Merge branch 'master' into summary_memory

.devcontainer/devcontainer.json

@@ -7,12 +7,11 @@
     "ghcr.io/devcontainers/features/common-utils:2": {
       "installZsh": "true",
       "username": "vscode",
-      "userUid": "1000",
-      "userGid": "1000",
+      "userUid": "6942",
+      "userGid": "6942",
       "upgradePackages": "true"
     },
     "ghcr.io/devcontainers/features/desktop-lite:1": {},
-    "ghcr.io/devcontainers/features/github-cli:1": {},
     "ghcr.io/devcontainers/features/python:1": "none",
     "ghcr.io/devcontainers/features/node:1": "none",
     "ghcr.io/devcontainers/features/git:1": {
@@ -26,20 +25,8 @@
     "vscode": {
       // Set *default* container specific settings.json values on container create.
       "settings": {
-        "python.defaultInterpreterPath": "/usr/local/bin/python",
-        "python.testing.pytestEnabled": true,
-        "python.testing.unittestEnabled": false
-      },
-      "extensions": [
-        "ms-python.python",
-        "VisualStudioExptTeam.vscodeintellicode",
-        "ms-python.vscode-pylance",
-        "ms-python.black-formatter",
-        "ms-python.isort",
-        "GitHub.vscode-pull-request-github",
-        "GitHub.copilot",
-        "github.vscode-github-actions"
-      ]
+        "python.defaultInterpreterPath": "/usr/local/bin/python"
+      }
     }
   },
   // Use 'forwardPorts' to make a list of ports inside the container available locally.
@@ -49,8 +36,5 @@
   // "postCreateCommand": "pip3 install --user -r requirements.txt",
 
   // Set `remoteUser` to `root` to connect as root instead. More info: https://aka.ms/vscode-remote/containers/non-root.
-  "remoteUser": "vscode",
-
-  // Add the freshly containerized repo to the list of safe repositories
-  "postCreateCommand": "git config --global --add safe.directory /workspace/Auto-GPT && pip3 install --user -r requirements.txt"
-}
+  "remoteUser": "vscode"
+}

.devcontainer/docker-compose.yml

@@ -4,9 +4,16 @@ version: '3.9'
 
 services:
   auto-gpt:
+    depends_on:
+      - redis
     build:
       dockerfile: .devcontainer/Dockerfile
       context: ../
     tty: true
+    environment:
+      MEMORY_BACKEND: ${MEMORY_BACKEND:-redis}
+      REDIS_HOST: ${REDIS_HOST:-redis}
     volumes:
       - ../:/workspace/Auto-GPT
+  redis:
+    image: 'redis/redis-stack-server:latest'

.dockerignore

@@ -2,7 +2,6 @@
 *.template
 *.yaml
 *.yml
-!prompt_settings.yaml
 
 *.md
 *.png

.env.template

@@ -1,16 +1,10 @@
-# For further descriptions of these settings see docs/configuration/options.md or go to docs.agpt.co
-
 ################################################################################
 ### AUTO-GPT - GENERAL SETTINGS
 ################################################################################
 
-## OPENAI_API_KEY - OpenAI API Key (Example: my-openai-api-key)
-OPENAI_API_KEY=your-openai-api-key
-
 ## EXECUTE_LOCAL_COMMANDS - Allow local command execution (Default: False)
-# EXECUTE_LOCAL_COMMANDS=False
-
 ## RESTRICT_TO_WORKSPACE - Restrict file operations to workspace ./auto_gpt_workspace (Default: True)
+# EXECUTE_LOCAL_COMMANDS=False
 # RESTRICT_TO_WORKSPACE=True
 
 ## USER_AGENT - Define the user-agent used by the requests library to browse website (string)
@@ -19,194 +13,202 @@ OPENAI_API_KEY=your-openai-api-key
 ## AI_SETTINGS_FILE - Specifies which AI Settings file to use (defaults to ai_settings.yaml)
 # AI_SETTINGS_FILE=ai_settings.yaml
 
-## PLUGINS_CONFIG_FILE - The path to the plugins_config.yaml file (Default plugins_config.yaml)
-# PLUGINS_CONFIG_FILE=plugins_config.yaml
-
-## PROMPT_SETTINGS_FILE - Specifies which Prompt Settings file to use (defaults to prompt_settings.yaml)
-# PROMPT_SETTINGS_FILE=prompt_settings.yaml
-
-## OPENAI_API_BASE_URL - Custom url for the OpenAI API, useful for connecting to custom backends. No effect if USE_AZURE is true, leave blank to keep the default url
-# the following is an example:
-# OPENAI_API_BASE_URL=http://localhost:443/v1
-
-## OPENAI_FUNCTIONS - Enables OpenAI functions: https://platform.openai.com/docs/guides/gpt/function-calling
-## WARNING: this feature is only supported by OpenAI's newest models. Until these models become the default on 27 June, add a '-0613' suffix to the model of your choosing.
-# OPENAI_FUNCTIONS=False
-
 ## AUTHORISE COMMAND KEY - Key to authorise commands
 # AUTHORISE_COMMAND_KEY=y
-
 ## EXIT_KEY - Key to exit AUTO-GPT
 # EXIT_KEY=n
 
-## PLAIN_OUTPUT - Plain output, which disables the spinner (Default: False)
-# PLAIN_OUTPUT=False
-
-## DISABLED_COMMAND_CATEGORIES - The list of categories of commands that are disabled (Default: None)
-# DISABLED_COMMAND_CATEGORIES=
-
 ################################################################################
 ### LLM PROVIDER
 ################################################################################
 
+### OPENAI
+## OPENAI_API_KEY - OpenAI API Key (Example: my-openai-api-key)
 ## TEMPERATURE - Sets temperature in OpenAI (Default: 0)
-# TEMPERATURE=0
-
-## OPENAI_ORGANIZATION - Your OpenAI Organization key (Default: None)
-# OPENAI_ORGANIZATION=
-
 ## USE_AZURE - Use Azure OpenAI or not (Default: False)
+OPENAI_API_KEY=your-openai-api-key
+# TEMPERATURE=0
 # USE_AZURE=False
 
-## AZURE_CONFIG_FILE - The path to the azure.yaml file (Default: azure.yaml)
-# AZURE_CONFIG_FILE=azure.yaml
-
+### AZURE
+# moved to `azure.yaml.template`
 
 ################################################################################
 ### LLM MODELS
 ################################################################################
 
-## SMART_LLM - Smart language model (Default: gpt-4)
-# SMART_LLM=gpt-4
+## SMART_LLM_MODEL - Smart language model (Default: gpt-4)
+## FAST_LLM_MODEL - Fast language model (Default: gpt-3.5-turbo)
+# SMART_LLM_MODEL=gpt-4
+# FAST_LLM_MODEL=gpt-3.5-turbo
 
