Dana: The World’s First Agentic OS

Build deterministic expert agent easily with Dana.

A complete Expert Agent Development Toolkit: Agentic out of the box. Grounded in domain expertise.

Why Dana?

Most frameworks make you choose:

• Too rigid → narrow, specialized agents.
• Too generic → LLM wrappers that fail in production.
• Too much glue → orchestration code everywhere.

Dana gives you the missing foundation:

• Deterministic → flexible on input, consistent on output — reliable results every run.
• Contextual → built-in memory and knowledge grounding let agents recall, adapt, and reason with domain expertise.
• Concurrent by default → non-blocking execution; agents run tasks in parallel without threads or async code.
• Composable workflows → chain simple steps into complex, reproducible processes that capture expert know-how.
• Local → runs on your laptop or secure environments, ensuring privacy, speed, and mission-critical deployment.
• Robust → fault-tolerant by design, agents recover gracefully from errors and edge cases.
• Adaptive → agents learn from feedback and evolving conditions, improving performance over time.

Install and Launch Dana

💡 Tip: Always activate your virtual environment before running or installing anything for Dana.

# Activate your virtual environment (recommended)
source venv/bin/activate  # On macOS/Linux
# or
venv\Scripts\activate     # On Windows

pip install dana
dana studio # Launch Dana Agent Studio
dana repl # Launch Dana Repl

• For detailed setup (Python versions, OS quirks, IDE integration), see Tech Setup.

What’s Included in v0.5

Agent Studio

Turn a problem statement into a draft expert agent with three parts — agent, resources, workflows. Studio generates a best-match workflow and lets you extend it with resources (documents, generated knowledge, web search) or edit workflows directly.

Agent-Native Programming Language

A Python-like .na language with a built-in runtime that provides agentic behaviors out of the box — concurrency, knowledge grounding, and deterministic execution — so you don’t have to wire these up yourself.

What this means for you: You can build and iterate on expert agents faster, with less setup and more confidence they’ll run reliably in production.

Full release notes → v0.5 Release.

First Expert Agent in 4 Steps

1. Define an Agent

   agent RiskAdvisor
   

2. Add Resources

   resource_financial_docs = get_resources("rag", sources=["10-K.pdf", "Q2.xlsx"])
   

3. Follow an Expert Workflow

   def analyze(...): return ...
   def score(...): return ...  
   def recommend(...): return ...
   
   def wf_risk_check(resources) = analyze | score | recommend
   
   result = RiskAdvisor.solve("Identify liquidity risks", resources=[resource_financial_docs], workflows=[wf_risk_check])
   
   print(result)
   

4. Run or Deploy

   dana run my_agent.na       # Run locally
   dana deploy my_agent.na    # Deploy as REST API
   

Learn More

• Core Concepts → Agents, Resources, Workflows, Studio.
• Reference → Language syntax and semantics.
• Primers → Deep dives into Dana language design.

Community

• 🐞 Issues
• 💬 Discuss on Discord

Enterprise support

• Contact Aitomatic Sales

License

Dana is released under the MIT License.
© 2025 Aitomatic, Inc.