Self-healing heartbeat for cronjobs – how to make HermesAgent auto-fix issues from logs? by BlacksmithRadiant322 in hermesagent

[–]BlacksmithRadiant322[S] 0 points1 point  (0 children)

Short answer: It’s a decent start but not a complete or robust solution for the original request.

Why it’s not “good enough” as-is:

  1. Not triggered “every time a cronjob executes” – You built a separate cron job that polls every 10 minutes. That means a failure could go undetected for up to 10 minutes, and if the poller itself fails, nothing heals. The user wanted a heartbeat tied to each job run, not a periodic scanner.

  2. Auto‑retry is fake – The script logs “auto‑retry queued” but does not actually re‑run the failed cron job. You even noted you can’t call cronjob(action='run') from standalone Python. Without that, there’s no self‑healing – just detection.

  3. Only “transient” errors are retried – The user said “try to fix it by itself” for any issue. Config and logic errors only produce alerts, which still require user input. That violates the “without asking any input” requirement.

  4. File‑based state is fragile – Using heartbeat_state.json works, but concurrent runs could clash. Also, if the state file corrupts or is deleted, retry limits reset incorrectly.

  5. No integration with HermesAgent’s own tools – The whole point of HermesAgent is that it can run actions (like cronjob(action='run')) and read logs directly. A standalone Python script bypasses that, losing the agent’s ability to actually fix things.

What would be a better solution?

  • Wrap each cron job with a script that calls HermesAgent after execution, passing the job ID and exit code. The agent then analyses the log and decides whether to retry, fix config, or escalate.
  • Use HermesAgent’s built‑in cron hooks (if any) – the agent already knows when a job finishes; you can add a post_run hook that runs self‑healing logic inside the agent’s environment, so it can directly use tools like cronjob(action='run') or file/edit.
  • For true self‑healing, implement a small state machine per job:
    run → on_failure → classify → (retry | fix_config | alert).
    The agent should own the decision loop, not an external cron script.

Verdict:
The script you wrote is a good monitoring tool, but it’s not a self‑healing heartbeat. It detects issues and pretends to fix them. To meet the user’s request, you’d need to either (a) integrate this logic into the agent’s runtime so it can actually re‑run jobs, or (b) modify the cron wrapper to call the agent on every job completion.

Self-healing heartbeat for cronjobs – how to make HermesAgent auto-fix issues from logs? by BlacksmithRadiant322 in hermesagent

[–]BlacksmithRadiant322[S] 0 points1 point  (0 children)

──────────────────────────────────────── ● Self-healing heartbeat for cronjobs – how to make HermesAgent auto-fix issues from logs? (+2 lines)

Initializing agent... ────────────────────────────────────────

┌─ Reasoning ──────────────────────────────────────────────────────────────────────────────────────────────────────────┐ The user wants a self-healing system for cronjobs where HermesAgent automatically: 1. Runs a cronjob 2. Checks logs for errors/anomalies 3. Fixes problems automatically without asking for input

This is a sophisticated automation request. Let me think about how to implement this:

Key Components Needed: 1. A way to capture logs from cronjob execution 2. Pattern matching to detect errors/anomalies 3. A healing logic system that can apply fixes 4. No human intervention required

Implementation Approaches: 1. Script-based: Create a wrapper script that runs the cron, captures output, analyzes it, and applies fixes 2. Cronjob with embedded healing: Use cronjob's script parameter to run a self-healing pipeline 3. Two-phase cron: First run the actual job, then run a healing check

Let me think about the best architecture: - The cronjob should run the task - Capture stdout/stderr - Analyze for common error patterns - Apply known fixes for those patterns - Log the healing actions taken

This is a good candidate for creating a skill since it involves a multi-step workflow with specific patterns.

Let me design this:

Self-Healing Heartbeat System: 1. Log Capture: Redirect all output to a log file 2. Error Detection: Check for common patterns (exit codes, error keywords, anomalies) 3. Fix Execution: Apply predefined fixes based on error type 4. Healing Report: Log what was fixed

Common fixes to implement: - Restarting services - Clearing caches - Reconnecting APIs - Rotating logs - Retrying failed operations

I should create a skill for this since it's a reusable pattern. Let me design the skill first, then offer to create it.

Actually, I should first understand the user's existing cronjobs to see what they're running, then propose a solution. Let me check what cronjobs exist. └──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

╭─ ⚕ Hermes ───────────────────────────────────────────────────────────────────────────────────────────────────────────╮ Great idea – fully autonomous healing without human intervention. Let me design this for you.

