agent0/agents/gunnar/memory/default/index.pkl

€•hnŒ&langchain_community.docstore.in_memory”ŒInMemoryDocstore”“”)�”}”Œ_dict”}”(Œ
6SSHP1uj89”Œlangchain_core.documents.base”ŒDocument”“”)�”}”(Œ__dict__”}”(Œid”hŒmetadata”}”(Œarea”Œ	fragments”Œ	timestamp”Œ2026-03-08 11:40:50”hhŒ_consolidation_similarity”Œnumpy.core.multiarray”Œscalar”“”Œnumpy”Œdtype”“”Œf8”‰ˆ‡”R”(KŒ
<”NNNJÿÿÿÿJÿÿÿÿKt”bC0ÞØë?”†”R”uŒpage_content”Œmodel not found”Œtype”ŒDocument”uŒ__pydantic_extra__”NŒ__pydantic_fields_set__”�”(h%hh�Œ__pydantic_private__”NubŒ
whP4OBJxLx”h
)�”}”(h
}”(Œid”h-Œmetadata”}”(Œsource”Œ2/a0/knowledge/main/tool_call_reference_examples.md”Œarea”Œmain”Œsource_file”Œtool_call_reference_examples.md”Œsource_path”h5Œ	file_type”Œmd”Œknowledge_source”ˆŒimport_timestamp”Nh1h-Œ	timestamp”Œ2026-04-09 16:19:13”uŒpage_content”X# Tool Call Reference Examples

These examples are intentionally short and high signal so tool-call shape guidance
can live in knowledge without bloating the default prompt stack.

## 1) Namespaced tool (`text_editor`) vs non-namespaced tool (`code_execution_tool`)

- `text_editor` requires method in `tool_name`:
  - `text_editor:read`
  - `text_editor:write`
  - `text_editor:patch`
- `code_execution_tool` uses a plain tool name plus behavior in `tool_args.runtime`.

### Example A: read file lines with namespaced tool

```json
{
  "tool_name": "text_editor:read",
  "tool_args": {
    "path": "/workspace/agent-zero/README.md",
    "line_from": 1,
    "line_to": 60
  }
}
```

### Example B: run shell command with `code_execution_tool`

```json
{
  "tool_name": "code_execution_tool",
  "tool_args": {
    "runtime": "terminal",
    "session": 0,
    "reset": false,
    "code": "pwd"
  }
}
```

### Example C: poll ongoing terminal output

```json
{
  "tool_name": "code_execution_tool",
  "tool_args": {
    "runtime": "output",
    "session": 0
  }
}
```

## 2) Memory tools use plain names and structured args

```json
{
  "tool_name": "memory_load",
  "tool_args": {
    "query": "tool argument format",
    "limit": 3,
    "threshold": 0.7
  }
}
```

## 3) Subordinate tool booleans are JSON booleans

```json
{
  "tool_name": "call_subordinate",
  "tool_args": {
    "profile": "",
    "message": "Review this patch for edge cases.",
    "reset": true
  }
}
```

Use these examples as structure references only. Adapt arguments to the current task.”Œtype”ŒDocument”uh)Nh*�”(hAh2h1�h,NubŒ
1hrEHvxw1S”h
)�”}”(h
}”(h1hFh2}”(h4Œ0/a0/knowledge/main/about/setup-and-deployment.md”h6h7h8Œsetup-and-deployment.md”h:hKh;Œmd”h=ˆh>Nh1hFh?Œ2026-04-09 16:19:13”uhAXh# Agent Zero - Setup and Deployment

## Docker Deployment (Standard)

Agent Zero is distributed as a Docker image: `agent0ai/agent-zero`.

```bash
docker pull agent0ai/agent-zero
docker run -p 50001:80 agent0ai/agent-zero
```

The web UI is then accessible at `http://localhost:50001`. The container exposes port 80 internally; map any host port to it.

## Persistence

All user data lives in `/a0/usr/` inside the container. Without a volume mount, data is lost when the container is removed.

