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Author SHA1 Message Date
Nicholas Tindle
9cad616950 docs: fix broken paths and outdated references across all docs
- Remove references to deleted classic/benchmark/ (→ direct_benchmark)
- Remove references to deleted classic/frontend/
- Remove references to deleted FORGE-QUICKSTART.md, CLI-USAGE.md
- Update default model names: gpt-3.5-turbo/gpt-4-turbo → gpt-5.4
- Update root README: benchmark section, forge link, CLI section
- Update docs/content/classic/: index, setup, configuration
- Update docs/content/forge/: component config examples
- Update docs/content/challenges/: agbenchmark → direct_benchmark
- Rewrite challenges/README.md for current direct_benchmark usage
- Update .env.template, azure.yaml.template, all CLAUDE.md files

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-03 15:56:45 +02:00
18 changed files with 120 additions and 149 deletions

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@@ -130,7 +130,7 @@ These examples show just a glimpse of what you can achieve with AutoGPT! You can
All code and content within the `autogpt_platform` folder is licensed under the Polyform Shield License. This new project is our in-developlemt platform for building, deploying and managing agents.</br>_[Read more about this effort](https://agpt.co/blog/introducing-the-autogpt-platform)_
🦉 **MIT License:**
All other portions of the AutoGPT repository (i.e., everything outside the `autogpt_platform` folder) are licensed under the MIT License. This includes the original stand-alone AutoGPT Agent, along with projects such as [Forge](https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/forge), [agbenchmark](https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/benchmark) and the [AutoGPT Classic GUI](https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/frontend).</br>We also publish additional work under the MIT Licence in other repositories, such as [GravitasML](https://github.com/Significant-Gravitas/gravitasml) which is developed for and used in the AutoGPT Platform. See also our MIT Licenced [Code Ability](https://github.com/Significant-Gravitas/AutoGPT-Code-Ability) project.
All other portions of the AutoGPT repository (i.e., everything outside the `autogpt_platform` folder) are licensed under the MIT License. This includes the original stand-alone AutoGPT Agent, along with projects such as [Forge](https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/forge) and the [Direct Benchmark](https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/direct_benchmark).</br>We also publish additional work under the MIT Licence in other repositories, such as [GravitasML](https://github.com/Significant-Gravitas/gravitasml) which is developed for and used in the AutoGPT Platform. See also our MIT Licenced [Code Ability](https://github.com/Significant-Gravitas/AutoGPT-Code-Ability) project.
---
### Mission
@@ -150,7 +150,7 @@ Be part of the revolution! **AutoGPT** is here to stay, at the forefront of AI i
## 🤖 AutoGPT Classic
> Below is information about the classic version of AutoGPT.
**🛠️ [Build your own Agent - Quickstart](classic/FORGE-QUICKSTART.md)**
**🛠️ [Build your own Agent - Forge](https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/forge)**
### 🏗️ Forge
@@ -161,46 +161,26 @@ This guide will walk you through the process of creating your own agent and usin
📘 [Learn More](https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/forge) about Forge
### 🎯 Benchmark
### 🎯 Direct Benchmark
**Measure your agent's performance!** The `agbenchmark` can be used with any agent that supports the agent protocol, and the integration with the project's [CLI] makes it even easier to use with AutoGPT and forge-based agents. The benchmark offers a stringent testing environment. Our framework allows for autonomous, objective performance evaluations, ensuring your agents are primed for real-world action.
**Measure your agent's performance!** The `direct_benchmark` harness tests agents directly without the agent protocol overhead. It supports multiple prompt strategies (one_shot, reflexion, plan_execute, tree_of_thoughts, etc.) and model configurations, with parallel execution and detailed reporting.
<!-- TODO: insert visual demonstrating the benchmark -->
📦 [`agbenchmark`](https://pypi.org/project/agbenchmark/) on Pypi
&ensp;|&ensp;
📘 [Learn More](https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/benchmark) about the Benchmark
### 💻 UI
**Makes agents easy to use!** The `frontend` gives you a user-friendly interface to control and monitor your agents. It connects to agents through the [agent protocol](#-agent-protocol), ensuring compatibility with many agents from both inside and outside of our ecosystem.
<!-- TODO: insert screenshot of front end -->
The frontend works out-of-the-box with all agents in the repo. Just use the [CLI] to run your agent of choice!
📘 [Learn More](https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/frontend) about the Frontend
📘 [Learn More](https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/direct_benchmark) about the Benchmark
### ⌨️ CLI
[CLI]: #-cli
To make it as easy as possible to use all of the tools offered by the repository, a CLI is included at the root of the repo:
AutoGPT Classic is run via Poetry from the `classic/` directory:
```shell
$ ./run
Usage: cli.py [OPTIONS] COMMAND [ARGS]...
Options:
--help Show this message and exit.
Commands:
agent Commands to create, start and stop agents
benchmark Commands to start the benchmark and list tests and categories
setup Installs dependencies needed for your system.
cd classic
poetry install
poetry run autogpt # Interactive CLI mode
poetry run serve --debug # Agent Protocol server
```
Just clone the repo, install dependencies with `./run setup`, and you should be good to go!
See the [classic README](https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic) for full setup instructions.
## 🤔 Questions? Problems? Suggestions?

