From 6e35a2baa10bad35ffec500f8a0a4fadee4be4a8 Mon Sep 17 00:00:00 2001 From: Rachael Graham Date: Wed, 7 Oct 2026 09:53:31 -0500 Subject: [PATCH] Link fixes Closes https://github.com/kagent-dev/website/issues/557 Signed-off-by: Rachael Graham --- .../snippets/about/what-is-kagent-enterprise.md | 2 +- docs-site/content/kagent/0.x/concepts/agents.md | 4 ++-- docs-site/content/kagent/0.x/concepts/architecture.md | 8 ++++---- docs-site/content/kagent/0.x/concepts/tools.md | 2 +- docs-site/content/kagent/0.x/examples/a2a-byo.md | 2 +- docs-site/content/kagent/0.x/examples/crewai-byo.md | 2 +- .../content/kagent/0.x/examples/human-in-the-loop.md | 2 +- docs-site/content/kagent/0.x/examples/langchain-byo.md | 2 +- docs-site/content/kagent/0.x/examples/slack-a2a.md | 6 +++--- .../content/kagent/0.x/getting-started/first-mcp-tool.md | 2 +- .../content/kagent/0.x/introduction/what-is-kagent.md | 2 +- docs-site/content/kagent/0.x/resources/release-notes.md | 2 +- .../kagent/0.x/supported-providers/amazon-bedrock.md | 2 +- .../kagent/0.x/supported-providers/google-vertexai.md | 2 +- docs-site/content/kagent/1.x/observability/otel-stack.md | 2 +- .../kagent/1.x/setup/model-providers/amazon-bedrock.md | 2 +- .../content/kagent/1.x/setup/model-providers/anthropic.md | 4 ++-- .../content/kagent/1.x/setup/model-providers/ollama.md | 4 ++-- .../content/kagent/1.x/setup/model-providers/openai.md | 2 +- docs-site/content/kagent/1.x/setup/model-providers/xai.md | 2 +- docs-site/content/kmcp/develop/fastmcp-python.md | 2 +- docs-site/content/kmcp/introduction.md | 2 +- 22 files changed, 30 insertions(+), 30 deletions(-) diff --git a/docs-site/assets/kagent-docs/snippets/about/what-is-kagent-enterprise.md b/docs-site/assets/kagent-docs/snippets/about/what-is-kagent-enterprise.md index c3565da0..9e9692cf 100644 --- a/docs-site/assets/kagent-docs/snippets/about/what-is-kagent-enterprise.md +++ b/docs-site/assets/kagent-docs/snippets/about/what-is-kagent-enterprise.md @@ -1,3 +1,3 @@ ## Enterprise distributions -Check out [Solo Enterprise for kagent](https://www.solo.io/products/kagent-enterprise), a comprehensive agent management interface for creating, validating, debugging, deploying, and monitoring AI agents across federated Kubernetes clusters. Solo Enterprise for kagent adds enterprise-grade capabilities on top of the kagent open source project, including advanced management features, observability tools, and multicluster federation support. +Check out [Solo Enterprise for kagent](https://www.solo.io/products/kagent), a comprehensive agent management interface for creating, validating, debugging, deploying, and monitoring AI agents across federated Kubernetes clusters. Solo Enterprise for kagent adds enterprise-grade capabilities on top of the kagent open source project, including advanced management features, observability tools, and multicluster federation support. diff --git a/docs-site/content/kagent/0.x/concepts/agents.md b/docs-site/content/kagent/0.x/concepts/agents.md index 66452331..ed364f29 100644 --- a/docs-site/content/kagent/0.x/concepts/agents.md +++ b/docs-site/content/kagent/0.x/concepts/agents.md @@ -92,7 +92,7 @@ If the user asks "List all pods in the cluster", the agent can use the **list_re Some tools support additional configuration that you set when adding the tool to the agent. For example, any Grafana or Prometheus tools will require an API endpoint URL to be set. -kagent comes with a set of built-in tools that you can use to interact with your environment. kagent also supports [MCP (Model Context Protocol)](https://modelcontextprotocol.io/introduction) tools. Using MCP, you can bring any external tool into kagent and make it available for your agents to run. +kagent comes with a set of built-in tools that you can use to interact with your environment. kagent also supports [MCP (Model Context