- Gemini 1.5 Flash gives 1,500 free requests/day — enough for personal agent use without a paid plan
- Groq's free tier provides fast inference on Llama and Mistral models with generous daily limits
- OpenRouter exposes free model access for several open-weight models including Meta Llama and Mistral
- Ollama + local models eliminates API costs entirely at the cost of hardware requirements
- DuckDuckGo search integration requires no API key and works immediately out of the box
People ask "what's the free API for OpenClaw" expecting one answer. There are four different answers depending on what part of the stack you're trying to make free. The LLM, the search provider, the hosting, and any integrations each have their own free options — and they stack.
Get all four right and you have a fully functional OpenClaw agent running at zero recurring cost. Here's exactly how, with the specific config for each.
Free LLM APIs That Work With OpenClaw
OpenClaw supports any OpenAI-compatible API endpoint. Most free-tier LLM providers expose an OpenAI-compatible interface, which means you configure them with a base URL and API key — same pattern, different endpoint. You don't need special OpenClaw support for each provider.
Here are the free options we've verified work reliably as of early 2025:
| Provider | Free Limit | Best Model | Speed |
|---|---|---|---|
| Google Gemini | 1,500 req/day | Gemini 1.5 Flash | Fast |
| Groq | ~14,400 tokens/min | Llama 3.1 70B | Very fast |
| OpenRouter | Varies by model | Llama 3.1 8B free | Moderate |
| Ollama (local) | Unlimited | Llama 3.1 8B | Depends on hardware |
Gemini Flash Free Tier
Google's Gemini free tier is the most generous free LLM option available right now. Gemini 1.5 Flash supports a 1 million token context window — far larger than most agents will use — and 1,500 requests per day at zero cost.
Get a free API key at aistudio.google.com. Then configure it in OpenClaw:
model:
provider: gemini
name: gemini-1.5-flash
api_key: "${GEMINI_API_KEY}"
base_url: "https://generativelanguage.googleapis.com/v1beta/openai/"The 15 requests per minute rate limit is the main constraint. For a single agent doing a few tasks per hour, this is invisible. For an agent that runs frequent autonomous loops, you'll hit it. Monitor your request rate and add delays between automated task cycles if needed.
Groq Free Tier
Groq's hardware-accelerated inference is genuinely fast — often 10–20x faster than standard cloud API endpoints. Their free tier is rate-limited but generous enough for real work. Llama 3.1 70B on Groq's free tier outperforms much smaller models on most tasks.
model:
provider: groq
name: llama-3.1-70b-versatile
api_key: "${GROQ_API_KEY}"
base_url: "https://api.groq.com/openai/v1"The free tier limit is approximately 14,400 tokens per minute on Llama models. This is comfortable for interactive agent use. It tightens if you're processing long documents repeatedly — chunk your documents and process in batches if you hit limits.
OpenRouter Free Models
OpenRouter aggregates dozens of model providers through a single API. Some models are available at zero cost — specifically the smaller open-weight models like Llama 3.1 8B Instruct and some Mistral variants. Free model availability changes; check the OpenRouter model list for current `:free` tagged models.
model:
provider: openrouter
name: "meta-llama/llama-3.1-8b-instruct:free"
api_key: "${OPENROUTER_API_KEY}"
base_url: "https://openrouter.ai/api/v1"Local Models with Ollama
Ollama runs open-weight models locally. No API key. No rate limits. No recurring cost beyond electricity. This is the ultimate free setup — but it requires hardware.
Minimum hardware for a usable local model:
- 8GB unified RAM for Llama 3.1 8B (Apple Silicon, M1 or later)
- 16GB RAM for a Windows/Linux machine running Llama 3.1 8B with acceptable speed
- GPU with 8GB VRAM for significantly faster inference on any platform
# Install Ollama, then pull a model
ollama pull llama3.1:8b
# Configure in OpenClaw
model:
provider: ollama
name: llama3.1:8b
base_url: "http://localhost:11434/v1"Llama 3.1 8B handles most agent tasks well: summarization, classification, routing, Q&A over documents. Where it falls short relative to frontier models: complex multi-step reasoning, code generation for advanced languages, and nuanced judgment calls. Know its limits and design your agent tasks accordingly.
Free Search APIs
Agents that can search the web are dramatically more useful than those operating purely on their training data. Two free options work well with OpenClaw:
DuckDuckGo — No API key required. The OpenClaw DuckDuckGo MCP integration works out of the box. Rate limits are informal but sufficient for personal agent use. Not suitable for high-frequency automated searches.
Brave Search — 2,000 free queries per month with an API key from brave.com/search/api. Better result quality than DuckDuckGo for technical queries. The free tier is plenty for most personal agent setups that do occasional research tasks.
mcp:
- name: brave-search
command: npx
args: ["@modelcontextprotocol/server-brave-search"]
env:
BRAVE_API_KEY: "${BRAVE_API_KEY}"Common Mistakes
The biggest mistake with free API tiers is building a workflow that relies on a specific rate limit and then watching it break when your usage grows. Design for rate limit tolerance from day one. Add exponential backoff to your agent's retry logic and configure a local model as a fallback when the primary API is rate-limited.
Not checking data privacy terms for free tiers is a significant oversight. Some providers use API traffic from free accounts to train future models. If you're processing sensitive information — customer data, proprietary documents — check the provider's data retention and training policies before sending anything sensitive.
The third mistake is running a free-tier API for multiple agents simultaneously without rate limit awareness. Free tiers are per-account, not per-agent. Five agents each making modest API calls can exceed a single-account free limit in minutes. Use a single model routing layer and fan out from there, rather than giving each agent its own direct API connection.
Frequently Asked Questions
What free APIs work with OpenClaw?
Several providers offer meaningful free tiers: Google Gemini Flash gives 1,500 requests/day free, Groq offers fast inference on Llama models with generous free limits, and OpenRouter aggregates providers including some with free access. For search, Brave Search API gives 2,000 free queries/month and DuckDuckGo requires no API key at all.
Is running Ollama locally truly free?
Yes, with one caveat: Ollama itself is free, and local models have no per-call cost. The only ongoing cost is electricity — roughly $1–$3/month for a Mac mini or mid-range laptop running inference periodically. Hardware cost is one-time. If you already own a capable machine, local inference is genuinely zero ongoing cost.
How many free API calls does Gemini Flash give per day?
Google's Gemini 1.5 Flash free tier provides 1,500 requests per day with a 1 million token context window — far more than most personal agent setups consume. The free tier has rate limits of 15 requests per minute. For moderate agent workloads, this is sufficient without upgrading to a paid plan.
What's the catch with free API tiers?
Free tiers typically have rate limits (requests per minute), daily quotas, and may lack features like function calling or streaming available to paid users. Data from free-tier API calls may be used for model training by some providers — check each provider's terms if data privacy matters for your use case.
Can I use OpenRouter for free model access?
Yes. OpenRouter aggregates dozens of models and some are available at zero cost — including Meta's Llama models and Mistral models. Free models on OpenRouter have lower rate limits and may experience higher latency during peak times. Still useful for testing and light production workloads.
Will free API tiers handle production agent workloads?
For personal automation, light-duty agents, and development, yes. For business-grade workloads with multiple agents running continuously, free tiers will hit rate limits. The practical sweet spot is using free APIs for development and low-frequency production tasks, then upgrading specific integrations where volume demands it.