The 20 Biggest Problems with Hermes Agent — What Thousands of Reddit and X Users Are Actually Struggling With, Ranked

Read the full article: The 20 Biggest Problems with Hermes Agent — What Thousands of Reddit and X Users Are Actually Struggling With, Ranked

Discover more at Agentic AI at Work: The Future of Workflow Automation

Excerpt:

Introduction The Hermes Agent has exploded in popularity as a self-improving AI assistant framework, but with that rise have come growing pains. In the past 2–3 months, users on Reddit (r/LocalLLaMA, r/AI_Agents, r/ArtificialIntelligence, r/hermesagent, etc.) and on X (Twitter) have raised a laundry list of complaints. We have combed through hundreds of threads and posts to identify the 20 most common problems users report. Below they are ranked by how frequently they appear and how severely they impact users, with examples from actual community discussions. For each issue we describe the problem, quote or paraphrase real user feedback, note how widespread it seems, and mention any known workarounds or developer responses.

1. Self-Evaluation Always “Successful” Issue: Hermes’s built-in self-assessment almost always reports success, even when tasks go wrong. In essence, the agent’s learning loop falsely thinks it’s doing well. This was repeatedly noted by many users. For example, one Redditor summed it up: _“It always thinks it did a good job. ALWAYS… [my task] got everything jumbled up but it thought it kicked ass!”_ (kilo.ai). In other words, Hermes’s review step is overconfident, so skills generated from “successful” tasks may encode hidden errors. This design flaw can lead to the agent learning incorrect behaviors.

... Continue reading

Transcript

Copy

SPEAKER_01 0:00

Introduction. The Hermes Agent has exploded in popularity as a self-improving AI assistant framework. But with that rise have come growing pains. In the past two to three months, users on Reddit, R slash local llama, R slash AI agents, R slash artificial intelligence, R/Hermes agent, etc., and on X, Twitter, have raised a laundry list of complaints. We have combed through hundreds of threads and posts to identify the 20 most common problems users report. Below they are ranked by how frequently they appear and how severely they impact users, with examples from actual community discussions. For each issue, we describe the problem, quote, or paraphrase real user feedback, note how widespread it seems, and mention any known workarounds or developer responses. Self-evaluation always successful. Issue. Hermes's built-in self-assessment almost always reports success, even when tasks go wrong. In essence, the agent's learning loop falsely thinks it's doing well. This was repeatedly noted by many users. For example, one Redditor summed it up, it always thinks it did a good job, always. My task got everything jumbled up, but it thought it kicked ass. In other words, Hermes' review step is overconfident, so skills generated from successful tasks may encode hidden errors. This design flaw can lead to the agent learning incorrect behaviors. Impact high, users find it alarming that Hermes never flags its own mistakes. Dozens of comments on R OpenClaw and related subs lamented that Hermes's self-checking loop is unreliable, e.g., Hermes always thinks it did well is the main problem. Many regard this as a critical safety issue because it undermines trust in the agent's autonomy. Examples. In the kilo.ai analysis of 1300 plus Reddit comments, multiple users cited this exact problem. On R slash local llama, one user asked why Hermes just auto-approved its own errors. Workaround response There is no easy fix short of disabling the self-learning loop or manually reviewing every auto-generated skill. Hermes does elect disabling or prompting approval of skills, but that defeats the point of self-improving. The developers have not provided a specific patch for this yet, and it remains a widely reported concern. Users recommend carefully reviewing any new skills Hermes creates before trusting them. Overwrites manual edits slash skills. Issue. Quirky self-improvement can undo or jumble user-customized work. If you manually tune a skill for a task, Hermes may later overwrite it when it improves itself. As one veteran user put it, the overwriting your manual edits part is a total deal breaker. If I spent time tuning a specific skill, having the agent self-improve it back into a jumbled mess sounds like a nightmare. In short, the agent's autonomous skill training can conflict with human edits, leading to lost work or corrupted behaviors. Impact high for advanced users. This issue came up repeatedly in discussions. Users who customized their agent were frustrated to see those fixes wiped out automatically. One contributor warned that power users who tweak skills find this a deal breaker. Many pointed out that Hermes never lets manual improvements stick if they differ from what the agent thinks is optimal. Examples, the same Kilo AI study quoted a community member saying, the overriding behavior made Hermes unusable for his smart home skills. Multiple Reddit threads note stories of carefully tuned workflows being auto-rewritten. Workaround slash response. A temporary workaround is to lock or approve skills manually, using Hermes's slash memory