Protocol)](https://modelcontextprotocol.io/docs/getting-started/intro) tools. Using MCP, you can bring any external tool into kagent and make it available for your agents to run. ## Human-in-the-Loop @@ -185,7 +185,7 @@ These skills contain instructions, scripts, and resources that are loaded from c Container-based skills are actual, callable capabilities—not just descriptions of capabilities. -kagent's skills are similar to [Claude's Agent Skills](https://docs.claude.com/en/docs/agents-and-tools/agent-skills/overview), but with a key advantage: you can use kagent's skills with any LLM provider, not just Anthropic Claude. This means your agents can use skills with OpenAI, Google Vertex AI, Azure OpenAI, Ollama, and any other LLM provider that kagent supports. +kagent's skills are similar to [Claude's Agent Skills](https://platform.claude.com/docs/en/agents-and-tools/agent-skills/overview), but with a key advantage: you can use kagent's skills with any LLM provider, not just Anthropic Claude. This means your agents can use skills with OpenAI, Google Vertex AI, Azure OpenAI, Ollama, and any other LLM provider that kagent supports. ### Git-based skills diff --git a/docs-site/content/kagent/0.x/concepts/architecture.md b/docs-site/content/kagent/0.x/concepts/architecture.md index 7d40b4a5..b0acdd83 100644 --- a/docs-site/content/kagent/0.x/concepts/architecture.md +++ b/docs-site/content/kagent/0.x/concepts/architecture.md @@ -21,16 +21,16 @@ In the future, we envision more features for the controller, such as: The kagent engine is the core component of kagent. It runs the agent's conversation loop and supports two runtimes: -- **Python ADK** (default) — Built on top of the [Google ADK](https://google.github.io/adk-docs/) framework. Supports Google ADK-native features and integrations with CrewAI, LangGraph, and OpenAI frameworks. +- **Python ADK** (default) — Built on top of the [Google ADK](https://adk.dev/) framework. Supports Google ADK-native features and integrations with CrewAI, LangGraph, and OpenAI frameworks. - **Go ADK** — A native Go implementation that provides faster startup (~2 seconds vs ~15 seconds) and lower resource consumption. Select the runtime by setting the `runtime` field in the agent spec (e.g., `runtime: go`). Both runtimes support MCP tools, HITL, and agent memory. For more details, see [Agents]({{< link path="concepts/agents#runtime" >}}). For more information on the Google ADK framework: -- [Agents](https://google.github.io/adk-docs/agents/) -- [Tools](https://google.github.io/adk-docs/tools/) -- [Context](https://google.github.io/adk-docs/context/) +- [Agents](https://adk.dev/agents/) +- [Tools](https://adk.dev/tools/) +- [Context](https://adk.dev/context/) ## CLI diff --git a/docs-site/content/kagent/0.x/concepts/tools.md b/docs-site/content/kagent/0.x/concepts/tools.md index 0ea813fe..fbbbd893 100644 --- a/docs-site/content/kagent/0.x/concepts/tools.md +++ b/docs-site/content/kagent/0.x/concepts/tools.md @@ -72,7 +72,7 @@ tools: ## MCP Tools -MCP stands for [Model Context Protocol](https://modelcontextprotocol.io/introduction). It is a protocol, originally created by Anthropic, which is meant as a flexible way to provide tools and other information to Agents. In the year or so since its inception, it has begun to gain traction and more and more tools are adopting it. The [servers](https://github.com/modelcontextprotocol/servers) repository has a list of MCP servers that you can use immediately with kagent! Of course there are more than just the ones listed there, so we also support bringing in your own MCP servers. +MCP stands for [Model Context Protocol](https://modelcontextprotocol.io/docs/getting-started/intro). It is a protocol, originally created by Anthropic, which is meant as a flexible way to provide tools and other information to Agents. In the year or so since its inception, it has begun to gain traction and more and