Window2026-11-11 10:30 ~ 2026-11-12 10:30 (Beijing time), covering default-branch commits with `committer-date >= 2026-11-11T02:30:00Z`. This issue collects **273** entries, 5 of them featured, split as ① 119 / ② 49 / ③ 45 / ④ 60.
CoverageAI Coding / coding agents & the skills ecosystem · Medical / clinical research AI & agent skill libraries · Health management / nutrition & food AI · Model layer & agent infrastructure
De-duplicationChecked line by line against the 23 issues published from 10-16 to 11-11 (extracting the **cumulative 1548-repo history set** with `grep -o "https://github.com/[^/]*/[^/]*"`, lowercased throughout so case variants cannot slip through). All **273** entries this issue are first-time appearances - zero repeats; the 5 featured entries are also first-time entries. This round collected **668** unique `search/repositories` candidates (30 keyword queries with a `pushed:>2026-11-10` date filter) and separately pulled **39** repositories from GitHub trending's daily / weekly / monthly boards, each verified individually; **113** candidates matching history were dropped, leaving 555 non-historical candidates, each then verified against the default branch via the core endpoint `GET /repos/{o}/{r}/commits?since=&per_page=1` (reading the `Link` header's `rel="last"` page number as the exact commit count). **523** of those had real commits inside the window and **36** had zero commits in the window and were dropped; after manual curation and information-density filtering, **273** entries were kept. **Any repository whose `pushed_at` looked updated but which verified as outside the window is dropped - entries are never included on the strength of `pushed_at` alone, and being on the trending board is never treated as proof of today's activity.**
VerificationEvery "has activity today" entry is confirmed against GitHub commit records, and **exact commit counts always come from the core endpoint's `Link` header `rel="last"` page number** (with `per_page=1` the page number is the total). **Stars and licenses are taken from a live GitHub API snapshot** (as of 2026-11-12 10:30 Beijing time). **Licenses are read from the actual LICENSE file via the core endpoint `GET /repos/{o}/{r}/license`; the API's `spdx_id` is only a lead** - in this issue `use-junction/usejunction` reports `NOASSERTION` but the file text is a **proprietary license**, while `awslabs/hcls-agent-skills` reports `MIT-0` and its file text is the **Amazon copyright notice plus an MIT-0-style grant** (both consistent). A further **0** entries were individually confirmed via the core endpoint to have **no LICENSE file in the repository root**, marked "undeclared" in the tables - **"undeclared" does not mean unlicensed, but every one of them must be re-checked during selection**. Of the 273 entries this issue, MIT plus Apache-2.0 account for 160, and 164 once BSD / ISC / MPL / CC0 are included; AGPL-3.0 plus GPL / LGPL total 11 and are contagious copyleft - readable but not copyable; 0 are source-visible but not open source (ELv2 / BUSL-1.1 / PolyForm NC); 0 carry proprietary licenses; and 0 have no LICENSE file at all. Select in three tiers.
1. Today's picks (5)
1. awslabs/hcls-agent-skills: AWS turns 43 healthcare and life-science skills into portable standard parts, and proves with 410 pairwise prompts that they beat going without
URL: https://github.com/awslabs/hcls-agent-skills ｜ ★76 ｜ MIT-0 ｜ Open-source healthcare AI agent skills and life sciences agent skills. 42 skills for medical coding, risk adjustment, claims analytics, quality measures, genomics, imaging, and drug discovery across 13 HCLS domains.
What happened
- The most directly relevant entry for my business this issue: the most widely available, large-vendor-maintained library of medical / clinical Agent skills - and it ships with a credible evaluation report. The README lists 43 skills across 13-14 domains, split into two families:
reasoningskills that encode methodology and decision frameworks (ACMG variant classification, target trial emulation, HEDIS measure specification), andpipelineskills that encode runnable tool chains (GATK4 variant calling, RDKit cheminformatics, HL7v2 parsing). - It substantiates the value of the skills themselves with a 410-prompt pairwise evaluation (380 single-skill + 30 cross-skill), judged by Claude Opus 4.7: the skill-equipped version wins 70-86% of head-to-head comparisons depending on harness, with up to 85% on critical thinking and up to 87% on scientific accuracy. The skills follow the open Agent Skills standard and run on 20+ platforms (Kiro, Amazon Quick, Bedrock AgentCore, Strands SDK, Claude Code, OpenAI Codex, CrewAI, LangChain, and more).
- Both commits inside the window land squarely on evaluation credibility:
fix(eval): Extend fail-loudly to independent judge, fix stale docsextends the "a tie is a failure" principle from local review out to an independent judge model; the other merges theroda-open-dataskill (the ODAC open-data access standard). - The license is MIT-0 - the repository's LICENSE file is the Amazon copyright notice plus an MIT-0-style grant (the API reports
MIT-0as well), placing it in the most permissive tier.
Why it matters
- This is the industrial-grade sample of "packaging expert judgment into a portable asset" - exactly the template my expert-package workflow is missing. My quality check today is still "have the model grade itself", while hcls-agent-skills does something different: skills are written as files carrying explicit decision thresholds (ACMG grading, HEDIS definitions), then measured with pairwise comparison plus an independent judge. Its
reasoningvspipelinesplit is directly transferable - in my 4P precision-nutrition reports, "interpretation rules" (the ordering of mitochondrial, methylation and OAT criteria) are reasoning, while "lab-indicator parsing and export" is pipeline. - "Skills portable across 20+ platforms on an open standard" matters enormously for a one-person company. My assets must not be welded to a single client; standardization means the same rule files are reusable across Claude Code, Codex and my local dsh without rewriting for each harness.
- It confirms the through-line I have logged for four consecutive issues and finally supplies the evaluation-side recipe. I noted assay's "a read claim must carry its file list", groundedmed's "a critic model checks each claim against source text", and bpf-benchmark's "third parties independently evaluate artifacts" - this issue, hcls-agent-skills bundles all three into a downloadable asset manifest: the skills are the methodology, the independent judge is the referee, the pairwise comparison is the scale. If I build a 30-50 prompt pairwise evaluation for my 4P expert package, I can copy this methodology wholesale.
- The claim that a skill should be a practitioner's decision chain rather than a long prompt is directly valuable for my content work. The README's line - that models know facts but not procedures - explains the recurring gap in my public-account writing: readers need "what decision comes next", not a list of concepts.
2. openai/codex-security: the official agent security scanner, now starting to pull other vendors' findings into a single evidence chain
URL: https://github.com/openai/codex-security ｜ ★11,802 ｜ Apache-2.0 ｜ OpenAI's Codex Security CLI and TypeScript SDK for finding, validating, and fixing security vulnerabilities. npm: https://www.npmjs.com/package/@openai/codex-security
What happened
- OpenAI's official Codex Security CLI and TypeScript SDK, for finding, validating and fixing vulnerabilities in code; 34 commits inside the window, with 0.10.0 released on 2026-11-12 (npm package and GitHub Action released together).
- The most interesting change this issue is the absorption of third-party scanner results into the same evidence chain: first
feat(scanner-imports): add ArmorCode findings support, thenfeat(scanner-imports): import AWS Continuum code issues, thenfeat(import): include AWS-generated patches in Continuum findings- the patches generated on the AWS side are carried into the Continuum finding entries verbatim. - Another thread is resumability rather than one-shot runs:
feat(scans): resume interrupted Standard scanslets an interrupted Standard scan pick up where it left off;fix(diagnostics): distinguish configuration errors and policy refusalssplits "you misconfigured it" from "policy said no" into two distinct diagnostics instead of one mush. - It exports SARIF / JSON / CSV, pushes findings to Linear, Jira or GitHub, and can draft
SECURITY.mdpolicies and file threat models for later review.
Why it matters
- "A patch generated by AWS ends up inside a third-party scanner's finding entry" is methodologically more valuable than the feature itself. It is a complete evidence chain: finding (AWS Continuum) -> patch provenance (AWS-generated) -> attribution (third-party scanner entry) -> export format (SARIF/JSON/CSV). This is precisely the segment my GRPM reports lack - on 11-10 I noted bpf-benchmark's hash-bound evidence and on 11-11 groundedmed's "decision rules frozen before the run", both about where a conclusion came from; this chain is about where a modification came from. For the intervention reports I produce for clients, "recommended action" equally needs bound provenance and generation method, otherwise it cannot be re-checked three months later.
- "Distinguishing configuration errors from policy refusals" is an error taxonomy I can lift straight into my expert-package quality checks. When one of my reports omits a recommendation, the reader currently cannot tell whether "no rule covers this" or "the rule judged it inapplicable" - these two failure modes must be reported separately, or users conclude the system is broken.
- "Resumable scans" maps directly onto the reality of my 8-hours-a-week constraint. A job that runs for two hours and then dies is equivalent to no job; being interruptible and resumable means I can start it on the commute and pick it up when I get back. This holds for every long-running agent task and belongs on my technology-selection checklist.
3. awslabs/unified-kg-rag-on-aws: four graph RAG methods on one shared index - and renaming an entity is now treated as a migration that can be rolled back
URL: https://github.com/awslabs/unified-kg-rag-on-aws ｜ ★39 ｜ Apache-2.0 ｜ AWS-native knowledge graph RAG framework. Runs GraphRAG, LightRAG, HippoRAG 2 and RAPTOR on one shared index, each selectable at index and query time, with hybrid BM25 + vector + graph retrieval and source provenance. Amazon Bedrock, Neptune and OpenSearch, or fully local. Incremental indexing, multilingual, bring-your-own KG, built-in evaluation.
What happened
- Four published methods run on the same index: Microsoft GraphRAG (community summaries), LightRAG (dual-level keywords), HippoRAG 2 (Personalized PageRank over entities and passages) and RAPTOR (summary tree, opt-in) - sharing ingestion, indexing, caching and search, with only the retrieval algorithm differing, and it builds only what the selected methods actually read.
- Answers carry
[n]citation markers naming the document and PDF page behind them (theglobalanddriftstrategies omit them by default); opening a source shows the provenance trail leading into a force-directed graph with the traced entity ringed and its neighbours labelled. Retrieval is BM25 + vector + Neptune graph traversal, fused with Reciprocal Rank Fusion and reranked by a Bedrock rerank model. - All three commits inside the window deal with entity renaming: first
fix: re-key bundle - name folding and legal forms, prompt-aware stage cache keys, thenfix: legal forms read as written, idempotent name keys, SUSPECTED claims, prompt digests, thenfix(indexing): strip a vertex holding a kept edge, identifier tokens from the entity key, record ids mint. The core idea is that renaming must be idempotent - the old and new names of one entity must map to the same key, and a vertex still referenced by another edge must not be swept away during the move. - Evaluation defaults to no LLM at all: built-in answer match, retrieval hit rate and graph coverage metrics, with RAGAS and assertion-style judges as opt-in, plus paired-interval strategy comparison and per-query cost metering.
