Daily AI Implementation Scout Council

Today's ranked top 20

#1

build nowBoth runtimes30 / 35

Omnigent repo

An open source meta harness, Apache 2.0, that gives you one orchestration layer over Claude Code, Codex, Cursor, OpenCode, Hermes and Pi: swap or combine harnesses without rewriting your setup, enforce policies and sandboxing on what any of them can do, and collaborate on a live session from any device.

What it does for you: YY already runs two harnesses side by side, Claude Code for this workspace and Hermes for the Telegram bridge, with no shared policy layer between them. Omnigent names Hermes by name as one of the harnesses it orchestrates, which means it is a direct fit for putting one set of sandboxing and approval rules over both instead of maintaining two separate permission setups. It also gives a teammate a link to follow a running session live, which maps onto how YY currently has to describe what Claude is doing rather than show it.

In practice: Young but unusually well aimed: a team backed by Databricks built the exact cross harness control layer that a Claude Code plus Hermes shop like this one has been missing, and it grew fast because other shops have the same two harness problem.

For: Both runtimes. It is a meta harness, a layer that sits above Claude Code, Codex, Cursor, Pi and Hermes and lets you swap or combine them, so it is explicitly built to run both runtimes rather than belonging to one.

Security4

Quality5

Auditability3

Useful to you5

Useful to community4

Buildable now4

Hermes5

Verdict: build now. Apache 2.0, a PyPI package, and it is the only tool found this run that explicitly supports both of YY's actual runtimes rather than one. Low setup cost (pip install) against a real, named gap.

Build #1 Omnigent: use the ai-implementation-build-intake skill to build this safely. Source: https://github.com/omnigent-ai/omnigent. Save canonical skill/agent under AXION\Skills and AXION\Agents. Copy

Source · Open sourced under Apache 2.0 by the Databricks AI team, reaching over 8,500 GitHub stars within two months of launch per Databricks' own blog post and a Help Net Security report dated 6 July 2026. Ships as a PyPI package (omnigent) with a live Discord community.

#2

build nowBoth runtimes29 / 35

Arize Phoenix repo

Arize Phoenix is an open source, OpenTelemetry native AI observability and evaluation project: it captures agent traces, runs RAG evaluation and LLM as judge metrics, and keeps a dataset store for comparing runs, installable as a single pip package with no external database required.

What it does for you: This very newsletter skill has a documented history of running out of context and thrashing on the same failed command. A local Phoenix trace of a run like this one would show exactly which tool call, prompt, or context size caused the blowout instead of relying on a postmortem written after the fact. It is the lightest weight of the observability tools found this run, which matters on a single Windows box with no spare infrastructure to run.

In practice: Mature and unglamorous in the best way: it does one job, tracing and evaluating LLM calls, and does not ask for a database cluster to do it.

For: Both runtimes. It is an OpenTelemetry native tracing library with Python and JS clients, language and harness agnostic, so it works the same way whether the traced calls come from Claude Code or a Hermes session.

Security4

Quality5

Auditability4

Useful to you4

Useful to community4

Buildable now5

Hermes3

Verdict: build now. Pip installable, no external service needed, 11.7k stars, and it solves a concrete, already documented problem (unattended runs failing silently) rather than a hypothetical one.

Build #2 Arize Phoenix: use the ai-implementation-build-intake skill to build this safely. Source: https://github.com/Arize-ai/phoenix. Save canonical skill/agent under AXION\Skills and AXION\Agents. Copy

Source · 11.7k GitHub stars, 1.2k forks per the repo header checked this run. Arize AI backed, OpenTelemetry native, pip install arize-phoenix with no external database dependency.

#3

test firstBoth runtimes28 / 35

Langfuse repo

Langfuse is the leading open source agent evaluation and observability platform, MIT licensed, covering tracing with multi turn conversation support, prompt versioning with a built in playground, and evaluation through LLM as judge, user feedback, or custom metrics.

What it does for you: Where Phoenix is the light weight option, Langfuse is the fuller platform: prompt versioning and a playground would let YY compare how a skill prompt change actually performs before shipping it, instead of judging by feel. It is the more credible long term home for observability across every AXION automation if YY ever wants one shared dashboard rather than per tool traces.

In practice: The default answer when anyone asks what open source LLM observability tool to use, which is exactly why it carries more setup weight than the newsletter skill's own problem actually needs today.

