Tokenadevsheadroom

Best alternative to headroom

Tokenade is the best alternative to headroom — Universal token-optimization engine for AI coding agents — native hooks for 18 agents combine output filtering, semantic code search, skeleton compression, sandboxed execution, MCP proxying and a live savings dashboard in a single dependency-free binary.

Get Tokenade

Tokenadeheadroom

Output Filtering

Format-aware compactors cover git, cargo, kubectl, terraform, docker and more — 60–99% reduction on the noisiest commands. Command rewriting further trims source-side before the shell even runs.

Output Filtering

Indirect only: headroom compresses tool outputs after they are already in the message array, using generic per-format compressors — not command-aware filters at the tool boundary.

Semantic Code Search

Finds the most relevant files for a task and sends only those to the model, instead of the whole repo. Runs fully on-device with no external vector database and no model downloads — fast even on large codebases.

Semantic Code Search

Not available. headroom does not index or search codebases.

Skeleton Compression

Signatures-only view of source files, YAML, Markdown and Terraform — −64% on file reads while preserving every top-level declaration. Stacks on top of output filtering for maximum savings.

Conversation-Window Compression

Three-stage pipeline (CacheAligner → ContentRouter → IntelligentContext) with per-format compressors reaching 40–95% on JSON, code, text, logs and diffs, plus a CCR retrieve tool to pull originals back. Strong per-workload numbers (92% on code-search results).

Third-Party MCP Optimization

tokenade mcp-proxy wraps any third-party MCP server's launch command in the agent's MCP config, so every tool result (verbose JSON, logs, console output) is folded on the way back — set once, not per call. Image results pass through untouched.

MCP Integration

Ships as an MCP server and injects a headroom_retrieve tool. Does not fold third-party MCP tool results at the boundary or reduce manifest costs.

Mechanism Breadth

The only tool combining output filtering + semantic search + skeleton compression + sandbox execution + MCP proxying + secret redaction + content-addressed cache in a single binary. On the open THOL benchmark it is the only one of the twelve tools tested that measurably cuts costs: 23% cheaper than running no tool at all, and 39% cheaper on long sessions.

Mechanism Breadth

Single layer: conversation-window management (cache alignment + compression + context scoring). On the open THOL benchmark it measured 53% MORE expensive than running no tool at all — last of the twelve tools tested, because per-message compression wins never translated into cheaper sessions.

Setup & Installation

npm install -g @tokenade/cli then tokenade install — native hooks auto-detected for 18 agents (Claude Code, Cursor, Codex, Gemini CLI, Copilot, Windsurf and more). Works without an account: 10M tokens offered per machine to try. Not yet on crates.io or Homebrew.

Setup & Installation

pip install, then configure the CLI proxy or MCP server. Python 3.10+ with a 150M-parameter model download and ML content detection — a heavier footprint than hook-based binaries.

Savings Dashboard

tokenade dashboard shows measured savings, per-command and per-project breakdown, and framework-detection status. Local logs rotate automatically with built-in secret redaction.

Savings Stats

headroom_stats reports compression statistics. No live dashboard with per-command, per-project or per-model breakdown.

headroom at a glance

headroom starts at Free (Apache 2.0). Python SDK + CLI proxy + MCP server (~18.7k stars, Apache 2.0) that compresses the full LLM message array before each API call: cache alignment, ML-routed per-format compressors (40–95%) and relevance-scored context selection, with a retrieve tool to pull originals back.

Pros

Operates on the whole conversation window — a layer tool-output filters don't touch; composable with tool-boundary optimizers
CacheAligner stabilizes the static prompt prefix (moves timestamps/UUIDs to a non-cached tail) for up to 90% provider KV-cache hit rates
Strong per-workload compression numbers: 92% on code-search results and SRE incident logs, 73% on issue triage
Reversible: originals kept in a local LRU cache with an injected headroom_retrieve tool (optional BM25 sub-search)

Cons

Measured 53% MORE expensive than running no tool at all on long sessions in the open THOL benchmark — last of the twelve tools tested, because aggressive window compression costs more than it saves
Heavy footprint: Python 3.10+ with a 150M-parameter ModernBERT model and ML content detection (Magika); 16–50 ms per-call overhead
Rewriting the message array on every call can interact badly with provider prompt caching when compression decisions change between turns
No code navigation, no semantic code search, no output filtering at the tool boundary