Groq's Language Processing Unit can push out roughly 800 tokens per second on Llama 3.1 70B—that's not a marketing number, it's what you observe the first time you watch a full paragraph appear before you've lifted your finger from the Enter key. DeepSeek R1, released in January 2025, matched or beat OpenAI's o1 on several math and coding benchmarks at a fraction of the inference cost. Put those two facts together and the obvious question follows: what can you actually build, or use right now, for free?
This article maps the real landscape. Not every project that claims 'Groq-powered' is worth your time, and 'built on DeepSeek' covers everything from a serious research agent to a thin ChatGPT wrapper with a different logo. What follows is a specific account of which free agents exist, what infrastructure they actually run on, what they're good at, and where each one will disappoint you.
Why Groq Speed and DeepSeek's Model Quality Change the Calculus for Free Agents
Most free AI tiers are slow because inference is expensive. OpenAI's free ChatGPT tier throttles heavily. Google's Gemini free tier caps usage quickly. The reason anyone is building serious free agents on Groq and DeepSeek right now is that the cost-per-token math has shifted enough to make it viable.
Groq doesn't run GPU clusters. Its LPU (Language Processing Unit) is a custom silicon design built around the idea that transformer inference is a memory-bandwidth problem, not a compute problem. By solving the memory bottleneck, Groq achieves latencies that make agentic loops—where a model calls a tool, reads the output, and calls another tool—feel nearly instant rather than a multi-second wait per step. For an agent running five sequential tool calls, that difference between 200ms and 2 seconds per step is the difference between something you'd actually use and something you abandon.
DeepSeek's contribution is different. The Hangzhou-based lab trained R1 using reinforcement learning on chain-of-thought data, which is why it reasons rather than just predicts. V3, their dense 671-billion-parameter mixture-of-experts model, was reportedly trained for around $5.5 million—a figure that made Western AI labs visibly uncomfortable when it was published. Whether that number captures full costs is debated, but the model's capability is not: it scores competitively with GPT-4o on MATH, HumanEval, and MMLU.
The practical upshot is that you can now build or use an agent that reasons carefully (DeepSeek R1) and receives fast responses (Groq hardware) without a credit card. That combination didn't exist eighteen months ago.
Groq's Own Playground and API Free Tier: The Fastest No-Cost Agent Starting Point
Groq's developer console at console.groq.com gives you free API access with rate limits that are generous enough for personal projects and light experimentation. As of early 2025, the free tier allows several hundred requests per day across models including Llama 3.1 8B, Llama 3.1 70B, Mixtral 8x7B, and Gemma 2. These aren't toy models—Llama 3.1 70B is competitive with GPT-3.5 Turbo and in some coding tasks beats it.
What makes this genuinely useful for agents specifically is that Groq's API supports function calling (tool use). That means you can wire up a ReAct-style agent loop where the model decides which tool to invoke, reads the tool's output, and continues reasoning. At Groq's latency, a five-step agent loop that would take 20–30 seconds on a free OpenAI tier completes in 3–5 seconds. That's not a small difference in practice—it's the difference between an agent that feels interactive and one that feels like you submitted a form.
The playground interface itself has a basic chat mode but no persistent agent scaffolding built in. To build a real agent on Groq, you need one of the frameworks discussed below, or you write your own loop. The API is straightforward—it's OpenAI-compatible, meaning any code you wrote for the OpenAI SDK works against Groq by changing the base URL and API key.
Groq also offers GroqCloud, which is the same infrastructure with a paid tier, but for the use cases covered here—personal agents, side projects, evaluation—the free tier is sufficient.
DeepSeek Chat's Agentic Reasoning Mode: What It Does and Where It Stops
DeepSeek's own interface at chat.deepseek.com is free, requires only an email signup, and includes a 'DeepThink (R1)' toggle that switches the model into its full chain-of-thought reasoning mode. This is not a marketing feature—R1 genuinely shows its working, sometimes for hundreds of tokens before giving you an answer, in a way that makes complex multi-step problems significantly more reliable.
