restructure cli (#656)

cli/README.md

@@ -0,0 +1,68 @@
+## Setup Instructions(User): plano CLI
+
+This guide will walk you through the steps to set up the plano cli on your local machine
+
+### Step 1: Create a Python virtual environment
+
+In the tools directory, create a Python virtual environment by running:
+
+```bash
+python -m venv venv
+```
+
+### Step 2: Activate the virtual environment
+* On Linux/MacOS:
+
+```bash
+source venv/bin/activate
+```
+
+### Step 3: Run the build script
+```bash
+pip install planoai==0.4.0
+```
+
+## Uninstall Instructions: plano CLI
+```bash
+pip uninstall planoai
+```
+
+## Setup Instructions (Dev): plano CLI
+
+This guide will walk you through the steps to set up the plano cli on your local machine when you want to develop the plano CLI
+
+### Step 1: Create a Python virtual environment
+
+In the tools directory, create a Python virtual environment by running:
+
+```bash
+python -m venv venv
+```
+
+### Step 2: Activate the virtual environment
+* On Linux/MacOS:
+
+```bash
+source venv/bin/activate
+```
+
+### Step 3: Run the build script
+```bash
+poetry install
+```
+
+### Step 4: build Arch
+```bash
+planoai build
+```
+
+### Logs
+`plano` command can also view logs from the gateway. Use following command to view logs,
+
+```bash
+planoai logs --follow
+```
+
+## Uninstall Instructions: plano CLI
+```bash
+pip uninstall planoai

cli/build_cli.sh

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+#!/bin/bash
+
+echo "Building the cli"
+poetry install

cli/planoai/config_generator.py

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+import json
+import os
+from planoai.utils import convert_legacy_listeners
+from jinja2 import Environment, FileSystemLoader
+import yaml
+from jsonschema import validate
+from urllib.parse import urlparse
+from copy import deepcopy
+
+
+SUPPORTED_PROVIDERS_WITH_BASE_URL = [
+    "azure_openai",
+    "ollama",
+    "qwen",
+    "amazon_bedrock",
+    "arch",
+]
+
+SUPPORTED_PROVIDERS_WITHOUT_BASE_URL = [
+    "deepseek",
+    "groq",
+    "mistral",
+    "openai",
+    "gemini",
+    "anthropic",
+    "together_ai",
+    "xai",
+    "moonshotai",
+    "zhipu",
+]
+
+SUPPORTED_PROVIDERS = (
+    SUPPORTED_PROVIDERS_WITHOUT_BASE_URL + SUPPORTED_PROVIDERS_WITH_BASE_URL
+)
+
+
+def get_endpoint_and_port(endpoint, protocol):
+    endpoint_tokens = endpoint.split(":")
+    if len(endpoint_tokens) > 1:
+        endpoint = endpoint_tokens[0]
+        port = int(endpoint_tokens[1])
+        return endpoint, port
+    else:
+        if protocol == "http":
+            port = 80
+        else:
+            port = 443
+        return endpoint, port
+
+
+def validate_and_render_schema():
+    ENVOY_CONFIG_TEMPLATE_FILE = os.getenv(
+        "ENVOY_CONFIG_TEMPLATE_FILE", "envoy.template.yaml"
+    )
+    ARCH_CONFIG_FILE = os.getenv("ARCH_CONFIG_FILE", "/app/arch_config.yaml")
+    ARCH_CONFIG_FILE_RENDERED = os.getenv(
+        "ARCH_CONFIG_FILE_RENDERED", "/app/arch_config_rendered.yaml"
+    )
+    ENVOY_CONFIG_FILE_RENDERED = os.getenv(
+        "ENVOY_CONFIG_FILE_RENDERED", "/etc/envoy/envoy.yaml"
+    )
+    ARCH_CONFIG_SCHEMA_FILE = os.getenv(
+        "ARCH_CONFIG_SCHEMA_FILE", "arch_config_schema.yaml"
+    )
+
+    env = Environment(loader=FileSystemLoader(os.getenv("TEMPLATE_ROOT", "./")))
+    template = env.get_template(ENVOY_CONFIG_TEMPLATE_FILE)
+
+    try:
+        validate_prompt_config(ARCH_CONFIG_FILE, ARCH_CONFIG_SCHEMA_FILE)
+    except Exception as e:
+        print(str(e))
+        exit(1)  # validate_prompt_config failed. Exit
+
+    with open(ARCH_CONFIG_FILE, "r") as file:
+        arch_config = file.read()
+
+    with open(ARCH_CONFIG_SCHEMA_FILE, "r") as file:
+        arch_config_schema = file.read()
+
+    config_yaml = yaml.safe_load(arch_config)
+    _ = yaml.safe_load(arch_config_schema)
+    inferred_clusters = {}
+
+    # Convert legacy llm_providers to model_providers
+    if "llm_providers" in config_yaml:
+        if "model_providers" in config_yaml:
+            raise Exception(
+                "Please provide either llm_providers or model_providers, not both. llm_providers is deprecated, please use model_providers instead"
+            )
+        config_yaml["model_providers"] = config_yaml["llm_providers"]
+        del config_yaml["llm_providers"]
+
+    listeners, llm_gateway, prompt_gateway = convert_legacy_listeners(
+        config_yaml.get("listeners"), config_yaml.get("model_providers")
+    )
+
+    config_yaml["listeners"] = listeners
+
+    endpoints = config_yaml.get("endpoints", {})
+
+    # Process agents section and convert to endpoints
+    agents = config_yaml.get("agents", [])
+    filters = config_yaml.get("filters", [])
+    agents_combined = agents + filters
+    agent_id_keys = set()
+
+    for agent in agents_combined:
+        agent_id = agent.get("id")
+        if agent_id in agent_id_keys:
+            raise Exception(
+                f"Duplicate agent id {agent_id}, please provide unique id for each agent"
+            )
+        agent_id_keys.add(agent_id)
+        agent_endpoint = agent.get("url")
+
+        if agent_id and agent_endpoint:
+            urlparse_result = urlparse(agent_endpoint)
+            if urlparse_result.scheme and urlparse_result.hostname:
+                protocol = urlparse_result.scheme
+
+                port = urlparse_result.port
+                if port is None:
+                    if protocol == "http":
+                        port = 80
+                    else:
+                        port = 443
+
+                endpoints[agent_id] = {
+                    "endpoint": urlparse_result.hostname,
+                    "port": port,
+                    "protocol": protocol,
+                }
+
+    # override the inferred clusters with the ones defined in the config
+    for name, endpoint_details in endpoints.items():
+        inferred_clusters[name] = endpoint_details
+        # Only call get_endpoint_and_port for manually defined endpoints, not agent-derived ones
+        if "port" not in endpoint_details:
+            endpoint = inferred_clusters[name]["endpoint"]
+            protocol = inferred_clusters[name].get("protocol", "http")
+            (
+                inferred_clusters[name]["endpoint"],
+                inferred_clusters[name]["port"],
+            ) = get_endpoint_and_port(endpoint, protocol)
+
+    print("defined clusters from arch_config.yaml: ", json.dumps(inferred_clusters))
+
+    if "prompt_targets" in config_yaml:
+        for prompt_target in config_yaml["prompt_targets"]:
+            name = prompt_target.get("endpoint", {}).get("name", None)
+            if not name:
+                continue
+            if name not in inferred_clusters:
+                raise Exception(
+                    f"Unknown endpoint {name}, please add it in endpoints section in your arch_config.yaml file"
+                )
+
+    arch_tracing = config_yaml.get("tracing", {})
+
+    llms_with_endpoint = []
+    llms_with_endpoint_cluster_names = set()
+    updated_model_providers = []
+    model_provider_name_set = set()
+    llms_with_usage = []
+    model_name_keys = set()
+    model_usage_name_keys = set()
+
+    print("listeners: ", listeners)
+
+    for listener in listeners:
+        if (
+            listener.get("model_providers") is None
+            or listener.get("model_providers") == []
+        ):
+            continue
+        print("Processing listener with model_providers: ", listener)
+        name = listener.get("name", None)
+
+        for model_provider in listener.get("model_providers", []):
+            if model_provider.get("usage", None):
+                llms_with_usage.append(model_provider["name"])
+            if model_provider.get("name") in model_provider_name_set:
+                raise Exception(
+                    f"Duplicate model_provider name {model_provider.get('name')}, please provide unique name for each model_provider"
+                )
+
+            model_name = model_provider.get("model")
+            print("Processing model_provider: ", model_provider)
+            if model_name in model_name_keys:
+                raise Exception(
+                    f"Duplicate model name {model_name}, please provide unique model name for each model_provider"
+                )
+            model_name_keys.add(model_name)
+            if model_provider.get("name") is None:
+                model_provider["name"] = model_name
+
+            model_provider_name_set.add(model_provider.get("name"))
+
+            model_name_tokens = model_name.split("/")
+            if len(model_name_tokens) < 2:
+                raise Exception(
+                    f"Invalid model name {model_name}. Please provide model name in the format <provider>/<model_id>."
+                )
+            provider = model_name_tokens[0]
+
+            # Validate azure_openai and ollama provider requires base_url
+            if (provider in SUPPORTED_PROVIDERS_WITH_BASE_URL) and model_provider.get(
+                "base_url"
+            ) is None:
+                raise Exception(
+                    f"Provider '{provider}' requires 'base_url' to be set for model {model_name}"
+                )
+
+            model_id = "/".join(model_name_tokens[1:])
+            if provider not in SUPPORTED_PROVIDERS:
+                if (
+                    model_provider.get("base_url", None) is None
+                    or model_provider.get("provider_interface", None) is None
+                ):
+                    raise Exception(
+                        f"Must provide base_url and provider_interface for unsupported provider {provider} for model {model_name}. Supported providers are: {', '.join(SUPPORTED_PROVIDERS)}"
+                    )
+                provider = model_provider.get("provider_interface", None)
+            elif model_provider.get("provider_interface", None) is not None:
+                raise Exception(
+                    f"Please provide provider interface as part of model name {model_name} using the format <provider>/<model_id>. For example, use 'openai/gpt-3.5-turbo' instead of 'gpt-3.5-turbo' "
+                )
+
+            if model_id in model_name_keys:
+                raise Exception(
+                    f"Duplicate model_id {model_id}, please provide unique model_id for each model_provider"
+                )
+            model_name_keys.add(model_id)
+
+            for routing_preference in model_provider.get("routing_preferences", []):
+                if routing_preference.get("name") in model_usage_name_keys:
+                    raise Exception(
+                        f"Duplicate routing preference name \"{routing_preference.get('name')}\", please provide unique name for each routing preference"
+                    )
+                model_usage_name_keys.add(routing_preference.get("name"))
+
+            model_provider["model"] = model_id
+            model_provider["provider_interface"] = provider
+            model_provider_name_set.add(model_provider.get("name"))
+            if model_provider.get("provider") and model_provider.get(
+                "provider_interface"
+            ):
+                raise Exception(
+                    "Please provide either provider or provider_interface, not both"
+                )
+            if model_provider.get("provider"):
+                provider = model_provider["provider"]
+                model_provider["provider_interface"] = provider
+                del model_provider["provider"]
+            updated_model_providers.append(model_provider)
+
+            if model_provider.get("base_url", None):
+                base_url = model_provider["base_url"]
+                urlparse_result = urlparse(base_url)
+                base_url_path_prefix = urlparse_result.path
+                if base_url_path_prefix and base_url_path_prefix != "/":
+                    # we will now support base_url_path_prefix. This means that the user can provide base_url like http://example.com/path and we will extract /path as base_url_path_prefix
+                    model_provider["base_url_path_prefix"] = base_url_path_prefix
+
+                if urlparse_result.scheme == "" or urlparse_result.scheme not in [
+                    "http",
+                    "https",
+                ]:
+                    raise Exception(
+                        "Please provide a valid URL with scheme (http/https) in base_url"
+                    )
+                protocol = urlparse_result.scheme
+                port = urlparse_result.port
+                if port is None:
+                    if protocol == "http":
+                        port = 80
+                    else:
+                        port = 443
+                endpoint = urlparse_result.hostname
+                model_provider["endpoint"] = endpoint
+                model_provider["port"] = port
+                model_provider["protocol"] = protocol
+                cluster_name = (
+                    provider + "_" + endpoint
+                )  # make name unique by appending endpoint
+                model_provider["cluster_name"] = cluster_name
+                # Only add if cluster_name is not already present to avoid duplicates
+                if cluster_name not in llms_with_endpoint_cluster_names:
+                    llms_with_endpoint.append(model_provider)
+                    llms_with_endpoint_cluster_names.add(cluster_name)
+
+    if len(model_usage_name_keys) > 0:
+        routing_model_provider = config_yaml.get("routing", {}).get(
+            "model_provider", None
+        )
+        if (
+            routing_model_provider
+            and routing_model_provider not in model_provider_name_set
+        ):
+            raise Exception(
+                f"Routing model_provider {routing_model_provider} is not defined in model_providers"
+            )
+        if (
+            routing_model_provider is None
+            and "arch-router" not in model_provider_name_set
+        ):
+            updated_model_providers.append(
+                {
+                    "name": "arch-router",
+                    "provider_interface": "arch",
+                    "model": config_yaml.get("routing", {}).get("model", "Arch-Router"),
+                }
+            )
+
+    # Always add arch-function model provider if not already defined
+    if "arch-function" not in model_provider_name_set:
+        updated_model_providers.append(
+            {
+                "name": "arch-function",
+                "provider_interface": "arch",
+                "model": "Arch-Function",
+            }
+        )
+
+    if "plano-orchestrator" not in model_provider_name_set:
+        updated_model_providers.append(
+            {
+                "name": "plano-orchestrator",
+                "provider_interface": "arch",
+                "model": "Plano-Orchestrator",
+            }
+        )
+
+    config_yaml["model_providers"] = deepcopy(updated_model_providers)
+
+    listeners_with_provider = 0
+    for listener in listeners:
+        print("Processing listener: ", listener)
+        model_providers = listener.get("model_providers", None)
+        if model_providers is not None:
+            listeners_with_provider += 1
+            if listeners_with_provider > 1:
+                raise Exception(
+                    "Please provide model_providers either under listeners or at root level, not both. Currently we don't support multiple listeners with model_providers"
+                )
+
+    # Validate model aliases if present
+    if "model_aliases" in config_yaml:
+        model_aliases = config_yaml["model_aliases"]
+        for alias_name, alias_config in model_aliases.items():
+            target = alias_config.get("target")
+            if target not in model_name_keys:
+                raise Exception(
+                    f"Model alias 2 - '{alias_name}' targets '{target}' which is not defined as a model. Available models: {', '.join(sorted(model_name_keys))}"
+                )
+
+    arch_config_string = yaml.dump(config_yaml)
+    arch_llm_config_string = yaml.dump(config_yaml)
+
+    use_agent_orchestrator = config_yaml.get("overrides", {}).get(
+        "use_agent_orchestrator", False
+    )
+
+    agent_orchestrator = None
+    if use_agent_orchestrator:
+        print("Using agent orchestrator")
+
+        if len(endpoints) == 0:
+            raise Exception(
+                "Please provide agent orchestrator in the endpoints section in your arch_config.yaml file"
+            )
+        elif len(endpoints) > 1:
+            raise Exception(
+                "Please provide single agent orchestrator in the endpoints section in your arch_config.yaml file"
+            )
+        else:
+            agent_orchestrator = list(endpoints.keys())[0]
+
+    print("agent_orchestrator: ", agent_orchestrator)
+
+    data = {
+        "prompt_gateway_listener": prompt_gateway,
+        "llm_gateway_listener": llm_gateway,
+        "arch_config": arch_config_string,
+        "arch_llm_config": arch_llm_config_string,
+        "arch_clusters": inferred_clusters,
+        "arch_model_providers": updated_model_providers,
+        "arch_tracing": arch_tracing,
+        "local_llms": llms_with_endpoint,
+        "agent_orchestrator": agent_orchestrator,
+        "listeners": listeners,
+    }
+
+    rendered = template.render(data)
+    print(ENVOY_CONFIG_FILE_RENDERED)
+    print(rendered)
+    with open(ENVOY_CONFIG_FILE_RENDERED, "w") as file:
+        file.write(rendered)
+
+    with open(ARCH_CONFIG_FILE_RENDERED, "w") as file:
+        file.write(arch_config_string)
+
+
+def validate_prompt_config(arch_config_file, arch_config_schema_file):
+    with open(arch_config_file, "r") as file:
+        arch_config = file.read()
+
+    with open(arch_config_schema_file, "r") as file:
+        arch_config_schema = file.read()
+
+    config_yaml = yaml.safe_load(arch_config)
+    config_schema_yaml = yaml.safe_load(arch_config_schema)
+
+    try:
+        validate(config_yaml, config_schema_yaml)
+    except Exception as e:
+        print(
+            f"Error validating arch_config file: {arch_config_file}, schema file: {arch_config_schema_file}, error: {e}"
+        )
+        raise e
+
+
+if __name__ == "__main__":
+    validate_and_render_schema()