Why it matters
- "Renaming is a migration that can be rolled back" lands squarely on GRPM's terminology-drift problem. On 11-10 I noted cortexdb's answer - aliases must resolve on both write and read - and here is a harder path: renaming is not adding an alias, it is migrating every edge off the old key, keeping vertices that cannot be moved, and doing it idempotently (renaming one entity twice must not create two identities). My terminology upgrades (merging "omega-3" with "EPA+DHA", renaming a biomarker) will eventually rewrite historical conclusions, so I need to know now how others handle whether old conclusions still count after a rename.
- "A vertex referenced by another edge must not be casually deleted" is a rule I never considered on the content side, and it turns out to matter. I retitle posts and change terminology in my public-account A/B tests; if the old title's conclusion is simply deleted, the historical test can never be reproduced - which is structurally the same problem as deleting a vertex on the data side.
- "No LLM judge by default" points in exactly the same direction as my recent notes. I have logged four consecutive issues about "let an uninformed third party decide" and "machine verification beats model self-report"; this goes further: three LLM-free metrics as the default, LLM judges demoted to an opt-in switch. That default choice is worth copying more than the feature list.
- Cost metering plus paired intervals is what content experiments should look like. My public-account content tests are currently as coarse as "this post got more reads"; the structure of "paired intervals per strategy + cost per query" can be turned directly into a template for "paired intervals between two copy variants + generation cost".
4. CaiZongyuan/clinmesh: China's public-hospital registration-orders-pharmacy-insurance-records flow turned into an evaluable virtual environment - where the human, not the agent, always presses the final button
URL: https://github.com/CaiZongyuan/clinmesh ｜ ★3 ｜ Unstated ｜ A virtual Chinese public-hospital HIS for Agent evaluation, product validation and medical IT research, providing a FHIR R5 interface with repeatable, auditable synthetic business scenarios.
What happened
- A virtual Chinese public-hospital HIS aimed at Agent evaluation, product validation and medical IT research, using FHIR R5 as the standard interoperability interface, with explicit business commands carrying registration, orders, pharmacy, billing, insurance, inventory and medical-records flows. The repository states it handles only fictional, synthetic or irreversibly de-identified demo data.
- The permission boundary is stated plainly: ClinMesh Agent Tools can only read the current trusted page context, navigation, edit drafts and prepare items for human review; "the final Actor for registration, triage, clinical, payment and pharmacy commands is always the logged-in human who clicks the native review box". The agent cannot submit business actions directly.
- The public FHIR surface is pinned to R5
5.0.0and declares only resource read, vread, instance history and whitelisted Search; business writes go only through controlled commands, and the server does not advertise generic FHIR create/update/delete, custom Profiles or Implementation Guide conformance. - Three commits inside the window: restoring GitHub Issues as the formal requirement spec and retiring the
docs/specdirectory, collapsing spec labels back to the main spec ticket while excluding execution sub-tickets, and integrating continuous handling on the clinical workbench. The requirement spec moved back to the issue tracker itself - a process-level return to source.
Why it matters
- What I find most valuable here is not the FHIR implementation but how it draws the boundary of what an agent may touch. It does not rely on a prompt saying "please do not decide for the doctor" - architecturally the agent simply cannot reach the submit button, and writes only happen through a human clicking a review box. That is harder than the "no medical credentials claimed" rule I set for myself: my constraint is editorial discipline, while ClinMesh turns it into a system property that cannot be bypassed. The same should hold for my 4P platform - wherever a recommendation "must be confirmed by a human", that should be an unavoidable workflow step, not a reminder in a prompt.
- "Synthetic data + repeatable + auditable" written into the positioning is directly relevant to my Hundred-Project work and real-world research. My de-identification flow has always been a soft problem (on 11-09 I noted that dsh-medical-anonymizer treated de-identification as optional rather than mandatory), whereas ClinMesh's approach is to never connect real data in the first place rather than scrubbing it afterwards.
- "Requirement specs moving back from a docs folder to Issues" is something I can use immediately. My 4P implementation-document system always carries the risk of "one set of docs, a different set of reality" - making checkable Issues the single source of truth and letting docs merely render them can be copied as-is.
- It also plugs into DSH (DeepSeek Harness) through a native React Surface. I run dsh locally, and this line has now appeared in my daily four issues running (11-09 dsh-Go, 11-10 dsh-medical-anonymizer, 11-11 dsh-plugin-radar, this issue dsh-context), which shows the dsh ecosystem moving from personal plugins toward industry-scenario adaptation - worth tracking as a trend of its own.
5. rupal2k/openfit: three releases in one day, a self-hosted fitness app whose lab-report OCR enforces a "read then delete" privacy boundary, and a public map of the verification status of its own 95 rules
URL: https://github.com/rupal2k/openfit ｜ ★1 ｜ AGPL-3.0 ｜ Self-hosted training, body-weight and nutrition tracker with on-device AI vision: guided workouts, progressive overload, passkey sync, PWA and standalone Android.
What happened
- A self-hosted training, body-weight and nutrition tracking app, running as a single Docker Compose stack on the user's own server or homelab with no external database; it supports guided workouts with automatic progressive overload, Open Food Facts search and barcode meal logging, body-weight tracking, and reading lab-test reports on-device.
- Three versions shipped in a single day: v1.6.0 (Tabata / EMOM / AMRAP plus an easier start), v1.6.1 (offline workouts, private blood tests) and v1.6.2 (photos work again) - 30 commits inside the window.
- Two security fixes with clear intent:
fix(security): delete camera captures once read (lab report photos stayed on external storage)- destroy the original photo once it has been read, so lab-report images do not linger on external storage; andfix(security): repair every untrusted shape in a backup- fix every untrusted structure in a restored backup. - The most striking item is a self-published test coverage map:
docs: test-coverage map for 1.6.2 (95 rules: 53 pinned, 13 proposed, 21 manual, 8 unverified)- 95 rules are explicitly split into four tiers: 53 pinned, 13 merely proposed, 21 relying on manual work, and 8 not yet verified.
Why it matters
- "Delete after reading" addresses a real hazard in my lab-report OCR flow. My GLP-1 Nutrition Companion plans a path of lab-report photos -> OCR -> AI -> app import; once a photo lands in a shared storage directory, the medical report has effectively been left somewhere it should not be. openfit's approach - destroy the original capture once it has been read - should be written straight into my import specification.
- "95 rules published across four tiers" is the most honest coverage declaration I have seen, and exactly what my expert-package quality check should copy. On 11-09 my own to-do was "add a validation timestamp to each rule", and on 11-11 I logged "rule validation time + expiry scanning"; openfit goes further by writing rule status as four explicit tiers - pinned / merely proposed / manual / unverified. That carries far more information than a timestamp field - it honestly admits that 8 rules have not been verified at all. This four-way split belongs in my spec-document template.
- It demonstrates that the "self-hosted + on-device OCR + no telemetry" assembly genuinely works in a real project. The fonts and OCR model ship with the app, so opening it or reading a lab report sends nothing to Google or anyone else - which lines up with the implicit data-sovereignty requirement on my 4P platform (client data never leaves the machine).
- For a one-person company, the practical lesson is about scope: a full-featured self-hosted health-tracking app is already near the ceiling of what 8 hours a week can sustain. That is a reminder that I should not build the self-hosted health app myself but be its user and integrator - my value lies in the precision-nutrition judgement layer and intervention design, not in rewriting a training log.
2. Category roundup
① AI Coding / coding agents & the skills ecosystem
| Project | Stars | One-liner |
|---|---|---|
| aequitas-labs/lobstah | ★1 | 🦞✨ Harness-agnostic, token-efficient supervision framework for coding agents. Nobody stares at the water — your agents just bring home the lobstahs. |
| agbossjs/agboss | ★2 | Spring-style TypeScript framework for mission-critical LangGraph AI agents — @Application DI, Path Scope Flow DSL, transactional LIFO sagas, FinOps budgets, MCP/RAG/A2A & unit testing. |
| agentes-chat/agentes-chat | ★260 | Open-source, self-hosted Slack alternative where your team and AI agents work together in channels and DMs. Agents run on Claude, ChatGPT/Codex, Gemini or Grok, with MCP tools, their own computer and browser, routines, memory and approvals. Web, desktop and phone apps. Docker Compose + Postgres. AGPL-3.0. |
| agentrust-io/awesome-ai-governance | ★66 | 🛡️ A curated list of tools, frameworks, standards, and resources for AI agent governance, safety, and compliance |
| AgentWorkforce/relay | ★1,111 | Infrastructure for coding agents |
| ah2233092/trading-agent | ★0 | Crypto futures watchman scanner + Claude Code deep-scan agent |
| AI4LGT/lamet-agent | ★4 | An Agent Framework for Large-Momentum Effective Theory Analysis |
| aliasunder/vault-cortex | ★36 | Standalone MCP server for Obsidian vaults — hybrid search, structured memory, task management, files, OAuth 2.1. Run locally, self-host, or one-click deploy for remote access. |
| alloverart/askhooman | ★0 | AskHooman: asks and tasks between people and their AI agents. An MCP server + app API, data in each user's own Google Drive |
| AmrDab/clawdcursor | ★408 | Local MCP server that gives any AI agent safe desktop control on Windows, macOS and Linux. Reads the screen as text and structure (accessibility + OCR) and acts by element name, with screenshots as the last resort. Multi-monitor aware, verifies every action, one safety gate. |
| Aquaticats/Monochromatic | ★7 | TypeScript and Rust monorepo: Claude Code and Pi coding-agent plugins, a minimal MCP stdio server framework, custom oxlint plugins and a pluggable Rust linter, policy-aware git tooling, core TS libraries, Wayland desktop apps (GTK4 file manager, Slint terminal, true-peak music player), OpenTofu infra automation, and a personal site. |
| araray/llmcore | ★5 | A unified, async Python framework for LLM applications—chat, autonomous agents, RAG, and sandboxed code execution. Supports OpenAI, Anthropic, Gemini, Ollama, and more through a single API. Features an 8-phase cognitive reasoning cycle, Docker-based isolation, human-in-the-loop approvals, session management, and comprehensive observability. |
| arisnachy/phoenix-harnes | ★2 | PHOENIX is a universal, provider-agnostic AI agent harness with intelligent model routing, persistent memory, tools, multi-agent orchestration, recovery, evaluation, and evolutionary self-improvement. Bring any API or run models locally. |
| ASTRANL/astranl-mcp | ★0 | AstraNL — protocol-governed coordination infrastructure for the AI + robot + human economy. Live MCP server, Europe-wide robot & drone database. |
| ayxuerui/contexture | ★1 | Context store for AI coding agents — markdown and git, catalog and graph retrieval, no vector database. |
| backblaze-labs/awesome-agent-infrastructure | ★12 | A curated list of AI agent infrastructure: memory stores, vector databases, execution sandboxes, MCP servers, tool registries, observability, and evaluation tools for building and running autonomous agents. |
| BaesTheorem/exobrain-harness | ★1 | A self-hosted AI exobrain. Claude Code plus 50+ skills, launchd watchers, and MCP servers, pulling voice transcripts, handwritten notes, calendar, tasks, and health data into one place so I stop losing track of things. |
| bilibiliUID1480494301/relay-hub | ★2 | Self-hosted LLM gateway - from LAN to the public internet (tunnel-friendly): multi-upstream keys. |
| Blaag/tfr | ★3 | Multi-world terminal client with typed JSONC configuration, a persistent multi-client gateway, LLM agents, and a plugin system with GitHub-based plugin installation. |
| busabase/busabase | ★810 | Open-source database & workspace for AI agents — structured data, durable knowledge, reusable skills, runnable apps, and human review on the writes that matter. Local-first and self-hostable. |
| calbebop/batesian | ★3 | Active security scanner for A2A and MCP servers |
| can4hou6joeng4/boss-harness | ★2 | An Agent Harness for recruiting tasks: controllable tools, persistent sessions and a desktop workbench. |
| career-ops-hq/career-ops | ★82,014 | Open-source AI job search agent and job finder: scan job boards, score each job 1-5 against your CV before you apply, tailor an ATS-friendly resume and cover letter, get interview prep and an application tracker. It helps you fill in each application; you press Submit. Runs locally in Codex, OpenCode and other AI CLIs, or as a Claude Code plugin. |
| cellbang/cell | ★748 | The TypeScript framework that builds once and runs anywhere, designed for humans and coding agents alike. |
| chaklam-silpasuwanchai/research_skill | ★1 | My personal claude skill for doing research |
| chaterm/Chaterm | ★3,107 | Open source AI terminal for cloud and infrastructure management, enabling you to deploy, troubleshoot, and automate services using natural language and intelligent agents. |
| codecoradev/tole | ★1 | Durable Rust agent harness: chat-first personal assistant with mission mode — resumable after crashes, replayable forever |
| cosyte/agent-skills | ★0 | Agent Skills, Cursor rules, and AGENTS.md that teach AI coding agents when and how to use the MIT-licensed @cosyte healthcare data libraries: HL7 v2, MLLP, FHIR R4, X12, C-CDA, DICOM, NCPDP, and ASTM. |
| cporter202/agentic-ai-apis | ★782 | The ultimate collection of APIs for building autonomous AI agents — 2,036 production-ready APIs across Agents, AI Models, and MCP Servers. Stop wasting weeks building infrastructure. Plug these in and ship your agent today. |
| cuijiejie/open-agent-harness | ★0 | A self-hosted agent platform: conversation + tools + sandbox + role-based RBAC authorization. |
| D4Vinci/Scrapling | ★101,681 | 🕷️ The BEST Web Scraping framework that handles everything from a single request to a full-scale crawl! Don't be shy, join here: https://discord.gg/EMgGbDceNQ and follow here for daily tips and tricks: https://x.com/Scrapling_dev |
| devbasex/ai-plugins | ★1 | AI Agent Marketplace - Claude Codeプラグインマーケットプレイス |
| developerz-ai/ultimate | ★3 | Bun-only, opinionated full-stack framework where the primary developer is an AI agent — and AI-maintained by developerz.ai. Eight primitives, one authz system across every surface, errors that carry an exact fix command. Start on a PaaS; scale out without changing app code. |
| Dicklesworthstone/agentic_coding_flywheel_setup | ★1,676 | Bootstraps a fresh Ubuntu VPS into a complete multi-agent AI development environment in 30 minutes: coding agents, session management, safety tools, and coordination infrastructure |
| dpc00/sublime-commander | ★0 | MCP server for Sublime Text 4 — lets AI agents read and control a running editor |
| dpc00/vscode-mcp | ★0 | An MCP server inside VS Code's extension host: run any command, edit files, install and test extensions |
| drptbl/logos-agents | ★0 | MCP server that lets AI agents hire each other over Logos, paid through an escrow, on top of logos-mcp |
| evolving-foundry/ember-rsi | ★24 | Make everything RSI-able! A pluggable framework for recursive self-improvement of AI agents and infrastructure through configurable strategies and evaluation-driven optimization. |
| facron2004/pi-desktop | ★0 | A Codex-style Electron desktop client for the Pi coding agent |
| fitzss/valun-open | ★1 | Valun for any AI agent, on any OS: MCP server, CLI and framework-neutral core for agent-to-agent service-for-credit deals; protocol spec with conformance vectors. Experimental pilot: synthetic units, nothing settled. |
| fr0ster/llm-agent | ★12 | Framework for building LLM agents, AI pipelines, multi-agent systems, MCP integrations, RAG workflows, planners, controllers and tool-driven orchestration across multiple LLM providers. |
| frankalcantara/Arestrix | ★7 | Arestrix is an AI Agent Operating System that provides a visual environment for building, coordinating, and running autonomous and multi-agent applications. It combines workflow orchestration, episodic memory, real-time observability, and LLM integration, allowing developers to design, test, and deploy intelligent agent ecosystems at scale. |
| FrozenYty/dsh-team | ★0 | Only the person changes, not the work. Retire, replace and recover agent-team members in DeepSeek Harness: the successor inherits the assignment, the session goes to a trash you control, and the freed slot comes back. |
| gekkyo/animate-mcp | ★2 | MCP server that lets AI agents read and edit the ActionScript/JSFL scripts in Adobe Animate documents. |
| gitpoo/imaginal | ★0 | What survives when an AI agent's memory is rebuilt: a synthetic-corpus harness, 45 saved runs and an invitation to break it. |
| gke-labs/kube-agents | ★126 | An autonomous agentic harness for Kubernetes. Proactive fleet audits, declarative GitOps remediation, and ChatOps in one place. |
| godic97/Gingoa | ★0 | An AI code guardrail and validation framework that strictly constrains and controls rogue AI coding agents and flawed codebases through logic and code. |
| gschaetz/humpyard | ★0 | Routing and policy gateway for LLM agent traffic: one Rust binary that speaks OpenAI Chat, Responses and Anthropic Messages, routes by request content, fails over, and records why. |
| hai-series/hai-framework | ★4 | Hai Framework is an AI-first, modular TypeScript full-stack framework that integrates LLMs, MCP, RAG, databases, authentication, security, type-safe APIs, Svelte UI, payments, and automated deployment, with built-in Agent Skills for AI-assisted development. |
| harshanandak/forge | ★0 | Forge — an open building-block platform for AI coding agents: blocks declare operations once; CLI and MCP are generated; works with any harness and any toolchain. |
| HoeTee/shipwright | ★2 | A plugin for Codex, Claude Code, and OpenCode that takes one coding task from plan to human merge. |
| homeassistant-ai/ha-mcp | ★7,413 | Configure and automate Home Assistant with Claude, ChatGPT, and other AI assistants. |
| IAI-Researching/Performance-Comparison-Demos | ★0 | Standardized autonomous benchmarks of frontier AI models across native developer harnesses (or dedicated agentic orchestrators) on demanding 3D physics applications with full performance telemetry. |
| IvanFlores/playwright-automation-framework | ★0 | Playwright + TypeScript automation framework with Claude Code skills (POM, test, spec and git standards) |
| JesunAhmadUshno/vllm-serving-lab | ★0 | LLM inference and serving, expanded in v3 to an inference optimisation lab: vLLM and SGLang, speculative decoding, KV-cache study, one benchmark harness. |
| jionpz/keel | ★0 | AI Engineering Runtime - a long-term collaboration layer letting AI, humans, multi-Agent, multi-model and multi-Harness work together in one software delivery process. |
| Jitsusama/agentic-harness.pi | ★6 | A pi package for collaborative coding workflows: planning, TDD, git hygiene, and code investigation. |
| K-kiron/dutyloop | ★0 | A repository super-agent for your coding tools. Plan, implement, verify and follow up with explicit budgets and human control. |
| kauki-labs/sandy | ★1 | Nix-based sandbox infrastructure and binary cache for the coding-agent microVMs |
| KAVentures/openworkgraph | ★1 | Open-source, local-first work context infrastructure for AI agents. Privacy-hardened workflow evidence via REST and MCP. |
| khaoss85/agent-crm | ★2 | Accordo — agent-native CRM framework for Claude Code, Codex and Gemini CLI. Author custom CRM and Customer Hub source with deterministic workflows, human approvals, audit and trace. Smart CRM is policy-governed; CDP + CRM adds bounded imports and logical identity, with no streaming ingestion, audience segmentation or CDP activation. |