For: Both runtimes. It is a self hosted, language agnostic tracing and evaluation platform reached over an SDK or API, so any harness that can make an HTTP call, Claude Code or Hermes, can send traces to it.

Security4

Quality5

Auditability4

Useful to you4

Useful to community5

Buildable now3

Hermes3

Verdict: test first. Self hosting Langfuse properly needs Postgres, ClickHouse and Redis together per its own docker compose, real infrastructure to maintain, so prove the value on one workflow before committing the stack.

Build #3 Langfuse: use the ai-implementation-build-intake skill to build this safely. Source: https://github.com/langfuse/langfuse. Save canonical skill/agent under AXION\Skills and AXION\Agents. Copy

Source · 35.4k GitHub stars, 3.9k forks per the repo header checked this run. MIT licensed, self hostable with no enterprise paywall per the project's own documentation.

#4

test firstBoth runtimes26 / 35

DeepTeam repo

DeepTeam is a framework to red team LLMs and AI agents from Confident AI, with a stable v1 release shipping over 50 ready to use vulnerabilities and more than 20 attack vectors across data privacy, responsible AI, and security, covering single turn and multi turn attacks against chatbots, RAG pipelines, and agents.

What it does for you: YY has dozens of live agent flows handling buyer messages, confirmations, and unsub logic for Ouria and MCL. Running DeepTeam against a staging copy of one of those flows before trusting it unattended would surface prompt injection or data leak paths before a live contact hits them, rather than after.

In practice: Serious and purpose built, the kind of tool you run once before trusting an agent with real conversations rather than something to leave running continuously.

For: Both runtimes. It is a Python package that red teams any LLM endpoint or agent you point it at, so it tests Claude Code based or Hermes based agents equally well.

Security4

Quality4

Auditability4

Useful to you3

Useful to community4

Buildable now4

Hermes3

Verdict: test first. Mature enough to trust for a one time pre launch check, MIT style license, pip installable, but it needs a real target agent and a read of what each vulnerability actually probes before relying on its verdict.

Build #4 DeepTeam: use the ai-implementation-build-intake skill to build this safely. Source: https://github.com/confident-ai/deepteam. Save canonical skill/agent under AXION\Skills and AXION\Agents. Copy

Source · Reported star counts vary by mirror, from about 1.9k to 3.0k, with the official confident-ai/deepteam repo showing 3.0k stars when checked this run. Stable v1 release, 50 plus vulnerabilities and 20 plus attack vectors documented in its own README.

#5

test firstBoth runtimes25 / 35

browser-use repo

browser-use is an open source Python library, agents that use the browser, letting an LLM click, type, and navigate a real browser session to complete a task, with 117k GitHub stars and 12.9k forks.

What it does for you: YY has recurring friction with exactly this problem: Sheets edits over Chrome MCP that do not stick, GHL being read only so nothing automated can write there, and render verify steps that require clicking through every button by hand. A properly sandboxed browser-use session, never pointed at a logged in GHL or live payment page per the standing hard rules, could take over the mechanical parts of render verify (clicking every link, filling a test form) and report back, rather than YY or Claude doing it by hand every time.

In practice: Huge and fast moving, the de facto standard for this category, which cuts both ways: lots of examples to copy, but also a large attack surface to read before trusting it near anything that matters.

For: Both runtimes. It is a Python library that drives a real browser from any LLM backend you configure, so it is usable from a Claude Code tool call or from a Hermes script equally.

Security2

Quality4

Auditability3

Useful to you4

Useful to community5

Buildable now4

Hermes3

Verdict: test first. The adoption number is real and the fit is real, but an agent that reads arbitrary web content is also an agent that can be steered by that content, so it needs a throwaway browser profile and a non production test before it touches anything connected to real accounts.

Build #5 browser-use: use the ai-implementation-build-intake skill to build this safely. Source: https://github.com/browser-use/browser-use. Save canonical skill/agent under AXION\Skills and AXION\Agents. Copy

Source · 117k GitHub stars, 12.9k forks per the repo header checked this run. Works with any LLM backend configured by the user, pip installable.

#6

newtest firstClaude (Axion)24 / 35

memsearch repo

memsearch is a persistent, unified memory layer for AI coding agents, Claude Code, Codex, and DSH are named explicitly, backed by plain Markdown files plus a Milvus vector database for search, from Zilliz, the company behind Milvus.

