feat: Support Anthropic for Langgraph

docs/pyagentspec/source/_components/llm_config_tabs.rst

@@ -60,3 +60,14 @@
                 name="Ollama Config",
                 model_id="model-id",
             )
+
+    .. tab:: Anthropic
+
+        .. code-block:: python
+
+            from pyagentspec.llms import AnthropicLlmConfig
+
+            llm_config = AnthropicLlmConfig(
+                name="Anthropic Config",
+                model_id="claude-haiku-4-5-20251001",
+            )

docs/pyagentspec/source/agentspec/json_spec/agentspec_json_spec_26_2_0.json

@@ -82,6 +82,16 @@
         }
       ]
     },
+    "AnthropicLlmConfig": {
+      "anyOf": [
+        {
+          "$ref": "#/$defs/BaseAnthropicLlmConfig"
+        },
+        {
+          "$ref": "#/$defs/ComponentReference"
+        }
+      ]
+    },
     "ApiNode": {
       "anyOf": [
         {
@@ -1385,6 +1395,98 @@
       ],
       "x-abstract-component": true
     },
+    "BaseAnthropicLlmConfig": {
+      "additionalProperties": false,
+      "description": "Class to configure a connection to an Anthropic Claude model.\n\nRequires to specify the model identity. The API key and endpoint are provided by the runtime environment.",
+      "properties": {
+        "id": {
+          "title": "Id",
+          "type": "string"
+        },
+        "name": {
+          "title": "Name",
+          "type": "string"
+        },
+        "description": {
+          "anyOf": [
+            {
+              "type": "string"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "default": null,
+          "title": "Description"
+        },
+        "metadata": {
+          "anyOf": [
+            {
+              "additionalProperties": true,
+              "type": "object"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "title": "Metadata"
+        },
+        "default_generation_parameters": {
+          "anyOf": [
+            {
+              "type": "null"
+            },
+            {
+              "$ref": "#/$defs/LlmGenerationConfig"
+            }
+          ],
+          "default": null
+        },
+        "model_id": {
+          "title": "Model Id",
+          "type": "string"
+        },
+        "url": {
+          "anyOf": [
+            {
+              "type": "string"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "default": null,
+          "title": "Url"
+        },
+        "api_key": {
+          "anyOf": [
+            {
+              "format": "password",
+              "type": "string",
+              "writeOnly": true
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "default": null,
+          "title": "Api Key"
+        },
+        "$referenced_components": {
+          "$ref": "#/$defs/ReferencedComponents"
+        },
+        "component_type": {
+          "const": "AnthropicLlmConfig"
+        }
+      },
+      "required": [
+        "model_id",
+        "name"
+      ],
+      "title": "AnthropicLlmConfig",
+      "type": "object",
+      "x-abstract-component": false
+    },
     "BaseApiNode": {
       "additionalProperties": false,
       "description": "Make an API call.\n\nThis node is intended to be a part of a Flow.\n\n- **Inputs**\n    Inferred from the json spec retrieved from API Spec URI, if available and reachable.\n    Otherwise, users have to manually specify them.\n- **Outputs**\n    Inferred from the json spec retrieved from API Spec URI, if available and reachable.\n    Otherwise, users should manually specify them.\n\n    If None is given, ``pyagentspec`` infers a generic property of any type named ``response``.\n- **Branches**\n    One, the default next.\n\n\nExamples\n--------\n>>> from pyagentspec.flows.nodes import ApiNode\n>>> from pyagentspec.property import Property\n>>> weather_result_property = Property(\n...     json_schema={\n...         \"title\": \"zurich_weather\",\n...         \"type\": \"object\",\n...         \"properties\": {\n...             \"temperature\": {\n...                 \"type\": \"number\",\n...                 \"description\": \"Temperature in celsius degrees\",\n...             },\n...             \"weather\": {\"type\": \"string\"}\n...         },\n...     }\n... )\n>>> call_current_weather_step = ApiNode(\n...     name=\"Weather API call node\",\n...     url=\"https://example.com/weather\",\n...     http_method = \"GET\",\n...     query_params={\n...         \"location\": \"zurich\",\n...     },\n...     outputs=[weather_result_property]\n... )\n>>>\n>>> item_id_property = Property(\n...     json_schema={\"title\": \"item_id\", \"type\": \"string\"}\n... )\n>>> order_id_property = Property(\n...     json_schema={\"title\": \"order_id\", \"type\": \"string\"}\n... )\n>>> store_id_property = Property(\n...     json_schema={\"title\": \"store_id\", \"type\": \"string\"}\n... )\n>>> session_id_property = Property(\n...     json_schema={\"title\": \"session_id\", \"type\": \"string\"}\n... )\n>>> create_order_step = ApiNode(\n...     name=\"Orders api call node\",\n...     url=\"https://example.com/orders/{{ order_id }}\",\n...     http_method=\"POST\",\n...     # sending an object which will automatically be transformed into JSON\n...     data={\n...         # define a static body parameter\n...         \"topic_id\": 12345,\n...         # define a templated body parameter.\n...         # The value for {{ item_id }} will be taken from the IO system at runtime\n...         \"item_id\": \"{{ item_id }}\",\n...     },\n...     query_params={\n...         # provide one templated query parameter called \"store_id\"\n...         # which will take its value from the IO system from key \"store_id\"\n...         \"store_id\": \"{{ store_id }}\",\n...     },\n...     headers={\n...         # set header session_id. the value is coming from the IO system\n...         \"session_id\": \"{{ session_id }}\",\n...     },\n...     inputs=[item_id_property, order_id_property, store_id_property, session_id_property],\n... )",
@@ -2779,6 +2881,9 @@
     },
     "BaseLlmConfig": {
       "anyOf": [
+        {
+          "$ref": "#/$defs/AnthropicLlmConfig"
+        },
         {
           "$ref": "#/$defs/OciGenAiConfig"
         },
@@ -6259,6 +6364,9 @@
           {
             "$ref": "#/$defs/BaseAgenticComponent"
           },
+          {
+            "$ref": "#/$defs/BaseAnthropicLlmConfig"
+          },
           {
             "$ref": "#/$defs/BaseApiNode"
           },
@@ -6580,6 +6688,21 @@
         }
       }
     },
+    "VersionedAnthropicLlmConfig": {
+      "anyOf": [
+        {
+          "$ref": "#/$defs/BaseAnthropicLlmConfig"
+        },
+        {
+          "$ref": "#/$defs/ComponentReference"
+        }
+      ],
+      "properties": {
+        "agentspec_version": {
+          "$ref": "#/$defs/AgentSpecVersionEnum"
+        }
+      }
+    },
     "VersionedApiNode": {
       "anyOf": [
         {
@@ -7504,6 +7627,9 @@
     {
       "$ref": "#/$defs/VersionedAgenticComponent"
     },
+    {
+      "$ref": "#/$defs/VersionedAnthropicLlmConfig"
+    },
     {
       "$ref": "#/$defs/VersionedApiNode"
     },

