docs: add adapters section in docs

adapters_examples/langgraph/langgraph_graph.py

@@ -45,9 +45,10 @@
 from langchain_openai import ChatOpenAI
 from langgraph.graph import END, START, StateGraph
 from langgraph.prebuilt import ToolNode
-from langgraph_agentspec_adapter.agentspecexporter import AgentSpecExporter
 from pydantic import SecretStr
 
+from pyagentspec.adapters.langgraph import AgentSpecExporter
+
 
 class MessagesState(TypedDict):
     messages: list[Any]

docs/pyagentspec/source/_templates/navbar-new.html

@@ -13,8 +13,8 @@
     <li class="nav-item {% if pagename == 'changelog' %} current active{% endif %}">
         <a class="nav-link nav-internal" href="{{ pathto("changelog") }}">Release Notes</a>
     </li>
-    <li class="nav-item {% if pagename == 'misc/reference_sheet' %} current active{% endif %}">
-        <a class="nav-link nav-internal" href="{{ pathto("misc/reference_sheet") }}">Reference Sheet</a>
+    <li class="nav-item {% if pagename == 'adapters/langgraph' %} current active{% endif %}">
+        <a class="nav-link nav-internal" href="{{ pathto("adapters/langgraph") }}">Adapters</a>
     </li>
     <li class="nav-item {% if pagename == 'ecosystem/integrations' %} current active{% endif %}">
         <a class="nav-link nav-internal" href="{{ pathto("ecosystem/integrations") }}">Ecosystem</a>

docs/pyagentspec/source/adapters/autogen.rst

@@ -0,0 +1,61 @@
+.. _autogenadapter:
+
+=============================
+Agent Spec Adapters - AutoGen
+=============================
+
+
+.. figure:: ../_static/icons/autogen-adapter.jpg
+    :align: center
+    :scale: 20%
+    :alt: Agent Spec adapter for AutoGen
+
+    ↑ With the **Agent Spec adapter for AutoGen**, you can easily import agents from external frameworks using Agent Spec and run them with AutoGen.
+
+
+*Microsoft AutoGen supports the development of multi-agent conversational systems,
+allowing agents to communicate and collaborate to solve tasks.*
+
+
+Get started
+===========
+
+To get started, set up your Python environment (Python 3.10 to 3.12 required),
+and then install the PyAgentSpec package with the AutoGen extension.
+
+
+.. code-block:: bash
+
+    python -m venv .venv
+    source .venv/bin/activate  # On Windows: .venv\Scripts\activate
+    pip install "pyagentspec[autogen]"
+
+
+You are now ready to use the adapter:
+
+- Run Agent Spec configurations with AutoGen (see more details :ref:`below <spectoautogen>`)
+- Convert AutoGen agents to Agent Spec (see more details :ref:`below <autogentospec>`)
+
+
+
+.. _spectoautogen:
+
+Run Agent Spec configurations with AutoGen
+==========================================
+
+
+.. literalinclude:: ../code_examples/adapter_autogen_quickstart.py
+    :language: python
+    :start-after: .. start-agentspec_to_runtime
+    :end-before: .. end-agentspec_to_runtime
+
+
+.. _autogentospec:
+
+Convert AutoGen agents to Agent Spec
+====================================
+
+.. literalinclude:: ../code_examples/adapter_autogen_quickstart.py
+    :language: python
+    :start-after: .. start-runtime_to_agentspec
+    :end-before: .. end-runtime_to_agentspec

docs/pyagentspec/source/adapters/crewai.rst

@@ -0,0 +1,60 @@
+.. _crewaiadapter:
+
+============================
+Agent Spec Adapters - CrewAI
+============================
+
+
+.. figure:: ../_static/icons/crewai-adapter.jpg
+    :align: center
+    :scale: 18%
+    :alt: Agent Spec adapter for CrewAI
+
+    ↑ With the **Agent Spec adapter for CrewAI**, you can easily import agents from external frameworks using Agent Spec and run them with CrewAI.
+
+*CrewAI enables the design of collaborative AI agents and workflows, incorporating guardrails, memory,
+and observability for production-ready multi-agent systems.*
+
+
+Get started
+===========
+
+To get started, set up your Python environment (Python 3.10 to 3.13 required),
+and then install the PyAgentSpec package with the CrewAI extension.
+
+
+.. code-block:: bash
+
+    python -m venv .venv
+    source .venv/bin/activate  # On Windows: .venv\Scripts\activate
+    pip install "pyagentspec[crewai]"
+
+
+You are now ready to use the adapter:
+
+- Run Agent Spec configurations with CrewAI (see more details :ref:`below <spectocrewai>`)
+- Convert CrewAI agents to Agent Spec (see more details :ref:`below <crewaitospec>`)
+
+
+
+.. _spectocrewai:
+
+Run Agent Spec configurations with CrewAI
+=========================================
+
+
+.. literalinclude:: ../code_examples/adapter_crewai_quickstart.py
+    :language: python
+    :start-after: .. start-agentspec_to_runtime
+    :end-before: .. end-agentspec_to_runtime
+
+
+.. _crewaitospec:
+
+Convert CrewAI agents to Agent Spec
+===================================
+
+.. literalinclude:: ../code_examples/adapter_crewai_quickstart.py
+    :language: python
+    :start-after: .. start-runtime_to_agentspec
+    :end-before: .. end-runtime_to_agentspec