What it does for you: This is a working version of the exact problem this newsletter skill itself has been documented failing on: reading a 167KB registry or a 142KB index whole and running out of context. A tool built for the same failure mode, persistent memory for a coding agent backed by Markdown plus vector search, is directly relevant to the Second Brain upgrade plan that is still waiting on YY's picks.

In practice: Small, focused, and built by people who clearly hit the same wall this workspace has hit, not a platform play.

For: Claude (Axion). It is built and documented specifically for coding harness sessions, Claude Code, Codex and DSH are the three named in its own description, with no evidence yet of Hermes support.

Security3

Quality4

Auditability4

Useful to you5

Useful to community3

Buildable now3

Hermes2

Verdict: test first. Good fit on paper and from a credible vendor (Zilliz), but it needs its own Milvus service running, real infrastructure on a Windows box that currently runs none, so prove it on one brain folder before adopting it everywhere.

Build #6 memsearch: use the ai-implementation-build-intake skill to build this safely. Source: https://github.com/zilliztech/memsearch. Save canonical skill/agent under AXION\Skills and AXION\Agents. Copy

Source · 2.7k GitHub stars per the repo About panel checked this run. Published as a PyPI package (memsearch) with a dedicated docs site and a passing tests badge.

#7

newtest firstBoth runtimes24 / 35

Tencent AI-Infra-Guard repo

AI-Infra-Guard is a full stack AI red teaming platform from Tencent's Zhuque Lab, Apache 2.0 licensed, that scans agents, skills, MCP configurations, and AI infrastructure for vulnerabilities, plus LLM jailbreak evaluation, at version 4.5.1.

What it does for you: YY's Skills folder has over 710 entries per the skills inventory hub, built by many different sessions over months. A scan that specifically checks Skills and MCP configuration, not just chat prompts, is the closest match found this run to a tool that could audit that whole inventory for anything that reads, writes, or sends more than its description claims.

In practice: Enterprise grade and a little intimidating, built by a dedicated security lab rather than a side project, which is reassuring for a tool whose whole job is finding what else is untrustworthy.

For: Both runtimes. It is a standalone scanning platform run against targets you point it at, agents, skills, MCP servers, or AI infra, so it does not care which harness produced what it is scanning.

Security4

Quality4

Auditability3

Useful to you4

Useful to community4

Buildable now3

Hermes2

Verdict: test first. Apache 2.0, a named security lab behind it, and scope that matches the Skills and MCP audit problem directly, but run it against a handful of known skills first and check its findings make sense before trusting a full sweep of the inventory.

Build #7 Tencent AI-Infra-Guard: use the ai-implementation-build-intake skill to build this safely. Source: https://github.com/Tencent/AI-Infra-Guard. Save canonical skill/agent under AXION\Skills and AXION\Agents. Copy

Source · 6.8k GitHub stars per the repo About panel checked this run. Apache License 2.0, current release 4.5.1 dated 30 July 2026 per the project's own release notes, with Agent Scan, Skills Scan, MCP scan and AI infra scan named as its four scan types.

#8

newwatchClaude (Axion)24 / 35

awesome-claude-code repo

A curated list of tools, IDE integrations, frameworks, skills, plugins, and other resources for developers working with Claude Code, 523 stars, 630 forks, with the maintainer noting that 18 resources were added across Agent Skills and related categories on 21 September 2026.

What it does for you: A navigation aid, not a tool to run. It is the kind of list worth a periodic skim when looking for prior art before building a new skill from scratch, closer to how this newsletter itself exists to save that same search time.

In practice: A normal, honestly maintained awesome list, useful exactly as much as any such list is and no more.

For: Claude (Axion). It is a curated directory of tools and resources specifically for Anthropic's Claude Code, by its own description, not a general agent list.

Security5

Quality3

Auditability5

Useful to you2

Useful to community3

Buildable now5

Hermes1

Verdict: watch. Low direct usefulness today since it is a directory rather than a tool, but worth tracking for what gets added, and the maintainer's own disclosure about stale star counts is a good model for honesty.

Build #8 awesome-claude-code: use the ai-implementation-build-intake skill to build this safely. Source: https://github.com/jqueryscript/awesome-claude-code. Save canonical skill/agent under AXION\Skills and AXION\Agents. Copy

Source · 523 GitHub stars, 630 forks per the repo header checked this run. The maintainer's own README states 18 resources were added on 21 September 2026 and that star counts shown for linked resources are static snapshots, not live.