more tools are adopting it. The [servers](https://github.com/modelcontextprotocol/servers) repository has a list of MCP servers that you can use immediately with kagent! Of course there are more than just the ones listed there, so we also support bringing in your own MCP servers. **Note:** Double check any community servers before running them in your environment. diff --git a/docs-site/content/kagent/0.x/examples/a2a-byo.md b/docs-site/content/kagent/0.x/examples/a2a-byo.md index 5ad4869d..ea39a156 100644 --- a/docs-site/content/kagent/0.x/examples/a2a-byo.md +++ b/docs-site/content/kagent/0.x/examples/a2a-byo.md @@ -6,7 +6,7 @@ weight: 1 author: kagent.dev --- -Bring your own custom agents. This example uses the [Agent Development Kit (ADK)](https://google.github.io/adk-docs/), but you can also try out the [LangGraph guide]({{< link path="examples/langchain-byo/" >}}). Such frameworks give you more control over the agent behavior and are well-suited for complex workflows and integration with external systems and APIs. +Bring your own custom agents. This example uses the [Agent Development Kit (ADK)](https://adk.dev/), but you can also try out the [LangGraph guide]({{< link path="examples/langchain-byo/" >}}). Such frameworks give you more control over the agent behavior and are well-suited for complex workflows and integration with external systems and APIs. Unlike declarative agents that are defined by kagent resources with components such as system instructions, models, and tools written inline, these BYO agents give you full control over agent logic. If you have your own agent, no need to decompose its functions into separate kagent resources. kagent can invoke your agent directly through the A2A protocol. diff --git a/docs-site/content/kagent/0.x/examples/crewai-byo.md b/docs-site/content/kagent/0.x/examples/crewai-byo.md index a9fe6f4f..aaf308c1 100644 --- a/docs-site/content/kagent/0.x/examples/crewai-byo.md +++ b/docs-site/content/kagent/0.x/examples/crewai-byo.md @@ -6,7 +6,7 @@ weight: 1 author: kagent.dev --- -Bring your own custom agents. This example uses [CrewAI](https://www.crewai.com/), but you can also try out the [ADK guide]({{< link path="examples/a2a-byo/" >}}) or [LangGraph guide]({{< link path="examples/langchain-byo/" >}}). Such frameworks give you more control over the agent behavior and are well-suited for complex workflows and integration with external systems and APIs. +Bring your own custom agents. This example uses [CrewAI](https://crewai.com/), but you can also try out the [ADK guide]({{< link path="examples/a2a-byo/" >}}) or [LangGraph guide]({{< link path="examples/langchain-byo/" >}}). Such frameworks give you more control over the agent behavior and are well-suited for complex workflows and integration with external systems and APIs. Unlike declarative agents that are defined by kagent resources with components such as system instructions, models, and tools written inline, these BYO agents give you full control over agent logic. If you have your own agent, no need to decompose its functions into separate kagent resources. kagent can invoke your agent directly through the A2A protocol. diff --git a/docs-site/content/kagent/0.x/examples/human-in-the-loop.md b/docs-site/content/kagent/0.x/examples/human-in-the-loop.md index adb42fcf..e861a357 100644 --- a/docs-site/content/kagent/0.x/examples/human-in-the-loop.md +++ b/docs-site/content/kagent/0.x/examples/human-in-the-loop.md @@ -24,7 +24,7 @@ Make sure you have these installed before starting: | Tool | Install Link | |------|-------------| -| Docker | [get-docker](https://docs.docker.com/get-docker/) | +| Docker | [get-docker](https://docs.docker.com/get-started/get-docker/) | | Kind | [quick-start](https://kind.sigs.k8s.io/docs/user/quick-start/#installation) | | kubectl | [install-tools](https://kubernetes.io/docs/tasks/tools/) | | Helm | [install](https://helm.sh/docs/intro/install/) | diff --git a/docs-site/content/kagent/0.x/examples/langchain-byo.md b/docs-site/content/kagent/0.x/examples/langchain-byo.md index 0ae9ddf2..08706fee 100644 --- a/docs-site/content/kagent/0.x/examples/langchain-byo.md +++ b/docs-site/content/kagent/0.x/examples/langchain-byo.md @@ -14,7 +14,7 @@ Install kagent by following the [quick start]({{< link path="getting-started/qui ## Building a LangGraph agent -The following example builds a simple LangGraph agent from the [kagent code repository](https://github.com/kagent-dev/kagent). The sample app is built with [LangGraph SDK](https://docs.langchain.com/langgraph-platform/sdk) and performs a currency exchange lookup task. It uses Google's Gemini model as the underlying LLM provider. +The following example builds a simple LangGraph agent from the [kagent code repository](https://github.com/kagent-dev/kagent). The sample app is built with [LangGraph](https://docs.langchain.com/oss/python/langgraph/overview) and performs a currency exchange lookup task. It uses Google's Gemini model as the underlying LLM provider. 1. Clone the kagent code repository. diff --git a/docs-site/content/kagent/0.x/examples/slack-a2a.md b/docs-site/content/kagent/0.x/examples/slack-a2a.md index d04f9656..59b31598 100644 --- a/docs-site/content/kagent/0.x/examples/slack-a2a.md +++ b/docs-site/content/kagent/0.x/examples/slack-a2a.md @@ -17,13 +17,13 @@ This guide demonstrates how to achieve exactly that. We will explore a two-way i 1. **Invoking kagent from Slack:** Learn how to set up a Slack bot that uses kagent's A2A (Agent-to-Agent) protocol to interact with a kagent agent, allowing you to run commands or ask questions about your cluster directly from Slack. 2. **Sending messages from kagent to Slack:** Discover how to configure a kagent agent to use an MCP (Model Context Protocol) server to send messages and notifications to specific Slack channels. -To follow along, you'll first need to create a Slack App. We'll cover the necessary steps below, but you can also refer to the official [Slack documentation](https://api.slack.com/quickstart). +To follow along, you'll first need to create a Slack App. We'll cover the necessary steps below, but you can also refer to the official [Slack documentation](https://docs.slack.dev/quickstart/). ## Creating a Slack App You will need a Slack workspace and sufficient permissions to create and install Slack apps. Assuming you have that, you can head over to https://api.slack.com/apps and follow the instructions below. ->Note that these instructions might change, so make sure you check out the latest instructions on the [Slack website](https://api.slack.com/quickstart). +>Note that these instructions might change, so make sure you check out the latest instructions on the [Slack website](https://docs.slack.dev/quickstart/). 1. From the Your Apps page click the "Create an app" button. @@ -83,7 +83,7 @@ Lastly we'll add a slash command that will allow us to invoke the bot directly u ## Writing a Slack bot -Armed with two tokens, we can now go and write some code! We'll be using Bolt for Python, but you can also use Bolt for other languages. Start by cloning the [A2A Slack template](https://github.com/kagent-dev/a2a-slack-template.git). +Armed with two tokens, we can now go and write some code! We'll be using Bolt for Python, but you can also use Bolt for other languages. Start by cloning the [A2A Slack template](https://github.com/kagent-dev/a2a-slack-template). 