cli/planoai/consts.py

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+import os
+
+SERVICE_NAME_ARCHGW = "plano"
+PLANO_DOCKER_NAME = "plano"
+PLANO_DOCKER_IMAGE = os.getenv("PLANO_DOCKER_IMAGE", "katanemo/plano:0.4.0")

cli/planoai/core.py

@@ -0,0 +1,234 @@
+import json
+import subprocess
+import os
+import time
+import sys
+
+import yaml
+from planoai.utils import convert_legacy_listeners, getLogger
+from planoai.consts import (
+    PLANO_DOCKER_IMAGE,
+    PLANO_DOCKER_NAME,
+)
+import subprocess
+from planoai.docker_cli import (
+    docker_container_status,
+    docker_remove_container,
+    docker_start_plano_detached,
+    docker_stop_container,
+    health_check_endpoint,
+    stream_gateway_logs,
+)
+
+
+log = getLogger(__name__)
+
+
+def _get_gateway_ports(arch_config_file: str) -> list[int]:
+    PROMPT_GATEWAY_DEFAULT_PORT = 10000
+    LLM_GATEWAY_DEFAULT_PORT = 12000
+
+    # parse arch_config_file yaml file and get prompt_gateway_port
+    arch_config_dict = {}
+    with open(arch_config_file) as f:
+        arch_config_dict = yaml.safe_load(f)
+
+    listeners, _, _ = convert_legacy_listeners(
+        arch_config_dict.get("listeners"), arch_config_dict.get("llm_providers")
+    )
+
+    all_ports = [listener.get("port") for listener in listeners]
+
+    # unique ports
+    all_ports = list(set(all_ports))
+
+    return all_ports
+
+
+def start_arch(arch_config_file, env, log_timeout=120, foreground=False):
+    """
+    Start Docker Compose in detached mode and stream logs until services are healthy.
+
+    Args:
+        path (str): The path where the prompt_config.yml file is located.
+        log_timeout (int): Time in seconds to show logs before checking for healthy state.
+    """
+    log.info(
+        f"Starting arch gateway, image name: {PLANO_DOCKER_NAME}, tag: {PLANO_DOCKER_IMAGE}"
+    )
+
+    try:
+        plano_container_status = docker_container_status(PLANO_DOCKER_NAME)
+        if plano_container_status != "not found":
+            log.info("plano found in docker, stopping and removing it")
+            docker_stop_container(PLANO_DOCKER_NAME)
+            docker_remove_container(PLANO_DOCKER_NAME)
+
+        gateway_ports = _get_gateway_ports(arch_config_file)
+
+        return_code, _, plano_stderr = docker_start_plano_detached(
+            arch_config_file,
+            env,
+            gateway_ports,
+        )
+        if return_code != 0:
+            log.info("Failed to start plano gateway: " + str(return_code))
+            log.info("stderr: " + plano_stderr)
+            sys.exit(1)
+
+        start_time = time.time()
+        while True:
+            all_listeners_healthy = True
+            for port in gateway_ports:
+                health_check_status = health_check_endpoint(
+                    f"http://localhost:{port}/healthz"
+                )
+                if not health_check_status:
+                    all_listeners_healthy = False
+
+            plano_status = docker_container_status(PLANO_DOCKER_NAME)
+            current_time = time.time()
+            elapsed_time = current_time - start_time
+
+            if plano_status == "exited":
+                log.info("plano container exited unexpectedly.")
+                stream_gateway_logs(follow=False)
+                sys.exit(1)
+
+            # Check if timeout is reached
+            if elapsed_time > log_timeout:
+                log.info(f"stopping log monitoring after {log_timeout} seconds.")
+                stream_gateway_logs(follow=False)
+                sys.exit(1)
+
+            if all_listeners_healthy:
+                log.info("plano is running and is healthy!")
+                break
+            else:
+                health_check_status_str = (
+                    "healthy" if health_check_status else "not healthy"
+                )
+                log.info(
+                    f"plano status: {plano_status}, health status: {health_check_status_str}"
+                )
+                time.sleep(1)
+
+        if foreground:
+            stream_gateway_logs(follow=True)
+
+    except KeyboardInterrupt:
+        log.info("Keyboard interrupt received, stopping arch gateway service.")
+        stop_docker_container()
+
+
+def stop_docker_container(service=PLANO_DOCKER_NAME):
+    """
+    Shutdown all Docker Compose services by running `docker-compose down`.
+
+    Args:
+        path (str): The path where the docker-compose.yml file is located.
+    """
+    log.info(f"Shutting down {service} service.")
+
+    try:
+        subprocess.run(
+            ["docker", "stop", service],
+        )
+        subprocess.run(
+            ["docker", "rm", service],
+        )
+
+        log.info(f"Successfully shut down {service} service.")
+
+    except subprocess.CalledProcessError as e:
+        log.info(f"Failed to shut down services: {str(e)}")
+
+
+def start_cli_agent(arch_config_file=None, settings_json="{}"):
+    """Start a CLI client connected to Arch."""
+
+    with open(arch_config_file, "r") as file:
+        arch_config = file.read()
+        arch_config_yaml = yaml.safe_load(arch_config)
+
+    # Get egress listener configuration
+    egress_config = arch_config_yaml.get("listeners", {}).get("egress_traffic", {})
+    host = egress_config.get("host", "127.0.0.1")
+    port = egress_config.get("port", 12000)
+
+    # Parse additional settings from command line
+    try:
+        additional_settings = json.loads(settings_json) if settings_json else {}
+    except json.JSONDecodeError:
+        log.error("Settings must be valid JSON")
+        sys.exit(1)
+
+    # Set up environment variables
+    env = os.environ.copy()
+    env.update(
+        {
+            "ANTHROPIC_AUTH_TOKEN": "test",  # Use test token for arch
+            "ANTHROPIC_API_KEY": "",
+            "ANTHROPIC_BASE_URL": f"http://{host}:{port}",
+            "NO_PROXY": host,
+            "DISABLE_TELEMETRY": "true",
+            "DISABLE_COST_WARNINGS": "true",
+            "API_TIMEOUT_MS": "600000",
+        }
+    )
+
+    # Set ANTHROPIC_SMALL_FAST_MODEL from additional_settings or model alias
+    if "ANTHROPIC_SMALL_FAST_MODEL" in additional_settings:
+        env["ANTHROPIC_SMALL_FAST_MODEL"] = additional_settings[
+            "ANTHROPIC_SMALL_FAST_MODEL"
+        ]
+    else:
+        # Check if arch.claude.code.small.fast alias exists in model_aliases
+        model_aliases = arch_config_yaml.get("model_aliases", {})
+        if "arch.claude.code.small.fast" in model_aliases:
+            env["ANTHROPIC_SMALL_FAST_MODEL"] = "arch.claude.code.small.fast"
+        else:
+            log.info(
+                "Tip: Set an alias 'arch.claude.code.small.fast' in your model_aliases config to set a small fast model Claude Code"
+            )
+            log.info("Or provide ANTHROPIC_SMALL_FAST_MODEL in --settings JSON")
+
+    # Non-interactive mode configuration from additional_settings only
+    if additional_settings.get("NON_INTERACTIVE_MODE", False):
+        env.update(
+            {
+                "CI": "true",
+                "FORCE_COLOR": "0",
+                "NODE_NO_READLINE": "1",
+                "TERM": "dumb",
+            }
+        )
+
+    # Build claude command arguments
+    claude_args = []
+
+    # Add settings if provided, excluding those already handled as environment variables
+    if additional_settings:
+        # Filter out settings that are already processed as environment variables
+        claude_settings = {
+            k: v
+            for k, v in additional_settings.items()
+            if k not in ["ANTHROPIC_SMALL_FAST_MODEL", "NON_INTERACTIVE_MODE"]
+        }
+        if claude_settings:
+            claude_args.append(f"--settings={json.dumps(claude_settings)}")
+
+    # Use claude from PATH
+    claude_path = "claude"
+    log.info(f"Connecting Claude Code Agent to Arch at {host}:{port}")
+
+    try:
+        subprocess.run([claude_path] + claude_args, env=env, check=True)
+    except subprocess.CalledProcessError as e:
+        log.error(f"Error starting claude: {e}")
+        sys.exit(1)
+    except FileNotFoundError:
+        log.error(
+            f"{claude_path} not found. Make sure Claude Code is installed: npm install -g @anthropic-ai/claude-code"
+        )
+        sys.exit(1)

cli/planoai/docker_cli.py

@@ -0,0 +1,136 @@
+import subprocess
+import json
+import sys
+import requests
+
+from planoai.consts import (
+    PLANO_DOCKER_IMAGE,
+    PLANO_DOCKER_NAME,
+)
+from planoai.utils import getLogger
+
+log = getLogger(__name__)
+
+
+def docker_container_status(container: str) -> str:
+    result = subprocess.run(
+        ["docker", "inspect", "--type=container", container],
+        capture_output=True,
+        text=True,
+        check=False,
+    )
+    if result.returncode != 0:
+        return "not found"
+
+    container_status = json.loads(result.stdout)[0]
+    return container_status.get("State", {}).get("Status", "")
+
+
+def docker_stop_container(container: str) -> str:
+    result = subprocess.run(
+        ["docker", "stop", container], capture_output=True, text=True, check=False
+    )
+    return result.returncode
+
+
+def docker_remove_container(container: str) -> str:
+    result = subprocess.run(
+        ["docker", "rm", container], capture_output=True, text=True, check=False
+    )
+    return result.returncode
+
+
+def docker_start_plano_detached(
+    arch_config_file: str,
+    env: dict,
+    gateway_ports: list[int],
+) -> str:
+    env_args = [item for key, value in env.items() for item in ["-e", f"{key}={value}"]]
+
+    port_mappings = [
+        f"{12001}:{12001}",
+        "19901:9901",
+    ]
+
+    for port in gateway_ports:
+        port_mappings.append(f"{port}:{port}")
+
+    port_mappings_args = [item for port in port_mappings for item in ("-p", port)]
+
+    volume_mappings = [
+        f"{arch_config_file}:/app/arch_config.yaml:ro",
+    ]
+    volume_mappings_args = [
+        item for volume in volume_mappings for item in ("-v", volume)
+    ]
+
+    options = [
+        "docker",
+        "run",
+        "-d",
+        "--name",
+        PLANO_DOCKER_NAME,
+        *port_mappings_args,
+        *volume_mappings_args,
+        *env_args,
+        "--add-host",
+        "host.docker.internal:host-gateway",
+        PLANO_DOCKER_IMAGE,
+    ]
+
+    result = subprocess.run(options, capture_output=True, text=True, check=False)
+    return result.returncode, result.stdout, result.stderr
+
+
+def health_check_endpoint(endpoint: str) -> bool:
+    try:
+        response = requests.get(endpoint)
+        if response.status_code == 200:
+            return True
+    except requests.RequestException as e:
+        pass
+    return False
+
+
+def stream_gateway_logs(follow, service="plano"):
+    """
+    Stream logs from the plano gateway service.
+    """
+    log.info("Logs from plano gateway service.")
+
+    options = ["docker", "logs"]
+    if follow:
+        options.append("-f")
+    options.append(service)
+    try:
+        # Run `docker-compose logs` to stream logs from the gateway service
+        subprocess.run(
+            options,
+            check=True,
+            stdout=sys.stdout,
+            stderr=sys.stderr,
+        )
+
+    except subprocess.CalledProcessError as e:
+        log.info(f"Failed to stream logs: {str(e)}")
+
+
+def docker_validate_plano_schema(arch_config_file):
+    result = subprocess.run(
+        [
+            "docker",
+            "run",
+            "--rm",
+            "-v",
+            f"{arch_config_file}:/app/arch_config.yaml:ro",
+            "--entrypoint",
+            "python",
+            PLANO_DOCKER_IMAGE,
+            "-m",
+            "planoai.config_generator",
+        ],
+        capture_output=True,
+        text=True,
+        check=False,
+    )
+    return result.returncode, result.stdout, result.stderr

