headroom/wiki/proxy.md

Proxy Server Documentation

The Headroom proxy server is a production-ready HTTP server that applies context optimization to all requests passing through it.

New: The proxy now supports the TypeScript SDK via the POST /v1/compress endpoint, enabling compression-as-a-service for any HTTP client without calling an LLM.

Starting the Proxy

# Basic usage
headroom proxy

# Custom port
headroom proxy --port 8080

# With all options
headroom proxy \
  --host 0.0.0.0 \
  --port 8787 \
  --log-file /var/log/headroom.jsonl \
  --budget 100.0

Common agent CLI entrypoints

# Claude Code
ANTHROPIC_BASE_URL=http://localhost:8787 claude

# GitHub Copilot CLI
headroom wrap copilot -- --model claude-sonnet-4-20250514

# OpenAI-compatible clients
OPENAI_BASE_URL=http://localhost:8787/v1 your-app

headroom wrap copilot uses Copilot CLI's BYOK provider settings under the hood. In provider-type=auto, it chooses Headroom's Anthropic route for the default proxy backend and the OpenAI-compatible /v1 route for translated backends such as anyllm and LiteLLM.

Anonymous aggregate telemetry is enabled by default. Opt out with HEADROOM_TELEMETRY=off or headroom proxy --no-telemetry. Downstream apps can set HEADROOM_SDK=headroom-app to override the anonymous telemetry sdk label; the default remains proxy.

Operational OTEL metrics are configured separately and are off by default. Install headroom-ai[proxy,otel] and set:

HEADROOM_OTEL_METRICS_ENABLED=1
HEADROOM_OTEL_METRICS_EXPORTER=otlp_http
HEADROOM_OTEL_METRICS_ENDPOINT=http://127.0.0.1:4318/v1/metrics
HEADROOM_OTEL_SERVICE_NAME=headroom-proxy

Use HEADROOM_OTEL_METRICS_EXPORTER=console for local smoke testing. HEADROOM_TELEMETRY controls the anonymous data-flywheel beacon only; it does not disable or enable OTEL export.

Langfuse can be enabled alongside this OTEL path for trace ingestion. Langfuse does not ingest OTEL metrics, so Headroom keeps metrics and Langfuse traces as complementary signals:

HEADROOM_LANGFUSE_ENABLED=1
LANGFUSE_PUBLIC_KEY=pk-lf-...
LANGFUSE_SECRET_KEY=sk-lf-...
LANGFUSE_BASE_URL=https://cloud.langfuse.com

When configured, Headroom emits OTLP traces for the shared compression pipeline to Langfuse while continuing to expose metrics through /metrics and OTEL metric exporters.

Command Line Options

Core Options

Option	Default	Description
--host	127.0.0.1	Host to bind to
--port	8787	Port to bind to
--mode	token	Run mode: token (maximize compression) or cache (freeze prior turns)
--no-optimize	false	Disable optimization (passthrough mode)
--no-cache	false	Disable semantic caching
--no-rate-limit	false	Disable rate limiting
--log-file	None	Path to JSONL log file
--budget	None	Daily budget limit in USD
--openai-api-url	https://api.openai.com	Custom OpenAI API URL endpoint

Run Modes

Headroom proxy has two explicit run modes:

• token mode: prioritize token reduction. Prior history may be rewritten when that improves compression.
• cache mode: prioritize provider prefix cache stability. Prior turns are frozen; only the newest turn is mutable.

Set via CLI or env:

headroom proxy --mode token
HEADROOM_MODE=cache headroom proxy

When to pick each:

• token: best for maximizing immediate compression savings.
• cache: best for long conversations where preserving prior-turn bytes improves prefix-cache reuse.

Legacy values (token_headroom, cost_savings) are still accepted as aliases.

Context Management Options

Option	Default	Description
--no-intelligent-context	false	Disable IntelligentContextManager (fall back to RollingWindow)
--no-intelligent-scoring	false	Disable multi-factor importance scoring (use position-based)
--no-compress-first	false	Disable trying deeper compression before dropping messages

By default, the proxy uses IntelligentContextManager which scores messages by multiple factors (recency, semantic similarity, TOIN-learned patterns, error indicators, forward references) and drops lowest-scored messages first. This is smarter than simple age-based truncation.

