Short answer: Docker can isolate where DeepAgents runs commands and handles files, but it does not supply the model that DeepAgents needs for inference. The documented deepagents-docker example uses OpenAI and an API key. Docker separately documents local models for its built-in sandbox agents, but that is not a verified DeepAgents setup. So there is no documented, turnkey DeepAgents-and-Docker recipe here that avoids cloud API credentials.
Understand the two separate parts
A DeepAgents application has at least two distinct jobs:
- Model provider: generates the agent’s responses and decides what to do next. A hosted provider generally requires credentials.
- Backend: handles operations such as running commands and managing files. A Docker backend puts command execution in a container.
Using Docker for the backend does not move inference into that container or remove the model provider’s credential requirement. The deepagents-docker package documentation demonstrates a hosted OpenAI model, while Docker’s local-model instructions describe a separate feature for Docker’s built-in agents.
What the documented DeepAgents Docker setup does
The third-party deepagents-docker package shows how to install its backend and pass it to create_deep_agent. Its current package page lists Python 3.12 or higher and an OpenAI API key as prerequisites. The quickstart selects openai:gpt-5.5, so this example does not meet the no-cloud-API-key requirement. Check the package page for current release and compatibility information before using these commands; the page lists a release dated September 14, 2026.
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- Install Docker and Python 3.12 or higher, and choose one of the package’s documented installation commands:
uv add deepagents-dockerorpip install deepagents-docker. - In Python, import
DockerSandboxfrom the package and passbackend=DockerSandbox()tocreate_deep_agent, following the package’s quickstart. - Configure the model provider separately. In the documented quickstart, that means choosing
openai:gpt-5.5and supplying the OpenAI API key required by the package example.
That is a way to put the documented DeepAgents command backend in Docker, not a way to avoid hosted model credentials.
Can DeepAgents use Ollama without a cloud API key?
It is a plausible direction, but the cited documentation does not verify an end-to-end combination of DeepAgents, ChatOllama, and deepagents-docker. LangChain documents Ollama as a way to run open models locally and provides a ChatOllama integration; the Deep Agents overview describes its framework as model-provider agnostic. Those facts alone do not establish that the specific combination works with particular library versions. Treat it as an integration to validate against the versions you intend to deploy, not a documented turnkey recipe.
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Docker’s own local-model options do not close that gap: they configure Docker Sandboxes’ built-in agents, not create_deep_agent.
What Docker’s local-model instructions cover instead
Docker documents a separate sbx feature for selecting models with its built-in claude, codex, and opencode agents. Model selection is marked experimental and requires enabling the relevant experimental settings.
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| Route | Documented example | What it establishes |
|---|---|---|
| Docker-managed local model | sbx run --model gemma4 |
Runs a local model managed by llmman with the built-in Docker sandbox agents. |
| Existing Ollama installation | sbx run --model gemma4 --provider ollama claude |
Uses an existing host Ollama installation with the built-in claude agent. |
These examples are not configuration instructions for DeepAgents. In the Ollama route, Docker connects to the host at localhost:11434; Docker does not install, start, or manage Ollama. For hosted providers, Docker’s documentation describes passing credentials through the host daemon environment.
Docker notes that the local model runs on the host, so its memory and compute requirements are separate from the sandbox’s resource limits. The cited documentation does not quantify hardware requirements; choose a model that fits the host you have.
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Choose a route based on the credential requirement
| Approach | Cloud model API key | DeepAgents integration evidence |
|---|---|---|
deepagents-docker documented quickstart |
Yes; its example uses OpenAI and lists an API key prerequisite. | Directly documented for DeepAgents with a hosted model. |
Docker sbx with llmman local model |
No hosted-provider credential in the documented local-model flow. | Documented for Docker’s built-in agents, not create_deep_agent. |
Docker sbx with host Ollama |
No hosted-provider credential in the documented local-model flow. | Documented for Docker’s built-in agents, not create_deep_agent. |
DeepAgents with Ollama plus deepagents-docker |
Potentially avoids a hosted model key if inference is local. | Exact combination is not established by the cited documentation; validate the specific library versions. |
Set boundaries for files and commands
The Docker backend changes where commands execute, but shared files still matter. The deepagents-docker package starts a long-running container for command execution. If you set shared_dir, the chosen host directory is mounted inside the container at /shared. If you omit it, the package creates a temporary host directory and removes it when the backend closes. By default, the container is removed when the Python process exits; the repository also documents using a context manager for earlier cleanup.
The package exposes configuration for the image, outbound traffic, timeout, memory, CPUs, PID limit, and extra Docker run flags. Those controls are configurable settings, not evidence that the backend is a hardened security boundary. The package advises using it for trusted workloads and development rather than as a hard multi-tenant boundary, and says not to put secrets in the shared folder.
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Docker’s general sandbox tutorial likewise describes a private environment with its own operating system and Docker daemon, but the project directory is shared read-write. An agent can modify or delete project files visible on the host. Do not treat the workspace as immutable just because commands run in a sandbox.
Avoid substituting DeepAgents’ LocalShellBackend when host isolation is a requirement. Its source documentation says commands run directly on the host, without sandboxing, process isolation, or security restrictions; the agent may access files available to the user, including credentials. Use an appropriately isolated backend when command containment matters.
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