Question 1

Does the NemoClaw integration work with CrewAI's memory feature?

Accepted Answer

Yes, but each agent's memory is isolated within its sandbox. CrewAI's short-term memory (within a task) and long-term memory (across tasks) are stored in the agent's designated write directory. Agent A cannot read Agent B's memory store. Shared memory across agents is only possible through the IPC channel, which the supervisor mediates. This prevents memory poisoning attacks where a compromised agent writes malicious context into another agent's memory.

Question 2

Is NemoClaw's Supervisor compatible with LangGraph as well as CrewAI?

Accepted Answer

Yes. NemoClaw's Supervisor-Worker architecture is compatible with both LangGraph and CrewAI orchestration frameworks. If your team has existing LangGraph workflows, you can migrate them to NemoClaw without rewriting the orchestration logic — swap the execution backend and apply NemoClaw sandbox policies. The NeMo Agent Toolkit v1.5.0 includes integration modules for both frameworks. This means teams are not locked into a single orchestration choice; you can run CrewAI crews alongside LangGraph graphs under the same NemoClaw security umbrella.

Question 3

Can I use CrewAI with NemoClaw without NeMo Agent Toolkit?

Accepted Answer

Technically, yes — you can run each CrewAI agent as a separate process inside individual NemoClaw sandboxes and manage the IPC yourself. But NeMo Agent Toolkit v1.5.0 handles the sandbox lifecycle, IPC channel setup, policy loading, and audit log routing automatically. Building this plumbing manually is error-prone and not recommended. The toolkit is the supported integration path, and it is what CrewAI's official blog post documents.

Question 4

What is the performance overhead of per-agent sandboxing?

Accepted Answer

Sandbox creation adds approximately 200–400ms per agent at crew startup. During execution, the overhead comes from Landlock filesystem checks (sub-millisecond per call), seccomp syscall filtering (negligible), and IPC message passing (1–5ms per message depending on payload size). For a typical crew of 3–5 agents running tasks that take seconds to minutes each, the sandbox overhead is less than 1% of total execution time. The overhead becomes noticeable only in crews with hundreds of agents making rapid sub-second tool calls.

Question 5

How does this compare to running CrewAI agents in separate Docker containers?

Accepted Answer

Docker containers provide process and filesystem isolation but require significantly more resources per agent (each container needs its own Python runtime, dependencies, and memory allocation). NemoClaw sandboxes use Landlock and seccomp at the kernel level without separate container runtimes — all agents share the same Python process but with kernel-enforced isolation. Startup is 200ms vs 2–5 seconds for Docker. Memory overhead is negligible vs 200–500MB per container. For crews with more than 3 agents, NemoClaw sandboxing is materially more efficient.

Tag: AI Agents

NemoClaw + CrewAI: Orchestrating Self-Evolving Agents with Enterprise Security