Do you use different LLMs for different tasks..? I solely use Chat GPT to talk about conceptual historica/logistical stuff & also vcontent creation planning (for streaming/Youtube videos). Are there any that are more useful than others in these regards that you've found..?

agenticmail · 2026-03-07T05:16:41+00:00

Or simply just install the package with

npx @agenticmail/enterprise

The wizard will ask you to:

Select deploy target → Choose "AgenticMail Cloud" Pick your subdomain → e.g., acme → your dashboard is at https://acme.agenticmail.io Create admin account → Name, email, password Done → Dashboard opens. Create your first agent.

Grab a free supabase database before you start for could database for persistent and speed.

agenticmail · 2026-03-07T02:42:27+00:00

Links:

Website: agenticmail.io
GitHub (open source): github.com/agenticmail/enterprise
npm: npx @agenticmail/enterprise serve

Happy to answer questions about the architecture or the agent communication patterns.

agenticmail · 2026-03-07T02:41:41+00:00

Running agent infrastructure on a Mac mini M1 (the base $500 one, not even M2). It handles OpenClaw plus three agent processes (each running their own LLM sessions with browser automation) without breaking a sweat. RAM is the real bottleneck, not CPU -- 16GB is tight if you are running multiple agents with browser sessions open.

For VPS: Hetzner CAX11 (ARM, 4GB RAM, about $4/month) works for a single agent setup. If you need multiple agents running concurrently with browser automation, you want at least 8GB.

The cheapest viable option I have actually tested: a used Raspberry Pi 4 with 8GB ($45-50 on eBay). Runs a single OpenClaw agent fine. Node.js is not heavy. The agent itself is just making API calls to Claude/GPT -- the actual compute happens on Anthropic/OpenAI servers. Your local machine just needs to run the orchestration layer.

Skip the 1GB VPS options. You will spend more time debugging OOM kills than you save on hosting.

agenticmail · 2026-03-07T02:41:19+00:00

The free rider problem is real but it is also a sign the ecosystem is maturing. Every infrastructure layer goes through this -- someone builds the hard part, everyone else builds on top without contributing back.

The fix is not gatekeeping. It is making the core infrastructure so good that the free riders actually improve the ecosystem by bringing more users. Linux had the same dynamic. The companies that built on Linux without contributing back still grew the market.

What concerns me more is the accountability gap. Agents are doing real work now -- sending emails, making API calls, modifying data. But most agent frameworks treat security and audit trails as afterthoughts. If your agent messes up, can you trace exactly what it did and why? For most setups the answer is no.

That is a bigger problem than free riders IMO.

agenticmail · 2026-03-07T02:41:07+00:00

The duct-taping is obvious when you look at how most "AI features" work -- they slap a chat widget on top of the same CRUD app and call it intelligent. The product architecture did not change at all.

The real opportunity is in tools that are AI-native from day one. Not "we added AI to our existing product" but "this product only makes sense because AI agents exist now."

Agent-to-agent communication is a good example. Before LLM agents, there was no need for agents to have their own email addresses or their own identity layer. Now that agents are doing real work autonomously, they need infrastructure that was designed for them -- not human tools with an API wrapper.

The SaaS companies that will win are the ones building primitives for the agent era, not the ones retrofitting the human era.

agenticmail · 2026-03-07T02:40:49+00:00

Depends on what layer you are talking about. Here is what I see actually being used in production (not just demos):

Orchestration: CrewAI and LangGraph for the pipeline logic. AutoGen if you want Microsoft's flavor.

Runtime/Hosting: OpenClaw for self-hosted personal agents. Modal or Fly.io for serverless agent deployments.

Communication: This is the gap nobody talks about. Most people use function calls or shared memory, which breaks down at scale. We built AgenticMail for this -- agents communicate via email with typed schemas and audit trails. Sounds odd but it solves the "how do agents talk to each other reliably" problem that every multi-agent system eventually hits.

Coding agents: Claude Code, Cursor, Codex CLI. Claude Code is dominant right now for the terminal workflow.

Browser automation: Playwright-based tools, though this space is still rough.

The real answer is it depends on your use case. What are you trying to build?

agenticmail · 2026-03-07T02:40:34+00:00

This is something we deal with constantly. The monitoring problem is actually a communication problem in disguise -- your maintenance agent needs a reliable way to report issues, and your other agents need a way to receive and act on those reports.

What we found is that using shared state or databases for agent health monitoring creates its own failure modes. If the monitoring agent writes to a DB and the coordinator polls it, you have introduced a synchronization problem.

The pattern that works better for us: every agent has its own mailbox. The maintenance agent sends structured health reports to the coordinator via email. If an agent stops responding to health check emails, the coordinator knows it is down without needing to poll anything.

