langroid · pchalasani · Mar 3, 2025 · Mar 3, 2025 · Mar 7, 2025 · Mar 9, 2025
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -3,4 +3,4 @@ repos:
   # Ruff version.
   rev: v0.11.4
   hooks:
-    - id: ruff
+    - id: ruff
diff --git a/langroid/agent/base.py b/langroid/agent/base.py
@@ -35,6 +35,7 @@
 from langroid.agent.xml_tool_message import XMLToolMessage
 from langroid.exceptions import XMLException
 from langroid.language_models.base import (
+    AnthropicToolCall,
     LanguageModel,
     LLMConfig,
     LLMFunctionCall,
@@ -139,6 +140,10 @@ class Agent(ABC):
     # Index of ALL tool calls generated by the agent
     oai_tool_id2call: Dict[str, OpenAIToolCall] = {}
 
+    # anthropic tool calls
+    ant_tool_calls: List[AnthropicToolCall] = []
+    ant_tool_id2call: Dict[str, AnthropicToolCall] = {}
+
     def __init__(self, config: AgentConfig = AgentConfig()):
         self.config = config
         self.lock = asyncio.Lock()  # for async access to update self.llm.usage_cost
@@ -1168,6 +1173,7 @@ def get_tool_messages(
             msg.content != ""
             and msg.oai_tool_calls is None
             and msg.function_call is None
+            and msg.ant_tool_calls is None
         ):
 
             tools = self.get_formatted_tool_messages(
@@ -1184,7 +1190,7 @@ def get_tool_messages(
                 return my_tools
 
         # otherwise, we look for `tool_calls` (possibly multiple)
-        tools = self.get_oai_tool_calls_classes(msg)
+        tools = self.get_tool_calls_classes(msg)
         msg.all_tool_messages = tools
         my_tools = [t for t in tools if self._tool_recipient_match(t)]
         msg.tool_messages = my_tools
@@ -1270,17 +1276,22 @@ def get_function_call_class(self, msg: ChatDocument) -> Optional[ToolMessage]:
         tool = tool_class.parse_obj(tool_msg)
         return tool
 
-    def get_oai_tool_calls_classes(self, msg: ChatDocument) -> List[ToolMessage]:
+    def get_tool_calls_classes(self, msg: ChatDocument) -> List[ToolMessage]:
         """
         From ChatDocument (constructed from an LLM Response), get
          a list of ToolMessages corresponding to the `tool_calls`, if any.
         """
 
-        if msg.oai_tool_calls is None:
+        if msg.oai_tool_calls is None and msg.ant_tool_calls is None:
             return []
         tools = []
         all_errors = True
-        for tc in msg.oai_tool_calls:
+
+        tools_to_iterate = (
+            msg.oai_tool_calls if msg.oai_tool_calls else msg.ant_tool_calls
+        ) or []
-        tools_to_iterate = (
-            msg.oai_tool_calls if msg.oai_tool_calls else msg.ant_tool_calls
-        ) or []
+        tools_to_iterate = []
+        if msg.oai_tool_calls:
+            tools_to_iterate.extend(msg.oai_tool_calls)
+        if msg.ant_tool_calls:
+            tools_to_iterate.extend(msg.ant_tool_calls)
-        tools_to_iterate = (
-            msg.oai_tool_calls if msg.oai_tool_calls else msg.ant_tool_calls
-        ) or []
+        tools_to_iterate = []
+        if msg.oai_tool_calls:
+            tools_to_iterate.extend(msg.oai_tool_calls)
+        if msg.ant_tool_calls:
+            tools_to_iterate.extend(msg.ant_tool_calls)
+
+        for tc in tools_to_iterate:
             if tc.function is None:
                 continue
             tool_name = tc.function.name

diff --git a/langroid/agent/chat_agent.py b/langroid/agent/chat_agent.py
@@ -557,6 +557,16 @@ def delete_last_message(self, role: str = Role.USER) -> None:
                 self.message_history.pop(i)
                 break
 
+    def _common_system_and_tools_message(self) -> str:
+        content = self.system_message
+        if self.system_tool_instructions != "":
+            content += "\n\n" + self.system_tool_instructions
+        if self.system_tool_format_instructions != "":
+            content += "\n\n" + self.system_tool_format_instructions
+        if self.output_format_instructions != "":
+            content += "\n\n" + self.output_format_instructions
+        return content
+
     def _create_system_and_tools_message(self) -> LLMMessage:
         """
         (Re-)Create the system message for the LLM of the agent,
@@ -572,17 +582,27 @@ def _create_system_and_tools_message(self) -> LLMMessage:
         Returns:
             LLMMessage object
         """
-        content = self.system_message
-        if self.system_tool_instructions != "":
-            content += "\n\n" + self.system_tool_instructions
-        if self.system_tool_format_instructions != "":
-            content += "\n\n" + self.system_tool_format_instructions
-        if self.output_format_instructions != "":
-            content += "\n\n" + self.output_format_instructions
+        content = self._common_system_and_tools_message()
 
