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Scope
Repositories that bridge MeTTa/Hyperon with neural networks, large language models, and embedding-based retrieval systems. This family covers integration infrastructure — the libraries and services that connect symbolic reasoning to neural capabilities. Pure ML research repos without MeTTa/Hyperon coupling are excluded.
Active Repositories
Core Integration Library
RepoLanguageUpstreamMaturityPurpose
metta-mottoPython / MeTTazarqa-aiOperationalPrimary LLM agent library for MeTTa: OpenAI, Anthropic, OpenRouter integration with stateless wrappers, stateful agents, RAG, SPARQL, functional calling. PyPI: v0.0.12.
petta_lib_localvlmSWI-Prolog / MeTTapatham9OperationalLightweight Prolog library (~28 lines) for calling local VLM HTTP endpoints (chat completions + embeddings) from PeTTa.
RAG and Semantic Search
RepoLanguageUpstreamMaturityPurpose
MeTTa-AI-AssistantPython (FastAPI) + ReactiCog-Labs-DevOperationalRAG-based MeTTa coding assistant with document/repo ingestion, MeTTa-aware chunking, and chat UI. Docker deployment.
rag-apiPython (Django)iCog-Labs-DevExperimentalDjango wrapper around GraphRAG for file upload and global/local retrieval queries. Minimal.
semantic-search-enginePython (Flask)iCog-Labs-DevOperationalSemantic search API over Slack/Mattermost data using ChromaDB embeddings + Together AI. Has pytest suite.
semantic-search-pluginReact / TypeScriptiCog-Labs-DevOperationalMattermost plugin frontend for the semantic-search-engine backend.
mm-semantic-searchGo + ReactiCog-Labs-DevOperationalSelf-contained Mattermost plugin with Go server + React webapp for semantic search via ChromaDB. GitHub Actions CI.
MCP and Tool Integration
RepoLanguageUpstreamMaturityPurpose
mcp-xpPython (FastAPI)iCog-Labs-DevOperationalChatbot with Galaxy bioinformatics MCP integration. Multi-LLM support (Azure OpenAI, Groq, Gemini). Docker deployment.
How They Fit Together
metta-motto is the central integration layer — the canonical way to call LLMs from MeTTa code. Most other repos in this family either build on top of it or address specific deployment surfaces:
petta_lib_localvlm complements metta-motto for Prolog-side VLM access (local models instead of cloud APIs).
MeTTa-AI-Assistant is a full application using RAG + MeTTa-aware chunking to create a MeTTa coding assistant — the most complete demonstration of neural-symbolic integration in the ecosystem.
The semantic search cluster (semantic-search-engine + plugin + mm-semantic-search) provides embedding-based retrieval for Mattermost — not MeTTa-specific, but developed by the same iCog Labs team as AI integration infrastructure.
mcp-xp demonstrates Model Context Protocol integration with external tool services (Galaxy bioinformatics).
Quick Start
# metta-motto (primary LLM integration)
pip install hyperon-metta-motto
# or from source:
cd metta-motto && pip install -e . && cd tests && pytest
# MeTTa-AI-Assistant (RAG coding assistant, Docker)
cd MeTTa-AI-Assistant/Backend && docker compose build && docker compose up
# semantic-search-engine (embedding search API)
cd semantic-search-engine && pip install -r requirements.txt && python src/server.py
# mcp-xp (Galaxy MCP chatbot)
cd mcp-xp && pip install -r requirements.txt && uvicorn app.main:app --reload --port 8000
Cross-References
metta-nl-corpus (NL-to-MeTTa annotation pipeline) and nl2pln_demo (NL-to-PLN conversion) are covered in {{Hyperon AI Algorithms+Semantic Parsing+Semantic Parsing Full|view:link;title:Semantic Parsing Full}} and {{Implementation Families+Reasoning and Search|view:link;title:Reasoning and Search}}.
metta-motto2 (patham9/metta-motto) is a fork slightly behind the primary zarqa-ai repo. Not listed separately.
Excluded from This Family
ai-dsl: Research archive for AI service composition DSL — legacy, no active development.
FabricPC, PC-Transformers, NGC-PC-Transformers: Pure ML research libraries with no MeTTa/Hyperon coupling.
quantum-neural-architecture: Hybrid quantum-classical ML — no MeTTa integration.
Current State vs. Whitepaper
Outside integration (whitepaper §7.1): Operational via metta-motto. LLMs wrapped as MeTTa-queryable Spaces.
Inside integration / QuantiMORK (whitepaper §7.4): Proposed. No implementation encodes neural structures natively in MORK.
Symbolic Transformer Heads (whitepaper §7.2): Proposed. No implementation augments Transformers with mined AtomSpace templates.
Gaps and Consolidation Opportunities
Three separate semantic search implementations: semantic-search-engine (Python), semantic-search-plugin (React frontend), mm-semantic-search (Go + React, self-contained). The Go version may supersede the Python+React pair.
No MeTTa-native embedding Space: The whitepaper describes Neural Spaces wrapping DNNs as queryable AtomSpaces. No implementation exists.
metta-motto is the single point of LLM access: All MeTTa-LLM integration flows through metta-motto. Its upstream (zarqa-ai) is outside the core Hyperon GitHub organizations.
