Three logically-related sets of proxy changes ship in this branch:
1. Strands integration on the Bedrock path (HeadroomBundle + 4 OpenAI
handler fixes + LiteLLM cache stats + dep pin)
2. /stats MCP aggregation (cross-process events log → proxy summary)
3. Codex compression-failure fail-closed (WS + HTTP /v1/responses)
== 1. Strands integration on the Bedrock path ==
* HeadroomBundle (headroom/integrations/strands/bundle.py): single-helper
MCP wiring for a Strands Agent — Headroom MCP server (headroom_compress
/ headroom_retrieve / headroom_stats) plus optional Serena MCP and
optional in-process compression hook. Constructor builds unstarted
MCPClient instances per server; Strands' Agent owns the subprocess
lifecycle. Default config: MCP enabled, Serena enabled, hook OFF
(proxy is the single source of truth for compression). User-side
integration is two lines in any Strands app.
* headroom/proxy/handlers/openai.py — backend path now:
- calls PrefixCacheTracker.update_from_response (was direct-OpenAI only)
- intercepts CCR headroom_retrieve tool_calls server-side, mirroring
the Anthropic handler pattern; NO silent fallback, re-raises on
CCR errors (per feedback_no_silent_fallbacks)
- works for both non-streaming and streaming paths
* headroom/proxy/handlers/streaming.py: _stream_openai_via_backend now
accepts prefix_tracker + optimized_messages, parses cache stats from
the SSE final-usage frame (cache_creation_input_tokens added to the
state machine), records CCR retrieve feedback via a new
_record_ccr_feedback_from_openai_sse helper. Streaming CCR intercept
is intentionally out of scope (mirrors Anthropic streaming behaviour).
* headroom/backends/litellm.py: send_openai_message response usage block
now carries cache_read_input_tokens / cache_creation_input_tokens
(Anthropic/Bedrock dialect) and prompt_tokens_details.cached_tokens
(OpenAI dialect). Backwards-compatible — cold-start callers see the
same 3-key shape; cache keys appear only when the underlying provider
returns them. Pinned by test_no_cache_fields_means_no_cache_keys.
* headroom/proxy/auth_mode.py: ("strands-agents/", "strands") added to
CLIENT_UA_MAP. Production callers should also set X-Client: strands
since the default openai-python UA carries no Strands signal.
* pyproject.toml: huggingface-hub>=1.5.0,<2.0 pinned in [ml] so a sibling
install (e.g. strands-agents) can't drag the version below the floor
transformers 5.x requires (otherwise Kompress silently goes
"unavailable").
== 2. /stats MCP aggregation ==
* headroom/proxy/cost.py: _aggregate_mcp_events() reads the cross-process
shared events file the Headroom MCP server already writes to and
surfaces summary.mcp with three new keys:
- compressions (count of headroom_compress invocations)
- tokens_removed (sum of input - output across those)
- retrievals (count of headroom_retrieve — the load-bearing
over-compression alarm; if it grows linearly
with turn count, lossy compressors are
dropping info the model actually needs)
Defensive on every axis — missing MCP SDK, missing file, malformed
events, read errors — never blocks /stats.
* examples/strands_bundle_demo.py: stats panel prints the new fields so
the demo shows the full proxy-HTTP + MCP-tool story in one view.
== 3. Codex compression-failure fail-closed protection ==
Reported by Camille (2026-05-21): Codex threads were locking with
"ran out of room in the model's context window" after Headroom's
compression timed out on an oversized response.create frame and
forwarded the original ~1.7 MB frame to the upstream, which then
rejected it. Codex's auto-compact heuristic gates on the upstream-
reported total_usage_tokens (which Headroom had been shrinking on
earlier turns), so its compaction never fired and the thread locked.
