Uvi vs Langfuse
Different layers, complementary tools. Langfuse measures + versions. Uvi cuts the volume at the source.
The thesis in one paragraph
Langfuse is open-source observability + prompt management - traces every LLM call, versions every prompt, runs evals against outputs. Necessary for shipping LLM features in production. Uvi is a Reasoning Layer - the data layer your agents query against, returning structured chains of decisions instead of speculative documents. The two solve different problems. Compose them and your Langfuse eval scores improve, your spend curve flattens, and your prompt iteration count drops.
Side by side
Layer of the stack
- Uvi
- Context delivery (upstream of model)
- Langfuse
- Observability + prompt management (downstream of model)
What it returns to the agent
- Uvi
- Structured chain of decisions, owners, evidence
- Langfuse
- Traces, evals, prompt versions - inputs to your dev cycle, not to the agent
Primary user
- Uvi
- Agents (via GraphQL + MCP)
- Langfuse
- Developers shipping LLM apps
What it changes about cost
- Uvi
- Cuts token volume by replacing speculative retrieval with precision
- Langfuse
- Surfaces spend per trace, per prompt version - prerequisite for cost discipline
Deployment
- Uvi
- On-prem or VPC, native RBAC at retrieval
- Langfuse
- Self-hosted (open-source) or cloud
Integration point
- Uvi
- GraphQL + MCP server queried by your agents
- Langfuse
- SDK in your app emitting traces / fetching versioned prompts
Time to value
- Uvi
- 2-4 weeks via Forward Deployed Agent
- Langfuse
- Days via SDK + dashboard setup
Open source?
- Uvi
- Decision Graph spec is open; engine is proprietary
- Langfuse
- Yes, MIT licensed
Compose well?
- Uvi
- Designed to run alongside observability
- Langfuse
- Yes - keeps tracing the LLM calls Uvi makes
| Uvi | Langfuse | |
|---|---|---|
| Layer of the stack | Context delivery (upstream of model) | Observability + prompt management (downstream of model) |
| What it returns to the agent | Structured chain of decisions, owners, evidence | Traces, evals, prompt versions - inputs to your dev cycle, not to the agent |
| Primary user | Agents (via GraphQL + MCP) | Developers shipping LLM apps |
| What it changes about cost | Cuts token volume by replacing speculative retrieval with precision | Surfaces spend per trace, per prompt version - prerequisite for cost discipline |
| Deployment | On-prem or VPC, native RBAC at retrieval | Self-hosted (open-source) or cloud |
| Integration point | GraphQL + MCP server queried by your agents | SDK in your app emitting traces / fetching versioned prompts |
| Time to value | 2-4 weeks via Forward Deployed Agent | Days via SDK + dashboard setup |
| Open source? | Decision Graph spec is open; engine is proprietary | Yes, MIT licensed |
| Compose well? | Designed to run alongside observability | Yes - keeps tracing the LLM calls Uvi makes |
FAQ
Why would I add Uvi if I already have Langfuse?
Langfuse tells you which prompts perform, what they cost, and how outputs change as you iterate. It does not change what your agent sees when it makes a call. Uvi changes what the agent sees - structured decisions instead of speculative document chunks - so the prompts you're versioning in Langfuse start succeeding on the first try. The result: fewer prompt iterations, fewer eval failures, lower spend.
Can I run Langfuse traces over Uvi-grounded calls?
Yes - Uvi makes LLM calls on behalf of agents and Langfuse can trace each one. Customers running both see Langfuse eval scores improve sharply within weeks of integrating Uvi because the agent stops failing on context-retrieval issues, which are a large share of enterprise eval failures.
Is Langfuse a competitor to Uvi's accuracy claims?
Different category. Langfuse measures accuracy via evals; Uvi improves accuracy via better inputs. The 97-of-100 head-to-head against MCP-enabled GPT-4.1 at Global-e is the kind of result an eval pipeline (Langfuse or otherwise) would measure - Uvi is the upstream change that produces it.
Which do I deploy first?
Langfuse first if you're shipping new LLM features and need traces + prompt management to iterate. Uvi first if you have evals already and the accuracy / cost numbers are the problem. Most R&D teams end up with both - one measures, one cuts at the source.
Better evals start with better inputs
Uvi cuts context-retrieval failures upstream of Langfuse evals. Engineers stop iterating prompts to compensate for missing context.