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Archives

Create Archive
client.archives.create(ArchiveCreateParams { name, description, embedding, embedding_config } body, RequestOptionsoptions?): Archive { id, created_at, embedding_config, 7 more }
post/v1/archives/
List Archives
client.archives.list(ArchiveListParams { after, agent_id, before, 4 more } query?, RequestOptionsoptions?): ArrayPage<Archive { id, created_at, embedding_config, 7 more } >
get/v1/archives/
Update Archive
client.archives.update(stringarchiveID, ArchiveUpdateParams { description, name } body, RequestOptionsoptions?): Archive { id, created_at, embedding_config, 7 more }
patch/v1/archives/{archive_id}
Get Archive By Id
client.archives.retrieve(stringarchiveID, RequestOptionsoptions?): Archive { id, created_at, embedding_config, 7 more }
get/v1/archives/{archive_id}
Delete Archive
client.archives.delete(stringarchiveID, RequestOptionsoptions?): Archive { id, created_at, embedding_config, 7 more }
delete/v1/archives/{archive_id}
ModelsExpand Collapse
Archive { id, created_at, embedding_config, 7 more }

Representation of an archive - a collection of archival passages that can be shared between agents.

id: string

The human-friendly ID of the Archive

created_at: string

The creation date of the archive

formatdate-time
embedding_config: EmbeddingConfig { embedding_dim, embedding_endpoint_type, embedding_model, 7 more }

Embedding configuration for passages in this archive

embedding_dim: number

The dimension of the embedding.

embedding_endpoint_type: "openai" | "anthropic" | "bedrock" | 16 more

The endpoint type for the model.

Accepts one of the following:
"openai"
"anthropic"
"bedrock"
"google_ai"
"google_vertex"
"azure"
"groq"
"ollama"
"webui"
"webui-legacy"
"lmstudio"
"lmstudio-legacy"
"llamacpp"
"koboldcpp"
"vllm"
"hugging-face"
"mistral"
"together"
"pinecone"
embedding_model: string

The model for the embedding.

azure_deployment?: string | null

The Azure deployment for the model.

azure_endpoint?: string | null

The Azure endpoint for the model.

azure_version?: string | null

The Azure version for the model.

batch_size?: number

The maximum batch size for processing embeddings.

embedding_chunk_size?: number | null

The chunk size of the embedding.

embedding_endpoint?: string | null

The endpoint for the model (None if local).

handle?: string | null

The handle for this config, in the format provider/model-name.

name: string

The name of the archive

created_by_id?: string | null

The id of the user that made this object.

description?: string | null

A description of the archive

last_updated_by_id?: string | null

The id of the user that made this object.

metadata?: Record<string, unknown> | null

Additional metadata

updated_at?: string | null

The timestamp when the object was last updated.

formatdate-time
vector_db_provider?: VectorDBProvider

The vector database provider used for this archive's passages

Accepts one of the following:
"native"
"tpuf"
"pinecone"
VectorDBProvider = "native" | "tpuf" | "pinecone"

Supported vector database providers for archival memory

Accepts one of the following:
"native"
"tpuf"
"pinecone"

ArchivesPassages

Create Passage In Archive
client.archives.passages.create(stringarchiveID, PassageCreateParams { text, metadata, tags } body, RequestOptionsoptions?): PassageCreateResponse { embedding, embedding_config, text, 12 more }
post/v1/archives/{archive_id}/passages
Delete Passage From Archive
client.archives.passages.delete(stringpassageID, PassageDeleteParams { archive_id } params, RequestOptionsoptions?): void
delete/v1/archives/{archive_id}/passages/{passage_id}