Causaly
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.
At a glance
- Type
- Persistent causal knowledge graph
- Tier
- T1
- Created
- 2018 (founded 2018 by Yiannis Kiachopoulos and Artur Saudabayev; London UK)
- Latest release
- not applicable — not OSS
- License
- not applicable — not OSS
- GitHub
- not applicable — no GitHub repo
- Pricing
- Enterprise only
- Funding
- Series B $60M (ICONIQ Growth) Jul 2023; total $93M
Taxonomy
- storage
- graph
- retrieval
- graph-traversal
- persistence
- long-term
- update
- extraction
- unit
- fact
- governance
- inspectable
- conflict
- overwrite
When to use
Optimised for: research-workflow integration + provenance + claim grounding
Anti-fit: not for non-research / non-academic use cases
Pros & cons
Pros
Causal-biology graph extracted from full literature corpus is unique — most science-AI competitors retrieve documents rather than causal links.
Cons
Biology / pharma scope; not general scientific search; subscription pricing.
Claims & capabilities
500M facts, 70M directional relationships. Major pharma partners. Agentic layer launched early-access Mar 2025.
Technical surface
- API surface
- searched not found
- Backend storage
- searched not found
- Deployment
- Managed-only
- 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
Compare Causaly with…
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.
- 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.
- Charisma.ai T1
No-code interactive narrative platform. Characters track what they know, when they were lied to, what they misremember; story state changes downstream behaviour. Characters can deliberately lie / misremember and track when players have lied.