-## FAST_LLM - Fast language model (Default: gpt-3.5-turbo)
-# FAST_LLM=gpt-3.5-turbo
-
-## EMBEDDING_MODEL - Model to use for creating embeddings
-# EMBEDDING_MODEL=text-embedding-ada-002
-
-################################################################################
-### SHELL EXECUTION
-################################################################################
-
-## SHELL_COMMAND_CONTROL - Whether to use "allowlist" or "denylist" to determine what shell commands can be executed (Default: denylist)
-# SHELL_COMMAND_CONTROL=denylist
-
-## ONLY if SHELL_COMMAND_CONTROL is set to denylist:
-## SHELL_DENYLIST - List of shell commands that ARE NOT allowed to be executed by Auto-GPT (Default: sudo,su)
-# SHELL_DENYLIST=sudo,su
-
-## ONLY if SHELL_COMMAND_CONTROL is set to allowlist:
-## SHELL_ALLOWLIST - List of shell commands that ARE allowed to be executed by Auto-GPT (Default: None)
-# SHELL_ALLOWLIST=
+### LLM MODEL SETTINGS
+## FAST_TOKEN_LIMIT - Fast token limit for OpenAI (Default: 4000)
+## SMART_TOKEN_LIMIT - Smart token limit for OpenAI (Default: 8000)
+## When using --gpt3only this needs to be set to 4000.
+# FAST_TOKEN_LIMIT=4000
+# SMART_TOKEN_LIMIT=8000
 
 ################################################################################
 ### MEMORY
 ################################################################################
 
-### General
-
-## MEMORY_BACKEND - Memory backend type
-# MEMORY_BACKEND=json_file
-
-## MEMORY_INDEX - Value used in the Memory backend for scoping, naming, or indexing (Default: auto-gpt)
+### MEMORY_BACKEND - Memory backend type
+## local - Default
+## pinecone - Pinecone (if configured)
+## redis - Redis (if configured)
+## milvus - Milvus (if configured - also works with Zilliz)
+## MEMORY_INDEX - Name of index created in Memory backend (Default: auto-gpt)
+# MEMORY_BACKEND=local
 # MEMORY_INDEX=auto-gpt
 
-### Redis
+### PINECONE
+## PINECONE_API_KEY - Pinecone API Key (Example: my-pinecone-api-key)
+## PINECONE_ENV - Pinecone environment (region) (Example: us-west-2)
+# PINECONE_API_KEY=your-pinecone-api-key
+# PINECONE_ENV=your-pinecone-region
 
+### REDIS
 ## REDIS_HOST - Redis host (Default: localhost, use "redis" for docker-compose)
-# REDIS_HOST=localhost
-
 ## REDIS_PORT - Redis port (Default: 6379)
-# REDIS_PORT=6379
-
 ## REDIS_PASSWORD - Redis password (Default: "")
-# REDIS_PASSWORD=
-
 ## WIPE_REDIS_ON_START - Wipes data / index on start (Default: True)
+# REDIS_HOST=localhost
+# REDIS_PORT=6379
+# REDIS_PASSWORD=
 # WIPE_REDIS_ON_START=True
 
+### WEAVIATE
+## MEMORY_BACKEND - Use 'weaviate' to use Weaviate vector storage
+## WEAVIATE_HOST - Weaviate host IP
+## WEAVIATE_PORT - Weaviate host port
+## WEAVIATE_PROTOCOL - Weaviate host protocol (e.g. 'http')
+## USE_WEAVIATE_EMBEDDED - Whether to use Embedded Weaviate
+## WEAVIATE_EMBEDDED_PATH - File system path were to persist data when running Embedded Weaviate
+## WEAVIATE_USERNAME - Weaviate username
+## WEAVIATE_PASSWORD - Weaviate password
+## WEAVIATE_API_KEY - Weaviate API key if using API-key-based authentication
+# WEAVIATE_HOST="127.0.0.1"
+# WEAVIATE_PORT=8080
+# WEAVIATE_PROTOCOL="http"
+# USE_WEAVIATE_EMBEDDED=False
+# WEAVIATE_EMBEDDED_PATH="/home/me/.local/share/weaviate"
+# WEAVIATE_USERNAME=
+# WEAVIATE_PASSWORD=
+# WEAVIATE_API_KEY=
+
+### MILVUS
+## MILVUS_ADDR - Milvus remote address (e.g. localhost:19530, https://xxx-xxxx.xxxx.xxxx.zillizcloud.com:443)
+## MILVUS_USERNAME - username for your Milvus database
+## MILVUS_PASSWORD - password for your Milvus database
+## MILVUS_SECURE - True to enable TLS. (Default: False)
+##   Setting MILVUS_ADDR to a `https://` URL will override this setting.
+## MILVUS_COLLECTION - Milvus collection, change it if you want to start a new memory and retain the old memory.
+# MILVUS_ADDR=localhost:19530
+# MILVUS_USERNAME=
+# MILVUS_PASSWORD=
+# MILVUS_SECURE=
+# MILVUS_COLLECTION=autogpt
+
 ################################################################################
 ### IMAGE GENERATION PROVIDER
 ################################################################################
 
-### Common
-
-## IMAGE_PROVIDER - Image provider (Default: dalle)
+### OPEN AI
+## IMAGE_PROVIDER - Image provider (Example: dalle)
+## IMAGE_SIZE - Image size (Example: 256)
+##   DALLE: 256, 512, 1024
 # IMAGE_PROVIDER=dalle
-
-## IMAGE_SIZE - Image size (Default: 256)
 # IMAGE_SIZE=256
 
-### Huggingface (IMAGE_PROVIDER=huggingface)
-
+### HUGGINGFACE
 ## HUGGINGFACE_IMAGE_MODEL - Text-to-image model from Huggingface (Default: CompVis/stable-diffusion-v1-4)
+## HUGGINGFACE_API_TOKEN - HuggingFace API token (Example: my-huggingface-api-token)
 # HUGGINGFACE_IMAGE_MODEL=CompVis/stable-diffusion-v1-4
+# HUGGINGFACE_API_TOKEN=your-huggingface-api-token
 
-## HUGGINGFACE_API_TOKEN - HuggingFace API token (Default: None)
-# HUGGINGFACE_API_TOKEN=
-
-### Stable Diffusion (IMAGE_PROVIDER=sdwebui)
-
-## SD_WEBUI_AUTH - Stable Diffusion Web UI username:password pair (Default: None)
+### STABLE DIFFUSION WEBUI
+## SD_WEBUI_AUTH - Stable diffusion webui username:password pair (Example: username:password)
+## SD_WEBUI_URL - Stable diffusion webui API URL (Example: http://127.0.0.1:7860)
 # SD_WEBUI_AUTH=
-
-## SD_WEBUI_URL - Stable Diffusion Web UI API URL (Default: http://localhost:7860)
-# SD_WEBUI_URL=http://localhost:7860
+# SD_WEBUI_URL=http://127.0.0.1:7860
 