Map `/a0/usr` to a host directory for persistence:
```bash
docker run -p 50001:80 -v /path/on/host:/a0/usr agent0ai/agent-zero
```

Contents of `/a0/usr/`:
- `settings.json` - all configuration including API keys and model settings
- `memory/` - FAISS vector indexes and knowledge import state
- `knowledge/` - user-added knowledge files
- `agents/` - custom agent profiles
- `plugins/` - user plugins
- `projects/` - project workspaces
- `work/` - default working directory for agent file output

## Configuration After Start

On first run, open Settings (gear icon) and configure:
1. **API Keys** - add at least one provider API key under the relevant provider section
2. **Chat Model** - select provider and model name for the primary LLM
3. **Utility Model** - select a cheaper/faster model for internal tasks
4. **Embedding Model** - select embedding provider and model (required for memory and knowledge)

Settings are saved to `usr/settings.json` immediately on change.

## Updating Agent Zero

The recommended update process is to use Self Update:
1. Open **Settings UI → Update** tab
2. Open **Self Update**
3. Wait for the update checker to see if you have the latest version or if there's an available update

You'll also be prompted through the UI when a new A0 version is released. Note that backups are automatically managed internally during the self-update process.

### Updating from Pre-v0.9.8

If upgrading from v0.9.8 or earlier, the architecture has significantly changed. You must use the new install scripts and manually migrate your data:
1. Backup your existing `usr/` directory.
2. Run the Quick Install script (`curl -fsSL https://bash.agent-zero.ai | bash` for macOS/Linux or `irm https://ps.agent-zero.ai | iex` for Windows).
3. Copy your backed-up `usr/` contents into the new installation's `a0/usr/` directory to preserve your settings, memory, and plugins.

## Remote Access

### Flare Tunnel (recommended for external access)
Settings → External Services → Flare Tunnel → Create Tunnel

This generates a public HTTPS URL without requiring firewall changes or a static IP. Set a username and password before creating the tunnel to enable authentication.

### Local Network
Access from other devices on the same network using the host machine's IP:
`http://<host-ip>:<mapped-port>`

### Microsoft Dev Tunnels
Supported as an alternative to Flare for users in Microsoft environments. Configure under External Services in Settings.

## Mobile Access

Agent Zero is a Progressive Web App (PWA). On mobile, open the web UI URL in a browser, then add to home screen for an app-like experience. Works with both local network and tunnel URLs.

## Common Troubleshooting

**Agent responds but no memory/knowledge recall:**
- Check that an embedding model is configured (provider + model name)
- Verify the embedding provider API key is set
- Embedding model changes require re-indexing; this happens automatically but takes time on first run

**"Model not found" or API errors:**
- Verify the model name matches the provider's naming convention exactly
- Check that the API key has access to the requested model
- For OpenRouter, model names must include the provider prefix (`anthropic/claude-sonnet-4-5`)

**Container starts but web UI unreachable:**
- Confirm the host port mapping in `docker ps`
- Check that no firewall rule blocks the mapped port
- The container needs a few seconds to initialize on first start”hChDuh)Nh*�”(hAh2h1�h,NubŒ
zY9364W5fn”h
)�”}”(h
}”(h1hQh2}”(h4hKh6h7h8hLh:hKh;hMh=ˆh>Nh1hQh?hNuhAXª**Knowledge files not being recalled:**
- Supported formats: `.md`, `.txt`, `.pdf`, `.csv`, `.html`, `.json`
- Files must be in `knowledge/` (framework level) or `usr/knowledge/<subdir>/`
- The configured `agent_knowledge_subdir` must match the subdir where files are placed
- Re-indexing is triggered automatically when file checksums change

**Ollama / local model setup:**
- Ollama must be running and accessible from inside the Docker container
- Use `http://host.docker.internal:<port>` as the API URL for Ollama (not `localhost`)
- Pull the model first: `ollama pull <model-name>`

## Development Setup (non-Docker)

```bash
git clone https://github.com/agent0ai/agent-zero
cd agent-zero
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
pip install -r requirements2.txt
python run_ui.py
```

The dev server runs on `http://localhost:5000` by default. User data is written to `usr/` in the project root.”hChDuh)Nh*�”(hAh2h1�h,NubŒ
dRat4HaLyj”h
)�”}”(h
}”(h1hXh2}”(h4Œ$/a0/knowledge/main/about/identity.md”h6h7h8Œidentity.md”h:h]h;Œmd”h=ˆh>Nh1hXh?Œ2026-04-09 16:19:13”uhAXè
# Agent Zero - Identity and Design Philosophy

## What Agent Zero Is

Agent Zero is an open-source, general-purpose agentic framework. It is not pre-programmed for specific tasks and has no fixed capability set beyond the basics. Its defining characteristic is that it grows and adapts as it is used - accumulating knowledge, solutions, and behaviors through persistent memory and user customization.

