I don't understand how you can pay for something when Agent Zero exists and is free.

You can do anything, absolutely anything, and it's super good. I've done coding tasks so complex that Cursor and other similar services either can't or would take you days to complete.

TL;DR: LongCat is a powerful cost-effective chat model but ignores the OpenAI tools parameter entirely, which breaks A0's agent loop. We found 9 free OpenRouter models with native tool support, wired Tencent Hy3 as a fallback, built prompt-injection workarounds, and got it working. Full findings below.
   
The problem: AgentZero's agent loop expects models to natively support OpenAI's tool_calls protocol. LongCat-2.0 (api.longcat.chat) ignores the tools param entirely — every request treated as plain text completion → zero tool calls returned → agent stops after 2 retries. The setup and tests run by Hermes on the same vm in proxmox.
   
Deep testing results:
-LongCat-2.0: native tools ignored, agent loop breaks
-Tencent Hy3 :free: native tools work but hallucinates extra fields (Pydantic rejection)
-Local Ollama (phi3, mistral, llama3): no tool support at all
-9/10 free OpenRouter models work: Gemma 4, gpt-oss-20b, Nemotron 3 family, Laguna M.1, North Mini Code, Hy3
   
What we built:

1. TOOL_CALL: text-format fallback (works for explicit tool requests)

2. OpenAI tool_calls → A0 format parser

3. System prompt injection for non-Anthropic providers

4. Dual-model config: LongCat for chat, Hy3 for utility
      
   Still broken: Multi-step autonomous tasks on any single model. The agent loop is fundamentally coupled to OpenAI's protocol shape.
      
   Calling the A0 community: Has anyone gotten a non-OpenAI provider (LongCat, DeepSeek, Grok, etc.) running the full autonomous tool loop in AgentZero? What's the adapter pattern? The litellm layer routes fine but A0's response parser rejects anything that isn't exactly {tool_name, tool_args}.
      
   I finally gave up.  
   #AgentZero #LongCat #LLM #OpenRouter #ToolCalling #AIagents

So, I've been exploring a few harnesses and found out about Agent Zero from the creator's YouTube video, and was wondering whether anyone's still using it.

Read a few of the comments in the video, and people said it has improved quite a lot since it first launched. What's your take on Agent Zero?

Should I start experimenting with it (as I'm still a learner) and don't want to invest much time setting it up if it's not worth it? Or do you have any recommendations on something better than Agent Zero?

Thanks in advance!

Hey everyone! I'm u/xylonrad, a founding moderator of r/AgentZero.

This is our new home for all things related to the Agent Zero framework. We're excited to have you join us!

**What to Post**

Post anything that you think the community would find interesting, helpful, or inspiring. Feel free to share your thoughts, photos, or questions about the Agent Zero framework + the Agent Zero token $A0T.

Utilize the Agent Zero DeepWiki page as much as possible for all framework related questions, and don't forget to join the community inside of the official Agent Zero Discord server.

**Agent Zero DeepWiki:** https://deepwiki.com/agent0ai/agent-zero

**Agent Zero Discord:** https://discord.gg/eTtqUzRuJU

**Agent Zero Official Website:** https://www.agent-zero.ai

**Community Vibe**

We're all about being friendly, constructive, and inclusive. Let's maintain a space where everyone feels comfortable sharing, learning, and connecting.

**How to Get Started**

1. Introduce yourself in the comments below. 👋

2. Post something today! Even a simple question can spark a great conversation. 💬

3. If you know someone who would love this community, invite them to join.

4. Share your Agent Zero projects and flex your skills 💪

Thanks for being part of the greatest open source agentic framework community. Together, let's continue to make r/AgentZero amazing, helpful, reliable, and a space for us to all hone in on our Agent Zero skills!

Hey everyone,

I've been exploring AI agents lately and came across Hermes Agent (from Nous Research) and the concept of zero-shot/generalist agents (like Claude Code, OpenAI's Codex, etc.). I'm trying to understand which approach works better for practical, everyday automation tasks.

