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feat: add echo agent for testing. (#62)
* feat: add echo agent for testing. Signed-off-by: Jeff Napper <[email protected]> * fix: fixed typo in manifest. Hat tip, Marco. Signed-off-by: Jeff Napper <[email protected]> * chore: update for workflow server type mismatch. Signed-off-by: Jeff Napper <[email protected]> * chore: add code in main to imitate workflow server. Signed-off-by: Jeff Napper <[email protected]> * feat: added test client that can use file describing API operations to test against an ACP client. Signed-off-by: Jeff Napper <[email protected]> --------- Signed-off-by: Jeff Napper <[email protected]>
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‎agntcy_acp/__init__.py‎

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# SPDX-License-Identifier: Apache-2.0
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import json
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from os import getenv
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from pydantic_core import CoreSchema, core_schema
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from pydantic import GetCoreSchemaHandler, BaseModel
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from typing import Optional, Any, Dict, Union
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from .acp_v0.sync_client.api_client import ApiClient
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env_varname = prefix + varname.upper()
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return getenv(env_varname.translate(__ENV_VAR_SPECIAL_CHAR_TABLE), None)
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class ApiClientConfiguration(Configuration):
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class ApiClientConfiguration(Configuration,BaseModel):
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"""This class contains various settings of the API client.
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:param host: Base url.
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debug=debug,
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)
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__all__ = [
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"ACPClient",
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"AsyncACPClient",

‎examples/echo-agent/README.md‎

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# Echo Agent
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The echo agent simply repeats input to output adding a new
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"assistant" message with a limited number of supported
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transformations of the last "human" type message:
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* to-upper: convert text to upper case
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* to-lower: convert text to lower case
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The agent is intended to be used for simple test cases where
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access to an LLM is either unsupported or undesired. The agent
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outputs a deterministic value based on the input to better
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support testable outcomes.
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## Prerequisites
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Before running the application, ensure you have the following:
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- **Python 3.9 or higher**
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- [Poetry](https://python-poetry.org/)
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## Running the Echo Agent directly
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* Install dependencies
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```
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poetry install
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```
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* Run the agent CLI using poetry
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```
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export TO_LOWER=true ; poetry run echo_agent --human "What is actually up?"
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{
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"messages": [
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{
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"type": "human",
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"content": "What is actually up?"
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},
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{
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"type": "assistant",
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"content": "what is actually up?"
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}
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]
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}
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```
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## Running the Echo Agent using Agent Workflow Server
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* Create an environment variable file if desired based on the example
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* Make sure that the [workflow server manager](https://docs.agntcy.org/pages/agws/workflow_server_manager.html#getting-started) cli (wfsm) is added to your path
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* Start the workflow server. Note the existing manifest
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assumes you are deploying from the directory with the `pyproject.toml`
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file for the echo agent. Adjust as needed.
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```
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wfsm deploy --manifestPath deploy/echo-agent.json --envFilePath deploy/echo_agent_example.yaml
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```
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Using the output of the logs to get the values for the
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`API_KEY`, `AGENT_ID`, and `WORKFLOW_SERVER_PORT`, we can
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make a request to the agent using `curl`:
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```
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curl -s -H 'content-type: application/json' -H "x-api-key: ${API_KEY}" -d '{"agent_id": "'${AGENT_ID}'", "input": { "echo_input": { "messages": [ { "type": "human", "content": "What is up, Dude?" } ] } }, "config": { "configurable": { "to_upper": true } } }' http://127.0.0.1:${WORKFLOW_SERVER_PORT}/runs/wait
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```

