Headroom SDK: A Complete Explanation¶

Architecture Overview¶

flowchart TB
    subgraph Entry["Entry Points"]
        Proxy["Proxy Mode<br/><i>Zero code changes</i>"]
        SDK["SDK Mode<br/><i>HeadroomClient</i>"]
        Integrations["Integrations<br/><i>LangChain / Agno</i>"]
    end

    subgraph Pipeline["Transform Pipeline"]
        direction TB

        CA["Cache Aligner<br/>━━━━━━━━━━━━━━━<br/>Extracts dynamic content<br/>(dates, UUIDs, tokens)<br/>Stable prefix for caching"]

        SC["Smart Crusher<br/>━━━━━━━━━━━━━━━<br/>Analyzes tool outputs<br/>Keeps: first, last, errors, outliers<br/>70-95% reduction"]

        CM["Context Manager<br/>━━━━━━━━━━━━━━━<br/>Enforces token limits<br/>Scores by recency and relevance<br/>Fits context window"]

        CA --> SC --> CM
    end

    subgraph Cache["Provider Cache Optimization"]
        direction LR
        Anthropic["Anthropic<br/><i>cache_control blocks</i><br/>90% savings"]
        OpenAI["OpenAI<br/><i>Prefix alignment</i><br/>50% savings"]
        Google["Google<br/><i>CachedContent API</i><br/>75% savings"]
    end

    subgraph CCR["CCR: Compress-Cache-Retrieve"]
        Store[("Compression<br/>Store")]
        Tool["Retrieve Tool<br/><i>LLM requests original</i>"]
        Store <--> Tool
    end

    LLM["LLM API<br/><i>OpenAI / Anthropic / Google</i>"]

    Entry --> Pipeline
    Pipeline --> Cache
    Cache --> LLM
    SC -.->|"Stores original"| Store
    LLM -.->|"If needed"| Tool

What Problem Does Headroom Solve?¶

When you use AI models like GPT-4 or Claude, you pay for tokens - the pieces of text you send (input) and receive (output). The problem is:

Tool outputs are HUGE: When an AI agent calls tools (search, database queries, APIs), the responses are often massive JSON blobs with thousands of tokens
Most of that data is REDUNDANT: 60 metric data points showing cpu: 45% repeated, or 50 log entries with the same error message
You're paying for waste: Every token costs money and adds latency
Context windows fill up: Models have limits (128K tokens), and bloated tool outputs eat into your available space

Headroom creates "headroom" - it intelligently compresses your input tokens so you have more room (and budget) for what matters.

How Headroom Works: The Big Picture¶

┌─────────────────────────────────────────────────────────────────┐
│                        YOUR APPLICATION                          │
└─────────────────────────────────────────────────────────────────┘
                               │
                               ▼
┌─────────────────────────────────────────────────────────────────┐
│                      HEADROOM CLIENT                             │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐             │
│  │   ANALYZE   │→ │  TRANSFORM  │→ │    CALL     │             │
│  │  (Parser)   │  │  (Pipeline) │  │   (API)     │             │
│  └─────────────┘  └─────────────┘  └─────────────┘             │
│         │                │                │                     │
│         ▼                ▼                ▼                     │
│   Count tokens     Apply compressions   Send to OpenAI/Claude  │
│   Detect waste     Preserve meaning     Log metrics            │
└─────────────────────────────────────────────────────────────────┘
                               │
                               ▼
┌─────────────────────────────────────────────────────────────────┐
│                    OPENAI / ANTHROPIC API                        │
└─────────────────────────────────────────────────────────────────┘

The Core Components (In Simple Terms)¶

1. HeadroomClient (`client.py`) - The Wrapper¶

This is what you interact with. It wraps your existing OpenAI or Anthropic client:

# Before (normal OpenAI)
client = OpenAI(api_key="...")
response = client.chat.completions.create(model="gpt-4o", messages=[...])

# After (with Headroom)
base = OpenAI(api_key="...")
client = HeadroomClient(original_client=base, provider=OpenAIProvider())
response = client.chat.completions.create(model="gpt-4o", messages=[...])

What it does: - Intercepts your API calls - Runs messages through the transform pipeline - Calls the real API with optimized messages - Logs metrics to a database - Returns the response unchanged

Two modes: - audit: Just observe and log (no changes) - optimize: Apply transforms to reduce tokens

2. Providers (`providers/`) - Model-Specific Knowledge¶

Different AI providers have different rules:

class OpenAIProvider:
    # Knows GPT-4o has 128K context
    # Knows how to count tokens (tiktoken)
    # Knows pricing ($2.50 per million input tokens)

class AnthropicProvider:
    # Knows Claude has 200K context
    # Uses different tokenization (~4 chars per token)
    # Different pricing structure

Why this matters: Token counting is model-specific. GPT-4 uses different tokenization than Claude. Headroom needs accurate counts to know how much to compress.