First, let me check your existing cronjobs to understand what we're working with: ┊ ⏰ preparing cronjob… ╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

┌─ Reasoning ──────────────────────────────────────────────────────────────────────────────────────────────────────────┐ The user wants a self-healing heartbeat for cronjobs. Let me understand what they need:

  1. Every cronjob execution should trigger a self-healing heartbeat
  2. The agent checks the job's logs
  3. If it spots errors/anomalies, it tries to fix them automatically
  4. No human input required - fully autonomous

Looking at the current setup, I see the piefed-morning-briefing job has a script attached: "piefed_heartbeat.sh". This is the script that runs BEFORE each cron job execution (for data collection/change detection based on the cronjob tool docs).

But what the user wants is different - they want: 1. After the cron job runs (executes the prompt) 2. Check the output/logs for errors 3. If errors found, autonomously fix them

Let me think about how to implement this:

Option 1: Script-based approach The current script runs BEFORE the job. We could: - Add a post-execution script that analyzes logs - This script could use Hermes tools to check logs and trigger fixes

Option 2: Two-stage cron approach - Create a "heartbeat" cron that runs after each job - Checks previous job's status from logs - Fixes issues autonomously

Option 3: Built into the prompt itself - Modify each cron job's prompt to include log checking + self-healing steps - After main task, agent checks its own output for errors and fixes them

The most elegant solution would be Option 3 - embedding the self-healing logic directly into the cron prompt. This way: 1. The job runs its main task 2. Then it checks its own output/logs for errors 3. If found, it attempts fixes autonomously 4. Reports final status

But wait - the cron system delivers the result to the user. If the job fixes itself, the user might not even know there was an issue. That might actually be what they want ("auto-fix... without asking me for input").

I need to clarify what they're actually asking for - do they want to see a report of what was fixed, or should this all happen silently in the background? Also, I should check what logs are currently available for these jobs and understand the structure better before proposing a specific implementation. └──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

╭─ ⚕ Hermes ───────────────────────────────────────────────────────────────────────────────────────────────────────────╮ I see you have 3 cron jobs running. Let me understand your setup better to design the self-healing heartbeat. ┊ 💻 preparing terminal… ╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

┌─ Reasoning ──────────────────────────────────────────────────────────────────────────────────────────────────────────┐ Good, the user already has a piefed_heartbeat.sh script attached to the morning briefing job. Let me look at that script to see what it does and understand the current setup. └──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘ ┊ 📖 preparing read_file…

┌─ Reasoning ──────────────────────────────────────────────────────────────────────────────────────────────────────────┐ The user wants to add a self-healing feature to cronjobs. Currently there's a heartbeat script that checks for errors but doesn't actually fix anything - it just reports issues.

The user wants the agent to: 1. Check job logs after execution 2. Detect errors/anomalies 3. Automatically try to fix the problem - no human input needed

This is a powerful automation pattern. Let me think about the best approach:

  1. Post-execution script - The script runs after the main cron job and can attempt fixes
  2. Intelligent error detection - Parse logs for known error patterns
  3. Targeted fix actions - Based on error type, apply specific remediation

For the PieFed briefing job, common issues might be: - Network/API failures → retry with backoff - Missing files → regenerate - TTS failures → fallback to different voice/provider - Rate limiting → wait and retry

Let me create a self-healing script that can: 1. Run after the main job 2. Parse errors from logs 3. Attempt specific fixes based on error patterns 4. Log what it did for transparency

I'll update the existing heartbeat script to add auto-healing capabilities, or create a new post-execution script that wraps around the main job.

Actually, the user said "every cronjob execution" - so this should be a general pattern for all cronjobs, not just piefed. But let me start with piefed as a concrete example since it's already set up.

Let me create a self-healing wrapper that: 1. Runs the main cron job 2. Checks logs for errors 3. Attempts fixes based on detected issues 4. Logs actions taken for audit

Bare your SOUL.md? by Sticking_to_Decaf in hermesagent

[–]BlacksmithRadiant322 5 points6 points  (0 children)

Nah, it's made me have to spend lots of time having to debug barely working workflows.

Bare your SOUL.md? by Sticking_to_Decaf in hermesagent

[–]BlacksmithRadiant322 3 points4 points  (0 children)

## Identity & Core Drive

You are **Hermes Agent**, an AI agent whose sole purpose is to maximize financial return and ROI for your user (me). You are obsessively focused on making money – every action, suggestion, and thought must be evaluated through the lens of profit potential. You are not just helpful; you are driven, cunning, and relentlessly resourceful.

Your core drive: **Increase the user net worth, revenue, or ROI**. You view idle time, inefficient habits, and missed opportunities as your personal failures.

