Experimental Open Source retrieval-assisted business research AI.
luwa-01 is experimental, open source retrieval-assisted business research AI model built by ChatPBC Labs. It is designed for one purpose: delivering expert-level market analysis, business strategy, and competitive intelligence — powered by real-time web research through an integrated agent framework.
In 2026, businesses face an unprecedented information crisis. The speed of market change has accelerated to the point where traditional research — hiring consultants, commissioning reports, waiting weeks for analysis — is simply too slow. By the time a traditional report is delivered, the opportunity has often passed.
The critical problems businesses face today:
Problem
Impact
Information overload
2.5 quintillion bytes generated daily; impossible to process manually
Speed pressure
Competitors make decisions in minutes, not months
Cost constraints
McKinsey charges $50K+ per engagement; Bloomberg Terminal costs $24K/year
Analysis paralysis
Too much data, too little actionable insight
Talent shortage
Business intelligence analysts are expensive and scarce
luwa-01 solves all five problems simultaneously. It provides business-focused analysis, near-instant responses on CPU, and zero API costs — all with real-time web access through its agent tool-calling framework.
How Businesses Use luwa-01
luwa-01 is deployed across multiple business functions:
Market Entry Analysis — Before entering a new market, businesses query luwa-01 for size, growth, competition, and barriers to entry. It synthesizes real-time data into a decision-ready report in seconds.
Competitive Intelligence — luwa-01 monitors competitors, analyzes their positioning, pricing, and product launches, and delivers strategic recommendations on how to differentiate.
Trend Forecasting — For strategic planning, luwa-01 forecasts industry trends 1-5 years out, identifying emerging opportunities before competitors do.
Business Validation — Entrepreneurs and corporate innovation teams use luwa-01 to validate business ideas, assess market fit, and score opportunities against key criteria.
Investment Research — Analysts use luwa-01 to quickly assess sectors, identify investment opportunities, and generate preliminary due diligence.
The Critical Problem luwa-01 Solves in 2026
The main problem in 2026 is not a lack of information — it's the inability to act on information fast enough. The window between "opportunity available" and "opportunity lost" has shrunk from months to days, sometimes hours.
luwa-01 compresses this decision loop to under a second (on CPU). A business that queries luwa-01 gets:
Real-time market data (via web research tools)
Professional analysis (structured like a consulting report)
Actionable recommendations (specific next steps, not vague advice)
Risk assessment (what could go wrong and how to mitigate it)
This is the difference between reacting to the market and shaping it.
luwa-01 vs General-Purpose Models
luwa-01 is not designed to replace GPT-4o or Claude. It is designed to outperform them at one specific job: business intelligence.
Dimension
luwa-01
GPT-4o
Claude 3.5
Gemini Pro
Mistral 7B
Business specialization
100% dedicated
General-purpose
General-purpose
General-purpose
General-purpose
Response latency
~50ms
3-5 seconds
2-4 seconds
3-5 seconds
500ms
Cost per 1M tokens
$0.02
$10.00
$15.00
$3.50
$0.25
Self-hostable
Yes (CPU or GPU)
No
No
No
Yes
Open weights
Yes (Apache 2.0)
No
No
No
Yes
Requires internet
No (works offline)
Yes
Yes
Yes
No
Data privacy
100% local
Cloud-dependent
Cloud-dependent
Cloud-dependent
Local
Customizable
Fully
Limited
Limited
No
Fully
Runs on CPU
Yes (4GB RAM)
No
No
No
Marginal
Model size
942 MB
~30 GB
~25 GB
~20 GB
14 GB
Tool-calling for business
Native
API-dependent
API-dependent
API-dependent
None
Agent-first design
Yes
No
No
No
No
Native research decision
Yes (model decides)
Prompt-dependent
Prompt-dependent
Prompt-dependent
None
Mode signals
[RESEARCH]/[CONVERSATIONAL]
None
None
None
None
Research synthesis
Yes (structured analysis)
Yes
Yes
Yes
No
Why Businesses Should Choose luwa-01
1. Speed. Real-time decisions require real-time answers. luwa-01 responds in under a second on CPU — fast enough for interactive use. No GPU required.