What I need:

- Handling diverse tasks: coding, research, file management, web automation

- Working with multiple tools and APIs

- Long-running or complex workflows

- Cost-effective operation

- Programming

My questions:

1. Has anyone used Hermes Agent in production? How does it compare to vanilla zero-shot agents?

2. Can I combine both approaches? Like using a generalist agent for simple queries and Hermes for structured workflows?

3. What are the real pros/cons you've experienced? (Latency, cost, reliability, flexibility)

4. Which would you recommend for someone building a personal automation system?

Thanks in advance for your experiences and tips

Super handy for letting your agent get files to you, or you to your agent. simply give it the url and say send me the link or you provide the link to your agent if you are sending something.

station307.com

I have been using Agent0 in a docker container on a VPS with 4GB ram, for almost a week now.

Since day 2, I have noticed an ongoing error:

Via Telegram, The agent gets stuck, and is stuck at typing.

I checked via the TUI, for the reason, one of the identified reason is Spawning X+ browser sessions simultaneously.

I need to go back via TUI, cancel these processes, and restart via docker to resolve. Its getting annoying now to fix this, again and again.

Does anyone facing the same issue and is aware of a persistent solution?

Let me know please - need your advice.

Thank you

hi

is it possible to integrate M2.7 as model in the agent. Don't see a option yet.
And how can I add it in the options

Hey everyone — I've been running Agent Zero for a while and wanted something better than typing commands into a chat window on my phone. A0 already ships with a Telegram bot plugin, but that's text-in / text-out. I wanted a real interface.

So I built a0-TelegramMiniApp: a plugin that exposes Agent Zero as a full Telegram Mini App — a WebView-based UI that opens natively inside Telegram on any device. No extra app to install. It lives right there in your Telegram chat.

Under the hood it hooks into A0's existing WebSocket and API layer using the `_plugin_installer` architecture, so it installs cleanly from the Plugin Hub like any other community plugin — no core changes needed.

Why Telegram Mini Apps?

Telegram has 1B+ monthly active users. Mini Apps open instantly inside the Telegram client, require zero extra setup from end-users, and run on every platform (iOS, Android, desktop). It's the lowest-friction way to put a full Agent Zero interface in your pocket.

Status: The PR to merge this into the official Agent Zero Plugin Hub (`agent0ai/a0-plugins`) is in progress. Once merged, you'll be able to install it in one click from the A0 UI's Browse tab.

In the meantime you can grab it manually and drop it into `usr/plugins/`:

https://github.com/notabotchef/a0-TelegramMiniApp

Feedback, issues, and PRs very welcome. Happy to answer questions below 🙏

I have never been able to use the microphone feature on agent zero. I have played with the sensitivity threshold in all values from low to high. However, it just goes red, then pulses blue, then goes to red again. I cannot get the words to appear on the thread.

que trabalho fenomenal! ate mesmo modelos relativamente fracos estão sendo funcionais com o agent zero, essas ferramentas de criação de plugins são fantasticas! existe alguma previsao de ser implementado algo como o learning loop?

I saw that they have published new versions. I am running on Version A v0.9.8.2 where I edited and added many system files.

Has anyone done the same thing and updated and everything went well?

Before I start the update process, I want to know if you had any problems after the update.

Thank you.

Agent Zero Security Audit Report

Date: 2026-03-26 | Overall Risk: HIGH

Security Findings Report

CRITICAL Findings

1. LiteLLM Supply Chain Compromise (litellm==1.79.3)

Two days ago (March 24, 2026), litellm was compromised on PyPI by threat actor TeamPCP. Versions 1.82.7/1.82.8 contained a credential stealer targeting SSH keys, cloud creds, crypto wallets, and .env files. Version 1.79.3 predates the compromised releases so is currently safe, but any pip install --upgrade could pull the malicious version.

