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Support for Distributed Workflow Context Across Multiple Pods #22

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opened 2026-02-16 02:16:09 -05:00 by yindo · 0 comments
yindo commented 2026-02-16 02:16:09 -05:00
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Originally created by @hidhana on GitHub (Nov 3, 2025).

In scalable deployments, the LlamaIndex Workflow Server may run across multiple pods or instances (e.g., in a Kubernetes environment) to handle high concurrency and fault tolerance.

Currently, the workflow execution context is maintained locally within the pod where the workflow was initiated. As a result, when an external event (such as a human response or webhook) needs to continue a specific workflow run, the client or event source has no deterministic way to know which pod hosts that workflow context.

Proposed Changes:

  1. Workflow execution context is persisted in a centralized store accessible by all pods
  2. Any pod in the cluster can load and resume workflow execution for a given workflow ID.
  3. Optionally, a lightweight coordination or locking mechanism (e.g., via leader election or distributed locks) ensures safe concurrent access.
Originally created by @hidhana on GitHub (Nov 3, 2025). In scalable deployments, the LlamaIndex Workflow Server may run across multiple pods or instances (e.g., in a Kubernetes environment) to handle high concurrency and fault tolerance. Currently, the workflow execution context is maintained locally within the pod where the workflow was initiated. As a result, when an external event (such as a human response or webhook) needs to continue a specific workflow run, the client or event source has no deterministic way to know which pod hosts that workflow context. Proposed Changes: 1) Workflow execution context is persisted in a centralized store accessible by all pods 2) Any pod in the cluster can load and resume workflow execution for a given workflow ID. 3) Optionally, a lightweight coordination or locking mechanism (e.g., via leader election or distributed locks) ensures safe concurrent access.
yindo added the enhancement label 2026-02-16 02:16:09 -05:00
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Reference: run-llama/workflows-py#22
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