ResearchRabbit
https://www.researchrabbit.ai/
Literature discovery + citation mapping. Oct 2025 revamp formalises checkpointed search paths as named "rabbit holes" — each checkpoint is a saved state of the citation graph the user can branch from. Navigational memory rather than stateless re-querying.
At a glance
- Type
- Iterative paper-graph "rabbit-hole" checkpoints
- Tier
- T2
- Created
- 2021 (founded 2021 in Seattle; acquired by Litmaps May 2025)
- Latest release
- not applicable — not OSS
- License
- not applicable — not OSS
- GitHub
- not applicable — no GitHub repo
- Pricing
- Free (RR+ premium tier with country-parity pricing)
- Funding
- TA Ventures investor; acquired by Litmaps May 2025
Taxonomy
- storage
- graph
- retrieval
- graph-traversal
- persistence
- cross-session
- update
- append-only
- unit
- document
- governance
- user-controllable
- 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
Visual citation graph navigation — memory is the mental model of related-work landscapes.
Cons
Visual-discovery scope; less useful for direct Q&A or summarization.
Claims & capabilities
Graph-checkpoint memory model.
Technical surface
- API surface
- searched not found
- Backend storage
- searched not found
- Deployment
- Managed-only; acquired by Litmaps May 2025
- 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.