docs/pyagentspec/source/api/llmmodels.rst

@@ -66,6 +66,13 @@ OpenAI Models
 .. autoclass:: pyagentspec.llms.openaiconfig.OpenAiConfig
     :exclude-members: model_post_init, model_config
 
+Anthropic Models
+^^^^^^^^^^^^^^^^
+
+.. _anthropicllmconfig:
+.. autoclass:: pyagentspec.llms.anthropicconfig.AnthropicLlmConfig
+    :exclude-members: model_post_init, model_config
+
 OciGenAi Models
 ^^^^^^^^^^^^^^^
 

docs/pyagentspec/source/code_examples/howto_llm_from_different_providers.py

@@ -82,6 +82,20 @@
 )
 # .. openai-end
 
+# .. anthropic-start
+from pyagentspec.llms import AnthropicLlmConfig
+
+generation_config = LlmGenerationConfig(max_tokens=256, temperature=0.7, top_p=0.95)
+
+llm = AnthropicLlmConfig(
+    name="anthropic-claude-haiku",
+    model_id="claude-haiku-4-5-20251001",
+    url="https://api.anthropic.com",
+    api_key="optional_api_key",
+    default_generation_parameters=generation_config,
+)
+# .. anthropic-end
+
 # .. ollama-start
 from pyagentspec.llms import OllamaConfig
 