 ################################################################################
 ### AUDIO TO TEXT PROVIDER
 ################################################################################
 
-## AUDIO_TO_TEXT_PROVIDER - Audio-to-text provider (Default: huggingface)
-# AUDIO_TO_TEXT_PROVIDER=huggingface
-
-## HUGGINGFACE_AUDIO_TO_TEXT_MODEL - The model for HuggingFace to use (Default: CompVis/stable-diffusion-v1-4)
-# HUGGINGFACE_AUDIO_TO_TEXT_MODEL=CompVis/stable-diffusion-v1-4
+### HUGGINGFACE
+# HUGGINGFACE_AUDIO_TO_TEXT_MODEL=facebook/wav2vec2-base-960h
 
 ################################################################################
+### GIT Provider for repository actions
+################################################################################
+
 ### GITHUB
-################################################################################
-
-## GITHUB_API_KEY - Github API key / PAT (Default: None)
-# GITHUB_API_KEY=
-
-## GITHUB_USERNAME - Github username (Default: None)
-# GITHUB_USERNAME=
+## GITHUB_API_KEY - Github API key / PAT (Example: github_pat_123)
+## GITHUB_USERNAME - Github username
+# GITHUB_API_KEY=github_pat_123
+# GITHUB_USERNAME=your-github-username
 
 ################################################################################
 ### WEB BROWSING
 ################################################################################
 
+### BROWSER
 ## HEADLESS_BROWSER - Whether to run the browser in headless mode (default: True)
+## USE_WEB_BROWSER - Sets the web-browser driver to use with selenium (default: chrome).
+##   Note: set this to either 'chrome', 'firefox', or 'safari' depending on your current browser
 # HEADLESS_BROWSER=True
-
-## USE_WEB_BROWSER - Sets the web-browser driver to use with selenium (default: chrome)
 # USE_WEB_BROWSER=chrome
-
-## BROWSE_CHUNK_MAX_LENGTH - When browsing website, define the length of chunks to summarize (Default: 3000)
+## BROWSE_CHUNK_MAX_LENGTH - When browsing website, define the length of chunks to summarize (in number of tokens, excluding the response. 75 % of FAST_TOKEN_LIMIT is usually wise )
 # BROWSE_CHUNK_MAX_LENGTH=3000
-
-## BROWSE_SPACY_LANGUAGE_MODEL - spaCy language model](https://spacy.io/usage/models) to use when creating chunks. (Default: en_core_web_sm)
+## BROWSE_SPACY_LANGUAGE_MODEL is used to split sentences. Install additional languages via pip, and set the model name here. Example Chinese:  python -m spacy download zh_core_web_sm
 # BROWSE_SPACY_LANGUAGE_MODEL=en_core_web_sm
 
-## GOOGLE_API_KEY - Google API key (Default: None)
-# GOOGLE_API_KEY=
-
-## GOOGLE_CUSTOM_SEARCH_ENGINE_ID - Google custom search engine ID (Default: None)
-# GOOGLE_CUSTOM_SEARCH_ENGINE_ID=
+### GOOGLE
+## GOOGLE_API_KEY - Google API key (Example: my-google-api-key)
+## CUSTOM_SEARCH_ENGINE_ID - Custom search engine ID (Example: my-custom-search-engine-id)
+# GOOGLE_API_KEY=your-google-api-key
+# CUSTOM_SEARCH_ENGINE_ID=your-custom-search-engine-id
 
 ################################################################################
-### TEXT TO SPEECH PROVIDER
+### TTS PROVIDER
 ################################################################################
 
-## TEXT_TO_SPEECH_PROVIDER - Which Text to Speech provider to use (Default: gtts)
-# TEXT_TO_SPEECH_PROVIDER=gtts
+### MAC OS
+## USE_MAC_OS_TTS - Use Mac OS TTS or not (Default: False)
+# USE_MAC_OS_TTS=False
 
-### Only if TEXT_TO_SPEECH_PROVIDER=streamelements
-## STREAMELEMENTS_VOICE - Voice to use for StreamElements (Default: Brian)
-# STREAMELEMENTS_VOICE=Brian
+### STREAMELEMENTS
+## USE_BRIAN_TTS - Use Brian TTS or not (Default: False)
+# USE_BRIAN_TTS=False
 
-### Only if TEXT_TO_SPEECH_PROVIDER=elevenlabs
-## ELEVENLABS_API_KEY - Eleven Labs API key (Default: None)
-# ELEVENLABS_API_KEY=
-
-## ELEVENLABS_VOICE_ID - Eleven Labs voice ID (Example: None)
-# ELEVENLABS_VOICE_ID=
+### ELEVENLABS
+## ELEVENLABS_API_KEY - Eleven Labs API key (Example: my-elevenlabs-api-key)
+## ELEVENLABS_VOICE_1_ID - Eleven Labs voice 1 ID (Example: my-voice-id-1)
+## ELEVENLABS_VOICE_2_ID - Eleven Labs voice 2 ID (Example: my-voice-id-2)
+# ELEVENLABS_API_KEY=your-elevenlabs-api-key
+# ELEVENLABS_VOICE_1_ID=your-voice-id-1
+# ELEVENLABS_VOICE_2_ID=your-voice-id-2
 
 ################################################################################
-### CHAT MESSAGES
+### TWITTER API
 ################################################################################
 
-## CHAT_MESSAGES_ENABLED - Enable chat messages (Default: False)
+# TW_CONSUMER_KEY=
+# TW_CONSUMER_SECRET=
+# TW_ACCESS_TOKEN=
+# TW_ACCESS_TOKEN_SECRET=
+
+################################################################################
+### ALLOWLISTED PLUGINS
+################################################################################
+
+#ALLOWLISTED_PLUGINS - Sets the listed plugins that are allowed (Example: plugin1,plugin2,plugin3)
+ALLOWLISTED_PLUGINS=
+
+################################################################################
+### CHAT PLUGIN SETTINGS
+################################################################################
+# CHAT_MESSAGES_ENABLED - Enable chat messages (Default: False)
 # CHAT_MESSAGES_ENABLED=False

.gitattributes

@@ -1,5 +1,5 @@
 # Exclude VCR cassettes from stats
-tests/Auto-GPT-test-cassettes/**/**.y*ml linguist-generated
+tests/**/cassettes/**.y*ml linguist-generated
 
