Scite.ai
Stores surrounding textual context of every citation plus a deep-learning classification (supports / disputes / mentions). Persisted across the database, not computed on demand. MCP integration (2025) exposes the memory layer to Claude and ChatGPT.
At a glance
- Type
- Intent-classified citation context index
- Tier
- T2
- Created
- 2018 (founded 2018 by Josh Nicholson and Yuri Lazebnik in Brooklyn NY)
- Latest release
- not applicable — not OSS
- License
- not applicable — not OSS
- GitHub
- not applicable — no GitHub repo
- Pricing
- Free / Basic $7.99/mo / Premium $19.99/mo / Premium+ $59.99/mo / Enterprise custom
- Funding
- $1.93M raised; acquired by Research Solutions Dec 2023
Taxonomy
- storage
- vector
- retrieval
- similarity
- persistence
- long-term
- update
- extraction
- unit
- document
- governance
- inspectable
- conflict
- append
When to use
Optimised for: research-workflow integration + provenance + claim grounding
Anti-fit: not for non-research / non-academic use cases
Pros & cons
Pros
Smart Citations — memory of how each citation was used (supporting / contrasting / mentioning) is unique.
Cons
Niche to citation analysis; not a general scientific search product.
Claims & capabilities
Pre-classified citation intents — distinct from plain semantic search.
Technical surface
- API surface
- searched not found
- Backend storage
- searched not found
- Deployment
- Managed-only; acquired by Research Solutions Dec 2023
- Embedding model
- searched not found
- Multi-tenancy
- searched not found
- MCP
- no MCP support advertised — vertical product, no MCP server / client integration documented
- A2A
- no A2A protocol support advertised — vertical product, no A2A integration documented
- OpenTelemetry
- no OpenTelemetry integration advertised — vendor logs/observability not publicly documented
Similar systems
Other vertical / domain-specific ai memory in the catalog, ranked by inbound references.
- NVIDIA ReMEmbR T3
Builds long-horizon memory by captioning video segments with VILA, storing captions with timestamps + 3D position coordinates in MilvusDB. At query time, LLM iterates retrieval across text, time, and position modalities. Deployed on Nova Carter robot (Jetson Orin).
- Abridge T1
Clinician-assist ambient documentation. Source mapping: every AI-generated summary element traced back to the source utterance. Audit-and-trust layer over episodic memory. Built on proprietary 1.5M+ medical-encounter dataset.
- ASAPP GenerativeAgent T1
Treats memory as first-class architecture. Captures the digital footprint of every interaction; retrieves preference and history at engagement time. Public example: airline knowing a frequent flyer wants aisle seats with her son — preference-aware, not just history-lookup.
- BenevolentAI T1
Target identification / drug repurposing / mechanism tracing. 85+ data sources, petabyte-scale, rebuilt every few weeks. Wet-lab results re-enter the graph and shift downstream predictions — institutional experimental memory closing a feedback loop.
- Causaly T1
Drug discovery / target identification / causal mechanism tracing. The graph is the memory: 7 years of curated biomedical cause-effect relationships compounding with each new ingestion. Scientific RAG retrieves from a versioned causal substrate.
- Character.ai T1
Chat Memories (user-defined facts), auto-memories for c.ai+ subscribers, pinned memories, in-context retention. PipSqueak 2 model (April 2026) reduces in-conversation drift. Memory Visualization meter forthcoming.