time
+import logging
+import os
+from dotenv import load_dotenv
+from src.connection_manager import ConnectionManager
+from src.helpers import print_h_bar
+from src.action_handler import execute_action
+import src.actions.twitter_actions
+import src.actions.echochamber_actions
+import src.actions.solana_actions
+from datetime import datetime
+
+logger = logging.getLogger("agent")
+
+class LegacyZerePyAgent:
+    def __init__(self, agent_config: dict):
+        try:
+            # Load agent configuration
+            self._setup_agent_config(agent_config)
+
+            self.is_llm_set = False
+
+            # Set up empty agent state
+            self.context = {}
+
+        except Exception as e:
+            logger.error("Could not load ZerePy agent")
+            raise e
+
+    def _setup_agent_config(self, agent_config: dict):
+        try:
+            # GENERAL CONFIG
+            general_config = agent_config["config"]
+            self.name = general_config["name"]
+            self.loop_delay = general_config["loop_delay"]
+            self.time_based_multipliers = general_config.get("time_based_multipliers", None)
+
+            # CONNECTIONS
+            connections_config = agent_config["connections"]
+            self.connection_manager = ConnectionManager(connections_config)
+
+            # LLM CONFIG
+            llm_config = agent_config["llms"]
+            character_config = llm_config["character"]
+            self.character_name = character_config["name"]
+            self.bio = character_config.get("bio", [])
+            self.traits = character_config.get("traits", [])
+            self.examples = character_config.get("examples", [])
+            self.example_accounts = character_config.get("example_accounts", [])
+            self.model_provider = character_config["model_provider"]
+            self.model = character_config["model"]
+
+            # SYSTEM PROMPT
+            self._system_prompt = None
+            self._system_prompt = self._construct_system_prompt()
+
+            # TASK CONFIGS
+            self.tasks = agent_config.get("tasks", [])
+            self.task_weights = [task.get("weight", 0) for task in self.tasks]
+
+            # TWITTER CONFIG
+            # TODO: None of this does anything! These values are set and never used!
+            has_twitter_tasks = any("tweet" in task["name"] for task in self.tasks)
+            twitter_config = next((config for config in connections_config if config["name"] == "twitter"), None)
+
+            if has_twitter_tasks and twitter_config:
+                self.tweet_interval = twitter_config["config"].get("tweet_interval", 900)
+                self.own_tweet_replies_count = twitter_config["config"].get("own_tweet_replies_count", 2)
+                # Get the username from the .env file to avoid self reply
+                load_dotenv()
+                self.username = os.getenv('TWITTER_USERNAME', '').lower()
+                if not self.username:
+                    logger.warning("Twitter username not found, some Twitter functionalities may be limited")
+
+            # ECHOCAMBERS CONFIG
+            echochambers_config = next((config for config in connections_config if config["name"] == "echochambers"), None)
+            if echochambers_config:
+                self.echochambers_message_interval = echochambers_config["config"].get("message_interval", 60)
+                self.echochambers_history_count = echochambers_config["config"].get("history_read_count", 50)
+
+        except Exception as e:
+            logger.error("Could not load ZerePy agent")
+            raise e
+
+    def _setup_llm_provider(self):
+        # Get first available LLM provider and its model
+        llm_providers = self.connection_manager.get_model_providers()
+        if not llm_providers:
+            raise ValueError("No configured LLM provider found")
+        self.model_provider = llm_providers[0]
+
+    def _construct_system_prompt(self) -> str:
+        """Construct the system prompt from agent configuration"""
+        if self._system_prompt is None:
+            prompt_parts = []
+            prompt_parts.extend(self.bio)
+
+            if self.traits:
+                prompt_parts.append("\nYour key traits are:")
+                prompt_parts.extend(f"- {trait}" for trait in self.traits)
+
+            if self.examples or self.example_accounts:
+                prompt_parts.append("\nHere are some examples of your style (Please avoid repeating any of these):")
+                if self.examples:
+                    prompt_parts.extend(f"- {example}" for example in self.examples)
+
+                if self.example_accounts:
+                    for example_account in self.example_accounts:
+                        tweets = self.connection_manager.perform_action(
+                            connection_name="twitter",
+                            action_name="get-latest-tweets-from-user",
+                            params=[example_account]
+                        )
+                        if tweets:
+                            prompt_parts.extend(f"- {tweet['text']}" for tweet in tweets)
+
+            self._system_prompt = "\n".join(prompt_parts)
+
+        return self._system_prompt
+
+    def _adjust_weights_for_time(self, current_hour: int, task_weights: list) -> list:
+        weights = task_weights.copy()
+
+        # Reduce tweet frequency during night hours (1 AM - 5 AM)
+        if 1 <= current_hour <= 5:
+            weights = [
+                weight * self.time_based_multipliers.get("tweet_night_multiplier", 0.4) if task["name"] == "post-tweet"
+                else weight
+                for weight, task in zip(weights, self.tasks)
+            ]
+
+        # Increase engagement frequency during day hours (8 AM - 8 PM) (peak hours?🤔)
+        if 8 <= current_hour <= 20:
+            weights = [
+                weight * self.time_based_multipliers.get("engagement_day_multiplier", 1.5) if task["name"] in ("reply-to-tweet", "like-tweet")
+                else weight
+                for weight, task in zip(weights, self.tasks)
+            ]
+
+        return weights
+
+    def prompt_llm(self, prompt: str, system_prompt: str = None) -> str:
+        """Generate text using the configured LLM provider"""
+        system_prompt = system_prompt or self._construct_system_prompt()
+
+        return self.connection_manager.perform_action(
+            connection_name=self.model_provider,
+            action_name="generate-text",
+            params=[prompt, system_prompt]
+        )
+
+    def perform_action(self, connection: str, action: str, **kwargs) -> None:
+        return self.connection_manager.perform_action(connection, action, **kwargs)
+
+    def select_action(self) -> dict:
+        task_weights = [weight for weight in self.task_weights.copy()]
+
+        if self.time_based_multipliers:
+            current_hour = datetime.now().hour
+            task_weights = self._adjust_weights_for_time(current_hour, task_weights)
+
+        return random.choices(self.tasks, weights=task_weights, k=1)[0]
+
+    def loop(self):
+        """Main agent loop for autonomous behavior"""
+        if not self.is_llm_set:
+            self._setup_llm_provider()
+
+        logger.info("\n🚀 Starting agent loop...")
