qrmadness/agentx-api repository overview

⁠AgentX API

The backend API for AgentX — an AI agent platform with multi-model orchestration, MCP tool integration, reasoning frameworks, and a layered memory system (episodic / semantic / procedural / working).

This image is the server. It is designed to run as a self-contained ("isolated") deployment alongside its databases (Neo4j + PostgreSQL/pgvector + Redis) via Docker Compose — no source checkout or Taskfile required.

⁠Quick start

Grab the deployment bundle (compose + .env.example) from the latest release⁠, then:

cp .env.example .env
# edit .env: DJANGO_SECRET_KEY, database passwords, DJANGO_ALLOWED_HOSTS,
# and at least one LLM provider API key
docker compose up -d

The API self-initializes its database schemas on first boot (idempotent) and seeds default config. First start also downloads the embedding model (~2.3 GB; translation models download lazily on first use) into a persistent cache, so later starts are fast.

curl "http://localhost:12319/api/health?include_memory=true"

If auth is enabled (default), set the root password once:

docker compose exec api agentx setup-auth

⁠Operations — the agentx CLI

Day-2 operations run through the in-image CLI (versioned with the image):

docker compose exec api agentx help        # list commands
docker compose exec api agentx status      # health + memory status
docker compose exec api agentx migrate     # re-apply migrations + memory schema
docker compose exec api agentx warmup      # pre-load the embedding model
docker compose exec api agentx setup-auth --force   # reset the root password

⁠Tags

• latest — the most recent release
• X.Y.Z — pinned release (recommended for reproducible deploys)

linux/amd64. The image bundles PyTorch + Transformers + sentence-transformers, so it is large (~4.4 GB); the embedding/translation models themselves download at runtime into the mounted cache volume.

⁠Configuration

Set via the .env file. Key variables: DJANGO_SECRET_KEY (required), DJANGO_ALLOWED_HOSTS, NEO4J_PASSWORD / POSTGRES_PASSWORD, AGENTX_AUTH_ENABLED, EMBEDDING_PROVIDER (local | openai), AGENTX_DEVICE (auto | cpu | cuda), and provider keys (ANTHROPIC_API_KEY, OPENAI_API_KEY, …).

GPU acceleration is available via the docker-compose.gpu.yml overlay (NVIDIA Container Toolkit required).

⁠Links

• 📚 Documentation: https://agentx.thejpnet.net/docs/deployment/self-hosting⁠
• 🐙 Source: https://github.com/QR-Madness/agentx⁠
• 🐛 Issues: https://github.com/QR-Madness/agentx/issues⁠

Licensed under MIT.