Strands Integration

Headroom integrates with Strands Agents to provide automatic context optimization. Two integration patterns: wrap the model, or hook into tool calls.

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Installation

pip install headroom-ai strands-agents

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Quick Start

from strands import Agent
from strands.models.bedrock import BedrockModel
from headroom.integrations.strands import HeadroomStrandsModel

# Wrap your model
model = BedrockModel(model_id="us.anthropic.claude-sonnet-4-20250514-v1:0")
optimized = HeadroomStrandsModel(wrapped_model=model)

# Create agent as usual
agent = Agent(model=optimized)
response = agent("Investigate the production incident")

# Check savings
print(f"Tokens saved: {optimized.total_tokens_saved}")

Every API call the agent makes — including tool result round-trips — gets compressed automatically.

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Integration Patterns

1. Model Wrapping

Wraps the Strands Model interface. Every call to stream() compresses the messages before they hit the provider.

from strands.models.bedrock import BedrockModel
from headroom.integrations.strands import HeadroomStrandsModel

model = BedrockModel(model_id="us.anthropic.claude-sonnet-4-20250514-v1:0")
optimized = HeadroomStrandsModel(wrapped_model=model)

# Streaming works identically
agent = Agent(model=optimized)
response = agent("Analyze these logs")

With custom config:

from headroom import HeadroomConfig

config = HeadroomConfig()
optimized = HeadroomStrandsModel(wrapped_model=model, config=config)

2. Hook Provider (Tool Output Compression)

Compresses tool call results via Strands' hook system. Uses SmartCrusher on JSON arrays returned by tools.

from strands import Agent
from strands.models.bedrock import BedrockModel
from headroom.integrations.strands import HeadroomHookProvider

model = BedrockModel(model_id="us.anthropic.claude-sonnet-4-20250514-v1:0")
hooks = HeadroomHookProvider(
    compress_tool_outputs=True,
    min_tokens_to_compress=200,
    preserve_errors=True,
)

agent = Agent(model=model, hooks=[hooks])
response = agent("Search the database for recent failures")

# Check tool compression savings
print(f"Tokens saved by hooks: {hooks.total_tokens_saved}")

The hook preserves:

• Error items (error indicators, exceptions)
• Anomalous values (statistical outliers)
• Items matching the user's query context
• First/last items for boundary context

3. Both Together

Model wrapping compresses conversation history. Hooks compress individual tool results. Use both for maximum savings.

from headroom.integrations.strands import HeadroomStrandsModel, HeadroomHookProvider

optimized = HeadroomStrandsModel(wrapped_model=model)
hooks = HeadroomHookProvider(compress_tool_outputs=True)

agent = Agent(model=optimized, hooks=[hooks])

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Structured Output

HeadroomStrandsModel supports Strands' structured output feature:

from pydantic import BaseModel

class Analysis(BaseModel):
    severity: str
    root_cause: str
    recommendation: str

result = optimized.structured_output(Analysis, messages)

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Metrics

# Per-request metrics
for m in optimized.metrics_history:
    print(f"  {m.tokens_before} → {m.tokens_after} ({m.tokens_saved} saved)")

# Running total
print(f"Total saved: {optimized.total_tokens_saved}")

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How It Works

Agent decides to call tool
    │
    ▼
Tool executes, returns result
    │
    ▼
HeadroomHookProvider (optional)
    compresses tool result JSON
    │
    ▼
Agent builds next API request
    │
    ▼
HeadroomStrandsModel.stream()
    compresses full message list
    │
    ▼
Provider API (Bedrock, etc.)

The model wrapper uses Headroom's full pipeline (CacheAligner → ContentRouter → IntelligentContext). The hook provider uses SmartCrusher directly for fast JSON compression of individual tool results.

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Supported Providers

HeadroomStrandsModel auto-detects the provider from the wrapped model:

Strands Model	Provider Detected
BedrockModel	Anthropic (via Bedrock)
OllamaModel	OpenAI-compatible
Custom Model	Falls back to estimation