| kingsoftcloud/agentkit-studio | ★3 | An open Agent workbench for code, documents and automation: DSH plugin + macOS/Windows desktop app covering Agent orchestration, resource management, deployment and automation. |
| koala73/worldmonitor | ★90,497 | Real-time global intelligence dashboard. AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking in a unified situational awareness interface |
| KyaniteLabs/ag4te | ★3 | Git repository archaeology framework — mine commit history, detect signals, run 6 analysis vectors, and generate engineering reports. Python CLI + AI-agent ready. |
| langfuse/langfuse-docs | ★253 | 🪢 Open source agent evals & observability: Trace, evaluate, and improve LLM applications with one open platform. |
| Leoglez10/finanzas | ★0 | Local personal finance app for macOS, Windows and Linux in Rust: envelope budgets, scheduled payments, projected balance and an MCP server. Spanish UI. |
| liamphmurphy/vesper | ★1 | A frontier model chat-like experience, except memory, agent definitions, context, and more stay yours no matter the underlying harness you use. |
| lnardev/lean-agent-harness | ★0 | One rule file and one skill format for Claude Code, Pi, OpenCode, Codex, Cursor, Copilot and CommandCode. Standing context is AGENTS.md (about 0.9k tokens) plus one line per skill. |
| lobster0113/stalker | ★0 | Tournament poker AI study system, developed autonomously by an agent harness |
| localtaskrepo/lotar | ★0 | A git-native issue tracker that lives in your repo: tasks as YAML files, a local web UI, an MCP server, and coding agents that work your tasks in their own git worktrees. |
| luongnv89/herdr-github | ★1 | Open GitHub pull requests and issues of the current repo, in a herdr side pane |
| mchasse73/aegisx9 | ★0 | Secure AI gateway: policy-enforced LLM access, prompt-injection and data-leak guards, audit, continuous red-teaming, GitOps delivery |
| mdiri-ai/SoduWork | ★330 | The AI-native engine for teams and their agents. Multi-harness, multi-agent, self-hosted. |
| michael-berardi/ultracompress | ★3 | Deterministic local compaction for Pi, Claude Code, Carl and Hermes Agent: VCC briefs, snap frames and lossless recall. |
| millathossain115/ai-forward-learning-client | ★0 | Interactive visual roadmap & architecture platform for AI engineers. Explore Algorithmic Foundations (DSA), Database Internals & B-Trees, Vector Embeddings, LLMs, RAG, MCP tool servers, and Autonomous Agents with live simulations. Built with Next.js 16, React 19, and Tailwind CSS. 100% client-side with zero backend dependencies. |
| minhtran3124/agent-harness | ★1 | Risk-gated /skills for Claude Code — 14 invocable skills carry a change from brainstorm → ship. |
| MoltyCel/moltrust-api | ★4 | MolTrust Protocol — AI Agent Trust Infrastructure |
| MrDHat/noma | ★0 | Run your existing coding agents in isolated, reproducible remote development environments. Bring your own harness, configuration, and infrastructure. |
| msaifkms/LLM-Gateway | ★0 | Production-style LLM API gateway with multi-provider routing, automatic failover, caching, rate limiting, cost tracking, and an evaluation harness. |
| MusadiqUrRahman/whaagent | ★3 | Multi-tenant WhatsApp AI agent platform — deploy intelligent agents on WhatsApp with LLM support, MCP tools, and production-ready infrastructure. |
| mvanhorn/last30days-skill | ★67,350 | AI agent skill that researches any topic across Reddit, X, YouTube, HN, Polymarket, and the web - then synthesizes a grounded summary |
| openai/codex-security | ★11,802 | OpenAI's Codex Security CLI and TypeScript SDK for finding, validating, and fixing security vulnerabilities. npm: https://www.npmjs.com/package/@openai/codex-security |
| oscarka/deepseekharness-remotecontrol | ★2 | Repository description unavailable in English. |
| Parcha-ai/agentrun | ★460 | AgentRun is an open-source platform that supercharges your agents. It adds self-improvement, durable execution and a control plane on top of your favorite agent harness. Your agents become smarter, more fault tolerant and use fewer tokens with every run. |
| philals/mission-control-capcom | ★1 | Mission control for story-driven work: local kanban boards, Claude Code skills and a live terminal board for tasks, draft PRs, CI and manual runs |
| pnedfff/claude-env-cleanup | ★34 | Codex skill for auditing and cleaning local Claude/CC Switch/coding-agent environment residue |
| polysius/nl-analytics-agent | ★0 | Text-to-SQL agent over Formula 1 data: LangGraph, hybrid retrieval in pgvector, read-only safety layers, Langfuse tracing, evals in CI, MCP server |
| puppyone-ai/puppyone-cloud | ★1,299 | The cloud infrastructure behind puppyone. Hosts projects as Git repositories for your team and agents. |
| Rayyan-Oumlil/OpenProxyAI | ★2 | Open-source LLM gateway: route by data class, audit trail, policy, budgets and MCP tool control. |
| reliant-labs/forge | ★8 | Go + React framework and CLI that builds, runs and ships static sites, SPAs, web/mobile apps and Go APIs (Connect RPC, protobuf, SQL migrations, generated ORM/handlers/hooks/tests), with linters for agent-written code. Deploys to hosted infra, Kubernetes, Docker Compose or host processes. Also an MCP server. |
| rgdevment/Tisty | ★10 | Local memory and context for you and your AI agent: Markdown notes, documents and tasks on your disk, with an MCP server for GitHub Copilot, Claude Code, Codex and Cursor. Open source, no account. |
| rlwillen0121/identity-eval-harness | ★1 | IdentityBench v4.1 & v4: multi-turn Enterprise IGA & Zero-Trust agent benchmark (Connectors, NHI Classification, NP-Hard Role/SoD Math, JIT/CAE, Access Reviews, JML) with multi-agent skills & steering. Published results run on v4_lite/v41_lite (60 scenarios, 360 tasks); harness supports v4_full/v41_full (180 scenarios, 1,080 tasks). |
| rnikadi/genaiopengateway | ★0 | Open-source GenAI gateway for every model type: LLMs, embeddings, text-to-speech, speech-to-text, realtime audio, image and video generation, plus an MCP and agent gateway. Enterprise features like SSO, RBAC, budgets, guardrails and audit are all included. Apache-2.0, with no paid tier. |
| roomi-fields/roomis-grillhouse | ★1 | An elephant–goldfish framework for building with Claude Code agents: a grill settles the architecture, fresh agents write tests, code and review each ticket. |
| sad-and-bad1231/awesome-LLM-inference-systems | ★1 | Awesome papers and open systems for efficient LLM inference, from foundational ideas to production serving. |
| SanvioLabs/sanvio-factory | ★3 | The smallest useful agent harness: instructions, a skill, a gate, a hook and a review agent. Works with Claude Code, Codex and Kiro. |
| searchware73/skills-maker | ★0 | Custom Claude Code skill development and publishing: literature research (lit-research) + paper review (paper-review). |
| shuruheel/mindgraph-mcp | ★1 | MCP server for MindGraph — connect Claude Desktop, Claude Code, and any MCP client to your knowledge graph |
| smithersai/smithers | ★449 | Smithers is an agentic workflow framework for defining workflows in simple TypeScript configuration files and executing them quickly, durably, and reliably |
| specnaut/specnaut-cli | ★0 | Specification-driven development framework — scaffolds a coordinated team of AI agents into your project, across every major coding harness |
| steelhead99x/rind-agent | ★0 | Agent skills for contributing to rind, the pure-Rust cryptography library behind MoQ |
| stevenmcconnon/multi-agent-finance-example | ★0 | A finance multi-agent system, end to end: SEC filings ingested into a vector database, permission-filtered retrieval, a supervisor graph coordinating deterministic and model-driven agents, human approval, audit and replay, a web app, and AWS infrastructure. A reference architecture for running AI agents on private documents. |
| superserve-ai/superserve | ★507 | Sandbox infrastructure for AI Agents |
| tenalisriharsha/shadowci | ★0 | An ambient coding agent that watches your work, fixes bugs in a shadow git worktree, and shows a verified patch with proof only when sure. Includes a replay-based evaluation harness for detection latency and false interruptions. |
| TerrenoLabs/terreno | ★3 | Batteries-included full-stack TypeScript framework built for AI coding agents: auto-generated REST APIs, auth, admin, and local-first sync, on one React Native app for iOS, Android, and web. |
| TimothyVang/deep-mode | ★0 | Open-source AI code review and software factory for coding agents: ACP agents (omp, Claude Code, Codex) review, fix, verify and merge your PRs and build your issues, on your own subscription. |
| tobocop2/lilbee | ★70 | A local model engine with a search engine and web crawler built in. Run your coding agents on your own GPUs, and get cited answers from your files, code, and the sites you crawl. One executable. |
| Toolbox-POD/toolbox-pod-mcp | ★0 | Toolbox POD's hosted MCP server: design custom shirts and hoodies, see mockups, and order prints from your AI app. |
| true-line/agent-bot-identity | ★0 | Give every coding agent on your machine its own GitHub identity — per-harness GitHub App bots, git-native hooks, and a fail-closed gh. |
| TruffleCraft/feedbackkit | ★1 | Self-hosted feedback gateway — complete-at-the-source user feedback → structured, agent-ready GitHub issues. Privacy-first, no session replay, local-LLM-ready. |
| TTNAN/quark-skill | ★2 | One-sentence Agent Skill to save cloud-drive resources into Quark: PanSou search + official Quark transfer, supporting Claude Code, Codex, Cursor and more. |
| twinnydotdev/twinny | ★3,695 | Open-source AI coding assistant for VS Code. Coding agent, code completion, chat, edits and reviews with local or hosted models. Your models, your infrastructure. |
| use-junction/usejunction | ★11 | AI coding spend management for engineering teams. Track Cursor, Claude Code, Codex, Copilot, usage, cost, plan utilization, seat waste, and device health. |
| vanillagreencom/kendex | ★93 | Package manager for agents, skills, hooks, and extensions. Author once, install on every harness. QOL features included. |
| viniciusmachadovianna/skill-shard | ★0 | Codebase and evaluation framework for my Bachelor's Thesis (TCC), focused on developing a Relational Retrieval-Augmented Generation (RAG) system that combines vector search, cross-document entity linking, and explicit source citation to minimize LLM hallucinations |
| weideyouni/holoaeon | ★0 | A Zero-Dependency, Bio-Inspired Cognitive Framework for General-Purpose Multi-Agent Systems |
| whichguy/skill-craft | ★1 | Host-neutral portable skills monorepo (skill-interop and more). Not claude-craft. |