3. Parser (`parser.py`) - Understanding Your Messages¶

Before optimizing, Headroom needs to understand what's in your messages:

messages = [
    {"role": "system", "content": "You are helpful..."},
    {"role": "user", "content": "Search for X"},
    {"role": "assistant", "tool_calls": [...]},
    {"role": "tool", "content": "{huge JSON}"},
]

# Parser breaks this into "blocks":
blocks = [
    Block(kind="system", tokens=50, ...),
    Block(kind="user", tokens=10, ...),
    Block(kind="tool_call", tokens=20, ...),
    Block(kind="tool_result", tokens=5000, ...),  # ← This is the problem!
]

It also detects waste signals: - Large JSON blobs (>500 tokens) - HTML tags and comments - Base64 encoded data - Excessive whitespace

4. Transforms (`transforms/`) - The Compression Magic¶

This is where the real work happens. Headroom has 4 transforms that run in sequence:

Transform 1: Cache Aligner¶

Problem: LLM providers cache your prompts, but only if they're byte-identical. If your system prompt has today's date, every day is a cache miss.

# Before:
"You are helpful. Current Date: 2024-12-15"  # Changes daily = no cache

# After:
"You are helpful."  # Static = cacheable
"[Context: Current Date: 2024-12-15]"  # Dynamic part moved to end

How it works: 1. Find date patterns in system prompt 2. Extract them 3. Move to end of message 4. Now the PREFIX is stable → cache hits!

Transform 2: Tool Crusher (Naive) - DISABLED BY DEFAULT¶

This was our first approach - simple but limited:

# Before: 60 items
[{"ts": 1, "cpu": 45}, {"ts": 2, "cpu": 45}, ..., {"ts": 60, "cpu": 95}]

# After: First 10 items only
[{"ts": 1, "cpu": 45}, ..., {"ts": 10, "cpu": 45}, {"__truncated": 50}]

Problem: If the important data (CPU spike) is at position 45, it gets thrown away!

Transform 3: Smart Crusher (NEW DEFAULT)¶

This is the intelligent approach using statistical analysis:

# Analyzes the data first:
analysis = {
    "ts": {"type": "sequential", "unique_ratio": 1.0},
    "host": {"type": "constant", "value": "prod-1"},  # ← Same everywhere!
    "cpu": {"variance": 892, "change_points": [45]},  # ← Spike detected!
}

# Smart compression:
{
    "__headroom_constants": {"host": "prod-1"},  # Factor out
    "__headroom_summary": "items 0-44: cpu stable at ~45",  # Summarize boring part
    "data": [
        {"ts": 45, "cpu": 92},  # Keep the spike!
        {"ts": 46, "cpu": 95},
        ...
    ]
}

Strategies it uses: 1. TIME_SERIES: Detect variance spikes, keep change points 2. CLUSTER: Group similar log messages, keep 1-2 per cluster 3. TOP_N: For search results, keep highest scored 4. SMART_SAMPLE: Statistical sampling with constant extraction

Transform 4: LLMLingua Compressor (Optional)¶

When to use: Maximum compression needed and latency is acceptable.

# Opt-in ML-based compression using Microsoft's LLMLingua-2
# BERT-based token classifier trained via GPT-4 distillation

# Before: Long tool output text
"The function processUserData takes a user object and validates all fields..."

# After: Compressed while preserving semantic meaning
"function processUserData validates user fields..."

Key characteristics: - Uses microsoft/llmlingua-2-xlm-roberta-large-meetingbank model - Auto-detects content type (code, JSON, text) for optimal compression rates - Stores original in CCR for retrieval if needed - Adds 50-200ms latency per request - Requires ~1GB RAM when loaded

Proxy integration (opt-in):

headroom proxy --llmlingua --llmlingua-device cuda

Transform 5: Rolling Window¶

Problem: Even after compression, you might exceed the model's context limit.

# Model limit: 128K tokens
# Your messages: 150K tokens
# Need to drop 22K tokens

# Rolling Window drops OLDEST messages first:
# - Keeps system prompt (always)
# - Keeps last 2 turns (always)
# - Drops old tool calls + their responses as atomic units

Safety rule: If we drop a tool CALL, we MUST drop its RESPONSE too (or vice versa). Otherwise the model sees orphaned data.

Transform 6: Intelligent Context Manager (Advanced)¶

Problem: Rolling Window drops by position (oldest first), but position doesn't equal importance.

