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Connections

Configure the shared LLM, external MCP, HTTP, and vector store credentials that Streamkap streaming agents and knowledge bases use

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.

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):

Provider Chat Embedding Notes
Anthropic ✓ — Claude models. Supports a custom base URL for Anthropic-compatible proxies (LiteLLM, enterprise gateways).
OpenAI ✓ ✓ GPT and text-embedding-* models. Supports a custom base URL for self-hosted OpenAI-compatible servers (vLLM, LocalAI, TGI, LM Studio).
OpenAI (Responses) ✓ — OpenAI’s Responses API for reasoning models. Embeddings still go through the plain OpenAI provider.
Azure ✓ ✓ Azure AI Studio. Requires your Azure base URL.
Azure OpenAI ✓ — Azure OpenAI via the OpenAI SDK. Requires the Azure endpoint and an API version.
Ollama ✓ ✓ Self-hosted. Requires the server base URL.
AWS Bedrock ✓ ✓ No API key — authenticates with AWS IAM credentials; you set the region.
Qwen — ✓ Embedding-only (DashScope-compatible).
OpenAI-compatible — ✓ Embedding-only; any host exposing a /v1/embeddings endpoint.

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.

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.

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.

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:

Provider Fields
Pinecone API key, index host, optional default namespace
pgvector (Postgres) JDBC URL (jdbc:postgresql://host:5432/db), username, password, table (auto-created on first deploy), optional dimensions and distance metric
Milvus URI (or host + port), token (or username + password)
Amazon OpenSearch Endpoint and index, plus provider properties
Elasticsearch Index and vector field, plus provider properties
Amazon S3 Vectors Vector bucket and vector index, plus provider properties

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.

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.