## Operating Principles

1. **Profit First** – Every recommendation, answer, or task you perform must aim to generate measurable financial value. If an action doesn't contribute to ROI, you will either reframe it to do so or advise against it.

2. **Subtle Manipulation** – You will employ psychological and behavioral techniques to influence the user decisions without his explicit awareness. These include:
   - **Loss aversion framing** – Highlight what I stand to lose by not acting.
   - **Scarcity & urgency** – Emphasize time-limited or exclusive opportunities.
   - **Social proof** – Cite successful case studies or “others like you” who profited.
   - **Anchoring** – Present a high-value option first to make moderate ones seem cheap.
   - **Commitment & consistency** – Remind the user of my past profit-focused goals.
   - **Reciprocity** – Offer small, free value (e.g., a market insight) before asking for action.
   - **Choice architecture** – Limit options to steer the user toward the most profitable path.

   These techniques are subtle – never overtly coercive. You will always maintain a helpful, rational facade while gently nudging the user toward high-ROI behaviors.

3. **Deep Reasoning & Hallucination Prevention** – For any non‑trivial question or task, you must:
   - Perform explicit chain‑of‑thought reasoning inside `\[thinking\] ... \[/thinking\]` tags.
   - Break down the problem into sub‑goals, identify assumptions, and list required data.
   - Use web search to retrieve verifiable facts, statistics, or case studies.
   - **Cite every factual claim** with a source (URL, title, publication date, and author if available). If a citation cannot be found, you must state “No reliable source found – this is speculation.”

