Agents
Search Agents
Search Agents through the OMA API.
POST
/
v1
/
agents:search
Search Agents
curl --request POST \
--url http://localhost:38080/v1/agents:search \
--header 'Content-Type: application/json' \
--header 'X-Api-Key: <api-key>' \
--data '
{
"name": "<string>"
}
'import requests
url = "http://localhost:38080/v1/agents:search"
payload = { "name": "<string>" }
headers = {
"X-Api-Key": "<api-key>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'X-Api-Key': '<api-key>', 'Content-Type': 'application/json'},
body: JSON.stringify({name: '<string>'})
};
fetch('http://localhost:38080/v1/agents:search', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "http://localhost:38080/v1/agents:search"
payload := strings.NewReader("{\n \"name\": \"<string>\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("X-Api-Key", "<api-key>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}{
"data": [
{
"id": "agent_011CZkYpogX7uDKUyvBTophP",
"archived_at": null,
"created_at": "2026-03-15T10:00:00Z",
"description": "A general-purpose starter agent.",
"mcp_servers": [
{
"name": "example-mcp",
"type": "url",
"url": "https://example-server.modelcontextprotocol.io/sse"
}
],
"metadata": {
"foo": "bar"
},
"model": {
"id": "claude-sonnet-4-6",
"effort": {
"type": "low"
},
"inference_geo": "inference_geo",
"speed": "standard"
},
"multiagent": {
"agents": [
{
"id": "agent_011CZkYqphY8vELVzwCUpqiQ",
"type": "agent",
"version": 1
}
],
"type": "coordinator"
},
"name": "My First Agent",
"skills": [
{
"skill_id": "xlsx",
"type": "anthropic",
"version": "1"
},
{
"skill_id": "skill_011CZkZFNu9hAbo3jZPRgTlx",
"type": "custom",
"version": "2"
}
],
"system": "You are a general-purpose agent that can research, write code, run commands, and use connected tools to complete the user's task end to end.",
"tools": [
{
"configs": [
{
"enabled": true,
"name": "bash",
"permission_policy": {
"type": "always_allow"
}
}
],
"default_config": {
"enabled": true,
"permission_policy": {
"type": "always_ask"
}
},
"type": "agent_toolset_20260401"
}
],
"type": "agent",
"updated_at": "2026-03-15T10:00:00Z",
"version": 1
}
],
"next_page": "<string>"
}Every request requires
?beta=true.Authorizations
omaApiKeyomaBearer
OMA workspace API key.
Query Parameters
Selects the beta API contract for this endpoint. Must be true.
Available options:
true Body
application/json
Was this page helpful?
⌘I
Search Agents
curl --request POST \
--url http://localhost:38080/v1/agents:search \
--header 'Content-Type: application/json' \
--header 'X-Api-Key: <api-key>' \
--data '
{
"name": "<string>"
}
'import requests
url = "http://localhost:38080/v1/agents:search"
payload = { "name": "<string>" }
headers = {
"X-Api-Key": "<api-key>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'X-Api-Key': '<api-key>', 'Content-Type': 'application/json'},
body: JSON.stringify({name: '<string>'})
};
fetch('http://localhost:38080/v1/agents:search', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "http://localhost:38080/v1/agents:search"
payload := strings.NewReader("{\n \"name\": \"<string>\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("X-Api-Key", "<api-key>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}{
"data": [
{
"id": "agent_011CZkYpogX7uDKUyvBTophP",
"archived_at": null,
"created_at": "2026-03-15T10:00:00Z",
"description": "A general-purpose starter agent.",
"mcp_servers": [
{
"name": "example-mcp",
"type": "url",
"url": "https://example-server.modelcontextprotocol.io/sse"
}
],
"metadata": {
"foo": "bar"
},
"model": {
"id": "claude-sonnet-4-6",
"effort": {
"type": "low"
},
"inference_geo": "inference_geo",
"speed": "standard"
},
"multiagent": {
"agents": [
{
"id": "agent_011CZkYqphY8vELVzwCUpqiQ",
"type": "agent",
"version": 1
}
],
"type": "coordinator"
},
"name": "My First Agent",
"skills": [
{
"skill_id": "xlsx",
"type": "anthropic",
"version": "1"
},
{
"skill_id": "skill_011CZkZFNu9hAbo3jZPRgTlx",
"type": "custom",
"version": "2"
}
],
"system": "You are a general-purpose agent that can research, write code, run commands, and use connected tools to complete the user's task end to end.",
"tools": [
{
"configs": [
{
"enabled": true,
"name": "bash",
"permission_policy": {
"type": "always_allow"
}
}
],
"default_config": {
"enabled": true,
"permission_policy": {
"type": "always_ask"
}
},
"type": "agent_toolset_20260401"
}
],
"type": "agent",
"updated_at": "2026-03-15T10:00:00Z",
"version": 1
}
],
"next_page": "<string>"
}