---
title: "Deploy to AgentKit"
---

Besides deploying to VeFaaS, you can deploy your agent to the Volcengine **AgentKit** platform. The VeADK CLI is compatible with AgentKit commands—just prefix the familiar AgentKit commands with `veadk` (using the `agentkit` command directly works as well).

## Prerequisites

- VeADK is installed (it bundles the `agentkit` dependency).
- Volcengine access credentials (Access Key / Secret Key) are configured in the `.env` file at your project root.

## Shared application component

New projects generated by Studio contain only agent business logic and a small
`app.py`. `veadk.integrations.agentkit` provides the AgentKit APIs, health
check, agent topology, bundled Web UI, default short-term memory, and optional
Feishu lifecycle:

```python
from agents.my_agent.agent import AGENT_DISPLAY_NAMES, root_agent
from veadk.integrations.agentkit import create_agentkit_app

app = create_agentkit_app(
    root_agent,
    AGENT_DISPLAY_NAMES,
)
```

The resulting service exposes AgentKit conversation APIs together with
`/ping`, `/web/agent-info/{app_name}`, `/web/agent-graph`, `/web/search`, and
the bundled Web UI. See
[`examples/generated_agentkit_project`](https://github.com/volcengine/veadk-python/tree/main/examples/generated_agentkit_project)
for a complete generated project.

`/web/agent-info/{app_name}` returns the root Agent's name, description, model,
sub-Agents, tools, skills, and mounted component summaries. Each
`/web/agent-graph` node also includes its skills, components, path, and whether
it can be selected in a conversation. Each Runtime row in Studio has explicit
connect and info actions. The info action opens one panel whose tabs switch
between the live Agent metadata and Runtime control-plane detail. These endpoints
do not expose system prompts, credentials, environment-variable values, or
arbitrary serialized objects. The metadata response also reports
`searchSources`. Studio disables unavailable sources before the user searches,
and enables network, KnowledgeBase, or long-term-memory search only when the
corresponding `web_search` tool or component is mounted. `/web/search` reuses the
Agent's existing retrieval implementations. KnowledgeBase results identify the
configured index and backend type separately so users can verify the source.

## Steps

<Steps>

<Step>
### Create the project

Generate a standard AgentKit project:

```bash
veadk agentkit init
```
</Step>

<Step>
### Configure the project

Configure the project as needed:

```bash
veadk agentkit config
```
</Step>

<Step>
### Deploy the project

From the project root, deploy it to the Volcengine AgentKit platform:

```bash
veadk agentkit launch
```
</Step>

</Steps>

When deploying through Studio, the required Feishu, knowledge-base,
short-/long-term memory, and observability settings appear in their respective
feature sections. Their values are converted to VeADK runtime environment
variables only during deployment, and selecting an observability exporter also
enables its corresponding `ENABLE_*` flag. These values are not written to
generated source or exported YAML. The deployment page mirrors them in a
component-generated environment-variable group for review. Use a database-backed
short-term memory store for multi-instance runtimes; when `run_sse` returns 404,
the UI also suggests checking whether an in-memory or SQLite session landed on a
different instance.

When deploying from the bundled Web UI, cloud image build failures include a
credential-safe build-log excerpt so dependency resolution and Dockerfile
errors can be diagnosed directly.

`veadk agentkit` passes through all of AgentKit's subcommands (such as `build`, `deploy`, `status`, and `destroy`). For the full command list and descriptions, see [AgentKit-Compatible Commands](/en/docs/cli/agentkit-cli); for more detailed platform deployment steps, see the [AgentKit documentation](https://volcengine.github.io/agentkit-sdk-python/content/2.agentkit-cli/1.overview.html).
