feat: add OpenAI support to all single-model demos

a2a/.gitignore

@@ -1,3 +1,4 @@
+/secret.*
 /.vscode
 /.venv
 /.mypy_cache

a2a/Dockerfile

@@ -14,11 +14,22 @@ RUN python -m compileall -q .
 ENV AGENT_CONFIG=/app/agent.yaml
 
 COPY <<EOF ./entrypoint.sh
-#!/bin/bash
+#!/bin/sh
 set -e
 
-export LLM_AGENT_API_URL=\${MODEL_RUNNER_URL}
-export LLM_AGENT_MODEL_NAME=\${MODEL_RUNNER_MODEL}
+if test -f /run/secrets/openai-api-key; then
+    export OPENAI_API_KEY=$(cat /run/secrets/openai-api-key)
+fi
+
+if test -n "\${OPENAI_API_KEY}"; then
+    echo "Using OpenAI with \${MODEL_NAME}"
+    export LLM_AGENT_MODEL_PROVIDER=openai
+    export LLM_AGENT_MODEL_NAME=\${OPENAI_MODEL_NAME}
+else
+    echo "Using Docker Model Runner with \${MODEL_RUNNER_MODEL}"
+    export LLM_AGENT_MODEL_PROVIDER=docker
+    export LLM_AGENT_MODEL_NAME=\${MODEL_RUNNER_MODEL}
+fi
 exec \$@
 EOF
 RUN chmod +x ./entrypoint.sh