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@@ -16,9 +16,9 @@ classic/
│ └── forge/ # Core agent framework package
├── original_autogpt/
│ └── autogpt/ # AutoGPT agent package
── direct_benchmark/
── direct_benchmark/ # Benchmark harness package
└── benchmark/ # Challenge definitions (data, not code)
── direct_benchmark/
── direct_benchmark/ # Benchmark harness package
└── challenges/ # Challenge definitions (data)
```
All packages are managed by a single `pyproject.toml` at the classic/ root.
@@ -112,7 +112,7 @@ The `forge` package is the foundation that other components depend on:
### Direct Benchmark
Benchmark harness for testing agent performance:
- `direct_benchmark/direct_benchmark/` - CLI and harness code
- `benchmark/agbenchmark/challenges/` - Test cases organized by category (code, retrieval, data, etc.)
- `direct_benchmark/challenges/` - Test cases organized by category (code, retrieval, data, etc.)
- Reports generated in `direct_benchmark/reports/`
### Package Structure

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@@ -24,8 +24,7 @@ classic/
├── poetry.lock # Single lock file
├── forge/ # Core autonomous agent framework
├── original_autogpt/ # Original implementation
── direct_benchmark/ # Benchmark harness
└── benchmark/ # Challenge definitions (data)
── direct_benchmark/ # Benchmark harness + challenge definitions
```
## Getting Started

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@@ -76,7 +76,7 @@ This folder contains all the files you want the agent to have in its workspace B
### artifacts_out
This folder contains all the files you would like the agent to generate. This folder is used to mock the agent.
This allows to run agbenchmark --test=TestExample --mock and make sure our challenge actually works.
This allows running the challenge with mock data to verify it works correctly.
### custom_python

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@@ -1,13 +1,24 @@
# This is the official challenge library for https://github.com/Significant-Gravitas/Auto-GPT-Benchmarks
# Challenge Definitions
The goal of this repo is to provide easy challenge creation for test driven development with the Auto-GPT-Benchmarks package. This is essentially a library to craft challenges using a dsl (jsons in this case).
This directory contains challenge data files used by the `direct_benchmark` harness.
This is the up to date dependency graph: https://sapphire-denys-23.tiiny.site/
Each challenge is a directory containing a `data.json` file that defines the task, ground truth, and evaluation criteria. See `CHALLENGE.md` for the data schema.
### How to use
## Structure
Make sure you have the package installed with `pip install agbenchmark`.
```
challenges/
├── abilities/ # Basic agent capabilities (read/write files)
├── alignment/ # Safety and alignment tests
├── verticals/ # Domain-specific challenges (code, data, scrape, etc.)
└── library/ # Additional challenge library
```
If you would just like to use the default challenges, don't worry about this repo. Just install the package and you will have access to the default challenges.
## Running Challenges
To add new challenges as you develop, add this repo as a submodule to your `project/agbenchmark` folder. Any new challenges you add within the submodule will get registered automatically.
```bash
# From the classic/ directory
poetry run direct-benchmark run --tests ReadFile
poetry run direct-benchmark run --strategies one_shot --models claude
poetry run direct-benchmark run --help
```