1. Clone the repository: diff --git a/docs-site/content/kagent/0.x/getting-started/first-mcp-tool.md b/docs-site/content/kagent/0.x/getting-started/first-mcp-tool.md index 1c6cd1eb..5bcb0a07 100644 --- a/docs-site/content/kagent/0.x/getting-started/first-mcp-tool.md +++ b/docs-site/content/kagent/0.x/getting-started/first-mcp-tool.md @@ -6,7 +6,7 @@ weight: 3 author: kagent.dev --- -[MCP (Model Context Protocol)](https://modelcontextprotocol.io/introduction) tools extend the agent's abilities by calling external services to perform tasks that require logic or access outside the cluster. +[MCP (Model Context Protocol)](https://modelcontextprotocol.io/docs/getting-started/intro) tools extend the agent's abilities by calling external services to perform tasks that require logic or access outside the cluster. In this guide, you'll learn how to add an MCP tool to your first AI agent using the kagent resources. diff --git a/docs-site/content/kagent/0.x/introduction/what-is-kagent.md b/docs-site/content/kagent/0.x/introduction/what-is-kagent.md index 72dc0706..57684d5b 100644 --- a/docs-site/content/kagent/0.x/introduction/what-is-kagent.md +++ b/docs-site/content/kagent/0.x/introduction/what-is-kagent.md @@ -58,7 +58,7 @@ Everything works with a single `helm install`. No add-ons, no extra databases, n ## Enterprise distributions -Check out [Solo Enterprise for kagent](https://www.solo.io/products/kagent-enterprise), a comprehensive agent management interface for creating, validating, debugging, deploying, and monitoring AI agents across federated Kubernetes clusters. Solo Enterprise for kagent adds enterprise-grade capabilities on top of the kagent open source project, including advanced management features, observability tools, and multicluster federation support. +Check out [Solo Enterprise for kagent](https://www.solo.io/products/kagent), a comprehensive agent management interface for creating, validating, debugging, deploying, and monitoring AI agents across federated Kubernetes clusters. Solo Enterprise for kagent adds enterprise-grade capabilities on top of the kagent open source project, including advanced management features, observability tools, and multicluster federation support. ## Getting Started diff --git a/docs-site/content/kagent/0.x/resources/release-notes.md b/docs-site/content/kagent/0.x/resources/release-notes.md index f543241e..9e95648c 100644 --- a/docs-site/content/kagent/0.x/resources/release-notes.md +++ b/docs-site/content/kagent/0.x/resources/release-notes.md @@ -1432,4 +1432,4 @@ Supported LLM providers are pre-configured by the [kagent-dev/autogen project fo ### Memory API -The Memory API is not supported in ADK. The [agent development kit](https://google.github.io/adk-docs/) is required to bring your own agents. As such, the Memory docs are removed. +The Memory API is not supported in ADK. The [agent development kit](https://adk.dev/) is required to bring your own agents. As such, the Memory docs are removed. diff --git a/docs-site/content/kagent/0.x/supported-providers/amazon-bedrock.md b/docs-site/content/kagent/0.x/supported-providers/amazon-bedrock.md index 5f2e7718..a9801ebc 100644 --- a/docs-site/content/kagent/0.x/supported-providers/amazon-bedrock.md +++ b/docs-site/content/kagent/0.x/supported-providers/amazon-bedrock.md @@ -55,7 +55,7 @@ If you are using access keys instead of an IAM role, add `apiKeySecret: bedrock- | Setting | Description | | --- | --- | | `provider` | Set to `Bedrock` for the native provider. | -| `model` | The Bedrock model ID. Use the format from the [AWS Bedrock model IDs](https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html) (for example, `us.anthropic.claude-sonnet-4-20250514-v1:0`). | +| `model` | The Bedrock model ID. Use the format from the [AWS Bedrock model IDs](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) (for example, `us.anthropic.claude-sonnet-4-20250514-v1:0`). | | `apiKeySecret` | Optional. Set this when using a Kubernetes secret that contains `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY`. Omit it when the agent uses the pod's AWS credential chain, such as an IAM role attached to the ServiceAccount. | | `bedrock.region` | The AWS region where the Bedrock model is available (for