feat(io-mapper): io mapping from manifests

Author: CostaRegi

Diff for: agntcy_iomapper/__init__.py

@@ -1,13 +1,11 @@
 # SPDX-FileCopyrightText: Copyright (c) 2025 Cisco and/or its affiliates.
 # SPDX-License-Identifier: Apache-2.0
 # ruff: noqa: F401
-from .agent_iomapper import (
+from .base import (
     AgentIOMapper,
     AgentIOMapperConfig,
     AgentIOMapperInput,
     AgentIOMapperOutput,
-)
-from .base import (
     BaseIOMapper,
     BaseIOMapperConfig,
     BaseIOMapperInput,

Diff for: agntcy_iomapper/base/__init__.py

@@ -0,0 +1,4 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 Cisco and/or its affiliates.
+# SPDX-License-Identifier: Apache-2.0
+from .agent_iomapper import *
+from .base import *

Diff for: agntcy_iomapper/agent_iomapper.py renamed to agntcy_iomapper/base/agent_iomapper.py

@@ -9,27 +9,62 @@
 
 
 class ArgumentsDescription(BaseModel):
+    """
+    ArgumentsDescription a pydantic model that defines
+    the details necessary to perfom io mapping between two agents
+    """
+
     json_schema: Schema | None = Field(
         default=None, description="Data format JSON schema"
     )
     description: str | None = Field(
         default=None, description="Data (semantic) natural language description"
     )
+    agent_manifest: dict[str, Any] | None = Field(
+        default=None,
+        description="Agent Manifest definition as per https://supreme-adventure-wg7nnwm.pages.github.io/docs/openapi.html#model/agentmanifest",
+    )
 
     @model_validator(mode="after")
     def _validate_obj(self) -> Self:
-        if self.json_schema is None and self.description is None:
+        if (
+            self.json_schema is None
+            and self.description is None
+            and self.agent_manifest
+        ):
             raise ValueError(
-                'Either the "schema" field and/or the "description" field must be specified.'
+                'Either the "schema" field and/or the "description" or agent_manifest field must be specified.'
             )
         return self
 
 
 class BaseIOMapperInput(BaseModel):
-    input: ArgumentsDescription = Field(description="Input data descriptions")
-    output: ArgumentsDescription = Field(description="Output data descriptions")
+    input: ArgumentsDescription = Field(
+        description="Input data descriptions",
+    )
+    output: ArgumentsDescription = Field(
+        description="Output data descriptions",
+    )
     data: Any = Field(description="Data to translate")
 
+    @model_validator(mode="after")
+    def _validate_obj(self) -> Self:
+        if self.input.agent_manifest is not None:
+            # given an input agents manifest map its ouput definition
+            # because the data to be mapped is the result of calling the input agent
+            self.input.json_schema = Schema.model_validate(
+                self.input.agent_manifest["specs"]["output"]
+            )
+
+        if self.output.agent_manifest:
+            # given an output agents manifest map its input definition
+            # because the data to be mapped would be mapped to it's input
+            self.output.json_schema = Schema.model_validate(
+                self.output.agent_manifest["specs"]["input"]
+            )
+
+        return self
+
 
 class BaseIOMapperOutput(BaseModel):
     data: Any = Field(default=None, description="Data after translation")
@@ -73,3 +108,13 @@ async def ainvoke(self, input: BaseIOMapperInput) -> BaseIOMapperOutput:
         Args:
             input: the data to be mapped
         """
+
+
+class _ArgumentsDescription:
+    @staticmethod
+    def input_args_description(data):
+        return ArgumentsDescription(is_source_agent=True, **data)
+
+    @staticmethod
+    def output_args_description(data):
+        return ArgumentsDescription(is_source_agent=False, **data)

Diff for: agntcy_iomapper/base.py renamed to agntcy_iomapper/base/base.py

@@ -0,0 +1,8 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 Cisco and/or its affiliates.
+# SPDX-License-Identifier: Apache-2.0"
+
+from .imperative import (
+    ImperativeIOMapper,
+    ImperativeIOMapperInput,
+    ImperativeIOMapperOutput,
+)

Diff for: agntcy_iomapper/imperative/__init__.py

@@ -22,7 +22,7 @@
 import jsonschema
 from jsonpath_ng.ext import parse
 
-from .base import (
+from agntcy_iomapper.base import (
     BaseIOMapper,
     BaseIOMapperConfig,
     BaseIOMapperInput,

Diff for: agntcy_iomapper/imperative.py renamed to agntcy_iomapper/imperative/imperative.py

@@ -0,0 +1,11 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 Cisco and/or its affiliates.
+# SPDX-License-Identifier: Apache-2.0"
+
+from .langgraph import (
+    LangGraphIOMapper,
+    LangGraphIOMapperConfig,
+    LangGraphIOMapperInput,
+    LangGraphIOMapperOutput,
+)
+
+from .create_langraph_iomapper import create_langraph_iomapper

