Eden AI

Overview

Property	Details
Description	Eden AI is an AI gateway: one API key and one bill for 1000+ LLMs from 30+ providers (OpenAI, Anthropic, Google, Mistral, DeepSeek, xAI, Amazon Bedrock, Azure and more), with the real cost of every request reported in the response.
Provider Route on LiteLLM	edenai/
Link to Provider Doc	Eden AI Documentation ↗
Base URL	https://api.edenai.run/v3
Supported Operations	/chat/completions, /responses, /v1/messages, /embeddings, /audio/transcriptions, /audio/speech, /images/generations, /videos

https://www.edenai.co/docs

We support ALL Eden AI chat models, just set edenai/ as a prefix when sending completion requests

Required Variables

Environment Variables

os.environ["EDENAI_API_KEY"] = ""  # your Eden AI API key

Keys are created in the Eden AI dashboard at https://app.edenai.run under Settings, API Keys.

Optional Variables

Environment Variables

os.environ["EDENAI_API_BASE"] = "https://api.eu.edenai.run/v3"  # EU endpoint, same key. Default is https://api.edenai.run/v3

The EU endpoint accepts the same API key but serves only the subset of the catalog hosted in the EU, so model ids that work on the default host, including the openai/gpt-mini-latest and anthropic/claude-sonnet-latest examples on this page, may not exist there. Check https://api.eu.edenai.run/v3/models for the ids the EU host serves before switching.

Model Names

Eden AI model ids are provider/model, for example openai/gpt-mini-latest, anthropic/claude-sonnet-latest or google/gemini-3.7-flash. Add the edenai/ prefix and LiteLLM strips only that prefix, so edenai/openai/gpt-mini-latest reaches Eden AI as openai/gpt-mini-latest. A bare model name such as edenai/mistral-small-latest also works: Eden AI then picks the seller that serves the model (provider routing). Eden AI's own stable aliases, the catalog entries with an alias_of such as openai/gpt-mini-latest, only resolve with their vendor prefix. Region variants keep their suffix, as in edenai/vertex/gemini-3.7-flash@eu.

The catalog is public at https://app.edenai.run/models. From LiteLLM, litellm.get_valid_models(custom_llm_provider="edenai", check_provider_endpoint=True) returns the same list with the edenai/ prefix applied.

Route Every Eden AI Model Through One Deployment

The proxy can expose the whole catalog with a wildcard deployment. With check_provider_endpoint on, /v1/models lists every model from https://api.edenai.run/v3/models under the edenai/ prefix, and a request for any of them, such as edenai/mistral/mistral-small-latest, routes through this deployment.

config.yaml

model_list:
  - model_name: edenai/*
    litellm_params:
      model: edenai/*
      api_key: os.environ/EDENAI_API_KEY

litellm_settings:
  check_provider_endpoint: true

Cost Tracking

Every Eden AI response reports the request's cost in USD, after any account discount, and LiteLLM records that number as the request's spend instead of a price map estimate. On chat completion streams the cost arrives on the final usage chunk. LiteLLM always asks Eden AI for that chunk, and forwards it to your client only when you set stream_options={"include_usage": True}, so streaming clients see exactly the OpenAI behavior they expect.

Endpoint	Non-streaming	Streaming
/chat/completions	Eden AI's cost	Eden AI's cost, from the final usage chunk
/responses	Eden AI's cost	Eden AI's cost, from usage.cost on the response.completed event
/v1/messages	Eden AI's cost	Price map estimate, which is 0 unless you register the model's prices: Eden AI does not report a cost inside a Messages stream
/embeddings	Eden AI's cost	Not streamed
/audio/transcriptions	Eden AI's cost for the JSON formats; price map estimate from the clip's duration for text, srt and vtt, which Eden AI returns without a cost	Not streamed
/audio/speech	Eden AI's cost, from the x-edenai-cost response header	Not streamed
/images/generations	Eden AI's cost	Not streamed
/videos	0 on the create call, which is what Eden AI reports while the job is queued; the settled cost appears on the job's status once it completes, as usage.provider_reported_cost_usd. Register the model's per-second price to bill an estimate on the create call instead	Not streamed

