nasiko-labs/nasiko

Developer Control Plane for your AI Agents

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Summary Information

Updated 4 minutes ago
Added to GitGenius on September 10th, 2026
Created on February 12th, 2026
Open Issues & Pull Requests: 22 (+0)
GitHub issues: Enabled
Number of forks: 1,227
Total Stargazers: 6,615 (+0)
Total Subscribers: 15 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 0.0 hours
Mean response time: 6.3 days
90th percentile: 25.3 days
Tracked items: 4

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How this project is maintained

Around half of the issues opened in the past year never receive a reply. Only 5% of issues opened in the past year have been closed.

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Issue Activity (beta)

Open issues: 14
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 145 days
Stale 30+ days: 14
Stale 90+ days: 12

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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Detailed Description

Nasiko is a developer control plane for deploying, routing, securing, and observing AI agents that speak the A2A protocol.

The tool addresses the operational complexity of running AI agents in production. Rather than requiring separate gateway infrastructure, sidecars, or custom integration code, Nasiko consolidates agent deployment, request routing, security policies, and observability into a single command-line interface. It targets agents built on the A2A specification, providing a unified control surface for multi-agent systems.

Nasiko suits teams building production AI agent systems who want to avoid the operational overhead of managing distributed infrastructure. It is particularly relevant for projects involving multiple agents that need coordinated security policies, token usage tracking, and observability across agent interactions. The tool's approach of eliminating gateway and sidecar requirements makes it attractive for teams seeking simpler deployment topologies.

The project shows active development with regular commits addressing both core functionality and operational concerns. Work spans agent security hardening, observability improvements, and token operations tracking. The codebase demonstrates attention to production readiness through incremental refinement of deployment patterns and security mechanisms.