# Scenario: Error at turn 3, verbose success at turn 10
# Rolling Window: Drops turn 3 error (oldest first)
# Intelligent Context: Keeps turn 3 error (high TOIN error score)

The Solution: Multi-factor importance scoring using TOIN-learned patterns:

# Message scores (all learned, no hardcodes):
scores = {
    "recency": 0.20,           # Exponential decay from end
    "semantic_similarity": 0.20,  # Embedding similarity to recent context
    "toin_importance": 0.25,   # TOIN retrieval_rate (high = important)
    "error_indicator": 0.15,   # TOIN field_semantics.inferred_type
    "forward_reference": 0.15, # Referenced by later messages
    "token_density": 0.05,     # Unique tokens / total tokens
}

# Drops lowest-scored messages first
# Preserves critical errors even if old

Key principle: No hardcoded patterns. Error detection uses TOIN's learned field_semantics.inferred_type == "error_indicator", not keyword matching like "error" or "fail".

TOIN + CCR Integration:

IntelligentContext is a message-level compressor — just like SmartCrusher compresses items in an array, IntelligentContext "compresses" messages in a conversation. This means full CCR integration:

# When messages are dropped:
# 1. Store dropped messages in CCR for potential retrieval
ccr_ref = store.store(
    original=json.dumps(dropped_messages),
    compressed="[60 messages dropped]",
    tool_name="intelligent_context_drop",
)

# 2. Record drop to TOIN for cross-user learning
toin.record_compression(
    tool_signature=message_signature,  # Pattern of roles, tools, errors
    original_count=len(dropped_messages),
    compressed_count=1,  # The marker
    strategy="intelligent_context_drop",
)

# 3. Insert marker with CCR reference
marker = f"[Earlier context compressed: 60 messages dropped. Retrieve: {ccr_ref}]"

The feedback loop: - If users retrieve dropped messages via CCR, TOIN learns those patterns are important - Future drops of similar message patterns get higher importance scores - The system gets smarter across all users, not just within one session

5. Storage (`storage/`) - Metrics Database¶

Every request is logged:

CREATE TABLE requests (
    id TEXT PRIMARY KEY,
    timestamp TEXT,
    model TEXT,
    mode TEXT,  -- audit or optimize
    tokens_input_before INTEGER,  -- Before Headroom
    tokens_input_after INTEGER,   -- After Headroom
    tokens_saved INTEGER,         -- The win!
    transforms_applied TEXT,      -- What we did
    ...
);

This lets you: - See how much you're saving - Generate reports - Track trends over time

The Data Flow (Step by Step)¶

Let's trace a real request:

Step 1: You call the API¶

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are an SRE. Date: 2024-12-15"},
        {"role": "user", "content": "Check the metrics"},
        {"role": "assistant", "tool_calls": [...]},
        {"role": "tool", "content": "{60 metric points...}"},  # 5000 tokens!
        {"role": "user", "content": "What's wrong?"},
    ],
    headroom_mode="optimize",
)

Step 2: HeadroomClient intercepts¶

# In client.py:
def _create(self, messages, ...):
    # 1. Parse messages into blocks
    blocks, breakdown, waste = parse_messages(messages, tokenizer)
    # breakdown = {"system": 50, "user": 20, "tool_result": 5000, ...}

    # 2. Count original tokens
    tokens_before = 5100

Step 3: Transform Pipeline runs¶

# In pipeline.py:
def apply(self, messages, ...):
    # Transform 1: Cache Aligner
    # - Extracts "Date: 2024-12-15" from system prompt
    # - Moves to end

    # Transform 2: Smart Crusher
    # - Analyzes 60 metric points
    # - Detects CPU spike at point 45
    # - Compresses to 17 points (preserving spike)
    # - Factors out constant "host" field

    # Transform 3: LLMLingua (if enabled via --llmlingua)
    # - ML-based compression on remaining long text
    # - Auto-detects content type for optimal rate
    # - Stores original in CCR for retrieval

    # Transform 4: Rolling Window
    # - Checks if we're under limit (we are)
    # - No drops needed

    return TransformResult(
        messages=optimized,
        tokens_before=5100,
        tokens_after=1200,  # 76% reduction!
        transforms=["cache_align", "smart_crush:1"]
    )

Step 4: Call real API¶

# In client.py:
response = self._original.chat.completions.create(
    model="gpt-4o",
    messages=optimized_messages,  # Only 1200 tokens now!
)

Step 5: Log metrics and return¶

# Save to database
metrics = RequestMetrics(
    tokens_input_before=5100,
    tokens_input_after=1200,
    tokens_saved=3900,  # 76%!
    ...
)
storage.save(metrics)

return response  # Unchanged from API

The Smart Crusher Deep Dive¶

This is the most sophisticated part. Here's how it analyzes data:

Field Analysis¶

def analyze_field(key, items):
    values = [item[key] for item in items]

    return {
        "unique_ratio": len(set(values)) / len(values),
        # 0.0 = all same (constant)
        # 1.0 = all different (unique IDs)

        "variance": statistics.variance(values),  # For numbers
        # Low = stable
        # High = changing

        "change_points": detect_spikes(values),
        # Indices where value jumps significantly
    }

Pattern Detection¶

def detect_pattern(field_stats):
    # Has timestamp + numeric variance? → TIME_SERIES
    if has_timestamp and has_numeric_variance:
        return "time_series"

    # Has message field + level field? → LOGS
    if has_message_field and has_level_field:
        return "logs"

    # Has score/rank field? → SEARCH_RESULTS
    if has_score_field:
        return "search_results"

    return "generic"

Compression Strategy¶

def compress(items, analysis):
    if analysis.pattern == "time_series":
        # Keep points around change points
        # Summarize stable regions
        return time_series_compress(items, analysis.change_points)

    elif analysis.pattern == "logs":
        # Cluster similar messages
        # Keep 1-2 per cluster
        return cluster_compress(items, analysis.clusters)

    elif analysis.pattern == "search_results":
        # Sort by score
        # Keep top N
        return top_n_compress(items, analysis.score_field)

CCR Architecture: Compress-Cache-Retrieve¶

The Key Insight¶

"Prefer raw > Compaction > Summarization only when compaction no longer yields enough space. Compaction (Reversible) strips out information that is redundant because it exists in the environment—if the agent needs to read the data later, it can use a tool to retrieve it." — Phil Schmid, Context Engineering

The problem with traditional compression: If we guess wrong about what's important, we've permanently lost data. The LLM might need something we threw away.

CCR's solution: Make compression reversible. When SmartCrusher compresses, the original data is cached. If the LLM needs more, it can retrieve instantly.

┌──────────────────────────────────────────────────────────────────┐
│  TOOL OUTPUT (1000 items)                                         │
└────────────────────────┬─────────────────────────────────────────┘
                         │
                         ▼
┌──────────────────────────────────────────────────────────────────┐
│  HEADROOM CCR LAYER                                               │
│                                                                   │
│  1. COMPRESS: Keep 20 items (errors, anomalies, relevant)        │
│  2. CACHE: Store full 1000 items in fast local cache             │
│  3. INJECT: Add retrieval capability to LLM context              │
│                                                                   │
│  "20 items shown. Use /v1/retrieve?hash=xxx for more."           │
└────────────────────────┬─────────────────────────────────────────┘
                         │
                         ▼
┌──────────────────────────────────────────────────────────────────┐
│  LLM PROCESSING                                                   │
│                                                                   │
│  Option A: LLM solves task with 20 items → Done                  │
│  Option B: LLM needs more → retrieves via API                    │
│            → We fetch from cache → Return instantly              │
└────────────────────────┬─────────────────────────────────────────┘
                         │
                         ▼
┌──────────────────────────────────────────────────────────────────┐
│  FEEDBACK LOOP                                                    │
│                                                                   │
│  Track: What did the LLM retrieve? What queries?                 │
│  Learn: "For this tool, keep items matching common queries"      │
│  Improve: Next compression uses learned patterns                 │
└──────────────────────────────────────────────────────────────────┘

CCR Phase 1: Compression Store¶

Location: headroom/cache/compression_store.py

When SmartCrusher compresses, the original content is stored for on-demand retrieval:

@dataclass
class CompressionEntry:
    hash: str                    # 16-char SHA256 for retrieval
    original_content: str        # Full JSON before compression
    compressed_content: str      # Compressed JSON
    original_item_count: int
    compressed_item_count: int
    tool_name: str | None        # For feedback tracking
    created_at: float
    ttl: int = 300               # 5 minute default

Features: - Thread-safe in-memory storage - TTL-based expiration (default 5 minutes) - LRU-style eviction when capacity reached - Built-in BM25 search within cached content

Usage:

store = get_compression_store()

# Store compressed content
hash_key = store.store(
    original=original_json,
    compressed=compressed_json,
    original_item_count=1000,
    compressed_item_count=20,
    tool_name="search_api",
)

# Retrieve later
entry = store.retrieve(hash_key)

# Or search within cached content
results = store.search(hash_key, "user query")

CCR Phase 2: Retrieval API¶

Endpoints:

Endpoint	Method	Description
`/v1/retrieve`	POST	Retrieve original content by hash
`/v1/retrieve?query=X`	POST	Search within cached content

Retrieval Request:

{
    "hash": "abc123def456...",
    "query": "find errors"     // Optional: search within
}

Response (full retrieval):

{
    "hash": "abc123def456...",
    "original_content": "[{...}, {...}, ...]",
    "original_item_count": 1000,
    "tool_name": "search_api"
}

Response (search):

{
    "hash": "abc123def456...",
    "query": "find errors",
    "results": [{...}, {...}, ...],
    "count": 15
}

CCR Phase 3: Tool Injection¶

When compression happens, Headroom injects retrieval instructions into the LLM context.

