OpenSSA: Small Specialist Agents for Problem-Solving¶
-OpenSSA is an agentic AI framework for solving complex problems in real-world industry domains,
-overcoming the limitations of LLMs and RAG in such settings.
Level-2 Intelligence with Planning, Reasoning, domain-specific Knowledge and diverse Informational Resources¶
-OpenSSA agents, built with powerful Hierarchical Task Planning (HTP) and Observe-Orient-Decide-Act Reasoning (OODAR),
-go far beyond the Level-1 pattern-matching intelligence performed by LLMs and RAG and achieve superior outcomes
-in complex multi-faceted, multi-step tasks. See our comparative study.
OpenSSA agents can also be armed with domain-specific Knowledge, connected to diverse Informational Resources
-(files, databases, web sources, etc.), and/or be guided by specialized industry experts
-to maximize the accuracy and comprehensiveness in their planning, reasoning and deliberative/iterative problem-solving.
Open and Extensible Architecture¶
-Committed to promoting and supporting open development in generative AI,
-OpenSSA would strive to integrate with a diverse array of LLM backends, especially open-source LLMs.
-If you would like certain LLMs to be supported, please suggest through a GitHub issue, or, even better, submit your PRs.
Additionally, OpenSSA’s key Planning, Reasoning, Knowledge and Resource interfaces
-are designed with customizability and extensibility as first-class concerns,
-in order to enable developers to effectively solve problems in their specific industries and specialized domains.
Small and Resource-Efficient Agents for Practical Real-World Deployment¶
-Specialized, Level-2 intelligence allows OpenSSA agents to work well in many applications
-using significantly smaller component models, thereby greatly economizing computing resources.
OpenSSA: Neurosymbolic Agentic AI for Industrial Problem-Solving¶
+Why OpenSSA? +OpenSSA is an open-source neurosymbolic agentic AI framework +designed to solve complex, high-stakes problems in industries like semiconductor, manufacturing and finance, +where consistency, accuracy and deterministic outcomes are essential.
+At the core of OpenSSA is the Domain-Aware Neurosymbolic Agent (DANA) architecture, +advancing AI from basic pattern-matching and information retrieval to true problem-solving. +It overcomes the limitations of traditional LLMs and RAG in high-precision, multi-step problem-solving +by combining Hierarchical Task Plans (HTPs) to structure complex programs and the Observe-Orient-Decide-Act Reasoning (OODAR) paradigm to execute such programs. +By integrating domain-specific knowledge with neural and symbolic planning and reasoning, +OpenSSA consistently delivers accurate solutions for complex industrial challenges.
+Key Benefits of OpenSSA¶
+-
+
Consistent Results: Delivers repeatable, high-precision outcomes for complex tasks.
+Advanced Problem-Solving: Combines HTPs and OODAR for multi-step planning and reasoning.
+Scalable Expertise: Leverages domain knowledge to scale AI without heavy data requirements.
+Resource Efficiency: Uses smaller, resource-efficient models, minimizing computational costs.
+Extensible and Developer-Friendly: Supports diverse LLM backends and is fully customizable for industry-specific needs.
+
Getting Started¶
-Install by pip install openssa (on Python 3.12 only).
-
-
for bleeding-edge latest capabilities:
pip install https://github.com/aitomatic/openssa/archive/main.zip
+Install with
pip install openssa+(supports Python 3.12 and 3.13)
+For the latest capabilities: +
pip install https://github.com/aitomatic/openssa/archive/main.zip.
+Explore the
examples/directory and developer guides and tutorials on our documentation site.
Explore the examples/ directory and developer guides and tutorials on our documentation site.
API Documentation¶
@@ -131,7 +128,7 @@ContributingCommunity Forum
Submit pull requests for bug fixes, enhancements, or new features
For more information, see our Contribution Guide.
+For detailed guidelines, refer to our Contribution Guide.