Uvi vs LangChain
LangChain orchestrates the agent. Uvi gives it the substrate to reason over. Composing the two is the right enterprise architecture.
The thesis in one paragraph
LangChain is the dominant agent orchestration framework - chains, agents, tools, memory, and now LangGraph for cyclic flows. It is the control layer for how an agent decides what to do next. Uvi is a Reasoning Layer - the data layer the agent queries to know what state the company is in. LangChain orchestrates; Uvi grounds. The two compose naturally: a LangChain or LangGraph agent calls Uvi's MCP server, gets back a structured chain of decisions, and reasons over it. The right enterprise architecture uses both.
Side by side
Category
- Uvi
- Reasoning Layer (data layer)
- LangChain
- Agent orchestration framework (control layer)
What it provides
- Uvi
- Structured chain of decisions, owners, evidence
- LangChain
- Chains, agents, tools, memory abstractions
Primary surface
- Uvi
- GraphQL + MCP queried by agents
- LangChain
- Python / TypeScript SDK developers build agents with
Cross-system joins
- Uvi
- Live joins across code / tickets / PRs / Slack / internal services
- LangChain
- Tools that wrap individual APIs - joins are agent-orchestrated, not first-class
Permission model
- Uvi
- Native RBAC at retrieval, mirrored from source ACLs
- LangChain
- Application-level - whatever you wire into the tools
Deployment
- Uvi
- On-prem or VPC, zero data egress
- LangChain
- Self-hosted, LangGraph Cloud, hybrid VPC
Time to value
- Uvi
- 2-4 weeks via Forward Deployed Agent (graph + integration)
- LangChain
- Hours for a first agent; weeks for production hardening
Open source?
- Uvi
- Decision Graph spec is open; engine is proprietary
- LangChain
- Yes, MIT licensed
Compose with each other?
- Uvi
- Uvi's MCP server is queried by LangChain / LangGraph agents
- LangChain
- Yes - native MCP support, treats Uvi as a structured tool
| Uvi | LangChain | |
|---|---|---|
| Category | Reasoning Layer (data layer) | Agent orchestration framework (control layer) |
| What it provides | Structured chain of decisions, owners, evidence | Chains, agents, tools, memory abstractions |
| Primary surface | GraphQL + MCP queried by agents | Python / TypeScript SDK developers build agents with |
| Cross-system joins | Live joins across code / tickets / PRs / Slack / internal services | Tools that wrap individual APIs - joins are agent-orchestrated, not first-class |
| Permission model | Native RBAC at retrieval, mirrored from source ACLs | Application-level - whatever you wire into the tools |
| Deployment | On-prem or VPC, zero data egress | Self-hosted, LangGraph Cloud, hybrid VPC |
| Time to value | 2-4 weeks via Forward Deployed Agent (graph + integration) | Hours for a first agent; weeks for production hardening |
| Open source? | Decision Graph spec is open; engine is proprietary | Yes, MIT licensed |
| Compose with each other? | Uvi's MCP server is queried by LangChain / LangGraph agents | Yes - native MCP support, treats Uvi as a structured tool |
FAQ
Is Uvi a LangChain competitor?
No - different layers. LangChain orchestrates an agent: it manages chains of tool calls, memory across turns, and the control flow between an LLM and external systems. Uvi is the data layer that LangChain agents query against. A LangChain agent calls Uvi's MCP server, gets back a structured chain of decisions, and reasons over it. The two are complementary - many Uvi customers use LangChain as their agent framework.
Does LangGraph change anything?
LangGraph is LangChain's graph-based agent orchestration - it lets you build cyclic agent workflows instead of linear chains. Same layer relationship to Uvi: LangGraph orchestrates the agent flow, Uvi provides the substrate the agent queries. They compose naturally - LangGraph nodes that call Uvi's MCP server return structured decision data the rest of the graph can branch on.
Can I build a Decision Graph with LangChain?
LangChain doesn't have a Decision Graph primitive - it has Tools, Memory, and Chains. You could write a series of LangChain tools that approximate Uvi's behavior (one tool per source system, one chain to assemble them), but you'd be reimplementing live joins, identity resolution, permission mirroring, and entity definitions from scratch. The Forward Deployed Agent engagement that ships a Uvi Decision Graph in 2-4 weeks would take months to replicate in LangChain.
How does the MCP integration work?
Uvi exposes an MCP (Model Context Protocol) server. Any MCP-aware client - including LangChain, Anthropic's Claude, Cursor, and any custom agent that speaks the protocol - can connect, list the available tools (typed entity queries), and call them. The agent expresses intent in natural language; the MCP server translates to a GraphQL query against the Decision Graph and returns structured results.
Which do I start with?
LangChain first if you're building agents and don't have one yet - it's the right control layer. Uvi first if you have agents that work in demos but fail in production because they can't reason about your company's actual state. Long term, the right enterprise architecture is LangChain (or similar) orchestrating + Uvi providing the substrate.
Orchestrate with LangChain. Ground with Uvi.
Uvi's MCP server plugs into LangChain / LangGraph agents in minutes. Your agents stop guessing about company state.