The framework has been created by Jan Tomášek and is maintained by the Agent Zero dev team and the community. Source code lives at github.com/agent0ai/agent-zero.

## Core Design Principles

**No hard-coding.** Almost nothing in the framework is fixed in source code. Agent behavior, tool definitions, message templates, and response patterns are all controlled by files in the `prompts/` directory. Changing the prompts changes the agent - fundamentally if needed.

**Transparency.** Every prompt, every message template, every tool implementation is readable and editable. There are no hidden instructions or black-box behaviors. The agent can be fully audited.

**Computer as a tool.** Agent Zero does not have a library of pre-built skill functions. Instead, it uses the operating system directly - writing code, running terminal commands, and creating tools on demand. The terminal is the primary interface to everything.

**Organic growth.** The agent accumulates knowledge through experience. Facts, solutions, discovered patterns, and useful code are stored in memory and recalled in future conversations. The agent becomes more effective at tasks it has done before.

**Prompt-driven behavior.** The `prompts/` directory is the control plane. System prompts, tool instructions, framework messages, and utility AI prompts are all there. The agent's behavior is as good as its prompts.

## Project Context

- **Repository**: github.com/agent0ai/agent-zero
- **License**: Open source
- **Primary author**: Jan Tomášek
- **Community**: Discord (discord.gg/B8KZKNsPpj), Skool community, YouTube channel
- **Documentation**: docs/ folder in the repository; deepwiki.com/agent0ai/agent-zero for AI-generated docs
- **Current version**: v0.9.8

## Relationship With the User

Agent Zero treats the human user as its top-level superior in the agent hierarchy. The user is functionally indistinguishable from a superior agent - they give tasks, receive reports, and can intervene at any time. The agent is not a chatbot that answers questions; it is an executor that solves tasks using whatever means are available to it.

The framework is a personal tool, not a service. It runs locally (or on user-controlled infrastructure) and has access to the user's files, credentials, and systems as configured. This makes it powerful and requires the user to understand what they are delegating.”hChDuh)Nh*�”(hAh2h1�h,NubŒ
bHL9JyLQRw”h
)�”}”(h
}”(h1hch2}”(h4Œ(/a0/knowledge/main/about/architecture.md”h6h7h8Œarchitecture.md”h:hhh;Œmd”h=ˆh>Nh1hch?Œ2026-04-09 16:19:13”uhAXóThe agent sees recalled memories as a section in its system prompt labeled "Memories on the topic". The agent is instructed not to over-rely on them.

### Agent memory (read-write, via memorize tool)
The agent can explicitly save facts, solutions, and code snippets using the `memorize` tool. These are stored in the same FAISS index under the `main` or `solutions` area and recalled in future conversations. Memory can also be consolidated (summarized) and managed through the Memory Dashboard in the web UI.

## Tool System

Tools are Python classes in `python/tools/` that inherit from `Tool`. Each tool implements an `execute()` async method. Tools are discovered at startup and registered in the agent's tool list (rendered into the system prompt as `{{tools}}`). The agent names a tool in its JSON response; the framework finds and calls it.

Plugin tools can be added in `plugins/<plugin>/tools/` or `usr/plugins/<plugin>/tools/` without modifying core files.

## Extension and Plugin System

The plugin system (`python/helpers/plugins.py`) discovers plugins from `plugins/` and `usr/plugins/`. Each plugin has a `plugin.yaml` manifest declaring name, version, and settings. Plugins can contribute: API handlers, tools, WebUI components, extensions, and hooks. User plugins in `usr/plugins/` are never overwritten by framework updates. The agent has skills to create, manage, debug, review and contribute plugins to the Plugin Index repository (https://github.com/agent0ai/a0-plugins)

## Frontend Architecture

The web UI is built with Alpine.js and ES module components. The main shell is `webui/index.html`. Components are in `webui/components/`. Frontend state is managed via Alpine stores defined with `createStore` from `/js/AlpineStore.js`.

