The performance layer for AI agents
Every time your AI agent looks at a page, it processes the whole thing. Again. JD Codec is a stateful compression layer that sends only what changed. Your agent sees less, understands more, and acts faster.
Same task. Same model. Same tools.
Task: Filter products by status 4/6
Same model · Same task · Same tools
99% fewer tokens. $2.23 saved per task.
Setup
One command. No dependencies beyond Python.
pip install jdcodec
Drop-in layer between your agent and the browser. No changes to your agent, model, or tools.
jdcodec start
86% fewer input tokens. Higher task success rates. Faster execution. Your agent sees only what changed, and performs better because of it.
Benchmarked
Same model (Claude Sonnet 4). Same tasks. Same environment. The only difference: standard snapshots vs. JD Codec.
| Task | Baseline | JD Codec | Baseline tokens | JD Codec tokens | Reduction |
|---|---|---|---|---|---|
| Navigate to Products | PASS | PASS | 108.8k | 10.2k | -91% |
| Navigate to Customers | PASS | PASS | 30.6k | 8.6k | -72% |
| Navigate to Orders | PASS | PASS | 54.3k | 9.9k | -82% |
| Filter products by status | PASS | PASS | 748.9k | 5.2k | -99% |
| Change customer group | FAIL | FAIL | 203.2k | 50.8k | -75% |
| Toggle product status | FAIL | PASS | 543.5k | 145.3k | -73% |
| Total (6 tasks) | 4/6 | 5/6 | 1,689k | 230k | -86.4% |
Methodology: 6 real-world tasks on production web app. Input tokens via tiktoken. Wall-clock includes all latency.
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