Process Session Messages

Papr Memory API (1.0.0)

API for managing personal memory items with authentication and user-specific data.

Authentication

This API supports three authentication methods:

API Key: Include your API key in the X-API-Key header
```
X-API-Key: <your-api-key>
```
Session Token: Include your session token in the X-Session-Token header
```
X-Session-Token: <your-session-token>
```
Bearer Token: Include your OAuth2 token from Auth0 in the Authorization header
```
Authorization: Bearer <token>
```

All endpoints require one of these authentication methods.

Download OpenAPI description

index.json

index.yaml

Servers

Production server

https://memoryserver-staging-223473570766.us-west1.run.app/

v1

Operations

Compress Session

Request

Get compressed conversation context for a session.

Compress your conversation into hierarchical summaries with rich metadata, perfect for reducing token usage in LLM context windows.

Authentication Required: Bearer token, API key, or session token

What it returns:

short_term: Last 15 messages compressed
medium_term: Last ~100 messages compressed
long_term: Full session compressed
topics: Key topics discussed
enhanced_fields: Project context, tech stack, key decisions, next steps, files accessed

Perfect for:

Reducing token usage in LLM prompts (96% savings)
Providing conversation context without full history
Quick conversation overview for AI agents
Project documentation and status snapshots

Input: Just the session ID - all context is extracted automatically

Path

session_idstring(Session Id)required

curl -i -X GET \
  'https://memoryserver-staging-223473570766.us-west1.run.app/v1/messages/sessions/{session_id}/compress' \
  -H 'Authorization: Bearer <YOUR_JWT_HERE>'

Responses

Session summary (compressed context)

Bodyapplication/json

session_idstring(Session Id)required

Session ID of the conversation

Example: "session_123"

summariesobject(ConversationSummaryResponse)required

Hierarchical conversation summaries

Example: {"last_updated":"2024-01-15T10:31:00Z","long_term":"Product planning and strategy conversation focused on Q4 roadmap development and execution.","medium_term":"Ongoing product planning discussion for Q4, covering objectives, timelines, and resource allocation.","short_term":"User requested help planning Q4 product roadmap. Assistant offered to help identify key objectives.","topics":["product planning","Q4 roadmap","objectives","strategy"]}

summaries.short_termShort Term (string) or Short Term (null)(Short Term)

Summary of last 15 messages

Example: "User requested help planning Q4 product roadmap. Assistant offered to help identify key objectives."

Any of:

Summary of last 15 messages

string(Short Term)

Summary of last 15 messages

summaries.medium_termMedium Term (string) or Medium Term (null)(Medium Term)

Summary of last ~100 messages

Example: "Ongoing product planning discussion for Q4, covering objectives, timelines, and resource allocation."

Any of:

Summary of last ~100 messages

string(Medium Term)

Summary of last ~100 messages

summaries.long_termLong Term (string) or Long Term (null)(Long Term)

Full session summary

Example: "Product planning and strategy conversation focused on Q4 roadmap development and execution."

Any of:

Full session summary

string(Long Term)

Full session summary

summaries.topicsArray of strings(Topics)

Key topics discussed

Example: ["product planning","Q4 roadmap","objectives","strategy"]

summaries.last_updatedLast Updated (string) or Last Updated (null)(Last Updated)

When summaries were last updated

Example: "2024-01-15T10:31:00Z"

Any of:

When summaries were last updated

string(date-time)(Last Updated)

When summaries were last updated

ai_agent_notestring(Ai Agent Note)required

Instructions for AI agents on how to search for more details about this conversation

Example: "To find more details about this conversation, search memories with metadata filter: sessionId='session_123'"

from_cacheboolean(From Cache)required

Whether summaries were retrieved from cache (true) or just generated (false)

Example: true

message_countMessage Count (integer) or Message Count (null)(Message Count)

Number of messages summarized (only present if just generated)

Any of:

Number of messages summarized (only present if just generated)

integer(Message Count)

Number of messages summarized (only present if just generated)

Response

application/json

{
  "ai_agent_note": "To find more details about this conversation, search memories with metadata filter: sessionId='session_123'",
  "from_cache": true,
  "session_id": "session_123",
  "summaries": {
    "last_updated": "2024-01-15T10:31:00Z",
    "long_term": "Product planning and strategy conversation focused on Q4 roadmap development and execution.",
    "medium_term": "Ongoing product planning discussion for Q4, covering objectives, timelines, and resource allocation.",
    "short_term": "User requested help planning Q4 product roadmap. Assistant offered to help identify key objectives.",
    "topics": [ … ]
  }
}

Process Session Messages

Request

Process all stored messages in a session that were previously stored with process_messages=false.

Authentication Required: Bearer token, API key, or session token

This endpoint allows you to retroactively process messages that were initially stored without processing. Useful for:

Processing messages after deciding you want them as memories
Batch processing large conversation sessions
Re-processing sessions with updated AI models

Processing Behavior:

Only processes messages with status 'stored_only' or 'pending'
Uses the same smart batch analysis (every 15 messages)
Respects existing memory creation pipeline

Path

session_idstring(Session Id)required

curl -i -X POST \
  'https://memoryserver-staging-223473570766.us-west1.run.app/v1/messages/sessions/{session_id}/process' \
  -H 'Authorization: Bearer <YOUR_JWT_HERE>'

Responses

Session messages queued for processing

Bodyapplication/json

any

Response

application/json

null

Export memories to OMO format

Request

Export memories in Open Memory Object (OMO) standard format.

This enables memory portability to other OMO-compliant platforms. The exported format follows the OMO v1 schema.

OMO Standard: https://github.com/papr-ai/open-memory-object

Bodyapplication/jsonrequired

memory_idsArray of strings(Memory Ids)required

List of memory IDs to export

curl -i -X POST \
  https://memoryserver-staging-223473570766.us-west1.run.app/v1/omo/export \
  -H 'Content-Type: application/json' \
  -H 'X-API-Key: YOUR_API_KEY_HERE' \
  -d '{
    "memory_ids": [
      "string"
    ]
  }'

Responses

Successful Response

Bodyapplication/json

codeinteger(Code)

Default 200

statusstring(Status)

Default "success"

countinteger(Count)required

Number of memories exported

memoriesArray of objects(Memories)required

Memories in OMO v1 format

memories[].property name*anyadditional property

errorError (string) or Error (null)(Error)

Any of:

string(Error)

Response

application/json

{
  "code": 200,
  "status": "success",
  "count": 0,
  "memories": [
    {}
  ],
  "error": "string"
}

Memory

Operations

Memory Status

Operations

User

Operations

Feedback

Operations

Schema Management

Operations

Messages

Operations

omo

Operations

Namespace

Operations

Frequency Schemas

Operations

Holographic Transform

Operations

Instance Configuration

Operations

AI Proxy

Operations

Sync

Operations

Telemetry

Operations

Document

Operations

GraphQL

Operations

Authentication

Operations

Papr Memory API (1.0.0)

Authentication

v1

Compress Session

Request

Responses

Was this helpful?

Process Session Messages

Request

Responses

Was this helpful?

Export memories to OMO format

Request

Responses

Was this helpful?

Memory

Memory Status

User

Feedback

Schema Management

Messages

omo

Namespace

Frequency Schemas

Holographic Transform

Instance Configuration

AI Proxy

Sync

Telemetry

Document

GraphQL

Authentication