Connect

LLM providers

Turn the built-in agent on with Anthropic or an OpenAI-compatible server.

The agent turns a prompt into a saved dashboard by calling tools against your datasources. It is on only when SLASHOBSERVE_LLM_PROVIDER is set. With it unset the server starts normally, and:

POST /api/v1/observe and revision prompts answer 503 agent_unavailable.
Comments are saved but do not queue a revision, and turning the agent on later does not replay old comments.
You can still create and edit dashboards through the API or through MCP with an agent of your own.

The agent sends what its tools return to the provider: datasource and metric names, label values, sampled rows and query results. Choose a provider whose data handling fits what your datasources contain.

The server calls api.anthropic.com for anthropic and api.openai.com for openai, over HTTPS, unless SLASHOBSERVE_LLM_BASE_URL names another server. These calls are not covered by the outbound allowlist, so a firewall that restricts egress must allow the host. Neither adapter reads the provider’s own environment variables, such as ANTHROPIC_API_KEY or OPENAI_BASE_URL; set every value through SLASHOBSERVE_LLM_*. A failed call is retried up to 2 times, inside the request timeout.

Anthropic

Anthropic is the default model family. Only the key is required:

SLASHOBSERVE_LLM_PROVIDER=anthropic
SLASHOBSERVE_LLM_API_KEY=<your Anthropic API key>

The default model is claude-sonnet-5-5. Set SLASHOBSERVE_LLM_MODEL to use another, for example claude-opus-5-5 for harder prompts or claude-haiku-5-5 for lower cost. The server does not validate the model name itself. SLASHOBSERVE_LLM_EFFORT sets the model’s effort level and applies to this provider only. To read the key from a file, such as a Docker or Kubernetes secret, use SLASHOBSERVE_LLM_API_KEY_FILE instead.

OpenAI-compatible

openai talks to any server that implements the Chat Completions API with tool calling. The model is required, because there is no safe default:

SLASHOBSERVE_LLM_PROVIDER=openai
SLASHOBSERVE_LLM_MODEL=<model name>
SLASHOBSERVE_LLM_API_KEY=<API key>

Leave SLASHOBSERVE_LLM_BASE_URL unset to use OpenAI. Point it at another server to use that instead. For a local Ollama:

SLASHOBSERVE_LLM_PROVIDER=openai
SLASHOBSERVE_LLM_MODEL=llama3.1
SLASHOBSERVE_LLM_BASE_URL=http://localhost:11434/v1

The key is optional for servers that need none, as Ollama does. The model must support tool calling. Inside a container, localhost is the container itself; use an address the container can reach, such as http://host.docker.internal:11434/v1 on Docker Desktop. The project does not vouch for any particular local model, and dashboard quality depends heavily on the model.