Migrating from Other Tools

DecimalAI is designed to coexist with your existing observability tool, not replace it overnight. Most teams adopt DecimalAI for the manifest-aware regression check while keeping their existing tool for general trace search. This page maps the concepts so you can wire up DecimalAI quickly.

Quick decision: replace or coexist?

Coexist (recommended for most)

Send the same traces to both tools. DecimalAI adds the manifest layer; your existing tool keeps doing what it does. Switching cost: ~10 minutes.

Migrate fully

Replace your existing tool entirely. Larger change — see the per-tool sections below for what features map cleanly and what doesn’t.

LangSmith → DecimalAI

LangSmith concept	DecimalAI equivalent	Notes
Run	Trace	Same atomic unit — input + LLM calls + output.
Project	Agent	LangSmith projects are bag-of-runs; DecimalAI agents are named entities with manifest history. Set `agent_name` in `decimalai.init()`.
Dataset	Dataset	Both formats supported. Pull a LangSmith dataset and pipe rows into `POST /api/v1/datasets/{id}/build`.
Annotation	Eval Score	LangSmith feedback maps to a DecimalAI eval score via `source="langsmith"`. Use `decimalai.push_langsmith_scores(...)`.
Evaluator	Evaluator	Both pre-built and custom evaluators supported. The signature is similar — see Evaluations.
Regression test	Experiment	LangSmith uses run-on-eval-set for regression; DecimalAI uses manifest impact for pre-deploy + experiments for post-deploy verification.

Coexistence pattern. Use LangChain’s standard callback handler list:

import decimalai
from langsmith.run_helpers import traceable

decimalai.init(api_key="dai_sk_...", langchain=True)
# Your existing LangSmith @traceable decorators continue to work.
# Both tools receive the same trace data via separate callback paths.

Braintrust → DecimalAI

Braintrust concept	DecimalAI equivalent	Notes
Span / Experiment Run	Trace	Identical model.
Project	Agent	Same as LangSmith.
Eval function	Custom evaluator	Wrap your Braintrust scorer in `@decimalai.eval()`.
Dataset	Dataset	Use `decimalai.pull_dataset()` to round-trip.
Regression check	Manifest impact + Experiment	Braintrust runs evals on a new model version; DecimalAI’s regression check is structural (no eval suite needed) — add experiments on top for behavioral verification.

Langfuse → DecimalAI

Langfuse concept	DecimalAI equivalent	Notes
Trace	Trace	Same model.
Observation	Span or LLM Call	Langfuse “observations” map to DecimalAI spans (`span_type=tool/retriever/other`) or LLM calls (`span_type=llm`).
Generation	LLM Call	Direct mapping with full fidelity.
Score	Eval Score	Push via `decimalai.push_custom_scores(...)`.
Session	Session	Same model; share `session_id` across traces.
Prompt management	(Not built-in)	DecimalAI tracks prompts as manifest components but doesn’t have prompt-management UI. Continue using Langfuse for prompt registries.

Phoenix / Arize → DecimalAI

Both Phoenix and Arize emit OpenTelemetry GenAI spans. DecimalAI consumes OTel directly:

import decimalai
decimalai.init(api_key="dai_sk_...", otel=True)
# Phoenix instrumentation continues to emit spans; DecimalAI receives them.

Phoenix concept	DecimalAI equivalent
Span	Span (1:1)
Dataset	Dataset
Annotation	Eval Score

Bulk import of historical traces

If you have a JSONL backup from any of the above tools, use either the CLI or the REST API:

decimalai traces import historical.jsonl

import httpx, json

with open("historical.jsonl") as f:
    traces = [json.loads(line) for line in f]

resp = httpx.post(
    "https://api.decimal.ai/api/v1/import/traces",
    headers={"Authorization": "Bearer dai_sk_..."},
    json={"traces": traces},
    timeout=60.0,
)
print(resp.json())

Each trace payload follows the same shape as POST /api/v1/traces. Imported traces:

Count at half rate against your traces_ingested quota
Get tagged with source_type="imported"
Are deduplicated by trace_id — re-running is safe

See the Import endpoints for the full schema.

What doesn’t migrate cleanly

Be honest about gaps:

Prompt registries. DecimalAI tracks prompts inside manifests but isn’t a dedicated prompt-management tool. If you use LangSmith Prompts or Langfuse Prompt Management for non-engineers, keep using it.
Tracing UI feature parity. Our trace viewer covers the common case (timeline tree + LLM call inspection) but doesn’t yet have feature parity with mature observability tools for things like service-level dependency graphs or distributed tracing across non-LLM services.
Pre-existing eval datasets. Datasets transfer (we accept JSONL of any shape), but the eval definitions don’t — you’ll re-implement custom evaluators.

Get migration help

For enterprise migrations (>500k historical traces, custom eval definitions, SSO setup), email support@decimal.ai — we’ll do the schema mapping and one-time backfill for you.

​Quick decision: replace or coexist?

Coexist (recommended for most)

Migrate fully

​LangSmith → DecimalAI

​Braintrust → DecimalAI

​Langfuse → DecimalAI

​Phoenix / Arize → DecimalAI

​Bulk import of historical traces

​What doesn’t migrate cleanly

​Get migration help