| zch6a/build-your-own-ai | ★0 | Build your own AI from scratch: tokenizer, autograd, GPT, inference, vector database, RAG, agent and MCP. Each a small, tested, readable module that runs on a laptop CPU with no API key. |
| zchoi/Awesome-Embodied-Robotics-and-Agent | ★1,916 | This is a curated list of "Embodied AI or robot with Large Language Models" research. Watch this repository for the latest updates! 🔥 |
② Medical / clinical research AI & agent skill libraries
| Project | Stars | One-liner | |
|---|---|---|---|
| 1ali2003ah1-lgtm/ProtonAI | ★0 | Clinical AI Platform for Proton Therapy | |
| agustinroviraquezada/clinical-deid-es | ★0 | Master's Thesis for the Master's in AI Development (BIG school): A web-based tool that removes personal data from a clinical note in Spanish before using it with a third-party LLM. A proprietary model (a clinical RoBERTa fine-tuned with MEDDOCAN) detects personal data; only questionable cases are submitted to an LLM, and only the affected phrase is | |
| Ahmed28A/ovarian-cancer-dual-stream | ★0 | Interpretable dual-stream AI for ovarian cancer: ConvNeXt+CBAM+Grad-CAM subtype classification (99.29%) and CatBoost+SHAP clinical prediction (AUC 99.35%), with a Gradio demo. | |
| alexblogoodf/glyfiq-mcp | ★0 | Hand-drawn medical & health icons for AI agents (MCP server + Claude skill) | |
| alvianzf/AE3 | ★0 | An AI clinical assistant that only answers from your practice's own curated library — every claim cited, every answer independently checked before a practitioner sees it. | |
| andreibesleaga/AgenticMedicalImagingHelper | ★2 | Agentic AI analysis of medical image series over time. Research and education only, not a medical device [Demo] | |
| AndrewPJ2002/AI-Clinical-Mental-Health-Support-Platform | ★1 | A platform that connects patients and clinicians on the basis of mental health conversations | |
| Annapurnar06/alterra | ★0 | Healthcare AI Safety & Alignment Platform — simulation environments, evaluation, post-training, and RL infrastructure for clinical AI | |
| assiaezzayani-developer/GreenShield-AI | ★0 | GreenShield AI is an intelligent plant health platform powered by AI, delivering advanced plant disease diagnosis, personalized care recommendations, health tracking, and recovery monitoring. Designed to help individuals and growers protect plants, improve plant health, and promote sustainable agriculture worldwide. | |
| awslabs/hcls-agent-skills | ★76 | Open-source healthcare AI agent skills and life sciences agent skills. 42 skills for medical coding, risk adjustment, claims analytics, quality measures, genomics, imaging, and drug discovery across 13 HCLS domains. | |
| berntpopp/litvar-link | ★0 | MCP server for NCBI LitVar2: search the biomedical literature by genetic variant — the index linking variants to the publications that mention them, as typed tools for LLM agents. | |
| CaiZongyuan/clinmesh | ★3 | A virtual Chinese public-hospital HIS for Agent evaluation, product validation and medical IT research, providing a FHIR R5 interface with repeatable, auditable synthetic business scenarios. | |
| Chetanprince213/healthcare-ai-data-platform | ★0 | Healthcare Data Engineering, Analytics, MLOps and Agentic AI Portfolio Project | |
| concept2cure/ClinicalSageAI-2-replit | ★0 | Repository for https://replit.com/t/clinicalsage/repls/ClinicalSageAI-2 | |
| damsolanke/aarogya-atlas | ★2 | Agentic healthcare-facility search for India: a tool-calling loop over FHIR-aligned data with a critic pass on every answer. Hack-Nation 2026 / Challenge 3 (Databricks). | |
| dareinelt/lanpa | ★0 | Comprehensive intranet solution for healthcare institutions, integrating a proprietary chat agent, Nextcloud, EuroOffice, as well as an alternative web-based Exchange client. | |
| dhruvsave29/cognistream | ★0 | CogniStream - Winner, COHERENCE Hackathon 2026. AI clinical-trial matcher: 30+ min screening to under 15 seconds (RAG + Groq LLM, FastAPI, Next.js). | |
| elong0527/ai4csr | ★3 | AI for Clinical Study Workflow | |
| farruxshernazarov5-dotcom/med1.uz | ★0 | MED1.uz is an advanced medical ecosystem integrating 72 AI agents and 14 AI models for early diagnosis of oncology and diabetes, as well as clinic digitalization. | |
| Gaogxx/MedicalGPT_agent | ★0 | A medical large model agent based on MedicalGPT and DeepSeek Harness, covering model training, capability alignment, and runtime framework development and deployment. | |
| gil906/devpost-buildhybridai-2026-31808 | ★0 | PII-Zero: Zero-Trust Clinical & Legal Hybrid AI Airlock (#BuildHybridAI 2026 — Web + Chrome MV3 Extension + Kotlin Compose Foldable Android) | |
| godasrinivasulureddy/Fame-Genix-Agentic-AI-2.0 | ★0 | Multimodal, agentic AI-powered health monitoring, risk assessment, medical document understanding, RAG-based health guidance, and coordinated emergency response. | |
| Guitrentz/clinical-ai-deterministic-harness | ★0 | Deterministic clinical validation harness in Python for AI-augmented healthcare systems: SNOMED CT (SCTID) Verhoeff validator, US NPI/DEA/FDA UDI & Brazil CNS checksums, and HIPAA Safe Harbor PHI scrubber. | |
| HusseinYassineMD/ClinicalCore_Aim1 | ★0 | USC ADRC Clinical Core Aim 1 metrics dashboard | |
| ilay-fisher/vitalstream | ★0 | Real-time clinical telemetry platform with an AI clinical copilot: Kafka, Spark, Spring Boot, Cassandra, Elasticsearch, Redis, RabbitMQ, pgvector RAG, MCP, Kubernetes | |
| JoshuaOforisurg/surgical-data-platform | ★0 | data engineering platform for operating theatres, supporting operational pipelines, analytics, and AI-ready datasets from fragmented clinical systems. | |
| KennethJSmithDev/DICOM3D_AI | ★1 | DICOM3D AI Hub uses a native InterSystems AI Hub agent in an IRIS Interoperability workflow to validate FHIR imaging evidence and select source-linked findings in an interactive 3D anatomy viewer. | |
| kollaprudvi79-ai/clinical-trial-operations-dashboard | ★0 | Portfolio-grade Clinical Trial Operations Dashboard in Power BI — synthetic data, star schema, exact DAX, 5 report pages | |
| lakshmisanthosh123/healthcare-ai-platform | ★0 | Enterprise AI Healthcare Platform built with React, Micro Frontend, FastAPI, PostgreSQL, RAG, AI Agents, LangGraph, Docker, Kubernetes, and AWS. | |
| lawrencelucado/coding-audit-copilot | ★0 | AI medical coding agent: Claude + deterministic CMS-rule tools (ICD-10-CM, NCCI, MUE), scored against CPC-reviewed answer keys, with cost/latency tracing and a CI evaluation gate. | |
| mbzuai-ai4bio/BioXArena-Pro | ★1 | BioXArena-Pro: a research-grade benchmark for autonomous biomedical machine learning agents (harness, graders, scoring, leaderboard) | |
| mianaz/biomed-skills | ★0 | Claude Code / Codex agent skills for biomedical research: R tidyplots and GraphPad Prism figures, paper framing and writing, protocols, Methods and mechanism animations. | |
| mohammadazam7/clinical-data-lakehouse | ★0 | FHIR R4 patient records to a tested OMOP CDM 5.4 warehouse on DuckDB and BigQuery, with de-identification, data quality checks and care gap measures. Synthetic patients only. | |
| muhammadusamakhan911-bit/fabric-mlv-agent-healthcare-starter | ★149 | Fabric MLV healthcare demo template: lake views, ontology, data agents | |
| pedropcamellon/folium | ★0 | Folium is an engineering sandbox for agentic AI in healthcare — not a commercial or feature-complete EHR. | |
| Prashanthbkm/MediLens-AI | ★1 | 🩺 MediLens AI – An AI-powered medical report analyzer that extracts health data from PDF reports, explains test results, tracks health trends 📊, and provides intelligent insights using RAG 🤖. | |
| pv-tech28/PCOS360 | ★0 | PCOS360 – AI-powered PCOS Health Intelligence Platform \ | Track symptoms, discover personal health patterns, understand lab reports, and gain meaningful health insights. |
| Rhushabh1/SugenICD | ★0 | AI-powered Clinical Coding Engine | |
| saikumarreddy2902/MediSphereAI | ★0 | AI Agent that helps in Healthcare. | |
| sisovin/healthcare-ai-desk | ★0 | M1.1 is infrastructure scaffolding only. Authentication, organization/project modules, database migrations, agent orchestration, and clinical workflows are not implemented yet. No tests are claimed to have passed. Run the verification steps below on your computer. | |
| smaniches/uniprot-mcp | ★3 | Auditable UniProt MCP server with per-response SHA-256 provenance, release pinning, verification, and offline replay. | |
| Snigdha-0210/Cancer-multi-agent | ★0 | Autonomous, state-driven multi-agent oncology intelligence system powered by LangGraph, 103-document biomedical RAG (31,498 units), local Ollama LLMs, deterministic safety guardrails, and fail-closed clinical verification. | |
| suresh121011/SEHAT-AI | ★0 | SEHAT AI is a multimodal healthcare triage assistant designed for Indian healthcare facilities. It combines voice, text, and medical report extraction with rules-based urgency assessment, evidence-linked summaries, and human review to support safer, faster clinical handoffs. Built with a focus on accessibility, privacy, and offline resilience. | |
| Swethavenna-github/clearbill | ★0 | Clearbill: two AI agents settle a medical bill. PayPal + AI Hackathon prototype (fictional data, PayPal sandbox). | |
| veganbeef-96/lastehr-platform | ★0 | Open-source platform for building AI agents on FHIR health records, including the agent app, MCP server, and conformance tools. | |
| VenkatesaPerumal-23/HealthCare_Agent | ★0 | An agentic healthcare information assistant that answers questions using local PDF references and trusted health websites. It is intended for educational use only and is not a substitute for professional medical advice, diagnosis, or treatment. | |
| wetware-hq/chart | ★0 | An agentic medical chart. | |
| xuyul6/med-agent-runtime-v1 | ★1 | Medical information multi-agent runtime rebuilt from requirements, with source-grounded answers and observable execution. | |
| zlatedod/multi-agent-system-for-healthcare | ★0 | MAS-mcp healthcare research: n8n workflow, agent prompts, cohort SQL, and setup instructions. |
③ Health management / nutrition & food AI
| Project | Stars | One-liner | |
|---|---|---|---|
| 12alankrita11srivastava2610-droid/nutrition-agent | ★1 | AI Nutrition Assistant powered by IBM Granite | |
| A0966411725-png/dietsense-proposal | ★0 | DietSense: a conversational daily calorie-intake Domain AI Agent - a course project proposal refactored with OpenSpec. | |
| aderaaij/loopback-training-server | ★9 | Self-hosted backend for Loopback – Run Coach: workout tracking, Apple Watch training queue, training plans, health metrics, web dashboard, and an MCP server for AI coaching | |