Usage - LiteLLM Python SDK

Non-streaming

Eden AI Non-streaming Completion

import os
from litellm import completion

os.environ["EDENAI_API_KEY"] = ""  # your Eden AI API key

messages = [{"content": "Hello, how are you?", "role": "user"}]

response = completion(
    model="edenai/openai/gpt-mini-latest",
    messages=messages,
)

print(response)

Streaming

Eden AI Streaming Completion

import os
from litellm import completion

os.environ["EDENAI_API_KEY"] = ""  # your Eden AI API key

messages = [{"content": "Write a short story about AI", "role": "user"}]

response = completion(
    model="edenai/anthropic/claude-sonnet-latest",
    messages=messages,
    stream=True,
    stream_options={"include_usage": True},
)

for chunk in response:
    print(chunk)

Eden AI parameters: fallbacks and routing

Eden AI accepts a few fields beyond the OpenAI set. fallbacks lists up to three provider/model ids tried in order when the primary model fails, and routing steers provider routing for bare model names (sort is cost, speed, latency or exact, and allowed_providers restricts the sellers). Pass them through extra_body:

Eden AI fallbacks and routing

import os
from litellm import completion

os.environ["EDENAI_API_KEY"] = ""  # your Eden AI API key

response = completion(
    model="edenai/gpt-5-mini",
    messages=[{"content": "Hello, how are you?", "role": "user"}],
    extra_body={
        "fallbacks": ["anthropic/claude-sonnet-latest"],
        "routing": {"sort": "latency", "allowed_providers": ["openai", "azure"]},
    },
)

print(response)

Usage - Responses API

Eden AI serves OpenAI's Responses API at /v3/responses for every model in its catalog, and LiteLLM routes litellm.responses and the proxy's /v1/responses there natively rather than emulating them over chat completions. Stateful features (previous_response_id, store, retrieving or deleting a response) only work when the seller natively supports the Responses API, which today means the OpenAI models; other sellers answer statelessly.

Eden AI Responses API

import os
import litellm

os.environ["EDENAI_API_KEY"] = ""  # your Eden AI API key

response = litellm.responses(
    model="edenai/openai/gpt-mini-latest",
    input="Hello, how are you?",
    max_output_tokens=200,
)

print(response.output_text)

stream = litellm.responses(
    model="edenai/anthropic/claude-sonnet-latest",
    input="Write a short story about AI",
    stream=True,
)

for event in stream:
    print(event)

fallbacks and routing go through extra_body here too.

Usage - Anthropic Messages API

Eden AI serves Anthropic's Messages API at /v3/v1/messages for every model in its catalog, OpenAI and Google models included, and LiteLLM forwards litellm.anthropic.messages calls and the proxy's /v1/messages there untranslated, so system blocks with cache_control, thinking and tool results reach Eden AI exactly as your client sent them. Eden AI's fallbacks and routing fields cannot be sent on this route.

Eden AI Anthropic Messages API

import os
import litellm

os.environ["EDENAI_API_KEY"] = ""  # your Eden AI API key

response = await litellm.anthropic.messages.acreate(
    model="edenai/anthropic/claude-sonnet-latest",
    max_tokens=200,
    messages=[{"role": "user", "content": "Hello, how are you?"}],
)

print(response["content"][0]["text"])

Usage - Embeddings

Eden AI serves OpenAI's embeddings API at /v3/embeddings for every embedding model in its catalog (OpenAI, Google, Cohere, Mistral, Amazon and more). dimensions, encoding_format and user go through as-is; Eden AI's own fields such as metadata go through extra_body.

Eden AI Embeddings

import os
import litellm

os.environ["EDENAI_API_KEY"] = ""  # your Eden AI API key

response = litellm.embedding(
    model="edenai/openai/text-embedding-3-small",
    input=["Hello, how are you?", "Fine, thanks"],
    dimensions=256,
)

print(len(response.data[0]["embedding"]))
print(response._hidden_params["response_cost"])  # the cost Eden AI reported

Usage - Audio (transcription and speech)

Eden AI serves OpenAI's speech-to-text API at /v3/audio/transcriptions and text-to-speech API at /v3/audio/speech. Transcription takes the usual multipart upload plus language, prompt, response_format, temperature and timestamp_granularities; speech takes voice, response_format, speed and instructions. Both report Eden AI's cost: transcription in the body, speech in the x-edenai-cost response header since the body is the audio itself.