@@ -148,6 +162,17 @@
     default_generation_parameters=generation_config,
 )
 
+from pyagentspec.llms import AnthropicLlmConfig
+
+generation_config = LlmGenerationConfig(max_tokens=256, temperature=0.7, top_p=0.95)
+
+llm = AnthropicLlmConfig(
+    name="anthropic-claude-haiku",
+    model_id="claude-haiku-4-5-20251001",
+    url="https://api.anthropic.com",
+    default_generation_parameters=generation_config,
+)
+
 from pyagentspec.llms import OllamaConfig
 
 generation_config = LlmGenerationConfig(max_tokens=512, temperature=0.9, top_p=0.9)

docs/pyagentspec/source/howtoguides/howto_llm_from_different_providers.rst

@@ -6,6 +6,7 @@ Agent Spec supports several LLM providers, each one having its own LlmConfig com
 The available LLMs are:
 
 - :ref:`OpenAiConfig <openaiconfig>`
+- :ref:`AnthropicLlmConfig <anthropicllmconfig>`
 - :ref:`OciGenAiConfig <ocigenaiconfig>`
 - :ref:`OpenAiCompatibleConfig <openaicompatibleconfig>`
 - :ref:`VllmConfig <vllmconfig>`
@@ -163,6 +164,39 @@ You can refer to one of those models by using the ``OpenAiConfig`` Component.
     :start-after: .. openai-start
     :end-before: .. openai-end
 
+AnthropicLlmConfig
+==================
+
+`Anthropic Models <https://docs.anthropic.com/en/docs/about-claude/models>`_ are powered by Anthropic.
+You can refer to one of those models by using the ``AnthropicLlmConfig`` component.
+
+**Parameters**
+
+.. option:: model_id: str
+
+  ID of the Anthropic model to use.
+
+.. option:: url: str, null
+
+  URL of the Anthropic API endpoint.
+  If omitted, the Anthropic API URL from the runtime environment is used.
+
+.. option:: api_key: SecretStr, null
+
+  An optional API key for authentication to the Anthropic API endpoint.
+  If omitted, configure credentials in your runtime environment.
+
+.. option:: default_generation_parameters: dict, null
+
+  Default parameters for text generation with this model.
+
+**Examples**
+
+.. literalinclude:: ../code_examples/howto_llm_from_different_providers.py
+    :language: python
+    :start-after: .. anthropic-start
+    :end-before: .. anthropic-end
+
 
 OpenAiCompatibleConfig
 ======================

pyagentspec/setup.py

@@ -30,6 +30,7 @@ def read(file_name):
     "langchain>=1.2.0",
     "langchain-openai>=1.1.7",
     "langchain-ollama>=1.0.1",
+    "langchain-anthropic>=1.3.2,<2.0.0",
     "anyio>=4.10.0,<4.12.0",
     "langgraph-checkpoint>=3.0.1,<4.0.0",  # To mitigate CVE-2025-64439
 ]

pyagentspec/src/pyagentspec/_component_registry.py

@@ -43,6 +43,7 @@
     ToolNode,
 )
 from pyagentspec.llms import (
+    AnthropicLlmConfig,
     OciGenAiConfig,
     OllamaConfig,
     OpenAiCompatibleConfig,
@@ -87,6 +88,7 @@
     "AgenticComponent": AgenticComponent,
     "AgentNode": AgentNode,
     "AgentSpecializationParameters": AgentSpecializationParameters,
+    "AnthropicLlmConfig": AnthropicLlmConfig,
     "ApiNode": ApiNode,
     "BranchingNode": BranchingNode,
     "CatchExceptionNode": CatchExceptionNode,

pyagentspec/src/pyagentspec/adapters/langgraph/_agentspecconverter.py

@@ -8,6 +8,8 @@
 from types import FunctionType
 from typing import TYPE_CHECKING, Any, Dict, List, Mapping, Optional, Union, cast
 