 # Mark documentation as such
 docs/**.md linguist-documentation

.github/CODEOWNERS

@@ -1,2 +0,0 @@
-.github/workflows/ @Significant-Gravitas/maintainers
-autogpt/core @collijk

.github/ISSUE_TEMPLATE/1.bug.yml

@@ -8,16 +8,14 @@ body:
         ### ⚠️ Before you continue
         * Check out our [backlog], [roadmap] and join our [discord] to discuss what's going on
         * If you need help, you can ask in the [discussions] section or in [#tech-support]
-        * **Thoroughly search the [existing issues] before creating a new one**
-        * Read our [wiki page on Contributing]
+        * **Throughly search the [existing issues] before creating a new one**
+
         [backlog]: https://github.com/orgs/Significant-Gravitas/projects/1
         [roadmap]: https://github.com/orgs/Significant-Gravitas/projects/2  
         [discord]: https://discord.gg/autogpt
         [discussions]: https://github.com/Significant-Gravitas/Auto-GPT/discussions
         [#tech-support]: https://discord.com/channels/1092243196446249134/1092275629602394184
         [existing issues]: https://github.com/Significant-Gravitas/Auto-GPT/issues?q=is%3Aissue
-        [wiki page on Contributing]: https://github.com/Significant-Gravitas/Nexus/wiki/Contributing
-
   - type: checkboxes
     attributes:
       label: ⚠️ Search for existing issues first ⚠️
@@ -27,29 +25,23 @@ body:
       options:
         - label: I have searched the existing issues, and there is no existing issue for my problem
           required: true
-
   - type: markdown
     attributes:
       value: |
-        Please confirm that the issue you have is described well and precise in the title above ⬆️.
-        A good rule of thumb: What would you type if you were searching for the issue?
-        
-        For example:
-        BAD - my auto-gpt keeps looping
-        GOOD - After performing execute_python_file, auto-gpt goes into a loop where it keeps trying to execute the file.
-        
-        ⚠️ SUPER-busy repo, please help the volunteer maintainers.
-        The less time we spend here, the more time we can spend building AutoGPT.
-        
-        Please help us help you by following these steps:
-        - Search for existing issues, adding a comment when you have the same or similar issue is tidier than "new issue" and 
-          newer issues will not be reviewed earlier, this is dependent on the current priorities set by our wonderful team
-        - Ask on our Discord if your issue is known when you are unsure (https://discord.gg/autogpt)
-        - Provide relevant info:
-          - Provide commit-hash (`git rev-parse HEAD` gets it) if possible
-          - If it's a pip/packages issue, mention this in the title and provide pip version, python version
-          - If it's a crash, provide traceback and describe the error you got as precise as possible in the title.
+        Please provide a searchable summary of the issue in the title above ⬆️.
 
+        ⚠️ SUPER-busy repo, please help the volunteer maintainers.
+        The less time we spend here, the more time we spend building AutoGPT.
+        
+        Please help us help you:
+        - Does it work on `stable` branch (https://github.com/Torantulino/Auto-GPT/tree/stable)?
+        - Does it work on current `master` (https://github.com/Torantulino/Auto-GPT/tree/master)?
+        - Search for existing issues, "add comment" is tidier than "new issue"
+        - Ask on our Discord (https://discord.gg/autogpt)
+        - Provide relevant info:
+          - Provide commit-hash (`git rev-parse HEAD` gets it)
+          - If it's a pip/packages issue, provide pip version, python version
+          - If it's a crash, provide traceback.
   - type: dropdown
     attributes:
       label: Which Operating System are you using?
@@ -62,15 +54,9 @@ body:
         - Docker
         - Devcontainer / Codespace
         - Windows Subsystem for Linux (WSL)
-        - Other
+        - Other (Please specify in your problem)
     validations:
       required: true
-      nested_fields:
-        - type: text
-          attributes:
-            label: Specify the system
-            description: Please specify the system you are working on.
-
   - type: dropdown
     attributes:
       label: Which version of Auto-GPT are you using?
@@ -85,80 +71,61 @@ body:
         - Master (branch)
     validations:
       required: true
-
   - type: dropdown
     attributes:
-      label: Do you use OpenAI GPT-3 or GPT-4?
+      label: GPT-3 or GPT-4?
       description: >
         If you are using Auto-GPT with `--gpt3only`, your problems may be caused by
         the [limitations](https://github.com/Significant-Gravitas/Auto-GPT/issues?q=is%3Aissue+label%3A%22AI+model+limitation%22) of GPT-3.5.
       options:
         - GPT-3.5
         - GPT-4
-        - GPT-4(32k)
     validations:
       required: true
-
-  - type: dropdown
+  - type: textarea
     attributes:
-      label: Which area covers your issue best?
+      label: Steps to reproduce 🕹
+      description: |
+        **⚠️ Issues that we can't reproduce will be closed.**
+  - type: textarea
+    attributes:
+      label: Current behavior 😯
+      description: Describe what happens instead of the expected behavior.
+  - type: textarea
+    attributes:
+      label: Expected behavior 🤔
+      description: Describe what should happen.
+  - type: textarea
+    attributes:
+      label: Your prompt 📝
       description: >
-        Select the area related to the issue you are reporting.
-      options:
-        - Installation and setup
-        - Memory
-        - Performance
-        - Prompt
-        - Commands
-        - Plugins
-        - AI Model Limitations
-        - Challenges
-        - Documentation
-        - Logging
-        - Agents
-        - Other
-    validations:
-      required: true
-      autolabels: true
-      nested_fields:
-        - type: text
-          attributes:
-            label: Specify the area
-            description: Please specify the area you think is best related to the issue.
-
-  - type: textarea
-    attributes:
-      label: Describe your issue.
-      description: Describe the problem you are experiencing. Try to describe only the issue and phrase it short but clear. ⚠️ Provide NO other data in this field  
-    validations:
-      required: true
-
-  #Following are optional file content uploads
-  - type: markdown
-    attributes:
+        If applicable please provide the prompt you are using. Your prompt is stored in your `ai_settings.yaml` file.
       value: |
-        ⚠️The following is OPTIONAL, please keep in mind that the log files may contain personal information such as credentials.⚠️
+        ```yaml
+        # Paste your prompt here
+        ```
+  - type: textarea
+    attributes:
+      label: Your Logs 📒
+      description: |
+        Please include the log showing your error and the command that caused it, if applicable.
+        You can copy it from your terminal or from `logs/activity.log`.
+        This will help us understand your issue better!
         
