Add application-scoped state API (ctx.app_state)#542
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Adds server and dataplane support for application-scoped state, enabling functions to share key-value state across request boundaries within the same application. This complements the existing per-request ctx.state. Proto changes: - Add FunctionExecutorMetadata message with app_state_uri_prefix to ContainerDescription in executor_api.proto - Add AllocationAppState operation/result messages to function_executor.proto - Wire into AllocationState and AllocationUpdate Server changes: - Add app_state_key_prefix() to DataPayload for URI computation - Populate FunctionExecutorMetadata on ContainerDescription in both build_full_snapshot() and build_add_container_command() Dataplane changes: - Add AppStateHandler with full reconcile logic in state_ops.rs - Add app_state_uri_prefix to AllocationContext - Read FunctionExecutorMetadata from container description in allocation_lifecycle.rs Integration test: - Add test_app_state.py covering set/get, downstream reads, cross-request persistence, overwrites, and default values Companion PR: tensorlakeai/tensorlake#542 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Some agents require updating context about users or objects they are
managing. Application State is a way for applications to share state
across request boundaries as they learn about the problem domain.
For example, a fitness tracker agent managing fitness logs of multiple
users can store per-user context in app_state as a flexible K/V store,
which is much more practical than using rigid database schemas for
evolving agent knowledge.
The API mirrors the existing request-scoped ctx.state but persists
across all requests within the same application:
ctx = RequestContext.get()
ctx.app_state.set("key", value) # write
ctx.app_state.get("key") # read from any request
Implementation uses the same 3-phase blob protocol as request state
(prepare_read -> transfer -> commit_write) with a different URI
namespace: {namespace}/{application}/app_state/{key}.
Changes:
- Add ApplicationState abstract class and app_state property on
RequestContext
- Add AllocationAppState for FE-side operation handling
- Add app state proto messages (operations + results) to
function_executor.proto and regenerate stubs
- Add HTTP handlers (base, FE, local) for app state operations
- Add ApplicationStateHTTPClient and wire into RequestContextHTTPClient
- Wire app state into allocation runner and state wrapper
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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diptanu
added a commit
to tensorlakeai/indexify
that referenced
this pull request
Mar 2, 2026
Adds server and dataplane support for application-scoped state, enabling functions to share key-value state across request boundaries within the same application. This complements the existing per-request ctx.state. Proto changes: - Add FunctionExecutorMetadata message with app_state_uri_prefix to ContainerDescription in executor_api.proto - Add AllocationAppState operation/result messages to function_executor.proto - Wire into AllocationState and AllocationUpdate Server changes: - Add app_state_key_prefix() to DataPayload for URI computation - Populate FunctionExecutorMetadata on ContainerDescription in both build_full_snapshot() and build_add_container_command() Dataplane changes: - Add AppStateHandler with full reconcile logic in state_ops.rs - Add app_state_uri_prefix to AllocationContext - Read FunctionExecutorMetadata from container description in allocation_lifecycle.rs Integration test: - Add test_app_state.py covering set/get, downstream reads, cross-request persistence, overwrites, and default values Companion PR: tensorlakeai/tensorlake#542 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Summary
Some agents require updating context about users or objects they are managing. Application State is a way for applications to share state across request boundaries as they learn about the problem domain.
For example, a fitness tracker agent managing fitness logs of multiple users can store per-user context in
app_stateas a flexible K/V store — much more practical than using rigid database schemas for evolving agent knowledge.API
How it works
ctx.statearchitecture at every layerprepare_read→ transfer →commit_write{namespace}/{application}/app_state/{key}(vs{namespace}/{application}/{request_id}/state/{key}for request state)Changes
ApplicationStateclass +app_stateproperty onRequestContextfunction_executor.proto+ regenerated stubsAllocationAppStateclass, wired intoAllocationRunnerandAllocationStateWrapperApplicationStateHTTPClientwired intoRequestContextHTTPClientTest plan