cli/planoai/main.py

@@ -0,0 +1,302 @@
+import click
+import os
+import sys
+import subprocess
+import multiprocessing
+import importlib.metadata
+import json
+from planoai import targets
+from planoai.docker_cli import (
+    docker_validate_plano_schema,
+    stream_gateway_logs,
+    docker_container_status,
+)
+from planoai.utils import (
+    getLogger,
+    get_llm_provider_access_keys,
+    has_ingress_listener,
+    load_env_file_to_dict,
+    stream_access_logs,
+    find_config_file,
+    find_repo_root,
+)
+from planoai.core import (
+    start_arch,
+    stop_docker_container,
+    start_cli_agent,
+)
+from planoai.consts import (
+    PLANO_DOCKER_IMAGE,
+    PLANO_DOCKER_NAME,
+    SERVICE_NAME_ARCHGW,
+)
+
+log = getLogger(__name__)
+
+# ref https://patorjk.com/software/taag/#p=display&f=Doom&t=Plano&x=none&v=4&h=4&w=80&we=false
+logo = r"""
+______ _
+| ___ \ |
+| |_/ / | __ _ _ __   ___
+|  __/| |/ _` | '_ \ / _ \
+| |   | | (_| | | | | (_) |
+\_|   |_|\__,_|_| |_|\___/
+
+"""
+
+# Command to build plano Docker images
+ARCHGW_DOCKERFILE = "./Dockerfile"
+
+
+def get_version():
+    try:
+        version = importlib.metadata.version("planoai")
+        return version
+    except importlib.metadata.PackageNotFoundError:
+        return "version not found"
+
+
+@click.group(invoke_without_command=True)
+@click.option("--version", is_flag=True, help="Show the plano cli version and exit.")
+@click.pass_context
+def main(ctx, version):
+    if version:
+        click.echo(f"plano cli version: {get_version()}")
+        ctx.exit()
+
+    log.info(f"Starting plano cli version: {get_version()}")
+
+    if ctx.invoked_subcommand is None:
+        click.echo("""Arch (The Intelligent Prompt Gateway) CLI""")
+        click.echo(logo)
+        click.echo(ctx.get_help())
+
+
+@click.command()
+def build():
+    """Build Arch from source. Works from any directory within the repo."""
+
+    # Find the repo root
+    repo_root = find_repo_root()
+    if not repo_root:
+        click.echo(
+            "Error: Could not find repository root. Make sure you're inside the plano repository."
+        )
+        sys.exit(1)
+
+    dockerfile_path = os.path.join(repo_root, "Dockerfile")
+
+    if not os.path.exists(dockerfile_path):
+        click.echo(f"Error: Dockerfile not found at {dockerfile_path}")
+        sys.exit(1)
+
+    click.echo(f"Building plano image from {repo_root}...")
+    try:
+        subprocess.run(
+            [
+                "docker",
+                "build",
+                "-f",
+                dockerfile_path,
+                "-t",
+                f"{PLANO_DOCKER_IMAGE}",
+                repo_root,
+                "--add-host=host.docker.internal:host-gateway",
+            ],
+            check=True,
+        )
+        click.echo("archgw image built successfully.")
+    except subprocess.CalledProcessError as e:
+        click.echo(f"Error building plano image: {e}")
+        sys.exit(1)
+
+
+@click.command()
+@click.argument("file", required=False)  # Optional file argument
+@click.option(
+    "--path", default=".", help="Path to the directory containing arch_config.yaml"
+)
+@click.option(
+    "--foreground",
+    default=False,
+    help="Run Arch in the foreground. Default is False",
+    is_flag=True,
+)
+def up(file, path, foreground):
+    """Starts Arch."""
+    # Use the utility function to find config file
+    arch_config_file = find_config_file(path, file)
+
+    # Check if the file exists
+    if not os.path.exists(arch_config_file):
+        log.info(f"Error: {arch_config_file} does not exist.")
+        return
+
+    log.info(f"Validating {arch_config_file}")
+    (
+        validation_return_code,
+        validation_stdout,
+        validation_stderr,
+    ) = docker_validate_plano_schema(arch_config_file)
+    if validation_return_code != 0:
+        log.info(f"Error: Validation failed. Exiting")
+        log.info(f"Validation stdout: {validation_stdout}")
+        log.info(f"Validation stderr: {validation_stderr}")
+        sys.exit(1)
+
+    # Set the ARCH_CONFIG_FILE environment variable
+    env_stage = {
+        "OTEL_TRACING_HTTP_ENDPOINT": "http://host.docker.internal:4318/v1/traces",
+    }
+    env = os.environ.copy()
+    # Remove PATH variable if present
+    env.pop("PATH", None)
+    # check if access_keys are preesnt in the config file
+    access_keys = get_llm_provider_access_keys(arch_config_file=arch_config_file)
+
+    # remove duplicates
+    access_keys = set(access_keys)
+    # remove the $ from the access_keys
+    access_keys = [item[1:] if item.startswith("$") else item for item in access_keys]
+
+    if access_keys:
+        if file:
+            app_env_file = os.path.join(
+                os.path.dirname(os.path.abspath(file)), ".env"
+            )  # check the .env file in the path
+        else:
+            app_env_file = os.path.abspath(os.path.join(path, ".env"))
+
+        if not os.path.exists(
+            app_env_file
+        ):  # check to see if the environment variables in the current environment or not
+            for access_key in access_keys:
+                if env.get(access_key) is None:
+                    log.info(f"Access Key: {access_key} not found. Exiting Start")
+                    sys.exit(1)
+                else:
+                    env_stage[access_key] = env.get(access_key)
+        else:  # .env file exists, use that to send parameters to Arch
+            env_file_dict = load_env_file_to_dict(app_env_file)
+            for access_key in access_keys:
+                if env_file_dict.get(access_key) is None:
+                    log.info(f"Access Key: {access_key} not found. Exiting Start")
+                    sys.exit(1)
+                else:
+                    env_stage[access_key] = env_file_dict[access_key]
+
+    env.update(env_stage)
+    start_arch(arch_config_file, env, foreground=foreground)
+
+
+@click.command()
+def down():
+    """Stops Arch."""
+    stop_docker_container()
+
+
+@click.command()
+@click.option(
+    "--f",
+    "--file",
+    type=click.Path(exists=True),
+    required=True,
+    help="Path to the Python file",
+)
+def generate_prompt_targets(file):
+    """Generats prompt_targets from python methods.
+    Note: This works for simple data types like ['int', 'float', 'bool', 'str', 'list', 'tuple', 'set', 'dict']:
+    If you have a complex pydantic data type, you will have to flatten those manually until we add support for it.
+    """
+
+    print(f"Processing file: {file}")
+    if not file.endswith(".py"):
+        print("Error: Input file must be a .py file")
+        sys.exit(1)
+
+    targets.generate_prompt_targets(file)
+
+
+@click.command()
+@click.option(
+    "--debug",
+    help="For detailed debug logs to trace calls from plano <> api_server, etc",
+    is_flag=True,
+)
+@click.option("--follow", help="Follow the logs", is_flag=True)
+def logs(debug, follow):
+    """Stream logs from access logs services."""
+
+    archgw_process = None
+    try:
+        if debug:
+            archgw_process = multiprocessing.Process(
+                target=stream_gateway_logs, args=(follow,)
+            )
+            archgw_process.start()
+
+        archgw_access_logs_process = multiprocessing.Process(
+            target=stream_access_logs, args=(follow,)
+        )
+        archgw_access_logs_process.start()
+        archgw_access_logs_process.join()
+
+        if archgw_process:
+            archgw_process.join()
+    except KeyboardInterrupt:
+        log.info("KeyboardInterrupt detected. Exiting.")
+        if archgw_access_logs_process.is_alive():
+            archgw_access_logs_process.terminate()
+        if archgw_process and archgw_process.is_alive():
+            archgw_process.terminate()
+
+
+@click.command()
+@click.argument("type", type=click.Choice(["claude"]), required=True)
+@click.argument("file", required=False)  # Optional file argument
+@click.option(
+    "--path", default=".", help="Path to the directory containing arch_config.yaml"
+)
+@click.option(
+    "--settings",
+    default="{}",
+    help="Additional settings as JSON string for the CLI agent.",
+)
+def cli_agent(type, file, path, settings):
+    """Start a CLI agent connected to Arch.
+
+    CLI_AGENT: The type of CLI agent to start (currently only 'claude' is supported)
+    """
+
+    # Check if plano docker container is running
+    archgw_status = docker_container_status(PLANO_DOCKER_NAME)
+    if archgw_status != "running":
+        log.error(f"archgw docker container is not running (status: {archgw_status})")
+        log.error("Please start plano using the 'planoai up' command.")
+        sys.exit(1)
+
+    # Determine arch_config.yaml path
+    arch_config_file = find_config_file(path, file)
+    if not os.path.exists(arch_config_file):
+        log.error(f"Config file not found: {arch_config_file}")
+        sys.exit(1)
+
+    try:
+        start_cli_agent(arch_config_file, settings)
+    except SystemExit:
+        # Re-raise SystemExit to preserve exit codes
+        raise
+    except Exception as e:
+        click.echo(f"Error: {e}")
+        sys.exit(1)
+
+
+main.add_command(up)
+main.add_command(down)
+main.add_command(build)
+main.add_command(logs)
+main.add_command(cli_agent)
+main.add_command(generate_prompt_targets)
+
+if __name__ == "__main__":
+    main()