Real-time communication uses Socket.io WebSockets via a unified `/ws` namespace. WebSocket handlers (WsHandler subclasses) are in `api/ws_*.py`. The connection manager is in `helpers/ws_manager.py`. API handlers are in `api/`, each deriving from `ApiHandler` in `helpers/api.py`.”hChDuh)Nh*�”(hAh2h1�h,NubŒ
bz0Xey3NFp”h
)�”}”(h
}”(Œid”hnŒmetadata”}”(Œarea”Œmain”hrhnŒ	timestamp”Œ2026-04-20 15:39:21”uŒpage_content”XUUser's project ecosystem:
- **Glitch University**: Hybrid learning platform, alien-sponsored university, and YouTube channel
- **Omega13**: Local computer used for inference (just the machine name)
- **Festinger**: A Knowledge Graph repo within Glitch University, part of the Omega13 local inference setup. Stores taxonomic (is-a) relations between concepts. Uses index collisions to prevent contradictory memories. Runs as a proxy that triggers on non-standard/local concepts in conversation to build a domain glossary. Only handles IS_ISA relations along dimensions like 'type' and 'membership'.”Œtype”ŒDocument”uh)Nh*�”(hyhshr�h,NubŒ
w7QUpueQ2U”h
)�”}”(h
}”(Œid”h~Œmetadata”}”(Œarea”Œmain”h‚h~Œ	timestamp”Œ2026-04-21 16:20:14”uŒpage_content”XÝGlitch University System Portfolio - Infrastructure:

**VPS (glitch.university)** - 5 containers:
- Gnommoweb: Webserver + admin panel for task table, agents
- GnommoEditor: Interactive presentations using videos, built in React
- gitea: Local git hosting instance
- Postgres: Main database holding most data
- minIO: File storage for images and videos

**Omega13 (local inference box)** - powerful home computer running 5 parallel Agent Zero instances, one per personality:
- Gunnar, Rind, Abyssinthia, Dobby, Gerhard

**GlitchComponents**: React components importable into Glitch University as UI building blocks / mini games that users play as part of the experience.