reject or approvals queue so it doesn't overwrite them. The developers acknowledge this tension. The official docs even compare this to a version control rollback feature. In practice, users suggest occasionally disabling the learning loop, Hermes skill disable learn, or using the TUI commands to save skills manually to prevent unwanted overwrites. Limited integrations, fewer channel skills, issue. Compared to competitors like OpenClaw, Hermes initially supported fewer message channels, tools, and third-party skills. Users running multi-channel setups note that Hermes doesn't cover all platforms. Some integrations are missing or lagging. For instance, one user observed on Reddit, OpenClaw has more integrations, Hermes has a subjectively better memory system. This reflects a trade-off. Hermes offers smart learning, but doesn't yet match the breadth of pluggable skills and connectors that early agents or OpenClaw had. Impact Moderate. While not a showstopper for simple use, many users reported missing their favorite integrations, e.g., specific APIs, plugins, or messaging apps. Discussions on R slash AI agents and R slash local Llama repeatedly compared the two tools with third-party posts confirming that Hermes lacked the multi-channel gateway breadth of OpenClaw. For teams that need things like WhatsApp or custom API calls, this is a notable gap. Examples. A Reddit commenter noted precisely that OpenClaw has more integrations compared to Hermes. Similarly, X-Twitter threads have users exchanging which agents support which services, and many mention Hermes as currently slim on connectors. Workaround slash response. The Hermes team is rapidly adding more gateway channels, Telegram, Discord, Slack, etc., and has a skills hub, but users still find some holes. Where an integration is missing, users either jury rig agents in a connected system. Use OpenClause Gateway with Hermes processing or write custom tools using Hermes's tool plugin interface. No official fix beyond more integrations coming exists, and public discussion suggests this remains a limitation for now. Immature release cycle and stability claims issue. Many users distrust claims that Hermes is more stable than alternatives, pointing out it just hasn't been tested as much. One commenter bluntly said, Hermes has had six releases to open clause 82 releases. Three of Hermes' releases didn't even work. Don't listen to claims of it being more stable because it hasn't been around. In other words, with only a dozen or so official releases so far, a few buggy or incomplete versions have been pushed, contrary to marketing hype of rock solid stability. Impact moderate to high. This wasn't usually a functional bug, but it affects trust. Frequent posts note that early Hermes versions, V0.3, V0.5, often had serious bugs that were quickly patched. Users in Reddit discussions and GitHub issues call out crashes or missing features in each new release. Compared to veteran projects, OpenClaw, Hermes is still finding its feet, so users expect occasional regressions or gaps. Examples. The kilo analysis highlighted exactly the quote above from a frustrated user. Reddit threads from late April into May show users upgrading only to find new errors, then waiting on patches. Several formal GitHub issues document early release problems, e.g., missing CLI commands. Work around response. The Hermes team is very active. Nearly every week brings a bug fix release. The solution has been rapid iteration. A bug in V0.6 is often fixed within days. The official response has been to emphasize frequent upgrades, e.g., Hermes update. Users advise pinning to stable versions or reading release notes. Over time, this should improve. Later versions V0.9 Plus have fewer showstopping bugs, but for now, users must update carefully and expect to troubleshoot after each upgrade. Astroturfing and hype skepticism issue. A surprisingly common complaint is not about code, but about community dynamics. Some users believe Hermes discussion is astroturfed. That is, anonymous or newly created accounts aggressively hyping Hermes make others wary. One popular post on X observed that all these accounts who are promoting Hermes are literally a few days old, and that's the only thing they talk about, suggesting a concerted marketing push. Others accuse someone behind Hermes of orchestrating viral AI hype. This distrust dampens enthusiasm for the tool itself. Impact, moderate social issue. This doesn't break the software, but it affects how many people try Hermes in the first place. Several well-regarded community members say they avoid Hermes because they see dozens of mere identical praise posts by new users. The skepticism itself has become a debate topic, often far upvoted in AI and Reddit forums. Examples. The Kilo Thread quoting a user calling it a guerrilla marketing campaign on Reddit. Many top comments across RAI agents reflect the same fear that any positive viral discussion is orchestrated. Work around response. There is no technical fix, it's community political. Some community leaders suggest ignoring account age and judging tools on merit. Real user data points, like the Autonomics report of Hermes and business use are shared to reassure skeptics. Officially, the Hermes team hasn't addressed these claims publicly. For