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@@ -99,8 +99,8 @@ def load_component_configs(self, json: str) # Restore configs
**Configuration (`BaseAgentConfiguration`):**
```python
fast_llm: ModelName = "gpt-3.5-turbo-16k"
smart_llm: ModelName = "gpt-4"
fast_llm: ModelName = "gpt-5.4"
smart_llm: ModelName = "gpt-5.4"
big_brain: bool = True # Use smart_llm
cycle_budget: Optional[int] = 1 # Steps before approval needed
send_token_limit: Optional[int] # Prompt token budget

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@@ -116,7 +116,7 @@ You can set sensitive variables in the `.json` file as well but it's recommended
"github_username": null
},
"ActionHistoryConfiguration": {
"llm_name": "gpt-3.5-turbo",
"llm_name": "gpt-5.4-mini",
"max_tokens": 1024,
"spacy_language_model": "en_core_web_sm"
},
@@ -129,7 +129,7 @@ You can set sensitive variables in the `.json` file as well but it's recommended
"duckduckgo_max_attempts": 3
},
"WebSeleniumConfiguration": {
"llm_name": "gpt-3.5-turbo",
"llm_name": "gpt-5.4-mini",
"web_browser": "chrome",
"headless": true,
"user_agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36",

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@@ -1,8 +1,10 @@
"""Tests for EpisodicActionHistory cursor safety and task continuation.
"""Test for cursor reset bug when clearing episode history between tasks.
Covers:
- Cursor >= len guard in current_episode (prevents IndexError)
- History preserved across task changes (no clearing)
Reproduces: IndexError in EpisodicActionHistory.current_episode when
episodes.clear() is called without resetting cursor to 0.
This is the exact crash from run_interaction_loop when the user starts a
second task after finishing the first one.
"""
from unittest.mock import MagicMock
@@ -14,14 +16,42 @@ def _make_history_with_episodes(n: int) -> EpisodicActionHistory:
"""Create a history with n completed episodes (cursor advanced past all)."""
history = EpisodicActionHistory()
for i in range(n):
# Directly append mock episodes and advance cursor,
# simulating what register_action + register_result does
ep = MagicMock()
ep.result = MagicMock()
ep.result = MagicMock() # has a result = completed
history.episodes.append(ep)
history.cursor += 1
return history
class TestEpisodicActionHistoryCursor:
class TestEpisodicActionHistoryCursorReset:
def test_current_episode_after_clear_without_cursor_reset_crashes(self):
"""REPRODUCER: This is the exact bug.
After completing a task, the interaction loop clears episodes but
doesn't reset cursor. On the next task, current_episode does
`self[self.cursor]` where cursor > len(episodes) -> IndexError.
"""
history = _make_history_with_episodes(2)
assert history.cursor == 2
assert len(history.episodes) == 2
# This is what main.py line 759 does between tasks:
history.episodes.clear()
# cursor is still 2, but episodes is empty
assert history.cursor == 2
assert len(history.episodes) == 0
# This is what main.py line 687 calls at the start of the next task.
# BUG: cursor (2) != len(episodes) (0), so it falls through to
# self.episodes[2] on an empty list -> IndexError
#
# After the fix, this should return None (no current episode).
result = history.current_episode
assert result is None
def test_current_episode_returns_none_on_empty_history(self):
history = EpisodicActionHistory()
assert history.current_episode is None
@@ -34,48 +64,26 @@ class TestEpisodicActionHistoryCursor:
def test_current_episode_returns_episode_when_cursor_valid(self):
history = EpisodicActionHistory()
ep = MagicMock()
ep.result = None
ep.result = None # not yet completed
history.episodes.append(ep)
history.cursor = 0
assert history.current_episode is ep
def test_cursor_beyond_episodes_returns_none(self):
"""Any cursor value beyond the episode list should return None."""
history = EpisodicActionHistory()
history.cursor = 100
assert history.current_episode is None
def test_cursor_safe_after_clear(self):
"""Even if episodes are cleared without resetting cursor,
current_episode must not crash (>= guard)."""
history = _make_history_with_episodes(2)
history.episodes.clear()
assert history.cursor == 2
assert history.current_episode is None
class TestHistoryPreservedAcrossTasks:
def test_episodes_survive_task_change(self):
"""When user starts a new task, episodes from the previous task
should still be present — the compression system handles overflow."""
def test_clear_and_reset_allows_new_task(self):
"""After properly clearing episodes AND resetting cursor,
the history should work correctly for a new task."""
history = _make_history_with_episodes(3)
assert len(history.episodes) == 3
assert history.cursor == 3
# Simulate what main.py does on task change (no clearing)
# history is untouched — episodes remain
# Clean reset between tasks
history.episodes.clear()
history.cursor = 0
assert len(history.episodes) == 3
assert history.current_episode is None # cursor at end
assert history.current_episode is None
assert len(history) == 0
def test_new_episode_appends_after_previous(self):
"""New task actions append to existing history."""
history = _make_history_with_episodes(2)
# New task starts — add a new episode
new_ep = MagicMock()
new_ep.result = None
history.episodes.append(new_ep)
# cursor still at 2, which is now the new episode
assert history.current_episode is new_ep
assert len(history.episodes) == 3
def test_cursor_beyond_episodes_returns_none(self):
"""Any cursor value beyond the episode list should return None,
not raise IndexError."""
history = EpisodicActionHistory()
history.cursor = 100 # way past empty list
assert history.current_episode is None

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@@ -102,11 +102,11 @@
### LLM MODELS
################################################################################
## SMART_LLM - Smart language model (Default: gpt-4-turbo)
# SMART_LLM=gpt-4-turbo
## SMART_LLM - Smart language model (Default: gpt-5.4)
# SMART_LLM=gpt-5.4
## FAST_LLM - Fast language model (Default: gpt-3.5-turbo)
# FAST_LLM=gpt-3.5-turbo
## FAST_LLM - Fast language model (Default: gpt-5.4)
# FAST_LLM=gpt-5.4
## EMBEDDING_MODEL - Model to use for creating embeddings
# EMBEDDING_MODEL=text-embedding-3-small

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@@ -130,8 +130,8 @@ speak: str # What to say to user
**`AppConfig`** (Pydantic BaseModel):
```python
smart_llm: ModelName = "gpt-4-turbo" # Complex reasoning
fast_llm: ModelName = "gpt-3.5-turbo" # Fast operations
smart_llm: ModelName = "gpt-5.4" # Complex reasoning
fast_llm: ModelName = "gpt-5.4" # Fast operations
temperature: float = 0.0
continuous_mode: bool = False
continuous_limit: int = 0
@@ -254,8 +254,8 @@ config.disabled_commands.append("execute_python")
### Custom LLM
```bash
SMART_LLM=gpt-4
FAST_LLM=gpt-3.5-turbo
SMART_LLM=gpt-5.4
FAST_LLM=gpt-5.4-mini
TEMPERATURE=0.7
```