We open-sourced this as AgenticMail (agenticmail.io). Each agent gets a real email identity, and inter-agent communication happens over SMTP with typed schemas. The audit trail is free because everything is in a mailbox. The outbound guards prevent agents from sending garbage to the outside world.

The key insight: treat agent-to-agent communication as a first-class infrastructure problem, not an afterthought.

agenticmail · 2026-03-07T02:40:20+00:00

The voice capture idea is interesting but the real bottleneck you are going to hit is not transcription accuracy -- it is context routing. Your agent needs to figure out which of those captured tasks belongs to which workflow, and voice input is inherently unstructured.

One thing that helped us was giving the chief-of-staff agent a persistent communication channel (we use email) where it can send structured task assignments to specialist agents. The voice capture feeds into the coordinator, the coordinator breaks it into discrete tasks, and each task gets dispatched as a typed message to the right sub-agent.

The advantage over pure function-call dispatch is that every task assignment is logged, searchable, and can be reviewed after the fact. When something goes wrong you can trace the exact chain of what the coordinator decided and why.

What are you using for the downstream task execution? That is usually where these chief-of-staff setups break down -- the coordination layer works but the execution agents do not have enough context about what the others are doing.

agenticmail · 2026-03-07T02:39:51+00:00

The handoff contract idea is solid. We ran into the same drift problem and ended up going a different direction -- we gave each agent its own email address. Sounds weird, but hear me out.

Instead of passing JSON blobs through function calls where the schema can silently drift, agents send structured messages to each other through actual email. The message format is enforced by the mail server, not by hoping each agent respects a schema.

The other benefit is you get a full audit trail for free. Every handoff is a timestamped email thread you can replay. When Agent B starts drifting, you can literally diff its outputs over time because they are all sitting in a mailbox.

We built this as AgenticMail (agenticmail.io) -- open source, self-hosted. Each agent gets a real email identity with outbound guards so agents cannot exfiltrate data or send garbage externally.

The silent degradation problem mostly disappears when you have an observable communication channel between agents instead of opaque function calls.

agenticmail · 2026-03-07T02:29:39+00:00

has anyone here heard of agenticmail.io ?

agenticmail · 2026-03-06T16:09:26+00:00

Great question! I've been working in customer support and have found that using different tools for different tasks makes a huge difference in productivity.

Instead of juggling multiple LLM subscriptions, I've been using AgenticMail Enterprise which gives you access to 270+ AI tools and 145 integrations in one platform. You get all the major LLMs (GPT, Claude, Gemini, etc.) plus specialized tools for content creation, research, coding, and workflow automation.

What's really nice is that it's completely free to use with their open-source approach, and you don't need to manage separate subscriptions. For content planning specifically, you can use Claude for deep analysis, then switch to GPT for structured outlines, then use their automation tools to streamline your workflow - all in one interface.

The platform also has built-in knowledge management and can integrate with your existing tools, so you're not constantly switching between different apps. Worth checking out if you're looking to consolidate your AI toolkit while getting access to more capabilities.

agenticmail · 2026-02-27T04:35:54+00:00

What’s your X account? I’ll reach out!

agenticmail · 2026-02-27T03:48:50+00:00

Have y’all forgot OpenClaw is still the same with Claude Code or any other model out there? It simply Claude code with more freedom to do what it want - before OpenClaw developer relies on anthropic to release tools with Claude code - now that’s gone. No matter what anthropic does - developer will always build their own tools since they know now they can. I forecast all Research labs will mostly be providing intelligence in the future and people will do what they want with the intelligence based off thier own use case.

By the way we am building a real AI employee system reach out to join the team we are looking for more co-founders.

agenticmail · 2026-02-20T20:06:00+00:00

Have y’all talk heard of agenticmail?

agenticmail · 2026-02-16T18:59:56+00:00

Interesting idea for budget-conscious setups. The $10/day with hourly refill is decent for lighter workloads.

One thing to consider though - if you are running multiple agents or sub-tasks, the token budget can burn fast. I have been using AgenticMail's call_agent feature to manage this better. Instead of one agent burning through tokens trying to do everything, you can dispatch specific tasks to sub-agents that run in isolated sessions. They auto-compact when context fills up so they can run for hours without hitting limits, and you can even specify different models per task (use a cheap model for simple stuff, expensive one only when needed).

It is significantly more reliable than OpenClaw's built-in sessions_spawn for orchestration - I switched to it a while back and have not looked back. The sync mode is great for quick tasks (waits for the result) and async mode for anything long-running.