Diff for: agntcy_iomapper/langgraph/__init__.py

@@ -0,0 +1,22 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 Cisco and/or its affiliates.
+# SPDX-License-Identifier: Apache-2.0"
+from langchain_core.runnables import Runnable
+
+from .langgraph import (
+    LangGraphIOMapper,
+    LangGraphIOMapperConfig,
+    LangGraphIOMapperInput,
+    LangGraphIOMapperOutput,
+)
+
+
+def create_langraph_iomapper(
+    config: LangGraphIOMapperConfig,
+) -> Runnable[LangGraphIOMapperInput, LangGraphIOMapperOutput]:
+    """Creates a langgraph agent
+    Args:
+      config: The configuration of the llm that would be used during the mapping
+    Returns:
+      A runnable representing an agent. It returns as output the mapping result
+    """
+    return LangGraphIOMapper(config).as_runnable()

Diff for: agntcy_iomapper/langgraph/create_langraph_iomapper.py

@@ -5,11 +5,11 @@
 
 from langchain.chat_models import init_chat_model
 from langchain_core.language_models import BaseChatModel
-from langchain_core.runnables import Runnable, RunnableConfig
+from langchain_core.runnables import RunnableConfig
 from langgraph.utils.runnable import RunnableCallable
 from pydantic import Field
 
-from .agent_iomapper import (
+from agntcy_iomapper.base import (
     AgentIOMapper,
     AgentIOMapperConfig,
     AgentIOMapperInput,
@@ -86,9 +86,3 @@ def invoke(self, state: dict[str, Any], config: RunnableConfig) -> dict:
 
     def as_runnable(self):
         return RunnableCallable(self.invoke, self.ainvoke, name="extract", trace=False)
-
-
-def create_iomapper(
-    config: LangGraphIOMapperConfig,
-) -> Runnable[LangGraphIOMapperInput, LangGraphIOMapperOutput]:
-    return LangGraphIOMapper(config).as_runnable()

Diff for: agntcy_iomapper/langgraph.py renamed to agntcy_iomapper/langgraph/langgraph.py

@@ -18,7 +18,7 @@
 from pydantic_ai.models.openai import OpenAIModel
 from typing_extensions import Self
 
-from .agent_iomapper import (
+from agntcy_iomapper.base import (
     AgentIOMapper,
     AgentIOMapperConfig,
     AgentIOMapperInput,

Diff for: agntcy_iomapper/pydantic_ai.py

@@ -1,7 +1,7 @@
 # Usage
 
-The Agntcy IO Mapper functions provided an easy to use package for mapping output from 
-one agent to another. The data can be described in JSON or with natural language. The 
+The Agntcy IO Mapper functions provided an easy to use package for mapping output from
+one agent to another. The data can be described in JSON or with natural language. The
 (incomplete) [pydantic](https://docs.pydantic.dev/latest/) models follow:
 
 ```python
@@ -23,16 +23,16 @@ class BaseIOMapperConfig(BaseModel):
     validate_json_output: bool = Field(description="Validate output against JSON schema.")
 ```
 
-There are several different ways to leverage the IO Mapper functions in Python. There 
-is an [agentic interface](#use-agent-io-mapper) using models that can be invoked on 
-different AI platforms and a [imperative interface](#use-imperative--deterministic-io-mapper) 
+There are several different ways to leverage the IO Mapper functions in Python. There
+is an [agentic interface](#use-agent-io-mapper) using models that can be invoked on
+different AI platforms and a [imperative interface](#use-imperative--deterministic-io-mapper)
 that does deterministic JSON remapping without using any AI models.
 
 ## Use Agent IO Mapper
 
-The Agent IO Mapper uses an LLM/model to transform the inputs (typically output of the 
+The Agent IO Mapper uses an LLM/model to transform the inputs (typically output of the
 first agent) to match the desired output (typically the input of a second agent). As such,
-it additionally supports specifying the model prompts for the translation. The configuration 
+it additionally supports specifying the model prompts for the translation. The configuration
 object provides a specification for the system and default user prompts:
 
 ```python
@@ -54,11 +54,11 @@ class AgentIOMapperInput(BaseIOMapperInput):
     )
 ```
 
-Further specification of models and their arguments is left to the underlying supported 
+Further specification of models and their arguments is left to the underlying supported
 packages:
-  * [Pydantic-AI](#pydantic-ai)
-  * [LangGraph](#langgraph)
 
+- [Pydantic-AI](#pydantic-ai)
+- [LangGraph](#langgraph)
 
 ### Pydantic-AI
 
@@ -70,8 +70,8 @@ This project supports specifying model interations using [LangGraph](https://lan
 
 ## Use Imperative / Deterministic IO Mapper
 
-The code snippet below illustrates a fully functional deterministic mapping that 
-transforms the output of one agent into input for a second agent. The code for the 
+The code snippet below illustrates a fully functional deterministic mapping that
+transforms the output of one agent into input for a second agent. The code for the
 agents is omitted.
 
 ```python
@@ -89,7 +89,7 @@ agents is omitted.
      }
  }
 
- # the mapping object using jsonpath, note: the value of the mapping 
+ # the mapping object using jsonpath, note: the value of the mapping
  # can be either a jsonpath or a function
  mapping_object = {
      "prof_question": "$.question",
@@ -115,8 +115,8 @@ agents is omitted.
 
 ### Use Examples
 
-1. To run the examples we strongly recommend that a 
-  [virtual environment is created](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/)
+1. To run the examples we strongly recommend that a
+   [virtual environment is created](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/)
 2. Install the requirements file
 3. From within examples folder run: