Rastir

LLM & Agent Observability Library for Python

Rastir provides decorator-based instrumentation for LLM applications and AI agents. It captures structured traces and Prometheus metrics with minimal code changes — no monkey-patching, no framework lock-in.

Why Rastir?

Most LLM observability tools require SDK wrappers, monkey-patching, or vendor-specific clients. Rastir takes a different approach:

  • One decorator per framework — @framework_agent auto-detects the framework; or use @langgraph_agent, @crew_kickoff, @llamaindex_agent, @adk_agent, @strands_agent for explicit control
  • Adapters, not patches — 15 adapters extract model, tokens, and provider from return values. Works across SDK versions
  • Two-phase enrichment — metadata captured from function kwargs before the call, refined from the response after. Survives API failures
  • Self-hosted collector — a lightweight FastAPI server you own. Prometheus metrics, OTLP export, zero external infrastructure

Quick Example 

from rastir import configure, framework_agent

configure(service="my-app", push_url="http://localhost:8080")

@framework_agent(agent_name="react_agent")
def run(graph, query):
    return graph.invoke({"messages": [("user", query)]})

Every LLM call, tool invocation, and node execution is now traced:

react_agent (AGENT)
  ├── node:agent (TRACE)
  │   └── langgraph.llm.gpt-4o.invoke (LLM)
  ├── node:tools (TRACE)
  │   └── langgraph.tool.search.invoke (TOOL)
  └── node:agent (TRACE)
      └── langgraph.llm.gpt-4o.invoke (LLM)

Key Features

  • Framework decorators — @framework_agent (auto-detect), plus @langgraph_agent, @crew_kickoff, @llamaindex_agent, @adk_agent, @strands_agent with automatic LLM/tool discovery
  • 8 provider adapters — OpenAI, Azure OpenAI, Anthropic, Bedrock, Gemini, Cohere, Mistral, Groq — auto-detected from client module paths
  • Two-phase enrichment — metadata captured from function kwargs before the call, refined from response after. Survives API failures
  • MCP distributed tracing — wrap(session) propagates trace context across MCP tool boundaries — same trace_id links client and server
  • Generic wrap() — instrument any object (Redis, databases, MCP sessions) without decorator access
  • Cost observability — per-model USD cost tracking with PricingRegistry, pricing profiles, cost histograms
  • Streaming TTFT — Time-To-First-Token measurement on streaming LLM calls
  • Guardrail tracking — automatic AWS Bedrock guardrail violation metrics
  • Error normalisation — exceptions mapped to 6 fixed categories: timeout, rate_limit, validation_error, provider_error, internal_error, unknown
  • Self-hosted collector — FastAPI server you own. Prometheus /metrics, in-memory trace store, OTLP export to Tempo/Jaeger
  • SRE budgets & burn rates — error and cost budget tracking via Prometheus recording rules — SLO status, burn rates, days-to-exhaustion
  • 7 Grafana dashboards — LLM Performance, Agent-Tool, Cost-TTFT, Evaluation, Guardrail, SRE Budgets, System Health

Architecture 

┌─────────────────────────────────────────────────┐
│  Your Application                               │
│  ┌──────────────────────────────────────────┐   │
│  │  @framework_agent (auto-detect)         │   │
│  │  @langgraph_agent / @crew_kickoff /      │   │
│  │  @llamaindex_agent / @adk_agent /         │   │
│  │  @strands_agent                           │   │
│  │  @agent / @llm / wrap()                 │   │
│  │  Decorators → SpanRecord → Queue         │   │
│  └───────────────┬──────────────────────────┘   │
│                  │ HTTP POST /v1/telemetry       │
└──────────────────┼──────────────────────────────┘
                   ▼
┌─────────────────────────────────────────────────┐
│  Rastir Collector Server                        │
│  ┌────────────┐  ┌──────────┐  ┌────────────┐  │
│  │ Ingestion  │→ │ Metrics  │→ │ Prometheus │  │
│  │ Worker     │  │ Registry │  │ /metrics   │  │
│  │            │→ │ Trace    │  │            │  │
│  │            │  │ Store    │  │ /v1/traces │  │
│  │            │→ │ OTLP     │→ │ Jaeger/    │  │
│  │            │  │ Exporter │  │ Tempo      │  │
│  └────────────┘  └──────────┘  └────────────┘  │
└─────────────────────────────────────────────────┘

Pages

Getting Started

Core

Frameworks

Operations

Deployment

Reference

Rastir — LLM & Agent Observability Library