+        logger.info("Press Ctrl+C at any time to stop the loop.")
+        print_h_bar()
+
+        time.sleep(2)
+        logger.info("Starting loop in 5 seconds...")
+        for i in range(5, 0, -1):
+            logger.info(f"{i}...")
+            time.sleep(1)
+
+        try:
+            while True:
+                success = False
+                try:
+                    # REPLENISH INPUTS
+                    if "timeline_tweets" not in self.context or self.context["timeline_tweets"] is None or len(self.context["timeline_tweets"]) == 0:
+                        if any("tweet" in task["name"] for task in self.tasks):
+                            logger.info("\n👀 READING TIMELINE")
+                            self.context["timeline_tweets"] = self.connection_manager.perform_action(
+                                connection_name="twitter",
+                                action_name="read-timeline",
+                                params=[]
+                            )
+
+                    if "room_info" not in self.context or self.context["room_info"] is None:
+                        if any("echochambers" in task["name"] for task in self.tasks):
+                            logger.info("\n👀 READING ECHOCHAMBERS ROOM INFO")
+                            self.context["room_info"] = self.connection_manager.perform_action(
+                                connection_name="echochambers",
+                                action_name="get-room-info",
+                                params={}
+                            )
+
+                    # CHOOSE AN ACTION
+                    action = self.select_action()
+                    action_name = action["name"]
+
+                    # PERFORM ACTION
+                    success = execute_action(self, action_name)
+
+                    logger.info(f"\n⏳ Waiting {self.loop_delay} seconds before next loop...")
+                    print_h_bar()
+                    time.sleep(self.loop_delay if success else 60)
+
+                except Exception as e:
+                    logger.error(f"\n❌ Error in agent loop iteration: {e}")
+                    logger.info(f"⏳ Waiting {self.loop_delay} seconds before retrying...")
+                    time.sleep(self.loop_delay)
+
+        except KeyboardInterrupt:
+            logger.info("\n🛑 Agent loop stopped by user.")
+            return
\ No newline at end of file
diff --git a/src/migration_script.py b/src/migration_script.py
new file mode 100644
index 00000000..3eaaccd6
--- /dev/null
+++ b/src/migration_script.py
@@ -0,0 +1,53 @@
+import json
+
+#Easily convert the old JSON format to the new agent configuration format
+
+def migrate_old_to_new(old_json, conversion_type="legacy"):
+    new_json = {
+        "config": {
+            "name": old_json["name"],
+            "type": conversion_type,
+            "loop_delay": old_json["loop_delay"],
+        },
+        "llms": {
+            "character": {
+                "name": old_json["name"],
+                "bio": old_json["bio"],
+                "traits": old_json["traits"],
+                "examples": old_json["examples"],
+                "example_accounts": old_json["example_accounts"],
+                "model_provider": "openai",
+                "model": "gpt-3.5-turbo",
+            }
+        },
+        "connections": [],
+    }
+
+    if conversion_type == "autonomous":
+        new_json["llms"]["executor"] = {
+            "model_provider": "openai",  # openai or anthropic for langchain
+            "model": "gpt-4o-mini",
+        }
+    else:
+        new_json["config"]["time_based_multipliers"] = old_json["time_based_multipliers"]
+        new_json["tasks"] = old_json.get("tasks", [])
+
+    new_json["connections"] = [
+        {"name": item["name"], "config": {k: v for k, v in item.items() if k != "name"}}
+        for item in old_json["config"]
+    ]
+
+    return new_json
+
+def migrate_config(agent_file_name,agent_type):
+    with open("agents/{}.json".format(agent_file_name), "r") as f:
+        old_data = json.load(f)
+
+    new_data = migrate_old_to_new(old_data, agent_type)
+
+    # Save new JSON to a file
+    new_file_name = "agents/{}-migrated.json".format(agent_file_name)
+    with open(new_file_name, "w") as f:
+        json.dump(new_data, f, indent=2)
+
+    print("Migration complete. New agent configuration of type {} saved to {}".format(agent_type,new_file_name))