Method A: System Message Injection

## Compressed Context Available
The following tool outputs have been compressed. If you need more detail,
call the retrieve_compressed tool with the hash.

Available: hash=abc123 (1000→20 items from search_api)

Method B: MCP Tool Registration (Hybrid) When running as MCP server, Headroom exposes retrieval as a tool:

{
    "name": "headroom_retrieve",
    "description": "Retrieve more items from compressed tool output",
    "inputSchema": {
        "type": "object",
        "properties": {
            "hash": {"type": "string"},
            "query": {"type": "string"}
        }
    }
}

Marker Injection: Compressed content includes retrieval markers:

{
    "__headroom_compressed": true,
    "__headroom_hash": "abc123def456",
    "__headroom_stats": {
        "original_items": 1000,
        "kept_items": 20,
        "errors_preserved": 5
    },
    "data": [...]
}

CCR Phase 4: Feedback Loop¶

Location: headroom/cache/compression_feedback.py

The feedback system learns from retrieval patterns to improve future compression.

Tracked Patterns per Tool:

@dataclass
class ToolPattern:
    tool_name: str
    total_compressions: int      # Times we compressed this tool
    total_retrievals: int        # Times LLM asked for more
    full_retrievals: int         # Retrieved everything
    search_retrievals: int       # Used search query
    common_queries: dict[str, int]   # Query frequency
    queried_fields: dict[str, int]   # Fields mentioned in queries

Key Metrics: - Retrieval Rate: total_retrievals / total_compressions - High (>50%) → Compressing too aggressively - Low (<20%) → Compression is effective - Full Retrieval Rate: full_retrievals / total_retrievals - High (>80%) → Data is unique, consider skipping compression

Compression Hints:

@dataclass
class CompressionHints:
    max_items: int = 15          # Target item count
    suggested_items: int | None  # Calculated optimal
    skip_compression: bool       # Don't compress at all
    preserve_fields: list[str]   # Always keep these fields
    aggressiveness: float        # 0.0 = aggressive, 1.0 = conservative
    reason: str                  # Explanation

Feedback-Driven Adjustment:

# In SmartCrusher._crush_array()
if self.config.use_feedback_hints and tool_name:
    feedback = get_compression_feedback()
    hints = feedback.get_compression_hints(tool_name)

    if hints.skip_compression:
        return items, f"skip:feedback({hints.reason})", None

    if hints.suggested_items is not None:
        self.config.max_items_after_crush = hints.suggested_items

Feedback Endpoints:

Endpoint	Method	Description
`/v1/feedback`	GET	Get all learned patterns
`/v1/feedback/{tool_name}`	GET	Get hints for specific tool

Example Response:

{
    "total_compressions": 150,
    "total_retrievals": 23,
    "global_retrieval_rate": 0.15,
    "tools_tracked": 5,
    "tool_patterns": {
        "search_api": {
            "compressions": 50,
            "retrievals": 5,
            "retrieval_rate": 0.10,
            "full_rate": 0.20,
            "search_rate": 0.80,
            "common_queries": ["status:error", "level:critical"],
            "queried_fields": ["status", "level", "message"]
        }
    }
}

CCR Phase 5: Response Handler (Automatic Tool Call Handling)¶

Location: headroom/ccr/response_handler.py

The Problem: When the proxy injects the headroom_retrieve tool, the LLM might call it. But who handles that tool call? Without response handling, the tool call would go back to the client unhandled.

The Solution: The Response Handler intercepts LLM responses, detects CCR tool calls, executes retrievals automatically, and continues the conversation until the LLM produces a final response.

┌──────────────────────────────────────────────────────────────────┐
│  RESPONSE HANDLER FLOW                                           │
│                                                                   │
│  1. LLM Response arrives                                         │
│     └─ Contains: tool_use(headroom_retrieve, hash=abc123)       │
│                                                                   │
│  2. Handler detects CCR tool call                                │
│     └─ Extracts hash and optional query                         │
│                                                                   │
│  3. Handler executes retrieval                                   │
│     └─ Full retrieval: store.retrieve(hash)                     │
│     └─ Search: store.search(hash, query)                        │
│                                                                   │
│  4. Handler continues conversation                               │
│     └─ Adds tool result to messages                             │
│     └─ Makes another API call                                   │
│                                                                   │
│  5. Repeat until no CCR tool calls                              │
│     └─ Max 3 rounds (configurable)                              │
│                                                                   │
│  6. Return final response to client                             │
└──────────────────────────────────────────────────────────────────┘