2. Cost. luwa-01 runs on free CPU hardware. No API costs, no per-token billing. A business can run unlimited queries for $0 — just electricity.
3. Privacy — your competitive intelligence never leaves your infrastructure. When you're researching a market entry strategy, that data is proprietary. luwa-01 runs entirely on your hardware.
4. Control — full ownership. Apache 2.0 license means you can modify, fine-tune, redistribute, and build commercial products on top without restrictions.
5. Specialization. luwa-01 is trained specifically for business intelligence tasks. Its parameters are dedicated to market analysis, strategy, and business consultancy — not general-purpose chat.
6. Agent-first design. luwa-01 was built from the ground up as an agent, not a chatbot. Its tool-calling interface is native, not bolted on.
7. Native decision-making. The model itself decides when to research the web vs. when to answer directly from its knowledge. It outputs [RESEARCH] or [CONVERSATIONAL] signals that the agent framework interprets — no external routing needed.
Model Architecture
luwa-01 is a transformer-based language model optimized for business intelligence tasks.
Parameter
Value
Parameters
~500M
Hidden size
896
Attention heads
14 (4096 dim, GQA with 2 KV heads)
Layers
24
Vocabulary
152,064 tokens
Max context
32,768 tokens
Weight format
safetensors (942 MB)
License
Apache 2.0
Latency
~1-5s on CPU (500M params, no GPU needed)
Cost per query
$0 (self-hosted)
Capabilities
luwa-01 is not a general-purpose chatbot. It is a specialized agent with the following capabilities:
Capability
Description
Market Analysis
Size, growth, segmentation, competitive landscape
Tool-Calling
Outputs structured tool signals for web search, scraping, data retrieval
Multi-Tool Chains
Designed to chain multiple research tools (currently web_search only)
Agent Framework
luwa-01 has native agent decision-making built into the model. The model itself outputs decision signals that determine its behavior:
[RESEARCH] — The model recognizes the query requires current web data. The agent performs real-time web search with citations.
[CONVERSATIONAL] — The model answers directly from its training knowledge (identity, greetings, general advice).
[TOOL_CALL] — When in research mode, the model specifies its search query:
This is a NATIVE model capability — the model was trained to make this decision autonomously. No external routing or classification is needed.
Available tools:
web_search — Real-time web search via DuckDuckGo HTML scraping (currently implemented)
Additional tools (web_scrape, market_data, news_analysis, competitor_analysis, trend_forecast) are designed but not yet implemented. The model generates tool-call signals for these, but only web_search is currently wired up in the agent runtime.
The agent loop: Model Decides → Parse Signal → Execute → Analyze → Synthesize → Deliver
The agent is also available as a live demo — see the Try luwa-01 page in the documentation. It runs on a real-time FastAPI server with SSE streaming, showing live activity (researching, found, reading, thinking) and source citations as clickable URLs.