CCR Integration: When messages are dropped, they're stored in CCR so the LLM can retrieve them if needed. The inserted marker includes the CCR reference. Drops are also recorded to TOIN, so the system learns which message patterns are important across all users.

# Use legacy RollingWindow (drops oldest first)
headroom proxy --no-intelligent-context

# Disable semantic scoring (faster, but less intelligent)
headroom proxy --no-intelligent-scoring

LLMLingua Options (ML Compression)

Option	Default	Description
--llmlingua	false	Enable LLMLingua-2 ML-based compression
--llmlingua-device	auto	Device for model: auto, cuda, cpu, mps
--llmlingua-rate	0.3	Target compression rate (0.3 = keep 30% of tokens)

Note: LLMLingua requires additional dependencies: pip install headroom-ai[llmlingua]

# Enable LLMLingua with GPU acceleration
headroom proxy --llmlingua --llmlingua-device cuda

# More aggressive compression (keep only 20%)
headroom proxy --llmlingua --llmlingua-rate 0.2

# Conservative compression for code (keep 50%)
headroom proxy --llmlingua --llmlingua-rate 0.5

API Endpoints

Liveness

curl http://localhost:8787/livez

Response:

{
  "service": "headroom-proxy",
  "status": "healthy",
  "alive": true,
  "version": "0.5.21",
  "timestamp": "2026-04-10T16:36:25Z",
  "uptime_seconds": 12.483
}

Readiness

curl http://localhost:8787/readyz

Response:

{
  "service": "headroom-proxy",
  "status": "healthy",
  "ready": true,
  "version": "0.5.21",
  "timestamp": "2026-04-10T16:36:25Z",
  "uptime_seconds": 12.483,
  "checks": {
    "startup": {"enabled": true, "ready": true, "status": "healthy"},
    "http_client": {"enabled": true, "ready": true, "status": "healthy"},
    "cache": {"enabled": true, "ready": true, "status": "healthy"},
    "rate_limiter": {"enabled": true, "ready": true, "status": "healthy"},
    "memory": {"enabled": false, "ready": true, "status": "disabled"}
  }
}

/readyz returns HTTP 503 when Headroom has not completed startup or a required enabled subsystem is unavailable. This is the endpoint used by the container health checks.

Aggregate Health

curl http://localhost:8787/health

Response:

{
  "status": "healthy",
  "ready": true,
  "version": "0.5.21",
  "config": {
    "backend": "anthropic",
    "optimize": true,
    "cache": true,
    "rate_limit": true
  },
  "checks": {
    "startup": {"enabled": true, "ready": true, "status": "healthy"},
    "http_client": {"enabled": true, "ready": true, "status": "healthy"}
  }
}

Detailed Statistics

curl http://localhost:8787/stats

/stats remains the live/session-oriented endpoint and now also includes a persistent_savings block with durable proxy compression lifetime totals plus a small recent preview. The existing savings_history field is still present and remains session-scoped for backward compatibility.

For providers that return cache-write TTL bucket usage, /stats also includes observed TTL breakdowns under prefix_cache:

• observed_ttl_buckets.5m.tokens
• observed_ttl_buckets.1h.tokens
• observed_ttl_mix

These are provider-reported observations, not configured TTL and not remaining expiration time.

Historical Savings

curl http://localhost:8787/stats-history

/stats-history exposes durable proxy compression history for dashboards and other Headroom frontends. It returns:

• lifetime proxy compression totals
• compact checkpoint history by default, with history_mode=full available for export/debug flows
• derived hourly, daily, weekly, and monthly rollups for charts
• a history_summary block describing stored versus returned checkpoint counts
• UTC timestamps throughout

By default the proxy stores this history at ${HEADROOM_WORKSPACE_DIR}/proxy_savings.json (i.e. ~/.headroom/proxy_savings.json when HEADROOM_WORKSPACE_DIR is unset). Set HEADROOM_SAVINGS_PATH to override the location directly, or set HEADROOM_WORKSPACE_DIR to relocate the full state root. See the Filesystem Contract.

/dashboard uses this endpoint directly for its historical view, including the daily/weekly/monthly rollups and built-in JSON / CSV export buttons.

curl "http://localhost:8787/stats-history?format=csv&series=weekly"
curl "http://localhost:8787/stats-history?format=csv&series=monthly"
curl "http://localhost:8787/stats-history?history_mode=full"

Prometheus Metrics

curl http://localhost:8787/metrics

/metrics remains the built-in Prometheus-formatted operational view. The proxy now also emits the same operational events through the OTEL facade when OTEL metrics are configured.

