Intent-compressed
intelligence orchestration.
88% cheaper at turn 10. A maestro for any LLM. O(N²) → O(N) by math.
Pick any model as the Maestro — Claude, GPT, Gemini, OpenClaw, a local Ollama. It orchestrates gold/silver/bronze Workers from any vendor and shares a cached protocol prefix. 88% cheaper at turn 10, not by trick — by arithmetic on the published pricing pages.
Burnless turns long LLM conversations into compact capsule memory. Under the hood, it is evolving into a candidate protocol layer for human-LLM and LLM-LLM communication: a living, compressed, privacy-aware language between humans, maestros, and workers.
We compress pages of conversation history into a single 80-character line. Your AI keeps the memory. You stop paying for the excess.
Calibrated from real Anthropic API runs. Reproduce: python bench/v2.py --simulate
Turns Standalone Burnless Savings 2 $0.80 $0.14 82.7% 5 $2.06 $0.29 86.1% 10 $4.34 $0.54 87.6% 20 $9.59 $1.07 88.9% 50 $30.72 $2.83 90.8%
Without Burnless $2.45 With Burnless $0.28 Real saving 88%
pip install burnlessBeyond cost. The protocol layer.
A token is not an abstraction. It is compute. Compute is electricity. Electricity is water and infrastructure. O(N²) at the scale LLM inference is heading — 1–5% of global electricity within a decade — is not a pricing quirk. It is a trajectory.
Burnless is the transparent, provider-agnostic layer that sits between any user and any LLM, converting O(N²) context growth into O(N) by design. Not a feature. Not an SDK wrapper. A candidate protocol — like TCP/IP was to packets. The model receives a capsule. The user sends a message. The orchestration layer speaks compact state.
60–90 TWh/year
Estimated energy saved at 1% of global LLM inference. Denmark's entire electricity consumption is 35 TWh/year. This is not a rounding error.
Structurally unblockable
You cannot prohibit sending a summary of a conversation. That is indistinguishable from normal behavior. The protocol is invisible by design.
Inevitable. Open.
This layer will exist. The question is who defines it and in whose interest. Burnless answers: MIT, documented, first.
Why the curve is quadratic.
Every turn in a standalone agent loop replays the full conversation as input. Cost on turn N is proportional to N, so total cost across N turns is Θ(N²). That is arithmetic from the pricing page, not a property of any SDK.
1. Capsules, not transcripts
Brain history holds ~80-char summaries of each turn, not the raw exchange. Full output stays on disk, read on demand.
2. Shared prefix cache
System prompt is byte-identical every turn with cache_control. Read price ($0.15/MTok) instead of write price ($15/MTok). 100× spread.
3. Tiers are roles, not models
Any model as Brain. Any model as Worker. GPT-4o, Opus, Sonnet, Codex, Ollama — one-line config change.
4. Living glossary
Core terms, project terms and session deltas form a compact language between human intent, Maestro and Workers.
5. Privacy by mode
Cost mode reduces repeated exposure today. Redact, audit, opaque and burnkey modes define the path to stronger local-key privacy.
We don't ask you to trust the table. Reproduce it: python bench/run.py --turns 8 with your own API key.
How it looks.
A chat-first CLI. Slash commands users already understand. Compact state under .burnless/. No hosted backend.
# in any project $ pip install burnless $ burnless setup $ burnless # Burnless Chat — /commands, /maestro, /model, /workers, /native $ burnless delegate "fix the failing tests" → d001 routed to silver/codex (matched: test) $ burnless delegate "summarize the logs" → d002 routed to bronze/haiku (matched: summarize) $ burnless run d002 OK:d002 — cache_read=23,000 tokens (warm)
Gold/silver/bronze are quality/cost bands, not vendors. The default setup can mix Claude, Codex and Ollama; users can replace any tier in .burnless/config.yaml.
Cache compaction is realtime ROI math: Burnless freezes immutable blocks and only creates a new super-capsule when future cache-read savings beat the write+compaction cost.
Editions.
Run the protocol locally for free. Pay for governance, auditability, privacy operations and team infrastructure.
Burnless
Free · MIT
- Open protocol, MIT licensed
- Full CLI, runs locally
- Brain + Worker + capsule history
- Shared prefix cache, realtime capsule compaction
- Local burnkey semantics planned in the protocol
- Provider-agnostic — any LLM as Brain or Worker
- Reproducible benchmark
Burnless Cloud/Enterprise
Soon · waitlist
- Shared cache across machines and teammates
- KMS/HSM key custody and retention policy
- Audit logs, legal hold and destruction reports
- SSO + team permissions
- Dashboards, SIEM/webhooks and priority support
Don't trust the table. python bench/run.py --turns 8 with your own API key.
Burnless Cloud — waitlist.
Hosted features for teams: shared cache, dashboards, KMS/HSM key custody, audit logs, SSO and retention policy. The protocol is free and open source — Cloud/Enterprise is for organizations that need governance.