Validated against open Codex issues (CLI + Desktop share codex-rs/core):
* #16068 — confirms compaction gates on total_usage_tokens,
estimated_token_count is computed but only logged
* #19806 — confirms image token estimator unbounded, contributes to
the same ContextManager.get_total_token_usage → auto-compaction chain
* headroom/proxy/helpers.py: decide_compression_failure_action() with a
unit-tested decision matrix:
- asyncio.TimeoutError → refuse, always
- non-timeout failure + frame > 256 KiB (configurable) → refuse
- non-timeout failure + small frame → forward (legacy)
Operator escape hatches:
- HEADROOM_WS_FAIL_OPEN_ON_COMPRESSION_FAILURE=1 restores legacy
- HEADROOM_WS_COMPRESSION_FAIL_THRESHOLD_BYTES tunes the threshold
* headroom/proxy/handlers/openai.py (WS /v1/responses): consults the
helper after compression failure. On refuse: close client websocket
code 1009 with "headroom: compression <reason> — please compact
context and retry" reason; set termination_cause for the outer
lifecycle finally; return.
* headroom/proxy/handlers/openai.py (HTTP /v1/responses): same helper.
On refuse: raise HTTPException(413) with a structured error body so
FastAPI's HTTPException handler emits a clean 413. The existing
`except HTTPException: raise` guard in this handler already ensures
the 413 propagates without being swallowed by the 502 catch-all.
Anthropic /v1/messages NOT changed in this branch: no equivalent bug
report on Anthropic-protocol clients, Claude Code (Anthropic-owned)
handles context overflow via its own cache_control/ephemeral
primitives, and Cursor/Aider don't maintain the local-Y estimate the
Codex bug requires. Deferred until a real report lands; the patch is
a one-liner reusing the same helper.
== Tests + verification ==
* tests/test_backends/test_litellm_cache_stats.py — 3 tests pinning
cache-stat surfacing across Anthropic/OpenAI dialects + backwards-
compat for no-cache responses.
* tests/test_proxy/test_openai_backend_path.py — 5 tests (Bedrock cache
fields, OpenAI fallback shape, CCR intercept with provider="openai",
CCR re-raise on exception, streaming signature contract).
* tests/test_proxy/test_mcp_stats_aggregation.py — 5 tests pinning the
aggregator across compress+retrieve mixes, empty events, unknown event
types, missing token fields, and read failures.
* tests/test_proxy/test_compression_failure_action.py — 12 tests pinning
the fail-closed decision matrix (timeout always refuses, small
transient passes through, oversize refuses, env override variants,
custom threshold, invalid threshold falls back, 0/negative ignored).
* examples/strands_bedrock_demo.py — model_id bumped from deprecated
Claude 3 Haiku to Sonnet 4.5 (the deprecated model now errors on
account access).
* examples/strands_via_proxy_demo.py — proxy + Bedrock cache + streaming
smoke test.
* examples/strands_mcp_dispatch_test.py — pure MCP round-trip probe.
* examples/strands_bundle_demo.py — full Strands + HeadroomBundle E2E
demo (this is the shape a real Strands user copies into their app).
Full pytest: 5327 passed, 178 skipped. The previously-failing
test_core_operations.py::TestAddBatch::test_add_batch_basic passes now
that the huggingface-hub pin in pyproject.toml unblocks transformers
imports.
E2E verified live against AWS Bedrock (Sonnet 4.5):
* cache_write=10,438 on turn A → cache_read=10,438 on turn B
* streaming SSE final usage frame carries cache_read_input_tokens
* 78.7% reduction on a 50 KB JSON tool_result via SmartCrusher (
dispatched per-content-type by ContentRouter)
* Strands Agent + HeadroomBundle: model autonomously called
headroom_compress + headroom_retrieve via MCP; CompressionStore
round-trip succeeded; final answer correct.
|
||
|---|---|---|
| .. | ||
| deployment/macos-launchagent | ||
| langchain_demo | ||
| mcp_demo | ||
| vercel-ai-sdk-pr/node-headroom-compression/node_modules | ||
| 07-context-compression.ipynb | ||
| context_compression_demo.py | ||
| README.md | ||
| strands_bedrock_demo.py | ||
| strands_bundle_demo.py | ||
| strands_mcp_dispatch_test.py | ||
| strands_via_proxy_demo.py | ||
| test_ccr.py | ||
| test_intelligent_context_toin_ccr.py | ||
Headroom Examples
This directory contains examples demonstrating Headroom's capabilities.
