State Eviction Policy

The Least Recently Used (LRU) policy evicts the state data that has not been accessed for the longest time. It operates on the principle that recently used data is likely to be used again soon.

• Implementation: Typically uses a doubly-linked list and a hash map. When an item is accessed, it's moved to the front (most recent). The item at the back of the list is evicted.
• Use Case: Ideal for agent conversation context or session state where recent interactions are most relevant. It's a default choice for many caching layers.

The Least Frequently Used (LFU) policy evicts the state data with the lowest number of accesses over a given period. It prioritizes keeping commonly referenced data in memory.

• Implementation: Maintains a counter for each item. Requires more overhead to track and decay frequencies to handle shifts in access patterns.
• Use Case: Effective for agents with stable, long-term reference data, such as cached tool schemas or frequently accessed knowledge graph entities.

The First-In, First-Out (FIFO) policy evicts state data in the order it was loaded into memory, regardless of how often it has been used. It's a simple queue-based approach.

• Implementation: Uses a standard queue. New entries are added to the back; the entry at the front is evicted when needed.
• Use Case: Suitable for streaming or sequential agent tasks where data has a natural expiration, like processing a linear execution trace or a time-ordered event buffer.

The Random Replacement (RR) policy selects a candidate for eviction at random. Its simplicity avoids the tracking overhead of LRU or LFU.

• Implementation: On eviction, a random index or key is selected from the state store.
• Use Case: Can be effective when access patterns are truly unpredictable or as a lightweight baseline. Sometimes used in large-scale distributed agent state caches where perfect optimality is less critical than low management cost.

Time-To-Live (TTL) Expiration is not a choice-based algorithm but a time-based rule. Each state entry has a timestamp and a predefined lifespan; it is evicted automatically when it expires.

• Implementation: Requires a background process or a priority queue (heap) ordered by expiration time to efficiently find and remove stale entries.
• Use Case: Critical for ephemeral session state, authentication tokens, or any agent data with a natural shelf-life, ensuring automatic cleanup and preventing memory leaks.

A Cost-Aware Eviction policy incorporates multiple factors—such as computational cost to recompute the state, retrieval latency from persistent storage, or business priority—to make an optimal eviction decision.

• Implementation: Assigns a score or cost to each state item. The item with the lowest score (highest benefit to keep) is evicted. This often combines LRU/LFU with custom metrics.
• Use Case: Essential for complex agents where state has variable importance. For example, evicting a cheap-to-recompute intermediate reasoning step before a costly RAG context window that took seconds to retrieve.

In-memory state refers to an agent's active operational data—such as conversation context, intermediate reasoning, and tool call results—held in volatile RAM for fast access during execution. This is the primary target of an eviction policy.

• Volatile Storage: Data is lost if the process terminates.
• Performance Critical: Enables low-latency decision-making.
• Resource Bound: Size is limited by available system memory, necessitating eviction.

Persistent state is the portion of an agent's operational data that is durably stored on disk or in a database, ensuring survival across sessions, restarts, or hardware failures. An eviction policy often moves data from in-memory to persistent state.

• Durable Storage: Survives process and system failures.
• Higher Latency: Slower to read/write than RAM.
• Eviction Target: The destination for offloaded in-memory state.

A state persistence layer is the software component responsible for durably storing and retrieving an agent's state. It provides the read/write interface for the eviction policy to offload and rehydrate state segments.

• Abstraction: Hides complexity of databases or object stores.
• Serialization/Deserialization: Converts in-memory objects to storable formats.
• Integration Point: Directly used by the eviction mechanism.

State rehydration is the process of reconstructing an agent's full, operational in-memory state from a persisted snapshot or checkpoint. This is the inverse operation triggered when evicted data is needed again.

• On-Demand Loading: Occurs when an agent accesses evicted data.
• Performance Cost: Introduces latency compared to in-memory access.
• Policy Dependency: The efficiency of the eviction policy is judged by the frequency and cost of rehydration.

A state schema is a formal definition specifying the structure, data types, and validation rules for an agent's internal state. It dictates how state can be partitioned and serialized for eviction.

• Data Contract: Ensures consistency across saves and loads.
• Eviction Granularity: Defines the units (e.g., objects, keys) that can be individually evicted.
• Versioning: Schemas evolve, requiring compatibility handling during rehydration of old state.

Context window usage is a telemetry metric measuring the proportion of an LLM agent's available token-based memory currently occupied. For LLM agents, this is a critical resource limit that drives eviction policies.

• Primary Driver: In LLM agents, eviction is often triggered by context window exhaustion.
• Telemetry Signal: Monitored to tune eviction policy aggressiveness.
• Strategic Eviction: Policies may prioritize evicting less relevant conversation turns or retrieved documents to stay within the limit.