-        "The log files are located in the folder 'logs' inside the main auto-gpt folder."
-
-  - type: textarea
-    attributes:
-      label: Upload Activity Log Content
-      description: |
-        Upload the activity log content, this can help us understand the issue better. 
-        To do this, go to the folder logs in your main auto-gpt folder, open activity.log and copy/paste the contents to this field. 
-        ⚠️ The activity log may contain personal data given to auto-gpt by you in prompt or input as well as 
-        any personal information that auto-gpt collected out of files during last run. Do not add the activity log if you are not comfortable with sharing it. ⚠️
-    validations:
-      required: false
-
-  - type: textarea
-    attributes:
-      label: Upload Error Log Content
-      description: |
-        Upload the error log content, this will help us understand the issue better. 
-        To do this, go to the folder logs in your main auto-gpt folder, open error.log and copy/paste the contents to this field. 
-        ⚠️ The error log may contain personal data given to auto-gpt by you in prompt or input as well as 
-        any personal information that auto-gpt collected out of files during last run. Do not add the activity log if you are not comfortable with sharing it. ⚠️
-    validations:
-      required: false
+        <details>
+        <summary><i>Example</i></summary>
+        ```log
+        INFO NEXT ACTION:  COMMAND = execute_shell  ARGUMENTS = {'command_line': 'some_command'}
+        INFO -=-=-=-=-=-=-= COMMAND AUTHORISED BY USER -=-=-=-=-=-=-=
+        Traceback (most recent call last):
+        File "/home/anaconda3/lib/python3.9/site-packages/openai/api_requestor.py", line 619, in _interpret_response
+        self._interpret_response_line(
+        File "/home/anaconda3/lib/python3.9/site-packages/openai/api_requestor.py", line 682, in _interpret_response_line
+        raise self.handle_error_response(
+        openai.error.InvalidRequestError: This model's maximum context length is 8191 tokens, however you requested 10982 tokens (10982 in your prompt; 0 for the completion). Please reduce your prompt; or completion length.
+        ```
+        </details>
+      value: |
+        ```log
+        <insert your logs here>
+        ```

.github/ISSUE_TEMPLATE/2.feature.yml

@@ -1,12 +1,13 @@
 name: Feature request 🚀
-description: Suggest a new idea for Auto-GPT!
+description: Suggest a new idea for Auto-GPT.
 labels: ['status: needs triage']
 body:
   - type: markdown
     attributes:
       value: |
-        First, check out our [wiki page on Contributing](https://github.com/Significant-Gravitas/Nexus/wiki/Contributing)
         Please provide a searchable summary of the issue in the title above ⬆️.
+
+        Thanks for contributing by creating an issue! ❤️
   - type: checkboxes
     attributes:
       label: Duplicates
@@ -25,4 +26,4 @@ body:
   - type: textarea
     attributes:
       label: Motivation 🔦
-      description: What are you trying to accomplish? How has the lack of this feature affected you? Providing context helps us come up with a solution that is more useful in the real world.
+      description: What are you trying to accomplish? How has the lack of this feature affected you? Providing context helps us come up with a solution that is more useful in the real world.

.github/PULL_REQUEST_TEMPLATE.md

@@ -14,8 +14,6 @@ Provide clear documentation and explanations of the changes made.
 Ensure diffs are limited to the intended lines — no applying preferred formatting styles or line endings (unless that's what the PR is about).
 For guidance on committing only the specific lines you have changed, refer to this helpful video: https://youtu.be/8-hSNHHbiZg
 
-Check out our [wiki page on Contributing](https://github.com/Significant-Gravitas/Nexus/wiki/Contributing)
-
 By following these guidelines, your PRs are more likely to be merged quickly after testing, as long as they align with the project's overall direction. -->
 
 ### Background
@@ -35,14 +33,7 @@ By following these guidelines, your PRs are more likely to be merged quickly aft
 - [ ] I have thoroughly tested my changes with multiple different prompts.
 - [ ] I have considered potential risks and mitigations for my changes.
 - [ ] I have documented my changes clearly and comprehensively.
-- [ ] I have not snuck in any "extra" small tweaks changes. <!-- Submit these as separate Pull Requests, they are the easiest to merge! -->
-- [ ] I have run the following commands against my code to ensure it passes our linters:
-    ```shell
-    black .
-    isort .
-    mypy
-    autoflake --remove-all-unused-imports --recursive --ignore-init-module-imports --ignore-pass-after-docstring autogpt tests --in-place
-    ```
+- [ ] I have not snuck in any "extra" small tweaks changes <!-- Submit these as separate Pull Requests, they are the easiest to merge! -->
 
 <!-- If you haven't added tests, please explain why. If you have, check the appropriate box. If you've ensured your PR is atomic and well-documented, check the corresponding boxes. -->
 

.github/workflows/benchmarks.yml

@@ -1,73 +1,31 @@
-name: Benchmarks
+name: Run Benchmarks
 
 on:
-  schedule:
-    - cron: '0 8 * * *'
   workflow_dispatch:
 
 jobs:
-  Benchmark:
-    name: ${{ matrix.config.task-name }}
+  build:
     runs-on: ubuntu-latest
-    timeout-minutes: 30
-    strategy:
-      fail-fast: false
-      matrix:
-        config:
-          - python-version: "3.10"
-            task: "tests/challenges"
-            task-name: "Mandatory Tasks"
-          - python-version: "3.10"
-            task: "--beat-challenges -ra tests/challenges"
-            task-name: "Challenging Tasks"
+
+    env:
+      python-version: '3.10'
 
     steps:
-      - name: Checkout repository
-        uses: actions/checkout@v3
-        with:
-          ref: master
+    - name: Checkout repository
+      uses: actions/checkout@v3
 
-      - name: Set up Python ${{ matrix.config.python-version }}
-        uses: actions/setup-python@v4
-        with:
-          python-version: ${{ matrix.config.python-version }}
+    - name: Set up Python ${{ env.python-version }}
+      uses: actions/setup-python@v4
+      with:
+        python-version: ${{ env.python-version }}
 
-      - id: get_date
-        name: Get date
-        run: echo "date=$(date +'%Y-%m-%d')" >> $GITHUB_OUTPUT
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt
 
-      - name: Set up Python dependency cache
-        uses: actions/cache@v3
-        with:
-          path: ~/.cache/pip
-          key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}-${{ steps.get_date.outputs.date }}
-
-      - name: Install Python dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install -r requirements.txt
-
-      - name: Run pytest with coverage
-        run: |
-          rm -rf tests/Auto-GPT-test-cassettes
-          pytest -n auto --record-mode=all ${{ matrix.config.task }}
-        env:
-          CI: true
-          PROXY: ${{ secrets.PROXY }}
-          AGENT_MODE: ${{ secrets.AGENT_MODE }}
-          AGENT_TYPE: ${{ secrets.AGENT_TYPE }}
-          PLAIN_OUTPUT: True
-
-      - name: Upload logs as artifact
-        if: always()
-        uses: actions/upload-artifact@v3
-        with:
-          name: test-logs-${{ matrix.config.task-name }}
-          path: logs/
-
-      - name: Upload cassettes as artifact
-        if: always()
-        uses: actions/upload-artifact@v3
-        with:
-          name: cassettes-${{ matrix.config.task-name }}
-          path: tests/Auto-GPT-test-cassettes/
+    - name: benchmark
+      env:
+        OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+      run: |
+         python benchmark/benchmark_entrepreneur_gpt_with_undecisive_user.py

