GitHub user letaoj closed a discussion: Replay-based Per Action State
Consistency
# Background
Flink Agents execute various actions during record processing, including model
inference and tool invocation. Model inference involves calling LLMs for
reasoning, classification, or generation tasks, often through expensive API
calls to external providers. Tool invocation allows agents to interact with
external systems through UDFs with network access, with native support for
Model Context Protocol (MCP). These actions enable agents to perform contextual
searches, execute business logic, interact with enterprise systems, and invoke
specialized processing services.
# Problem
## Side Effects and Costs from Action Replay
While Flink provides exactly-once processing guarantees for stream processing
on a per message basis, agent actions create challenges around side effects,
costs, and recovery semantics. Both model inference and tool invocation can
produce effects that persist beyond the agent's execution context or incur
significant costs that should not be duplicated.
The core problem occurs when:
1. A Flink agent processes a record and executes multiple actions (model calls,
tool calls)
2. Some actions complete successfully, potentially modifying external state or
consuming costly resources
3. The agent crashes before completing record processing
4. Upon recovery, Flink reprocesses the same record, repeating all actions
This creates several issues:
* duplicate side effects in external systems (e.g. sending the same email
multiple times to the same recipient)
* unnecessary costs from repeated expensive model inference calls
* consistency violations from mixed successful and repeated actions
* resource waste from redundant operations
* observability challenges when debugging multiple executions of the same
logical operation.
## Non-Deterministic Model Outputs
A critical additional challenge is that model inference and generation is
inherently non-deterministic. Repeating model calls multiple times with
identical inputs may result in different outputs due to sampling, temperature
settings, or model provider variations. This creates severe consistency
problems when model outputs drive downstream decisions such as reasoning chains
or tool selection.
Consider this scenario: an agent makes a model call that decides to invoke Tool
A, but crashes before completion. Upon recovery, the same model call with
identical inputs may decide to invoke Tool B instead. This leaves the system in
an inconsistent state where Tool A was already executed based on the first
decision, but the agent now wants to execute Tool B based on the second
decision. The best approach is to ensure the model never makes the same
decision twice - the original model output should be preserved and reused
during recovery.
Flink's streaming architecture introduces additional complexity through
continuous processing on unbounded streams, distributed state management,
back-pressure from action failures, and a semantic gap where exactly-once
guarantees don't extend to external model providers or tool endpoints.
# Goals and Non-Goals
## Goals
* **Durability of Agent State (short-term memory)**: Ensure that the state can
be recovered when the agent crashes.
* **State Recovery**: Provide a mechanism to recover the agent's short-term
memory by replaying the action history from the state store.
* **Minimal Performance Overhead**: The solution should have minimal impact on
the performance of the Flink job during normal operation.
* **Pluggable Architecture**: The design should be flexible enough to support
different types of storage systems.
## Non-Goals
* **Long-Term Memory**: This design focuses on recovering the short-term memory
of the current task. It does not address the problem of long-term memory or
knowledge persistence.
* **Database Management**: This design assumes that the external storage system
is already set up and managed. It does not cover the details of database
administration.
# High-Level Design
## Execution Flow for Static Agent
```mermaid
sequenceDiagram
participant A as Agent
participant M as Short-term Memory
participant SS as State Store
A ->> SS: Check if the key exists or not
opt key exists
SS ->> A: saved state
A --> M: build the action to task action state map from saved state
end
A ->> A: receive Events (Chat Model/Tool/Prompt)
A ->> Chat Model/Tool/Prompt: take action(s)
A ->> A: check in-memory map to see if the key (message key + hash of action)
exists or not
alt not exists
A ->> SS: persist the input and a snapshot of the short-term memory
A ->> Chat Model/Tool/Prompt: send request
Chat Model/Tool/Prompt ->> A: receive response
A ->> SS: persist the output
A ->> A: process response
opt modify short-term memory
A ->> SS: persist the modification(s)
A ->> M: update short-term memory
end
A ->> A: generate the output event(s)
A ->> SS: persist the output event(s)
else exists
A ->> A: get the task action state from memory
A ->> M: apply short term memory modification
end
A ->> A: send output event
```
>From the above diagram, the save state will evolve like below
- `<message_key>-<action_hash_1>: {"request": request}`
- `<message_key>-<action_hash_1>: {"request": request, "response": response}`
- `<message_key>-<action_hash_1>: {"request": request, "response": response,
"short-term-memory-updates": [...]}`
- `<message_key>-<action_hash_1>: {"request": request, "response": response,
"short-term-memory-updates": [...], "output_event": [output_event]}`
- `<message_key>-<action_hash_2>: ...`
- `<message_key>-<action_hash_2>: ...`
- `<message_key>-<action_hash_2>: ...`
- `<message_key>-<action_hash_2>: ...`
- `<message_key>-<action_hash_3>: ...`
## APIs
### Task Action State
```python
@dataclasses
class TaskActionState:
"""
Data class to capture the TaskAction state
Attributes
----------
request: str
The request string associated with the task action.
