Relevance AI Memory
https://relevanceai.com/docs/agent/build-your-agent/memory
Two memory scopes: conversation metadata (KV per-conversation) and persistent memory (cross-session). Enabling metadata auto-adds Add/Read conversation metadata tools.
At a glance
- Type
- Short-term metadata + persistent KV store
- Tier
- T1
- Section
- Framework-embedded memory
- Created
- 2020 (Relevance AI founded 2020)
- Latest release
- not applicable — not OSS
- License
- not applicable — not OSS
- GitHub
- not applicable — no GitHub repo
- Pricing
- Free 200 actions/mo; Pro $19/mo; Team $234/mo; Business/Enterprise: 2 credits per run (vs 4 for free). Actions + Ve…
- Funding
- $24M Series B May 2025 led by Bessemer VP; total ~$30M+ raised; Series A ~$6M prior
Taxonomy
- storage
- kv
- retrieval
- injection
- persistence
- cross-session
- update
- overwrite
- unit
- fact
- governance
- inspectable
- conflict
- overwrite
When to use
Optimised for: low-code agent platform + dual-scope memory
Anti-fit: searched not found
Pros & cons
Pros
No-code agent platform with memory built in — fastest path to a deployed agent for non-engineers.
Cons
Memory abstractions are simple; sophisticated agent designs hit ceilings quickly.
Claims & capabilities
Free 200 actions/mo; Pro $19/mo; Team $234/mo.
Technical surface
- API surface
- searched not found
- Backend storage
- searched not found
- Deployment
- Managed cloud only (SaaS); multi-region on enterprise tier
- 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
Distinct from LangMem. Built-in checkpointer saves graph state per super-step (short-term, thread-scoped). Store System adds long-term hierarchical key-value memory across threads with optional vector search + TTL. Postgres / Mongo / Redis stores for production.
- AutoGen Memory T2
ListMemory chronological context + teachable agents that vectorise corrections. Integrates with Mem0/Zep rather than building deep memory natively.
- CrewAI Memory T2
Memory subsystem inside the CrewAI orchestration framework; integrates with Mem0 for the long-term tier.
- AGiXT Adaptive Memory T2
Open-source AI automation platform. Routes between short-term and long-term memory adaptively across any LLM provider; plugin system for storage backends. Memory managed at the instruction-management layer — task context, instruction state, conversation history as unified agent state.
- 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.