Free LLM API for AI Agents: LangChain, CrewAI, AutoGen

If you are building agents, you need a free LLM API for agents that does not fall over on the first rate limit. Agents are chatty. A single task can trigger dozens of model calls as the loop plans, calls tools, reads results, and tries again. When one provider returns a 429, a naive setup stops cold and your whole run dies halfway through. The DreamPrompting gateway is a free, OpenAI-compatible endpoint with automatic failover across many providers, so when one is rate-limited the next one is tried and the agent loop keeps running. It takes one free API key, which you create in seconds by signing in with Google at /account, with no credit card.

Because the API speaks the OpenAI format, any framework that talks to OpenAI works with a one-line change. You point the base URL at https://dreamprompting.com/api/v1, pass your free API key, and pick a model. That is it. Below are drop-in configs for the four frameworks most agent builders use.

Why failover matters for a free LLM API for AI agents

A chat app makes one call and a human waits. An agent makes many calls in a tight loop and nobody is watching. That difference is why free LLM API for agents traffic breaks on rate limits so often. If every call in a ten-step plan hits the same upstream, you are ten times as likely to trip a per-minute cap. The gateway spreads that risk. Set the model to auto and each request is routed to a healthy provider. If that provider is throttled, the request is retried against another one before it ever reaches your code as an error.

This resilience is worth more to an agent than to almost any other workload. A failed chat message is an annoyance you can retry by hand. A failed agent call, three steps deep into a plan that already spent tokens gathering context and calling tools, throws away all of that work. When the gateway absorbs a provider outage for you, the loop simply continues on the next model and your run finishes. You get the reliability of a paid multi-provider setup from a free LLM API for agents, without wiring up several accounts and API keys yourself.

Drop-in configs

LangChain (a free LLM for LangChain in one line):

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    base_url="https://dreamprompting.com/api/v1",
    api_key="YOUR_API_KEY",
    model="auto",
)

CrewAI:

from crewai import LLM

llm = LLM(
    model="openai/auto",
    base_url="https://dreamprompting.com/api/v1",
    api_key="YOUR_API_KEY",
)

AutoGen:

config_list = [{
    "model": "auto",
    "base_url": "https://dreamprompting.com/api/v1",
    "api_key": "YOUR_API_KEY",
}]

LlamaIndex:

from llama_index.llms.openai_like import OpenAILike

llm = OpenAILike(
    model="auto",
    api_base="https://dreamprompting.com/api/v1",
    api_key="YOUR_API_KEY",
    is_chat_model=True,
)

If you prefer raw HTTP, a plain curl call looks like this:

curl https://dreamprompting.com/api/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "auto",
    "messages": [{"role": "user", "content": "Plan a two-step research task."}]
  }'

Agent builder tips

A few settings make a free LLM API for agents behave well under load:

• Pin versus auto. Use "auto" for maximum resilience when any capable model will do. Pin a specific model such as "groq/openai/gpt-oss-120b" when a step needs consistent behavior or a known context window. See the full list on the models page.
• Back off on 429. The gateway allows 100 requests per minute, plus a rolling 24 hour account quota of 5,000 requests and 500,000 tokens. A busy multi-agent crew can hit either one. Wrap calls in exponential backoff so a 429 pauses briefly and retries instead of crashing the run.
• Stream long steps. Set "stream": true so planning or writing steps return tokens as they arrive, which keeps the loop responsive and lets you cut off runaway generations early.
• Check your budget. Call GET /api/v1/quota to see remaining fair-use capacity, and GET /api/v1/models to discover pinnable model names at runtime.

This is a fair-use free service. It is ideal for prototypes, side projects, evaluations, and moderate workloads. For a heavy production agent running around the clock, bring your own upstream provider keys so you control capacity and cost directly. Full request and response details live in the API docs, and the for-agents page collects everything specific to agent workloads.

FAQ

Will it survive rate limits? That is the point. With model: "auto" a throttled provider triggers automatic failover to another one. For very tight loops, still add backoff on 429 so you stay under the 100 requests per minute cap and inside your daily per-key quota.

Which framework works? Any framework that supports an OpenAI-compatible base URL, which covers LangChain, CrewAI, AutoGen, and LlamaIndex. Copy the config above, change the base URL and key, and you are done.

Can I use it in production? Yes for light and moderate workloads under fair use. For a high-volume production agent, add your own upstream keys so you own the rate limits and can scale predictably.

Ready to plug it in? Start on the for-agents page, then keep the API docs open while you wire up your first loop.