a2a/README.md

@@ -39,6 +39,25 @@ Using Docker Offload with GPU support, you can run the same demo with a larger m
 docker compose -f compose.yaml -f compose.offload.yaml up --build
 ```
 
+# 🧠 Inference Options
+
+By default, this project uses [Docker Model Runner] to handle LLM inference locally — no internet connection or external API key is required.
+
+If you’d prefer to use OpenAI instead:
+
+1. Create a `secret.openai-api-key` file with your OpenAI API key:
+
+```
+sk-...
+```
+
+2. Restart the project with the OpenAI configuration:
+
+```
+docker compose down -v
+docker compose -f compose.yaml -f compose.openai.yaml up
+```
+
 # ❓ What Can It Do?
 
 This system performs multi-agent fact verification, coordinated by an **Auditor**:
@@ -125,3 +144,4 @@ docker compose down -v
 [DuckDuckGo]: https://duckduckgo.com
 [Docker Compose]: https://github.com/docker/compose
 [Docker Desktop]: https://www.docker.com/products/docker-desktop/
+[Docker Model Runner]: https://docs.docker.com/ai/model-runner/

a2a/agents/critic.yaml

@@ -50,7 +50,9 @@ instructions: |
 
   Here is the question and answer you are going to double check:
 
-model: ${LLM_AGENT_MODEL_NAME}
+model:
+  name: ${LLM_AGENT_MODEL_NAME}
+  provider: ${LLM_AGENT_MODEL_PROVIDER}
 tools:
   - mcp/duckduckgo:search
 skills:

a2a/agents/reviser.yaml

@@ -29,7 +29,9 @@ instructions: |
     * If the answer is inaccurate, disputed, or unsupported, then you should output your revised answer text.
   In any case YOU MUST output only your answer. 
 
-model: ${LLM_AGENT_MODEL_NAME}
+model:
+  name: ${LLM_AGENT_MODEL_NAME}
+  provider: ${LLM_AGENT_MODEL_PROVIDER}
 skills:
   - id: revise_answer
     name: Revise and Correct Answer Text

a2a/compose.openai.yaml

@@ -0,0 +1,21 @@
+
+services:
+  auditor-agent-a2a:
+    environment:
+      - OPENAI_MODEL_NAME=o3
+    secrets:
+      - openai-api-key
+  critic-agent-a2a:
+    environment:
+      - OPENAI_MODEL_NAME=o3
+    secrets:
+      - openai-api-key
+  reviser-agent-a2a:
+    environment:
+      - OPENAI_MODEL_NAME=o3
+    secrets:
+      - openai-api-key
+
+secrets:
+  openai-api-key:
+    file: secret.openai-api-key

a2a/src/AgentKit/agent/llm_agent.py

@@ -34,23 +34,26 @@ def _build_model(self) -> BaseLlm:
             provider = self._config.model.provider
             name = self._config.model.name
 
+        if not name:
+            raise ValueError(
+                f"LLM agent {self._config.name} does not specify a model name"
+            )
+
         if not provider:
             provider = "docker"
 
         base_url = None
         api_key: str | None = None
         if provider == "docker":
             api_key = "does_not_matter_but_cannot_be_empty"
-            base_url = os.getenv("LLM_AGENT_API_URL")
+            base_url = os.getenv("MODEL_RUNNER_URL")
             if not base_url:
-                raise ValueError("AGENT_LLM_URL environment variable is not set")
+                raise ValueError("MODEL_RUNNER_URL environment variable is not set")
+            name = "openai/" + name
         elif provider == "openai":
             api_key = os.getenv("OPENAI_API_KEY")
+            if not api_key:
+                raise ValueError("OPENAI_API_KEY environment variable is not set")
         else:
             raise ValueError(f"unknown model provider {provider}")
-        if not name:
-            raise ValueError(
-                f"LLM agent {self._config.name} does not specify a model name"
-            )
-        print("LLMPARAMS", name, base_url)
-        return LiteLlm(model="openai/" + name, api_key=api_key, base_url=base_url)
+        return LiteLlm(model=name, api_key=api_key, base_url=base_url)

adk/.gitignore

@@ -1,3 +1,4 @@
+/secret.*
 /.vscode
 /.venv
 /.mypy_cache

adk/Dockerfile

@@ -13,9 +13,29 @@ RUN --mount=type=cache,target=/root/.cache/uv \
 COPY agents/ ./agents/
 RUN python -m compileall -q .
 
+COPY <<EOF /entrypoint.sh
+#!/bin/sh
