Sediment vs Git AI

Use Git AI to see which code came from an agent and track team usage. Sediment preserves coding-agent evidence on your infrastructure for reports, selected agent context, and training datasets.

Updated September 21, 2026 · Competitor sources reviewed September 2, 2026

01

Main differences

Git AI Sediment
What you record The agent, model, and session behind each AI-written line. Attribution docs Model calls, developer decisions, file edits, commits, and CI results. Sediment docs
What you get Attribution reports. Teams adds usage and cost dashboards plus warehouse exports. Team reports Warehouse exports Reports on agent work, selected captured evidence for agent context, and training datasets with their source evidence. Agent work reports Evidence retrieval Training exports
Where you run it The CLI works offline without a login. Teams offers cloud and paid self-hosting. Git support Self-hosting Capture, storage, evidence reads, reports, and exports run on your infrastructure. Your agent’s model endpoint controls inference traffic. Network boundaries

In no-login OSS mode, Git AI does not upload code, prompts, or agent-usage telemetry to its service. It enables error telemetry by default. Personal dashboards upload prompts and responses; Teams stores prompts and telemetry in its cloud or self-hosted instance. Privacy

Git AI updates attribution after supported Git rewrites. Renames and bulk history rewrites have gaps. Web-UI squash and rebase merges need Teams or Git AI CI Actions. Git support

02

Using both

Sediment's retrieval layer lets an operator select captured messages, tool calls, and tool results for a consuming agent. The continuation guide uses an intact workspace and explicit task instructions. Evidence retrieval

They use separate Git notes. Git AI records which lines each agent wrote in its notes and syncs them with your Git remotes. Attribution docs Sediment's notes link sessions to commits. Commit links Neither vendor documents an integration, so test both in your workflow.