Nasiko Agent Control Plane

What does this agent do, and when should you use it?

Nasiko is a developer control plane for production AI-agent ecosystems built as cloud-native microservices. Its documented components include a FastAPI Backend, Router Service, Auth Service, Chat History, Kong Registry, and Kong API Gateway, with Web UI, Python CLI, and desktop application entry points. Agents are uploaded as directories or ZIP packages containing AgentCard.json, a Dockerfile, and a Python project, then accessed through Kong at /agents/{agent-name}/. The Router Service uses LangChain to analyze a query, match AgentCard capabilities, and return an agent URL with a confidence score. It supports local Docker Compose deployment and documents Kubernetes deployment on DigitalOcean or AWS using Terraform, Helm, and BuildKit.

Users can upload an agent directory with nasiko agent upload-directory ./my-agent --name my-agent or a package with nasiko agent upload-zip agent.zip --name packaged-agent. Each agent must include AgentCard.json, which declares capabilities, examples, endpoints, and protocol version; the backend passes upload processing through the Redis Stream Listener into the build system, container registry, Kubernetes deployment, and Kong registration. Requests sent to /router/route?query=... are analyzed by the Router Service with LangChain for query intent, capability matching, confidence scoring, and fallback selection, producing agent_url and confidence. Kong Gateway routes /api/, /auth/, /router/, /app/, and dynamic agent paths to their services; Phoenix records request and LLM traces, while Chat History persists and retrieves conversations.

1. A platform engineer needs to register, build, and deploy internal FastAPI agents from a local Docker Compose environment.
2. An operations team wants requests automatically directed to translation, compliance-checking, or GitHub-oriented agents according to AgentCard.json capabilities.
3. A team needs Kong Gateway to provide a single access layer for /agents/{agent-name}/, /api/, /auth/, and /router/ routes.
4. A team needs Arize Phoenix traces for agent and LLM requests alongside searchable persisted conversation history.
5. An organization is preparing to deploy containerized agents to Kubernetes on DigitalOcean or AWS and build images through BuildKit.

What are this agent's strengths and limitations?

How do you install or deploy this agent?

Prerequisites are Docker, Docker Compose, Python 3.12+, and a recommended 4GB+ RAM. Clone and prepare the project:

git clone https://github.com/Nasiko-Labs/nasiko.git
cd nasiko
cp .nasiko-local.env.example .nasiko-local.env
pip install uv
uv sync

Edit .nasiko-local.env and configure USER_CREDENTIALS_ENCRYPTION_KEY for production security. OPENAI_API_KEY, MINIMAX_API_KEY, GITHUB_CLIENT_ID, and GITHUB_CLIENT_SECRET are optional. Start the platform:

docker compose -f docker-compose.local.yml --env-file .nasiko-local.env up -d

Verify it with curl http://localhost:8000/api/v1/healthcheck, then open http://localhost:9100/app/.

How do you use this agent?

Set NASIKO_API_URL=http://localhost:9100, run nasiko login with the automatically generated credentials in orchestrator/superuser_credentials.json, and check nasiko status. Upload an agent directory with:

nasiko agent upload-directory ./my-agent --name my-agent

The documented structure includes AgentCard.json, Dockerfile, pyproject.toml, and src/main.py. After deployment, call an agent with:

curl -X POST http://localhost:9100/agents/my-agent/analyze -H "Content-Type: application/json" -d '{"text": "Sample document content"}'

Or let the router choose an agent:

curl "http://localhost:9100/router/route?query=analyze this document"

How does this agent compare with similar options?

FAQ