docs/pyagentspec/source/adapters/langgraph.rst

@@ -0,0 +1,60 @@
+.. _langgraphadapter:
+
+===============================
+Agent Spec Adapters - LangGraph
+===============================
+
+
+.. figure:: ../_static/icons/langgraph-adapter.jpg
+    :align: center
+    :scale: 18%
+    :alt: Agent Spec adapter for LangGraph
+
+    ↑ With the **Agent Spec adapter for LangGraph**, you can easily import agents from external frameworks using Agent Spec and run them with LangGraph.
+
+
+*LangGraph facilitates the creation and management of long-running, stateful agents
+with durable execution and human-in-the-loop capabilities.*
+
+
+Get started
+===========
+
+To get started, set up your Python environment (Python 3.10 or newer required),
+and then install the PyAgentSpec package with the LangGraph extension.
+
+
+.. code-block:: bash
+
+    python -m venv .venv
+    source .venv/bin/activate  # On Windows: .venv\Scripts\activate
+    pip install "pyagentspec[langgraph]"
+
+
+You are now ready to use the adapter:
+
+- Run Agent Spec configurations with LangGraph (see more details :ref:`below <spectolanggraph>`)
+- Convert LangGraph agents to Agent Spec (see more details :ref:`below <langgraphtospec>`)
+
+
+.. _spectolanggraph:
+
+Run Agent Spec configurations with LangGraph
+============================================
+
+
+.. literalinclude:: ../code_examples/adapter_langgraph_quickstart.py
+    :language: python
+    :start-after: .. start-agentspec_to_runtime
+    :end-before: .. end-agentspec_to_runtime
+
+
+.. _langgraphtospec:
+
+Convert LangGraph agents to Agent Spec
+======================================
+
+.. literalinclude:: ../code_examples/adapter_langgraph_quickstart.py
+    :language: python
+    :start-after: .. start-runtime_to_agentspec
+    :end-before: .. end-runtime_to_agentspec

docs/pyagentspec/source/adapters/wayflow.rst

@@ -0,0 +1,61 @@
+.. _wayflowadapter:
+
+=============================
+Agent Spec Adapters - WayFlow
+=============================
+
+
+.. figure:: ../_static/icons/wayflow-adapter.jpg
+    :align: center
+    :scale: 18%
+    :alt: Agent Spec adapter for WayFlow
+
+    ↑ With the **Agent Spec adapter for WayFlow**, you can easily import agents from external frameworks using Agent Spec and run them with WayFlow.
+
+
+*WayFlow is the reference framework for Agent Spec, provides modular components for developing AI-powered assistants,
+supporting both workflow-based and agent-style applications.*
+
+
+Get started
+===========
+
+To get started, set up your Python environment (Python 3.10 or newer required),
+and then install the PyAgentSpec package as well as WayFlowCore.
+
+
+.. code-block:: bash
+
+    python -m venv .venv
+    source .venv/bin/activate  # On Windows: .venv\Scripts\activate
+    pip install pyagentspec wayflowcore
+
+
+You are now ready to use the adapter:
+
+- Run Agent Spec configurations with WayFlow (see more details :ref:`below <spectowayflow>`)
+- Convert WayFlow agents to Agent Spec (see more details :ref:`below <wayflowtospec>`)
+
+
+
+.. _spectowayflow:
+
+Run Agent Spec configurations with WayFlow
+==========================================
+
+
+.. literalinclude:: ../code_examples/adapter_wayflow_quickstart.py
+    :language: python
+    :start-after: .. start-agentspec_to_runtime
+    :end-before: .. end-agentspec_to_runtime
+
+
+.. _wayflowtospec:
+
+Convert WayFlow agents to Agent Spec
+====================================
+
+.. literalinclude:: ../code_examples/adapter_wayflow_quickstart.py
+    :language: python
+    :start-after: .. start-runtime_to_agentspec
+    :end-before: .. end-runtime_to_agentspec

docs/pyagentspec/source/changelog.rst

@@ -33,13 +33,13 @@ New features
 
 * **Sensitive Fields Support:**
 
-  New fields have been added to Agent Spec components that may carry sensitive data (e.g. the field `api_key` on :ref:`OpenAICompatibleModel <openaicompatiblemodel>`). To provide this functionality securely, we also introduced the annotation `SensitiveField` such that the sensitive fields are automatically excluded when exporting a Component to its JSON or yaml configuration.
+  New fields have been added to Agent Spec components that may carry sensitive data (e.g. the field `api_key` on :ref:`OpenAiCompatibleConfig <openaicompatibleconfig>`). To provide this functionality securely, we also introduced the annotation `SensitiveField` such that the sensitive fields are automatically excluded when exporting a Component to its JSON or yaml configuration.
 
