Merge branch 'main' into kyle/structured-output

Signed-off-by: Kyle Qian <kylexqian@gmail.com>

Author: kylexqian

README.md

@@ -24,7 +24,7 @@ OpenGradient enables developers to build AI applications with verifiable executi
 pip install opengradient
 ```
 
-**Note**: > **Windows users:** See the [Windows Installation Guide](./WINDOWS_INSTALL.md) for step-by-step setup instructions.
+> **Note for Windows users:** See the [Windows Installation Guide](./WINDOWS_INSTALL.md) for step-by-step setup instructions.
 
 ### Claude Code Integration
 

examples/llm_multi_turn_conversation.py

@@ -0,0 +1,119 @@
+"""
+Multi-turn conversation example with OpenGradient TEE-verified LLM.
+
+Demonstrates how to maintain conversation history across multiple turns,
+enabling context-aware responses with full cryptographic verification
+of every inference step.
+
+Usage:
+    export OG_PRIVATE_KEY="your_private_key"
+    python examples/llm_multi_turn_conversation.py
+"""
+
+import asyncio
+import os
+
+import opengradient as og
+
+
+def add_user_message(history: list, content: str) -> list:
+    """Append a user message to the conversation history."""
+    return history + [{"role": "user", "content": content}]
+
+
+def add_assistant_message(history: list, content: str) -> list:
+    """Append an assistant message to the conversation history."""
+    return history + [{"role": "assistant", "content": content}]
+
+
+async def chat_turn(
+    llm: og.LLM,
+    history: list,
+    user_input: str,
+    model: og.TEE_LLM = og.TEE_LLM.GEMINI_2_5_FLASH,
+) -> tuple[str, list, str]:
+    """
+    Execute a single conversation turn.
+
+    Args:
+        llm: Initialized LLM client.
+        history: Current conversation history.
+        user_input: The user's message for this turn.
+        model: TEE_LLM model to use.
+
+    Returns:
+        Tuple of (assistant_reply, updated_history, transaction_hash).
+    """
+    history = add_user_message(history, user_input)
+
+    result = await llm.chat(
+        model=model,
+        messages=history,
+        max_tokens=500,
+        temperature=0.7,
+    )
+    assert isinstance(result, og.TextGenerationOutput)
+
+    assert result.chat_output is not None
+    reply = str(result.chat_output["content"])
+    history = add_assistant_message(history, reply)
+
+    return reply, history, result.transaction_hash
+
+
+async def main():
+    private_key = os.environ.get("OG_PRIVATE_KEY")
+    if not private_key:
+        raise ValueError("OG_PRIVATE_KEY environment variable is not set.")
+
+    llm = og.LLM(private_key=private_key)
+    llm.ensure_opg_approval(min_allowance=0.5)
+
+    model = og.TEE_LLM.GEMINI_2_5_FLASH
+    print(f"Model : {model.value}")
+    print(f"Mode  : Multi-turn conversation with TEE verification")
+    print("=" * 60)
+
+    # System prompt sets the assistant persona for the whole conversation
+    history = [
+        {
+            "role": "system",
+            "content": ("You are a concise Python tutor. Give short, clear answers with code examples when helpful."),
+        }
+    ]
+
+    # --- Turn 1 ---
+    question_1 = "What is a Python decorator?"
+    print(f"\nUser : {question_1}")
+
+    reply_1, history, tx_1 = await chat_turn(llm, history, question_1, model)
+    print(f"Assistant : {reply_1}")
+    print(f"[tx: {tx_1}]")
+
+    # --- Turn 2 — follow-up referencing Turn 1 ---
+    question_2 = "Can you show me a real-world example of one?"
+    print(f"\nUser : {question_2}")
+
+    reply_2, history, tx_2 = await chat_turn(llm, history, question_2, model)
+    print(f"Assistant : {reply_2}")
+    print(f"[tx: {tx_2}]")
+
+    # --- Turn 3 — deeper follow-up ---
+    question_3 = "How would I stack two decorators on the same function?"
+    print(f"\nUser : {question_3}")
+
+    reply_3, history, tx_3 = await chat_turn(llm, history, question_3, model)
+    print(f"Assistant : {reply_3}")
+    print(f"[tx: {tx_3}]")
+
+    # Summary
+    print("\n" + "=" * 60)
+    print(f"Total turns     : {len([m for m in history if m['role'] == 'user'])}")
+    print(f"Total messages  : {len(history)}")
+    print("Transaction hashes (verifiable on-chain):")
+    for i, tx in enumerate([tx_1, tx_2, tx_3], 1):
+        print(f"  Turn {i}: {tx}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "opengradient"
-version = "0.9.5"
+version = "0.9.6"
 description = "Python SDK for OpenGradient decentralized model management & inference services"
 authors = [{name = "OpenGradient", email = "adam@vannalabs.ai"}]
 readme = "README.md"
@@ -76,6 +76,9 @@ target-version = "py310"
 select = ["E", "F", "I", "N"]
 ignore = []
 
+[tool.uv]
+exclude-newer = "7d"
+
 [tool.ruff.mccabe]
 max-complexity = 10
 

src/opengradient/agents/__init__.py

@@ -6,15 +6,24 @@
 into existing applications and agent frameworks.
 """
 
+from typing import Optional, Union
+
+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: Optional[str] = None,
+    model_cid: Optional[Union[TEE_LLM, str]] = None,
+    model: Optional[Union[TEE_LLM, str]] = None,
     max_tokens: int = 300,
+    temperature: float = 0.0,
     x402_settlement_mode: x402SettlementMode = x402SettlementMode.BATCH_HASHED,
+    client: Optional[LLM] = None,
+    rpc_url: Optional[str] = None,
+    tee_registry_address: Optional[str] = None,
+    llm_server_url: Optional[str] = None,
 ) -> OpenGradientChatModel:
     """
     Returns an OpenGradient LLM that implements LangChain's LLM interface
@@ -23,8 +32,14 @@ def langchain_adapter(
     return OpenGradientChatModel(
         private_key=private_key,
         model_cid=model_cid,
+        model=model,
         max_tokens=max_tokens,
+        temperature=temperature,
         x402_settlement_mode=x402_settlement_mode,
+        client=client,
+        rpc_url=rpc_url,
+        tee_registry_address=tee_registry_address,
+        llm_server_url=llm_server_url,
     )