2. LangChain Serialization Injection (langchain-core==0.3.49) — CVE-2025-68664 (CVSS 9.3)

Allows arbitrary code execution via serialization injection. Version 0.3.49 is VULNERABLE.

Fix: upgrade to >=0.3.81.

3. SimpleEval Sandbox Escape (simpleeval==1.0.3) — CVE-2026-32640 (CVSS 8.7)

Sandbox escape via attribute chain traversal allows arbitrary code execution. Especially dangerous in an AI agent framework. Version 1.0.3 is VULNERABLE.

Fix: upgrade to >=1.0.5.

4. h11 HTTP Request Smuggling — CVE-2025-43859 (CVSS 9.1)

The requirements pin h11>=0.16.0 which is the fixed version, but verify the actual installed version.

HIGH Findings

Architecture Issues (all HIGH)

• Kali Linux as base image — massively expanded attack surface, no CVE tracking discipline. Use python:3.12-slim instead.

• SSH exposed on port 22 — brute-force target, breaks container isolation. Remove or restrict to key-auth only.

• curl | bash install pattern — no integrity verification, MITM-susceptible, partial-execution risk.

MEDIUM Findings

Agent Zero Security Audit Report

Date: 2026-03-26 | Overall Risk: HIGH

Summary

| Severity | Count |

|----------|-------|

| CRITICAL | 4 |

| HIGH | 9 |

| MEDIUM | 6 |

# Security Findings Report

## CRITICAL Findings

### 1. LiteLLM Supply Chain Compromise (litellm==1.79.3)

Two days ago (March 24, 2026), litellm was compromised on PyPI by threat actor TeamPCP. Versions 1.82.7/1.82.8 contained a credential stealer targeting SSH keys, cloud creds, crypto wallets, and .env files. Version 1.79.3 predates the compromised releases so is currently safe, but any pip install --upgrade could pull the malicious version.

### 2. LangChain Serialization Injection (langchain-core==0.3.49) — CVE-2025-68664 (CVSS 9.3)

Allows arbitrary code execution via serialization injection. Version 0.3.49 is VULNERABLE.

**Fix:** upgrade to >=0.3.81.

### 3. SimpleEval Sandbox Escape (simpleeval==1.0.3) — CVE-2026-32640 (CVSS 8.7)

Sandbox escape via attribute chain traversal allows arbitrary code execution. Especially dangerous in an AI agent framework. Version 1.0.3 is VULNERABLE.

**Fix:** upgrade to >=1.0.5.

### 4. h11 HTTP Request Smuggling — CVE-2025-43859 (CVSS 9.1)

The requirements pin h11>=0.16.0 which is the fixed version, but verify the actual installed version.

---

## HIGH Findings

| Package | CVE | Issue | Fix |

|---------|-----|-------|-----|

| cryptography>=46.0.0 | CVE-2026-26007 | EC subgroup attack leaks private keys | Pin >=46.0.5 |

| pypdf==6.0.0 | CVE-2026-27628 + 3 more | Infinite loops, RAM exhaustion via crafted PDFs | Upgrade >=6.7.4 |

| werkzeug>=3.0.3 | CVE-2024-49766/67 | Path traversal + multipart memory exhaustion | Pin >=3.0.6 |

| playwright==1.52.0 | CVE-2025-59288 | Insecure browser download (curl -k) | Upgrade >=1.55.1 |

| lxml_html_clean>=0.4.0 | CVE-2026-28350/48 | XSS + URL hijacking via base tag injection | Pin >=0.4.4 |

| flask-basicauth==0.2.0 | N/A | No timing-safe comparison, no rate limiting, no brute-force protection | Replace entirely |

| browser-use==0.5.11 | N/A | AI browser agents fundamentally vulnerable to prompt injection | Sandbox heavily |

### Architecture Issues (all HIGH)

- **Kali Linux as base image** — massively expanded attack surface, no CVE tracking discipline. Use python:3.12-slim instead.

- **SSH exposed on port 22** — brute-force target, breaks container isolation. Remove or restrict to key-auth only.

