Start with the traces already in Langfuse
You can assess the dataset and evaluation workflow while continuing to use Langfuse. Begin with a representative slice of one production task. Model training is optional.
Step 1
Connect Langfuse
Open Observability, then Integrations, choose Langfuse, and add the public key, secret key and host for your project. The connector backfills retained history, then syncs incrementally. Sync now requests a manual pull.
Connector setupStep 2
Build an evaluation dataset
Review the imported traces, select examples for one task, and choose Add to dataset. Pick Evaluation, review the field mapping and source provenance, then inspect quality issues in Data Workshop.
Trace-to-dataset guideStep 3
Compare against a baseline
Define an eval set for the task and create an evaluation run from your dataset or captured traces. Pin a baseline and the quality criteria. Read individual failures as well as the aggregate score before choosing what to change next.
Evaluation setup
The connector reads from Langfuse, so available history depends on what Langfuse retains and what your API key can see. Imported traces become datasets, but scoring on arrival only applies to spans sent to Overmind's own OTLP endpoint. Evaluating outputs does not replay your production tools, and the optimizer runs from the CLI in your own repository and needs an executable agent.
Is Overmind a Langfuse alternative?
Traces
Datasets
Evals
Optimize
Train
Serve
Overmind
Langfuse
Langfuse describes fine-tuning as an export: trace data leaves for external tools. Overmind is one of the places it can go, connector included.
Should you replace Langfuse or run both?
Decision tree
Question 1 of 3, Is your agent in production, logging real runs?
Is your agent in production, logging real runs?
Three of the seven verdicts here end at Langfuse. A stack with no commercial services and regional data residency are two things Overmind has no answer to.
How does Overmind compare with Langfuse feature by feature?
| Overmind | Langfuse | |
|---|---|---|
| License | AGPL-3.0 platform, MIT SDK. Trained weights are yours | MIT, entire product, no usage limits |
| Self-hosting | Docker Compose or console.overmindlab.ai. Needs OpenRouter, Modal, S3, HF | Whole platform, free forever. Helm and Terraform |
| Fine-tuning | LoRA or full, hyperparameters recommended from your data | None. Data exports for external tools |
| Model serving | OpenAI-compatible endpoint, weights downloadable | None. Playground and judges use your API keys |
| Prompt improvement | Optimizer returns scored diffs you apply | Versioned prompts, instant rollbacks, manual editing |
| Proof before switching | Base vs trained, overlap reported. Production model opt-in | Experiments on datasets, app-level |
| Data residency | No published regional isolation. Self-host keeps traces in-house | Isolated EU, US, Japan, and HIPAA regions |
| Pricing | Free / Pro + credits. Ingest never credit-gated | Free 50k units/mo; $29, $199, $2,499 tiers; self-host $0 |
What does Langfuse do that Overmind does not?
Langfuse is MIT-licensed end to end and self-hosts free at any scale, with Helm and Terraform, versioned prompts with instant rollbacks, 100+ integrations, and isolated EU, US, Japan and HIPAA regions. Overmind's platform is open source under AGPL-3.0 and self-hosts with Docker Compose, but it needs OpenRouter, Modal, AWS S3 and Hugging Face to run, and its hosted service publishes no regional isolation. A no-commercial-SaaS rule or a named-region requirement settles the question in Langfuse's favor. Self-hosting and downloadable weights solve the trace store and serving, not training.
What else do teams ask about Langfuse and Overmind?
Is Overmind a replacement for Langfuse?
It does not have to be. The common pattern is Langfuse for observability, Overmind for the loop from its traces to datasets, evals, and a trained model, via the live import connector. Overmind ships its own tracing too, so starting fresh with one tool also works.
Does Langfuse do fine-tuning or serving?
No. As of August 2026 its docs describe exporting trace data for fine-tuning in external tools, and every model-touching feature uses your own provider API key. It observes and evaluates models; it does not train or host them.
Langfuse self-hosts for free. What does Overmind cost?
Free and Pro subscriptions plus credits for compute-heavy work: dataset analysis, evals, optimization, training, serving. Trace ingest never draws credits. Overmind's AGPL-3.0 platform self-hosts too, but it needs OpenRouter, Modal, AWS S3 and Hugging Face accounts to run. If the requirement is a free self-hosted trace store, Langfuse wins that outright; Overmind's pitch is the loop, not the store.
Can I migrate gradually?
Yes. Backfill via the connector while your instrumentation stays put, and curate datasets from imported traces. Add Overmind's OTel endpoint alongside Langfuse's later if you want live scoring. No rewrite at any step.