Skip to main content
Connections are saved, encrypted credentials shared across every agent in your organization (all projects). You define them once on the Connections tab, and agents and knowledge bases reference them by name, so keys aren’t re-entered per agent, and rotating a credential in one place updates everything that uses it. An LLM connection is required before you can build an agent. The other connection types are optional and depend on what your agents do. Where available, Streamkap also provides two managed default connections you can use for quick tests without bringing your own keys — see Streamkap Default Connections.
Secrets are encrypted at rest and masked when you view a connection. Read-only users (read:agents) can see connections with keys masked but cannot edit them; saving requires write:agents.

​
LLM Connections

An LLM connection holds a model provider’s credentials and defaults. It’s the identity an agent’s model uses, and the embedding model a knowledge base uses. Each connection declares one or both capabilities — Chat (used by agents to run the model) and Embedding (used by knowledge bases and long-term memory to embed text):
1

Add a connection

On the Connections tab, open LLM Connections and add a row. Enter a Name and pick a Provider, then paste the provider API key (Bedrock uses IAM credentials instead). For a self-hosted or proxy endpoint, enable the custom base URL and set it.
2

Test

Click Test. Streamkap validates the key against the provider and loads the live model list — the model dropdown is always fetched live from your provider, so new model releases show up without waiting for a Streamkap update.
3

Set capabilities and defaults

Choose the Chat and/or Embedding capabilities, then set defaults — chat model, temperature, max tokens, and timeout; embedding model for embedding connections. Agents can override these per agent.

​
External MCP Servers

An external MCP connection lets an agent call tools exposed by an MCP server you host. Configure a Name, an Auth mode (None, Bearer token, or Custom header), and the Server URL, then click Test to discover the tools the server exposes.
External MCP server URLs must be HTTPS on port 443. Streamkap rejects private, loopback, and cloud-metadata addresses, and blocks unsafe headers (for example Authorization in custom-header mode, Cookie, and Host).
To let an agent operate your own Streamkap platform instead, use the Streamkap MCP tool — see Streamkap MCP below.

​
HTTP Connections

An HTTP connection stores a reusable base URL and headers for HTTP tools. Define it once (name, base URL, headers, description) and select it when adding an HTTP tool to an agent, so the tool inherits the endpoint and auth without re-entering them.

​
Vector Stores

A vector store connection is where knowledge base embeddings and long-term memory are written and read. Pick a Provider and fill in its fields:
For pgvector, don’t embed credentials in the JDBC URL — put them in the username and password fields. Dimensions must match your embedding model’s output; leave blank to inherit it.

​
Streamkap Default Connections

Where available, two Streamkap-managed rows appear alongside your own connections, marked with a Streamkap Default badge:
  • Streamkap Default LLM — a managed chat + embedding model. Available for quick tests without bringing your own key; not intended for production data. Rate limits and model choice may change.
  • Streamkap Default Vector Store — a managed vector index. Available for quick tests; not intended for production data.
They let you deploy a working agent or knowledge base before you’ve set up any credentials of your own — select them anywhere a connection is picked (the agent wizard’s Model step, a knowledge base’s embedding model and vector store). They are read-only: the keys are managed by Streamkap, never displayed, and the rows can’t be edited or deleted.
Data written to the default vector store is isolated automatically — each organization (and each knowledge base) gets its own namespace on the managed index. You can’t choose the namespace yourself. For production workloads, bring your own LLM and vector store connections.

​
Streamkap MCP

Streamkap MCP lets an agent operate your Streamkap platform — inspecting topics, checking pipeline status, and looking up schemas — as a tool during a run. It isn’t a connection on this tab; it’s authenticated by an agentic-enabled Project Key. Enable Use with agents on a Project Key (see Use with Streaming Agents), then select it when adding the Streamkap MCP tool to an agent (see Build an Agent). The key’s MCP tool scoping restricts which platform tools the agent can call.
Disabling Use with agents on a Project Key breaks any agent bound to that key. Re-enable it or point the agent at another agentic-enabled key.