.github/workflows/ci.yml

@@ -2,24 +2,16 @@ name: Python CI
 
 on:
   push:
-    branches: [ master, ci-test* ]
-    paths-ignore:
-      - 'tests/Auto-GPT-test-cassettes'
-      - 'tests/challenges/current_score.json'
+    branches: [ master ]
   pull_request:
-    branches: [ stable, master, release-* ]
-  pull_request_target:
-    branches: [ master, release-*, ci-test* ]
+    branches: [ master ]
 
 concurrency:
-  group: ${{ format('ci-{0}', github.head_ref && format('{0}-{1}', github.event_name, github.event.pull_request.number) || github.sha) }}
-  cancel-in-progress: ${{ startsWith(github.event_name, 'pull_request') }}
+  group: ${{ format('ci-{0}', github.head_ref && format('pr-{0}', github.event.pull_request.number) || github.sha) }}
+  cancel-in-progress: ${{ github.event_name == 'pull_request' }}
 
 jobs:
   lint:
-    # eliminate duplicate runs
-    if: github.event_name == 'push' || (github.event.pull_request.head.repo.fork == (github.event_name == 'pull_request_target'))
-
     runs-on: ubuntu-latest
     env:
       min-python-version: "3.10"
@@ -27,26 +19,12 @@ jobs:
     steps:
       - name: Checkout repository
         uses: actions/checkout@v3
-        with:
-          fetch-depth: 0
-          ref: ${{ github.event.pull_request.head.ref }}
-          repository: ${{ github.event.pull_request.head.repo.full_name }}
 
       - name: Set up Python ${{ env.min-python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v2
         with:
           python-version: ${{ env.min-python-version }}
 
-      - id: get_date
-        name: Get date
-        run: echo "date=$(date +'%Y-%m-%d')" >> $GITHUB_OUTPUT
-
-      - name: Set up Python dependency cache
-        uses: actions/cache@v3
-        with:
-          path: ~/.cache/pip
-          key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}-${{ steps.get_date.outputs.date }}
-
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
@@ -63,19 +41,7 @@ jobs:
         run: isort . --check
         if: success() || failure()
 
-      - name: Check mypy formatting
-        run: mypy
-        if: success() || failure()
-
-      - name: Check for unused imports and pass statements
-        run: |
-          cmd="autoflake --remove-all-unused-imports --recursive --ignore-init-module-imports --ignore-pass-after-docstring autogpt tests"
-          $cmd --check || (echo "You have unused imports or pass statements, please run '${cmd} --in-place'" && exit 1)
-
   test:
-    # eliminate duplicate runs
-    if: github.event_name == 'push' || (github.event.pull_request.head.repo.fork == (github.event_name == 'pull_request_target'))
-
     permissions:
       # Gives the action the necessary permissions for publishing new
       # comments in pull requests.
@@ -85,177 +51,27 @@ jobs:
       # comments (to avoid publishing multiple comments in the same PR)
       contents: write
     runs-on: ubuntu-latest
-    timeout-minutes: 30
     strategy:
       matrix:
-        python-version: ["3.10"]
+        python-version: ["3.10", "3.11"]
 
     steps:
-      - name: Checkout repository
+      - name: Check out repository
         uses: actions/checkout@v3
-        with:
-          fetch-depth: 0
-          ref: ${{ github.event.pull_request.head.ref }}
-          repository: ${{ github.event.pull_request.head.repo.full_name }}
-          submodules: true
-
-      - name: Configure git user Auto-GPT-Bot
-        run: |
-          git config --global user.name "Auto-GPT-Bot"
-          git config --global user.email "github-bot@agpt.co"
-
-      - name: Checkout cassettes
-        if: ${{ startsWith(github.event_name, 'pull_request') }}
-        run: |
-          cassette_branch="${{ github.event.pull_request.user.login }}-${{ github.event.pull_request.head.ref }}"
-          cassette_base_branch="${{ github.event.pull_request.base.ref }}"
-          cd tests/Auto-GPT-test-cassettes
-
-          if ! git ls-remote --exit-code --heads origin $cassette_base_branch ; then
-            cassette_base_branch="master"
-          fi
-
-          if git ls-remote --exit-code --heads origin $cassette_branch ; then
-            git fetch origin $cassette_branch
-            git fetch origin $cassette_base_branch
-
-            git checkout $cassette_branch
-
-            # Pick non-conflicting cassette updates from the base branch
-            git merge --no-commit --strategy-option=ours origin/$cassette_base_branch
-            echo "Using cassettes from mirror branch '$cassette_branch'," \
-              "synced to upstream branch '$cassette_base_branch'."
-          else
-            git checkout -b $cassette_branch
-            echo "Branch '$cassette_branch' does not exist in cassette submodule." \
-              "Using cassettes from '$cassette_base_branch'."
-          fi
 
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python-version }}
 
-      - id: get_date
-        name: Get date
-        run: echo "date=$(date +'%Y-%m-%d')" >> $GITHUB_OUTPUT
-
-      - name: Set up Python dependency cache
-        uses: actions/cache@v3
-        with:
-          path: ~/.cache/pip
-          key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}-${{ steps.get_date.outputs.date }}
-
-      - name: Install Python dependencies
+      - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
           pip install -r requirements.txt
 
-      - name: Run pytest with coverage
+      - name: Run unittest tests with coverage
         run: |
-          pytest -vv --cov=autogpt --cov-branch --cov-report term-missing --cov-report xml \
-            --numprocesses=logical --durations=10 \
-            tests/unit tests/integration tests/challenges
-          python tests/challenges/utils/build_current_score.py
-        env:
-          CI: true
-          PROXY: ${{ github.event_name == 'pull_request_target' && secrets.PROXY || '' }}
-          AGENT_MODE: ${{ github.event_name == 'pull_request_target' && secrets.AGENT_MODE || '' }}
-          AGENT_TYPE: ${{ github.event_name == 'pull_request_target' && secrets.AGENT_TYPE || '' }}
-          OPENAI_API_KEY: ${{ github.event_name == 'pull_request' && secrets.OPENAI_API_KEY || '' }}
-          PLAIN_OUTPUT: True
+          pytest --cov=autogpt --cov-report term-missing --cov-branch --cov-report xml --cov-report term
 