cli/planoai/targets.py

@@ -0,0 +1,365 @@
+import ast
+import sys
+import yaml
+from typing import Any
+
+FLASK_ROUTE_DECORATORS = ["route", "get", "post", "put", "delete", "patch"]
+FASTAPI_ROUTE_DECORATORS = ["get", "post", "put", "delete", "patch"]
+
+
+def detect_framework(tree: Any) -> str:
+    """Detect whether the file is using Flask or FastAPI based on imports."""
+    for node in ast.walk(tree):
+        if isinstance(node, ast.ImportFrom):
+            if node.module == "flask":
+                return "flask"
+            elif node.module == "fastapi":
+                return "fastapi"
+    return "unknown"
+
+
+def get_route_decorators(node: Any, framework: str) -> list:
+    """Extract route decorators based on the framework."""
+    decorators = []
+    for decorator in node.decorator_list:
+        if isinstance(decorator, ast.Call) and isinstance(
+            decorator.func, ast.Attribute
+        ):
+            if framework == "flask" and decorator.func.attr in FLASK_ROUTE_DECORATORS:
+                decorators.append(decorator.func.attr)
+            elif (
+                framework == "fastapi"
+                and decorator.func.attr in FASTAPI_ROUTE_DECORATORS
+            ):
+                decorators.append(decorator.func.attr)
+    return decorators
+
+
+def get_route_path(node: Any, framework: str) -> str:
+    """Extract route path based on the framework."""
+    for decorator in node.decorator_list:
+        if isinstance(decorator, ast.Call) and decorator.args:
+            return decorator.args[0].s  # Assuming it's a string literal
+
+
+def is_pydantic_model(annotation: ast.expr, tree: ast.AST) -> bool:
+    """Check if a given type annotation is a Pydantic model."""
+    # We walk through the AST to find class definitions and check if they inherit from Pydantic's BaseModel
+    if isinstance(annotation, ast.Name):
+        for node in ast.walk(tree):
+            if isinstance(node, ast.ClassDef) and node.name == annotation.id:
+                for base in node.bases:
+                    if isinstance(base, ast.Name) and base.id == "BaseModel":
+                        return True
+    return False
+
+
+def get_pydantic_model_fields(model_name: str, tree: ast.AST) -> list:
+    """Extract fields from a Pydantic model, handling list, tuple, set, dict types, and direct default values."""
+    fields = []
+
+    for node in ast.walk(tree):
+        if isinstance(node, ast.ClassDef) and node.name == model_name:
+            for stmt in node.body:
+                if isinstance(stmt, ast.AnnAssign):
+                    # Initialize the default field description
+                    field_type = "Unknown: Please Fix This!"
+                    description = "Field, description not present. Please fix."
+                    default_value = None
+                    required = True  # Assume the field is required initially
+
+                    # Check if the field uses Field() with required status and description
+                    if (
+                        stmt.value
+                        and isinstance(stmt.value, ast.Call)
+                        and isinstance(stmt.value.func, ast.Name)
+                        and stmt.value.func.id == "Field"
+                    ):
+                        # Extract the description argument inside the Field call
+                        for keyword in stmt.value.keywords:
+                            if keyword.arg == "description" and isinstance(
+                                keyword.value, ast.Str
+                            ):
+                                description = keyword.value.s
+                            if keyword.arg == "default":
+                                default_value = keyword.value
+                        # If Ellipsis (...) is used, it means the field is required
+                        if (
+                            stmt.value.args
+                            and isinstance(stmt.value.args[0], ast.Constant)
+                            and stmt.value.args[0].value is Ellipsis
+                        ):
+                            required = True
+                        else:
+                            required = False
+
+                    # Handle direct default values (e.g., name: str = "John Doe")
+                    elif stmt.value is not None:
+                        if isinstance(stmt.value, ast.Constant):
+                            # Set the default value from the assignment (e.g., name: str = "John Doe")
+                            default_value = stmt.value.value
+                            required = (
+                                False  # Not required since it has a default value
+                            )
+
+                    # Always extract the field type, even if there's a default value
+                    if isinstance(stmt.annotation, ast.Subscript):
+                        # Get the base type (list, tuple, set, dict)
+                        base_type = (
+                            stmt.annotation.value.id
+                            if isinstance(stmt.annotation.value, ast.Name)
+                            else "Unknown"
+                        )
+
+                        # Handle only list, tuple, set, dict and ignore the inner types
+                        if base_type.lower() in ["list", "tuple", "set", "dict"]:
+                            field_type = base_type.lower()
+
+                    # Handle the ellipsis '...' for required fields if no Field() call
+                    elif (
+                        isinstance(stmt.value, ast.Constant)
+                        and stmt.value.value is Ellipsis
+                    ):
+                        required = True
+
+                    # Handle simple types like str, int, etc.
+                    if isinstance(stmt.annotation, ast.Name):
+                        field_type = stmt.annotation.id
+
+                    field_info = {
+                        "name": stmt.target.id,
+                        "type": field_type,  # Always set the field type
+                        "description": description,
+                        "default": default_value,  # Handle direct default values
+                        "required": required,
+                    }
+                    fields.append(field_info)
+
+    return fields
+
+
+def get_function_parameters(node: ast.FunctionDef, tree: ast.AST) -> list:
+    """Extract the parameters and their types from the function definition."""
+    parameters = []
+
+    # Extract docstring to find descriptions
+    docstring = ast.get_docstring(node)
+    arg_descriptions = extract_arg_descriptions_from_docstring(docstring)
+
+    # Extract default values
+    defaults = [None] * (
+        len(node.args.args) - len(node.args.defaults)
+    ) + node.args.defaults  # Align defaults with args
+    for arg, default in zip(node.args.args, defaults):
+        if arg.arg != "self":  # Skip 'self' or 'cls' in class methods
+            param_info = {
+                "name": arg.arg,
+                "description": arg_descriptions.get(arg.arg, "[ADD DESCRIPTION]"),
+            }
+
+            # Handle Pydantic model types
+            if hasattr(arg, "annotation") and is_pydantic_model(arg.annotation, tree):
+                # Extract and flatten Pydantic model fields
+                pydantic_fields = get_pydantic_model_fields(arg.annotation.id, tree)
+                parameters.extend(
+                    pydantic_fields
+                )  # Flatten the model fields into the parameters list
+                continue  # Skip adding the current param_info for the model since we expand the fields
+
+            # Handle standard Python types (int, float, str, etc.)
+            elif hasattr(arg, "annotation") and isinstance(arg.annotation, ast.Name):
+                if arg.annotation.id in [
+                    "int",
+                    "float",
+                    "bool",
+                    "str",
+                    "list",
+                    "tuple",
+                    "set",
+                    "dict",
+                ]:
+                    param_info["type"] = arg.annotation.id
+                else:
+                    param_info["type"] = "[UNKNOWN - PLEASE FIX]"
+
+            # Handle generic subscript types (e.g., Optional, List[Type], etc.)
+            elif hasattr(arg, "annotation") and isinstance(
+                arg.annotation, ast.Subscript
+            ):
+                if isinstance(
+                    arg.annotation.value, ast.Name
+                ) and arg.annotation.value.id in ["list", "tuple", "set", "dict"]:
+                    param_info[
+                        "type"
+                    ] = f"{arg.annotation.value.id}"  # e.g., "List", "Tuple", etc.
+                else:
+                    param_info["type"] = "[UNKNOWN - PLEASE FIX]"
+
+            # Default for unknown types
+            else:
+                param_info[
+                    "type"
+                ] = "[UNKNOWN - PLEASE FIX]"  # If unable to detect type
+
+            # Handle default values
+            if default is not None:
+                if isinstance(default, ast.Constant) or isinstance(
+                    default, ast.NameConstant
+                ):
+                    param_info[
+                        "default"
+                    ] = default.value  # Use the default value directly
+                else:
+                    param_info["default"] = "[UNKNOWN DEFAULT]"  # Unknown default type
+                param_info["required"] = False  # Optional since it has a default value
+            else:
+                param_info["default"] = None
+                param_info["required"] = True  # Required if no default value
+
+            parameters.append(param_info)
+
+    return parameters
+
+
+def get_function_docstring(node: Any) -> str:
+    """Extract the function's docstring description if present."""
+    # Check if the first node is a docstring
+    if isinstance(node.body[0], ast.Expr) and isinstance(node.body[0].value, ast.Str):
+        # Get the entire docstring
+        full_docstring = node.body[0].value.s.strip()
+
+        # Split the docstring by double newlines (to separate description from fields like Args)
+        description = full_docstring.split("\n\n")[0].strip()
+
+        return description
+
+    return "No description provided."
+
+
+def extract_arg_descriptions_from_docstring(docstring: str) -> dict:
+    """Extract descriptions for function parameters from the 'Args' section of the docstring."""
+    descriptions = {}
+    if not docstring:
+        return descriptions
+
+    in_args_section = False
+    current_param = None
+    for line in docstring.splitlines():
+        line = line.strip()
+
+        # Detect the start of the 'Args' section
+        if line.startswith("Args:"):
+            in_args_section = True
+            continue  # Proceed to the next line after 'Args:'
+
+        # End of 'Args' section if no indentation and no colon
+        if in_args_section and not line.startswith(" ") and ":" not in line:
+            break  # Stop processing if we reach a new section
+
+        # Process lines in the 'Args' section
+        if in_args_section:
+            if ":" in line:
+                # Extract parameter name and description
+                param_name, description = line.split(":", 1)
+                descriptions[param_name.strip()] = description.strip()
+                current_param = param_name.strip()
+            elif current_param and line.startswith(" "):
+                # Handle multiline descriptions (indented lines)
+                descriptions[current_param] += f" {line.strip()}"
+
+    return descriptions
+
+
+def generate_prompt_targets(input_file_path: str) -> None:
+    """Introspect routes and generate YAML for either Flask or FastAPI."""
+    with open(input_file_path, "r") as source:
+        tree = ast.parse(source.read())
+
+    # Detect the framework (Flask or FastAPI)
+    framework = detect_framework(tree)
+    if framework == "unknown":
+        print("Could not detect Flask or FastAPI in the file.")
+        return
+
+    # Extract routes
+    routes = []
+    for node in ast.walk(tree):
+        if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)):
+            route_decorators = get_route_decorators(node, framework)
+            if route_decorators:
+                route_path = get_route_path(node, framework)
+                function_params = get_function_parameters(
+                    node, tree
+                )  # Get parameters for the route
+                function_docstring = get_function_docstring(node)  # Extract docstring
+                routes.append(
+                    {
+                        "name": node.name,
+                        "path": route_path,
+                        "methods": route_decorators,
+                        "parameters": function_params,  # Add parameters to the route
+                        "description": function_docstring,  # Add the docstring as the description
+                    }
+                )
+
+    # Generate YAML structure
+    output_structure = {"prompt_targets": []}
+
+    for route in routes:
+        target = {
+            "name": route["name"],
+            "endpoint": [
+                {
+                    "name": "app_server",
+                    "path": route["path"],
+                }
+            ],
+            "description": route["description"],  # Use extracted docstring
+            "parameters": [
+                {
+                    "name": param["name"],
+                    "type": param["type"],
+                    "description": f"{param['description']}",
+                    **(
+                        {"default": param["default"]}
+                        if "default" in param and param["default"] is not None
+                        else {}
+                    ),  # Only add default if it's set
+                    "required": param["required"],
+                }
+                for param in route["parameters"]
+            ],
+        }
+
+        if route["name"] == "default":
+            # Special case for `information_extraction` based on your YAML format
+            target["type"] = "default"
+            target["auto-llm-dispatch-on-response"] = True
+
+        output_structure["prompt_targets"].append(target)
+
+    # Output as YAML
+    print(
+        yaml.dump(output_structure, sort_keys=False, default_flow_style=False, indent=3)
+    )
+
+
+if __name__ == "__main__":
+    if len(sys.argv) != 2:
+        print("Usage: python targets.py <input_file>")
+        sys.exit(1)
+
+    input_file = sys.argv[1]
+
+    # Automatically generate the output file name
+    if input_file.endswith(".py"):
+        output_file = input_file.replace(".py", "_prompt_targets.yml")
+    else:
+        print("Error: Input file must be a .py file")
+        sys.exit(1)
+
+    # Call the function with the input and generated output file names
+    generate_prompt_targets(input_file, output_file)
+
+# Example usage:
+# python targets.py api.yaml