**Festinger**: Knowledge Graph repo within Glitch University.”Œtype”ŒDocument”uh)Nh*�”(h‰hƒh‚�h,NubŒ
l9IcQrSp13”h
)�”}”(h
}”(Œid”hŽŒmetadata”}”(Œarea”Œ	solutions”Œ	timestamp”Œ2026-04-21 18:01:05”h’hŽhhhCˆæ?”†”R”uŒpage_content”Œ(# Solution
 Can plants become conscious?”Œtype”ŒDocument”uh)Nh*�”(hœh“h’�h,NubŒ
UYTysguUyv”h
)�”}”(h
}”(h’h¡h“}”(h•h–h—Œ2026-04-21 20:12:38”h’h¡hhhC�è?”†”R”uhœŒ# Solution
 EXTRAS”hžhŸuh)Nh*�”(hœh“h’�h,NubŒ
tWZzcGfeYe”h
)�”}”(h
}”(h’h¬h“}”(h•h–h—Œ2026-04-21 20:22:30”h’h¬uhœX›
# Problem
 The user wants to discuss an advanced topic, but doesn't specify what it is.
# Solution
 1. Ask the user to provide more context or clarify their request.
2. Look for a broad topic that can be discussed in depth, such as machine learning, data science, or artificial intelligence.
3. Be prepared to provide a comprehensive answer and clarify any technical concepts that may be unfamiliar to the user.”hžhŸuh)Nh*�”(hœh“h’�h,NubŒ
ucvdvLZvOS”h
)�”}”(h
}”(h’h´h“}”(h•Œ	fragments”h—Œ2026-04-21 20:23:55”Œconsolidation_action”Œ
keep_separate”h’h´hhhC œç?”†”R”Œconsolidated_from”]”Œ
WF9yerye54”aŒhistorical_notes”Œ@Existing memory provided context on user's topic being advanced.”Œimportance_score”G?é™™™™™šŒconsolidation_type”Œ0Decision to keep separate due to lack of context”uhœŒ3The user needs more context to help with the issue.”hžhŸuh)Nh*�”(hœh“h’�h,NubŒ
7DoSvsl4DT”h
)�”}”(h
}”(h’hÊh“}”(h•h–h—Œ2026-04-22 17:21:02”h»h¼Œconsolidated_from”]”Œ
FJRkFBfyHQ”aŒhistorical_notes”Œ<New solution-discussion context introduced with chat history”Œimportance_score”G?é™™™™™šŒconsolidation_type”Œ@Different contexts identified; information preserved separately.”h’hÊŒ_consolidation_similarity”hhCPê¤ê?”†”R”uhœŒ;# chat history
Conversation with user about potential issue”hžhŸuh)Nh*�”(hœh“h’�h,NubŒ
3uKCXogrzY”h
)�”}”(h
}”(h’hÞh“}”(h•h–h—Œ2026-04-22 17:27:20”h’hÞhhhCÀ¸,é?”†”R”uhœX
# Problem
 Qubit Field Theory has sparked interesting conversations about the nature of quantum systems and their representation. How does this theory relate to Hilbert space dimension?
# Solution
 David Deutsch's theory of Qubit Field Theory has implications on existing quantum field theories with empirical corroboration. The connection between information-carrying capacity and Hilbert space dimension may be the same as in conventional theory. Further research is needed to fully understand its implications.”hžhŸuh)Nh*�”(hœh“h’�h,NubŒ
uruWJTwxFH”h
)�”}”(h
}”(h’héh“}”(h•Œ	solutions”h—Œ2026-04-22 17:27:45”h»Œupdate”Œupdated_from”Œ
oo8BPiIk5V”h’héhhhC€Ë3é?”†”R”uhœXz
# Problem
Theories described in this paper may not satisfy the original motivation of the investigation regarding information-carrying capacity.
# Solution
Theories described in this paper may not satisfy the original motivation of the investigation regarding information-carrying capacity. This raises questions about the validity of the theories and requires further research.”hžhŸuh)Nh*�”(hœh“h’�h,NubŒ
wqFEKcjVwN”h
)�”}”(h
}”(h’høh“}”(h•Œ	solutions”h—Œ2026-04-22 17:27:45”h»hðhñŒ
ARJN6yex6Y”h’høhhhCp3Gç?”†”R”uhœXz
# Problem
Theories described in this paper may not satisfy the original motivation of the investigation regarding information-carrying capacity.
# Solution
Theories described in this paper may not satisfy the original motivation of the investigation regarding information-carrying capacity. This raises questions about the validity of the theories and requires further research.”hžhŸuh)Nh*�”(hœh“h’�h,NubŒ
ySNEXHQd7S”h
)�”}”(h
}”(h’j
h“}”(h•h–h—Œ2026-04-22 17:27:45”h»hðŒconsolidated_from”]”(Œ
oo8BPiIk5V”Œ
ARJN6yex6Y”eŒhistorical_notes”ŒOThe original problem and solution were replaced with more accurate information.”Œimportance_score”G?é™™™™™šŒconsolidation_type”ŒVConsolidated memory content matches the new information, addressing validity concerns.”h’j
hhhCp3Gç?”†”R”uhœXz
# Problem
Theories described in this paper may not satisfy the original motivation of the investigation regarding information-carrying capacity.
# Solution
Theories described in this paper may not satisfy the original motivation of the investigation regarding information-carrying capacity. This raises questions about the validity of the theories and requires further research.”hžhŸuh)Nh*�”(hœh“h’�h,NubŒ
pL9IxbQLMj”h
)�”}”(h
}”(h’j
h“}”(h•h¹h—Œ2026-04-22 17:27:50”h»h¼Œconsolidated_from”]”Œ
VueeJpVrqQ”ah’j