#9

watchBoth runtimes23 / 35

MCP Registry repo

A community driven registry service for Model Context Protocol servers, 7.3k GitHub stars, 1k forks, maintained by the Model Context Protocol organization, with its API at v0.1 and frozen for no breaking changes since late 2025.

What it does for you: This is the discovery surface lens 11 of this scouting process is built to check daily. It is not itself something to install, it is where new MCP servers like memsearch, aiogram-mcp, and others surface first, so it is logged here as infrastructure to keep watching rather than a tool to adopt.

In practice: Plumbing, not a product, and exactly as interesting as plumbing usually is until the day it breaks or a genuinely useful server shows up in it.

For: Both runtimes. It is a directory service for MCP servers, a protocol both Claude Code and any Hermes session with an MCP client can consume, so it is not tied to one runtime.

Security4

Quality4

Auditability3

Useful to you2

Useful to community4

Buildable now4

Hermes2

Verdict: watch. Valuable as a discovery surface for future scouting runs, not itself an actionable build this issue since no single standout new server surfaced from it today.

Build #9 MCP Registry: use the ai-implementation-build-intake skill to build this safely. Source: https://github.com/modelcontextprotocol/registry. Save canonical skill/agent under AXION\Skills and AXION\Agents. Copy

Source · 7.3k GitHub stars, 1k forks per the repo header checked this run. Registry API entered a v0.1 freeze on 24 October 2025 per the project's own blog, meaning no breaking changes since.

#10

watchClaude (Axion)23 / 35

Claude Code 2.1.289 repo

Claude Code 2.1.289, released 3 October 2026, adds agent.spawn for teammates plus a shared agent id across plugin hook events and idle and waiting states in the agent list, alongside fixes to plugin list and update commands showing stale copies, installed mods not loading after an upgrade, and deny rules not holding over nested compound shell commands.

What it does for you: This is the version this very unattended run and every scheduled task on this box is now sitting on or soon will be. agent.spawn and the plugin and mod reliability fixes directly affect how dependable the daily scout, ingest, and bridge routines are, so it is worth knowing what changed without needing to chase the changelog by hand.

In practice: A routine point release in isolation, the kind that is easy to ignore, but one line, agent.spawn for teammates, is a new primitive worth knowing is there.

For: Claude (Axion). This is a point release of Claude Code itself, so it only applies to the Claude runtime, not to Hermes.

Security4

Quality5

Auditability2

Useful to you3

Useful to community3

Buildable now5

Hermes1

Verdict: watch. Informational rather than something to build: confirm the installed version matches and note agent.spawn as a new capability to use later, no action needed today.

Build #10 Claude Code 2.1.289: use the ai-implementation-build-intake skill to build this safely. Source: https://code.claude.com/docs/en/changelog. Save canonical skill/agent under AXION\Skills and AXION\Agents. Copy

Source · Version 2.1.289, dated 3 October 2026, per the official Claude Code changelog generated from CHANGELOG.md on GitHub. Fixes and additions documented directly in that changelog entry.

#11

watchBoth runtimes23 / 35

LangGraph repo

LangGraph is a low level orchestration framework for building, managing, and deploying long running, stateful agents, MIT licensed, now at a v1.0 major release, modeling agent pipelines as directed graphs with nodes, edges, conditional routing, and persistent state checkpoints.

What it does for you: A technically strong, broadly used framework, but it solves a problem Claude Code's native subagents and workflows already solve for YY's actual day to day work in this workspace. Worth knowing it exists and reached v1.0, not worth building against today.

In practice: Impressive engineering aimed at a use case, custom multi agent apps built from scratch, that is one step removed from how this workspace actually operates.

For: Both runtimes. It is a Python and JS library for building stateful agents, usable from any Python or JS process regardless of which chat harness sits on top of it.

Security3

Quality5

Auditability3

Useful to you2

Useful to community5

Buildable now3

Hermes2

Verdict: watch. 42.7k stars and a genuine v1.0 milestone, but it overlaps with capability YY already has through Claude Code's own subagent and workflow tooling.

Build #11 LangGraph: use the ai-implementation-build-intake skill to build this safely. Source: https://github.com/langchain-ai/langgraph. Save canonical skill/agent under AXION\Skills and AXION\Agents. Copy

Source · 42.7k GitHub stars, 7.3k forks per the repo header checked this run. MIT licensed, now at a v1.0 release per its own PyPI version badge, trusted by companies including Klarna and Replit per its own README.

