OpenAnchor

LLM token-attribution middleware — capture, attribute, and observe every LLM call, with real OpenTelemetry export and real semantic caching.

What it is

OpenAnchor is an in-process SDK that sits alongside your LLM calls — via a LangChain middleware today, or by calling its collector API directly from any provider — and captures token-consumption events. It breaks usage down across six dimensions (phase, operation type, prompt template, session, model, and pattern) and exposes that data through a query API.

It also ships real OpenTelemetry span export and a real semantic-caching layer, both backed by actual embeddings and storage rather than mocked stand-ins.

Features / Highlights

• Token capture + 6D attribution — phase, operation type, prompt template, session, model, and pattern, via TokenCollector / AttributionModel / Analytics.
• Storage — in-memory and SQLite event stores (indexed, WAL, connection-reused) with optional retention/TTL purging.
• LangChain middleware — OpenAnchorMiddleware wraps a runnable and captures usage transparently, reading real usage_metadata.
• OpenTelemetry export — every capture_event is wrapped in a real OTEL span; tracing is off by default and enabled via configure_tracing(...).
• Semantic caching — embedding-based cache with real cosine-similarity search, SQLite storage, and hit-rate stats; 12 MCP tools (cache_prompt_embedding, find_cached_similar, analyze_cache_hit_rate, ...).
• Cost governance — okf_* token profiles, cost governance, and optimization tracking.
• Privacy by default — raw prompt/response capture is off by default (hash + length only), and opt-in captures are redacted for common PII/secrets.
• Docker image with a working entry point (python -m openanchor).

Who it's for / Use cases

• Attributing LLM cost/token spend across sessions, models, and prompt templates in an existing app, without switching providers.
• Wrapping a LangChain chain to capture usage transparently via OpenAnchorMiddleware.
• Reducing repeat-call cost with real semantic caching.
• Tracking optimization impact over time with the cost-governance and token-profile modules.

What it is not

• Not a proxy or a hosted platform. OpenAnchor is an in-process SDK with no network hop and no separate services to run — unlike proxy/async-logging platforms such as Helicone.
• Not framework-agnostic yet. The shipped integration is the LangChain middleware plus the direct collector API. The broader "drop-in across Cursor/Claude Code/Codex CLI/Deep Agents" pitch exists only in planning/ as an earlier, aspirational design.
• Not a content logger by default. Raw prompt/response text is not captured unless you explicitly opt in.
• Not production-certified. The project is classified Alpha; audit the privacy and MCP-connector defaults before relying on it in regulated environments.

Project status

Alpha (v0.6.1). The collector/attribution/analytics core, storage, LangChain middleware, OTEL export, semantic caching, the MCP tools, and the cost-governance modules are implemented and tested. See CHANGELOG.md for release history and docs/ for architecture and roadmap material.

Installation

pip install openanchor
# with OTLP exporter support:
pip install "openanchor[otel]"

Development install:

pip install -e ".[dev]"

Quick start

from openanchor import TokenCollector, Analytics, AttributionModel

collector = TokenCollector()
collector.set_session("session_1")

collector.capture_event(
    call_id="call_1",
    model="gpt-4",
    provider="openai",
    input_tokens=120,
    output_tokens=45,
)

attribution = AttributionModel(collector.store)
analytics = Analytics(collector, attribution)

summary = analytics.get_summary("session_1")
print(summary["total_tokens"], summary["by_operation"])

Or wrap a LangChain runnable directly:

from openanchor.middleware.langchain import OpenAnchorMiddleware

middleware = OpenAnchorMiddleware(project_name="my_app")
wrapped_chain = middleware(my_langchain_runnable)

result = wrapped_chain.invoke({"model": "gpt-4", "prompt": "..."})
print(middleware.get_session_stats())

Runnable examples live in examples/basic_usage.py and examples/mcp_openanchor.py.

Usage

Enable OpenTelemetry tracing:

from openanchor import configure_tracing

configure_tracing(exporter="console")  # safe default, no network calls
# or: configure_tracing(exporter="otlp", otlp_endpoint="http://localhost:4318/v1/traces")

Semantic caching:

from openanchor import SemanticCache
from openanchor._mcp_tools import OpenAnchorMCPHandler

cache = SemanticCache(cache_db_path="cache.db")
handler = OpenAnchorMCPHandler(cache)

await handler.cache_prompt_embedding("Summarize this report", response="...")
matches = await handler.find_cached_similar("Summarize this report for me")

Privacy controls:

from openanchor import SqliteEventStore
from openanchor.middleware.langchain import OpenAnchorMiddleware

store = SqliteEventStore("events.db", retention_days=30)
middleware = OpenAnchorMiddleware(
    store=store,
    capture_raw_content=False,       # default; only hash+length stored
    # capture_raw_content=True,      # opt in to store excerpts
    # redact_captured_content=True,  # default when opted in
)

Docker:

docker build -t openanchor .
docker run -p 8080:8080 openanchor
curl http://localhost:8080/health

Documentation

• QUICKSTART.md — the fastest path to a working capture.
• OTEL_SETUP_GUIDE.md — tracing config, env-var-only setup, and the span attribute reference.
• PRODUCTION_DEPLOYMENT.md — deployment and MCP connector security defaults.
• CHANGELOG.md — release history.
• examples/ — runnable examples.

Contributing

Contributions are welcome. Run the same checks CI runs:

pip install -e ".[dev]"
pytest tests/ -v
ruff check .
mypy openanchor/
bandit --ini .bandit -r openanchor/

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License

This project is licensed under the Apache License 2.0.