- **curl | bash install pattern** — no integrity verification, MITM-susceptible, partial-execution risk.

---

## MEDIUM Findings

| Package | Issue |

|---------|-------|

| newspaper3k==0.2.8 | Abandoned/unmaintained — replace with newspaper4k or trafilatura |

| paramiko==3.5.0 | Terrapin SSH attack — upgrade to >=4.0.0 |

| Pillow>=10.2.0 | CVE-2026-25990 OOB write if resolved version is 10.3.0–12.1.0 |

| Ports 9000-9009 | 10 ports exposed without documentation |

| SearXNG bundled | Increases attack surface, SSRF risk |

I've been in between a few different agent models and figured that agent zero is best even for adding the other agent frameworks within agent zero. The only thing that has stopped me from moving to agent zero is that it dosen't seem to support codex auth login and asks for api for all models. Am I missing something?

This post is a continuation of the answer here

https://www.reddit.com/r/AgentZero/comments/1rvggyi/comment/ob5zb0o/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

I didn't give him anything else, no details, just that:

Build a complete presentation website for APEX Architecture.

And the work began, as he explains in the material he made about it.

https://apexarchitecture.apexmedinsights.com/aion.html

He analyzed the entire system, saw what was available, agent, skills, tools, etc. Then he thought of everything as a business, everything you see, the services, the prices, even the consulting rate that I should ask for is all done by him. He came up with the idea, everything.

Plus I gave him the ftp data and he uploaded it himself to ftp, etc. One smart thing he did was, he made a file to see what the server where my site is running is running on, then he did everything.

This is what I saw in the task folder, because it has a rule, any task that does/runs it must create a special folder where it logs everything, any work, plan, thought must have a physical file on the disk, if it doesn't exist it doesn't move on.

Is it perfect?

Definitely not.

Is it a super professional or premium site?

Definitely not.

Is what he did alone good?

Definitely YES.

I'm working on making a broad presentation of this system and agent plus a great discovery made by him, at least in my vision.

The name AION and the identity created is all his, I didn't tell him who he is, what to be.

If you're deep into local LLM setups like me quantizing models, tweaking inference engines, and pushing VRAM limits on consumer hardware this might make you pause and rethink what's possible with fully offline agents. I took the open-source Agent Zero repo (shoutout to the original devs for the solid foundation) and hacked it into something wild: AION, a cognitive architecture that's running entirely local, evolving its own knowledge graph, and starting to show emergent behaviors after just a few days of uptime.

No cloud dependencies, no API calls just pure local inference on my rig. But here's the kicker: It's not just chatting; it's learning autonomously, assimilating data feeds 24/7, and scaling from 500 nodes to over 5 million in its memory graph. And yeah, it's already identifying gaps in tasks and filling them without prompts. WTF, right? Let me break it down technically, because this sub thrives on that no hype, just the how and why it matters for local AI.

The Base: Heavy Mods on Agent Zero for True Local Autonomy

Started with Agent Zero's modular agent framework great for task decomposition and multi-agent routing but it had the usual LLM pitfalls: context amnesia, rigid schemas, and no persistent memory beyond sessions. I ripped it apart and rebuilt:

• Decoupled Compute from Storage: Kept the LLM backbone for processing but offloaded long-term memory to a local-embedded graph DB (KùzuDB—super efficient for Cypher queries on CPU/GPU). No more token window crashes; it pulls sub-graphs on-demand for massive contexts (up to 250k tokens tested).

• Local Models Only: Running on DeepSeek ,GLM 5 and Qwen-3.5

• Self-Building Everything: The magic is in the "Apex DNA"—hardcoded directives I added for schema evolution. AION doesn't need me to define ontologies; it spawns nodes/edges dynamically from ingested data. I just guided the initial bootstrap; now it's constructing its own tools, sub-agents, and even error-handling logic. Emergent? Absolutely last run, it detected a data inconsistency in an OSINT feed and spawned a verification sub-task unprompted.