       - name: Upload coverage reports to Codecov
         uses: codecov/codecov-action@v3
-
-      - id: setup_git_auth
-        name: Set up git token authentication
-        # Cassettes may be pushed even when tests fail
-        if: success() || failure()
-        run: |
-          config_key="http.${{ github.server_url }}/.extraheader"
-          base64_pat=$(echo -n "pat:${{ secrets.PAT_REVIEW }}" | base64 -w0)
-
-          git config "$config_key" \
-            "Authorization: Basic $base64_pat"
-
-          cd tests/Auto-GPT-test-cassettes
-          git config "$config_key" \
-            "Authorization: Basic $base64_pat"
-
-          echo "config_key=$config_key" >> $GITHUB_OUTPUT
-
-      - name: Push updated challenge scores
-        if: github.event_name == 'push'
-        run: |
-          score_file="tests/challenges/current_score.json"
-
-          if ! git diff --quiet $score_file; then
-            git add $score_file
-            git commit -m "Update challenge scores"
-            git push origin HEAD:${{ github.ref_name }}
-          else
-            echo "The challenge scores didn't change."
-          fi
-
-      - id: push_cassettes
-        name: Push updated cassettes
-        # For pull requests, push updated cassettes even when tests fail
-        if: github.event_name == 'push' || success() || failure()
-        run: |
-          if [ "${{ startsWith(github.event_name, 'pull_request') }}" = "true" ]; then
-            is_pull_request=true
-            cassette_branch="${{ github.event.pull_request.user.login }}-${{ github.event.pull_request.head.ref }}"
-          else
-            cassette_branch="${{ github.ref_name }}"
-          fi
-
-          cd tests/Auto-GPT-test-cassettes
-          # Commit & push changes to cassettes if any
-          if ! git diff --quiet; then
-            git add .
-            git commit -m "Auto-update cassettes"
-            git push origin HEAD:$cassette_branch
-            if [ ! $is_pull_request ]; then
-              cd ../..
-              git add tests/Auto-GPT-test-cassettes
-              git commit -m "Update cassette submodule"
-              git push origin HEAD:$cassette_branch
-            fi
-            echo "updated=true" >> $GITHUB_OUTPUT
-          else
-            echo "updated=false" >> $GITHUB_OUTPUT
-            echo "No cassette changes to commit"
-          fi
-
-      - name: Post Set up git token auth
-        if: steps.setup_git_auth.outcome == 'success'
-        run: |
-          git config --unset-all '${{ steps.setup_git_auth.outputs.config_key }}'
-          git submodule foreach git config --unset-all '${{ steps.setup_git_auth.outputs.config_key }}'
-
-      - name: Apply "behaviour change" label and comment on PR
-        if: ${{ startsWith(github.event_name, 'pull_request') }}
-        run: |
-          PR_NUMBER=${{ github.event.pull_request.number }}
-          TOKEN=${{ secrets.PAT_REVIEW }}
-          REPO=${{ github.repository }}
-
-          if [[ "${{ steps.push_cassettes.outputs.updated }}" == "true" ]]; then
-            echo "Adding label and comment..."
-            curl -X POST \
-            -H "Authorization: Bearer $TOKEN" \
-            -H "Accept: application/vnd.github.v3+json" \
-            https://api.github.com/repos/$REPO/issues/$PR_NUMBER/labels \
-            -d '{"labels":["behaviour change"]}'
-
-            echo $TOKEN | gh auth login --with-token
-            gh api repos/$REPO/issues/$PR_NUMBER/comments -X POST -F body="You changed AutoGPT's behaviour. The cassettes have been updated and will be merged to the submodule when this Pull Request gets merged."
-          fi
-
-      - name: Upload logs to artifact
-        if: always()
-        uses: actions/upload-artifact@v3
-        with:
-          name: test-logs
-          path: logs/

.github/workflows/docker-ci.yml

@@ -3,11 +3,8 @@ name: Docker CI
 on:
   push:
     branches: [ master ]
-    paths-ignore:
-      - 'tests/Auto-GPT-test-cassettes'
-      - 'tests/challenges/current_score.json'
   pull_request:
-    branches: [ master, release-*, stable ]
+    branches: [ master ]
 
 concurrency:
   group: ${{ format('docker-ci-{0}', github.head_ref && format('pr-{0}', github.event.pull_request.number) || github.sha) }}
@@ -73,52 +70,46 @@ jobs:
       run: .github/workflows/scripts/docker-ci-summary.sh >> $GITHUB_STEP_SUMMARY
       continue-on-error: true
 
+  # Docker setup needs fixing before this is going to work: #1843
   test:
     runs-on: ubuntu-latest
-    timeout-minutes: 10
+    needs: build
     steps:
-      - name: Check out repository
-        uses: actions/checkout@v3
-        with:
-          submodules: true
+    - name: Checkout repository
+      uses: actions/checkout@v3
 
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v2
+    - name: Set up Docker Buildx
+      uses: docker/setup-buildx-action@v2
 
-      - id: build
-        name: Build image
-        uses: docker/build-push-action@v3
-        with:
-          build-args: BUILD_TYPE=dev  # include pytest
-          tags: ${{ env.IMAGE_NAME }}
-          load: true                  # save to docker images
-          # cache layers in GitHub Actions cache to speed up builds
-          cache-from: type=gha,scope=docker-dev
-          cache-to: type=gha,scope=docker-dev,mode=max
+    - id: build
+      name: Build image
+      uses: docker/build-push-action@v3
+      with:
+        build-args: BUILD_TYPE=dev  # include pytest
+        tags: ${{ env.IMAGE_NAME }}
+        load: true                  # save to docker images
+        # cache layers in GitHub Actions cache to speed up builds
+        cache-from: type=gha,scope=docker-dev
+        cache-to: type=gha,scope=docker-dev,mode=max
 
-      - id: test
-        name: Run tests
-        env:
-          CI: true
-          PLAIN_OUTPUT: True
-          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-        run: |
-          set +e
-          test_output=$(
-            docker run --env CI --env OPENAI_API_KEY --entrypoint python ${{ env.IMAGE_NAME }} -m \
-            pytest -v --cov=autogpt --cov-branch --cov-report term-missing \
-              --numprocesses=4 --durations=10 \
-              tests/unit tests/integration 2>&1
-          )
-          test_failure=$?
+    - id: test
+      name: Run tests
+      env:
+        CI: true
+        OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+      run: |
+        set +e
+        test_output=$(
+          docker run --env CI --env OPENAI_API_KEY --entrypoint python ${{ env.IMAGE_NAME }} -m \
+          pytest --cov=autogpt --cov-report term-missing --cov-branch --cov-report xml --cov-report term 2>&1
+        )
+        test_failure=$?
 