cli/planoai/utils.py

@@ -0,0 +1,263 @@
+import glob
+import os
+import subprocess
+import sys
+import yaml
+import logging
+from planoai.consts import PLANO_DOCKER_NAME
+
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+)
+
+
+def getLogger(name="cli"):
+    logger = logging.getLogger(name)
+    logger.setLevel(logging.INFO)
+    return logger
+
+
+log = getLogger(__name__)
+
+
+def find_repo_root(start_path=None):
+    """Find the repository root by looking for Dockerfile or .git directory."""
+    if start_path is None:
+        start_path = os.getcwd()
+
+    current = os.path.abspath(start_path)
+
+    while current != os.path.dirname(current):  # Stop at filesystem root
+        # Check for markers that indicate repo root
+        if (
+            os.path.exists(os.path.join(current, "Dockerfile"))
+            and os.path.exists(os.path.join(current, "crates"))
+            and os.path.exists(os.path.join(current, "config"))
+        ):
+            return current
+
+        # Also check for .git as fallback
+        if os.path.exists(os.path.join(current, ".git")):
+            # Verify it's the right repo by checking for expected structure
+            if os.path.exists(os.path.join(current, "crates")):
+                return current
+
+        current = os.path.dirname(current)
+
+    return None
+
+
+def has_ingress_listener(arch_config_file):
+    """Check if the arch config file has ingress_traffic listener configured."""
+    try:
+        with open(arch_config_file) as f:
+            arch_config_dict = yaml.safe_load(f)
+
+        ingress_traffic = arch_config_dict.get("listeners", {}).get(
+            "ingress_traffic", {}
+        )
+
+        return bool(ingress_traffic)
+    except Exception as e:
+        log.error(f"Error reading config file {arch_config_file}: {e}")
+        return False
+
+
+def convert_legacy_listeners(
+    listeners: dict | list, model_providers: list | None
+) -> tuple[list, dict | None, dict | None]:
+    llm_gateway_listener = {
+        "name": "egress_traffic",
+        "type": "model_listener",
+        "port": 12000,
+        "address": "0.0.0.0",
+        "timeout": "30s",
+        "model_providers": model_providers or [],
+    }
+
+    prompt_gateway_listener = {
+        "name": "ingress_traffic",
+        "type": "prompt_listener",
+        "port": 10000,
+        "address": "0.0.0.0",
+        "timeout": "30s",
+    }
+
+    # Handle None case
+    if listeners is None:
+        return [llm_gateway_listener], llm_gateway_listener, prompt_gateway_listener
+
+    if isinstance(listeners, dict):
+        # legacy listeners
+        # check if type is array or object
+        # if its dict its legacy format let's convert it to array
+        updated_listeners = []
+        ingress_traffic = listeners.get("ingress_traffic", {})
+        egress_traffic = listeners.get("egress_traffic", {})
+
+        llm_gateway_listener["port"] = egress_traffic.get(
+            "port", llm_gateway_listener["port"]
+        )
+        llm_gateway_listener["address"] = egress_traffic.get(
+            "address", llm_gateway_listener["address"]
+        )
+        llm_gateway_listener["timeout"] = egress_traffic.get(
+            "timeout", llm_gateway_listener["timeout"]
+        )
+        if model_providers is None or model_providers == []:
+            raise ValueError("model_providers cannot be empty when using legacy format")
+
+        llm_gateway_listener["model_providers"] = model_providers
+        updated_listeners.append(llm_gateway_listener)
+
+        if ingress_traffic and ingress_traffic != {}:
+            prompt_gateway_listener["port"] = ingress_traffic.get(
+                "port", prompt_gateway_listener["port"]
+            )
+            prompt_gateway_listener["address"] = ingress_traffic.get(
+                "address", prompt_gateway_listener["address"]
+            )
+            prompt_gateway_listener["timeout"] = ingress_traffic.get(
+                "timeout", prompt_gateway_listener["timeout"]
+            )
+            updated_listeners.append(prompt_gateway_listener)
+
+        return updated_listeners, llm_gateway_listener, prompt_gateway_listener
+
+    model_provider_set = False
+    for listener in listeners:
+        if listener.get("type") == "model_listener":
+            if model_provider_set:
+                raise ValueError(
+                    "Currently only one listener can have model_providers set"
+                )
+            listener["model_providers"] = model_providers or []
+            model_provider_set = True
+            llm_gateway_listener = listener
+    if not model_provider_set:
+        listeners.append(llm_gateway_listener)
+
+    return listeners, llm_gateway_listener, prompt_gateway_listener
+
+
+def get_llm_provider_access_keys(arch_config_file):
+    with open(arch_config_file, "r") as file:
+        arch_config = file.read()
+        arch_config_yaml = yaml.safe_load(arch_config)
+
+    access_key_list = []
+
+    # Convert legacy llm_providers to model_providers
+    if "llm_providers" in arch_config_yaml:
+        if "model_providers" in arch_config_yaml:
+            raise Exception(
+                "Please provide either llm_providers or model_providers, not both. llm_providers is deprecated, please use model_providers instead"
+            )
+        arch_config_yaml["model_providers"] = arch_config_yaml["llm_providers"]
+        del arch_config_yaml["llm_providers"]
+
+    listeners, _, _ = convert_legacy_listeners(
+        arch_config_yaml.get("listeners"), arch_config_yaml.get("model_providers")
+    )
+
+    for prompt_target in arch_config_yaml.get("prompt_targets", []):
+        for k, v in prompt_target.get("endpoint", {}).get("http_headers", {}).items():
+            if k.lower() == "authorization":
+                print(
+                    f"found auth header: {k} for prompt_target: {prompt_target.get('name')}/{prompt_target.get('endpoint').get('name')}"
+                )
+                auth_tokens = v.split(" ")
+                if len(auth_tokens) > 1:
+                    access_key_list.append(auth_tokens[1])
+                else:
+                    access_key_list.append(v)
+
+    for listener in listeners:
+        for llm_provider in listener.get("model_providers", []):
+            access_key = llm_provider.get("access_key")
+            if access_key is not None:
+                access_key_list.append(access_key)
+
+    # Extract environment variables from state_storage.connection_string
+    state_storage = arch_config_yaml.get("state_storage_v1_responses")
+    if state_storage:
+        connection_string = state_storage.get("connection_string")
+        if connection_string and isinstance(connection_string, str):
+            # Extract all $VAR and ${VAR} patterns from connection string
+            import re
+
+            # Match both $VAR and ${VAR} patterns
+            pattern = r"\$\{?([A-Z_][A-Z0-9_]*)\}?"
+            matches = re.findall(pattern, connection_string)
+            for var in matches:
+                access_key_list.append(f"${var}")
+        else:
+            raise ValueError(
+                "Invalid connection string received in state_storage_v1_responses"
+            )
+
+    return access_key_list
+
+
+def load_env_file_to_dict(file_path):
+    env_dict = {}
+
+    # Open and read the .env file
+    with open(file_path, "r") as file:
+        for line in file:
+            # Strip any leading/trailing whitespaces
+            line = line.strip()
+
+            # Skip empty lines and comments
+            if not line or line.startswith("#"):
+                continue
+
+            # Split the line into key and value at the first '=' sign
+            if "=" in line:
+                key, value = line.split("=", 1)
+                key = key.strip()
+                value = value.strip()
+
+                # Add key-value pair to the dictionary
+                env_dict[key] = value
+
+    return env_dict
+
+
+def find_config_file(path=".", file=None):
+    """Find the appropriate config file path."""
+    if file:
+        # If a file is provided, process that file
+        return os.path.abspath(file)
+    else:
+        # If no file is provided, use the path and look for arch_config.yaml first, then config.yaml for convenience
+        arch_config_file = os.path.abspath(os.path.join(path, "config.yaml"))
+        if not os.path.exists(arch_config_file):
+            arch_config_file = os.path.abspath(os.path.join(path, "arch_config.yaml"))
+        return arch_config_file
+
+
+def stream_access_logs(follow):
+    """
+    Get the archgw access logs
+    """
+
+    follow_arg = "-f" if follow else ""
+
+    stream_command = [
+        "docker",
+        "exec",
+        PLANO_DOCKER_NAME,
+        "sh",
+        "-c",
+        f"tail {follow_arg} /var/log/access_*.log",
+    ]
+
+    subprocess.run(
+        stream_command,
+        check=True,
+        stdout=sys.stdout,
+        stderr=sys.stderr,
+    )

