Langchain adapter updates

pyproject.toml

@@ -27,7 +27,7 @@ dependencies = [
     "langchain>=0.3.7",
     "openai>=1.58.1",
     "pydantic>=2.9.2",
-    "og-test-v2-x402==0.0.11"
+    "og-test-v2-x402==0.0.12.dev3"
 ]
 
 [project.scripts]

requirements.txt

@@ -7,4 +7,4 @@ requests>=2.32.3
 langchain>=0.3.7
 openai>=1.58.1
 pydantic>=2.9.2
-og-test-v2-x402==0.0.11
+og-test-v2-x402==0.0.12.dev3

src/opengradient/agents/__init__.py

@@ -6,25 +6,37 @@
 into existing applications and agent frameworks.
 """
 
+from ..client.llm import LLM
 from ..types import TEE_LLM, x402SettlementMode
 from .og_langchain import *
 
 
 def langchain_adapter(
-    private_key: str,
-    model_cid: TEE_LLM,
+    private_key: str | None = None,
+    model_cid: TEE_LLM | str | None = None,
+    model: TEE_LLM | str | None = None,
     max_tokens: int = 300,
+    temperature: float = 0.0,
     x402_settlement_mode: x402SettlementMode = x402SettlementMode.BATCH_HASHED,
+    client: LLM | None = None,
+    rpc_url: str | None = None,
+    tee_registry_address: str | None = None,
+    llm_server_url: str | None = None,
 ) -> OpenGradientChatModel:
     """
     Returns an OpenGradient LLM that implements LangChain's LLM interface
     and can be plugged into LangChain agents.
     """
     return OpenGradientChatModel(
         private_key=private_key,
-        model_cid=model_cid,
+        client=client,
+        model_cid=model_cid or model,
         max_tokens=max_tokens,
+        temperature=temperature,
         x402_settlement_mode=x402_settlement_mode,
+        rpc_url=rpc_url,
+        tee_registry_address=tee_registry_address,
+        llm_server_url=llm_server_url,
     )