+set -e
+
+if test -f /run/secrets/openai-api-key; then
+    export OPENAI_API_KEY=$(cat /run/secrets/openai-api-key)
+fi
+
+if test -n "\${OPENAI_API_KEY}"; then
+    echo "Using OpenAI with \${OPENAI_MODEL_NAME}"
+else
+    echo "Using Docker Model Runner with \${MODEL_RUNNER_MODEL}"
+    export OPENAI_BASE_URL=\${MODEL_RUNNER_URL}
+    export OPENAI_MODEL_NAME=openai/\${MODEL_RUNNER_MODEL}
+    export OPENAI_API_KEY=cannot_be_empty
+fi
+exec adk web --host 0.0.0.0 --port 8080 --log_level DEBUG
+EOF
+RUN chmod +x /entrypoint.sh
+
 # Create non-root user
 RUN useradd --create-home --shell /bin/bash app \
     && chown -R app:app /app
 USER app
 
-CMD [ "adk", "web", "--host", "0.0.0.0", "--port", "8080", "--log_level", "DEBUG" ]
+ENTRYPOINT [ "/entrypoint.sh" ]

adk/README.md

@@ -39,6 +39,24 @@ docker compose -f compose.yaml -f compose.offload.yaml up --build
 No configuration needed — everything runs from the container. Open `http://localhost:8080` in your browser to
 chat with the agents.
 
+# 🧠 Inference Options
+
+By default, this project uses [Docker Model Runner] to handle LLM inference locally — no internet connection or external API key is required.
+
+If you’d prefer to use OpenAI instead:
+
+1. Create a `secret.openai-api-key` file with your OpenAI API key:
+
+```
+sk-...
+```
+
+2. Restart the project with the OpenAI configuration:
+
+```
+docker compose down -v
+docker compose -f compose.yaml -f compose.openai.yaml up
+```
 
 # ❓ What Can It Do?
 
@@ -127,3 +145,4 @@ docker compose down -v
 [DuckDuckGo]: https://duckduckgo.com
 [Docker Compose]: https://github.com/docker/compose
 [Docker Desktop]: https://www.docker.com/products/docker-desktop/
+[Docker Model Runner]: https://docs.docker.com/ai/model-runner/

adk/agents/__init__.py

@@ -15,17 +15,11 @@
 """LLM Auditor for verifying & refining LLM-generated answers using the web."""
 
 import logging
-import os
 
 import litellm
 
 from . import agent
 
-# Set the base URL for the OpenAI API to the Docker Model Runner URL
-os.environ.setdefault("OPENAI_BASE_URL", os.getenv("MODEL_RUNNER_URL", ""))
-# Set the API key to a dummy value since it's not used
-os.environ.setdefault("OPENAI_API_KEY", "not-used")
-
 # Enable logging with reduced verbosity
 logging.basicConfig(
     level=logging.INFO,  # Less verbose than DEBUG

adk/agents/sub_agents/critic/agent.py

@@ -25,8 +25,8 @@
 tools = create_mcp_toolsets(tools_cfg=["mcp/duckduckgo:search"])
 
 critic_agent = Agent(
-    # MODEL_RUNNER_MODEL is set by model_runner provider with the model name
-    model=LiteLlm(model=f"openai/{os.environ.get('MODEL_RUNNER_MODEL')}"),
+    # OPENAI_MODEL_NAME is set by entrypoint.sh with the model name
+    model=LiteLlm(model=os.environ.get("OPENAI_MODEL_NAME", "")),
     name="critic_agent",
     instruction=prompt.CRITIC_PROMPT,
     tools=tools,  # type: ignore

adk/agents/sub_agents/reviser/agent.py

@@ -95,8 +95,8 @@ def force_string_content(
 
 
 reviser_agent = Agent(
-    # MODEL_RUNNER_MODEL is set by model_runner provider with the model name
-    model=LiteLlm(model=f"openai/{os.environ.get('MODEL_RUNNER_MODEL')}"),
+    # OPENAI_MODEL_NAME is set by entrypoint.sh with the model name
+    model=LiteLlm(model=os.environ.get("OPENAI_MODEL_NAME", "")),
     name="reviser_agent",
     instruction=prompt.REVISER_PROMPT,
     before_model_callback=force_string_content,

adk/compose.openai.yaml

@@ -0,0 +1,10 @@
+services:
+  adk:
+    environment:
+      - OPENAI_MODEL_NAME=o3
+    secrets:
+      - openai-api-key
+
+secrets:
+  openai-api-key:
+    file: secret.openai-api-key

crew-ai/.gitignore

@@ -1,3 +1,4 @@
+/secret.*
 /.vscode
 /.venv
 /.mypy_cache

crew-ai/Dockerfile

@@ -10,9 +10,19 @@ COPY . .
 RUN poetry install
 COPY <<EOF /entrypoint.sh
 #!/bin/sh
-export OPENAI_BASE_URL=\${MODEL_RUNNER_URL}
-export OPENAI_MODEL_NAME=openai/\${MODEL_RUNNER_MODEL}
-export OPENAI_API_KEY=does_not_matter_but_cannot_be_empty
+