cli/poetry.lock

@@ -0,0 +1,779 @@
+# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand.
+
+[[package]]
+name = "attrs"
+version = "25.4.0"
+description = "Classes Without Boilerplate"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "attrs-25.4.0-py3-none-any.whl", hash = "sha256:adcf7e2a1fb3b36ac48d97835bb6d8ade15b8dcce26aba8bf1d14847b57a3373"},
+    {file = "attrs-25.4.0.tar.gz", hash = "sha256:16d5969b87f0859ef33a48b35d55ac1be6e42ae49d5e853b597db70c35c57e11"},
+]
+
+[[package]]
+name = "certifi"
+version = "2025.11.12"
+description = "Python package for providing Mozilla's CA Bundle."
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "certifi-2025.11.12-py3-none-any.whl", hash = "sha256:97de8790030bbd5c2d96b7ec782fc2f7820ef8dba6db909ccf95449f2d062d4b"},
+    {file = "certifi-2025.11.12.tar.gz", hash = "sha256:d8ab5478f2ecd78af242878415affce761ca6bc54a22a27e026d7c25357c3316"},
+]
+
+[[package]]
+name = "charset-normalizer"
+version = "3.4.4"
+description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
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+    {file = "tomli-2.3.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:8a35dd0e643bb2610f156cca8db95d213a90015c11fee76c946aa62b7ae7e02f"},
+    {file = "tomli-2.3.0-cp314-cp314t-win32.whl", hash = "sha256:a1f7f282fe248311650081faafa5f4732bdbfef5d45fe3f2e702fbc6f2d496e0"},
+    {file = "tomli-2.3.0-cp314-cp314t-win_amd64.whl", hash = "sha256:70a251f8d4ba2d9ac2542eecf008b3c8a9fc5c3f9f02c56a9d7952612be2fdba"},
+    {file = "tomli-2.3.0-py3-none-any.whl", hash = "sha256:e95b1af3c5b07d9e643909b5abbec77cd9f1217e6d0bca72b0234736b9fb1f1b"},
+    {file = "tomli-2.3.0.tar.gz", hash = "sha256:64be704a875d2a59753d80ee8a533c3fe183e3f06807ff7dc2232938ccb01549"},
+]
+
+[[package]]
+name = "typing-extensions"
+version = "4.15.0"
+description = "Backported and Experimental Type Hints for Python 3.9+"
+optional = false
+python-versions = ">=3.9"
+groups = ["main", "dev"]
+files = [
+    {file = "typing_extensions-4.15.0-py3-none-any.whl", hash = "sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548"},
+    {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"},
+]
+markers = {main = "python_version < \"3.13\"", dev = "python_version == \"3.10\""}
+
+[[package]]
+name = "urllib3"
+version = "2.5.0"
+description = "HTTP library with thread-safe connection pooling, file post, and more."
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "urllib3-2.5.0-py3-none-any.whl", hash = "sha256:e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc"},
+    {file = "urllib3-2.5.0.tar.gz", hash = "sha256:3fc47733c7e419d4bc3f6b3dc2b4f890bb743906a30d56ba4a5bfa4bbff92760"},
+]
+
+[package.extras]
+brotli = ["brotli (>=1.0.9) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\""]
+h2 = ["h2 (>=4,<5)"]
+socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
+zstd = ["zstandard (>=0.18.0)"]
+
+[metadata]
+lock-version = "2.1"
+python-versions = ">=3.10"
+content-hash = "597534a122f4396a842eec6430e963f8fc6dd8d929cae1d5d6306aa262fcd1ee"

cli/pyproject.toml

@@ -0,0 +1,28 @@
+[tool.poetry]
+name = "planoai"
+version = "0.4.0"
+description = "Python-based CLI tool to manage Plano."
+authors = ["Katanemo Labs, Inc."]
+readme = "README.md"
+packages = [{ include = "planoai" }]
+
+[tool.poetry.dependencies]
+python = ">=3.10"
+click = ">=8.1.7,<9.0.0"
+jinja2 = ">=3.1.4,<4.0.0"
+jsonschema = ">=4.23.0,<5.0.0"
+pyyaml = ">=6.0.2,<7.0.0"
+requests = ">=2.31.0,<3.0.0"
+
+[tool.poetry.group.dev.dependencies]
+pytest = ">=8.4.1,<9.0.0"
+
+[tool.poetry.scripts]
+planoai = "planoai.main:main"
+
+[build-system]
+requires = ["poetry-core>=2.0.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.pytest.ini_options]
+addopts = ["-v"]

