Splyntra
Get demo
Solutions · Use Case

Complete observability for autonomous, multi-step AI agents

Debugging non-deterministic agents requires seeing the whole execution graph: planning steps, dynamic tool calls, vector retrievals, sub-agent handoffs, and state mutations across the full run lifecycle.

Start free on this feature →View documentationBook technical demo
Live Telemetry Feed
OTLP/gRPC
agent.run.execution200 OK · 412ms
planner_agent.execute_tool()
security.risk_score0 / 100 · Clean
DLP check: 0 secrets, 0 PII detected
finops.token_cost$0.0034 · Cached (84%)
Claude 3.5 Sonnet / 1,420 tokens
End-to-End
Trace Graphs
Visual execution DAGs across all sub-agents.
1-Click
State Replay
Step backward and forward through agent decisions.
< 2ms
Runtime Impact
Zero lag asynchronous OTLP span collection.
Real-Time
Anomaly Alerts
Instant notification on infinite reasoning loops.

Engineered for high-throughput autonomous agents

Every capability is built into the OpenTelemetry streaming pipeline with sub-millisecond ingestion overhead.

01Agent Graphs

Multi-Agent Graph & State Machine Debugging

Follow the execution flow across complex LangGraph state machines, CrewAI agent handoffs, and hierarchical supervisor patterns.

  • ✓Visualize state mutations and memory context across turns
  • ✓Identify why an agent chose a specific tool over others
  • ✓Break down latency between LLM generation and external tool APIs
02Failure Forensics

Root-Cause Failure Analysis & Trace Replay

Diagnose why an agent hallucinated, failed a tool schema, or entered an infinite loop using step-by-step trace replay.

  • ✓Filter production runs by status (Success, Error, Blocked, Timeout)
  • ✓Compare failed execution traces against golden baseline runs
  • ✓Export failed traces directly to evaluation datasets for regression testing
03FinOps Insight

Unified Performance & Cost Attribution

Correlate reasoning depth, token consumption, and response accuracy with real dollar costs across all models and tools.

  • ✓Attribute cost down to individual sub-agents and reasoning loops
  • ✓Track Anthropic and OpenAI prompt cache utilization
  • ✓Enforce hard spending and step limits to terminate runaway loops

Frequently Asked Questions

How does Splyntra handle multi-turn conversations and long-running agents?▼
Splyntra groups spans under a unified `session_id` and `trace_id`, allowing you to inspect both individual step latency and the cumulative conversation history over hours or days.
Can I self-host Splyntra on my own Kubernetes cluster?▼
Yes. Splyntra's core is source-available and can be self-hosted via Docker Compose or Helm charts on any cloud provider.

Related Platform Features & Guides

PlatformTraces & Replay →PlatformMetrics & Performance →SolutionsFor AI Engineers →

Ready to monitor and secure your AI agents?

Get started in under 3 minutes with zero credit card required. Free tier includes up to 5 projects and community telemetry.

Start Free on CloudRequest Custom Enterprise Demo