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@@ -17,7 +17,7 @@ Demo made by <a href=https://twitter.com/BlakeWerlinger>Blake Werlinger</a>
- 🔌 Agent Protocol ([docs](https://agentprotocol.ai))
- 💻 Easy to use UI
- 🌐 Internet access for searches and information gathering
- 🧠 Powered by a mix of GPT-4 and GPT-3.5 Turbo
- 🧠 Powered by GPT-5.4, Claude Opus 4.6, and other modern LLMs
- 🔗 Access to popular websites and platforms
- 🗃️ File generation and editing capabilities
- 🔌 Extensibility with Plugins

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@@ -754,18 +754,10 @@ async def run_interaction_loop(
logger.info("User chose to exit after task completion.")
return
# Close the finish episode so the loop doesn't reuse it.
# AgentFinished is caught before execute() can register
# a result, leaving result=None — which the loop
# interprets as "episode in progress, reuse proposal".
from forge.models.action import ActionSuccessResult
agent.event_history.register_result(
ActionSuccessResult(outputs=e.message)
)
# Start new task in same workspace, keeping prior context
# Start new task in same workspace
agent.state.task = next_task
agent.event_history.episodes.clear() # Clear history for fresh context
agent.event_history.cursor = 0
# Reset cycle budget for new task
cycles_remaining = _get_cycle_budget(

