risicare 0.1.11

AI agent observability and self-healing for Python — trace LLM calls, detect errors, get AI-generated fixes

risicare

AI agent observability and self-healing for Python.

Monitor your AI agents in production. Trace every LLM call, detect errors automatically, and get AI-generated fixes — with a single init().

Quickstart

pip install risicare

import risicare
from openai import OpenAI

# Initialize — auto-instruments all detected LLM providers
risicare.init(
    api_key="rsk-...",
    endpoint="https://app.risicare.ai",
)

client = OpenAI()  # Automatically traced by risicare

@risicare.agent(name="research-agent")
def research(query: str) -> str:
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": query}],
    )
    return response.choices[0].message.content

result = research("What is quantum computing?")
risicare.shutdown()

That's it. Your agent's LLM calls, latency, token usage, and costs now appear in the Risicare dashboard.

Features

• Auto-instrumentation — Detects and patches LLM providers on init(), zero code changes
• 12 LLM providers — OpenAI, Anthropic, Google, Mistral, Groq, Cohere, Together, Ollama, HuggingFace, Cerebras, Bedrock, Vertex AI
• 8 host-detected — DeepSeek, xAI, Fireworks, Baseten, Novita, BytePlus, vLLM, and any OpenAI-compatible API via base_url
• 10 framework integrations — LangChain, LangGraph, CrewAI, AutoGen, Instructor, LlamaIndex, LiteLLM, DSPy, Pydantic AI, OpenAI Agents
• Self-healing — Automatic error diagnosis and AI-generated fix suggestions
• 13 built-in scorers — Faithfulness, relevance, toxicity, hallucination, and more
• Streaming support — Full streaming trace enrichment with token counts
• OpenTelemetry bridge — Compatible with existing OTel pipelines
• Non-blocking — All telemetry is async, never slows your app

LLM Providers

# Auto-patching (default) — detects installed providers automatically
risicare.init(api_key="rsk-...", endpoint="https://app.risicare.ai")

# Disable auto-patching if needed
risicare.init(api_key="rsk-...", endpoint="https://app.risicare.ai", auto_patch=False)

All 12 native providers:

Provider	Package	Provider	Package
OpenAI	openai	Anthropic	anthropic
Google Gemini	google-generativeai	Mistral	mistralai
Cohere	cohere	Groq	groq
Together AI	together	Ollama	ollama
AWS Bedrock	boto3	Google Vertex AI	google-cloud-aiplatform
Cerebras	cerebras-cloud-sdk	HuggingFace	huggingface-hub

Plus 8 auto-detected via OpenAI base_url: DeepSeek, xAI, Fireworks, Baseten, Novita, BytePlus, vLLM, and any OpenAI-compatible API.

Framework Integrations

pip install risicare[langchain]    # LangChain + LangGraph
pip install risicare[crewai]       # CrewAI
pip install risicare[autogen]      # AutoGen
pip install risicare[instructor]   # Instructor
pip install risicare[litellm]      # LiteLLM
pip install risicare[dspy]         # DSPy
pip install risicare[pydantic-ai]  # Pydantic AI
pip install risicare[llamaindex]   # LlamaIndex
pip install risicare[all]          # Everything

Core API

import risicare

risicare.init(api_key, endpoint)            # Initialize (auto-patches providers)
risicare.shutdown(timeout_ms=5000)          # Flush pending spans and close

@risicare.agent(name="my-agent")            # Trace a function with agent identity
@risicare.trace                             # Trace any function (decorator or CM)
@risicare.session(session_id="sess-1")      # Group traces into user sessions

risicare.report_error(exception)            # Report caught errors for diagnosis
risicare.score(trace_id, "quality", 0.92)   # Record evaluation score [0.0-1.0]

risicare.enable() / risicare.disable()      # Runtime tracing control
risicare.is_enabled()                       # Check tracing status

Decision Phases

Structure your traces to see how your agent thinks, decides, and acts:

@risicare.agent(name="planner")
def plan(query: str):
    @risicare.trace_think
    def analyze():
        return llm.chat("Analyze this query...")

    @risicare.trace_decide
    def choose_action(analysis):
        return llm.chat("Pick the best action...")

    @risicare.trace_act
    def execute(action):
        return run_tool(action)

    analysis = analyze()
    action = choose_action(analysis)
    return execute(action)

Self-Healing

When your agent fails, Risicare automatically:

1. Classifies the error (154 codes across TOOL, MEMORY, REASONING, OUTPUT, etc.)
2. Diagnoses the root cause using AI analysis
3. Generates a fix you can review and apply

try:
    result = my_agent(user_input)
except Exception as e:
    risicare.report_error(e)  # Triggers automatic diagnosis pipeline

Scoring & Evaluation

# Custom scores
risicare.score(trace_id="tr-123", name="quality", value=0.92)

# 13 built-in scorers available in the dashboard:
# faithfulness, answer_relevance, context_precision, context_recall,
# hallucination, toxicity, bias, coherence, conciseness,
# fluency, g_eval, goal_accuracy, summarization

OpenTelemetry

pip install risicare[otel]

risicare.init(api_key="rsk-...", otel_bridge=True)
# Compatible with any OTel-instrumented application

Requirements

• Python 3.10+

Documentation

• Full docs
• Quickstart guide
• Python SDK reference
• LLM providers
• Self-healing
• Evaluations
• JS/TS SDK

License

MIT