+if test -f /run/secrets/openai-api-key; then
+    export OPENAI_API_KEY=$(cat /run/secrets/openai-api-key)
+fi
+
+if test -n "\${OPENAI_API_KEY}"; then
+    echo "Using OpenAI with \${OPENAI_MODEL_NAME}"
+else
+    echo "Using Docker Model Runner with \${MODEL_RUNNER_MODEL}"
+    export OPENAI_BASE_URL=\${MODEL_RUNNER_URL}
+    export OPENAI_MODEL_NAME=openai/\${MODEL_RUNNER_MODEL}
+    export OPENAI_API_KEY=cannot_be_empty
+fi
 exec poetry run marketing_posts
 EOF
 RUN chmod +x /entrypoint.sh

crew-ai/README.md

@@ -29,6 +29,24 @@ docker compose up --build
 That’s all. The agents will spin up and collaborate through a series of predefined roles and tasks to
 deliver a complete marketing strategy for the input project.
 
+# 🧠 Inference Options
+
+By default, this project uses [Docker Model Runner] to handle LLM inference locally — no internet connection or external API key is required.
+
+If you’d prefer to use OpenAI instead:
+
+1. Create a `secret.openai-api-key` file with your OpenAI API key:
+
+```
+sk-...
+```
+
+2. Restart the project with the OpenAI configuration:
+
+```
+docker compose down -v
+docker compose -f compose.yaml -f compose.openai.yaml up
+```
 
 ## ❓ What Can It Do?
 
@@ -142,3 +160,4 @@ docker compose down -v
 [DuckDuckGo]: https://duckduckgo.com
 [Docker Compose]: https://github.com/docker/compose
 [Docker Desktop]: https://www.docker.com/products/docker-desktop/
+[Docker Model Runner]: https://docs.docker.com/ai/model-runner/

crew-ai/compose.openai.yaml

@@ -0,0 +1,10 @@
+services:
+  agents:
+    environment:
+      - OPENAI_MODEL_NAME=gpt-4.1-mini
+    secrets:
+      - openai-api-key
+
+secrets:
+  openai-api-key:
+    file: secret.openai-api-key

langgraph/.gitignore

@@ -1,3 +1,4 @@
+/secret.*
 /.vscode
 /.venv
 /.mypy_cache

langgraph/Dockerfile

@@ -18,4 +18,24 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     UV_COMPILE_BYTECODE=1 uv pip install --system .
 COPY agent.py .
 RUN python -m compileall -q .
-ENTRYPOINT [ "python", "agent.py" ]
+COPY <<EOF /entrypoint.sh
+#!/bin/sh
+set -e
+
+if test -f /run/secrets/openai-api-key; then
+    export OPENAI_API_KEY=$(cat /run/secrets/openai-api-key)
+fi
+
+if test -n "\${OPENAI_API_KEY}"; then
+    echo "Using OpenAI with \${OPENAI_MODEL_NAME}"
+    export MODEL_NAME=\${OPENAI_MODEL_NAME}
+else
+    echo "Using Docker Model Runner with \${MODEL_RUNNER_MODEL}"
+    export OPENAI_BASE_URL=\${MODEL_RUNNER_URL}
+    export MODEL_NAME=\${MODEL_RUNNER_MODEL}
+    export OPENAI_API_KEY=cannot_be_empty
+fi
+exec python agent.py
+EOF
+RUN chmod +x /entrypoint.sh
+ENTRYPOINT [ "/entrypoint.sh" ]

langgraph/README.md

@@ -28,6 +28,24 @@ docker compose up
 
 That’s all. The agent spins up automatically, sets up PostgreSQL, loads a pre-seeded database (`Chinook.db`), and starts answering your questions.
 
+# 🧠 Inference Options
+
+By default, this project uses [Docker Model Runner] to handle LLM inference locally — no internet connection or external API key is required.
+
+If you’d prefer to use OpenAI instead:
+
+1. Create a `secret.openai-api-key` file with your OpenAI API key:
+
+```
+sk-...
+```
+
+2. Restart the project with the OpenAI configuration:
+
+```
+docker compose down -v
+docker compose -f compose.yaml -f compose.openai.yaml up
+```
 
 # ❓ What Can It Do?
 
@@ -99,3 +117,4 @@ docker compose down -v
 [PostgreSQL]: https://postgresql.org
 [Docker Compose]: https://github.com/docker/compose
 [Docker Desktop]: https://www.docker.com/products/docker-desktop/
+[Docker Model Runner]: https://docs.docker.com/ai/model-runner/

langgraph/agent.py

@@ -7,8 +7,8 @@
 from mcp import ClientSession
 from mcp.client.sse import sse_client
 
-base_url = os.getenv("MODEL_RUNNER_URL")
-model = os.getenv("MODEL_RUNNER_MODEL", "gpt-4.1")
+base_url = os.getenv("OPENAI_API_BASE_URL")
+model = os.getenv("MODEL_NAME")
 mcp_server_url = os.getenv("MCP_SERVER_URL")
 api_key = os.getenv("OPENAI_API_KEY", "does_not_matter")