OpenTelemetry-native agent tracing, cost attribution, and drift detection
Project description
agent-observability
OpenTelemetry-native agent tracing, cost attribution, and drift detection
Part of the AumOS open-source agent infrastructure portfolio.
Features
- Eight agent-semantic OTel span kinds —
llm_call,tool_invoke,memory_read,memory_write,reasoning_step,agent_delegate,human_approval, andagent_error— each with typed fluent setters for tokens, cost, model, and domain-specific fields AgentTracercontext-manager factory producesAgentSpaninstances backed by a real OTel tracer when the SDK is installed, or a zero-dependency no-op fallback when it is not- Per-call cost attribution with model pricing tables and
CostAnnotationdataclasses recorded directly on LLM call spans - Behavioral drift detection:
BaselineProfileraccumulates metrics (tool call frequency, prompt length, error rate), andDriftDetectorraises alerts when observations deviate beyond configurable thresholds - PII-safe telemetry via a configurable
Redactorthat scrubs span attributes before they are exported - Auto-instrumentation modules for LangChain, CrewAI, AutoGen, Anthropic SDK, OpenAI SDK, and MCP — call
instrument()once and all framework calls emit spans automatically - Pre-built Grafana dashboard definitions for agent throughput, cost trends, error rates, and drift alerts
Quick Start
Install from PyPI:
pip install agent-observability
Verify the installation:
agent-observability version
Basic usage:
import agent_observability
# See examples/01_quickstart.py for a working example
Documentation
Enterprise Upgrade
For production deployments requiring SLA-backed support and advanced integrations, contact the maintainers or see the commercial extensions documentation.
Contributing
Contributions are welcome. Please read CONTRIBUTING.md before opening a pull request.
License
Apache 2.0 — see LICENSE for full terms.
Part of AumOS — open-source agent infrastructure.
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