ACACE (Adaptive Context-Aware Content Engine) is an open-source Python library designed to optimize token efficiency and maintain contextual coherence in AI-generated content. It combines semantic compression, persistent multi-session context, and standardized integration patterns to support complex, long-form, and multi-agent LLM workflows.
- ✅ Semantic Compression: Reduce token usage by 20–40% through intelligent prompt summarization.
- 🧠 Context Awareness: Maintain state across multiple sessions (e.g., writing chapters or technical manuals).
- 🔌 Plug-and-Play Design: Compatible with OpenAI, Claude, LLaMA, and any API-compatible LLM.
- 🛠 Modular Architecture: Integrates seamlessly with other ACACE components.
- 📊 Built for Performance: Designed to cut token costs and preserve meaning at scale.
pip install acace✨ Features
🧠 Semantic Compression Engine: Reduces token usage by intelligently weighting key terms while filtering out redundant ones. 🪢 Context-Aware Generation: Persists and reuses session-level metadata across chapters, documents, or user journeys. 🧩 LLM-Agnostic Adapter: Works with OpenAI, Anthropic, HuggingFace models, or custom LLMs using a standardized API. 📊 Integrated Metrics & Logging: Monitor token savings, coherence scores, and generation performance across pipelines. 🧪 Plugin-Compatible Architecture: Easily integrates with preprocessing, compression, semantic, or UI modules from the ACACE family.
📦 Usage Python
from acace import AcaceEngine
engine = AcaceEngine()
compressed_prompt = engine.compress_prompt("Input content here...")
output = engine.generate_with_context(compressed_prompt)
print(output)📚 Documentation
All modules and architecture diagrams are available in the docs/ folder. The full proposal can also be referenced here for research and grant purposes.
🔌 Integrations
This core package interacts with the following modules (separate repos):
acace_utils, acace_logger, acace_validation (core functions) acace_tokenizer, acace_compression_engine, acace_context_storage acace_llm_adapter, acace_semantic_analyzer, acace_vector_store acace_web_interface for real-time UI integration View the complete component list in the ACACE GitHub org.
📈 Use Cases
AI writing assistants (books, blogs, docs) Scientific summarization Research communication Policy report drafting Personal memory in chat agents Semantic compression before LLM input 🧠 Why ACACE?
Large language models are powerful — but wasteful. ACACE cuts inefficiency by up to 40% in tokens, ensuring outputs stay:
Meaningful Aligned Memory-aware ✅ Roadmap
Cloud-native context persistence via S3 or Redis ACACE Studio (GUI + Playground) LLM benchmarking mode (compare GPT vs Claude vs Mistral) Dataset optimizers and training mode for token prioritization 📄 License
This project is licensed under the MIT License.