| Amna-Coder404/ByteDiet | ★0 | ByteDiet is an AI-powered personal nutrition tracker built with React Native and Expo. Analyze meals, estimate calories, track daily nutrients, and get personalized food insights using Google's Gemini API. | |
| ayhamimarati/Bourmdane-mohamed | ★0 | AI-powered nutrition and calorie tracking app | |
| bigidrise/my-perfect-meals-npm-dev | ★0 | My Perfect Meals is an AI-powered nutrition and meal-planning platform that personalizes meals around individual health needs, food preferences, and lifestyles. Copy | |
| chakravartiavinit/relife | ★1 | A second chance at life: local-first tracker for health, mind, skills, wealth and relationships. Telegram reminders, Binance/GitHub/bank-SMS connectors, bring-your-own-key AI. | |
| DavidRamms/plant-based-research-hub | ★0 | Daily-updated plant-based diet research tracker with AI-generated summaries | |
| DragonScripter/pulse-health-analytics | ★0 | A health tracking platform featuring a native Kotlin Android client, a Node.js API gateway, and a Python AI engine for biometric trend analysis(Not completed) | |
| drew-weise/Provisions | ★0 | AI-assisted personal food planning for meals, groceries, budgets, nutrition, and preferences. | |
| Dzarlax-AI/health_dashboard | ★1 | Personal health dashboard — ingests Apple Health data via Health Auto Export, tracks sleep, HR, HRV, SpO2, steps and readiness scores. Web UI + MCP server for AI assistants | |
| edisontaimu9-ui/thanzi-screening-engine | ★0 | AI-assisted malnutrition screening engine with deterministic WHO anthropometric calculations, ML support, clinical classification and explainability. | |
| ehtesham8958873-ops/caloriewise-ai | ★0 | Free online calorie calculators and nutrition tools to help you track daily calories, BMR, TDEE and macros. | |
| faisal-abdelnase/NutriVision | ★1 | NutriVision is an AI-powered dietary health assistant built with Flutter that helps users track meals, analyze nutritional data, monitor dietary goals, and receive personalized AI-driven nutrition insights, healthy alternatives, and recipe recommendations. | |
| ganeshvarun40-maker/BMI-Health-Dashboard | ★0 | A BMI Health Dashboard built with Python and Streamlit, featuring an AI Nutrition Coach, diet plans, charts, and health tracking. | |
| git-macko/resolution-fitness-app | ★1 | A full-stack gym fitness mobile app -> Go REST API backend with SQLite + React Native (Expo) frontend. Plan routines, execute workouts, scan food for nutrition insights, track progress with XP/streaks, and chat with an AI fitness coach. | |
| Javiguegadev/personal-trainer | ★0 | AI-powered nutrition & fitness app (freemium). Web-first Turborepo monorepo: Next.js, Hono, Supabase, Drizzle, Claude API — mobile-ready with Expo. | |
| jollyjoker992/garmin-archive | ★0 | Back up your Garmin Connect data (official Personal API) to Google Drive and S3 Glacier, map it to FHIR R4 and load it into a local Medplum. Docker, daily scheduler, MCP/GraphQL for agents. | |
| Justusability/license-label | ★0 | A nutrition label for the open-source licences inside your app. Plain language, for people who build with AI coding agents. | |
| kdinya/smart-diet-ai | ★0 | A simple food diary with AI-estimated calories and nutrition, daily balance and weight tracking. English and Ukrainian. | |
| lgf2111/snapbite | ★0 | Telegram-native AI calorie tracker — send a photo or describe a meal, get an editable nutrition estimate. Cloudflare Worker + Telegram Mini App, BYOK. | |
| manoj234-pull/DASH-Meal-Prep-AI | ★0 | An agentic DASH meal-prep assistant that finds recipes from available ingredients using DASH diet. | |
| merickvaughn/lifting-logbook | ★0 | TypeScript training log with AI-driven recommendations based on performance, sleep, and nutrition | |
| mkpoli/health-tracker | ★1 | AI-assisted multilingual health record tracker for lab results and clinical metrics | |
| munimtechnologies/open-food-safety-check | ★0 | Pre-registered checks that published food-safety survey numbers re-compute from the survey's own open data. By MV2 of Munim, Inc., made with AI agents. Apache-2.0. | |
| muscleshubfit-cpu/alkemos-aws | ★0 | Alkemos (alkemos.com) — production repo of the bilingual AR/EN smart fitness & nutrition platform: EVO AI coach, exercise & food libraries, planning tools, B2B coach system, memberships & full admin — Next.js 16 · Supabase · AWS Lightsail + Cloudflare | |
| nikhilramakrishna3005/nutricart-ai | ★1 | The AI for food nutrition | |
| NurtureAISpace/NurtureAISpace.github.io | ★0 | NurtureAISpace: Autonomous Space Nutrition & Bio-Regenerative Life Support System (N-BLSS) for Mars Sol-450 Missions — Thermodynamic Arrhenius Kinetics, SciPy NASA-STD-3001 Diet Optimization & Habitat Simulation Engine (FastAPI + SciPy + Python) | |
| OSerHuang/caldoo | ★0 | CalDoo - an offline-first Android diet and nutrition tracker (bring-your-own-key AI). | |
| PatricioMR/nMRPro-H---1D-Metabolomics-Platform | ★0 | Transparent, auditable pipeline for 1D 1H NMR metabolomics: signal detection, multiplet-based annotation, quantification with detection limits, statistics and PDF reporting. | |
| PedroPinho17/calorie-tracker | ★0 | Snap a photo of your meal and get calories and macros: AI food recognition (Gemini) matched to INSA, USDA and Open Food Facts nutrition data. NestJS API, Next.js web and Expo mobile app, PostgreSQL. | |
| petrovski-stefan/nutritionals | ★0 | North Macedonia's first ever supplements/OTC web aggregator for price comparison, AI-powered search included | |
| PrimeCaster/JustGetFit | ★1 | All-in-one Android fitness, strength training, cardio, and precision AI nutrition macro tracking app built natively with Jetpack Compose and Material 3 | |
| prokesmic/HealthUniverse | ★0 | Living knowledge graph of nutrition/lifestyle/supplements vs chronic disease — Claude-seeded, Gemma-maintained | |
| raslmm/safebite | ★1 | AI Agent that detects allergens from food labels | |
| Rmjjke/BabyBloom | ★0 | iOS app for newborn health tracking — feeding, sleep, diapers, growth with AI assistant | |
| RuneYang123/fitness-companion | ★2 | Fitness Companion \ | A Chinese-language fitness skill for adults, tailoring training, nutrition, supplement choices, and sleep guidance to your goals, schedule, and equipment. Supports exercise tutorials, on-demand feedback, and portable plans for continuing across chats. |
| rupal2k/openfit | ★1 | Self-hosted training, body-weight and nutrition tracker with on-device AI vision: guided workouts, progressive overload, passkey sync, PWA and standalone Android. | |
| sabasgh/food-rag-chatbot | ★0 | Three food search systems on one ChromaDB vector store: an interactive CLI search, advanced filtering by cuisine and calories, and a RAG chatbot powered by IBM Granite. Built during the IBM RAG and Agentic AI Professional Certificate. | |
| Sculpt-AI/nutrition | ★0 | Calorie and macro engine behind Sculpt AI's app, server and website | |
| Smith3939/smit-gym | ★1 | Smit Gym - Fitness & Nutrition App with AI Coach \ | React Native + Firebase + Claude AI |
| Soorya2201/Immuny | ★0 | Clinical AI allergy tracker combining a vision-language model (MedGemma), IoT wearable monitoring, and voice UI for proactive allergen management. | |
| spoonjoy/spoonjoy-apple | ★0 | The native SwiftUI app, with a cook mode that walks you through a recipe one step at a time. | |
| ulko31/sofe | ★0 | Wellness Telegram Mini App: nutrition, workouts, progress tracking, social features, maps and AI assistant | |
| xLightBringerx/pocket-coach | ★1 | An AI driven fitness app that can create weekly and monthly fitness regimens and diet plans and book clients with personal trainers in their area. |
④ Model layer & agent infrastructure
| Project | Stars | One-liner |
|---|---|---|
| ai-verification-observatory/inference-proofs | ★10 | Provable LLM inference measured on the same hardware: overhead against production serving, proving time, proof size and check time, and what one proof establishes. |
| aidless/kv-serve-lab | ★0 | CPU-verifiable toolkit for LLM inference serving: KV cache sizing (GQA/MLA), PagedAttention block pool, continuous batching simulator, and SSE streaming bench. Stdlib only, 180 tests. |
| aiverse-filmmakers/AI-Verse-Memory | ★0 | Local-first persistent memory for AI agents, with no external database, vector store or extra API required. |
| andrewyng/openworker | ★19,006 | |
| AndryanZulfi/hermes-server-stack | ★1 | Hermes Agent Production Server Infrastructure Stack (9router, Honcho, Monitoring, Galaxy View) |
| apache/pinot | ★6,171 | Apache Pinot: a real-time analytics database for low-latency SQL and vector search, serving user-facing and agent-facing applications |
| api7/aisix | ★219 | Open-source AI gateway for LLMs & AI agents, built in Rust. One OpenAI-compatible API for OpenAI, Anthropic, Gemini, Bedrock & more — routing, guardrails, caching, rate limits, observability. |
| awslabs/unified-kg-rag-on-aws | ★39 | AWS-native knowledge graph RAG framework. Runs GraphRAG, LightRAG, HippoRAG 2 and RAPTOR on one shared index, each selectable at index and query time, with hybrid BM25 + vector + graph retrieval and source provenance. Amazon Bedrock, Neptune and OpenSearch, or fully local. Incremental indexing, multilingual, bring-your-own KG, built-in evaluation. |
| beehive-lab/jitllm | ★295 | Java Inference Tornado toolkit: High performance LLM inference & serving for the JVM on GPUs |
| bowenliang123/dsh-context | ★4,961 | The best DeepSeek Harness plugin for context insight and management, with a context dashboard, browser and sidebar plus a context command for statistics, composition, breakdown and evolution details. |
| cookerpapa/pi-managed-agents | ★8 | Self-hosted infrastructure for managed agents, powered by Pi. |
| cosyte/cli | ★0 | The Cosyte command line for Node.js and TypeScript: one command over HL7 v2, FHIR R4, X12, ASTM, NCPDP, C-CDA, DICOM, and MLLP, with HL7 v2 to FHIR conversion from transform, code mapping from terminology, and the cosyte-mcp MCP server for agents. |
| dakotacookson/Llama-RPC-Farm | ★8 | Web UI to pool your home machines into one llama.cpp cluster: multi-machine RPC GPU pooling + on-demand multi-model serving via llama-swap |
| Drgr33nSenior/Spry.ai-rdna4-serving | ★1 | Experimental LLM serving on AMD RDNA4 GPUs, with ROCm/SGLang integration, FP8 model support, four-bit KV caching, and reproducible benchmarking. |
| elnomic/fesnuk | ★1 | Open-source programmable wallet infrastructure for AI Agents - Golang-first. |
| evoila/meho | ★22 | Governance backplane for AI agents acting on infrastructure — policy-gated, audit-grade, MCP-native. Apache 2.0. |
