Knowledge & Memory - Hexel Studio

Agents draw on two kinds of persistent context. Knowledge is reference material you ingest and search semantically. Memory is information an agent records and recalls across scopes. Both are provided by the Data Platform REST API.

Knowledge vs Memory

These serve different purposes and have different access patterns:

	Knowledge	Memory
Created by	You (or a connector sync). Content is ingested, chunked, and embedded before it’s searchable.	An agent at runtime. Items are written during task execution.
Access pattern	Searched at query time using embedding similarity (not exact match). Returns ranked results by semantic relevance.	Written and read by a specific agent, scoped by runtime context.
Sharing	Shared across agents that have read access to the store. Multiple agents can search the same knowledge base.	Private to the writing agent’s scope (task, session, agent, or workspace).
Mutability	Updated via re-ingestion or connector sync. Individual chunks are not directly editable.	Items can be written, read, and overwritten by the owning agent.
Persistence	Permanent until you delete the store or its content.	Governed by scope TTL (24h for task, 8h for session, 1yr for agent/workspace).

Knowledge search uses embedding similarity, not keyword or exact match. A query for “refund policy” will match content about “return procedures” or “money-back guarantees” if the semantic meaning is close. Tune score_threshold to control the relevance cutoff.

Key concepts

Term	Meaning
Knowledge store	A searchable collection of ingested content (documents, connector data).
Semantic search	Retrieval by meaning, ranked by vector similarity.
Memory store	A container where an agent writes and reads items over time.
Memory scope	Controls how long items persist and how broadly they’re shared.

Knowledge

Built by ingesting documents or connecting data sources. Content is chunked and embedded, then available for semantic search.

Configuration	Range	Default
`chunk_size`	100–4000	800
`chunk_overlap`	0–500	100
`embedding_dimensions`	128–4096	1024
`chunking_strategy`	`recursive` / `markdown` / `conversation`	`recursive`
`embedding_provider`	`openai` / `bedrock` / `custom`	—

Search: query (1–10,000 chars), top_k (1–100, default 10), optional score_threshold.

Memory scopes and TTL

Scope	TTL	Use for
`TASK`	24 hours	Working state within a single task.
`SESSION`	8 hours	Context across a multi-turn session.
`AGENT`	1 year	Long-lived knowledge an agent accumulates.
`WORKSPACE`	1 year	Memory shared across a workspace.

Accessing via REST API

Knowledge and memory are accessed via curl against the Data Platform REST API. There is no SDK client for data operations.

# Search a knowledge store
curl -X POST https://api.hexelstudio.com/data/v1/knowledge/YOUR_STORE_ID/search \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"query": "refund policy", "top_k": 5}'

# Search memory
curl -X POST https://api.hexelstudio.com/data/v1/memory/YOUR_STORE_ID/search \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"query": "user preferences", "top_k": 3}'

Knowledge Stores

Create and search knowledge.

Memory

Record and recall agent memory.

Connectors

Sync external data into knowledge.

Context Bundles

Assemble grounded context for runs.

Next steps

Continue to Organizations.

​Knowledge vs Memory

​Key concepts

​Knowledge

​Memory scopes and TTL

​Accessing via REST API

​Related pages

Knowledge Stores

Memory

Connectors

Context Bundles

​Next steps

Knowledge vs Memory

Key concepts

Knowledge

Memory scopes and TTL

Accessing via REST API

Related pages

Next steps