LLM Observability
Monitor and debug LLM applications with Parseable
Monitor, debug, and optimize your LLM applications with Parseable. Track API calls, token usage, latency, and errors across all major LLM providers and frameworks.
Why LLM Observability?
LLM applications present unique observability challenges:
- Non-deterministic outputs - Same input can produce different results
- High costs - Token usage directly impacts costs
- Latency sensitivity - Response times affect user experience
- Complex chains - Multi-step workflows are hard to debug
- Prompt engineering - Need visibility into prompt effectiveness
Supported Integrations
OpenAI
GPT-4, GPT-3.5, and other OpenAI models
Anthropic
Claude and Claude Instant models
LiteLLM
Unified API gateway for 100+ LLM providers
OpenRouter
Zero-code LLM observability via Broadcast
vLLM
High-performance LLM inference serving
LangChain
LangChain framework integration
LlamaIndex
LlamaIndex RAG applications
AutoGen
Microsoft AutoGen multi-agent systems
CrewAI
CrewAI agent orchestration
DSPy
DSPy programmatic prompting
n8n
n8n workflow automation
What to Monitor
API Calls & Responses
Track every interaction with LLM providers:
- Request parameters (model, temperature, max_tokens)
- Full prompts and completions
- Response metadata and finish reasons
Token Usage & Costs
Monitor consumption to control costs:
- Input and output tokens per request
- Cost calculations by model
- Usage trends over time
Latency & Performance
Measure response times:
- Time to first token (TTFT)
- Total response time
- Streaming vs non-streaming performance
Errors & Failures
Debug issues quickly:
- Rate limit errors
- API failures and retries
- Timeout tracking
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