Key Classes:

@dataclass
class CCRToolCall:
    tool_call_id: str      # For matching response
    hash_key: str          # CCR hash to retrieve
    query: str | None      # Optional search query

@dataclass
class CCRToolResult:
    tool_call_id: str
    content: str           # Retrieved data as JSON
    success: bool
    items_retrieved: int
    was_search: bool       # True if search, False if full retrieval

class CCRResponseHandler:
    async def handle_response(
        self,
        response: dict,           # Initial LLM response
        messages: list,           # Conversation history
        tools: list,              # Tool definitions
        api_call_fn: Callable,    # Function to make API calls
        provider: str,            # "anthropic" or "openai"
    ) -> dict:
        """Handle CCR tool calls until final response."""

Streaming Support:

The handler also supports streaming responses via StreamingCCRHandler:

class StreamingCCRBuffer:
    """Buffers streaming chunks to detect CCR tool calls."""
    chunks: list[bytes]
    detected_ccr: bool

class StreamingCCRHandler:
    """Handles CCR in streaming responses."""
    async def process_stream(self, stream, messages, tools, api_call_fn):
        """Yields chunks, switching to buffered mode if CCR detected."""

CCR Phase 6: Context Tracker (Multi-Turn Awareness)¶

Location: headroom/ccr/context_tracker.py

The Problem: In multi-turn conversations, earlier compressed data might become relevant later. Without tracking, the LLM has "context amnesia" - it can't reference data that was compressed in turn 1 when answering a question in turn 5.

The Solution: The Context Tracker maintains awareness of all compressed content across the conversation and can proactively expand relevant data when a new query might need it.

┌──────────────────────────────────────────────────────────────────┐
│  CONTEXT TRACKER FLOW                                            │
│                                                                   │
│  Turn 1: Search returns 100 files → compressed to 10            │
│          Tracker stores: hash=abc123, sample="auth.py, db.py"   │
│                                                                   │
│  Turn 5: User asks "What about the authentication middleware?"  │
│          Tracker analyzes query:                                 │
│          - "authentication" matches "auth.py" in sample         │
│          - Relevance score: 0.7 (above threshold)               │
│                                                                   │
│  Proactive Expansion:                                           │
│          - Retrieves abc123 before LLM responds                 │
│          - Adds expanded context to request                     │
│                                                                   │
│  Result: LLM sees full file list, can answer accurately         │
└──────────────────────────────────────────────────────────────────┘

Key Classes:

@dataclass
class CompressedContext:
    hash_key: str              # CCR hash
    turn_number: int           # When compression happened
    timestamp: float           # For age-based filtering
    tool_name: str | None      # Which tool was compressed
    original_item_count: int
    compressed_item_count: int
    query_context: str         # User query at compression time
    sample_content: str        # Preview for relevance matching

@dataclass
class ExpansionRecommendation:
    hash_key: str
    reason: str                # Human-readable explanation
    relevance_score: float     # 0-1, higher = more relevant
    expand_full: bool          # True = full retrieval
    search_query: str | None   # If expand_full=False

class ContextTracker:
    def track_compression(self, hash_key, turn_number, ...):
        """Track a compression event."""

    def analyze_query(self, query: str) -> list[ExpansionRecommendation]:
        """Find relevant compressed contexts for a query."""

    def execute_expansions(self, recommendations) -> list[dict]:
        """Execute recommended expansions."""

Relevance Calculation:

The tracker uses simple but effective heuristics:

Keyword overlap with sample content - Extract keywords from query, match against compressed content preview
Keyword overlap with original query - Match against the query that triggered compression
Tool name relevance - File operations more likely to need expansion for "file", "where", "find" queries
Age discount - Older contexts get lower scores

Configuration:

@dataclass
class ContextTrackerConfig:
    enabled: bool = True
    max_tracked_contexts: int = 100      # LRU eviction
    relevance_threshold: float = 0.3     # Min score to recommend
    max_context_age_seconds: float = 300 # 5 minutes
    proactive_expansion: bool = True
    max_proactive_expansions: int = 2    # Per query

Why CCR is a Moat¶

Reversible: No permanent information loss. Worst case = retrieve everything.
Transparent: LLM knows it can ask for more data.
Automatic: Response Handler executes retrievals without client intervention.
Context-Aware: Context Tracker prevents multi-turn amnesia.
Feedback Loop: Learn from actual needs, not guesses.
Network Effect: Retrieval patterns across users improve compression for everyone.
Zero-Risk: If compression fails, instant fallback to original data.

Image Compression Architecture¶

Vision models charge by the token, and images are expensive (765-2900 tokens for a typical image). Headroom's image compression uses a trained ML router to automatically select the optimal compression technique.