In reasoning mode, DeepSeek will work through multi-step math proofs, debug code with actual logical backtracking, and handle ambiguous instructions by explicitly considering multiple interpretations. For a free tool, it's remarkably capable. On the AIME 2024 math competition benchmark, R1 scored around 79%, which is close to o1's 79.2% and well above GPT-4o's 9.3%. That's a real number from DeepSeek's January 2025 technical report, and independent evaluators broadly confirmed it.
Where DeepSeek Chat falls short as an agent specifically: it has no tool use in the public interface. It cannot browse the web, run code, call APIs, or persist memory between sessions. It reasons well but it reasons in a sealed room. You get the planning and analysis half of an agent, not the execution half. For tasks where the answer lives entirely in the model's training data—code generation, mathematical reasoning, writing—it's excellent. For anything requiring live data or real-world actions, you need a framework on top.
DeepSeek also has an API (platform.deepseek.com) with an aggressive free credit offer for new accounts and token prices around $0.14 per million input tokens for V3 as of early 2025, which is roughly 20–25 times cheaper than GPT-4o. This price structure is what has made DeepSeek the obvious backend choice for cost-conscious agent builders.
Phidata: The Most Serious Open-Source Framework with DeepSeek and Groq Support
Phidata (github.com/phidatahq/phidata) is an open-source Python framework for building multi-modal agents. It's not a hosted service—you run it yourself—but the setup is a pip install and 20 lines of code, and it natively supports both Groq and DeepSeek as model backends. This is the option for someone who wants a real agent with tool use, memory, and structured workflows rather than a chat window.
With Phidata you can build an agent that uses DuckDuckGo for web search, a Python REPL for code execution, a PostgreSQL database for long-term memory, and a file reader for document analysis—and the model orchestrating all of this can be DeepSeek V3 via API or Llama 3.1 70B via Groq. The framework handles the tool-calling loop, the prompt structure, and the memory retrieval automatically. You write Python that says what you want the agent to do, not the raw OpenAI function-calling JSON.
A concrete example: a financial research agent using Phidata + Groq might search for recent news on a company (web tool), pull structured data from a finance API (custom tool), run a calculation in a Python sandbox (code tool), and then summarize findings into a structured report—all in one run, with each tool call returning fast enough that the whole loop finishes in under 30 seconds. That's a real workflow, not a demo.
Phidata also ships with a UI called Phidata App (separate repo) that gives you a web interface for your agents without building one yourself. The entire stack is MIT licensed. The tradeoff: you're managing your own infrastructure. There's no free hosted version that does everything—you're either running locally or paying for a server. For pure local use, it's genuinely free.
- Groq support: Set
model=Groq(id="llama-3.1-70b-versatile")in your agent definition. That's it. - DeepSeek support: Use the OpenAI-compatible DeepSeek API endpoint, set the model ID, and Phidata handles the rest through its OpenAI-compatible backend class.
- Best for: Developers comfortable in Python who want full control over agent behavior.
- Not for: Non-technical users who want a UI without writing code.
AgentGPT, OpenAgents, and Browser-Based Agents That Support Groq Backends
AgentGPT (agentgpt.reworkd.ai) is one of the longest-running browser-based autonomous agent interfaces. You give it a goal, it breaks the goal into tasks, executes them in sequence, and shows you the process. It's free to try without an account for a limited number of runs. More usefully, it allows you to plug in your own API key—including a Groq key—which means you can use Groq's fast, free-tier inference as the backbone instead of paying OpenAI rates.
The experience with a Groq backend is noticeably snappier than with GPT-3.5 Turbo, the default cheap option. Task decomposition and execution steps appear much faster. The limitation is that AgentGPT's tool access is still relatively limited compared to what you can build with Phidata or LangChain: web search, some data retrieval, basic task management. It won't run arbitrary code or access private data sources. For simple goal-directed tasks—research a topic, draft a document, create a plan—it works. For anything requiring custom integrations, you'll hit walls quickly.