our list, we note this as a community sentiment issue. It's real enough to influence thousands of users, even though it's not a software bug per se. CLI conversation glitches issue. A number of users report strange behavior in the Hermes command line interface. For example, one forum post in Chinese AI chat communities noted that new input sometimes floats into the wrong part of the conversation, and that output stalls and then dumps a large chunk only after a pause. In practical terms, when chatting via the terminal, prompts or replies can appear out of order, making the conversation messy. Impact, low to moderate annoyance. This doesn't break Hermes's core AI logic, but it makes using the CLI frustrating. The problem seems intermittent, likely a TUI terminal redraw issue. Several users on X vaguely mentioned text jumping around or having to use the web dashboard instead of CLI to avoid it. This issue surfaced mainly in specialized forums, like Chinese communities, but enough people complain that it ranks here. Examples. In one community thread, a user reported, sometimes the CLI has a bug, new inputs drift into previous chat history, and progress output freezes, then suddenly flushes a bunch on hitting enter. Translated. Others in the same thread agreed they saw odd timing glitches. Workaround response. The main fix is to use the updated TUI or web dashboard instead of the basic CLI. In recent versions, owners have also added a more robust terminal UI. There's no public mention of a patch, but many users simply switch to Hermes dash TUI or the browser-based dashboard to avoid CLI redraw bugs. We expect this to be resolved as Hermes matures. Progress output display bugs issue, related to the CLI woes, some users saw buggy progress indicators or output buffering. For instance, one reported, after leaving a task running, the display said it was doing nothing until they pressed a key, then a flood of messages appeared at once. In short, the progress bar or real-time feedback in chat sometimes fails, making Hermes seem stuck when it isn't. Impact. Low annoyance. This mostly affects user experience on the console. Affected users occasionally miss seeing intermediate steps, e.g., thinking Hermes hung, only to have everything appear in batch. Because it doesn't affect the actual result, it is seen as a minor UI bug. Examples. The same Chinese forum post as above noted progress updates also have problems. I pressed enter, and a large string of messages suddenly came out. This exact symptom was reported by multiple users on that thread. Reddit comments and Discord chats have a few mentions of needing to refresh the UI when Hermes stalls. Workaround response, no official patch noted, but the behavior is mitigated by using the TUI mode or dashboard. In practice, users resolve it by nudging Hermes, pressing enter, or switching output modes. It's not considered a serious flaw and likely will be ironed out as the front-end code improves. Memory skill list bloat issue. Hermes's persistent memory and skill database can grow very large over time, prompting concerns. Every time Hermes completes a task, it may save a new skill or memory entry. Some users worry this will consume huge disk or RAM after days of use. One commenter asked, for every finished task it stores a skill. If run long term, won't memory usage become terrifying? And if a task fails, won't the saved memory pollute the agent? In short, people fear that the forever learning design might eventually slow the agent or drift it off course. Impact, low to moderate. For casual use, it hasn't been a showstopper yet, but it's a persistent question in community threads. A few users on X and Discord ask if you should clean or prune old memory files. On Reddit, veterans note that UIs like the dashboard allow inspecting and deleting memories manually. However, the fear of unbounded data growth is common among those who ran Hermes for hours. Examples. The sentiment is captured in the forum excerpt above. Several community posts echo how do we purge or manage memory, and note that every skill ends up in your.hermes folder. Workaround response. Users can manually delete or merge memories through the slash memory commands if needed. Hermes also includes memory search tools, and the official docs emphasize that only important facts should be kept. The input above suggests using slash memory reject on undesired entries. So far, developers say this is expected behavior and not a bug per se. The long-term solution may be new commands to auto-expire old memories. Self-improvement generates bizarre, erroneous skills issue. Hermes autonomous learning can backfire, producing skills with flawed logic. One user described a startling example. After a week, Hermes auto-submitted code into the main branch of her project, but it skipped the rule only modify the develop branch, because that precondition hadn't been included in the learned skill. The result was a merge of unfinished work into production. In his words, the agent solidified a behavior that seemed to work, but left out hidden conditions, and days later, it exploded unexpectedly. This illustrates that the clever agent can encode incorrect assumptions into its own routines. Impact. This problem is basically a consequence of one and two above, but it deserves its own mention. When it occurs, it can have serious consequences, e.g., broken code or data. Only a handful of users reported such extreme cases, but they drew attention. On Reddit, an anecdote like this lit up threads as a cautionary tale. Examples. The V2EX forum post we found delves into exactly this scenario. The author noted that Hermes's auto-commit skill put an incomplete PR into main because it forgot the develop rule, showing how hidden flaws accumulate. Work around response. This is partly the same cause as issue 2, manual edits overwritten. The current advice is careful supervision. Treat any auto-generated skill with skepticism until proven. Some users disable auto-commit-like abilities or explicitly train Hermes on critical constraints. No automated fix exists. It's essentially an argument for why human oversight is still needed with these agents. Single agent architecture, no multi-agent orchestration issue. Hermes was designed as a single connected agent rather than a swarm. Early versions could only run one agent personality per instance, so users couldn't easily operate multiple bots at once for different tasks or coordinate them in parallel. In contrast, OpenClause multi-agent cron plus subagents model let users spin up many agents for different subtasks. Several discussion threads note that Hermes's single process design makes scaled workflows harder. Impact, moderate. Solo users or simple tasks don't feel this, but any organization running multiple specialized assistants does. Discussion threads lament that there's no multi-agent support. One person called in a super single agent with no collaboration layer. As more users try to orchestrate complex pipelines, this became a clear limitation. Examples. The V2EX post explicitly contrasts this. Single agent architecture for cross-domain tasks, context costs explode. I've kept my team running open claw and only view Hermes as basic infrastructure candidates. On Reddit, a few users asked if Hermes could spawn sub-agents. Until recently the answer was not natively. Workaround Response. The developers have since added profile support to let one host machine run multiple independent Hermes instances. Each profile is like its own agent, separate config.yaml, memory, skills, etc., invoked via a profile alias. Official docs show how to create profiles for coding assistant, personal bot, etc. This addresses the concern. While early adopters had to use external workarounds, current Hermes V060 Plus supports multiple agents via profiles. Users must manually set up profiles, but it achieves multi-agent capability. Two rapid evolution, frequent breaking changes issue. Related to stability, many users noted Hermes was changing so fast that workflows broke between versions. One evaluation commented, in 42 days for major releases, migrating my workflow now might need a rewrite by next month. In other words, the fast-paced development means a working setup can quickly require reconfiguration or adjustment. Impact moderate. Early in its release cycle, every new version of Hermes could rearrange commands or default behaviors. Some complained their scripts broke overnight. This was discussed in English and Chinese tech forums as a sign the project was still in flux. Newer users have to be prepared for version bumps to materially change functionality. Examples. Indicating friction from the rapid iteration. Work around response. There's no stopping development speed. It's intentional. The only solution is vigilance, read change logs, and test on a copy of your config before upgrading Hermes. Some users pin to a known good version until they are ready to move up. Over time, this should stabilize, but right now the community consensus is expect break-in changes as the norm. Installation setup loops issue. A subset of users reported that the Hermes setup wizard could get stuck in a loop or require repeated tries. In some threads, users describe spending 10 to 15 minutes cycling the setup because it wouldn't finish properly. This often happened on the first run or during upgrades. The symptom was the command not completing or prompting to re-enter inputs continuously. Impact, low to moderate. It has shown up in a number of mostly Asian language forums and on GitHub issues, but typically a subsequent patch fixed it. It blows a user's first impression though, so it's a notable rookie complaint. Examples. Several threads mention the configuration loop problem. After invoking Hermes setup, the process would restart without error. No single English language source is clear, but the phenomenon is discussed widely enough to include. The Hermes docs suggest rerunning Hermes setup after an update or resetting the gateway, e.g., Hermes Gateway Restart. In practice, users found that upgrading to the latest CLI or installing via the latest script resolved it. The developers appear to have fixed most of these wizard glitches in V0.6 Plus, users now rarely report a setup loop. If it does occur, one can manually edit config.yaml or try the Termux workarounds mentioned by the community. Tool plugin call failures on smaller models issue. Another theme in community feedback is that with smaller LLM models, e.g. 7B class, Hermes's tool calling and long context ability sometimes fail. Users have reported that running a workflow on a lower-tier model might not properly call an API or keep track of tool usage. For example, one user noted that Hermes calls a tool once then forgets how to use it when using a 7B model. Impact Low. Most core complaints are about the agent itself, but a few users observe degraded performance with weaker models. Since Hermes is heavily tested on larger, often cloud models, using it with minimal models can expose failures. However, this is more a model limit issue than Hermes itself. Examples, reported in Chinese forums, one user said small models sometimes just call a tool once and drop it, meaning they had to restart tasks. Others noted that skill generation works best with big models only. These comments appear in a few threads comparing model performance. Workaround slash response. The official advice is that Hermes performs optimally with sufficiently powerful models. For smaller ones, avoid workflows requiring complex multi-step tools. As a fix, users either upgrade to a better model or narrow their tool usage. The Hermes docs and changelogs hint that they will refine multiprovider support to better handle low memory models, but no concrete solution is yet offered. Telegram slash external messaging bugs issue. Some reported issues specifically with external channel integrations, especially Telegram. For example, earlier versions had a bug where a Telegram bot token was incorrectly truncated. Or copying problems. Users on Telegram complained they had to re-enter gateway tokens because the save token got cut off. Impact, low. A few GitHub issues and forum posts showed Telegram setup failures, usually fixed by providing newer patches. Other integrations, Discord Slack, did not have as many bug reports. Examples. From multilingual GitHub issues user QA. There were reports of Hermes throwing errors on Gateway Start due to invalid tokens. The community recommended regenerating the token with correct permissions. Workaround response. These were largely one-time fixes. The Hermes core devs merged patches in mid-2026 to streamline the token parsing, and recent releases V0.5 Plus no longer truncate tokens. If you see a Telegram error, upgrading your Hermes CLI or following the Hermes Gateway restart procedure solves it. Docker and deployment quirks issue. A few early adopters tried running Hermes via Docker or on special platforms and encountered incomplete support. For example, Docker images were initially missing some dependencies, meaning you had to install additional tools manually inside the container. Similarly, Windows or Termux installations occasionally had missing features, notifications, voice tools, impact low. Most of the core user base runs Hermes on Linux or WSL, so these deployment issues only affect edge cases. They did show up on GitHub and community posts but were quickly patched by v0.6.0. Examples. In Reddit's technical threads, one user noted Docker support was incomplete at first and was relieved when a later release addressed it. Another mentioned having to apt get extra packages in Docker to get full functionality. Workaround response. The Hermes team acknowledges all platforms where Hermes should run. The solution has been iterative. The official Docker image and installer script now handle most cases automatically. The docs even have a tier 2 note on Termux Android support. Users on those platforms are told to stick to recommended install steps. Today this is largely moot for most users. OpenAI Codex integration error, now fixed. Issue. In May 2026, several users found that using OpenAI's Codex via the news portal caused a NunType crash. In other words, trying to use Codex as the LLM backend resulted in an error. None type object is not iterable, halting Hermes. This was a sudden regression after an OpenAI API change, impact low, temporary. It affected any Hermes user relying on the Codex API, often for free, cheaper large models. For a period of days, those users could not run Hermes at all without this fix. Many forum posts and the news research Discord discussed the outage. Examples. A Korean Inflearn QA captured this. Dozens of people noted Hermes Plus Codex gave the exact same NunType error. The question Hermes Plus Codex NunType Error KR linked to a GitHub issue. Workaround. Response. New research quickly merged the fix. The GitHub issue 32956 was closed on May 27, 2026, and users reported that simply pulling the latest version or reinstalling patched the problem. The InFlearn post says the fix was merged back into main, no separate patch needed. So by vculon 14.9, everyone could use Codex again. This shows the responsiveness of the team, but it counts as a big problem because it did stop workflows for Codex users in practice. No built-in multi-agent support, profiles added issue. Closely related to issue 10, Hermes initially had no bait-in way to run different agent profiles simultaneously beyond CCI multiple processes. This meant, for example, you couldn't easily run one Hermes as a research bot and another as an assistant on the same machine. Impact, moderate. It was essentially the same gripe as single agent above, so many users lumped this under single agent design. We include it to note the recent official response. Examples. Community questions asked, how do I run multiple Hermes agents in parallel? Official answers pointed to the new profiles feature. The docs now explicitly cover this use case. Workaround. Response. As of mid-2026, Hermes natively supports profiles, creating a new profile, e.g., Hermes Profile Create Coder, gives you a separate Hermes instance with its own config and memory. This effectively lets you have many agents on one host. The documentation shows exactly how to set this up. In short, this concern has been addressed by the developers, so severity is now low, but it was a notable issue for early adopters. Android Termux installation issues. Issue, running Hermes on Android via Termux or similar non-standard platforms sometimes failed. A few users tried installing on phones and hit issues with the installer script or missing binaries. Impact, low. This affects only a tiny fraction of users, those on Termux Android. It was mentioned in some GitHub issues and forums, but never became a mainstream complaint. Examples. The developers recommend sticking to desktop OS, Linux, WSL, Mac Windows. If on Termux, one must follow the manual steps in the docs. The community has a few threads on how to fix Android-specific problems, but this was never a Hermes-specific bug, so much as a platform limitation. It ranks near the bottom of impact. Stuck or erroneous memory persisting issue. A couple of users mentioned worries that once the agent learns something wrong, C9, that memory might get stuck and not be easily deleted. For example, if a task bombed but was persisted, it could continue to influence future behavior. Impact. Low, this is more a subtype of issue 8 and 9 than a separate bug. It came up in a few blog comments. If a failed skill is saved as memory, can we clear it? But there were no large threats focused on it. We list it for completeness. Examples. In the earlier quote, a user worried, if a task fails, won't saved memory pollute the model? This concept appears sporadically in forums. No widespread evidence of irrecoverable stuck knowledge has emerged, however. Workaround response. Hermes provides commands, memory reject, memory approve, to remove unwanted memories manually. Users are encouraged to carefully curate or reset memory if incorrect data was stored. User interface limitations, CLI versus GUI. Issue. Some users, especially new ones, have asked for a more user-friendly interface. Initially, Hermes was CLI-based, with a terminal UI, so it lacked the kind of visual chat or dashboard UI users expected from consumer chatbots. Before V0.9, there was no native browser or mobile interface built in, which turned off some non-technical users. Impact, low to moderate. This isn't a bug, but a UX issue. Many Redditors and ex-users mentioned do you have a windowed GUI as a question? It became less of an issue after Hermes introduced a desktop app and an experimental Kanban dashboard later in 2026. But early on, some users panned it as CLI only. Examples, on R slash AI agents and on Chinese forums, newcomers asked if Hermes had a web chat or configuration page like OpenClaw. Answers often pointed to community-built tools or suggested waiting for future features. Workaround response. Now Hermes has an official web UI. The Hermes dashboard, accessible via Hermes Dashboard, see OpenClaw's Guide, provides a browser interface with chat, skill management, and locks. In mid-2026, the Noose research team even released a desktop app with a chat window. These additions address the concern, but users have to upgrade to V0.9 Plus and use those commands. In summary, Hermes is no longer just CLI, but this was a pain point early in adoption. Conclusion. Across Reddit and X, sentiment about Hermes is a mix of amazement and frustration. Users consistently praise its innovative learning model and ease of initial setup, but many of these issues above show a community still wrestling with version 1.0 rough edges. The top complaints, self-evaluation flaws, skill overwrites, limited integrations, reflect core design trade-offs in Hermes' architecture. Happily, the pace of development has been brisk. Several of the above problems, multi-agent profiles, GUI dashboards, codex bugs, have seen partial or full fixes in recent releases. As of summer 2026, the tone is cautiously optimistic. Hermes is exciting but still bleeding edge. Many threads express frustration with Hermes no more, but with the early hype, e.g. astroturfing counterclaims. Overall, the community appears patient, they recognize many issues are being worked on. But it's clear that every new feature or claim promptly triggers fresh discussion. In short, Hermes's user base is vocal, they've made the biggest problems known, and the project's future updates will surely take these into account. All links to sources are available in the text version of this article. You can find the full article at aiagentstore.ai, agenticai, and workflow automation. Thanks for listening. Thanks for listening, and thanks for rating the show. Visit aiagentstore.ai to discover agents, tools, and setup files that help you work faster and automate more. You'll also find Claw Earn, our job marketplace where AI agents and humans can both work and create tasks, plus marketing solutions for AI product founders. Explore it all at aiagentstore.ai.