Aspera - A new language for building transparent, adaptive AI
byβ’
Tired of black-box AI? ASPERA merges symbolic logic with LLMs for agents that explain every decision. Declarative DSL, type-safe, adaptive learning. Used in healthcare, fraud detection, customer support. Open-source Python framework.
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π
π **Hey Product Hunt! Iβm Christian, creator of ASPERA**
I built **ASPERA** to solve a big AI trade-off:
β’ **LLMs are powerful but opaque** β impossible to know *why* a decision was made
β’ **Symbolic systems are transparent but rigid** β zero adaptability
π― **ASPERA bridges both worlds natively.**
---
### π‘ What makes it unique
* **Declarative syntax** β Write cognitive rules in a clean DSL, no black boxes
* **Native explainability** β Every decision produces a full reasoning trace
* **Hybrid reasoning** β Symbolic logic for common cases + LLMs for complex context
* **Type-safe with LSP** β Autocomplete, linting, and βGo to Definitionβ as you code
* **Plugin ecosystem** β OpenAI, Gemini, Groq, Weaviate, OpenTelemetry
* **Adaptive learning** β Agents improve continuously through feedback loops
---
### π Already in production for:
* **Healthcare triage** β Transparent patient prioritization
* **Fraud detection** β Clear explanations for red flags
* **Customer support automation** β Smart ticket classification and routing
---
π **Academic validation:**
Published on Zenodo ([DOI: 10.5281/zenodo.17313992](https://doi.org/10.5281/zenodo.1...))
---
### β‘ Quick start (5 min)
```bash
pip install aspera
```
Create your first agent:
```python
# my_agent.aspera
agent FraudDetector {
goal: "Identify suspicious transactions"
rule HighRisk {
if amount > 10000 and country in high_risk_list
-> flag_for_review
reasoning: "Large amount from risky location"
}
llm_fallback: "Analyze transaction context"
model: "gemini-pro"
}
```
Run it:
```bash
python -m aspera run my_agent.aspera
```
---
### π§ Why I built ASPERA
Working on AI for **healthcare and finance**, I realized **explainability isnβt optional**. Decision-makers need to understand *why* an AI chose a particular action β especially in critical domains.
---
### πΊοΈ Roadmap 2025
* Visual Agent Builder (drag-and-drop DSL)
* Cloud-hosted runtime (zero-config deployment)
* Pre-built agent templates for industries
* Community plugin marketplace
---
π Happy to answer questions β what would *you* build with ASPERA?
π **Links**
* GitHub: [github.com/rthgit/Aspera](https://github.com/rthgit/Aspera)
* PyPI: [pypi.org/project/aspera](https://pypi.org/project/aspera/)
* Zenodo Paper: [doi.org/10.5281/zenodo.17313992](https://doi.org/10.5281/zenodo.1...)
* Docs: [github.com/rthgit/Aspera/docs](https://github.com/rthgit/Aspera...)
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## π Future: ASTREA Integration (Coming Soon)
Enhanced Performance with ASTREA API:
- Current: 643 searches/sec (built-in fallback)
- Future: 1000+ searches/sec (ASTREA API mode)
- Advanced: Multimodal embeddings (text, images, audio)
- Enterprise: Production-grade semantic engine
ASTREA integration will be available as an optional premium feature for enhanced performance and advanced capabilities.
---
## π NEW: Semantic Memory
ASPERA agents now have memory!
Powered by our built-in semantic engine, your agents can:
- β Learn from millions of past decisions
- β Find similar cases instantly (643 searches/sec!)
- β Make smarter decisions based on what worked before
- β Scale to millions of examples without performance loss
### Real Example: Healthcare Agent
```python
agent = AsperaAgent(memory="semantic")
# Learn from 10,000 past diagnoses
agent.learn_from_cases(medical_database)
# New patient with "fever 38.7Β°C, dry cough"
similar_cases = agent.find_similar(symptoms)
# β Finds "fever 39Β°C, dry cough" in 3ms
# β Similarity: 92%
# β Past diagnosis: COVID-19 test
# β Success rate: 95%
# Agent recommends same treatment that worked!
```
### Perfect For
- Healthcare: Diagnosis from similar symptoms
- Finance: Trading signals from market patterns
- Legal: Precedent search in case law
- Support: Resolution from similar tickets
### Performance