cli/test/test_config_generator.py

@@ -0,0 +1,475 @@
+import json
+import pytest
+from unittest import mock
+import sys
+from planoai.config_generator import validate_and_render_schema
+
+# Patch sys.path to allow import from cli/
+import os
+
+sys.path.insert(
+    0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "cli"))
+)
+
+
+@pytest.fixture(autouse=True)
+def cleanup_env(monkeypatch):
+    # Clean up environment variables and mocks after each test
+    yield
+    monkeypatch.undo()
+
+
+def test_validate_and_render_happy_path(monkeypatch):
+    monkeypatch.setenv("ARCH_CONFIG_FILE", "fake_arch_config.yaml")
+    monkeypatch.setenv("ARCH_CONFIG_SCHEMA_FILE", "fake_arch_config_schema.yaml")
+    monkeypatch.setenv("ENVOY_CONFIG_TEMPLATE_FILE", "./envoy.template.yaml")
+    monkeypatch.setenv("ARCH_CONFIG_FILE_RENDERED", "fake_arch_config_rendered.yaml")
+    monkeypatch.setenv("ENVOY_CONFIG_FILE_RENDERED", "fake_envoy.yaml")
+    monkeypatch.setenv("TEMPLATE_ROOT", "../")
+
+    arch_config = """
+version: v0.1.0
+
+listeners:
+  egress_traffic:
+    address: 0.0.0.0
+    port: 12000
+    message_format: openai
+    timeout: 30s
+
+llm_providers:
+
+  - model: openai/gpt-4o-mini
+    access_key: $OPENAI_API_KEY
+    default: true
+
+  - model: openai/gpt-4o
+    access_key: $OPENAI_API_KEY
+    routing_preferences:
+      - name: code understanding
+        description: understand and explain existing code snippets, functions, or libraries
+
+  - model: openai/gpt-4.1
+    access_key: $OPENAI_API_KEY
+    routing_preferences:
+      - name: code generation
+        description: generating new code snippets, functions, or boilerplate based on user prompts or requirements
+
+tracing:
+  random_sampling: 100
+"""
+    arch_config_schema = ""
+    with open("../config/arch_config_schema.yaml", "r") as file:
+        arch_config_schema = file.read()
+
+    m_open = mock.mock_open()
+    # Provide enough file handles for all open() calls in validate_and_render_schema
+    m_open.side_effect = [
+        # Removed empty read - was causing validation failures
+        mock.mock_open(read_data=arch_config).return_value,  # ARCH_CONFIG_FILE
+        mock.mock_open(
+            read_data=arch_config_schema
+        ).return_value,  # ARCH_CONFIG_SCHEMA_FILE
+        mock.mock_open(read_data=arch_config).return_value,  # ARCH_CONFIG_FILE
+        mock.mock_open(
+            read_data=arch_config_schema
+        ).return_value,  # ARCH_CONFIG_SCHEMA_FILE
+        mock.mock_open().return_value,  # ENVOY_CONFIG_FILE_RENDERED (write)
+        mock.mock_open().return_value,  # ARCH_CONFIG_FILE_RENDERED (write)
+    ]
+    with mock.patch("builtins.open", m_open):
+        with mock.patch("planoai.config_generator.Environment"):
+            validate_and_render_schema()
+
+
+def test_validate_and_render_happy_path_agent_config(monkeypatch):
+    monkeypatch.setenv("ARCH_CONFIG_FILE", "fake_arch_config.yaml")
+    monkeypatch.setenv("ARCH_CONFIG_SCHEMA_FILE", "fake_arch_config_schema.yaml")
+    monkeypatch.setenv("ENVOY_CONFIG_TEMPLATE_FILE", "./envoy.template.yaml")
+    monkeypatch.setenv("ARCH_CONFIG_FILE_RENDERED", "fake_arch_config_rendered.yaml")
+    monkeypatch.setenv("ENVOY_CONFIG_FILE_RENDERED", "fake_envoy.yaml")
+    monkeypatch.setenv("TEMPLATE_ROOT", "../")
+
+    arch_config = """
+version: v0.3.0
+
+agents:
+  - id: query_rewriter
+    url: http://localhost:10500
+  - id: context_builder
+    url: http://localhost:10501
+  - id: response_generator
+    url: http://localhost:10502
+  - id: research_agent
+    url: http://localhost:10500
+  - id: input_guard_rails
+    url: http://localhost:10503
+
+listeners:
+  - name: tmobile
+    type: agent
+    router: plano_orchestrator_v1
+    agents:
+      - name: simple_tmobile_rag_agent
+        description: t-mobile virtual assistant for device contracts.
+        filter_chain:
+          - query_rewriter
+          - context_builder
+          - response_generator
+      - name: research_agent
+        description: agent to research and gather information from various sources.
+        filter_chain:
+          - research_agent
+          - response_generator
+    port: 8000
+
+  - name: llm_provider
+    type: model
+    description: llm provider configuration
+    port: 12000
+    llm_providers:
+      - access_key: ${OPENAI_API_KEY}
+        model: openai/gpt-4o
+"""
+    arch_config_schema = ""
+    with open("../config/arch_config_schema.yaml", "r") as file:
+        arch_config_schema = file.read()
+
+    m_open = mock.mock_open()
+    # Provide enough file handles for all open() calls in validate_and_render_schema
+    m_open.side_effect = [
+        # Removed empty read - was causing validation failures
+        mock.mock_open(read_data=arch_config).return_value,  # ARCH_CONFIG_FILE
+        mock.mock_open(
+            read_data=arch_config_schema
+        ).return_value,  # ARCH_CONFIG_SCHEMA_FILE
+        mock.mock_open(read_data=arch_config).return_value,  # ARCH_CONFIG_FILE
+        mock.mock_open(
+            read_data=arch_config_schema
+        ).return_value,  # ARCH_CONFIG_SCHEMA_FILE
+        mock.mock_open().return_value,  # ENVOY_CONFIG_FILE_RENDERED (write)
+        mock.mock_open().return_value,  # ARCH_CONFIG_FILE_RENDERED (write)
+    ]
+    with mock.patch("builtins.open", m_open):
+        with mock.patch("planoai.config_generator.Environment"):
+            validate_and_render_schema()
+
+
+arch_config_test_cases = [
+    {
+        "id": "duplicate_provider_name",
+        "expected_error": "Duplicate model_provider name",
+        "arch_config": """
+version: v0.1.0
+
+listeners:
+  egress_traffic:
+    address: 0.0.0.0
+    port: 12000
+    message_format: openai
+    timeout: 30s
+
+llm_providers:
+
+  - name: test1
+    model: openai/gpt-4o
+    access_key: $OPENAI_API_KEY
+
+  - name: test1
+    model: openai/gpt-4o
+    access_key: $OPENAI_API_KEY
+
+""",
+    },
+    {
+        "id": "provider_interface_with_model_id",
+        "expected_error": "Please provide provider interface as part of model name",
+        "arch_config": """
+version: v0.1.0
+
+listeners:
+  egress_traffic:
+    address: 0.0.0.0
+    port: 12000
+    message_format: openai
+    timeout: 30s
+
+llm_providers:
+
+  - model: openai/gpt-4o
+    access_key: $OPENAI_API_KEY
+    provider_interface: openai
+
+""",
+    },
+    {
+        "id": "duplicate_model_id",
+        "expected_error": "Duplicate model_id",
+        "arch_config": """
+version: v0.1.0
+
+listeners:
+  egress_traffic:
+    address: 0.0.0.0
+    port: 12000
+    message_format: openai
+    timeout: 30s
+
+llm_providers:
+
+  - model: openai/gpt-4o
+    access_key: $OPENAI_API_KEY
+
+  - model: mistral/gpt-4o
+
+""",
+    },
+    {
+        "id": "custom_provider_base_url",
+        "expected_error": "Must provide base_url and provider_interface",
+        "arch_config": """
+version: v0.1.0
+
+listeners:
+  egress_traffic:
+    address: 0.0.0.0
+    port: 12000
+    message_format: openai
+    timeout: 30s
+
+llm_providers:
+  - model: custom/gpt-4o
+
+""",
+    },
+    {
+        "id": "base_url_with_path_prefix",
+        "expected_error": None,
+        "arch_config": """
+version: v0.1.0
+
+listeners:
+  egress_traffic:
+    address: 0.0.0.0
+    port: 12000
+    message_format: openai
+    timeout: 30s
+
+llm_providers:
+
+  - model: custom/gpt-4o
+    base_url: "http://custom.com/api/v2"
+    provider_interface: openai
+
+""",
+    },
+    {
+        "id": "duplicate_routeing_preference_name",
+        "expected_error": "Duplicate routing preference name",
+        "arch_config": """
+version: v0.1.0
+
+listeners:
+  egress_traffic:
+    address: 0.0.0.0
+    port: 12000
+    message_format: openai
+    timeout: 30s
+
+llm_providers:
+
+  - model: openai/gpt-4o-mini
+    access_key: $OPENAI_API_KEY
+    default: true
+
+  - model: openai/gpt-4o
