Comparison · Build it yourself
Curiosity and LangChain and LlamaIndex
Build the layer, or build on it
Curiosity is the system; LangChain and LlamaIndex are frameworks to build one.
Your data · your environment
Who each tool is for
Different problems, honestly stated
Curiosity
For industrial and regulated teams whose knowledge sits in engineering systems and structured records, where the answer depends on how those records connect, and where the deployment has to run on their own infrastructure.
LangChain and LlamaIndex
Open source frameworks for building applications on language models: retrieval, agents and the glue between them, with hosted services alongside. They give an engineering team full control and leave the system to that team.
How Curiosity compares
Where the boundary falls
A framework is the right choice when you want to own every part. Most of the work is not the chain: it is connectors, permission sync, keeping data current and the audit trail, which Curiosity ships.
The parts you would build
Connectors, incremental sync, a typed graph, hybrid retrieval and an interface come with the system rather than being the next quarter of work.
Permissions at query time
Access is synced from the source systems and enforced on every query, down to the record. In a framework that is code your team writes and maintains.
Your code still fits
Custom endpoints, AI tools and agents are written against the graph in C# or Python, so you keep the control a framework gives you.
Side by side
Feature comparison
The same five questions, answered for both.
Deployment
Cloud or self-hosted, ready to use
Your code, anywhere
Data model
Managed context layer and knowledge graph
Whatever you build
Industrial fit
Built for industrial and enterprise teams
Engineering teams
Data reach
70+ systems connected, and yours through a connector you write
Loaders you wire up
Residency
EU hosted, or entirely your own infrastructure
Your choice
Customization
What your team can change
Your own code, interface, data model and configuration. LangChain and LlamaIndex as its own documentation described it in October 2026. Where it says Not available, the vendor documents no such capability.
Your own code
C# endpoints, AI tools and tasks, compiled in Studio
Python and TypeScript code, runs where you deploy
Your own interface
Your own front end in C#, deployed from git
Your own front end, React hooks for workflow UIs
Data model
Typed node and edge schemas you define
Defined in your code, no managed data model
Configuration as code
Every change committed to git, pushed to your remote
Plain code in your git, CLI deployments
APIs and SDKs
REST API, C# and Python library, connector SDK
Python and TypeScript SDKs, Agent Server REST API
AI integration
What AI can build and reach
Whether AI can build on it, who reviews the result, and which models it runs. LangChain and LlamaIndex as its own documentation described it in October 2026.
AI that builds
Sudo drafts endpoints, tools, schemas and screens
Fleet and LlamaAgents Builder generate agents from a prompt
Review before it applies
Every change a diff a person approves, in a sandbox
Fleet build pauses for input, tool approvals at runtime
Model choice
Any major provider, or an OpenAI compatible endpoint
Most model providers, any OpenAI compatible endpoint
Models on your servers
Open weights on your servers, or air gapped
Local open weights through Ollama and similar runtimes
Assistant permissions
Built in tools search as the user, per record
Fleet acts per user via OAuth, else your code
Agent protocols
MCP server and an OpenAI compatible API
MCP client and server, A2A endpoint
At Airbus, we developed the next generation of customer support AI assistance tools with Curiosity, enhancing our technical support’s ability to access and navigate vast data from multiple sources, including millions of documents.
Services Innovation Team
Airline Services at Airbus
Build it yourself
Other comparisons in this group
Neo4j
Curiosity gives you a finished context layer; Neo4j gives you a graph database.
CompareAmazon Neptune
Curiosity gives you a finished context layer; Neptune gives you a managed graph store.
CompareGraphwise GraphDB
Curiosity gives you a finished context layer; GraphDB gives you a semantic database.
CompareYour data. Your infrastructure.