Quick Start Examples
basic_usage.py
Basic integration with OpenAI client:
export OPENAI_API_KEY='your-key'
python examples/basic_usage.py
anthropic_example.py
Integration with Anthropic Claude:
export ANTHROPIC_API_KEY='your-key'
python examples/anthropic_example.py
streaming_example.py
Streaming responses with optimization:
export OPENAI_API_KEY='your-key'
python examples/streaming_example.py
Evaluation Examples
smart_vs_naive_eval.py
Compare SmartCrusher against naive truncation:
export OPENAI_API_KEY='your-key'
python examples/smart_vs_naive_eval.py
real_world_eval.py
Comprehensive evaluation with Anthropic models:
export ANTHROPIC_API_KEY='your-key'
python examples/real_world_eval.py
real_world_openai_eval.py
Comprehensive evaluation with OpenAI models:
export OPENAI_API_KEY='your-key'
python examples/real_world_openai_eval.py
Demo Directories
langchain_demo/
Full LangChain agent integration demo:
# No API key needed for compression demo
PYTHONPATH=. python -m examples.langchain_demo.show_compression
# Full comparison (requires API key)
export OPENAI_API_KEY='your-key'
PYTHONPATH=. python -m examples.langchain_demo.run_comparison
See langchain_demo/README.md for details.
mcp_demo/
MCP (Model Context Protocol) integration demo:
export OPENAI_API_KEY='your-key'
PYTHONPATH=. python -m examples.mcp_demo.run_agent_eval
strands_bedrock_demo.py
AWS Strands Agents + Bedrock integration demo. Showcases two Headroom integration patterns:
- HeadroomHookProvider - Compresses tool outputs in real-time
- HeadroomStrandsModel - Optimizes entire conversation context
# Configure AWS credentials
export AWS_ACCESS_KEY_ID='your-access-key'
export AWS_SECRET_ACCESS_KEY='your-secret-key'
export AWS_DEFAULT_REGION='us-west-2' # Optional, defaults to us-west-2
# Or use AWS profile
export AWS_PROFILE='your-profile-name'
# Run the full demo (both integration patterns)
python examples/strands_bedrock_demo.py
# Run only the hook provider demo
python examples/strands_bedrock_demo.py --hook
# Run only the model wrapper demo
python examples/strands_bedrock_demo.py --model
# Specify a different AWS region
python examples/strands_bedrock_demo.py --region us-east-1
The demo uses Claude 3 Haiku via Bedrock for cost efficiency. It creates agents with 4 tools that return verbose JSON output (search results, logs, database records, metrics) and displays compression statistics with visual comparisons.
Requirements:
- AWS account with Bedrock enabled
- Claude 3 Haiku model access in your region
pip install strands-agents headroom-ai[strands]
Running Examples
All examples can be run from the repository root:
# Install dependencies
pip install -e ".[dev]"
# Run any example
python examples/<example_name>.py
Expected Results
| Example | Token Savings | Notes |
|---|---|---|
| basic_usage | 50-70% | Simple tool output compression |
| langchain_demo | 70-85% | Real agent with multiple tools |
| mcp_demo | 60-80% | MCP tool outputs |
| strands_bedrock_demo | 60-85% | Strands + Bedrock with verbose tools |
| real_world_eval | 50-90% | Varies by scenario |
Troubleshooting
ModuleNotFoundError: No module named 'headroom'
Run from the repository root with PYTHONPATH:
PYTHONPATH=. python examples/basic_usage.py
Or install in development mode:
pip install -e .
API Key Errors
Ensure your API keys are set:
export OPENAI_API_KEY='sk-...'
export ANTHROPIC_API_KEY='sk-ant-...'
AWS Credentials Errors (for Strands demo)
Ensure AWS credentials are configured:
# Option 1: Environment variables
export AWS_ACCESS_KEY_ID='your-access-key'
export AWS_SECRET_ACCESS_KEY='your-secret-key'
# Option 2: AWS profile
export AWS_PROFILE='your-profile-name'
# Option 3: AWS credentials file (~/.aws/credentials)
Also ensure Bedrock and the Claude 3 Haiku model are enabled in your AWS account.