         # remove leading and trailing newlines and other whitespace
         return LLMMessage(role=Role.SYSTEM, content=content.strip())
 
+    def _update_anthropic_configuration(self) -> LLMMessage:
+        """
+        For Anthropic's LLMs, the prescribed method for passing
+        instructions is to give a description using the User role.
+        The system configuration describes background on what context
+        the system is intended to respond in.
+        """
+        assert self.llm and self.llm.config.type == "anthropic"
+        content = self._common_system_and_tools_message()
+        return LLMMessage(
+            role=Role.USER,
+            content=(
+                content.strip() if content else "Please respond in a succinct manner."
+            ),
+        )
+
     def handle_message_fallback(self, msg: str | ChatDocument) -> Any:
         """
         Fallback method for the "no-tools" scenario.
@@ -1279,6 +1299,9 @@ def llm_response(
             and self.output_format is None
             and self._json_schema_available()
             and self.config.strict_recovery
+            or self.tool_error
+            and self.output_format is None
+            and self.config.strict_recovery
         ):
             self.tool_error = False
             AnyTool = self._get_any_tool_message()
@@ -1443,7 +1466,12 @@ def init_message_history(self) -> None:
         """
         Initialize the message history with the system message and user message
         """
-        self.message_history = [self._create_system_and_tools_message()]
+        assert self.llm
+        if self.llm.config.type == "anthropic":
+            self.message_history = [self._update_anthropic_configuration()]
+        else:
+            self.message_history = [self._create_system_and_tools_message()]
+
         if self.user_message:
             self.message_history.append(
                 LLMMessage(role=Role.USER, content=self.user_message)
@@ -1493,6 +1521,9 @@ def _prep_llm_messages(
                 [/grey37]
                 """
                 )
+        elif self.llm.config.type == "anthropic":
+            assert self.message_history[0].role == Role.USER
+            self.message_history[0] = self._update_anthropic_configuration()
         else:
             assert self.message_history[0].role == Role.SYSTEM
             # update the system message with the latest tool instructions
@@ -1501,11 +1532,21 @@ def _prep_llm_messages(
         if message is not None:
             if (
                 isinstance(message, str)
-                or message.id() != self.message_history[-1].chat_document_id
+                or self.message_history
+                and message.id() != self.message_history[-1].chat_document_id
+                or isinstance(message, ChatDocument)
+                and self.llm.config.type == "anthropic"
             ):
                 # either the message is a str, or it is a fresh ChatDocument
                 # different from the last message in the history
-                llm_msgs = ChatDocument.to_LLMMessage(message, self.oai_tool_calls)
+                if self.llm.config.type == "anthropic":
+                    llm_msgs = ChatDocument.to_LLMMessage(
+                        message, anthropic_tools=self.ant_tool_calls
+                    )
+                else:
+                    llm_msgs = ChatDocument.to_LLMMessage(
+                        message, oai_tools=self.oai_tool_calls
+                    )
                 # LLM only responds to the content, so only those msgs with
                 # non-empty content should be kept
                 llm_msgs = [m for m in llm_msgs if m.content.strip() != ""]
@@ -1516,6 +1557,9 @@ def _prep_llm_messages(
                 self.oai_tool_calls = [
                     t for t in self.oai_tool_calls if t.id not in done_tools
                 ]
+                self.ant_tool_calls = [
+                    t for t in self.ant_tool_calls if t.id not in done_tools
+                ]
                 self.message_history.extend(llm_msgs)
 
         hist = self.message_history
@@ -1756,6 +1800,8 @@ def llm_response_messages(
         self.oai_tool_id2call.update(
             {t.id: t for t in self.oai_tool_calls if t.id is not None}
         )
+        self.ant_tool_calls = response.ant_tool_calls or []
+        self.ant_tool_id2call.update({t.id: t for t in self.ant_tool_calls if t.id})
 