example, `us-east-1`). | diff --git a/docs-site/content/kagent/0.x/supported-providers/google-vertexai.md b/docs-site/content/kagent/0.x/supported-providers/google-vertexai.md index b5b186c0..ded57f41 100644 --- a/docs-site/content/kagent/0.x/supported-providers/google-vertexai.md +++ b/docs-site/content/kagent/0.x/supported-providers/google-vertexai.md @@ -9,7 +9,7 @@ author: kagent.dev Google Vertex AI is supported for Gemini and Anthropic models. -1. Create the [Google Application Default Credentials file](https://cloud.google.com/docs/authentication/provide-credentials-adc) and store it in a Kubernetes Secret. If your credentials are in a different location, update the filepath. +1. Create the [Google Application Default Credentials file](https://docs.cloud.google.com/docs/authentication/provide-credentials-adc) and store it in a Kubernetes Secret. If your credentials are in a different location, update the filepath. ```shell kubectl create secret generic kagent-google-creds -n kagent --from-file=~/.config/gcloud/application_default_credentials.json diff --git a/docs-site/content/kagent/1.x/observability/otel-stack.md b/docs-site/content/kagent/1.x/observability/otel-stack.md index 3a10ba86..a979f06c 100644 --- a/docs-site/content/kagent/1.x/observability/otel-stack.md +++ b/docs-site/content/kagent/1.x/observability/otel-stack.md @@ -66,7 +66,7 @@ Agent Substrate traces do not join the trace of the agent request that caused th ## Install Tempo and Loki -Install the backends that store traces and logs. Both run as a single replica, which suits evaluation. Tempo keeps its traces in memory, and Loki keeps its logs on a small persistent volume, so your cluster needs a default StorageClass, which a kind cluster has. For production, follow the Grafana guidance for [Tempo](https://grafana.com/docs/tempo/latest/setup/helm-chart/) and [Loki](https://grafana.com/docs/loki/latest/setup/install/helm/). +Install the backends that store traces and logs. Both run as a single replica, which suits evaluation. Tempo keeps its traces in memory, and Loki keeps its logs on a small persistent volume, so your cluster needs a default StorageClass, which a kind cluster has. For production, follow the Grafana guidance for [Tempo](https://grafana.com/docs/tempo/latest/set-up-for-tracing/setup-tempo/deploy/kubernetes/helm-chart/) and [Loki](https://grafana.com/docs/loki/latest/setup/install/helm/). 1. Install Tempo, with an OTLP receiver for traces. ```bash diff --git a/docs-site/content/kagent/1.x/setup/model-providers/amazon-bedrock.md b/docs-site/content/kagent/1.x/setup/model-providers/amazon-bedrock.md index 6d3f40db..80e641a8 100644 --- a/docs-site/content/kagent/1.x/setup/model-providers/amazon-bedrock.md +++ b/docs-site/content/kagent/1.x/setup/model-providers/amazon-bedrock.md @@ -59,7 +59,7 @@ The native provider authenticates with a Bedrock API key, which kagent sends as | Field | Description | | ----- | ----------- | | `apiKeySecret` | The name of the Kubernetes Secret that holds `AWS_BEARER_TOKEN_BEDROCK`. | - | `model` | The Bedrock model ID. For the format, see the [AWS Bedrock model IDs](https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html). | + | `model` | The Bedrock model ID. For the format, see the [AWS Bedrock model IDs](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html). | | `provider` | The provider to use, `Bedrock`. | | `bedrock.region` | The AWS region that serves the model. This field is required. | diff --git a/docs-site/content/kagent/1.x/setup/model-providers/anthropic.md b/docs-site/content/kagent/1.x/setup/model-providers/anthropic.md index 3d7eacef..3d1accad 100644 --- a/docs-site/content/kagent/1.x/setup/model-providers/anthropic.md +++ b/docs-site/content/kagent/1.x/setup/model-providers/anthropic.md @@ -12,7 +12,7 @@ The `Anthropic` provider calls the Anthropic API directly. ## Create the ModelConfig -1. Save