curl-X POST https://mnemoniqa.com/api/v1/instances/inst_abc/ingest \ -H“Authorization: Bearer ms_live_xxx” \ -d‘{“content”: “User prefers brief answers with code examples.”}’
curl-X POSThttps://mnemoniqa.com/api/v1/instances/inst_abc/query \ -H“Authorization: Bearer ms_live_xxx” \ -d‘{“query”: “What do we know about this user?”, “user_id”: “user_123”}’
Perfect for FAQ bots (RAG only) or simple session state (Working only).
POST /instances/:id/query
Agent · Unified
Up to 6 layers, one query
Combine RAG + Episodic + Working + Wiki + Graph + Reflective in a single query with weighted merge and synthesis.
Working
Reflective
Episodic
RAG
WIKI
Graph
Self-editing
The agent edits itself
Manages its own memory via tool calls during conversation: core memory, recall search, archival insert/update.
core · recall · archival
Get started
From zero to remembering agent in three steps
1
Create a memory instance
Choose a type (or build an Agent with multiple layers) in the dashboard. Get an API key.
2
Ingest your data
Upload docs, conversation logs, or structured facts. Async by default — returns task_id, progress via webhooks.
3
Connect your agent
REST API, Python/TypeScript SDK, or MCP for Claude Desktop / Cursor. Scope by user_id and session_id.
Why Mnemoniqa
Memory with provenance, not narrative generation
Full lineage
Every claim traces: source → segment → concept → answer. Responses include citations [Concept:ID] or [Source:UUID]. Not “text from similar chunks” — verified knowledge.
source→ segment → concept → answer
“Cancellation is self-service in Settings.” [Concept:c_9a1][Source:doc_44]
Fast model Structured extraction low reasoning · high volume
Smart model Synthesis & gardening high reasoning · low volume
−48% cost · +36% quality vs. single-model extraction
Split extraction pipeline
Fast model for structured extraction, smart model for synthesis and gardening. Lower cost, higher quality on concept-heavy memory (Wiki, Graph, Reflective).
Gardener — memory that maintains itself
Phase 0: cheap model proposes merges and splits. Phase 1: smart model applies surgical fixes. Proposals await your review — no silent auto-merge.
PHASE 0 Cheap model proposes merges & splits → queued for review
PHASE 1 Smart model applies surgical refactoring on approval
3 proposals pending · 0 auto-applied
fact plan = “Pro” valid_time2026-01-04 → now system_time2026-01-04 09:12 UTC query as-of 2025-12-01 → plan = “Starter”
Bi-temporal facts
Facts store valid time (when true in the world) and system time (when recorded). Query what the agent “knew” at any point in the past — critical for audit and compliance.
Use cases
Built for real agent products
🎮
Game / NPC
NPCs that remember the player across sessions.
Working
Reflective
Episodic
🧠
Personal coach
Grows with the user — goals, history, patterns.
Working
Reflective
Episodic
🎧
Support bot
Knows your product + each customer’s ticket history.
Working
Episodic
RAG
📚
Docs / KB bot
Answers with citations; concepts evolve as docs change.
Working
RAG
🎓
EdTech tutor
Remembers what each student learned and where they struggled.
Reflective
Episodic
RAG
💼
Sales / CRM agent
Every touchpoint, objection, and deal stage per contact.