Sediment vs LangSmith
Use LangSmith to debug and evaluate LLM applications. Sediment preserves coding-agent evidence on your infrastructure for reports, context agents can query, and proprietary training datasets.
Updated September 28, 2026 · Competitor sources reviewed September 2, 2026
Main differences
| Dimension | LangSmith | Sediment |
|---|---|---|
| What you record | Model calls and tool steps you send to LangSmith. Tracing docs | Model calls, developer decisions, file edits, commits, and CI results. Sediment docs |
| What you get | Evaluation datasets you can export as CSV, JSONL, or OpenAI fine-tuning format. Dataset exports | Reports on agent work, captured context that agents can query, and training datasets with their source evidence. Agent work reports Evidence retrieval Training exports |
| Where you run it | LangChain's cloud, your cloud account (BYOC), or self-hosted. BYOC and self-hosting require Enterprise. Agent deployment is optional. Hosting options | Capture, storage, evidence reads, reports, and exports run on your infrastructure. Your agent’s model endpoint controls inference traffic. Network boundaries |
LangSmith supports evaluation datasets and fine-tuning exports. Sediment focuses on coding-agent evidence linked to developer decisions, Git, and CI. You can inspect work outcomes, retrieve selected captured parts for an agent, or derive training labels from the recorded evidence.
Using both
Sediment returns exact captured messages, tool calls, and tool results from a previous session. A pi agent can query them itself, with a token limited to the sessions you grant, or an operator can prepare an evidence packet with the CLI. No model selects the evidence. Evidence retrieval
LangSmith can route coding-agent calls through its LLM Gateway (beta). Gateway docs Sediment also captures gateway traffic, then links it to Git and CI. Sediment docs Both may need to handle the same calls. Neither vendor documents an integration, so test the routing before using both.