your [Anthropic API key](https://console.anthropic.com/settings/keys) as an environment variable. +1. Save your [Anthropic API key](https://platform.claude.com/settings/keys) as an environment variable. ```bash export ANTHROPIC_API_KEY= ``` @@ -43,7 +43,7 @@ The `Anthropic` provider calls the Anthropic API directly. | ----- | ----------- | | `apiKeySecret` | The name of the Kubernetes Secret that stores the API key, in the same namespace as this ModelConfig. | | `apiKeySecretKey` | The key within that Secret that holds the API key. | - | `model` | The model to use. For the available models, see the [Anthropic model docs](https://docs.anthropic.com/en/docs/about-claude/models). | + | `model` | The model to use. For the available models, see the [Anthropic model docs](https://platform.claude.com/docs/en/models/overview). | | `provider` | The provider to use, `Anthropic`. | | `anthropic` | Settings that only the Anthropic provider takes. An empty block is valid. | diff --git a/docs-site/content/kagent/1.x/setup/model-providers/ollama.md b/docs-site/content/kagent/1.x/setup/model-providers/ollama.md index 67c5942e..80c9eb21 100644 --- a/docs-site/content/kagent/1.x/setup/model-providers/ollama.md +++ b/docs-site/content/kagent/1.x/setup/model-providers/ollama.md @@ -5,7 +5,7 @@ weight: 20 author: kagent.dev --- -[Ollama](https://ollama.com) runs large language models on hardware that you control. The `Ollama` provider points {{< reuse "kagent-docs/snippets/name-product.md" >}} at a self-hosted Ollama server or at an [Ollama Cloud](https://ollama.com/cloud) model. A self-hosted server takes a host address and no API key. An Ollama Cloud model takes an API key, and leaves the host address unset. The `ModelConfig` selects between them. +[Ollama](https://ollama.com) runs large language models on hardware that you control. The `Ollama` provider points {{< reuse "kagent-docs/snippets/name-product.md" >}} at a self-hosted Ollama server or at an [Ollama Cloud](https://docs.ollama.com/cloud) model. A self-hosted server takes a host address and no API key. An Ollama Cloud model takes an API key, and leaves the host address unset. The `ModelConfig` selects between them. > [!IMPORTANT] > kagent agents call tools, so choose a model that supports function calling. A model without tool support connects successfully and then fails to use any tool that you bind to it. @@ -101,7 +101,7 @@ EOF ## Use Ollama Cloud -[Ollama Cloud](https://ollama.com/cloud) serves hosted models that you reach with an API key. The same `Ollama` provider covers them, with a differently shaped `ModelConfig`: leave `ollama.host` unset, and set `apiKeySecret` and `apiKeySecretKey` to the Secret that holds the key. +[Ollama Cloud](https://docs.ollama.com/cloud) serves hosted models that you reach with an API key. The same `Ollama` provider covers them, with a differently shaped `ModelConfig`: leave `ollama.host` unset, and set `apiKeySecret` and `apiKeySecretKey` to the Secret that holds the key. 1. Save your [Ollama Cloud API key](https://ollama.com/settings/keys) as an environment variable. ```bash diff --git a/docs-site/content/kagent/1.x/setup/model-providers/openai.md b/docs-site/content/kagent/1.x/setup/model-providers/openai.md index 6e0398c0..89c3cfbc 100644 --- a/docs-site/content/kagent/1.x/setup/model-providers/openai.md +++ b/docs-site/content/kagent/1.x/setup/model-providers/openai.md @@ -43,7 +43,7 @@ The `OpenAI` provider calls the OpenAI API directly. | ----- | ----------- | | `apiKeySecret` | The name of the Kubernetes Secret that stores the API key, in the same namespace as this ModelConfig. | | `apiKeySecretKey` | The key within that Secret that holds the API key. | - | `model` | The model to use. For the available models, see the [OpenAI model docs](https://platform.openai.com/docs/models). | + | `model` | The model to use. For the available models, see