Merge a78157434e into 4641c03340

pyproject.toml

@@ -13,6 +13,7 @@ dependencies = [
     "backtrader>=1.9.78.123",
     "langchain-anthropic>=0.3.15",
     "langchain-experimental>=0.3.4",
+    "langchain-aws>=0.2.0",
     "langchain-google-genai>=2.1.5",
     "langchain-openai>=0.3.23",
     "langgraph>=0.4.8",

tradingagents/llm_clients/__init__.py

@@ -1,4 +1,5 @@
 from .base_client import BaseLLMClient
 from .factory import create_llm_client
+from .bedrock_client import BedrockClient
 
-__all__ = ["BaseLLMClient", "create_llm_client"]
+__all__ = ["BaseLLMClient", "create_llm_client", "BedrockClient"]

tradingagents/llm_clients/bedrock_client.py

@@ -0,0 +1,56 @@
+from typing import Any, Optional
+
+from langchain_aws import ChatBedrockConverse
+
+from .base_client import BaseLLMClient
+
+
+class BedrockClient(BaseLLMClient):
+    """Client for Amazon Bedrock models.
+
+    Supports any model available on Bedrock via IAM Role (no API key needed),
+    including Claude, Amazon Nova, Kimi, Qwen, GLM, DeepSeek, MiniMax, and more.
+
+    Authentication:
+        Uses boto3 default credential chain: IAM Role (EC2/Lambda), environment
+        variables (AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY), or ~/.aws/credentials.
+
+    Model ID formats:
+        - Cross-region inference profile (recommended):
+            ``global.anthropic.claude-sonnet-4-6``
+            ``eu.anthropic.claude-3-5-sonnet-20240620-v1:0``
+        - Direct on-demand (us-east-1 default region only):
+            ``amazon.nova-lite-v1:0``
+            ``moonshotai.kimi-k2.5``
+            ``qwen.qwen3-32b-v1:0``
+            ``zai.glm-4.7-flash``
+            ``deepseek.v3.2``
+
+    Note:
+        When specifying a non-default ``region_name``, use region-specific
+        inference profile IDs (e.g. ``us-west-2.anthropic.claude-...``),
+        as direct model IDs only support on-demand throughput in us-east-1.
+
+    Example::
+
+        config["llm_provider"]    = "bedrock"
+        config["deep_think_llm"]  = "global.anthropic.claude-sonnet-4-6"
+        config["quick_think_llm"] = "amazon.nova-micro-v1:0"
+    """
+
+    def __init__(self, model: str, base_url: Optional[str] = None, **kwargs):
+        super().__init__(model, base_url, **kwargs)
+
+    def get_llm(self) -> Any:
+        """Return configured ChatBedrockConverse instance."""
+        llm_kwargs: dict = {"model_id": self.model}
+
+        for key in ("region_name", "max_tokens", "callbacks", "timeout"):
+            if key in self.kwargs:
+                llm_kwargs[key] = self.kwargs[key]
+
+        return ChatBedrockConverse(**llm_kwargs)
+
+    def validate_model(self) -> bool:
+        """Bedrock model IDs are dynamic; skip static validation."""
+        return True

tradingagents/llm_clients/factory.py

@@ -4,6 +4,7 @@ from .base_client import BaseLLMClient
 from .openai_client import OpenAIClient
 from .anthropic_client import AnthropicClient
 from .google_client import GoogleClient
+from .bedrock_client import BedrockClient
 
 
 def create_llm_client(
@@ -46,4 +47,7 @@ def create_llm_client(
     if provider_lower == "google":
         return GoogleClient(model, base_url, **kwargs)
 
+    if provider_lower == "bedrock":
+        return BedrockClient(model, base_url, **kwargs)
+
     raise ValueError(f"Unsupported LLM provider: {provider}")