Repositories that bridge MeTTa/Hyperon with neural networks, large language models, and embedding-based retrieval systems. This family covers integration infrastructure — the libraries and services that connect symbolic reasoning to neural capabilities. Pure ML research repos without MeTTa/Hyperon coupling are excluded.
Active Repositories
Core Integration Library
RepoLanguageUpstreamMaturityPurpose
metta-mottoPython / MeTTazarqa-aiOperationalPrimary LLM agent library for MeTTa: OpenAI, Anthropic, OpenRouter integration with stateless wrappers, stateful agents, RAG, SPARQL, functional calling. PyPI: v0.0.12.
petta_lib_localvlmSWI-Prolog / MeTTapatham9OperationalLightweight Prolog library (~28 lines) for calling local VLM HTTP endpoints (chat completions + embeddings) from PeTTa.
RAG and Semantic Search
RepoLanguageUpstreamMaturityPurpose
MeTTa-AI-AssistantPython (FastAPI) + ReactiCog-Labs-DevOperationalRAG-based MeTTa coding assistant with document/repo ingestion, MeTTa-aware chunking, and chat UI. Docker deployment.
rag-apiPython (Django)iCog-Labs-DevExperimentalDjango wrapper around GraphRAG for file upload and global/local retrieval queries. Minimal.
semantic-search-enginePython (Flask)iCog-Labs-DevOperationalSemantic search API over Slack/Mattermost data using ChromaDB embeddings + Together AI. Has pytest suite.
semantic-search-pluginReact / TypeScriptiCog-Labs-DevOperationalMattermost plugin frontend for the semantic-search-engine backend.
mm-semantic-searchGo + ReactiCog-Labs-DevOperationalSelf-contained Mattermost plugin with Go server + React webapp for semantic search via ChromaDB. GitHub Actions CI.
MCP and Tool Integration
RepoLanguageUpstreamMaturityPurpose
mcp-xpPython (FastAPI)iCog-Labs-DevOperationalChatbot with Galaxy bioinformatics MCP integration. Multi-LLM support (Azure OpenAI, Groq, Gemini). Docker deployment.
How They Fit Together
metta-motto is the central integration layer — the canonical way to call LLMs from MeTTa code. Most other repos in this family either build on top of it or address specific deployment surfaces:
petta_lib_localvlm complements metta-motto for Prolog-side VLM access (local models instead of cloud APIs).
MeTTa-AI-Assistant is a full application using RAG + MeTTa-aware chunking to create a MeTTa coding assistant — the most complete demonstration of neural-symbolic integration in the ecosystem.
The semantic search cluster (semantic-search-engine + plugin + mm-semantic-search) provides embedding-based retrieval for Mattermost — not MeTTa-specific, but developed by the same iCog Labs team as AI integration infrastructure.
mcp-xp demonstrates Model Context Protocol integration with external tool services (Galaxy bioinformatics).
Quick Start
# metta-motto (primary LLM integration)
pip install hyperon-metta-motto
# or from source:
cd metta-motto && pip install -e . && cd tests && pytest
# MeTTa-AI-Assistant (RAG coding assistant, Docker)
cd MeTTa-AI-Assistant/Backend && docker compose build && docker compose up
# semantic-search-engine (embedding search API)
cd semantic-search-engine && pip install -r requirements.txt && python src/server.py
# mcp-xp (Galaxy MCP chatbot)
cd mcp-xp && pip install -r requirements.txt && uvicorn app.main:app --reload --port 8000
Cross-References
metta-nl-corpus (NL-to-MeTTa annotation pipeline) and nl2pln_demo (NL-to-PLN conversion) are covered in {{Hyperon AI Algorithms+Semantic Parsing+Semantic Parsing Full|view:link;title:Semantic Parsing Full}} and {{Implementation Families+Reasoning and Search|view:link;title:Reasoning and Search}}.
metta-motto2 (patham9/metta-motto) is a fork slightly behind the primary zarqa-ai repo. Not listed separately.
Excluded from This Family
ai-dsl: Research archive for AI service composition DSL — legacy, no active development.
FabricPC, PC-Transformers, NGC-PC-Transformers: Pure ML research libraries with no MeTTa/Hyperon coupling.
quantum-neural-architecture: Hybrid quantum-classical ML — no MeTTa integration.
Current State vs. Whitepaper
Outside integration (whitepaper §7.1): Operational via metta-motto. LLMs wrapped as MeTTa-queryable Spaces.
Inside integration / QuantiMORK (whitepaper §7.4): Proposed. No implementation encodes neural structures natively in MORK.
Symbolic Transformer Heads (whitepaper §7.2): Proposed. No implementation augments Transformers with mined AtomSpace templates.
Gaps and Consolidation Opportunities
Three separate semantic search implementations: semantic-search-engine (Python), semantic-search-plugin (React frontend), mm-semantic-search (Go + React, self-contained). The Go version may supersede the Python+React pair.
No MeTTa-native embedding Space: The whitepaper describes Neural Spaces wrapping DNNs as queryable AtomSpaces. No implementation exists.
metta-motto is the single point of LLM access: All MeTTa-LLM integration flows through metta-motto. Its upstream (zarqa-ai) is outside the core Hyperon GitHub organizations.