#12

test firstBoth runtimes22 / 35

RAGFlow repo

RAGFlow is an open source retrieval augmented generation engine from InfiniFlow that fuses RAG with agent capabilities, now at release 1.0.0-rc1 as of 29 September 2026, adding Knowledge Compilation to generate wikis, graphs, trees, and mind maps from documents, plus agentic RAG with low, medium, high, and ultra thinking modes.

What it does for you: The Knowledge Compilation feature, turning a pile of documents into a wiki or graph automatically, is directly relevant to the still parked Second Brain upgrade plan for YY-2nd Brain. It is a possible shortcut for the PARA and BASB restructure YY has not yet picked options for, rather than building that structure by hand.

In practice: Feature rich to the point of being a lot to evaluate at once, a platform more than a utility, which fits a deliberate Second Brain project better than a quick add on.

For: Both runtimes. It runs as its own self hosted service reached over an API, so any harness that can make an HTTP call can use it, not tied to Claude Code or Hermes specifically.

Security3

Quality4

Auditability3

Useful to you3

Useful to community4

Buildable now3

Hermes2

Verdict: test first. The Knowledge Compilation idea is a good match for the parked Second Brain plan, but this is still a release candidate and self hosting it needs Elasticsearch or Infinity plus MySQL and Redis together, so trial it on a copy of the brain, not the live vault.

Build #12 RAGFlow: use the ai-implementation-build-intake skill to build this safely. Source: https://github.com/infiniflow/ragflow. Save canonical skill/agent under AXION\Skills and AXION\Agents. Copy

Source · RAGFlow 1.0.0-rc1 released 29 September 2026 per the project's own release notes. Knowledge Compilation and agentic RAG thinking modes documented as added 19 August 2026.

#13

test firstClaude (Axion)21 / 35

awesome-claude-code-mods repo

A community catalogue of 1,745 public Claude Code mods, function hooks, scanned from GitHub and labeled with what each one can read, write, run, or send over the network, 191 stars, last scanned 4 October 2026, browsable at mods.aidojo.si.

What it does for you: YY's own hooks already gate Write, Edit, Bash, and secrets in this workspace, and this catalogue applies the same read, write, run, network thinking to every public Claude Code mod out there. Before installing any third party mod found through this or future scouting runs, checking it against this catalogue's capability labels is a cheap way to confirm it does not reach further than it claims.

In practice: A security minded community project that matches how this workspace already thinks about hooks and permissions, which is reassuring in itself.

For: Claude (Axion). It specifically catalogues Claude Code mods, function hooks, a Claude Code specific extension mechanism, so it has no meaning outside that runtime.

Security3

Quality3

Auditability3

Useful to you4

Useful to community3

Buildable now4

Hermes1

Verdict: test first. Directly useful as a pre install check for any future mod, but the labels come from an automated scan, not a manual audit, so confirm a mod's listed capabilities against its actual source before trusting the label alone.

Build #13 awesome-claude-code-mods: use the ai-implementation-build-intake skill to build this safely. Source: https://github.com/karanb192/awesome-claude-code-mods. Save canonical skill/agent under AXION\Skills and AXION\Agents. Copy

Source · 1,745 mods catalogued, 191 GitHub stars, 48 forks, last scanned 4 October 2026 per the repo header and description checked this run.

#14

watchBoth runtimes21 / 35

Microsoft Agent Framework repo

A framework from Microsoft for building, orchestrating, and deploying AI agents and multi agent workflows, with support for Python and .NET, 14.0k GitHub stars.

What it does for you: Solid, well backed engineering for building agent systems from scratch in Python or .NET. It is a fourth framework in a category, alongside CrewAI, LangGraph, and Omnigent, that YY does not currently need since Claude Code's own subagent and workflow tooling already covers this ground for this workspace.

In practice: Credible and well resourced, Microsoft's answer to the same orchestration question everyone else in this space is answering, without a specific hook into how this workspace already works.

For: Both runtimes. It ships Python and .NET SDKs usable from any process, not tied to a specific chat harness.

Security3

Quality4

Auditability3

Useful to you2

Useful to community4

Buildable now3

Hermes2

Verdict: watch. 14.0k stars and a serious backer, but it answers a question YY is not currently asking, since Claude Code's native subagents already cover multi agent orchestration here.

