aigie 0.2.39

Enterprise-grade AI agent reliability monitoring and autonomous remediation

Aigie Python SDK

Official Python SDK for Aigie -- AI agent monitoring, tracing, and autonomous remediation.

95% of AI agents never reach production due to context drift, tool errors, and runtime instability. Aigie provides the infrastructure that makes autonomous AI reliable and production-grade:

• Detects context drift and errors before they impact users
• Fixes issues automatically through self-healing workflows
• Prevents failures with predictive intervention

Installation

pip install aigie

With optional integrations

# Compression (recommended for production -- 50-90% bandwidth savings)
pip install aigie[compression]

# LLM providers
pip install aigie[openai]              # OpenAI
pip install aigie[anthropic]           # Anthropic Claude
pip install aigie[gemini]              # Google Gemini

# Agent frameworks
pip install aigie[langchain]           # LangChain
pip install aigie[langgraph]           # LangGraph
pip install aigie[openai-agents]       # OpenAI Agents SDK
pip install aigie[claude-agent-sdk]    # Anthropic Claude Agent SDK
pip install aigie[strands]             # Strands Agents (AWS/Anthropic)
pip install aigie[google-adk]          # Google Agent Development Kit
pip install aigie[crewai]              # CrewAI multi-agent
pip install aigie[autogen]             # AutoGen/AG2
pip install aigie[llamaindex]          # LlamaIndex RAG
pip install aigie[dspy]                # DSPy
pip install aigie[instructor]          # Instructor structured outputs
pip install aigie[semantic-kernel]     # Microsoft Semantic Kernel
pip install aigie[browser-use]         # Browser-Use automation
pip install aigie[livekit-agents]      # LiveKit real-time voice AI
pip install aigie[agno]                # Agno (formerly Phidata)
pip install aigie[pipecat]             # Pipecat

# Vector databases
pip install aigie[pinecone]            # Pinecone
pip install aigie[qdrant]              # Qdrant
pip install aigie[chromadb]            # ChromaDB
pip install aigie[weaviate]            # Weaviate
pip install aigie[vectordbs]           # All vector DBs

# Observability
pip install aigie[opentelemetry]       # OpenTelemetry inbound
pip install aigie[otlp]                # OTLP export

# Everything
pip install aigie[all]

Quick start

Decorator-based tracing (recommended)

from aigie import traceable

@traceable(run_type="agent")
async def my_agent(query: str):
    result = await process_query(query)
    return result

result = await my_agent("What is the weather?")

Auto-instrument LLM providers

from aigie import wrap_openai, wrap_anthropic
from openai import AsyncOpenAI
from anthropic import AsyncAnthropic

# OpenAI -- all calls automatically traced with model, tokens, cost, latency
client = wrap_openai(AsyncOpenAI())
response = await client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}],
)

# Anthropic
client = wrap_anthropic(AsyncAnthropic())

Context manager

from aigie import Aigie, Config

config = Config(
    api_url="https://api.aigie.com",
    api_key="your-key",
    batch_size=100,
    flush_interval=5.0,
)

aigie = Aigie(config=config)
await aigie.initialize()

async with aigie.trace("My Workflow") as trace:
    async with trace.span("operation", type="llm") as span:
        result = await do_work()
        span.set_output({"result": result})

Integrations

Agent frameworks

Framework	Install extra	Auto-instrument
LangChain	langchain	AigieCallbackHandler
LangGraph	langgraph	wrap_langgraph()
OpenAI Agents SDK	openai-agents	patch_openai_agents()
Claude Agent SDK	claude-agent-sdk	patch_claude_agent_sdk()
Strands Agents	strands	patch_strands()
Google ADK	google-adk	patch_google_adk()
CrewAI	crewai	patch_crewai()
AutoGen/AG2	autogen	patch_autogen()
LlamaIndex	llamaindex	patch_llamaindex()
DSPy	dspy	patch_dspy()
Instructor	instructor	patch_instructor()
Semantic Kernel	semantic-kernel	patch_semantic_kernel()
Browser-Use	browser-use	patch_browser_use()
LiveKit Agents	livekit-agents	patch_livekit_agents()
Pipecat	pipecat	patch_pipecat()
Agno	agno	patch_agno()

LLM providers

Provider	Wrapper
OpenAI	wrap_openai()
Anthropic	wrap_anthropic()
Google Gemini	wrap_gemini()
AWS Bedrock	wrap_bedrock()

Each integration lives in sdk/aigie/integrations/<framework>/ and follows a consistent pattern with auto-instrumentation, cost tracking, drift detection, error detection, retry logic, and session management. See CONTRIBUTING.md for details on adding new integrations.

Configuration

Environment variables

export AIGIE_API_URL=https://your-instance.aigie.io/api
export AIGIE_API_KEY=your-api-key
export AIGIE_BATCH_SIZE=100
export AIGIE_FLUSH_INTERVAL=5.0

Config object

from aigie import Config

config = Config(
    api_url="https://api.aigie.com",   # Aigie API endpoint
    api_key="your-key",                # API key
    batch_size=100,                    # Events per batch (default: 10)
    flush_interval=5.0,               # Flush interval in seconds
    enable_buffering=True,            # Enable event buffering (default: True)
    max_retries=3,                    # Retry count on failure
)

Advanced features

OpenTelemetry integration

from aigie import Aigie
from aigie.opentelemetry import setup_opentelemetry

aigie = Aigie()
await aigie.initialize()
setup_opentelemetry(aigie, service_name="my-service")

# All OTel spans now flow to Aigie
from opentelemetry import trace
tracer = trace.get_tracer(__name__)
with tracer.start_as_current_span("operation"):
    pass

Evaluation and scoring

from aigie import score, feedback

await score(trace_id, "accuracy", 0.95)
await feedback(trace_id, "user_feedback", "Great response!")

Prompt management

from aigie import Prompt

prompt = Prompt.chat(
    name="customer_support",
    messages=[{"role": "system", "content": "You are a helpful assistant."}],
    version="1.0",
)

Synchronous API

from aigie import AigieSync

aigie = AigieSync()
aigie.initialize()

with aigie.trace("workflow") as trace:
    with trace.span("operation") as span:
        result = do_work()
        span.set_output({"result": result})

API reference

Full API documentation is available at docs.aigie.io/sdk/python.

Development

# Clone and set up
git clone https://github.com/Kytte-AI/kytte-python-sdk.git
cd kytte-python-sdk
python -m venv .venv
source .venv/bin/activate
pip install -e "sdk/[dev]"

Common commands

make lint        # Run ruff linter
make format      # Format code with ruff
make test        # Run unit tests
make test-all    # Run all tests including integration
make coverage    # Run tests with coverage report
make typecheck   # Run mypy type checking
make check       # Run all checks (lint + test)
make build       # Build distribution packages

Running tests

# Unit tests
pytest tests/unit/ -v

# Integration tests (requires API keys)
pytest tests/integration/ -v

# Coverage report
pytest tests/unit/ --cov=sdk/aigie --cov-report=html --cov-report=term-missing

Publishing

Releases are published to PyPI automatically when a GitHub release is created, via the publish.yaml workflow.

Manual publishing is also supported:

./scripts/publish-sdk.sh <version>
# e.g. ./scripts/publish-sdk.sh 0.2.39

Contributing

See CONTRIBUTING.md for development setup and guidelines.

License

MIT -- see LICENSE for details.

Documentation

• API Reference
• Integration Guides
• Changelog

Related

Repository	Description
kytte-js-sdk	Aigie TypeScript/JavaScript SDK
docs-site	Documentation site