Memory Query Planner

The planner's first task is to parse the natural language or structured query to understand user intent. This involves:

• Semantic parsing to extract key entities, relationships, and constraints.
• Intent classification to categorize the query (e.g., factual lookup, temporal sequence, comparative analysis).
• Determining if the query requires semantic search, keyword filtering, temporal reasoning, or access to a knowledge graph. For example, the query "What did the user say about project budgets last week?" triggers intent for temporal and topical retrieval.

Based on the analyzed intent, the planner selects the most appropriate data indices and retrieval algorithms. A modern memory system uses multiple indices:

• Vector Index: For semantic similarity searches using embeddings.
• Inverted Index: For fast keyword or metadata filtering.
• Graph Index: For traversing relationships in a knowledge graph.
• Time-Series Index: For queries involving chronological order. The planner might decide to query a vector store for semantic context and an inverted index for user-ID filtering concurrently, then merge the results.

The planner creates a cost-efficient execution plan. Key optimizations include:

• Predicate Pushdown: Applying filters (e.g., date ranges, user IDs) as early as possible in the retrieval pipeline to reduce data volume.
• Query Rewriting: Transforming a complex query into a more efficient sequence of operations.
• Caching Strategy: Deciding whether to check a low-latency semantic cache for similar past queries before executing a full retrieval.
• Parallelization: Identifying independent sub-queries that can be executed in parallel against different indices to reduce overall latency.

For complex queries, the planner orchestrates a hybrid retrieval strategy, combining multiple search techniques. A common pattern is vector search + keyword boost.

• The planner might execute a broad semantic search via vectors, then re-rank the results using keyword relevance scores.
• Alternatively, it may use keyword search to create a candidate set, then apply semantic re-ranking. The planner decides the sequence and weightings based on query type and system performance data.

The planner is responsible for managing output within the LLM's context window constraints. This involves:

• Result Limiting & Pagination: Determining the optimal number of memory snippets ("chunks") to retrieve to balance completeness with token usage.
• Relevance Scoring & Filtering: Applying thresholds to low-confidence results before passing them to the agent.
• Synthesis Formatting: Structuring the retrieved context (e.g., adding metadata like timestamps, sources) in a format optimal for the downstream reasoning engine or LLM.

A sophisticated planner incorporates feedback loops. It:

• Instruments each query path, collecting metrics on latency, cache hit rate, and result relevance (via implicit or explicit feedback).
• Uses this telemetry to adapt future planning decisions. For instance, if certain query patterns consistently have low relevance scores with vector search alone, the planner can automatically default to a hybrid strategy for similar future queries.
• This enables the system to continuously optimize its retrieval strategies based on real usage patterns.

The automated collection and transmission of operational data from a memory system. For a query planner, this includes metrics on plan selection frequency, execution time per plan, and cache hit/miss rates for different retrieval strategies. This data is essential for:

• Optimizing planner heuristics based on real workload patterns.
• Detecting performance regressions after changes to the memory store or embedding models.
• Providing the raw data that feeds into memory dashboards and alerting systems.

A detailed, end-to-end record of all processing steps for a single query. A trace for a Memory Query Planner request would log:

• The original natural language query and its parsed intent.
• The candidate execution plans considered and their cost estimates.
• The selected plan and the rationale (e.g., 'lowest estimated latency').
• Sub-operation timings for index lookups, vector searches, and result fusion.
• The final retrieval score and returned items. This granular visibility is critical for debugging slow queries or understanding why a suboptimal plan was chosen.

Quantitative measurements of the memory system's performance and health. Key planner-specific metrics include:

• Planning Latency: Time taken to analyze the query and select an execution plan.
• Plan Effectiveness: Ratio of queries where the chosen plan matched the theoretically optimal one (requires ground-truth testing).
• Index Utilization: Frequency of use for different indexed paths (e.g., vector vs. keyword vs. graph).
• Retrieval Precision@K: Measures the relevance of results, indirectly evaluating planner decisions on algorithm and parameter selection. These metrics are exposed via the Memory Query API and aggregated for dashboards.

The programmatic interface through which queries are submitted to the memory system. The planner acts as the intelligent routing layer behind this API. The API specification defines:

• The query language (e.g., natural language, structured filters, hybrid search) that the planner must interpret.
• Parameters (e.g., top_k, score_threshold) that constrain the planner's search space.
• The response format, which includes the retrieved items and often the explainability payload detailing the plan used. The planner's goal is to fulfill the intent of an API call with maximal efficiency.

The preprocessing stage that creates the searchable data structures the planner evaluates. The planner's effectiveness is directly constrained by the quality of these underlying indexes:

• Chunking Strategies (semantic, fixed-size, recursive) determine the granularity of stored memories, affecting retrieval precision.
• Index Types created (e.g., HNSW for vectors, B-tree for metadata, graph for relationships) define the set of access paths available to the planner.
• Embedding Models used for vectorization determine the semantic similarity space; the planner must be aware of model strengths/weaknesses for certain query types. The planner does not create indexes but must understand their capabilities and costs.

The application of the vendor-agnostic OpenTelemetry standard to instrument memory systems. For a query planner, this involves creating spans for each planning phase and emitting metrics as OTLP data. Benefits include:

• Unified Observability: Planner traces can be correlated with application and downstream database traces.
• Standardized Export: Data can be sent to any compatible backend (e.g., Jaeger, Prometheus, Datadog).
• Automatic Instrumentation: Libraries can provide baseline tracing for common planner operations. This transforms planner observability from a custom implementation to a standardized engineering practice.