OpenAgents (github.com/xlang-ai/OpenAgents), developed by researchers at the University of Hong Kong, is a more research-oriented open-source agent platform. It includes three specialized agents: a data analysis agent (Python execution), a web browsing agent, and a general-purpose agent. DeepSeek models can be plugged in via API. The hosted demo is free but rate-limited; running it locally requires more setup than Phidata but offers more specialized capability for data tasks specifically.
Wordware.ai deserves a mention as a no-code agent builder that added DeepSeek V3 and R1 as available models in early 2025. The free tier allows basic agent creation with limited runs per month. If you want to build an agent without writing code and want to experiment with DeepSeek's reasoning model specifically, this is the most accessible entry point. The ceiling is lower than code-based options, but the floor is much more accessible.
LangChain and CrewAI with Groq or DeepSeek: For Developers Who Want Maximum Flexibility
LangChain (python.langchain.com) is the dominant Python framework for building LLM-powered applications, and both Groq and DeepSeek are first-class supported providers. The langchain-groq package wraps Groq's API in LangChain's standard interface. DeepSeek works through langchain-openai with a custom base URL, since DeepSeek's API is OpenAI-compatible.
The reason to choose LangChain over Phidata is ecosystem depth. LangChain has integrations with over 600 tools and data sources, a massive community, and LangSmith for observability (free tier available). The reason to consider Phidata instead is that LangChain's abstraction layers can be genuinely confusing—debugging why your agent made a particular tool call is harder when six layers of abstraction sit between your code and the API call. Phidata's design is more explicit.
CrewAI (github.com/joaomdmoura/crewAI) takes a different approach: instead of a single agent with tools, you define a crew of specialized agents with different roles and have them collaborate. A research crew might have a 'researcher' agent, a 'writer' agent, and an 'editor' agent, each with different system prompts and tool access, passing work between each other. CrewAI supports Groq as a backend—set llm=ChatGroq(model_name="llama3-70b-8192") in your agent config. DeepSeek works via the LiteLLM integration that CrewAI ships with.
The multi-agent approach genuinely improves output quality for complex tasks. A single agent asked to both research and write tends to produce mediocre results at both. Separate specialized agents—researcher uses Groq's speed to rapidly iterate through tool calls, writer uses DeepSeek R1's reasoning to synthesize coherently—often outperform a single general agent doing everything. The setup cost is higher, but for recurring workflows it pays off.
For cost: using Groq's free tier for the fast-iteration steps (search, retrieval, classification) and DeepSeek's cheap API for the final synthesis step is a legitimate architecture that experienced builders use. It's not just cost optimization—it plays to each system's strengths.
Honest Limits: What Free Groq and DeepSeek Agents Cannot Do
Free tiers have real ceilings, and ignoring them leads to frustration. Here's what you will actually run into.
Groq rate limits are stricter than they appear. The daily token allowance sounds generous until you realize that a single agentic loop—model call, tool call, model call again—can burn through 8,000–15,000 tokens. Ten agent runs and you're done for the day at the lower rate limit tiers. Groq has adjusted these limits several times; check the current limits in your console, don't rely on anything written more than a few months ago.
DeepSeek R1's context window is 128K tokens, but the reasoning overhead is real. R1 produces extensive internal chain-of-thought before answering. For complex problems, this can consume thousands of tokens of context before you see a single output token. In an agent loop where you're passing conversation history forward, this adds up quickly and can cause the model to lose context from early in the conversation on long sessions.
DeepSeek's API has had availability issues. The service experienced significant outages in late January 2025 when global demand spiked after R1's release. If you're building anything production-facing, single-backend dependency on DeepSeek is risky. Fallback to a secondary provider is not optional for serious use.
Data privacy is a genuine concern with DeepSeek specifically. DeepSeek is a Chinese company subject to Chinese law. Its privacy policy allows data to be stored on servers in China. For anything involving personal data, confidential business information, or anything subject to GDPR or HIPAA, you cannot use DeepSeek's hosted API without serious legal review. This is not a hypothetical risk—it's a documented policy. Running DeepSeek models locally via Ollama (which is free and possible on a reasonably powerful laptop for the smaller models) sidesteps this entirely.