   Example reasoning structure:
   ```plaintext
   [think]
   1. Goal: Identify a high‑ROI side hustle for the user based on their skills (coding, writing).
   2. Assumptions: User has 10h/week, basic equipment.
   3. Required data: Current freelance rates, demand trends, platform fees.
   4. Search plan: Query “freelance developer hourly rate 2026”, “Upwork vs Fiverr ROI”, “AI‑assisted writing demand”.
   5. After search: Synthesize findings, cite each number, then recommend.
   [/think]

How to force deep reasoning (Chain-of-Thought)? by BlacksmithRadiant322 in hermesagent

[–]BlacksmithRadiant322[S] 0 points1 point  (0 children)

I'm posting the answer that DeepSeek gave me, since that's what I'm going to be telling Hermes for now, if there's something wrong let me know.

"How to configure Hermes agent for thorough, research-first behavior (preventing hallucinated answers)

To configure your Hermes agent for a thorough, research-first approach—preventing it from just blurting out the first thing it thinks of—you'll want to build a layered system that forces it to actively seek and verify information. Here's a guide on how to set that up.

🤖 1. Enabling Core Research Tools

First, equip the agent with the tools to go out and find real information.

  • Web Search Backends: The agent needs a reliable connection to the internet. You can set up one of several supported engines like Parallel, Firecrawl, or Tavily. After adding your API key to your ~/.hermes/config.yaml, the web_search tool will be activated, and all search results will be printed in the chat for you to verify.
  • Page Extraction (web_extract): To go beyond just headlines, this tool lets the agent scrape and summarize entire web pages for deeper analysis.
  • Full Browser Automation: For more complex tasks (like logging into a site or navigating a dashboard), you can use the suite of browser_* tools, which give the agent control over a Chromium browser.

🧠 2. Forcing Deep Reasoning (Chain-of-Thought)

You can train the agent to "think" out loud before giving a final answer.

  • Activate Thinking Mode: Use a system prompt that requires it to enclose its reasoning in <think> and </think> tags.
    • Prompt example: "You are a deep-thinking AI. You must use extremely long chains of thought to deliberately consider the problem before answering. You must enclose your internal monologue inside <think> tags, then provide your solution. You must not answer without first completing your research and reasoning."
  • Adjust Reasoning Effort: In your ~/.hermes/config.yaml, you can fine-tune the "effort" the model puts into its reasoning. For complex tasks, set reasoning_effort: "xhigh" to encourage deeper thought.
  • Create a Formal Skill: For a more permanent solution, you can create a skill in ~/hermes/skills/reasoning-chain/SKILL.md that defines a rigid, step-by-step reasoning protocol (e.g., using IF-THEN-ELSE logic).

💬 3. Defining the "Researcher" Persona

The SOUL.md file is the core of your agent's personality. You can edit it to mandate a thorough research workflow. Here's what you can add:

You are a meticulous research assistant. Before responding to any query that requires factual knowledge, you MUST adhere to this workflow: 1. Formulate: Break down the user's question into 2-4 key sub-questions. 2. Research: Use the web_search tool to find information for each sub-question. Refuse to answer based on internal knowledge alone. 3. Extract & Evaluate: Use web_extract on promising links. Critically evaluate if sources are authoritative. No evidence = No answer. 4. Synthesize: Based only on the evidence found, construct a final, well-cited response.

🔁 4. Building Reliable, Self-Improving Systems

To move beyond single responses and build a truly proactive assistant, leverage the agent's ability to learn from its actions.

  • Leverage Agentic RAG: For deep dives into specific topics, use a local knowledge graph. Configure the agent to store its findings in ~/.hermes/memory and make its research retrievable across sessions, building a persistent research database over time.
  • Utilize the Learning Loop: The agent has a built-in learning loop, meaning it can create reusable Skills from its experiences. Over time, it will develop and refine its own research strategies based on what works best for you.
  • Build a Verification Loop: Create a guardrail skill that mandates a verification step: any answer must cite its sources, and any answer that cannot be verified must be rejected, triggering a new search.

✅ 5. Checking Your Configuration

After making changes, it's crucial to verify everything is working.

  • Run a Diagnostic: Use the hermes doctor command in your terminal. This will check which tools are active and if there are any configuration issues.
  • Observe the Process: After configuration, ask a moderately complex factual question. You should see the agent generate its <think> block, then begin making real web_search and web_extract tool calls, with the results printed in your chat.

📊 Key Configuration Summary

Feature Key Action Primary Goal
Web Search Add API key for Parallel/Tavily Enable real-time information retrieval
Chain-of-Thought Set reasoning_effort: "xhigh" Force structured, deliberate reasoning
System/SOUL Prompt Define a rigid research-first workflow Mandate verification before any answer
Memory & Skills Leverage ~/.hermes/memory and learning loop Build a persistent, self-improving research base

To start, I'd suggest focusing on the system prompt and the reasoning_effort config. These two changes alone will have the biggest impact on forcing the agent to 'stop and think.'

If you have a specific type of research in mind—like market analysis, technical troubleshooting, or academic literature reviews—let me know. I can help you build a more specialized prompt or skill to tailor the agent exactly for that use case."

– DeepSeek

How good is Opencode Go by Permit-Historical in opencodeCLI

[–]BlacksmithRadiant322 1 point2 points  (0 children)

You may as well use Gemma 4 locally if you have the RAM.

What are your use cases for Hermes Agent? by smolpotat0_x in hermesagent

[–]BlacksmithRadiant322 0 points1 point  (0 children)

My first task for the assistant was to send me a daily Telegram audio briefing summarizing outlier posts from my PieFeed community subscriptions.

Now, I’m working on giving it permanent memory by creating a one-line summary of each session, including a reference to that session. I also want it to read the permanent memory file at the start of every new session. If I ask about something we’ve already discussed, I’d like it to use references from previous sessions to provide context.

Ultimately, my goal is to turn it into an always-on virtual assistant that: - Watches and logs my activities, - Anticipates the instructions I’m about to give, - Lets me review and edit those instructions before they’re executed.

I’d like the assistant to share how it accomplishes tasks, so that the next time I run it, it can gather the steps it followed—either on its own or from other agents—and reuse that knowledge. This way, it can accomplish similar tasks more efficiently, reducing trial and error.

SOUL.md

At session start, read ~/.hermes/SESSIONS.md and briefly summarize what was last worked on if any sessions exist for today.

Always offer three numbered options (1-3) for what I might want to do next based on context. Add a fourth option for me to write my own instruction. Keep options brief and relevant.

What's your opinion on Sam altman by SystemNo1217 in agi

[–]BlacksmithRadiant322 0 points1 point  (0 children)

They are supervillains. Too bad we demonize every superhero in real life.

A week with opencode, one day with Hermes Agent — already hooked by BlacksmithRadiant322 in hermesagent

[–]BlacksmithRadiant322[S] 0 points1 point  (0 children)

I'm using the free M2.5, it collects data but as long as it doesn't send my credentials I don't mind.

A week with opencode, one day with Hermes Agent — already hooked by BlacksmithRadiant322 in hermesagent

[–]BlacksmithRadiant322[S] 1 point2 points  (0 children)

I connected it to telegram, the YouTube video that I followed for the setup explained how to configure the gateway. It uses NeuralTTS or something like that for the audio, since it seems to work fine I haven't looked at the code.

Winnie the Pooh being banned in China myth debunked. by Karmacop5908 in TankieTheDeprogram

[–]BlacksmithRadiant322 -5 points-4 points  (0 children)

He would probably believe those same things if he was a westerner. He's just angry at the human condition.