Semantica

Context Ontology Accelerator is an open-source semantic context layer for AWS and AI agents. It combines knowledge graphs, formal ontologies, rule-based systems, and modern AI in a Scan → Model → Serve workflow: it connects data sources, discovers schemas, enriches metadata, and ingests documents; induces and manages ontologies, defines metrics, and builds a unified semantic graph; then serves context through SPARQL federation using a Virtual Knowledge Graph, knowledge-graph traversal, queries, and MCP tools. The repository includes source-ingestion services for databases and documents, ontology induction and reasoning with HermiT and ELK, an Ontop-based Virtual Knowledge Graph, a context manager for query orchestration, and a React frontend. Access is controlled through namespace isolation and role-based authorization, with namespace-scoped roles and platform-level roles. The implementation uses Python and TypeScript, AWS CDK, Smithy-generated API contracts, and an Nx-managed monorepo; the project is licensed under Apache License 2.0.

Semantic Kernel is an open-source SDK developed by Microsoft for building AI applications that combine large language models with conventional code, memory, and external connectors. It provides orchestration primitives, memory abstractions, tooling for prompts and skills, and SDKs for .NET and Python.

SemIf is an independent open-source tool, formerly called OpenJev, for making runtime-defined semantic decisions with open language models. It accepts unstructured state, runtime-defined questions or criteria, and typed options, then reads the model’s native option logits to return conditional probabilities rather than generating an answer sentence, JSON, or running a decoding or parsing loop. It supports direct, serial, and shared-state execution, prefilling common state for evaluation of multiple criteria; results include option scores, timing, the exact model revision, and a prompt hash. It supports local GPU and CPU workflows, Apple Silicon MPS and MLX, and llama.cpp backends, and provides a browser-based WebGPU demo, reproducible fixtures and benchmark runners, calibration methods, and quality evaluations. The project is an independent reproduction of the interface pattern of TypeSafe’s closed Jev service, not its model or training, and is not affiliated with Jev or TypeSafe. The code is released under the MIT License.

OpenScience is an open-source AI workbench for scientific research developed by Synthetic Sciences. It runs as a browser-based workspace where a single research agent takes a goal through literature review, hypothesis formation, code writing and execution, experiments, analysis, and a written report. The agent can load domain skills, delegate bounded exploratory or execution work, query scientific databases, and preserve sessions as observable research traces. The local server hosts the workspace UI, agent runtime, skill library, and tool layer. The agent plans with a research harness and calls shell, editor, LSP, MCP, scientific database, and skill tools; sessions, skills, artifacts, and provenance are stored on disk. The workbench includes a file tree, code editor, terminal, session history, and inline rendering for molecules, structures, genomes, and plots. Its bundled skills cover training, evaluation, datasets, molecular and clinical biology, cheminformatics, papers and LaTeX, figures, and scientific runtimes, while database connectors provide direct access to services including UniProt, PDB, Ensembl, ChEMBL, PubChem, arXiv, OpenAlex, and Semantic Scholar. It supports frontier and open-weight models through user-provided keys, eligible ChatGPT or Codex access, local models, and optional Ace-managed models. Models are routed per request, allowing providers to be switched without changing the workspace. Extensibility includes LSP integration, MCP servers, plugins, custom agents and commands, experimental Python environments and BioNeMo adapters, and a TypeScript SDK. It is installed with the @synsci/openscience npm package or launched through npx; platform binaries and desktop installers are distributed through GitHub Releases. A free Synthetic Sciences account links an installation and can provide credential synchronization, private research graphs, enhanced search, and optional credit-backed models, while BYOK and local-model usage remain separate from Ace.

Lemmalog is a Datalog engine for LLM-agent memory, distributed as a Rust crate with an MCP server, REPL, and agent skill. It treats extracted statements as provenance-tracked base facts, then uses runtime-parsed stratified Datalog to derive temporal projections, closures, contradiction candidates, relevance relationships, and aggregates. The engine supports seminaive incremental fixpoint evaluation, stratified negation with negative-cycle rejection, bi-temporal facts, confidence and provenance annotations, proof trees through why(), scoped retraction recomputation, demand-driven ask_deep queries using magic sets, and persistence of episodes, rules, and base facts while rebuilding derived relations on load. Its AgentMemory facade connects an extractor to deterministic ADD, UPDATE, NOOP, and escalation policies, and assembles budgeted contexts from distilled facts and their source episodes. The MCP server exposes the engine to agent harnesses such as Claude Code and Kimi CLI through tools for observing facts, querying, explaining proofs, retrieving context, saving state, installing rules, and running hypotheticals.