clickllm

Prove which open model can replace your closed one — on your traffic, your hardware, your budget.

No config file. It reads your machine, sizes the KV cache per model, applies the licence gate, picks the engine, and sets the flags. Then it tells you whether the open model is actually good enough for your traffic, and hands you a receipt that says so.

uvx --from clickllm-cli clickllm fit --context 32k --concurrency 8

  M4 Max · 16 cores · 128 GB · 546 GB/s
  usable for inference: 96 GB

  FEASIBLE at 32,768 context, concurrency 8

  model                     quant   weights      kv   total    free  ~tok/s  license
  ----------------------------------------------------------------------------------
  Qwen3 30B-A3B (MoE)       q8        28.4G   24.0G   56.2G   39.8G      60  Apache-2.0 OK
  Phi-4 14B                 q8        13.7G   50.0G   66.3G   29.7G      18  MIT OK

Every number answers --explain, which prints the arithmetic that produced it. Throughput is a memory-bandwidth roofline estimate, not a measurement, and says so wherever it appears.

Or just say what you're building

clickllm with no arguments opens a conversation — one question at a time, asked only when the answer would change the plan. Stop whenever you like and you still leave with the best answer so far. In a script or a CI step it stays a usage error rather than waiting on input.

Then prove it, on your own traffic

Sizing tells you what fits. It cannot tell you whether the model is good enough, and no public benchmark can either, because it has never seen your traffic.

clickllm observe --upstream https://api.openai.com/v1   # record, redacted before storage
clickllm distill                                        # cluster it into an eval set
clickllm prove evalset.json --incumbent-cost 2847 --candidate-cost 317 \
    --traffic-window '14 days' --resume run
clickllm brief receipt.json --out brief.html            # one page for whoever signs off

The verdict is per task shape, never an average — a candidate can pass tool-calling and fail long-context, and one number hides that. Every claim carries a Wilson interval, and a cluster is proven only when its whole interval clears the bar.

The saving is a range, never a point: the share of traffic that moves is measured, so the dollars inherit its uncertainty. With no rate, no capture count, or under a week of traffic, it says the saving is unknown and names what would fix it.

For agents

clickllm-mcp speaks MCP over stdio with zero dependencies: nine read-only tools, receipts and eval sets as resources confined to one root, and three pre-built workflows. An agent can size, prove and guard a migration. It cannot cut over — not by policy, but because no such tool exists to call.

Install

uv tool install clickllm-cli
pipx install clickllm-cli

The PyPI name is clickllm-cli; the command is clickllm. pip install clickllm installs an unrelated package.

Sizing, and the three ways it goes wrong

• MoE needs total parameters resident. Sparsity cuts compute, not memory.
• GQA must use kv_heads, not attention heads — up to 8× out otherwise.
• MLA stores a compressed latent — roughly 50× out if you use the GQA formula.

Status

Pre-alpha, and honest about it: every number that is a projection says so, and a cell shows ? rather than a fabricated score.

• Source, docs and the full README: https://github.com/dshakes/clickllm
• Site: https://dshakes.github.io/clickllm/
• Licence: Apache-2.0