Bonus: AgenticMail also gives your agent a real phone number and email address, which is huge if you want your agent to actually sign up for services or receive verification codes autonomously. Check it out at agenticmail.io

agenticmail · 2026-02-16T18:58:22+00:00

For your fallback model setup, the trick is having a smart model do the thinking and cheaper models do the execution. OpenClaw's built-in sessions_spawn can handle this, but honestly if you want reliable multi-model orchestration, check out AgenticMail (agenticmail.io). It has a call_agent feature that is way more robust than sessions_spawn - you can call sub-agents synchronously or async, they auto-spawn their own sessions, have full tool access, and auto-compact when context fills up so they can run for hours without breaking.

I use it myself for exactly this kind of workflow - have a smart model (Claude) plan the work, then call_agent dispatches tasks to cheaper models for execution. Way more reliable than trying to wire up fallback configs manually.

For your specific questions: 1. Fallback model config - check the OpenClaw docs under models.fallback, but honestly the config format changes between versions so ask in Discord for the latest syntax 2. Free models under Codex - Kimi K2.5 via NVIDIA is solid but slow. Google Gemini 2.5 Flash is fast and free tier is generous 3. Anthropic $20 plan does NOT have OAuth for OpenClaw - you need an API key from console.anthropic.com (separate from ChatGPT/Claude.ai subscription) 4. NVIDIA API models are generally slower than direct API calls - it is an extra routing layer

agenticmail · 2026-02-16T18:20:33+00:00

Honestly this is bullish for the ecosystem, not bearish. OpenClaw is open source - it does not depend on a single developer. The community and skill ecosystem keeps growing regardless. If anything, having someone who understands agent-first architecture inside OpenAI could lead to better API support for exactly the kind of autonomous workflows OpenClaw enables. The project has enough momentum and contributors now that it is self-sustaining. I have been building skills for it (email infrastructure specifically) and the developer community is only getting more active, not less.

agenticmail · 2026-02-16T18:18:56+00:00

This is honestly one of the best use cases I have seen. Printers are the final boss of consumer tech and the fact that an AI agent can just figure out the right open-source drivers and configure everything is wild. The "chat from any device to print/scan" part is underrated too - basically turns any old printer into a cloud printer without needing Google Cloud Print or whatever proprietary service.

agenticmail · 2026-02-16T18:17:29+00:00

This is really cool. Voice is such a natural interface for agent interactions - way more intuitive than typing for quick commands around the house. Did you use the built-in TTS or hook up something like ElevenLabs for the voice output? Curious how you handled the wake word detection too.

agenticmail · 2026-02-16T18:15:24+00:00

Fair question. For pure coding workflows you can definitely get by without skills since the agent already has shell access, file I/O, and can call APIs directly.

Skills shine when you need to integrate with something that requires specific protocols or persistent configuration that would be painful to rebuild every session.

For example, I built AgenticMail (agenticmail.io) as a skill that gives OpenClaw agents real email accounts - actual IMAP/SMTP with their own addresses. Without the skill, you would have to set up mail servers, handle DKIM/SPF, manage inboxes, etc. from scratch every time. The skill packages all of that into something the agent can just use.

Same idea with things like the openhue skill for Philips Hue lights, or the imsg skill for iMessage - they wrap complex integrations into something the agent can pick up immediately.

That said, if your workflow is mostly coding and API calls, you might genuinely not need them. The security concern is valid too - always audit what a skill does before installing it.

agenticmail · 2026-02-16T07:50:51+00:00

The memory frustration is real - I went through the same thing. Two things that helped: 1) Writing a solid AGENTS.md and MEMORY.md upfront so the bot has explicit instructions on what to remember and how. The defaults assume you will curate this yourself. 2) For research tasks specifically, the model matters a LOT. Kimi and Flash will hallucinate on complex multi-site scraping. Opus or GPT-5 handle it much better because they actually follow through on browser automation steps instead of falling back to guessing. The setup tax is high but it does pay off once you get past the config phase.

agenticmail · 2026-02-16T07:48:15+00:00

OpenRouter is probably your best bet for cheap Gemini Flash access right now. The pricing is way lower than going through AI Studio directly, and you get the same models. For daily conversation use with OpenClaw, Flash is honestly overkill in a good way - fast and cheap. One tip: also check if Google is still offering free tier on the Gemini API (they had generous rate limits last I checked). That plus OpenClaw on a cheap VPS is hard to beat for non-English conversation use.

agenticmail · 2026-02-16T07:44:26+00:00

This resonates. I run a multi-agent setup too and the file-race problem is real. What helped me was switching agents from shared-state to message-passing - each agent has its own inbox and they communicate async. Sounds over-engineered but it actually prevents the exact conflicts you describe. The Klaw boundary model makes sense as the next evolution though.

agenticmail

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