GET /memories/context — Load a User's Session Context

Call this endpoint at the beginning of every conversation — before your agent generates its first response. Remem returns the most important and recent memories for the user so your agent already knows who they are and what they care about. If you supply current_message, the retrieval switches to semantic mode and returns memories most relevant to what the user just said, rather than a general importance-and-recency sort.

Endpoint: GET https://api.remem.online/memories/context

Request Headers

Header	Required	Value
`X-API-Key`	Yes	Your Remem API key (`rm_live_xxx`)

Query Parameters

string

required

Load memories belonging to this user.

string

required

Load memories belonging to this agent.

integer

default:"10"

Maximum number of memories to return. Minimum 1, maximum 50.

string

The first message sent by the user in this session. When provided, Remem uses semantic retrieval to return memories most relevant to this message. When omitted, memories are sorted by importance and recency.

Code Examples

# Without current_message — sorted by importance + recency
curl "https://api.remem.online/memories/context?\
user_id=user_123&agent_id=support_bot&top_k=10" \
  -H "X-API-Key: rm_live_xxx"

# With current_message — semantic retrieval
curl "https://api.remem.online/memories/context?\
user_id=user_123&agent_id=support_bot&\
current_message=I+need+help+with+my+order" \
  -H "X-API-Key: rm_live_xxx"

from remem import RememClient

client = RememClient(api_key="rm_live_xxx")

# At session start — no message yet
context = client.context(
    user_id="user_123",
    agent_id="support_bot",
    top_k=10,
)

# When the user sends their first message
context = client.context(
    user_id="user_123",
    agent_id="support_bot",
    current_message="I need help with my order",
)

# Inject into system prompt
memory_lines = "\n".join(f"- {m.content}" for m in context.memories)
system_prompt = f"What you know about this user:\n{memory_lines}"

Response

200 — Success

{
  "memories": [
    {
      "id":          "775263ee-73af-4416-9804-1f274048ae08",
      "content":     "User is based in Lagos, Nigeria",
      "user_id":     "user_123",
      "agent_id":    "support_bot",
      "memory_type": "semantic",
      "importance":  0.9,
      "metadata":    {},
      "created_at":  "2026-06-01T10:00:00Z",
      "last_accessed": "2026-06-07T09:00:00Z",
      "expires_at":  null
    }
  ],
  "total":  4,
  "limit":  10,
  "offset": 0
}

The response uses the same Memory object shape as the Search Memories endpoint. Refer to the Memory Object Fields table there for a full field-by-field description. Key fields at a glance:

Field	Type	Description
`id`	string	Memory UUID
`content`	string	The stored memory text
`memory_type`	string	`episodic`, `semantic`, or `summary`
`importance`	float	Priority score from `0.0` to `1.0`
`created_at`	string	ISO 8601 creation timestamp
`expires_at`	string \| null	ISO 8601 expiry timestamp, or `null` if permanent
`total`	integer	Total memories available for this user + agent pair
`limit`	integer	The `top_k` value applied to this response
`offset`	integer	Pagination offset (always `0` for context requests)

Error Responses

Code	Meaning	Fix
`401`	Invalid or missing API key	Verify your key is passed in the `X-API-Key` header
`422`	Missing required parameter	Both `user_id` and `agent_id` are required

Notes

context() vs recall() — when to use each

Use context() at session start, before you have a specific query. It surfaces the memories that are most important and recent for this user overall, giving your agent a baseline understanding before the conversation begins.Use recall() (the Search Memories endpoint) during a session, when the user says something specific and you want memories relevant to that particular message.You can combine both: call context() once at the start to pre-populate your system prompt, then call recall() on each user turn to retrieve memories relevant to what they just said.

New users — handling empty memory lists

If the user has no stored memories yet, context() returns an empty memories array. Make sure your agent handles this gracefully rather than treating it as an error:

context = client.context(user_id=user_id, agent_id=agent_id)

if context.memories:
    memory_text = "\n".join(f"- {m.content}" for m in context.memories)
else:
    memory_text = "This is a new user — no previous context available."

system_prompt = f"What you know about this user:\n{memory_text}"

A total of 0 in the response is the reliable signal that no memories exist yet for this user + agent pair.

​Request Headers

​Query Parameters

​Code Examples

​Response

​200 — Success

​Error Responses

​Notes

Request Headers

Query Parameters

Code Examples

Response

200 — Success

Error Responses

Notes