‎examples/echo-agent/deploy/echo-agent.json‎

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{
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"metadata": {
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"ref": {
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"name": "org.agntcy.echo_agent",
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"version": "0.0.1"
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},
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"description": "Provides a test interface to echo input to output."
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},
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"specs": {
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"capabilities": {
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"threads": false,
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"interrupts": false,
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"callbacks": false
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},
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"input": {
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"$defs": {
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"Message": {
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"properties": {
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"type": {
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"$ref": "#/$defs/Type",
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"description": "indicates the originator of the message, a human or an assistant"
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},
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"content": {
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"description": "the content of the message",
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"title": "Content",
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"type": "string"
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}
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},
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"required": [
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"type",
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"content"
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],
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"title": "Message",
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"type": "object"
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},
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"Type": {
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"enum": [
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"human",
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"assistant",
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"ai"
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],
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"title": "Type",
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"type": "string"
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}
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},
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"properties": {
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"messages": {
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"anyOf": [
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{
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"items": {
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"$ref": "#/$defs/Message"
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},
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"type": "array"
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},
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{
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"type": "null"
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}
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],
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"default": null,
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"title": "Messages"
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}
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},
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"title": "InputState",
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"type": "object"
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},
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"output": {
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"$defs": {
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"Message": {
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"properties": {
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"type": {
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"$ref": "#/$defs/Type",
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"description": "indicates the originator of the message, a human or an assistant"
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},
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"content": {
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"description": "the content of the message",
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"title": "Content",
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"type": "string"
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}
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},
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"required": [
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"type",
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"content"
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],
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"title": "Message",
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"type": "object"
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},
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"Type": {
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"enum": [
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"human",
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"assistant",
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"ai"
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],
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"title": "Type",
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"type": "string"
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}
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},
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"properties": {
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"messages": {
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"anyOf": [
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{
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"items": {
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"$ref": "#/$defs/Message"
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},
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"type": "array"
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},
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{
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"type": "null"
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}
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],
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"default": null,
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"title": "Messages"
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}
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},
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"title": "OutputState",
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"type": "object"
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},
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"config": {
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"properties": {
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"to_upper": {
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"title": "to_upper",
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"description": "If true, convert text to upper before echoing.",
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"type": "boolean",
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"default": false
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},
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"to_lower": {
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"title": "to_lower",
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"description": "If true, convert text to lower before echoing.",
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"type": "boolean",
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"default": false
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}
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},
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"title": "ConfigSchema",
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"type": "object"
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}
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},
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"deployment": {
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"deployment_options": [
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{
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"type": "source_code",
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"name": "source_code_local",
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"url": ".",
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"framework_config": {
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"framework_type": "langgraph",
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"graph": "echo_agent.langgraph:AGENT_GRAPH"
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}
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}
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],
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"env_vars": [
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{
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"desc": "If true-ish, convert text to lower before echoing. Superseded by config.",
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"name": "TO_LOWER"
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},
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{
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"desc": "If true-ish, convert text to upper before echoing. Superseded by config.",
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"name": "TO_UPPER"
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}
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],
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"dependencies": []
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}
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}

‎examples/echo-agent/deploy/echo_agent_example.yaml‎

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values:
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TO_UPPER: true

‎examples/echo-agent/echo_agent/__main__.py‎

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# Copyright AGNTCY Contributors (https://github.com/agntcy)
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# SPDX-License-Identifier: Apache-2.0
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import itertools
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import logging
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import click
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from langchain_core.runnables import RunnableConfig
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from .langgraph import AGENT_GRAPH
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from .state import AgentState, ConfigSchema, InputState, Message, MsgType
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logger = logging.getLogger(__name__)
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class ParamMessage(click.ParamType):
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name = "message"
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def __init__(self, **kwargs):
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self.msg_type = kwargs.pop("msg_type", MsgType.human)
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super().__init__(**kwargs)
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def convert(self, value, param, ctx):
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try:
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return Message(type=self.msg_type, content=value)
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except ValueError:
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self.fail(f"{value!r} is not valid message content", param, ctx)
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@click.command(short_help="Validate agent ACP descriptor")
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@click.option(
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"--to-upper",
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envvar="TO_UPPER",
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is_flag=True,
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show_envvar=True,
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help="Convert input to upper case.",
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)
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@click.option(
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"--to-lower",
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envvar="TO_LOWER",
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is_flag=True,
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show_envvar=True,
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help="Convert input to lower case.",
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)
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@click.option(
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"--log-level",
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type=click.Choice(
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["critical", "error", "warning", "info", "debug"], case_sensitive=False
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),
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default="info",
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help="Set logging level.",
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)
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@click.option(
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"--human",
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type=ParamMessage(msg_type=MsgType.human),
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multiple=True,
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help="Add a human message.",
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)
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@click.option(
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"--assistant",
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type=ParamMessage(msg_type=MsgType.assistant),
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multiple=True,
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help="Add a human message.",
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)
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def echo_server_agent(
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to_upper,
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to_lower,
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human,
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assistant,
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log_level,
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):
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""" """
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logging.basicConfig(level=log_level.upper())
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config = ConfigSchema()
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if to_upper is not None:
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config["to_upper"] = to_upper
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if to_lower is not None:
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config["to_lower"] = to_lower
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if human is not None and assistant is not None:
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# Interleave list starting with human. Stops at shortest list.
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messages = list(itertools.chain(*zip(human, assistant)))
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# Append rest of list.
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if len(human) > len(assistant):
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messages += human[len(assistant) :]
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elif len(assistant) > len(human):
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messages += assistant[len(human) :]
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elif human is not None:
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messages = human
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elif assistant is not None:
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messages = assistant
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else:
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messages = []
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echo_input = InputState(messages=messages)
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logger.debug(f"input messages: {echo_input.model_dump_json()}")
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# Imitate input from ACP API
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input_api_object = AgentState(echo_input=echo_input).model_dump(mode="json")
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output_state = AGENT_GRAPH.invoke(
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AGENT_GRAPH.builder.schema.model_validate(input_api_object),
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config=RunnableConfig(configurable=config),
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)
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logger.debug(f"output messages: {output_state}")
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print(output_state["echo_output"].model_dump_json(indent=2))
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if __name__ == "__main__":
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echo_server_agent() # type: ignore

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