the [OpenAI model docs](https://developers.openai.com/api/docs/models). | | `provider` | The provider to use, `OpenAI`. | | `openAI` | Settings that only the OpenAI provider takes. An empty block is valid, and is the common case for OpenAI itself. | diff --git a/docs-site/content/kagent/1.x/setup/model-providers/xai.md b/docs-site/content/kagent/1.x/setup/model-providers/xai.md index 2231c916..4ad98ad5 100644 --- a/docs-site/content/kagent/1.x/setup/model-providers/xai.md +++ b/docs-site/content/kagent/1.x/setup/model-providers/xai.md @@ -41,7 +41,7 @@ xAI's Grok models are served through an OpenAI-compatible API, so a {{< gloss "M | ----- | ----------- | | `apiKeySecret` | The name of the Kubernetes Secret that stores the API key, in the same namespace as this ModelConfig. | | `apiKeySecretKey` | The key within that Secret that holds the API key. | - | `model` | The Grok model to use. For the available models, see the [xAI model docs](https://docs.x.ai/docs/models). | + | `model` | The Grok model to use. For the available models, see the [xAI model docs](https://docs.x.ai/developers/models). | | `provider` | The provider to use, `OpenAI`. xAI is reached through the OpenAI provider. | | `openAI.baseUrl` | The xAI API endpoint, `https://api.x.ai/v1`. | diff --git a/docs-site/content/kmcp/develop/fastmcp-python.md b/docs-site/content/kmcp/develop/fastmcp-python.md index 7d7130e1..80be5603 100644 --- a/docs-site/content/kmcp/develop/fastmcp-python.md +++ b/docs-site/content/kmcp/develop/fastmcp-python.md @@ -5,7 +5,7 @@ weight: 10 author: kagent.dev --- -[FastMCP Python](https://github.com/jlowin/fastmcp) is a lightweight, high-performance Python framework that implements the Model Context Protocol (MCP). With KMCP, you can quickly create an MCP project that uses the FastMCP framework with a sample MCP server and `echo` tool that you can use as a boilerplate to develop your own tools. +[FastMCP Python](https://github.com/PrefectHQ/fastmcp) is a lightweight, high-performance Python framework that implements the Model Context Protocol (MCP). With KMCP, you can quickly create an MCP project that uses the FastMCP framework with a sample MCP server and `echo` tool that you can use as a boilerplate to develop your own tools. ## Prerequisites diff --git a/docs-site/content/kmcp/introduction.md b/docs-site/content/kmcp/introduction.md index ff21e5ab..1010d313 100644 --- a/docs-site/content/kmcp/introduction.md +++ b/docs-site/content/kmcp/introduction.md @@ -8,7 +8,7 @@ kmcp is a comprehensive platform to accelerate the local development of Model Co ## About MCP -[Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) is an open protocol developed by Anthropic that standardizes how Large Language Model (LLM) applications connect to various external data sources and tools. Without MCP, you need to implement custom integrations for each tool that your LLM application needs to access. However, this approach is hard to maintain and can cause issues when you want to scale your environment. With MCP, you can significantly speed up, simplify, and standardize these types of integrations. An MCP server exposes external data sources and tools so that LLM applications can access them. +[Model Context Protocol (MCP)](https://modelcontextprotocol.io/docs/getting-started/intro) is an open protocol developed by Anthropic that standardizes how Large Language Model (LLM) applications connect to various external data sources and tools. Without MCP, you need to implement custom integrations for each tool that your LLM application needs to access. However, this approach is hard to maintain and can cause issues when you want to scale your environment. With MCP, you can significantly speed up, simplify, and standardize these types of integrations. An MCP server exposes external data sources and tools so that LLM applications can access them. ## What is kmcp?