response: str
The response generated by the agent.
memory_updates: List[MemoryUpdate]
List of memory updates for recovery after crash.
output_events: List[Event]
The output events produced by the agent.
"""
request: str
response: str
memory_updates: List[MemoryUpdate]
output_events: List[Event]
```
### Memory Updates
```python
@dataclasses
class MemoryUpdate:
"""
Class to capture all the memory updates so that it can be used to recover the
short-term memory after agent crash
Attributes
----------
path: str
The path in memory where the update occurred.
value: Any
The value that was updated at the specified path.
"""
path: str
value: Any
```
### Action Result Store
Action result store is an abstract layer to the external database which handles
the serialization/deserialization from/to AgentState
```python
class TaskActionStore(ABC):
"""
Abstraction layer to the external database
"""
@abstractmethod
def put(self, key: str, action: Action, value: TaskActionState):
"""
Persist the object into the external database
Parameters
-----------
key: str
The key of the agent consist of the message key
action: Action
The action the agent is now taking
value: TaskActionState
The TaskActionState associate with one action for that message key
"""
def get(self, key: str, action: Action): -> TaskActionState:
"""
Get the TaskActionState for a message key and action
Parameters:
----------
key: str
The key of the message suffixed with the unique id of an action
Returns:
--------
TaskActionState
The TaskActionState associate with the key.
"""
def list(self, key: str) -> List<TaskActionState>:
"""
List all the TaskActionState associate with the given message key
Parameters:
-----------
key: str
The key of the message
Returns:
--------
List<TaskActionState>
List of all the task action states
"""
```
## External Database
### Database Consideration
Below are some characters of the agent state to consider when picking the right
external DB:
* Low QPS - Agent state updates typically exhibit lower Queries Per Second
(QPS) due to the prolonged response times from the LLM model and tool
invocation. Consequently, the storage system only needs to accommodate low QPS.
* Durability - The agent state is crucial for action recovery following runtime
failures and for debugging purposes. Therefore, the storage system must ensure
persistence.
* Strong Consistency - To facilitate rapid recovery from the state store after
a crash, the agent state must maintain strong consistency.
* High Data Volume - Given that both model and tool responses will be stored in
the state store, the data size can become substantial. Thus, the state store
should be capable of managing data at the megabyte level in extreme scenarios,
such as retrieving emails from an email server.
* Ranged Get - During recovery, we will use a ranged get the get based on the
key generated by the agent, so that the external database need to support
ranged get.
### Data Retention
To prevent unbounded growth in backend storage, we need to implement a data
retention policy since this data is only required for failure recovery. Once a
Flink checkpoint is successfully committed, we should automatically delete all
data that precedes that checkpoint, ensuring storage remains manageable while
maintaining the necessary recovery capabilities. This can be achieved by listen
and act on `notifyCheckpointComplete` sent by flink after each checkpoint.
### Viable solution
A practical solution for the external database is to use Kafka. All the state
will be written to Kafka as a separate message. The per-partition offset will
be recorded in the flink state. During recovery, Flink agent will get the
latest offset information from the latest checkpoint and reading from the
offset until the end to recover the task action state.
There are a couple of drawbacks of using Kafka as the data store
* Data retention: Kafka does not allow explicitly message removal, only through
retention period setting. So we will need to setup a reasonable retention
period to keep the state data at a lower-level
* Partition management: In the events of rescale of the Flink agent, since
Kafka's partition number is fixed, so there could be a chance that two Flink
agents reading from the same partition causing read amplification. However,
consume from Kafka is only during recover, so with extra latency, it would
still be acceptable.
GitHub link: https://github.com/apache/flink-agents/discussions/108
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