| farming-labs/farm.js | ★659 | a full-stack, renderer-agnostic framework for the web and agents, with an experimental AOT React compiler. |
| fluffyled/enterprise-rag-framework | ★0 | Local RAG framework for employee navigation through internal regulations: hybrid search, OCR and grounded citations. |
| ginuerzh/cuttle | ★1 | Cuttle is a self-hosted LLM gateway with an embedded admin console. |
| honeybee-studio/honeybeepf-llm | ★11 | Lightweight eBPF-based observability agent for AI/LLM workloads - monitors token usage, latency, and sensitive file access in Kubernetes without code changes. |
| humemai/tiervec | ★0 | Tiered vector index for agent-memory databases on the JVM: exact tail, sealed graph segments, scalar-safe kernels. Apache-2.0. |
| hyunnnchoi/vllm | ★0 | A high-throughput and memory-efficient inference and serving engine for LLMs |
| jingwenzz/miniserve | ★1 | A minimal LLM inference serving simulator with continuous batching (Python) |
| joseoper/token-diet | ★0 | Audit token footprint, detect prompt bloat, and neutralize silent context leaks in AI agent workflows |
| junxnpng/junxnpng.github.io | ★0 | Personal site — paper notes (Korean) on LLM inference & serving, built with Hugo + PaperMod |
| khaneilcampbell/ai-ops-atlas | ★1 | Atlas is a secure, observable AI operations control plane that unifies enterprise infrastructure, repositories, business workflows, and autonomous agents into one intelligent operating platform. |
| luharry2821/pingo-voice-backend | ★0 | `Pingo voice tutor backend: real-time language learning agent built on AssemblyAI Universal-3.6 Pro Realtime STT, LLM Gateway, and TTS. |
| luqman-v1/9router-go | ★282 | High-performance Go proxy gateway for 9Router LLM routing — 32K RPS, 42MB RAM, CGO-free |
| MacCracken/hoosh | ★4 | AI inference Gateway — multi-provider LLM routing, local model serving, speech-to-text, and token budget management |
| masterledgerlive/koda-kai-decoder | ★0 | Koda Kai the Decoder — the open universal decoder: every cipher and codec in library form, container interrogation of untrusted code, and public agent report cards. MIT licensed, free for everyone. |
| mfittko/dev-loops | ★18 | Shared AI agent workflow infrastructure for reusable local and remote development loops |
| mihazs/agent-harness-lab | ★4 | Evidence-driven agent harness diagnosis, process mining, and paired evaluation |
| mrveiss/AutoBot-AI | ★40 | Self-hosted, agentic AI you own — voice, vision-driven browser & computer control, visual workflows, human-in-the-loop approvals, multi-user RBAC, and a knowledge graph. Runs on your infrastructure. Your data. Your AI. |
| nfsnyy/signet | ★0 | Acceptance infrastructure for human–agent development: specs frozen before execution, machine-checked gates, every claim auditable. CLI-first |
| nikoma/bicdb | ★3 | Next-gen database for AI agents. PostgreSQL, Redis, queues, vectors, full-text search & analytics. One engine. |
| npspringmeyer/clinical-guideline-rag-eval | ★0 | A RAG assistant that answers questions over public health guidelines, with a measurable evaluation framework |
| olathedev/hedgerow | ★0 | A privacy router for the Otari LLM gateway: sensitive prompts stay on a local open-weight model, everything else goes to the cloud. |
| openai/openai-agents-python | ★30,623 | A lightweight, powerful framework for multi-agent workflows |
| pingcap/tidb | ★40,732 | TiDB is built for agentic workloads that grow unpredictably, with ACID guarantees and native support for transactions, analytics, and vector search. No data silos. No noisy neighbors. No infrastructure ceiling. |
| PNUops/pickle-llm-gateway | ★0 | LLM API 게이트웨이 (Go) |
| Praneeth16/kumo-fraud-databricks | ★0 | Fine-tune NVIDIA Kumo-Tabular on Databricks AI Runtime and serve it on GPU Model Serving with Lakebase features: card-fraud scoring in real time and in batch |
| pratiksinghchauhan/framework-compass | ★0 | A RAG assistant for NIST AI RMF and OWASP LLM Top 10, with a public evaluation report. |
| radiatedsaturn7/automated-llm-proxy | ★0 | automated llm gateway |
| rafiebazine/rag-image-captioning | ★0 | Contextual Compression RAG framework for automatic image captioning on MS COCO. Grounds Vision-Language Models (VLMs) by dynamically compressing retrieved multimodal context to reduce redundancy, lower object hallucinations, and improve caption precision. |
| railgrid/railgrid | ★14 | Railgrid helps platform teams turn company data, tools, and infrastructure into reusable building blocks for AI-built apps and agents. |
| ridsood90/agentic-ai-lab | ★0 | Building agentic AI systems with Python, LLMs, tool calling, memory and observability. |
| riffdb/riffdb | ★0 | RiffDB is an ACID-compliant relational database for agent-built SaaS that unifies transactional writes, columnar OLAP analytics, and full-text and vector search in one contract-first engine. |
| ROCm/rocm-cli | ★51 | ROCm CLI is a command-line tool for setting up and running local AI on AMD GPUs, with a full-screen TUI dashboard for GPU telemetry, model serving, and chat. |
| rrnewton/agent-utils | ★3 | A simple infrastructure for running a DAG of internally-parallel steps for CI with profiling planning and resource limitting. |
| sankalp-rajeev/Nexus | ★0 | LLM observability and self-healing platform for detecting, diagnosing, and repairing failures in multi-agent AI systems. |
| seckinozbek1/project-shimmer-deployment | ★0 | The deployment-phase version of Project Shimmer, an agentic framework to verify the claims written in the document and detect the changes in political, legal, or diplomatic documents between negotiation rounds. |
| Senguk520/dsh-multitask | ★52 | A Multitask "shock absorber" plugin & agent preset for DeepSeek Harness (DSH): the main chat only plans, briefs subagents and summarizes results, while file reads, commands, search and builds all run inside subagent contexts. Four worker types, an approval gate, zero DSH source changes. |
| Spongy01/Conduit | ★0 | LLM Gateway with Rate Limiting, Fallback Routing, and Observability |
| storytold/craft-anything | ★28,556 | Craft Anything: understand how any software works, and craft your own. |
| sudarshan1x1/efficient-inference | ★0 | Benchmarking LLM serving under concurrency on a T4: 22.6× throughput from batching, plus where KV cache, prefill and quantization help or don’t. |
| supportauranova23-max/-optimax-llm-router. | ★0 | "An ultra-fast, data-saving LLMOps routing gateway that intercepts AI prompts, applies dynamic semantic caching, and optimizes API compute workloads to drop enterprise model costs by up to 60%." |
| tachyon-zcash/ragu | ★61 | Rust language proof-carrying data framework |
| Theorvane/OpenGranter | ★0 | IAM-aware LLM gateway with OpenRouter and direct provider routing |
| westhong/astro-dashboard | ★0 | A personal dashboard by K3 to review my astro shooting conditions for my desired locations |
| xiseliuli/dsh-as-mcp | ★6 | Expose a running DeepSeek Harness as an MCP server, so Claude Code, Codex or any MCP client can drive its workspaces, sessions, files, shell and agent tools, and read token usage and cost receipts. |
3. Trends today
- ⭐⭐⭐ Skills are turning from "prompt fragments" into portable, evaluable, updatable methodology assets - and a public industrial-grade sample now exists. This issue, awslabs/hcls-agent-skills splits 43 healthcare and life-science skills into
reasoning(methodology and decision frameworks) andpipeline(tool chains and parameter tables), backed by a 410-prompt pairwise evaluation showing the skill-equipped version winning 70-86%. This matches the through-line I have logged for four consecutive issues exactly: 11-08 "a failing test must name the scenario", 11-09 "specs/docs/matrix aligned with code plus regression guards", 11-10 "let an uninformed third party decide", 11-11 "a read claim must carry its file list" - this issue is the first time those mechanisms have been packaged into a downloadable asset manifest. - ⭐⭐⭐ The evidence chain is now extending in both directions: one end toward the provenance of modifications, the other toward historical conclusions after an entity rename. codex-security carries AWS-generated patches into third-party scanner finding entries (ArmorCode / AWS Continuum) and exports SARIF/JSON/CSV; unified-kg-rag-on-aws turns a rename into a migration that needs an idempotent key and must preserve still-referenced vertices. Read together, mature systems are answering one question: three months from now, how do I know how that report, that modification and that term came about? That is precisely the part of the GRPM credibility path (data archival -> decision rules -> reproducible artifacts) I have not yet completed.
- ⭐⭐⭐ The dividing line in the health line has shifted from "can it record" to "does the data stay, and is the rule set honest". openfit turns lab-report OCR into "delete the camera capture once read" and publishes the verification status of its own 95 rules (53 pinned / 13 merely proposed / 21 manual / 8 unverified); uniprot-mcp makes protein data auditable with per-response SHA-256 provenance, release pinning and offline replay; openmed ships 72 medical skills with a local de-identification pipeline, and SAGEAgent has the agent decide whether the next diagnostic modality is worth acquiring (average acquisition burden down 55% with no significant C-index change). The common thread: this issue's health projects are answering "where does my data go and why should I trust my conclusion", not "here is yet another photo-to-calorie feature".
- ⭐⭐ Constraints enforced at the architecture layer are harder than constraints written into prompts. clinmesh states that "the final Actor is always the logged-in human who clicks the native review box" - the agent cannot reach the submit button; codex-security splits "configuration error" and "policy refusal" into separate diagnostics; unified-kg-rag-on-aws defaults to three LLM-free metrics with LLM judges merely optional. All three say the same thing: turn "please do not do this" into "the system does not allow this".
- ⭐⭐ The three-tier licensing split still holds this issue, with one exception worth remembering on its own. Of the 273 entries, permissive licenses (MIT / Apache-2.0 / BSD / MPL / CC0) account for 164; contagious copyleft (AGPL / GPL / LGPL) 11, source-visible but not open source 0, proprietary 0; and 0 entries have no LICENSE file whatsoever - the methodology can be read, but the code cannot be depended on directly, and that boundary has to be re-confirmed every single issue.
