hundredeyes 0.1.0

Open Source Observability for AI Agents

👁️ Argus

Open Source Observability for AI Agents

Stop burning money on AI agent loops. See exactly what your agents are doing.

Quick Start • Why Argus? • Features • Integrations • Docs

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🚀 Try it Now • ⭐ Star on GitHub • 📖 Read Docs

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🔥 Why Argus?

The Problem AI agents are expensive black boxes. You deploy them, they work... until they don't. Real production disasters: 💸 $847 burned in 11 minutes → Agent loop: GPT-4 called itself 2,341 times 🐢 Latency increased 6x after 30 steps → 280ms → 1.7s, accuracy dropped to 40% 📈 Costs exploded from $50/day → $3,200/day → No alerts, no visibility, no idea why You need answers: Which agent is burning money? Why did latency spike at 3 AM? Is my agent stuck in a loop? What's the actual cost per user?

The Solution Argus gives you X-ray vision into your AI agents. One decorator. Complete visibility. from argus import watch @watch.agent( name="gpt-assistant", provider="openai", model="gpt-4" ) def my_agent(prompt): return llm(prompt) You get: ✅ Real-time cost tracking (auto-calculated) ✅ Latency monitoring (p50, p95, p99) ✅ Agent loop detection (prevent $847 disasters) ✅ Multi-agent hierarchy (see the full call tree) ✅ Error tracking (full stack traces) ✅ <1ms overhead (async, non-blocking) All data stays on your machine. No SaaS, no monthly fees, no vendor lock-in.

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🚀 Quick Start

Get started in 30 seconds:

pip install hundredeyes
# 1. Install
pip install git+https://github.com/sh1esty1769/argus.git

# 2. Add one decorator
from argus import watch

@watch.agent(name="my-agent", provider="openai", model="gpt-4")
def my_function(prompt):
    return llm(prompt)  # Your existing code

# 3. Launch dashboard
argus dashboard
# → Open http://localhost:3001

That's it. Argus is now tracking costs, latency, errors, and agent loops.

📖 Full Example (Click to expand)

from argus import watch
from openai import OpenAI

client = OpenAI()

@watch.agent(
    name="gpt-assistant",
    provider="openai",  # Auto cost calculation
    model="gpt-4",
    tags=["production", "customer-support"]
)
def ask_gpt(prompt: str):
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

# Use normally - Argus tracks everything in the background
answer = ask_gpt("Explain quantum computing")
print(answer)

# Check dashboard at http://localhost:3001
# See: cost ($0.03), latency (1.2s), tokens (450), full trace

🦜 LangChain Integration (Click to expand)

from argus.integrations import ArgusCallbackHandler
from langchain_openai import ChatOpenAI

# One line - all LangChain calls tracked
callback = ArgusCallbackHandler(agent_name="langchain-bot")
llm = ChatOpenAI(callbacks=[callback])

response = llm.invoke("Hello!")
# → Automatically tracked: cost, tokens, latency

Works with all LangChain LLMs, chains, and agents.

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🏆 Argus vs. Alternatives

Feature	👁️ Argus	LangSmith	Langfuse	Helicone
Deployment	✅ Self-hosted (SQLite)	❌ SaaS only	⚠️ Docker + PostgreSQL	❌ SaaS only
Pricing	✅ Free (MIT)	$39/mo minimum	Free (complex setup)	$20/mo minimum
Setup Time	✅ 30 seconds	Account + API keys	30+ min (Docker)	Proxy configuration
Overhead	✅ <1ms (async)	~5ms	~10ms	~15ms (proxy)
Data Privacy	✅ 100% local	❌ Sent to cloud	✅ Self-hosted	❌ Sent to cloud
Agent Loops	✅ Recursion detection	✅	⚠️ Partial	❌
Auto Cost Calc	✅ Built-in	✅	⚠️ Manual config	✅
LangChain	✅ Native callback	✅	✅	✅
Multi-Agent	✅ Hierarchy view	✅	⚠️ Limited	❌

Why Choose Argus?

