Stack AI
Connects enterprise knowledge sources (SharePoint, Confluence, Notion, Drive, DBs) as memory layer with access controls, versioning, citations. Agents query at runtime; no separate session/cross-session store.
At a glance
- Type
- Enterprise-KB RAG (no agent memory store)
- Tier
- T2
- Section
- Framework-embedded memory
- Created
- 2022 (founded 2022 by Bernard Aceituno and Toni Rosinol; YC S23)
- Latest release
- not applicable — not OSS
- License
- not applicable — not OSS
- GitHub
- not applicable — no GitHub repo
- Pricing
- Free + paid
- Funding
- $16.6M total; Series A 2025 from Lobby Capital; YC W23
Taxonomy
- storage
- vector
- retrieval
- similarity
- persistence
- long-term
- update
- overwrite
- unit
- document
- governance
- auditable
- conflict
- n/a
When to use
Optimised for: enterprise KB connectors + access controls + citations
Anti-fit: not for agent-state memory; this is enterprise-KB RAG only
Pros & cons
Pros
No-code agent platform with memory integrated; fastest path for non-engineers.
Cons
Memory abstractions are simple; sophisticated agents hit ceilings quickly.
Claims & capabilities
Free tier; Enterprise custom.
Technical surface
- API surface
- searched not found
- Backend storage
- searched not found
- Deployment
- Both
- Embedding model
- searched not found
- Multi-tenancy
- searched not found
- MCP
- searched not found
- A2A
- searched not found
- OpenTelemetry
- searched not found
Similar systems
Other framework-embedded memory in the catalog, ranked by inbound references.
- LangGraph Persistence T2
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- AutoGen Memory T2
ListMemory chronological context + teachable agents that vectorise corrections. Integrates with Mem0/Zep rather than building deep memory natively.
- CrewAI Memory T2
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- AGiXT Adaptive Memory T2
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- Agno (Phidata) Memory T2
Agno (formerly Phidata). AgentStorage persists sessions to a DB; AgentMemory auto-classifies/store user preferences and conversation summaries. Single-line integrations with LanceDB, Pinecone, Weaviate, Qdrant.
- Botpress LLMz T1
Per-plan vector-DB storage quota + LLMz autonomous engine (in-session working memory) + Knowledge Base (semantic search over uploaded docs). Long-term user memory persists across sessions.