Strengths
- 1M-token context
- Text, image and video input
- Tool calling
- Published weights
Best For
- Long-horizon coding
- Knowledge work
- Deep reasoning and agent workflows
Run It in EDDI
The fastest route is the Manager: open the agent wizard or the Platform Operator and pick the provider and model. To configure it yourself, follow these steps for the provider you want.
Moonshot AI · type: "moonshot"
Model ids: kimi-k3, kimi-k2.6
1. Store the credential
Add the provider's API key to the Secrets Vault in the Manager, or with PUT /secretstore/secrets/default/moonshot-key, under the name used below. Configurations reference it as ${vault:…}, so the key never appears in plain text.
${vault:moonshot-key}2. Configure the LLM task
Use this as the langchain.json of the agent's workflow, or paste the parameters into the LLM editor in the Manager.
{
"tasks": [
{
"actions": [
"send_message"
],
"id": "chat",
"type": "moonshot",
"parameters": {
"apiKey": "${vault:moonshot-key}",
"modelName": "kimi-k3",
"systemMessage": "You are a helpful assistant",
"addToOutput": "true"
}
}
]
}Or create the whole agent in one call
setup_agent writes the rules, LLM configuration and workflow, then deploys the agent. Call it over MCP or POST /administration/agents/setup.
setup_agent(
agentName: "My kimi-k3 agent",
systemPrompt: "You are a helpful assistant.",
provider: "moonshot",
model: "kimi-k3",
apiKey: "${vault:moonshot-key}"
)Notes for EDDI
- This provider requires the model's reasoning to be sent back during tool loops. EDDI's preset does it for you: leave
returnThinkingandsendThinkingat their defaults.
Related Models
Sources
The facts on this page come from the vendor's documentation, checked on 2026-09-30. Models change quickly, so confirm limits with the vendor before you rely on them. Prices are not listed here: see the vendor's pricing page.