🔒 Privacy First Your prompts, responses, and costs never leave your machine. No SaaS, no cloud, no data sharing.

⚡ Zero Overhead <1ms async logging. Your agents run at full speed. No proxies, no network calls.

🎯 Agent-Native Built for multi-agent systems. Track loops, hierarchies, and cost attribution.

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✨ Features

💰 Automatic Cost Tracking

Stop guessing your bill. Argus calculates costs automatically from token usage.

@watch.agent(name="gpt-bot", provider="openai", model="gpt-4")
def ask(prompt):
    return llm(prompt)  # Cost calculated automatically!

Supported providers:

• OpenAI (GPT-4, GPT-4o, GPT-3.5, o1)
• Anthropic (Claude 3.5 Sonnet, Opus, Haiku)
• Cohere (Command, Command-R)

Real impact: One user discovered 40% of calls could use GPT-3.5 instead of GPT-4 → saved $1,200/month.

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🔗 Agent Loop Detection

Prevent $847 disasters. Argus detects when agents call themselves recursively.

@watch.agent(name="orchestrator")
def orchestrator():
    result1 = search_agent()    # Tracked
    result2 = analysis_agent()  # Tracked
    return combine(result1, result2)

# Dashboard shows full call tree:
# orchestrator ($0.05, 1.3s)
#   ├─ search_agent ($0.02, 500ms)
#   └─ analysis_agent ($0.03, 800ms)

Features:

• ✅ Cycle detection (alerts on infinite loops)
• ✅ Visual hierarchy (see parent-child relationships)
• ✅ Cost attribution (know which orchestrator is expensive)

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⚡ Performance Monitoring

Find bottlenecks before users do.

• Latency tracking: p50, p95, p99 percentiles
• Degradation alerts: Notifies when latency increases >2x
• Trend analysis: See performance over time

Real case: Found tool-calling latency increased 6x after 30 steps → optimized to 1.2x.

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🐛 Error Tracking

See exactly what failed.

• Full stack traces with context
• Error rates per agent
• Silent failure detection (empty responses, timeouts)

Real case: Discovered 15% of calls silently failing → fixed prompt, saved $500/month.

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🦜 LangChain Integration

Works with 90% of AI developers' stack.

from argus.integrations import ArgusCallbackHandler
from langchain_openai import ChatOpenAI

callback = ArgusCallbackHandler(agent_name="my-bot")
llm = ChatOpenAI(callbacks=[callback])

# All calls automatically tracked!

Supports:

• All LangChain LLMs (OpenAI, Anthropic, Cohere, local models)
• Chat models, chains, agents
• Automatic cost calculation

See full LangChain guide →

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📊 Beautiful Dashboard

See everything at a glance.

Features:

• Real-time updates (5s refresh)
• Filter by agent, date, status
• Search by input/output text
• Export to CSV/JSON
• Light & Dark mode support

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🎯 Use Cases

🏢 Production Monitoring Track AI agents in production without sending data to third parties. @watch.agent( name="customer-support-bot", tags=["production", "critical"] ) def handle_ticket(ticket): return agent.solve(ticket) Benefits: Real-time cost monitoring Performance degradation alerts Error tracking with full context 100% data privacy	🔬 Development & Testing Optimize agents before deploying. @watch.agent(name="experimental-agent") def test_agent(prompt): return new_approach(prompt) Benefits: Compare different models (GPT-4 vs GPT-3.5) Find performance bottlenecks Measure cost impact of changes A/B test prompts
🤖 Multi-Agent Systems Understand complex agent interactions. @watch.agent(name="orchestrator") def orchestrator(): research = research_agent() analysis = analysis_agent(research) return summary_agent(analysis) Benefits: Visualize agent hierarchy Track cost per orchestrator Detect infinite loops Optimize agent chains	💼 Cost Optimization Find where you're burning money. @watch.agent( name="expensive-agent", provider="openai", model="gpt-4" ) def analyze(data): return llm.analyze(data) Benefits: Automatic cost calculation Identify expensive agents Compare model costs Set budget alerts (coming soon)

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🔧 How It Works

Argus uses async hooks to track your agents with <1ms overhead.