Eden AI Audio

import os
import litellm

os.environ["EDENAI_API_KEY"] = ""  # your Eden AI API key

speech = litellm.speech(
    model="edenai/openai/tts-1",
    input="Hello, how are you?",
    voice="alloy",
    response_format="mp3",
)
speech.stream_to_file("hello.mp3")

with open("hello.mp3", "rb") as audio:
    transcript = litellm.transcription(
        model="edenai/openai/whisper-1",
        file=audio,
        language="en",
    )

print(transcript.text)

Usage - Image Generation

Eden AI serves OpenAI's image generation API at /v3/images/generations for every image model in its catalog (OpenAI, Google, Amazon, Stability and more). Images come back as b64_json or a hosted url depending on the seller, with Eden AI's cost on the response.

Eden AI Image Generation

import base64
import os
import litellm

os.environ["EDENAI_API_KEY"] = ""  # your Eden AI API key

response = litellm.image_generation(
    model="edenai/openai/gpt-image-1-mini",
    prompt="A watercolor lighthouse at dawn",
    size="1024x1024",
    quality="low",
)

image = response.data[0]
if image.b64_json:
    with open("lighthouse.png", "wb") as f:
        f.write(base64.b64decode(image.b64_json))
else:
    print(image.url)

Usage - Video Generation

Eden AI serves OpenAI's video API at /v3/videos for every video model in its catalog (OpenAI, Google, Amazon, MiniMax, Pixverse and more). A job is created, polled and downloaded through LiteLLM's video functions; the id LiteLLM returns routes the later calls back to Eden AI on its own. seconds and size go through as-is, a reference image goes through input_reference as a file or as {"image_url": ...} / {"file_id": ...}, and Eden AI's own seed, provider_params, webhook_receiver and user_webhook_parameters pass through for Eden AI to validate. OpenAI's characters and user are forwarded as well, and Eden AI answers with a 422 for them until it supports them.

Eden AI Video Generation

import os
import time
import litellm

os.environ["EDENAI_API_KEY"] = ""  # your Eden AI API key

job = litellm.video_generation(
    model="edenai/pruna/p-video",
    prompt="A red ball rolling across a wooden table",
    seconds="4",
    size="1280x720",
)

while job.status not in ("completed", "failed"):
    time.sleep(5)
    job = litellm.video_status(video_id=job.id)

print(job.status, job.usage)  # usage carries Eden AI's settled cost as provider_reported_cost_usd
with open("ball.mp4", "wb") as f:
    f.write(litellm.video_content(video_id=job.id))

Usage - LiteLLM Proxy Server

config.yaml

model_list:
  - model_name: gpt-mini-latest
    litellm_params:
      model: edenai/openai/gpt-mini-latest
      api_key: os.environ/EDENAI_API_KEY
  - model_name: claude-sonnet
    litellm_params:
      model: edenai/anthropic/claude-sonnet-latest
      api_key: os.environ/EDENAI_API_KEY
  - model_name: gemini-flash-eu
    litellm_params:
      model: edenai/vertex/gemini-3.7-flash
      api_key: os.environ/EDENAI_API_KEY
      api_base: https://api.eu.edenai.run/v3
  - model_name: text-embedding-3-small
    litellm_params:
      model: edenai/openai/text-embedding-3-small
      api_key: os.environ/EDENAI_API_KEY
  - model_name: whisper-1
    litellm_params:
      model: edenai/openai/whisper-1
      api_key: os.environ/EDENAI_API_KEY
  - model_name: tts-1
    litellm_params:
      model: edenai/openai/tts-1
      api_key: os.environ/EDENAI_API_KEY
  - model_name: gpt-image-1-mini
    litellm_params:
      model: edenai/openai/gpt-image-1-mini
      api_key: os.environ/EDENAI_API_KEY
  - model_name: p-video
    litellm_params:
      model: edenai/pruna/p-video
      api_key: os.environ/EDENAI_API_KEY