         # If using strict output format, parse the output JSON
         self._load_output_format(chat_doc)

diff --git a/langroid/agent/chat_document.py b/langroid/agent/chat_document.py
@@ -9,6 +9,7 @@
 from langroid.agent.tool_message import ToolMessage
 from langroid.agent.xml_tool_message import XMLToolMessage
 from langroid.language_models.base import (
+    AnthropicToolCall,
     LLMFunctionCall,
     LLMMessage,
     LLMResponse,
@@ -123,6 +124,8 @@ class ChatDocument(Document):
     oai_tool_id2result: Optional[OrderedDict[str, str]] = None
     oai_tool_choice: ToolChoiceTypes | Dict[str, Dict[str, str] | str] = "auto"
     function_call: Optional[LLMFunctionCall] = None
+    ant_tool_calls: Optional[List[AnthropicToolCall]] = None
+    ant_tool_id2result: Optional[OrderedDict[str, str]] = None
     # tools that are explicitly added by agent response/handler,
     # or tools recognized in the ChatDocument as handle-able tools
     tool_messages: List[ToolMessage] = []
@@ -296,12 +299,16 @@ def from_LLMResponse(
             # there must be at least one if it's not None
             for oai_tc in response.oai_tool_calls:
                 ChatDocument._clean_fn_call(oai_tc.function)
+        if response.ant_tool_calls:
+            for ant_tc in response.ant_tool_calls:
+                ChatDocument._clean_fn_call(ant_tc.function)
         return ChatDocument(
             content=message,
             reasoning=response.reasoning,
             content_any=message,
             oai_tool_calls=response.oai_tool_calls,
             function_call=response.function_call,
+            ant_tool_calls=response.ant_tool_calls,
             metadata=ChatDocMetaData(
                 source=Entity.LLM,
                 sender=Entity.LLM,
@@ -334,6 +341,7 @@ def from_str(msg: str) -> "ChatDocument":
     def to_LLMMessage(
         message: Union[str, "ChatDocument"],
         oai_tools: Optional[List[OpenAIToolCall]] = None,
+        anthropic_tools: Optional[List[AnthropicToolCall]] = None,
     ) -> List[LLMMessage]:
         """
         Convert to list of LLMMessage, to incorporate into msg-history sent to LLM API.
@@ -352,12 +360,14 @@ def to_LLMMessage(
         sender_role = Role.USER
         fun_call = None
         oai_tool_calls = None
+        ant_tool_calls = None
         tool_id = ""  # for OpenAI Assistant
         chat_document_id: str = ""
         if isinstance(message, ChatDocument):
             content = message.content or to_string(message.content_any) or ""
             fun_call = message.function_call
             oai_tool_calls = message.oai_tool_calls
+            ant_tool_calls = message.ant_tool_calls
             if message.metadata.sender == Entity.USER and fun_call is not None:
                 # This may happen when a (parent agent's) LLM generates a
                 # a Function-call, and it ends up being sent to the current task's
@@ -372,6 +382,9 @@ def to_LLMMessage(
                 # same reasoning as for function-call above
                 content += " " + "\n\n".join(str(tc) for tc in oai_tool_calls)
                 oai_tool_calls = None
+            if message.metadata.sender == Entity.USER and ant_tool_calls is not None:
+                content += " " + "\n\n".join(str(tc) for tc in ant_tool_calls)
+                ant_tool_calls = None
             sender_name = message.metadata.sender_name
             tool_ids = message.metadata.tool_ids
             tool_id = tool_ids[-1] if len(tool_ids) > 0 else ""
@@ -445,7 +458,7 @@ def to_LLMMessage(
                 tool_id=tool_id,  # for OpenAI Assistant
                 content=content,
                 function_call=fun_call,
-                tool_calls=oai_tool_calls,
+                tool_calls=oai_tool_calls if oai_tool_calls else ant_tool_calls,
                 name=sender_name,
                 chat_document_id=chat_document_id,
             )

diff --git a/langroid/language_models/__init__.py b/langroid/language_models/__init__.py
@@ -4,6 +4,7 @@
 from . import openai_gpt
 from . import azure_openai
 from . import prompt_formatter
+from . import anthropic
 
 from .base import (
     StreamEventType,
@@ -24,6 +25,7 @@
 from .openai_gpt import OpenAIGPTConfig, OpenAIGPT, OpenAICallParams
 from .mock_lm import MockLM, MockLMConfig
 from .azure_openai import AzureConfig, AzureGPT
+from .anthropic import AnthropicLLMConfig, AnthropicCallParams, AnthropicLLM
 
 
 __all__ = [
@@ -53,4 +55,8 @@
     "AzureGPT",
     "MockLM",
     "MockLMConfig",
+    "anthropic",
+    "AnthropicLLMConfig",
+    "AnthropicCallParams",
+    "AnthropicLLM",
 ]