vvm

You can run LLM-powered agent workflows with memory persistence by using the LLM as a runtime to execute structured programs. This approach supports memory-backed prompts, semantic control flow, and deterministic parallelism for robust multi-agent coordination.

The best way to coordinate multi-agent workflows using an LLM runtime is through actor-aware orchestration. This method utilizes semantic predicates, pattern matching, and explicit memory patches to guide agent decisions and ensure continuity across runs.

Patch-based memory in multi-agent LLM execution works by enforcing structured updates through memory patches. It uses digest and ledger memory with patch channels to provide continuity across runs while keeping prompts bounded.

Yes, you can audit and debug LLM agent workflows using an optional filesystem state mode. This runtime-agnostic design works across in-context and filesystem states, providing portability and auditability for your multi-agent execution.

A limitation of using semantic control flow for agent orchestration is that it requires patch-based memory updates to function correctly. Users must manage memory_mode variants like continue, dry_run, or fresh to ensure deterministic parallelism and avoid unexpected state overwrites.