LibreChat su Claude, GPT ir Gemini

Pridėk prie librechat.yaml vieną custom endpoint bloką, ir avots atsiranda kaip tiekėjas su visais modeliais viename balanse.

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Connect in three fields

LibreChat skaito pasirinktinius tiekėjus iš librechat.yaml. Vienas endpoint blokas prideda avots.

FieldValue
Base URLhttps://api.avots.ai/openai/v1
API keyav_mcp_… · get your key
Modelanthropic/claude-sonnet-4.6 or any id from the table below

Setup

1. Redaguokite librechat.yaml

Pridėkite pasirinktinį endpoint po endpoints.custom su avots base URL ir savo raktu.

2. Įdėkite raktą į .env

Pridėkite AVOTS_API_KEY=av_mcp_YOUR_KEY į savo .env ir nurodykite jį kaip ${AVOTS_API_KEY}.

3. Paleiskite LibreChat iš naujo

Paleiskite iš naujo ir pasirinkite avots kaip endpoint. Nustatykite models.fetch į true, kad modeliai būtų automatiškai išvardyti iš /models.

librechat.yaml
endpoints:
  custom:
    - name: "avots"
      apiKey: "${AVOTS_API_KEY}"
      baseURL: "https://api.avots.ai/openai/v1"
      models:
        default: ["anthropic/claude-sonnet-4.6"]
        fetch: true
      titleConvo: true
      modelDisplayLabel: "avots"

apiKey taip pat priima tiesioginį raktą arba "user_provided" (kiekvienas vartotojas įveda savo). Jei endpoint atmeta numatytąjį OpenAI parametrą, pridėkite jį į dropParams.

Example request

curl
curl https://api.avots.ai/openai/v1/chat/completions \
  -H "Authorization: Bearer av_mcp_YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "anthropic/claude-sonnet-4.6",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Model and MCP tools in one config

LibreChat keeps the chat provider and MCP servers in the same file, so you paste both at once and get Claude, GPT and Gemini plus the avots creative tools (image, video, audio, talking avatars) on one av_mcp_ key. You save it yourself, nothing is added automatically.

librechat.yaml, model + MCP tools
endpoints:
  custom:
    - name: "avots"
      apiKey: "${AVOTS_API_KEY}"
      baseURL: "https://api.avots.ai/openai/v1"
      models:
        default: ["anthropic/claude-sonnet-4.6"]
        fetch: true
      modelDisplayLabel: "avots"

mcpServers:
  avots:
    type: streamable-http
    url: "https://mcp.avots.ai/"
    headers:
      Authorization: "Bearer ${AVOTS_API_KEY}"

MCP setup guide →

Models and prices

Set the model field to any id below, or a lazy name like claude / gpt / gemini that maps to a sensible default.

ModelBest for⚡ / 1M tokens (in / out)
openai/gpt-5.5-proMax intelligence, hardest tasks60 000 / 360 000
anthropic/claude-opus-5Top Claude, long reasoning10 000 / 50 000
anthropic/claude-sonnet-4.6Balanced (default)6 000 / 30 000
google/gemini-3.1-pro-previewFresh Gemini Pro4 000 / 24 000
google/gemini-2.5-proStable Gemini Pro2 500 / 20 000
anthropic/claude-haiku-4.5Fast Claude2 000 / 10 000
deepseek/deepseek-r1Cheap reasoning1 400 / 5 000
google/gemini-2.5-flashFast and cheap600 / 5 000
openai/gpt-5-miniCheap OpenAI500 / 4 000
openai/gpt-oss-120b:freeFree (OpenAI open model, needs a positive balance)Free

* Price in ⚡ per 1,000,000 tokens, input / output (1000⚡ ≈ $1). Free models need a positive balance. Full list via GET /openai/v1/models.

LibreChat su Claude, GPT ir Gemini

Generate an av_mcp_ key in Settings, paste three fields, and reach 40+ models on one balance. The same key works for MCP.

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FAQ

Klausimai

Kur dedama konfigūracija?
Į librechat.yaml projekto šaknyje, po endpoints.custom. Raktas saugomas .env kaip AVOTS_API_KEY.
Ar modeliai gali užsikrauti automatiškai?
Taip, nustatykite models.fetch į true ir LibreChat išvardys juos iš GET /openai/v1/models.
Ar kiekvienas vartotojas gali naudoti savo raktą?
Nustatykite apiKey į "user_provided" ir LibreChat paprašys kiekvieno vartotojo rakto.
Kaip apmokestinama?
Už kiekvieną užklausą iš jūsų avots balanso.
Does it support function calling and tools?
Yes. The endpoint forwards the standard OpenAI tools, tool_choice and parallel_tool_calls fields (and the legacy functions/function_call), in both streaming and non-streaming mode, so agent frameworks like LangChain, LlamaIndex, Langflow and n8n get real tool_calls back. Whether a given request uses tools depends on the model you pick: Claude, GPT and Gemini all support function calling.

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