DeepFellow Server

To install and configure DeepFellow Server, start with:

deepfellow server install

You will proceed to configure built-in components:

• Docker network
• no-sql database (for example MongoDB)
• connection with DeepFellow Infra
• vector database and embedding model
• telemetry

Docker Network

The installer first asks which Docker network DeepFellow Server will join. Use the same network name you chose during Infra installation so Server and Infra can reach each other:

$ deepfellow server install
💡      Installing DeepFellow Server.
❓      Provide a docker network name (deepfellow-infra-net):

MongoDB

💡      DeepFellow Server requires a MongoDB to be installed.
❓      Install a local MongoDB for DeepFellow Server? [y/n] (y):
💡      A default MongoDB setup is created.

If you choose n instead, you will be asked to provide Mongo URL (not mongo connection string!):

❓      Provide host:port for MongoDB e.g. 192.168.1.5:27017: mongo:27017

If you use a service name like mongo, Docker will resolve it to the MongoDB container. Be careful: localhost will not work as it refers to the container's own loopback interface.

You will be asked about database name and credentials:

❓      Provide database name for MongoDB: deepfellow
❓      Provide username for MongoDB: deepfellow-usr
❓      Provide password for MongoDB:

Connection with Infra

Next, the installator will proceed to configure connection with Infra:

💡      DeepFellow Server is communicating with DeepFellow Infra.
❓      Provide DeepFellow Infra URL (http://infra:8086):
❓      Provide DeepFellow Infra API KEY:

Docker resolves a name like infra internally to a Compose service name. Be careful: localhost will not work as it refers to the container's own loopback interface.

Vector Database

Next, the installer configures the vector database and embedding model:

💡      DeepFellow Server might use a vector DB. If not provided some features will not work.
❓      Do you want to use a vector database with DeepFellow? [y/n] (y):
❓      Choose embedding type dense
❓      Choose the type of the vector database qdrant
❓      Install a local vector database for DeepFellow Server? [y/n] (y):
💡      DeepFellow will manage a Qdrant instance.
❓      Provide the model for embedding (mxbai-embed-large):
❓      Provide the embedding size (1024):

The available embedding types are dense and sparse. The available vector database backends are qdrant and milvus.

Answer n to "Install a local vector database?" if you want to connect to an existing instance. You will be asked for its URL:

❓      Install a local vector database for DeepFellow Server? [y/n] (y): n
❓      Provide Qdrant instance URL (http://qdrant:6333):

If you use a service name like qdrant, Docker will resolve it to the Qdrant service. Be careful: localhost will not work as it refers to the container's own loopback interface.

Telemetry

After that, OpenTelemetry configuration will start:

💡      DeepFellow Server might use an Open Telemetry.
❓      Do you have an Open Telemetry server ready? [y/n] (n): n
❓      Do you want to run Open Telemetry from this machine? [y/n] (n): y
💡      Let's configure Open Telemetry
❓      Do you want to export to Elasticsearch? [y/n] (n):

If you answer n to "Do you want to export to Elasticsearch?" (the default), DeepFellow generates a debug-only OTel collector configuration. No Elasticsearch instance is required.

Inspect telemetry via the container log once Server is running:

docker logs -f otel-collector-container

This mode suits local development, workshops, and demo environments.

To export to Elasticsearch, answer y. You will be prompted for your credentials:

❓      Do you want to export to Elasticsearch? [y/n] (n): y
❓      Provide your ElasticSearch endpoint: https://myelastic.com:9200
❓      Provide traces index: my-index
❓      Provide username: elastic_user
❓      Provide password:

In both cases, installation ends with:

⚠️       Open Telemetry configuration stored in file:
        /home/johndoe/.deepfellow/server/otel-collector-config.yaml
        Please review its content before starting the DeepFellow Server.

To use an existing OTel server instead of running one locally, answer y to "Do you have an Open Telemetry server ready?" You will be asked to provide the URL:

❓      Provide OTL url (http://otel-collector:4317):

If you use a service name like otel-collector, Docker will resolve it to the otel-collector service in compose. Be careful: localhost will not work as it refers to the container's own loopback interface.

By default, the server exports only traces to the collector. To also forward the server's Python log records, set DF_OTEL_LOGGING_ENABLED to true:

deepfellow server env set DF_OTEL_LOGGING_ENABLED true

The server ships logs to the endpoint defined by DF_OTEL_EXPORTER_OTLP_ENDPOINT (default http://localhost:4317). This endpoint must accept the OTLP log signal over insecure gRPC. The server exports logs under the service name llm-audit, which lets you correlate them with existing traces in your observability backend. Tracing and log export are independent. Enable each one with its own variable, DF_OTEL_TRACING_ENABLED and DF_OTEL_LOGGING_ENABLED.

After configuring the components, successful installation will be confirmed:

💡      Generated /home/johndoe/.deepfellow/server/.env.
💡      A default Qdrant setup is created.
💡      Updated /home/johndoe/.deepfellow/config.
💡      Saved Docker Compose configuration to /home/johndoe/.deepfellow/server/compose.yaml
✅      DeepFellow Server Installed.
        Call `deepfellow server start`.