Memory Write API

An atomic operation ensures that a write request either completes fully or fails entirely, preventing partial updates that could corrupt memory state. This is critical for maintaining data consistency when writing complex, multi-field memory objects. For example, writing a user's profile and their associated conversation history must succeed together to avoid orphaned data. Atomicity is typically implemented via database transactions or conditional writes.

Idempotency guarantees that performing the same write operation multiple times produces the same result as performing it once. This is essential for reliable retries in distributed systems where network timeouts are common. A Memory Write API achieves this by using client-supplied idempotency keys. For instance, sending a POST /memories request with the same key twice will create the memory record once; the second request returns the already-created record's ID without error.

The API enforces a schema on incoming data, validating structure, data types, and required fields before persistence. This prevents malformed data from entering the memory store, which could cause retrieval failures or runtime errors. Validation includes:

• Checking for mandatory fields like session_id or timestamp.
• Enforcing type constraints (e.g., embedding must be an array of floats).
• Applying domain-specific rules (e.g., relevance_score between 0 and 1).

A core function is the simultaneous writing of a dense vector embedding and its associated metadata. The embedding, generated by a model like OpenAI's text-embedding-3-small, is stored in a vector index for fast semantic search. The metadata (e.g., source text, timestamps, tags) is stored in a complementary database. The API abstracts this dual-write, ensuring both data representations are persisted atomically and remain linked via a common identifier.

To support collaborative or multi-agent scenarios, the API provides conditional write operations using mechanisms like optimistic concurrency control. Updates can specify an expected_version or last_modified timestamp; the write only proceeds if the condition matches, preventing overwrites from other processes. This enables safe, granular memory updates, such as appending to a conversation history only if no other agent has modified it since the last read.

For high-throughput ingestion, the API offers batch write endpoints. Instead of individual HTTP requests per memory item, clients can submit an array of records in a single payload. This drastically reduces network overhead and allows for internal optimizations like bulk indexing. Performance is measured in records per second (RPS). A typical implementation might accept batches of up to 100 items, with the API returning individual status codes for each item in the batch.

The complementary interface to a Memory Write API. While the Write API handles data ingestion, the Query API is used for semantic search, filtering, and retrieval of stored information. It typically accepts a query (text or embedding vector) and returns a ranked list of relevant memory items. This separation of concerns (CQRS - Command Query Responsibility Segregation) is common in scalable memory architectures.

An API that manages the data structure definitions within a memory store. Before a Write API can create records, a schema often defines the fields, data types, and indexing strategies. This API allows for:

• Dynamic schema creation for new types of agent memories.
• Schema validation to ensure data integrity on write operations.
• Index management to optimize for specific query patterns.

The automated collection of operational data from the memory system, including Write API calls. This is essential for observability and involves tracking:

• Write latency and throughput.
• Error rates and failure modes for create/update/delete operations.
• Resource utilization (CPU, memory, I/O) of the storage backend.
• Data volume growth over time.

A chronological, immutable log of all access and modification events. For a Write API, this creates a verifiable record of:

• Who (which agent/user) performed a write.
• What data was created, updated, or deleted.
• When the operation occurred (with high-resolution timestamps).
• The state change (useful for debugging data corruption or unauthorized changes).

A set of rules that governs data lifecycle within the memory store. It directly interacts with the Write and Delete operations of a Write API. Policies define:

• Time-to-Live (TTL) for ephemeral memories.
• Archival rules for moving old data to cold storage.
• Automatic deletion criteria based on age, relevance score, or storage limits.
• Compliance requirements for data sovereignty (e.g., GDPR).

The application of the OpenTelemetry standard to instrument memory systems, including Write APIs. This provides a vendor-neutral way to generate unified traces, metrics, and logs. Key benefits:

• Standardized instrumentation across different memory backends (vector DBs, graph DBs).
• End-to-end tracing to see how a write operation flows through the entire agentic stack.
• Correlation of logs and metrics using trace IDs for easier debugging of failed writes.