Build #14 Microsoft Agent Framework: use the ai-implementation-build-intake skill to build this safely. Source: https://github.com/microsoft/agent-framework. Save canonical skill/agent under AXION\Skills and AXION\Agents. Copy

Source · 14.0k GitHub stars per the repo About panel checked this run. Supports both Python and .NET per the repo's own description.

#15

watchBoth runtimes21 / 35

CrewAI repo

CrewAI is an open source framework for orchestrating role playing, autonomous AI agents, now at stable release 1.15.22 dated 16 September 2026, 59.4k GitHub stars, with a companion CrewAI AMP product for running agents in production.

What it does for you: Extremely popular and well maintained, but it is a role based multi agent framework for building agent teams from scratch, the same ground Claude Code's own subagent and workflow system already covers in how YY actually works day to day.

In practice: The biggest name in this category by star count, which speaks to how many people are solving this problem from scratch rather than to a gap in this workspace specifically.

For: Both runtimes. It is a Python framework usable from any process, with its own separate cloud product for production, not tied to one chat harness.

Security3

Quality4

Auditability3

Useful to you2

Useful to community4

Buildable now3

Hermes2

Verdict: watch. 59.4k stars and a stable, actively maintained release, but the same overlap with Claude Code's native subagent tooling applies here as to LangGraph and Microsoft Agent Framework.

Build #15 CrewAI: use the ai-implementation-build-intake skill to build this safely. Source: https://github.com/crewAIInc/crewAI. Save canonical skill/agent under AXION\Skills and AXION\Agents. Copy

Source · 59.4k GitHub stars, 8.7k forks per the repo header checked this run. Stable release 1.15.22 dated 16 September 2026 per the project's own changelog.

#16

watchBoth runtimes21 / 35

LiveKit Agents repo

A framework for building realtime voice AI agents from LiveKit, 14.6k GitHub stars, where agents join LiveKit rooms as programmatic participants to process realtime audio, video, and data, supporting voice, video, and data channels together.

What it does for you: This is built for live, two way voice calls, a different problem from the recorded reels and cloned narration MCL content work currently does. There is no concrete live call use case on YY's plate right now, so this is worth knowing about rather than building against today.

In practice: Polished and clearly built by people who run voice infrastructure for a living, aimed one step past where MCL's current content work actually sits.

For: Both runtimes. It is a Python framework for building realtime voice agents, usable from any process a harness can call out to, not tied to Claude Code or Hermes.

Security3

Quality4

Auditability3

Useful to you2

Useful to community4

Buildable now3

Hermes2

Verdict: watch. No live voice call use case exists in the current workload, so there is nothing concrete to build yet, revisit if a live voice agent need appears.

Build #16 LiveKit Agents: use the ai-implementation-build-intake skill to build this safely. Source: https://github.com/livekit/agents. Save canonical skill/agent under AXION\Skills and AXION\Agents. Copy

Source · 14.6k GitHub stars, 3.9k forks per the repo header checked this run.

#17

watchBoth runtimes21 / 35

Pipecat repo

Pipecat is an open source framework for voice agents, multimodal apps, and realtime AI, maintained by Daily and the community, 16.2k GitHub stars, built for sub 250 millisecond latency voice and multimodal pipelines.

What it does for you: The same mismatch applies here as to LiveKit Agents: optimized for live voice latency, not the asynchronous content work MCL currently does. No concrete use case is driving adoption today.

In practice: Well engineered for a problem, live latency sensitive voice, that this workspace is not currently solving.

For: Both runtimes. It is a Python framework for voice and multimodal agents, usable from any process, not tied to a specific chat harness.

Security3

Quality4

Auditability3

Useful to you2

Useful to community4

Buildable now3

Hermes2

Verdict: watch. Same reasoning as LiveKit Agents: no live voice use case exists yet to build against.

Build #17 Pipecat: use the ai-implementation-build-intake skill to build this safely. Source: https://github.com/pipecat-ai/pipecat. Save canonical skill/agent under AXION\Skills and AXION\Agents. Copy

Source · 16.2k GitHub stars per the repo About panel checked this run. Maintained by Daily per the project's own description.

#18

watchBoth runtimes20 / 35

OpenViking repo

OpenViking is a self evolving context database for AI agents from Volcengine, ByteDance's cloud arm, unifying agent memory, knowledge RAG, and skills behind one event driven system that extracts, updates, and consolidates long term memory as interactions accumulate, 39.2k GitHub stars.