+from pydantic import SecretStr
+
 from pyagentspec import Property
 from pyagentspec.adapters.langgraph._agentspec_converter_flow import (
     _langgraph_graph_convert_to_agentspec,
@@ -20,11 +22,13 @@
     StateNodeSpec,
     StructuredTool,
     SystemMessage,
+    langchain_anthropic,
     langchain_ollama,
     langchain_openai,
 )
 from pyagentspec.agent import Agent as AgentSpecAgent
 from pyagentspec.component import Component as AgentSpecComponent
+from pyagentspec.llms import AnthropicLlmConfig as AgentSpecAnthropicLlmConfig
 from pyagentspec.llms import LlmConfig as AgentSpecLlmConfig
 from pyagentspec.llms import OllamaConfig as AgentSpecOllamaConfig
 from pyagentspec.llms import OpenAiCompatibleConfig as AgentSpecOpenAiCompatibleConfig
@@ -160,6 +164,12 @@ def _basechatmodel_convert_to_agentspec(self, model: BaseChatModel) -> AgentSpec
                     model_id=model.model_name,
                     api_type=api_type,
                 )
+        elif isinstance(model, langchain_anthropic.ChatAnthropic):
+            return AgentSpecAnthropicLlmConfig(
+                name=model.model,
+                model_id=model.model,
+                url=model.anthropic_api_url,
+            )
         raise ValueError(f"The LLM instance provided is of an unsupported type `{type(model)}`.")
 
     def _langgraph_agent_convert_to_agentspec(

pyagentspec/src/pyagentspec/adapters/langgraph/_langgraphconverter.py

@@ -72,6 +72,7 @@
 from pyagentspec.flows.nodes import OutputMessageNode as AgentSpecOutputMessageNode
 from pyagentspec.flows.nodes import StartNode as AgentSpecStartNode
 from pyagentspec.flows.nodes import ToolNode as AgentSpecToolNode
+from pyagentspec.llms.anthropicconfig import AnthropicLlmConfig
 from pyagentspec.llms.llmconfig import LlmConfig as AgentSpecLlmConfig
 from pyagentspec.llms.ollamaconfig import OllamaConfig
 from pyagentspec.llms.openaicompatibleconfig import OpenAIAPIType, OpenAiCompatibleConfig
@@ -1240,6 +1241,27 @@ def _llm_convert_to_langgraph(
                 callbacks=callbacks,
                 **generation_config,
             )
+        elif isinstance(llm_config, AnthropicLlmConfig):
+            from langchain_anthropic import ChatAnthropic
+
+            anthropic_generation_config: dict[str, Any] = {
+                "temperature": generation_config.get("temperature"),
+                "max_tokens": generation_config.get("max_completion_tokens"),
+                "top_p": generation_config.get("top_p"),
+            }
+            anthropic_generation_config = {
+                k: v for k, v in anthropic_generation_config.items() if v is not None
+            }
+            anthropic_kwargs: Dict[str, Any] = {
+                "model": llm_config.model_id,
+                "callbacks": callbacks,
+                **anthropic_generation_config,
+            }
+            if llm_config.url:
+                anthropic_kwargs["base_url"] = llm_config.url
+            if llm_config.api_key:
+                anthropic_kwargs["anthropic_api_key"] = llm_config.api_key.get_secret_value()
+            return ChatAnthropic(**anthropic_kwargs)
         else:
             raise NotImplementedError(
                 f"Llm model of type {llm_config.__class__.__name__} is not yet supported."