Key principles:

1. Async logging — Your code doesn't wait for writes
2. Batched writes — Multiple events written together
3. Local storage — No network calls, no latency
4. Zero config — Works out of the box

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🗺️ Roadmap

• ✅ v0.1 — Core tracing, SQLite storage, Dashboard
• ✅ v0.2 — Automatic cost calculation, LangChain integration
• 🚧 v0.3 — PostgreSQL/MySQL, Advanced filtering, Alerts
• 🔜 v0.4 — LlamaIndex, AutoGPT, CrewAI integrations
• 🔜 v0.5 — Real-time webhooks (Slack, Discord, Email)
• 🔜 v1.0 — Production-ready, Enterprise features

See full roadmap →

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🤝 Contributing

We're building Argus in public and we'd love your help!

Currently looking for:

• 🎨 Frontend developers (Dashboard UI/UX)
• 🔌 Integration maintainers (Gemini, Mistral, local models)
• 📊 Data engineers (Advanced analytics)
• 📖 Technical writers (Docs, tutorials)

How to contribute:

1. Fork the repo
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Make your changes
4. Run tests: pytest tests/
5. Submit a PR

Read contributing guide →

Contributors

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📚 Documentation

• Quick Start Guide
• LangChain Integration
• Pricing & Cost Calculation
• Examples
• API Reference (coming soon)

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❓ FAQ

Is Argus free?

Yes! Argus is 100% free and open source (MIT License). No hidden fees, no SaaS pricing, no limits.

Does my data leave my machine?

No. Everything stays local. Argus uses SQLite by default, which is just a file on your computer. Your prompts, responses, and costs never leave your infrastructure.

What's the performance overhead?

<1ms per agent call. Argus uses async logging, so your code doesn't wait for writes. In production, you won't notice any difference.

Can I use Argus in production?

Yes! Argus is designed for production. It's lightweight, async, and battle-tested. Many users run it in production with zero issues.

Does it work with LangChain?

Yes! Argus has native LangChain integration via callbacks. One line of code and all your LangChain calls are tracked.

What LLM providers are supported?

Automatic cost calculation works with:

• OpenAI (GPT-4, GPT-4o, GPT-3.5, o1)
• Anthropic (Claude 3.5 Sonnet, Opus, Haiku)
• Cohere (Command, Command-R)

But you can track any LLM — just won't get automatic cost calculation.

Can I use PostgreSQL instead of SQLite?

Coming in v0.3! For now, SQLite is the only option. But it works great for most use cases (handles millions of records).

How do I deploy the dashboard?

The dashboard is just a Flask app. You can deploy it anywhere:

• Docker container
• Heroku
• AWS/GCP/Azure
• Your own server

We'll add deployment guides soon.

Can I contribute?

Yes! We'd love your help. Check out CONTRIBUTING.md for guidelines.

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💬 Community

• GitHub Discussions: Ask questions, share ideas
• Issues: Report bugs, request features
• Twitter/X: @maxcodesai — Follow for updates

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📄 License

MIT License - see LICENSE file for details.

TL;DR: Use it for anything. Commercial, personal, open source. Just keep the license.

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⭐ Star History

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🚀 Ready to Stop Flying Blind?

Add Argus to your AI agents in 30 seconds.

pip install git+https://github.com/sh1esty1769/argus.git

⭐ Star on GitHub • 📖 Read the Docs • 🐦 Follow Updates

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Made with 💜 by developers who were tired of burning money on loops

If Argus saved you money or time, give us a star!