What it does for you: Conceptually close to what the Second Brain upgrade plan is trying to achieve, one system unifying memory, knowledge, and skills, but it is built and marketed at enterprise scale with its own hosted Studio. Worth reading for ideas on how to structure that unification, not worth standing up as infrastructure for a solo operator today.

In practice: Impressively ambitious and clearly built for a much bigger team than this one, a source of ideas rather than a tool to install.

For: Both runtimes. It runs as its own hosted or self hosted context database reached over an API, so any harness that can call an API can use it, not tied to Claude Code or Hermes.

Security3

Quality4

Auditability2

Useful to you3

Useful to community4

Buildable now2

Hermes2

Verdict: watch. 39.2k stars and a genuinely relevant concept for the parked Second Brain plan, but the scale mismatch (built for enterprise deployment, not a solo Windows box) means read it for ideas, do not stand it up.

Build #18 OpenViking: use the ai-implementation-build-intake skill to build this safely. Source: https://github.com/volcengine/OpenViking. Save canonical skill/agent under AXION\Skills and AXION\Agents. Copy

Source · 39.2k GitHub stars per the repo About panel checked this run. Built by Volcengine with its own hosted Studio demo and documentation site.

#19

newwatchClaude (Axion)18 / 35

purlis (charter rebuild) repo

purlis is a from scratch rebuild of the diazoxide/charter tool as a cross platform desktop app on a Rust core, meant to run many harness sessions, Claude Code and Codex are named, in parallel across workspaces and repos and show which one needs attention, per the project's own ADR 0025. The old Python based charter, personas, workspaces, and memory for agentic frameworks, now lives separately at diazoxide/charter-plane.

What it does for you: This names exactly the problem YY already lives with running multiple AXION brains, skills, and agent sessions at once: knowing which session needs him without watching every window. It is the closest conceptual match to that problem found this run, which is worth tracking closely even though there is nothing usable yet.

In practice: Exactly the right idea at the wrong stage: a clean rebuild decision explained openly in its own ADRs, but still a skeleton rather than a tool.

For: Claude (Axion). It is a desktop app for running Claude Code and Codex harness sessions in parallel, named in its own README, with no mention of Hermes support.

Security3

Quality2

Auditability3

Useful to you4

Useful to community3

Buildable now1

Hermes2

Verdict: watch. The project's own README states its status plainly, not usable yet, so there is nothing to build with today despite the strong conceptual fit; revisit once a first real release lands.

Build #19 purlis (charter rebuild): use the ai-implementation-build-intake skill to build this safely. Source: https://github.com/purlis/purlis. Save canonical skill/agent under AXION\Skills and AXION\Agents. Copy

Source · Project status stated directly in its own README as M0, walking skeleton. Not usable yet, with the rebuild decision recorded in docs/adr/0025-charter-is-rebuilt-as-a-desktop-app-on-a-rust-core.md, checked this run.

#20

newwatchBoth runtimes18 / 35

dstack repo

dstack is a unified orchestration layer for heterogeneous AI compute, standardizing how to manage compute and run training and inference across GPU clouds, Kubernetes, VMs, or bare metal clusters, with release 0.21.0, Pydantic v2 and gateway replicas, dated August 2026.

What it does for you: Built for teams training or serving their own models across GPU fleets. YY's work runs on hosted model APIs, Claude and others, not self trained models, so there is no job for this tool to orchestrate in the current workflow.

In practice: Mature, well engineered infrastructure for a problem, running your own GPU training and inference jobs, that simply does not exist in this workspace's workload today.

For: Both runtimes. It is infrastructure for running GPU compute jobs across clouds, Kubernetes, or bare metal, unrelated to which chat harness issues the job.

Security3

Quality4

Auditability3

Useful to you1

Useful to community4

Buildable now2

Hermes1

Verdict: watch. No fit today since Axion and Hermes run on hosted model APIs rather than self managed GPU training or inference, revisit only if that changes.

Build #20 dstack: use the ai-implementation-build-intake skill to build this safely. Source: https://github.com/dstackai/dstack. Save canonical skill/agent under AXION\Skills and AXION\Agents. Copy

Source · Release 0.21.0 dated August 2026 per the project's own release notes, adding Pydantic v2 support and Gateway replicas.