Start LiteLLM Proxy

litellm --config config.yaml

# RUNNING on http://0.0.0.0:4000

OpenAI SDK

Eden AI via Proxy

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:4000",  # Your proxy URL
    api_key="your-proxy-api-key",      # Your proxy API key
)

response = client.chat.completions.create(
    model="gpt-mini-latest",
    messages=[{"role": "user", "content": "Hello, how are you?"}],
)

print(response.choices[0].message.content)

LiteLLM SDK

Eden AI via Proxy - LiteLLM SDK

import litellm

response = litellm.completion(
    model="litellm_proxy/gpt-mini-latest",
    messages=[{"role": "user", "content": "Hello, how are you?"}],
    api_base="http://localhost:4000",
    api_key="your-proxy-api-key",
)

print(response.choices[0].message.content)

cURL

Eden AI via Proxy - cURL

curl http://localhost:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-proxy-api-key" \
  -d '{
    "model": "gpt-mini-latest",
    "messages": [{"role": "user", "content": "Hello, how are you?"}]
  }'

The proxy's x-litellm-response-cost response header and the spend logs carry the cost Eden AI reported for the request.

The same deployments serve /v1/responses and /v1/messages:

Eden AI via Proxy - Responses API

curl http://localhost:4000/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-proxy-api-key" \
  -d '{
    "model": "gpt-mini-latest",
    "input": "Hello, how are you?"
  }'

Eden AI via Proxy - Anthropic Messages API

curl http://localhost:4000/v1/messages \
  -H "Content-Type: application/json" \
  -H "x-api-key: your-proxy-api-key" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "claude-sonnet",
    "max_tokens": 200,
    "messages": [{"role": "user", "content": "Hello, how are you?"}]
  }'

Embeddings, audio and images work the same way, on the OpenAI routes:

Eden AI via Proxy - Embeddings, Audio, Images

curl http://localhost:4000/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-proxy-api-key" \
  -d '{"model": "text-embedding-3-small", "input": "Hello, how are you?"}'

curl http://localhost:4000/v1/audio/speech \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-proxy-api-key" \
  -d '{"model": "tts-1", "input": "Hello, how are you?", "voice": "alloy"}' \
  --output hello.mp3

curl http://localhost:4000/v1/audio/transcriptions \
  -H "Authorization: Bearer your-proxy-api-key" \
  -F model=whisper-1 \
  -F file=@hello.mp3

curl http://localhost:4000/v1/images/generations \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-proxy-api-key" \
  -d '{"model": "gpt-image-1-mini", "prompt": "A watercolor lighthouse at dawn", "size": "1024x1024", "quality": "low"}'

Video jobs use the OpenAI video routes: create on /v1/videos, poll /v1/videos/{id} until status is completed, then download /v1/videos/{id}/content:

Eden AI via Proxy - Videos

curl http://localhost:4000/v1/videos \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-proxy-api-key" \
  -d '{"model": "p-video", "prompt": "A red ball rolling across a wooden table", "seconds": "4", "size": "1280x720"}'

curl http://localhost:4000/v1/videos/<id from the create response> \
  -H "Authorization: Bearer your-proxy-api-key"

curl http://localhost:4000/v1/videos/<id from the create response>/content \
  -H "Authorization: Bearer your-proxy-api-key" \
  --output ball.mp4

Supported OpenAI Parameters

Eden AI takes the full OpenAI chat completions parameter set: temperature, top_p, max_tokens, max_completion_tokens, n, stop, seed, stream, stream_options, tools, tool_choice, parallel_tool_calls, response_format, reasoning_effort, logprobs, top_logprobs, frequency_penalty, presence_penalty, logit_bias, web_search_options, modalities, audio, prediction and service_tier. Provider-specific parameters go through extra_body, which Eden AI passes to the underlying provider.