Lindy AI Memory
https://docs.lindy.ai/fundamentals/lindy-101/memory
Saves high-signal facts (scheduling preferences, communication patterns, recurring rules) selectively rather than raw transcripts. Memory entries editable, injected into every prompt call, persist across all future executions.
At a glance
- Type
- Selective KV memory injected into prompt
- Tier
- T1
- Section
- Framework-embedded memory
- Created
- 2023-01 (Lindy AI founded and launched January 2023 by Flo Crivello; YC W23)
- Latest release
- not applicable — not OSS
- License
- not applicable — not OSS
- GitHub
- not applicable — no GitHub repo
- Pricing
- Free plan; Plus $49.99/mo; Pro $99.99/mo; Max $199.99/mo; Enterprise custom with SSO/SCIM/audit logs
- Funding
- $49.9M total; Series B $35M Jan 2023; Battery Ventures key investor; YC W23
Taxonomy
- storage
- kv
- retrieval
- injection
- persistence
- cross-session
- update
- extraction
- unit
- fact
- governance
- user-controllable
- conflict
- llm-arbitrate
When to use
Optimised for: selective high-signal memory injection
Anti-fit: not for code-first developers
Pros & cons
Pros
Lifelong-agent positioning — memory isn't a feature but the product premise; most opinionated about memory of any framework.
Cons
Closed ecosystem; lock-in risk is highest of the framework-embedded options.
Claims & capabilities
Free plan; Plus from $49/mo; Enterprise custom.
Technical surface
- API surface
- searched not found
- Backend storage
- searched not found
- Deployment
- Managed cloud only (SaaS)
- Embedding model
- searched not found
- Multi-tenancy
- searched not found
- MCP
- searched not found
- A2A
- searched not found
- OpenTelemetry
- searched not found
Compare Lindy AI Memory with…
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.