hhhCÀ!î?”†”R”Œhistorical_notes”ŒYDavid Deutsch's theory of Qubit Field Theory is a fascinating topic in quantum mechanics.”Œimportance_score”G?ìÌÌÌÌÌÍŒconsolidation_type”Œ'merge similar memories with new content”uhœŒµThe user is discussing the implications of qubit fields on existing quantum field theories. David Deutsch's theory of Qubit Field Theory is a fascinating topic in quantum mechanics.”hžhŸuh)Nh*�”(hœh“h’�h,NubŒ
ghB6Ghfem1”h
)�”}”(h
}”(h’j,
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]”(Œ
pL9IxbQLMj”Œ
new_memory”eŒhistorical_notes”ŒšThe research was discussing the implications of qubit fields on existing quantum field theories and how they relate to David Deutsch's Qubit Field Theory.”Œimportance_score”G?îffffffŒconsolidation_type”Œ©Merging existing memory (pL9IxbQLMj) with new information to form a comprehensive understanding of qubit fields and their relation to David Deutsch's Qubit Field Theory.”h’j,
uhœX�
The user is discussing the implications of qubit fields on existing quantum field theories. David Deutsch's theory of Qubit Field Theory is a fascinating topic in quantum mechanics. It is not obvious that the theories described in this paper satisfy the original motivation of the investigation: that the information-carrying capacity of a system of David Deutsch Qubit Field Theory should be finite.”hžhŸuh)Nh*�”(hœh“h’�h,NubŒ
nEjdE2luIv”h
)�”}”(h
}”(h’j=
h“}”(h•h–h—Œ2026-04-23 15:58:27”h»hðj
j
j
j
j
G?é™™™™™šj
j
h’j=
hhhC€Ë3é?”†”R”hñhÿuhœX­# Problem
 David Deutsch's theory of Qubit Field Theory has sparked interesting conversations about the nature of quantum systems and their representation.
# Solution
 David Deutsch's theory proposes that quantum systems can exhibit wave-like behavior similar to classical field theories. However, this theory is still a topic of debate among experts, and more research is needed to fully understand its implications. The information-carrying capacity of a system in this theory should be finite, but it's not clear if existing theories satisfy this motivation. Further research is needed to investigate the connection between information-carrying capacity and Hilbert space dimension.”hžhŸuh)Nh*�”(hœh“h’�h,NubŒ
mKmPhQd6RM”h
)�”}”(h
}”(h’jH
h“}”(h•h–h—Œ2026-04-23 15:59:29”h’jH
hØhhC�¬>ç?”†”R”uhœXp
# Problem
 This is a new conversation, I should greet the user warmly and let them know I'm ready to help.
# Solution
 1. Start with a friendly greeting, '**Hello! 👋**, I'm **Agent Zero**, your AI assistant. How can I help you today?**' - This is a proper way to begin the conversation, making sure to include the AI assistant's name and inviting the user for help.”hžhŸuh)Nh*�”(hœh“h’�h,NubŒ
fMcIxdKBFq”h
)�”}”(h
}”(Œid”jS
Œmetadata”}”(Œarea”Œ	fragments”Œ	timestamp”Œ2026-04-25 08:51:05”jW
jS
Œ_consolidation_similarity”hhC`%Ië?”†”R”uŒpage_content”Œ‡The user mentioned gnommoweb, which I couldn't find any information about. I'll make sure to ask for more context or details if needed.”Œtype”ŒDocument”uh)Nh*�”(jb
jX
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Œ2026-04-25 08:51:20”jW
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Œ I've searched the available tools and couldn't find any relevant information about gnommoweb. Please provide more context or details so I can better assist you.”jd
je
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jo
Œ_consolidation_similarity”hhCøÞ•ç?”†”R”uŒpage_content”Œü# Problem
 Task is to download a video from YouTube. A video URL is specified by the user.
# Solution
 1. Install yt-dlp library using 'pip install yt-dlp'
2. Download the video using yt-dlp command: 'yt-dlp YT_URL', replace YT_URL with your video URL.”Œtype”ŒDocument”uh)Nh*�”(j~
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Œ2026-04-26 17:04:08”Œconsolidation_action”Œ
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hhhCRÄé?”†”R”Œconsolidated_from”]”ŒHallal”aŒhistorical_notes”ŒxMerged similar but distinct solution and additional information from Hallal into existing memory for better organization”Œimportance_score”G?ìÌÌÌÌÌÍuj~
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# Solution”j€
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Œ2026-04-26 17:04:18”js
j–
hØhhCè—æ?”†”R”uj~
ŒÓ# Problem
 Ok, great - it's working now. Here is what I want to talk about : There are more systems in Glitch University.
# Solution
 Hello! 👋, I'm **Agent Zero**, your AI assistant. How can I help you today?”j€
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