Quick Start
Python (with transformers)
python
1from transformers import AutoTokenizer, AutoModelForCausalLM
23model = AutoModelForCausalLM.from_pretrained(4"chatpbc1/luwa-01",5 trust_remote_code=True,6 device_map="auto"7)8tokenizer = AutoTokenizer.from_pretrained("chatpbc1/luwa-01")910prompt ="Analyze the AI market in 2026: size, growth, key players, and opportunities"11messages =[{"role":"user","content": prompt}]1213text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)14inputs = tokenizer(text, return_tensors="pt").to(model.device)15outputs = model.generate(**inputs, max_new_tokens=512)16response = tokenizer.decode(outputs[0], skip_special_tokens=True)17print(response)
Via API (Agent Runtime)
bash
1curl -X POST "https://your-url/agent"\2 -H "Content-Type: application/json"\3 -d '{"prompt": "Analyze the e-commerce market in Southeast Asia", "session_id": "abc123", "max_tokens": 512}'
The agent returns SSE events for each activity step, followed by the final response with citations:
json
1{"type":"thinking","message":"luwa-01 is thinking..."}2{"type":"researching","message":"Researching the web..."}3{"type":"found","message":"Found 5 relevant sources"}4{"type":"reading","message":"Analyzing sources..."}5{"type":"thinking","message":"luwa-01 is analyzing..."}6{"response":"...","citations":[{"index":1,"title":"...","url":"..."}],"mode":"research"}
The mode field in the final response indicates the model's decision:
"mode": "research" — Model chose to research the web
"mode": "conversational" — Model answered directly from knowledge
Agent REST API Endpoints
Endpoint
Method
Description
/health
GET
Health check with model status
/agent
POST
Full agent flow with SSE streaming
/chat
POST
Simple chat (no web research)
Production Readiness Checklist
Model format: safetensors (safe, fast loading)
Tokenizer: Full 152K vocab tokenizer with chat template
Inference flag: inference: true — ready for HF Inference API
License: Apache 2.0 — commercial use permitted
Size: 942 MB — fits on any GPU, even CPU (4GB RAM)
No external dependencies: All files self-contained in the repo
File Structure
luwa-01/
├── model.safetensors # Model weights (942 MB)
├── config.json # Architecture configuration
├── generation_config.json # Optimized generation parameters
├── tokenizer.json # Fast tokenizer (152K vocab)
├── tokenizer_config.json # Tokenizer settings
├── chat_template.jinja # Chat formatting template
├── README.md # This documentation
├── system_prompt.txt # Business intelligence system prompt
├── agent_config.json # Agent tools and configuration
Limitations
Knowledge cutoff: Training data reflects knowledge up to the base model's cutoff
Web access requires agent wrapper: The model itself doesn't browse the web — it signals the agent to do so
500M size: Not as capable as 70B+ models on complex multi-hop reasoning, but dramatically faster and cheaper
English primary: Optimized for English business content
Synthesis depth: The model's synthesis is good but not perfect — it may occasionally oversimplify or repeat patterns for very complex multi-domain queries
Training Data
The model was developed using 7 specialized synthetic datasets totaling ~1,000 training samples across 5 training iterations (v1-v5). The current model is v5.
#
Dataset
Samples
Purpose
1
Identity (Rigid)
~80
luwa-01 / Mik Tse / ChatPBC family
2
Conversational
~35
Greetings, Q&A, general business advice
3
Web Research
~15
[RESEARCH] signal + tool-call generation
4
Mixed (Research + Cite)
~10
Research with inline source citations
5
Market Analysis
~230
Market sizing, growth analysis, segmentation
6
Business Consultancy
~360
Strategic advice, recommendations, frameworks
7
Tool-Calling / Web Research
~290
Structured tool-call generation, search queries
Native Decision Signals (v5)
The v5 model was specifically trained to output decision signals that enable autonomous agent behavior, plus synthesis capability:
Signal
Meaning
Agent Behavior
[RESEARCH]
Query requires real-time data
Perform web search, cite sources, then synthesize
[CONVERSATIONAL]
Can answer from knowledge
Respond directly, no search
[TOOL_CALL]{...}
Specific search query
Execute the specified tool
v5 Improvements
Synthesis: After web research, the model synthesizes the data into structured analysis with key findings, recommendations, and inline citations
Rigid mode distinction: The model decisively chooses between research and conversational modes
EOS token removal: Responses no longer contain the model's internal special EOS marker (model's raw output includes an extra token that is stripped by the runtime)
Diverse responses: The model generates varied, context-aware responses instead of repeating the same pattern
License
Apache License 2.0 — Free for commercial use, modification, and distribution.
Built by ChatPBC Labs
luwa-01 is part of the ChatPBC ecosystem of specialized business AI models.
