saksh 1.0.0

Sahasraksh - The All-Seeing Observer for AI Agents

👁️ Saksh (Sahasraksh) v1.0.0

"The All-Seeing Observer for your AI Agents."

Saksh (derived from Sahasraksh — "The One with a Thousand Eyes") is a lightweight, non-intrusive observability layer designed specifically for modern AI Agent ecosystems.

Think of it as a Sidecar for your agents. whether you're running a swarm of CrewAI workers, a complex LangGraph state machine, or raw LLM chains, Saksh sits quietly in the background, monitoring health, latency, and vitality without ever blocking your main event loop.

⚡ Why Saksh?

Building agents is hard. Debugging why they "hung" or "timed out" is harder. Saksh gives you a Mission Control dashboard out of the box, so you can stop guessing if your agents are actually working or just hallucinating silence.

• Universal & Agnostic: We don't care if you use CrewAI, LangGraph, or raw OpenAI calls. If it has an API key and a URL, we can watch it.
• Zero-Touch Dashboard: Just instantiate the class, and a full React/FastAPI dashboard spins up on port 2604. No React code required.
• Vitality Scoring: We don't just check "Is it up?". We calculate a Vitality Score (0-100) based on latency spikes and auth failures.
• Non-Blocking Sidecar: Runs in a daemon thread. Your agents keep working even if Saksh takes a coffee break.

📦 Installation

Get it via pip:

pip install saksh

🛠️ Quick Start

You can attach Saksh to your existing stack in about 3 lines of code.

1. The Setup

from saksh import Saksh, CrewAINetra, LangGraphNetra
from crewai import Agent

# 1. Summon the Observer 👁️
# This auto-magically starts the dashboard at http://localhost:2604
observer = Saksh(start_dashboard=True)

2. Connect CrewAI

Saksh automatically detects the LLM configuration (URL, API Key) inside your CrewAI agents.

# Your standard CrewAI setup
researcher = Agent(
    role="Researcher", 
    goal="Analyze market trends", 
    backstory="You are a data wizard."
)

# Open an Eye on it
observer.open_eye(CrewAINetra(researcher, "Market-Researcher-01"))

3. Connect LangGraph

Since LangGraph is stateful and abstract, we monitor the underlying LLM provider it relies on.

# graph_app = workflow.compile()

# Register the graph (defaults to checking OpenAI availability)
observer.open_eye(LangGraphNetra(graph_app, "Math-Graph-State-Machine"))

That's it. Your terminal will now log: 👁️ Saksh is watching...

🧩 Architecture

Saksh uses a Netra (Eye) Adapter Pattern.

• Saksh (Core): The singleton observer that manages the background thread and dashboard.
• Netra (Adapter): A standardized interface that knows how to "Gaze" at a specific type of agent and normalize its health data into a HealthPulse.

🤝 Contributing

This is v1.0.0 — the "It Works on My Machine" release. We are actively looking for contributors to build Netras (Adapters) for:

• Autogen
• Semantic Kernel
• Local Ollama/Llama.cpp instances

PRs are welcome! Let's build the standard for Agent Observability together.

📜 License

MIT © 2026 Ram Bikkina