+    access_key: $OPENAI_API_KEY
+    routing_preferences:
+      - name: code understanding
+        description: understand and explain existing code snippets, functions, or libraries
+
+  - model: openai/gpt-4.1
+    access_key: $OPENAI_API_KEY
+    routing_preferences:
+      - name: code understanding
+        description: generating new code snippets, functions, or boilerplate based on user prompts or requirements
+
+tracing:
+  random_sampling: 100
+
+""",
+    },
+]
+
+
+@pytest.mark.parametrize(
+    "arch_config_test_case",
+    arch_config_test_cases,
+    ids=[case["id"] for case in arch_config_test_cases],
+)
+def test_validate_and_render_schema_tests(monkeypatch, arch_config_test_case):
+    monkeypatch.setenv("ARCH_CONFIG_FILE", "fake_arch_config.yaml")
+    monkeypatch.setenv("ARCH_CONFIG_SCHEMA_FILE", "fake_arch_config_schema.yaml")
+    monkeypatch.setenv("ENVOY_CONFIG_TEMPLATE_FILE", "./envoy.template.yaml")
+    monkeypatch.setenv("ARCH_CONFIG_FILE_RENDERED", "fake_arch_config_rendered.yaml")
+    monkeypatch.setenv("ENVOY_CONFIG_FILE_RENDERED", "fake_envoy.yaml")
+    monkeypatch.setenv("TEMPLATE_ROOT", "../")
+
+    arch_config = arch_config_test_case["arch_config"]
+    expected_error = arch_config_test_case.get("expected_error")
+
+    arch_config_schema = ""
+    with open("../config/arch_config_schema.yaml", "r") as file:
+        arch_config_schema = file.read()
+
+    m_open = mock.mock_open()
+    # Provide enough file handles for all open() calls in validate_and_render_schema
+    m_open.side_effect = [
+        mock.mock_open(
+            read_data=arch_config
+        ).return_value,  # validate_prompt_config: ARCH_CONFIG_FILE
+        mock.mock_open(
+            read_data=arch_config_schema
+        ).return_value,  # validate_prompt_config: ARCH_CONFIG_SCHEMA_FILE
+        mock.mock_open(
+            read_data=arch_config
+        ).return_value,  # validate_and_render_schema: ARCH_CONFIG_FILE
+        mock.mock_open(
+            read_data=arch_config_schema
+        ).return_value,  # validate_and_render_schema: ARCH_CONFIG_SCHEMA_FILE
+        mock.mock_open().return_value,  # ENVOY_CONFIG_FILE_RENDERED (write)
+        mock.mock_open().return_value,  # ARCH_CONFIG_FILE_RENDERED (write)
+    ]
+    with mock.patch("builtins.open", m_open):
+        with mock.patch("planoai.config_generator.Environment"):
+            if expected_error:
+                # Test expects an error
+                with pytest.raises(Exception) as excinfo:
+                    validate_and_render_schema()
+                assert expected_error in str(excinfo.value)
+            else:
+                # Test expects success - no exception should be raised
+                validate_and_render_schema()
+
+
+def test_convert_legacy_llm_providers():
+    from planoai.utils import convert_legacy_listeners
+
+    listeners = {
+        "ingress_traffic": {
+            "address": "0.0.0.0",
+            "port": 10000,
+            "timeout": "30s",
+        },
+        "egress_traffic": {
+            "address": "0.0.0.0",
+            "port": 12000,
+            "timeout": "30s",
+        },
+    }
+    llm_providers = [
+        {
+            "model": "openai/gpt-4o",
+            "access_key": "test_key",
+        }
+    ]
+
+    updated_providers, llm_gateway, prompt_gateway = convert_legacy_listeners(
+        listeners, llm_providers
+    )
+    assert isinstance(updated_providers, list)
+    assert llm_gateway is not None
+    assert prompt_gateway is not None
+    print(json.dumps(updated_providers))
+    assert updated_providers == [
+        {
+            "name": "egress_traffic",
+            "type": "model_listener",
+            "port": 12000,
+            "address": "0.0.0.0",
+            "timeout": "30s",
+            "model_providers": [{"model": "openai/gpt-4o", "access_key": "test_key"}],
+        },
+        {
+            "name": "ingress_traffic",
+            "type": "prompt_listener",
+            "port": 10000,
+            "address": "0.0.0.0",
+            "timeout": "30s",
+        },
+    ]
+
+    assert llm_gateway == {
+        "address": "0.0.0.0",
+        "model_providers": [
+            {
+                "access_key": "test_key",
+                "model": "openai/gpt-4o",
+            },
+        ],
+        "name": "egress_traffic",
+        "type": "model_listener",
+        "port": 12000,
+        "timeout": "30s",
+    }
+
+    assert prompt_gateway == {
+        "address": "0.0.0.0",
+        "name": "ingress_traffic",
+        "port": 10000,
+        "timeout": "30s",
+        "type": "prompt_listener",
+    }
+
+
+def test_convert_legacy_llm_providers_no_prompt_gateway():
+    from planoai.utils import convert_legacy_listeners
+
+    listeners = {
+        "egress_traffic": {
+            "address": "0.0.0.0",
+            "port": 12000,
+            "timeout": "30s",
+        }
+    }
+    llm_providers = [
+        {
+            "model": "openai/gpt-4o",
+            "access_key": "test_key",
+        }
+    ]
+
+    updated_providers, llm_gateway, prompt_gateway = convert_legacy_listeners(
+        listeners, llm_providers
+    )
+    assert isinstance(updated_providers, list)
+    assert llm_gateway is not None
+    assert prompt_gateway is not None
+    assert updated_providers == [
+        {
+            "address": "0.0.0.0",
+            "model_providers": [
+                {
+                    "access_key": "test_key",
+                    "model": "openai/gpt-4o",
+                },
+            ],
+            "name": "egress_traffic",
+            "port": 12000,
+            "timeout": "30s",
+            "type": "model_listener",
+        }
+    ]
+    assert llm_gateway == {
+        "address": "0.0.0.0",
+        "model_providers": [
+            {
+                "access_key": "test_key",
+                "model": "openai/gpt-4o",
+            },
+        ],
+        "name": "egress_traffic",
+        "type": "model_listener",
+        "port": 12000,
+        "timeout": "30s",
+    }

cli/uv.lock

@@ -0,0 +1,631 @@
+version = 1
+requires-python = ">=3.10"
+
+[[package]]
+name = "archgw"
+version = "0.3.10"
+source = { editable = "." }
+dependencies = [
+    { name = "click" },
+    { name = "huggingface-hub" },
+    { name = "jinja2" },
+    { name = "jsonschema" },
+    { name = "pytest" },
+    { name = "pyyaml" },
+    { name = "requests" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "click" },
+    { name = "huggingface-hub", specifier = ">=0.34.4" },
+    { name = "jinja2" },
+    { name = "jsonschema" },
+    { name = "pytest", specifier = ">=8.4.2" },
+    { name = "pyyaml" },
+    { name = "requests", specifier = ">=2.32.5" },
+]
+
+[[package]]
+name = "attrs"
+version = "25.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/5a/b0/1367933a8532ee6ff8d63537de4f1177af4bff9f3e829baf7331f595bb24/attrs-25.3.0.tar.gz", hash = "sha256:75d7cefc7fb576747b2c81b4442d4d4a1ce0900973527c011d1030fd3bf4af1b", size = 812032 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/77/06/bb80f5f86020c4551da315d78b3ab75e8228f89f0162f2c3a819e407941a/attrs-25.3.0-py3-none-any.whl", hash = "sha256:427318ce031701fea540783410126f03899a97ffc6f61596ad581ac2e40e3bc3", size = 63815 },
+]
+
+[[package]]
+name = "certifi"
+version = "2025.8.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/dc/67/960ebe6bf230a96cda2e0abcf73af550ec4f090005363542f0765df162e0/certifi-2025.8.3.tar.gz", hash = "sha256:e564105f78ded564e3ae7c923924435e1daa7463faeab5bb932bc53ffae63407", size = 162386 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e5/48/1549795ba7742c948d2ad169c1c8cdbae65bc450d6cd753d124b17c8cd32/certifi-2025.8.3-py3-none-any.whl", hash = "sha256:f6c12493cfb1b06ba2ff328595af9350c65d6644968e5d3a2ffd78699af217a5", size = 161216 },
+]
+
+[[package]]
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