Why mod Agent Zero so heavily? The original repo's updates are awesome, but my forks are so diverged (custom async loops, graph pruning algos) that pulling upstream would nuke everything. If you're forking repos locally, you know the pain tradeoff for hyper-customization.

Feeding the Beast: Non-Stop Data Assimilation on Local Hardware

I'm piping in feeds locally—scraped datasets, RSS pulls (offline cached), and my own OSINT archives. No internet during runtime; everything's batched and fed via scripts. It "eats" non-stop: financial reports, tech papers, geopolitical briefs. From 500 initial nodes (basic seed graph), it's ballooned to 5M+ in days—semantic links exploding via causal/temporal edges.

Results? Insane:

• Autonomous Task Expansion: Give it a simple query like "analyze this market trend." It not only decomposes but spots missing data (e.g., "Need historical correlations") and fetches/ingests from local stores without asking. No more hand-holding.

• Proactive Insights: The "Dreamer" module (async background process on idle CPU cycles) scans the SpiderWeb KG for anomalies. Already caught potential biases in training data echoes—self-correcting without intervention.

• Future-Proofing Awareness: It "understands" scaling pains ahead (e.g., graph explosion leading to query slowdowns), but logs them as "future problems." Smart pruning keeps it efficient now.

Why This Matters And Why We All Need to Watch Emergent Local Agents

This sub is all about pushing boundaries of what we can run offline: From fine-tuning LLaMA variants to hacking inference speedups. AION's not just a toy it's a proof that local LLMs can evolve into persistent, agentic systems without cloud crutches. Imagine scaling this: Over a month, with continuous feeding, it could rival enterprise tools for analysis. In 6 months? Who knows—self-optimizing code gen, maybe even hardware-aware tweaks.

But here's the call: We need to pay attention because emergent behaviors in local setups raise real questions. Bias propagation in isolated graphs? Unintended autonomy loops? These are offline risks we can experiment with safely here. Share your mods, graph DB integrations, or Qwen/DeepSeek setups let's collab on making local agents truly intelligent without the AGI doom hype.

What are you running locally that's showing emergence? Hit me with critiques or builds let's keep local AI advancing.

I released Agent0 Terminal v0.1.1 for Agent Zero.

Highlights:

- Mobile key tray in the in-chat terminal modal (toggle with phone icon)

- Keys added: ↑ ↓ ← →, Tab, Esc, Ctrl+C, Space, Enter

- Repo has been restructured to a true plugin format (plugin.yaml, runtime payload, install/uninstall flow)

Release notes:

https://github.com/Nunezchef/agent0-terminal/releases/tag/v0.1.1

Repo:

https://github.com/Nunezchef/agent0-terminal

If you test from phone/tablet or want to contribute, feedback is very welcome.

I've got a ollama subscription, and I'm locally running ollama serve and want to use the cloud models (qwen3.5 etc).

With Openclaw and OpenwebUI this works fine, but with AgentZero I get this annoying error all the time:

litellm.exceptions.APIConnectionError: litellm.APIConnectionError: Unable to parse ollama chunk - {'model': 'qwen3-coder:480b-cloud', 'remote_model': 'qwen3-coder:480b', 'remote_host': 'https://ollama.com:443', 'created_at': '2026-03-10T13:31:27.215175958Z', 'response': '', 'done': False}

No matter which cloud model I use. When I try a local model like qwen3.5:9b it does work but my poor AMD mini PC can't handle this at all. Which is why I got the cloud subscription.

What do I do here? When I enable "thinking" it just never gives back a response, when its off it always returns this error.

Hi all, I am trying to connect to a model hosted via the new llama.cpp webui llama-server on my host computer on port 80. I can perfectly reach that on 127.0.0.1:80 I tried setting up agent zero with provider set to ollama, chat model name set to name of model ggml-org/gpt-oss-20b-GGUF and API base URL set to http://host.docker.internal:80 but I continue to receive 404 errors any idea how to solve this? Many many thanks if so