#agentic-ai

【Star us! ⭐】 A declarative language that compiles to professional SVG floor plans — like Typst/LaTeX, but for architecture. Built for AI agents: deterministic output, JSON diagnostics that carry their own fix, and a describe/lint channel that verifies a plan against its brief without rendering an image. Zero-dependency TypeScript.

Delegation without abdication. — an Object Capability Security tool suite for Agentic Harnesses

Autopus-ADK is of the agents, by the agents. for the agents. Multi-model orchestration (consensus/pipeline/debate/fastest). Architecture-as-Code, Lore decision tracking, SPEC/EARS engine.

Ollama Agent is a powerful command-line tool (CLI and REPL) that allows you to interact with local AI models.

Detent is board-driven agentic work orchestration, shipped as a single Go binary — it watches a GitHub Projects board and runs each ready issue through an isolated Git worktree, a coding agent, your validation gate, and a serialized merge train, live on web and terminal dashboards.

Use sidecar next to CLI agents for diffs, file trees, conversation history, and task management with td

The design layer for agentic AI — design context, interface checks, and verification for coding agents.

Creating realistic RL environments requires collaboration between researchers, engineers, and domain experts across many dimensions: artifacts, environments tools, dynamism of the environment, reproducibility, and more. There is no open source framework for building these environments effectively. Until now.

Go SDK for Dakera AI agent memory — self-hosted, 88.2% LoCoMo. Vectors, hybrid search, knowledge graphs, sessions.

An orchestration runtime for multi-agent AI systems. Declare agents, tools, and policies as YAML; Orloj schedules, executes, routes, and governs them for production-grade operation.

Standardizing identity and connection orchestration for autonomous agents. A provider-agnostic, zero-trust framework for managing API integrations, OAuth2, and OIDC for in multi-agentic networks

Tablassert turns biomedical spreadsheets (Excel, CSV, TSV) into knowledge graphs ready for NCATS Translator. Declare how your columns map to subject–predicate–object statements in YAML; Tablassert resolves free text to standard CURIEs, attaches provenance and statistical annotations, and emits KGX-compliant nodes and edges.

An AI coding agent that reads your code, edits files, runs commands, and reasons through bugs — across a terminal REPL, a full-screen TUI, and a browser UI, while you keep your hand on every permission.

Durable execution framework for AI agents in Go. Keeps agent state, tool calls, and execution loops resilient across process crashes.

Terminal coding agent for local LLMs (llama.cpp, Ollama, vLLM) and any OpenAI-compatible API. OS-sandboxed autonomy, MCP, sessions. Go.

Open-source security-first runtime for AI agents that learn and act across sessions.

Kelos - The Kubernetes-native framework for orchestrating autonomous AI coding agents.

AI agent security harness, adversarial testing: 623 executable tests in the v4.21.3 release, across MCP, A2A, x402/L402, decision governance, benchmark integrity, human-in-the-loop, skill supply chain. Four target shapes: a verdict must be able to be wrong AND to be right. OWASP Agentic v1.1 T1-T17, AIUC-1 19/20 (2026-Q1/Q2 set), NIST AI 800-2.

The full-stack TypeScript framework to build, test, and deploy production-ready MCP servers and AI-native apps.

The AI agent that interacts with you in the real world.

Automated parallel code reviewer using AI Coding Agent CLIs

Your personal AI assistant - self-hosted, containerized, and intentionally opinionated.

an MCP server that interacts with SAP via the Desktop and/or Web GUI

Evidence-backed proof of done for Claude Code, Codex, Cursor, and other AI coding agents. Run real checks, bind results to current Git state, and know what is ready for human review.

CLI for managing AI agent squads. Status, memory, goals, feedback, and dashboard for your autonomous agents.

Open-source AI agent that reasons by blending many AI models, does real work on its own, and keeps learning — safe, self-hostable, and fully yours.

Open-source compiler for AI agents. Write one crewhaus.yaml; compile it to a CLI, a Slack bot, and an eval harness from the same spec. Apache-2.0.

pytest for AI agents - Autonomous red-teaming, behavioral monitoring & security testing for LLM agents

A Model Context Protocol (MCP) server implementation that connects Large Language Models (LLMs) to GIS operations using GIS libraries, enabling AI assistants to perform geospatial operations and transformations.