LLM APIs

The proxy supports both Anthropic and OpenAI API formats:

# Anthropic format
POST /v1/messages

# OpenAI format
POST /v1/chat/completions

POST /v1/compress

Compression-only endpoint. Compresses messages without calling any LLM. Used by the TypeScript SDK and any HTTP client that wants compression as a service.

Request:

{
  "messages": [...],     // OpenAI chat format
  "model": "gpt-4o"     // model name (for token counting)
}

Response:

{
  "messages": [...],            // compressed messages
  "tokens_before": 15000,
  "tokens_after": 3500,
  "tokens_saved": 11500,
  "compression_ratio": 0.23,
  "transforms_applied": ["router:smart_crusher:0.35"],
  "ccr_hashes": ["a1b2c3"]
}

Headers:

• x-headroom-bypass: true — skip compression, return messages as-is

Error responses: 400 (missing fields), 401 (bad API key), 503 (compression failed)

Using with Claude Code

# Start proxy
headroom proxy --port 8787

# In another terminal
ANTHROPIC_BASE_URL=http://localhost:8787 claude

Using with Cursor

1. Start the proxy: headroom proxy
2. In Cursor settings, set the base URL to http://localhost:8787

Using with OpenAI SDK

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8787/v1",
    api_key="your-api-key",  # Still needed for upstream
)

Features

LLMLingua ML Compression (Opt-In)

When enabled, the proxy uses Microsoft's LLMLingua-2 model for ML-based token compression:

headroom proxy --llmlingua

How it works:

• LLMLinguaCompressor is added to the transform pipeline (before RollingWindow)
• Automatically detects content type (JSON, code, text) and adjusts compression
• Stores original content in CCR for retrieval if needed

Startup feedback:

# When enabled and available:
LLMLingua: ENABLED  (device=cuda, rate=0.3)

# When installed but not enabled (helpful hint):
LLMLingua: available (enable with --llmlingua for ML compression)

# When enabled but not installed:
WARNING: LLMLingua requested but not installed. Install with: pip install headroom-ai[llmlingua]

Why opt-in?

Concern	Default Proxy	With LLMLingua
Dependencies	~50MB	+2GB (torch, transformers)
Cold start	<1s	10-30s (model load)
Memory	~100MB	+1GB (model in RAM)
Overhead	<5ms	50-200ms per request

Enable LLMLingua when maximum compression justifies the resource cost.

Semantic Caching

The proxy caches responses for repeated queries:

• LRU eviction with configurable max entries
• TTL-based expiration
• Cache key based on message content hash

Rate Limiting

Token bucket rate limiting protects against runaway costs:

• Configurable requests per minute
• Configurable tokens per minute
• Per-API-key tracking

Cost Tracking

Track spending and enforce budgets:

• Real-time cost estimation
• Budget periods: hourly, daily, monthly
• Automatic request rejection when over budget

Prometheus Metrics

Export metrics for monitoring:

headroom_requests_total
headroom_tokens_saved_total
headroom_cost_usd_total
headroom_latency_ms_sum

Configuration via Environment

export HEADROOM_HOST=0.0.0.0
export HEADROOM_PORT=8787
export HEADROOM_BUDGET=100.0
export OPENAI_TARGET_API_URL=https://custom.openai.endpoint.com
headroom proxy

Running in Production

For production deployments:

# Use a process manager
pip install gunicorn

# Run with gunicorn
gunicorn headroom.proxy.server:app \
  --workers 4 \
  --bind 0.0.0.0:8787 \
  --worker-class uvicorn.workers.UvicornWorker

Or with Docker:

FROM python:3.11-slim
RUN apt-get update && apt-get install -y --no-install-recommends build-essential \
    && pip install "headroom-ai[proxy]" \
    && apt-get purge -y build-essential && apt-get autoremove -y \
    && rm -rf /var/lib/apt/lists/*
EXPOSE 8787
CMD ["headroom", "proxy", "--host", "0.0.0.0"]

Note: build-essential is required at install time because headroom-ai includes hnswlib, a C++ extension that must be compiled from source. It is removed after installation to keep the image slim.