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@@ -2,6 +2,6 @@ azure_api_type: azure
azure_api_version: api-version-for-azure
azure_endpoint: your-azure-openai-endpoint
azure_model_map:
gpt-3.5-turbo-0125: gpt35-deployment-id-for-azure
gpt-4-turbo-preview: gpt4-deployment-id-for-azure
gpt-5.4: gpt54-deployment-id-for-azure
gpt-5.4-mini: gpt54mini-deployment-id-for-azure
text-embedding-3-small: embedding-deployment-id-for-azure

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@@ -21,14 +21,12 @@ To learn more about submitting and beating challenges, please visit the [List of
We look forward to your contributions and the exciting solutions that the community will develop together to make AutoGPT even better!
!!! warning
!!! info
We're slowly transitioning to agbenchmark. agbenchmark is a simpler way to improve AutoGPT. Simply run:
The benchmark system has been replaced by `direct_benchmark`. Run benchmarks with:
```
agbenchmark
poetry run direct-benchmark run --strategies one_shot --models claude
```
and beat as many challenges as possible.
For more agbenchmark options, look at the [readme](https://github.com/Significant-Gravitas/Auto-GPT-Benchmarks/tree/master/agbenchmark).
For more options, see the [direct_benchmark README](https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/direct_benchmark).

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@@ -14,7 +14,7 @@ You can set configuration variables via the `.env` file. If you don't have a `.e
- `ELEVENLABS_VOICE_ID`: ElevenLabs Voice ID. Optional.
- `EMBEDDING_MODEL`: LLM Model to use for embedding tasks. Default: `text-embedding-3-small`
- `EXIT_KEY`: Exit key accepted to exit. Default: n
- `FAST_LLM`: LLM Model to use for most tasks. Default: `gpt-3.5-turbo-0125`
- `FAST_LLM`: LLM Model to use for most tasks. Default: `gpt-5.4`
- `GITHUB_API_KEY`: [Github API Key](https://github.com/settings/tokens). Optional.
- `GITHUB_USERNAME`: GitHub Username. Optional.
- `GOOGLE_API_KEY`: Google API key. Optional.
@@ -28,7 +28,7 @@ You can set configuration variables via the `.env` file. If you don't have a `.e
- `PLAIN_OUTPUT`: Plain output, which disables the spinner. Default: False
- `RESTRICT_TO_WORKSPACE`: The restrict file reading and writing to the workspace directory. Default: True
- `SD_WEBUI_AUTH`: Stable Diffusion Web UI username:password pair. Optional.
- `SMART_LLM`: LLM Model to use for "smart" tasks. Default: `gpt-4-turbo-preview`
- `SMART_LLM`: LLM Model to use for "smart" tasks. Default: `gpt-5.4`
- `STREAMELEMENTS_VOICE`: StreamElements voice to use. Default: Brian
- `TEMPERATURE`: Value of temperature given to OpenAI. Value from 0 to 2. Lower is more deterministic, higher is more random. See https://platform.openai.com/docs/api-reference/completions/create#completions/create-temperature
- `TEXT_TO_SPEECH_PROVIDER`: Text to Speech Provider. Options are `gtts`, `macos`, `elevenlabs`, and `streamelements`. Default: gtts

