LLM Monitoring Without SDK: Zero-Code Integration Under 5ms
Adding another SDK to production felt like a mistake. So Tenet built a different path. Monitor every LLM call via proxy or OpenTelemetry sidecar — zero application code changes, under 5ms overhead. Point your LLM client's base_url at the Tenet proxy instead of OpenAI or Anthropic directly. Every decision gets a tamper-evident Reasoning Ledger record with SHA-256 + Ed25519 cryptographic signing.
Three Integration Modes — All Zero Application Code
Proxy mode: change one environment variable. Your LLM client's base_url points at the Tenet proxy; Tenet forwards the request and captures the full payload asynchronously. OpenTelemetry sidecar: if you already export OTel traces, add Tenet as a second OTLP exporter — no application code change required. Ghost SDK: 2-line fire-and-forget library that returns in under 0.1ms with all I/O on a background thread.
Latency Proof: Under 5ms vs Synchronous SDKs
Tenet proxy forwarding overhead is under 5ms p99. Ghost SDK blocking overhead is under 0.3ms p99. Typical synchronous monitoring SDKs add 30–200ms per call — blocking your agent thread on every network write. With Tenet, your agent's critical path sees only the forwarding latency, not the signing or write operations.
What Every Monitoring Record Contains
Full context snapshot (the complete LLM request payload), reasoning chain (model response including chain-of-thought and tool calls), SHA-256 + Ed25519 cryptographic signature, and structured metadata for compliance output. Every record is deterministically replayable for pre-deployment validation and behavioral drift detection.
PROOF, VERIFICATION, IMPROVEMENT
PROOF: every captured decision is cryptographically sealed before you need to explain it. VERIFICATION: Verification Replay re-executes any past decision against the current agent state to detect behavioral drift before deployment. IMPROVEMENT: human override captures are structured into fine-tuning datasets automatically — production mistakes become the next model's training signal.
Supported Frameworks and Integration Patterns
Ghost SDK works with any Python or Node.js AI agent implementation. Framework-specific integrations are available for LangChain (Python and JS), CrewAI, OpenAI Agents SDK, Google ADK, and AWS Bedrock. For custom agent implementations, the SDK provides a direct wrap API: pass the decision context and action, receive the confirmation ID. No changes to existing agent logic are required — the SDK runs as a non-blocking wrapper around the decision step.
Latency Benchmarks
Ghost SDK blocking overhead: under 0.3ms p99 (serialization + queue enqueue). Background I/O latency: under 5ms for cryptographic signing + ledger write. Compared to synchronous observability SDKs: 30–200ms blocking overhead per event. For production AI agents in time-sensitive workflows — loan decisions, real-time fraud detection, clinical recommendations — the difference between synchronous and async capture is the difference between acceptable and unacceptable monitoring overhead.