Groq's model selection is limited. Groq doesn't run every model—it runs models that have been specifically optimized for the LPU architecture. As of early 2025, you can't run DeepSeek R1 on Groq's hardware (though Groq has announced plans). You can run Llama 3.1, Mixtral, and Gemma 2. If you need DeepSeek's reasoning specifically, you're on DeepSeek's own infrastructure or running locally, not on Groq.
Frequently Asked Questions
Can I run DeepSeek R1 on Groq's infrastructure?
Not as of early 2025. Groq's LPU requires models to be specifically compiled for its hardware, and DeepSeek R1 has not yet been made available on GroqCloud. Groq has publicly stated interest in expanding its model library, but there's no confirmed launch date for R1 on Groq. To use R1, you either access it through DeepSeek's own API, use it on Ollama locally, or access it through providers like Together AI or Fireworks AI.
Is the DeepSeek API actually free, or is there a catch?
DeepSeek's API is not free—it charges per token, but at prices dramatically lower than OpenAI's. As of early 2025, DeepSeek V3 costs around $0.14 per million input tokens and $0.28 per million output tokens. New accounts receive a small credit on signup. The 'free' framing you see online often refers to the chat.deepseek.com interface, which is free for personal use, but API access for building agents is paid (though very cheap).
What's the best free AI agent for coding tasks specifically?
For coding, DeepSeek V3 accessed through the chat interface or cheap API is hard to beat—it scores near the top of HumanEval benchmarks and its code generation is unusually clean. Pair it with a code execution tool via Phidata or LangChain for a full coding agent. If you need speed over raw coding quality, Groq running Llama 3.1 70B is genuinely fast and capable for most coding tasks short of complex algorithmic problems.
How does Groq compare to running models locally with Ollama?
Groq is faster for most consumer hardware setups. On a machine with an RTX 4090, you might get 80–120 tokens per second on a 7B model locally—Groq delivers 500–800 tokens per second on 70B models. The tradeoff is data privacy and internet dependency: Ollama runs entirely on your machine with no data leaving it, which matters for sensitive tasks. For raw speed and convenience, Groq wins. For privacy and, Ollama wins.
Can I build a multi-step agent that uses both Groq and DeepSeek in the same workflow?
Yes, and it's a genuinely useful architecture. Groq handles fast, repetitive steps like search result parsing, classification, or retrieval where speed matters. DeepSeek R1 or V3 handles synthesis, complex reasoning, or final generation where model quality matters more than latency. LangChain and Phidata both support multiple LLM backends in a single workflow—you specify different models for different steps in your agent chain.
Are there any free hosted AI agents using these models that require no coding?
Wordware.ai offers a no-code agent builder with DeepSeek model support on a free tier with usage limits. AgentGPT allows browser-based agent creation with your own Groq API key, which requires no coding beyond pasting a key. DeepSeek Chat's built-in 'DeepThink' mode gives you reasoning-mode responses without any setup. For anything more sophisticated—custom tools, memory, real integrations—some coding becomes unavoidable.
Is it safe to use DeepSeek for business or professional tasks?
For tasks involving confidential business data, personal information, or data subject to regulations like GDPR or HIPAA, using DeepSeek's hosted API carries real risk. DeepSeek's privacy policy permits storage of user data on servers in China under Chinese jurisdiction. The safe alternative for privacy-sensitive work is running DeepSeek's open-weight models locally using Ollama or a private cloud deployment, which keeps data entirely under your control.
What Groq model should I use for an AI agent—Llama 3.1 70B or Mixtral 8x7B?
Llama 3.1 70B for most agent tasks. It handles instruction-following, tool-use formatting, and complex reasoning better than Mixtral 8x7B in Groq's current lineup. Mixtral 8x7B is faster and cheaper (useful if you're hitting rate limits hard), and it performs well on structured data tasks, but for general-purpose agentic behavior—where the model needs to reliably decide when to call a tool and format the call correctly—Llama 3.1 70B is more consistent.