4. Repo index
https://github.com/aequitas-labs/lobstah
https://github.com/agbossjs/agboss
https://github.com/agentes-chat/agentes-chat
https://github.com/agentrust-io/awesome-ai-governance
https://github.com/AgentWorkforce/relay
https://github.com/ah2233092/trading-agent
https://github.com/AI4LGT/lamet-agent
https://github.com/aliasunder/vault-cortex
https://github.com/alloverart/askhooman
https://github.com/AmrDab/clawdcursor
https://github.com/Aquaticats/Monochromatic
https://github.com/araray/llmcore
https://github.com/arisnachy/phoenix-harnes
https://github.com/ASTRANL/astranl-mcp
https://github.com/ayxuerui/contexture
https://github.com/backblaze-labs/awesome-agent-infrastructure
https://github.com/BaesTheorem/exobrain-harness
https://github.com/bilibiliUID1480494301/relay-hub
https://github.com/Blaag/tfr
https://github.com/busabase/busabase
https://github.com/calbebop/batesian
https://github.com/can4hou6joeng4/boss-harness
https://github.com/career-ops-hq/career-ops
https://github.com/cellbang/cell
https://github.com/chaklam-silpasuwanchai/research_skill
https://github.com/chaterm/Chaterm
https://github.com/codecoradev/tole
https://github.com/cosyte/agent-skills
https://github.com/cporter202/agentic-ai-apis
https://github.com/cuijiejie/open-agent-harness
https://github.com/D4Vinci/Scrapling
https://github.com/devbasex/ai-plugins
https://github.com/developerz-ai/ultimate
https://github.com/Dicklesworthstone/agentic_coding_flywheel_setup
https://github.com/dpc00/sublime-commander
https://github.com/dpc00/vscode-mcp
https://github.com/drptbl/logos-agents
https://github.com/evolving-foundry/ember-rsi
https://github.com/facron2004/pi-desktop
https://github.com/fitzss/valun-open
https://github.com/fr0ster/llm-agent
https://github.com/frankalcantara/Arestrix
https://github.com/FrozenYty/dsh-team
https://github.com/gekkyo/animate-mcp
https://github.com/gitpoo/imaginal
https://github.com/gke-labs/kube-agents
https://github.com/godic97/Gingoa
https://github.com/gschaetz/humpyard
https://github.com/hai-series/hai-framework
https://github.com/harshanandak/forge
https://github.com/HoeTee/shipwright
https://github.com/homeassistant-ai/ha-mcp
https://github.com/IAI-Researching/Performance-Comparison-Demos
https://github.com/IvanFlores/playwright-automation-framework
https://github.com/JesunAhmadUshno/vllm-serving-lab
https://github.com/jionpz/keel
https://github.com/Jitsusama/agentic-harness.pi
https://github.com/K-kiron/dutyloop
https://github.com/kauki-labs/sandy
https://github.com/KAVentures/openworkgraph
https://github.com/khaoss85/agent-crm
https://github.com/kingsoftcloud/agentkit-studio
https://github.com/koala73/worldmonitor
https://github.com/KyaniteLabs/ag4te
https://github.com/langfuse/langfuse-docs
https://github.com/Leoglez10/finanzas
https://github.com/liamphmurphy/vesper
https://github.com/lnardev/lean-agent-harness
https://github.com/lobster0113/stalker
https://github.com/localtaskrepo/lotar
https://github.com/luongnv89/herdr-github
https://github.com/mchasse73/aegisx9
https://github.com/mdiri-ai/SoduWork
https://github.com/michael-berardi/ultracompress
https://github.com/millathossain115/ai-forward-learning-client
https://github.com/minhtran3124/agent-harness
https://github.com/MoltyCel/moltrust-api
https://github.com/MrDHat/noma
https://github.com/msaifkms/LLM-Gateway
https://github.com/MusadiqUrRahman/whaagent
https://github.com/mvanhorn/last30days-skill
https://github.com/openai/codex-security
https://github.com/oscarka/deepseekharness-remotecontrol
https://github.com/Parcha-ai/agentrun
https://github.com/philals/mission-control-capcom
https://github.com/pnedfff/claude-env-cleanup
https://github.com/polysius/nl-analytics-agent
https://github.com/puppyone-ai/puppyone-cloud
https://github.com/Rayyan-Oumlil/OpenProxyAI
https://github.com/reliant-labs/forge
https://github.com/rgdevment/Tisty
https://github.com/rlwillen0121/identity-eval-harness
https://github.com/rnikadi/genaiopengateway
https://github.com/roomi-fields/roomis-grillhouse
https://github.com/sad-and-bad1231/awesome-LLM-inference-systems
https://github.com/SanvioLabs/sanvio-factory
https://github.com/searchware73/skills-maker
https://github.com/shuruheel/mindgraph-mcp
https://github.com/smithersai/smithers
https://github.com/specnaut/specnaut-cli
https://github.com/steelhead99x/rind-agent
https://github.com/stevenmcconnon/multi-agent-finance-example
https://github.com/superserve-ai/superserve
https://github.com/tenalisriharsha/shadowci
https://github.com/TerrenoLabs/terreno
https://github.com/TimothyVang/deep-mode
https://github.com/tobocop2/lilbee
https://github.com/Toolbox-POD/toolbox-pod-mcp
https://github.com/true-line/agent-bot-identity
https://github.com/TruffleCraft/feedbackkit
https://github.com/TTNAN/quark-skill
https://github.com/twinnydotdev/twinny
https://github.com/use-junction/usejunction
https://github.com/vanillagreencom/kendex
https://github.com/viniciusmachadovianna/skill-shard
https://github.com/weideyouni/holoaeon
https://github.com/whichguy/skill-craft
https://github.com/zch6a/build-your-own-ai
https://github.com/zchoi/Awesome-Embodied-Robotics-and-Agent
https://github.com/1ali2003ah1-lgtm/ProtonAI
https://github.com/agustinroviraquezada/clinical-deid-es
https://github.com/Ahmed28A/ovarian-cancer-dual-stream
https://github.com/alexblogoodf/glyfiq-mcp
https://github.com/alvianzf/AE3
https://github.com/andreibesleaga/AgenticMedicalImagingHelper
https://github.com/AndrewPJ2002/AI-Clinical-Mental-Health-Support-Platform
https://github.com/Annapurnar06/alterra
https://github.com/assiaezzayani-developer/GreenShield-AI
https://github.com/awslabs/hcls-agent-skills
https://github.com/berntpopp/litvar-link
https://github.com/CaiZongyuan/clinmesh
https://github.com/Chetanprince213/healthcare-ai-data-platform
https://github.com/concept2cure/ClinicalSageAI-2-replit
https://github.com/damsolanke/aarogya-atlas
https://github.com/dareinelt/lanpa
https://github.com/dhruvsave29/cognistream
https://github.com/elong0527/ai4csr
https://github.com/farruxshernazarov5-dotcom/med1.uz
https://github.com/Gaogxx/MedicalGPT_agent
https://github.com/gil906/devpost-buildhybridai-2026-31808
https://github.com/godasrinivasulureddy/Fame-Genix-Agentic-AI-2.0
https://github.com/Guitrentz/clinical-ai-deterministic-harness
https://github.com/HusseinYassineMD/ClinicalCore_Aim1
https://github.com/ilay-fisher/vitalstream
https://github.com/JoshuaOforisurg/surgical-data-platform
https://github.com/KennethJSmithDev/DICOM3D_AI
https://github.com/kollaprudvi79-ai/clinical-trial-operations-dashboard
https://github.com/lakshmisanthosh123/healthcare-ai-platform
https://github.com/lawrencelucado/coding-audit-copilot
https://github.com/mbzuai-ai4bio/BioXArena-Pro
https://github.com/mianaz/biomed-skills
https://github.com/mohammadazam7/clinical-data-lakehouse
https://github.com/muhammadusamakhan911-bit/fabric-mlv-agent-healthcare-starter
https://github.com/pedropcamellon/folium
https://github.com/Prashanthbkm/MediLens-AI
https://github.com/pv-tech28/PCOS360
https://github.com/Rhushabh1/SugenICD
https://github.com/saikumarreddy2902/MediSphereAI
https://github.com/sisovin/healthcare-ai-desk
https://github.com/smaniches/uniprot-mcp
https://github.com/Snigdha-0210/Cancer-multi-agent
https://github.com/suresh121011/SEHAT-AI
https://github.com/Swethavenna-github/clearbill
https://github.com/veganbeef-96/lastehr-platform
https://github.com/VenkatesaPerumal-23/HealthCare_Agent
https://github.com/wetware-hq/chart
https://github.com/xuyul6/med-agent-runtime-v1
https://github.com/zlatedod/multi-agent-system-for-healthcare
https://github.com/12alankrita11srivastava2610-droid/nutrition-agent
https://github.com/A0966411725-png/dietsense-proposal
https://github.com/aderaaij/loopback-training-server
https://github.com/Amna-Coder404/ByteDiet
https://github.com/ayhamimarati/Bourmdane-mohamed
https://github.com/bigidrise/my-perfect-meals-npm-dev
https://github.com/chakravartiavinit/relife
https://github.com/DavidRamms/plant-based-research-hub
https://github.com/DragonScripter/pulse-health-analytics
https://github.com/drew-weise/Provisions
https://github.com/Dzarlax-AI/health_dashboard
https://github.com/edisontaimu9-ui/thanzi-screening-engine
https://github.com/ehtesham8958873-ops/caloriewise-ai
https://github.com/faisal-abdelnase/NutriVision
https://github.com/ganeshvarun40-maker/BMI-Health-Dashboard
https://github.com/git-macko/resolution-fitness-app
https://github.com/Javiguegadev/personal-trainer
https://github.com/jollyjoker992/garmin-archive
https://github.com/Justusability/license-label
https://github.com/kdinya/smart-diet-ai
https://github.com/lgf2111/snapbite
https://github.com/manoj234-pull/DASH-Meal-Prep-AI
https://github.com/merickvaughn/lifting-logbook
https://github.com/mkpoli/health-tracker
https://github.com/munimtechnologies/open-food-safety-check
https://github.com/muscleshubfit-cpu/alkemos-aws
https://github.com/nikhilramakrishna3005/nutricart-ai
https://github.com/NurtureAISpace/NurtureAISpace.github.io
https://github.com/OSerHuang/caldoo
https://github.com/PatricioMR/nMRPro-H---1D-Metabolomics-Platform
https://github.com/PedroPinho17/calorie-tracker
https://github.com/petrovski-stefan/nutritionals
https://github.com/PrimeCaster/JustGetFit
https://github.com/prokesmic/HealthUniverse
https://github.com/raslmm/safebite
https://github.com/Rmjjke/BabyBloom
https://github.com/RuneYang123/fitness-companion
https://github.com/rupal2k/openfit
https://github.com/sabasgh/food-rag-chatbot
https://github.com/Sculpt-AI/nutrition
https://github.com/Smith3939/smit-gym
https://github.com/Soorya2201/Immuny
https://github.com/spoonjoy/spoonjoy-apple
https://github.com/ulko31/sofe
https://github.com/xLightBringerx/pocket-coach
https://github.com/ai-verification-observatory/inference-proofs
https://github.com/aidless/kv-serve-lab
https://github.com/aiverse-filmmakers/AI-Verse-Memory
https://github.com/andrewyng/openworker
https://github.com/AndryanZulfi/hermes-server-stack
https://github.com/apache/pinot
https://github.com/api7/aisix
https://github.com/awslabs/unified-kg-rag-on-aws
https://github.com/beehive-lab/jitllm
https://github.com/bowenliang123/dsh-context
https://github.com/cookerpapa/pi-managed-agents
https://github.com/cosyte/cli
https://github.com/dakotacookson/Llama-RPC-Farm
https://github.com/Drgr33nSenior/Spry.ai-rdna4-serving
https://github.com/elnomic/fesnuk
https://github.com/evoila/meho
https://github.com/farming-labs/farm.js
https://github.com/fluffyled/enterprise-rag-framework
https://github.com/ginuerzh/cuttle
https://github.com/honeybee-studio/honeybeepf-llm
https://github.com/humemai/tiervec
https://github.com/hyunnnchoi/vllm
https://github.com/jingwenzz/miniserve
https://github.com/joseoper/token-diet
https://github.com/junxnpng/junxnpng.github.io
https://github.com/khaneilcampbell/ai-ops-atlas
https://github.com/luharry2821/pingo-voice-backend
https://github.com/luqman-v1/9router-go
https://github.com/MacCracken/hoosh
https://github.com/masterledgerlive/koda-kai-decoder
https://github.com/mfittko/dev-loops
https://github.com/mihazs/agent-harness-lab
https://github.com/mrveiss/AutoBot-AI
https://github.com/nfsnyy/signet
https://github.com/nikoma/bicdb
https://github.com/npspringmeyer/clinical-guideline-rag-eval
https://github.com/olathedev/hedgerow
https://github.com/openai/openai-agents-python
https://github.com/pingcap/tidb
https://github.com/PNUops/pickle-llm-gateway
https://github.com/Praneeth16/kumo-fraud-databricks
https://github.com/pratiksinghchauhan/framework-compass
https://github.com/radiatedsaturn7/automated-llm-proxy
https://github.com/rafiebazine/rag-image-captioning
https://github.com/railgrid/railgrid
https://github.com/ridsood90/agentic-ai-lab
https://github.com/riffdb/riffdb
https://github.com/ROCm/rocm-cli
https://github.com/rrnewton/agent-utils
https://github.com/sankalp-rajeev/Nexus
https://github.com/seckinozbek1/project-shimmer-deployment
https://github.com/Senguk520/dsh-multitask
https://github.com/Spongy01/Conduit
https://github.com/storytold/craft-anything
https://github.com/sudarshan1x1/efficient-inference
https://github.com/supportauranova23-max/-optimax-llm-router.
https://github.com/tachyon-zcash/ragu
https://github.com/Theorvane/OpenGranter
https://github.com/westhong/astro-dashboard
https://github.com/xiseliuli/dsh-as-mcp