The Key Insight¶

Not all image queries need full resolution: - "What is this?" → Low detail is fine (87% savings) - "Count the whiskers" → Need full detail (0% savings) - "Read the sign" → Could convert to text (99% savings)

How It Works¶

User: [image] + "What animal is this?"
           ↓
┌─────────────────────────────────┐
│  1. Query Analysis              │
│  TrainedRouter (MiniLM)         │
│  Classifies → full_low          │
└─────────────────────────────────┘
           ↓
┌─────────────────────────────────┐
│  2. Image Analysis (Optional)   │
│  SigLIP checks:                 │
│  - Has text? Is complex?        │
│  - Fine details needed?         │
└─────────────────────────────────┘
           ↓
┌─────────────────────────────────┐
│  3. Apply Compression           │
│  OpenAI: detail="low"           │
│  Anthropic: Resize to 512px     │
│  Google: Resize to 768px        │
└─────────────────────────────────┘
           ↓
Compressed request → LLM → Response

The Trained Router¶

A fine-tuned MiniLM classifier hosted on HuggingFace:

Model: chopratejas/technique-router
Size: ~128MB (downloaded once, cached)
Accuracy: 93.7% on 1,157 training examples
Latency: ~10ms CPU, ~2ms GPU

The router learns from examples like: | Query | Technique | |-------|-----------| | "What is this?" | full_low | | "Count the items" | preserve | | "Read the text" | transcode | | "What's in the corner?" | crop |

Provider-Specific Compression¶

Each provider handles images differently:

Provider	Method	Savings
OpenAI	`detail="low"` parameter	~87%
Anthropic	PIL resize to 512px	~75%
Google	PIL resize to 768px (tile-optimized)	~75%

Integration Points¶

Image compression runs in the proxy before text compression:

Request arrives
      ↓
[Image Compression] ← NEW
      ↓
[Transform Pipeline: Cache Aligner → Smart Crusher → ...]
      ↓
Forward to LLM

This ensures images are compressed first, then text compression (CCR, SmartCrusher) handles the rest.

Code Location¶

headroom/
├── image/
│   ├── __init__.py         # Public API
│   ├── compressor.py       # ImageCompressor class
│   └── trained_router.py   # TrainedRouter (HuggingFace model)
├── proxy/
│   └── server.py           # Integration point

File Structure Explained¶

headroom/
├── __init__.py          # Public exports
├── client.py            # HeadroomClient - the main wrapper
├── config.py            # All configuration dataclasses
├── parser.py            # Message → Block decomposition
├── tokenizer.py         # Token counting abstraction
├── utils.py             # Hashing, markers, helpers
│
├── providers/
│   ├── base.py          # Provider/TokenCounter protocols
│   ├── openai.py        # OpenAI-specific (tiktoken)
│   └── anthropic.py     # Anthropic-specific
│
├── transforms/
│   ├── base.py              # Transform protocol
│   ├── pipeline.py          # Orchestrates all transforms
│   ├── cache_aligner.py     # Date extraction for caching
│   ├── tool_crusher.py      # Naive compression (disabled)
│   ├── smart_crusher.py     # Statistical compression (default)
│   ├── rolling_window.py    # Token limit enforcement (position-based)
│   ├── intelligent_context.py  # Semantic context management (score-based)
│   ├── scoring.py           # Message importance scoring
│   └── llmlingua_compressor.py  # ML-based compression (opt-in)
│
├── cache/               # CCR Architecture - Caching & Storage
│   ├── compression_store.py    # Phase 1: Store original content
│   ├── compression_feedback.py # Phase 4: Learn from retrievals
│   ├── anthropic.py     # Anthropic cache optimizer
│   ├── openai.py        # OpenAI cache optimizer
│   ├── google.py        # Google cache optimizer
│   └── dynamic_detector.py # Dynamic content detection
│
├── ccr/                 # CCR Architecture - Tool Injection & Response Handling
│   ├── __init__.py             # CCR module exports
│   ├── tool_injection.py       # Phase 3: Inject retrieval tool
│   ├── response_handler.py     # Phase 5: Handle CCR tool calls
│   ├── context_tracker.py      # Phase 6: Multi-turn context tracking
│   └── mcp_server.py           # MCP server for retrieval tool
│
├── relevance/           # Relevance scoring for compression
│   ├── bm25.py          # BM25 keyword scorer
│   ├── embedding.py     # Semantic embedding scorer
│   └── hybrid.py        # Adaptive fusion scorer
│
├── storage/
│   ├── base.py          # Storage protocol
│   ├── sqlite.py        # SQLite implementation
│   └── jsonl.py         # JSON Lines implementation
│
├── proxy/
│   └── server.py        # Production HTTP proxy (CCR endpoints)
│
└── reporting/
    └── generator.py     # HTML report generation

