Codag
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From a 100k-line window to the lines that matter

01
Group similar lines
02
Rank by severity, rarity, query
03
Keep surrounding lines
04
Return compact text

Codag Pro vs raw plus grep vs codag-drain

The same incidents, read three ways, paired at 1k / 10k / 100k lines.

98–99%
incidents answered
at every window size
5.9–7.5×
fewer agent tokens
vs raw + grep
100%
root evidence present
across the run
0
invalid outputs
0 judge errors

Debugging accuracy by window size

whiskers = 95% bootstrap CI  ·  n=102 paired runs / window (51 incidents × 2 agents)  ·  Codag Pro vs raw p<1e-4

Agent tokens by window size

bar = median, whisker = p90  ·  linear scale  ·  lower is cheaper

51 incidents × 2 agents  ·  blind cross-provider judges  ·  exact paired sign test  ·  bootstrap-2000 CIs  ·  methodology & artifacts

One command, no surprises

  • Tiny logs pass through raw
  • Errors fall back to deterministic drain
  • Every kept line cites a real line number
$ codag wrap -- kubectl logs api
any log command, compacted

$ codag setup
Claude Code: hook + MCP, wired

$ codag mcp serve
Codex or any MCP client

$ curl https://api.codag.ai/v1/compact
REST · /v1/compact over HTTPS

Give your agent logs it can actually read.

Start on the free tier. Go Pro when your windows get large.

$curl -fsSL https://codag.ai/install.sh | sh