   For more information read the :ref:`latest specification <agentspecsensitivefield_nightly>`.
 
 * **OpenAI Responses API Support:**
 
-  :ref:`OpenAICompatibleModel <openaicompatiblemodel>` and :ref:`OpenAIModel <openaiconfig>` now support the OpenAI Responses API, which can be configured
+  :ref:`OpenAiCompatibleConfig <openaicompatibleconfig>` and :ref:`OpenAIModel <openaiconfig>` now support the OpenAI Responses API, which can be configured
   using the ``api_type`` parameter, which accepts values from :ref:`OpenAIAPIType <openaiapitype>`.
 
   This enhancement allows recent OpenAI models to better leverage advanced reasoning capabilities, resulting in significant performance improvements in workflows.

docs/pyagentspec/source/code_examples/adapter_autogen_quickstart.py

@@ -0,0 +1,94 @@
+# Copyright © 2025 Oracle and/or its affiliates.
+#
+# This software is under the Apache License 2.0
+# (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0) or Universal Permissive License
+# (UPL) 1.0 (LICENSE-UPL or https://oss.oracle.com/licenses/upl), at your option.
+
+# isort:skip_file
+# fmt: off
+# mypy: ignore-errors
+
+# .. start-agentspec_to_runtime
+# Create a Agent Spec agent
+from pyagentspec.agent import Agent
+from pyagentspec.llms.openaicompatibleconfig import OpenAiCompatibleConfig
+from pyagentspec.property import FloatProperty
+from pyagentspec.tools import ServerTool
+
+subtraction_tool = ServerTool(
+    name="subtraction-tool",
+    description="subtract two numbers together",
+    inputs=[FloatProperty(title="a"), FloatProperty(title="b")],
+    outputs=[FloatProperty(title="difference")],
+)
+
+agentspec_llm_config = OpenAiCompatibleConfig(
+    name="llama-3.3-70b-instruct",
+    model_id="/storage/models/Llama-3.3-70B-Instruct",
+    url="url.to.my.llm",
+)
+
+agentspec_agent = Agent(
+    name="agentspec_tools_test",
+    description="agentspec_tools_test",
+    llm_config=agentspec_llm_config,
+    system_prompt="Perform subtraction with the given tool.",
+    tools=[subtraction_tool],
+)
+
+# Export the Agent Spec configuration
+from pyagentspec.serialization import AgentSpecSerializer
+
+agentspec_config = AgentSpecSerializer().to_json(agentspec_agent)
+
+# Load and run the Agent Spec configuration with AutoGen
+from pyagentspec.adapters.autogen import AgentSpecLoader
+
+def subtract(a: float, b: float) -> float:
+    return a - b
+
+async def main() -> None:
+    converter = AgentSpecLoader(tool_registry={"subtraction-tool": subtract})
+    component = converter.load_json(agentspec_config)
+    while True:
+        input_cmd = input("USER >> ")
+        if input_cmd == "q":
+            break
+        result = await component.run(task=input_cmd)
+        print(f"AGENT >> {result.messages[-1].content}")
+    await component._model_client.close()
+
+# anyio.run(main)
+# USER >> Compute 987654321-123456789
+# AGENT >> The result of the subtraction is 864197532.
+# .. end-agentspec_to_runtime
+# .. start-runtime_to_agentspec
+# Create an AutoGen Agent
+import os
+os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
+from autogen_agentchat.agents import AssistantAgent
+from autogen_ext.models.openai import OpenAIChatCompletionClient
+
+async def add_tool(a: int, b: int) -> int:
+    """Adds a to b and returns the result"""
+    return a + b
+
+autogen_tools = {"add_tool": add_tool}
+
+model_client = OpenAIChatCompletionClient(
+    model="gpt-4.1",
+)
+
+autogen_agent = AssistantAgent(
+    name="assistant",
+    model_client=model_client,
+    tools=list(autogen_tools.values()),
+    system_message="Use tools to solve tasks, and reformulate the answers that you get.",
+    reflect_on_tool_use=True,
+)
+
+# Convert to Agent Spec
+from pyagentspec.adapters.autogen import AgentSpecExporter
+
+agentspec_config = AgentSpecExporter().to_json(autogen_agent)
+# .. end-runtime_to_agentspec