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masondelan/selvedge

by Various

Long-term memory for AI-coded codebases. A git blame for AI agents — but for the why. MCP server that captures the agent's reasoning live, in context, as each change is made. Local

M

MCP

masondelan/selvedge

Added 1 June 2026

#agent-trace #ai-agent-reasoning #ai-agents #ai-code-provenance #ai-codebase-memory #ai-coding #claude-code #codebase-change-tracking

Overview

Selvedge is an MCP server that records an AI agent's reasoning as it makes changes to a codebase, storing the rationale in a local SQLite database. It functions like a git blame for AI edits, capturing the why behind each modification without external dependencies.

Best for

Best for
Developers using AI coding agents who need to track and review the rationale behind automated changes.

Use cases

  • Audit why an AI agent made a specific code change
  • Trace the reasoning behind past AI-driven modifications
  • Maintain a live log of agent decisions during development

Notes

Selvedge is an MCP server that records an AI agent’s reasoning as it makes changes to a codebase, storing the rationale in a local SQLite database. It functions like a git blame for AI edits, capturing the why behind each modification without external dependencies.

8 stars on GitHub. Last updated 2026-05-26. Licensed MIT.

Use cases

  • Audit why an AI agent made a specific code change
  • Trace the reasoning behind past AI-driven modifications
  • Maintain a live log of agent decisions during development

Pros

  • Zero external dependencies, runs locally with SQLite
  • Captures reasoning in real time as changes are made
  • Provides a clear audit trail for AI-generated code

Cons

  • Limited to MCP-compatible AI agents and workflows
  • Requires manual setup to integrate with existing projects
  • No built-in support for non-AI or manual code changes

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Zero external dependencies, runs locally with SQLite
  • Captures reasoning in real time as changes are made
  • Provides a clear audit trail for AI-generated code

Cons

  • Limited to MCP-compatible AI agents and workflows
  • Requires manual setup to integrate with existing projects
  • No built-in support for non-AI or manual code changes