Key Design Decisions¶

1. Provider-Agnostic¶

Works with ANY OpenAI-compatible API: - OpenAI - Azure OpenAI - Anthropic - Groq - Together - Local models (Ollama)

2. Deterministic Transforms¶

No LLM calls for compression. Everything is: - Statistical analysis - Pattern matching - Rule-based

This means: - Predictable results - Fast (<10ms overhead) - No added API costs

3. Safety First¶

Never modify user/assistant TEXT content
Tool call + response are atomic (drop both or neither)
Parse failures = no-op (return unchanged)
Audit mode for testing before optimizing

4. Smart by Default¶

SmartCrusher enabled (statistical analysis)
ToolCrusher disabled (naive rules)
Conservative settings that preserve important data

What Makes This Different?¶

vs. Summarization (LLM-based compression)¶

Headroom	Summarization
Deterministic	Non-deterministic
~10ms overhead	~2-5 seconds overhead
No extra API cost	Costs money to summarize
Preserves structure	Loses structure
Can't hallucinate	Can hallucinate

vs. Simple Truncation¶

Headroom	Truncation
Keeps important data	Loses end of data
Statistical analysis	No analysis
Detects spikes	Misses spikes
Factors out constants	Keeps redundancy

The Numbers (From Our Tests)¶

Real-world SRE incident investigation: - 5 tool calls: Metrics, logs, status, deployments, runbook - Original: 22,048 tokens - After SmartCrusher: 2,190 tokens - Reduction: 90% - Quality Score: 5.0/5 (no information loss)

The model could still: - Identify the CPU spike (preserved by change point detection) - Reference specific error rates (kept in compressed data) - Provide correct remediation commands

Summary¶

Headroom is a Context Budget Controller that:

Wraps your existing LLM client
Analyzes your messages to find waste
Compresses tool outputs intelligently (not blindly)
Preserves important information (spikes, anomalies, unique data)
Logs everything for observability
Saves 70-90% of tokens on tool-heavy workloads

The key insight: Most tool output redundancy is statistical (repeated values, constant fields, similar messages). By analyzing the data first, we can compress intelligently without losing the information that matters.

Headroom SDK: A Complete Explanation¶

Architecture Overview¶

What Problem Does Headroom Solve?¶

How Headroom Works: The Big Picture¶

The Core Components (In Simple Terms)¶

1. HeadroomClient (client.py) - The Wrapper¶

2. Providers (providers/) - Model-Specific Knowledge¶

3. Parser (parser.py) - Understanding Your Messages¶

4. Transforms (transforms/) - The Compression Magic¶

Transform 1: Cache Aligner¶

Transform 2: Tool Crusher (Naive) - DISABLED BY DEFAULT¶

Transform 3: Smart Crusher (NEW DEFAULT)¶

Transform 4: LLMLingua Compressor (Optional)¶

Transform 5: Rolling Window¶

Transform 6: Intelligent Context Manager (Advanced)¶

5. Storage (storage/) - Metrics Database¶

The Data Flow (Step by Step)¶

Step 1: You call the API¶

Step 2: HeadroomClient intercepts¶

Step 3: Transform Pipeline runs¶

Step 4: Call real API¶

Step 5: Log metrics and return¶

The Smart Crusher Deep Dive¶

Field Analysis¶

Pattern Detection¶

Compression Strategy¶

CCR Architecture: Compress-Cache-Retrieve¶

The Key Insight¶

CCR Phase 1: Compression Store¶

CCR Phase 2: Retrieval API¶

CCR Phase 3: Tool Injection¶

CCR Phase 4: Feedback Loop¶

CCR Phase 5: Response Handler (Automatic Tool Call Handling)¶

CCR Phase 6: Context Tracker (Multi-Turn Awareness)¶

Why CCR is a Moat¶

Image Compression Architecture¶

The Key Insight¶

How It Works¶

The Trained Router¶

Provider-Specific Compression¶

Integration Points¶

Code Location¶

File Structure Explained¶

Key Design Decisions¶

1. Provider-Agnostic¶

2. Deterministic Transforms¶

3. Safety First¶

4. Smart by Default¶

What Makes This Different?¶

vs. Summarization (LLM-based compression)¶

vs. Simple Truncation¶

The Numbers (From Our Tests)¶

Summary¶

1. HeadroomClient (`client.py`) - The Wrapper¶

2. Providers (`providers/`) - Model-Specific Knowledge¶

3. Parser (`parser.py`) - Understanding Your Messages¶

4. Transforms (`transforms/`) - The Compression Magic¶

5. Storage (`storage/`) - Metrics Database¶