Start with the traces already in LangSmith
You can assess the dataset and evaluation workflow while continuing to use LangSmith. Begin with a representative slice of one production task. Model training is optional.
Step 1
Connect LangSmith
Open Observability, then Integrations, choose LangSmith, and add your API key and the API URL for your region. 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 LangSmith, so available history depends on what LangSmith 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 LangSmith alternative?
Traces
Datasets
Evals
Optimize
Train
Serve
Overmind
LangSmith
Since September 2026 LangSmith can also fine-tune, through smithtune on Fireworks or Baseten. Overmind runs the loop on traces from any source and ends at a model it hosts, benchmarked against production, with weights you download.
Which one fits your team?
Decision tree
Question 1 of 3, Is your agent in production, logging real runs?
Is your agent in production, logging real runs?
Two of the six verdicts here send you to LangSmith, and one of those is a closed-network requirement Overmind cannot meet: its self-hosted platform still calls OpenRouter, Modal, AWS S3 and Hugging Face.
What are the key differences between Overmind and LangSmith?
| Overmind | LangSmith | |
|---|---|---|
| Fine-tuning | LoRA or full, on Qwen, Llama, and Antares | LoRA SFT via smithtune (beta) on Fireworks or Baseten, with your provider key |
| Model serving | Hosts your trained models, and proxies frontier ones | Proxies only. Gateway (beta) hosts nothing |
| Prompt improvement | Optimizer returns scored diffs you apply | Playground, versioned prompts, manual iteration |
| Proof before switching | Base vs trained on held-out data. Production model opt-in | Experiments compare app versions on datasets |
| Tracing | OTLP ingest, scored on arrival. Read-only LangSmith import | Py/TS/Go/Java SDKs, end-to-end OTel |
| Human review | Annotations that calibrate LLM judges | Annotation queues, pairwise A/B, bulk actions |
| License and self-hosting | AGPL-3.0 platform, MIT SDK. Docker Compose or console.overmindlab.ai | Commercial. Whole platform self-hosts on the Enterprise plan |
| Pricing | Free / Pro + credits. Ingest never credit-gated | Free (1 seat, 5k traces/mo); $39/seat/mo + usage |
What does LangSmith do that Overmind does not?
LangSmith goes deeper on seeing and reviewing what an agent did. Annotation queues, pairwise A/B review and bulk actions, tracing SDKs for Python, TypeScript, Go and Java with end-to-end OpenTelemetry, and self-hosting of the whole platform on the Enterprise plan. Overmind's platform self-hosts too, under AGPL-3.0 with Docker Compose, but it still calls OpenRouter, Modal, AWS S3 and Hugging Face, so it cannot run in a closed network. If the job is observing and scoring the agent you already have, LangSmith is the better buy.
What else do teams ask about LangSmith and Overmind?
Is Overmind a replacement for LangSmith?
Only if you want it to be. Overmind has its own tracing and evals, so it stands alone. Teams that like LangSmith keep it for observability and use Overmind for datasets, training, benchmarking, and serving.
Does LangSmith do fine-tuning?
Yes, since 24 September 2026. LangSmith Fine-Tuning, with the smithtune CLI (public beta), turns LangSmith trajectories into a curated dataset, runs supervised fine-tuning with LoRA on Fireworks or Baseten using your own provider account, compares the result with the base model in a replay evaluation, and can deploy it. Overmind covers the same steps and also takes traces from other tools (OTLP, Langfuse, Braintrust, Galileo and file uploads), audits datasets in Data Workshop, can benchmark against your production model, hosts the model on its own OpenAI-compatible endpoint, and lets you download the weights.
Do I need LangChain to use either?
No. LangSmith traces any framework via OpenTelemetry. Overmind has no framework at all: its Python SDK instruments OpenAI, Anthropic, and Gemini clients directly, and any OTel exporter can send to it.
What do they cost?
LangSmith: free for one seat and 5,000 traces a month, then $39 per seat per month plus usage units; self-hosting is Enterprise-only. Overmind: Free and Pro subscriptions plus credits, and trace ingest never draws credits. As of August 2026. Overmind's AGPL-3.0 platform can also be self-hosted with no license fee; you pay for the services it calls.