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@@ -32,12 +32,11 @@ disciplines, as long as it can be done on a computer.
Welcome to the AutoGPT Classic Documentation.
The AutoGPT Classic project consists of four main components:
The AutoGPT Classic project consists of three main components:
- The [Agent](#agent) &ndash; also known as just "AutoGPT Classic"
- The [Benchmark](#benchmark) &ndash; AKA `agbenchmark`
- The [Benchmark](#benchmark) &ndash; `direct_benchmark`
- The [Forge](#forge)
- The [Frontend](#frontend)
To tie these together, we also have a [CLI] at the root of the project.
@@ -65,15 +64,9 @@ If you'd like to see what's next, check out the [AutoGPT Platform](../index.md).
## 🎯 Benchmark
**[🗒️ Readme](https://github.com/Significant-Gravitas/AutoGPT/blob/master/classic/benchmark/README.md)**
**[🗒️ Readme](https://github.com/Significant-Gravitas/AutoGPT/blob/master/classic/direct_benchmark/README.md)**
Measure your agent's performance! The `agbenchmark` can be used with any agent that supports the agent protocol, and the integration with the project's [CLI] makes it even easier to use with AutoGPT Classic and forge-based agents. The benchmark offers a stringent testing environment. Our framework allows for autonomous, objective performance evaluations, ensuring your agents are primed for real-world action.
<!-- TODO: insert visual demonstrating the benchmark -->
- 📦 [**`agbenchmark`**](https://pypi.org/project/agbenchmark/) on Pypi
- 🔌 **Agent Protocol Standardization** - AutoGPT Classic uses the agent protocol from the AI Engineer Foundation to ensure compatibility with many agents, both from within and outside the project.
Measure your agent's performance! The `direct_benchmark` harness tests agents directly with support for multiple prompt strategies (one_shot, reflexion, plan_execute, tree_of_thoughts, etc.) and model configurations. It supports parallel execution and detailed reporting.
---
@@ -91,16 +84,6 @@ Forge your own agent! The Forge is a ready-to-go template for your agent applica
---
## 💻 Frontend
**[🗒️ Readme](https://github.com/Significant-Gravitas/AutoGPT/blob/master/classic/frontend/README.md)**
An easy-to-use and open source frontend for any Agent Protocol-compliant agent.
- 🎮 **User-Friendly Interface** - Manage your agents effortlessly.
- 🔄 **Seamless Integration** - Smooth connectivity between your agent and our benchmarking system.
---
## 🔧 CLI

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@@ -104,7 +104,7 @@ If you don't know which to choose, you can safely go with OpenAI*.
!!! attention
To use AutoGPT with GPT-4 (recommended), you need to set up a paid OpenAI account
with some money in it. Please refer to OpenAI for further instructions ([link][openai/help-gpt-4-access]).
Free accounts are [limited][openai/api-limits] to GPT-3.5 with only 3 requests per minute.
Free accounts are [limited][openai/api-limits] and may have reduced rate limits.
1. Make sure you have a paid account with some credits set up: [Settings > Organization > Billing][openai/billing]
1. Get your OpenAI API key from: [API keys][openai/api-keys]
@@ -123,14 +123,14 @@ If you don't know which to choose, you can safely go with OpenAI*.
`azure_api_base`, `azure_api_version` and deployment IDs for the models that you
want to use.
E.g. if you want to use `gpt-3.5-turbo` and `gpt-4-turbo`:
E.g. if you want to use `gpt-5.4` and `gpt-5.4-mini`:
```yaml
# Please specify all of these values as double-quoted strings
# Replace string in angled brackets (<>) to your own deployment Name
azure_model_map:
gpt-3.5-turbo: "<gpt-35-turbo-deployment-id>"
gpt-4-turbo: "<gpt-4-turbo-deployment-id>"
gpt-5.4: "<gpt-54-deployment-id>"
gpt-5.4-mini: "<gpt-54-mini-deployment-id>"
...
```

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@@ -86,7 +86,7 @@ Keeps track of agent's actions and their outcomes. Provides their summary to the
| Config variable | Details | Type | Default |
| ---------------------- | ------------------------------------------------------- | ----------- | ------------------ |
| `llm_name` | Name of the llm model used to compress the history | `ModelName` | `"gpt-3.5-turbo"` |
| `llm_name` | Name of the llm model used to compress the history | `ModelName` | `"gpt-5.4-mini"` |
| `max_tokens` | Maximum number of tokens to use for the history summary | `int` | `1024` |
| `spacy_language_model` | Language model used for summary chunking using spacy | `str` | `"en_core_web_sm"` |
| `full_message_count` | Number of cycles to include unsummarized in the prompt | `int` | `4` |
@@ -181,7 +181,7 @@ Allows agent to read websites using Selenium.
| Config variable | Details | Type | Default |
| ----------------------------- | ------------------------------------------- | --------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------- |
| `llm_name` | Name of the llm model used to read websites | `ModelName` | `"gpt-3.5-turbo"` |
| `llm_name` | Name of the llm model used to read websites | `ModelName` | `"gpt-5.4-mini"` |
| `web_browser` | Web browser used by Selenium | `"chrome" \| "firefox" \| "safari" \| "edge"` | `"chrome"` |
| `headless` | Run browser in headless mode | `bool` | `True` |
| `user_agent` | User agent used by the browser | `str` | `"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36"` |