-          echo "$test_output"
+        echo "$test_output"
 
-          cat << $EOF >> $GITHUB_STEP_SUMMARY
-          # Tests $([ $test_failure = 0 ] && echo '✅' || echo '❌')
-          \`\`\`
-          $test_output
-          \`\`\`
-          $EOF
-
-          exit $test_failure
+        cat << $EOF >> $GITHUB_STEP_SUMMARY
+        # Tests $([ $test_failure = 0 ] && echo '✅' || echo '❌')
+        \`\`\`
+        $test_output
+        \`\`\`
+        $EOF

.github/workflows/pr-label.yml

@@ -3,10 +3,7 @@ name: "Pull Request auto-label"
 on:
   # So that PRs touching the same files as the push are updated
   push:
-    branches: [ master, release-* ]
-    paths-ignore:
-      - 'tests/Auto-GPT-test-cassettes'
-      - 'tests/challenges/current_score.json'
+    branches: [ master ]
   # So that the `dirtyLabel` is removed if conflicts are resolve
   # We recommend `pull_request_target` so that github secrets are available.
   # In `pull_request` we wouldn't be able to change labels of fork PRs
@@ -48,10 +45,11 @@ jobs:
           s_label: 'size/s'
           s_max_size: 10
           m_label: 'size/m'
-          m_max_size: 100
+          m_max_size: 50
           l_label: 'size/l'
-          l_max_size: 500
+          l_max_size: 200
           xl_label: 'size/xl'
           message_if_xl: >
-            This PR exceeds the recommended size of 500 lines.
+            This PR exceeds the recommended size of 200 lines.
             Please make sure you are NOT addressing multiple issues with one PR.
+            Note this PR might be rejected due to its size  

.github/workflows/sponsors_readme.yml

@@ -0,0 +1,28 @@
+name: Generate Sponsors README
+
+on:
+  workflow_dispatch:
+  schedule:
+    - cron: '0 */12 * * *'
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout 🛎️
+        uses: actions/checkout@v3
+
+      - name: Generate Sponsors 💖
+        uses: JamesIves/github-sponsors-readme-action@v1
+        with:
+          token: ${{ secrets.README_UPDATER_PAT }}
+          file: 'README.md'
+          minimum: 2500
+          maximum: 99999
+
+      - name: Deploy to GitHub Pages 🚀
+        uses: JamesIves/github-pages-deploy-action@v4
+        with:
+          branch: master
+          folder: '.'
+          token: ${{ secrets.README_UPDATER_PAT }}

.gitignore

@@ -1,7 +1,12 @@
 ## Original ignores
 autogpt/keys.py
-autogpt/*.json
-**/auto_gpt_workspace/*
+autogpt/*json
+autogpt/node_modules/
+autogpt/__pycache__/keys.cpython-310.pyc
+autogpt/auto_gpt_workspace
+package-lock.json
+*.pyc
+auto_gpt_workspace/*
 *.mpeg
 .env
 azure.yaml
@@ -12,11 +17,10 @@ last_run_ai_settings.yaml
 auto-gpt.json
 log.txt
 log-ingestion.txt
-/logs
+logs
 *.log
 *.mp3
 mem.sqlite3
-venvAutoGPT
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
@@ -31,8 +35,7 @@ __pycache__/
 build/
 develop-eggs/
 dist/
-/plugins/
-plugins_config.yaml
+plugins/
 downloads/
 eggs/
 .eggs/
@@ -159,4 +162,4 @@ vicuna-*
 openai/
 
 # news
-CURRENT_BULLETIN.md
+CURRENT_BULLETIN.md

.gitmodules

@@ -1,4 +0,0 @@
-[submodule "tests/Auto-GPT-test-cassettes"]
-    path = tests/Auto-GPT-test-cassettes
-    url = https://github.com/Significant-Gravitas/Auto-GPT-test-cassettes
-    branch = master

.pre-commit-config.yaml

@@ -22,21 +22,11 @@ repos:
       - id: black
         language_version: python3.10
 
-  - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: 'v1.3.0'
-    hooks:
-      - id: mypy
-
   - repo: local
     hooks:
-      - id: autoflake
-        name: autoflake
-        entry: autoflake --in-place --remove-all-unused-imports --recursive --ignore-init-module-imports --ignore-pass-after-docstring autogpt tests
-        language: python
-        types: [ python ]
       - id: pytest-check
         name: pytest-check
-        entry: pytest --cov=autogpt tests/unit
+        entry: pytest --cov=autogpt --without-integration --without-slow-integration
         language: system
         pass_filenames: false
         always_run: true

BULLETIN.md

@@ -1,26 +1,9 @@
-# QUICK LINKS 🔗
-# --------------
-🌎 *Official Website*: https://agpt.co.
-📖 *User Guide*: https://docs.agpt.co.
-👩 *Contributors Wiki*: https://github.com/Significant-Gravitas/Auto-GPT/wiki/Contributing.
+Welcome to Auto-GPT! We'll keep you informed of the latest news and features by printing messages here.
+If you don't wish to see this message, you can run Auto-GPT with the --skip-news flag
 
-# v0.4.5 RELEASE HIGHLIGHTS! 🚀
-# -----------------------------
-This release includes under-the-hood improvements and bug fixes, such as more 
-accurate token counts for OpenAI functions, faster CI builds, improved plugin 
-handling, and refactoring of the Config class for better maintainability.
+# INCLUDED COMMAND 'send_tweet' IS DEPRICATED, AND WILL BE REMOVED IN THE NEXT STABLE RELEASE
+Base Twitter functionality (and more) is now covered by plugins: https://github.com/Significant-Gravitas/Auto-GPT-Plugins
 
-We have also released some documentation updates, including:
+## Changes to Docker configuration
+The workdir has been changed from /home/appuser to /app. Be sure to update any volume mounts accordingly.
 
-- *How to share your system logs*
-  Visit [docs/share-your-logs.md] to learn how to how to share logs with us 
-  via a log analyzer graciously contributed by https://www.e2b.dev/
-
-- *Auto-GPT re-architecture documentation*
-  You can learn more about the inner-workings of the Auto-GPT re-architecture 
-  released last cycle, via these links:
-  * [autogpt/core/README.md]
-  * [autogpt/core/ARCHITECTURE_NOTES.md]
-
-Take a look at the Release Notes on Github for the full changelog! 
-https://github.com/Significant-Gravitas/Auto-GPT/releases.

CONTRIBUTING.md

@@ -1,14 +1,148 @@
-We maintain a knowledgebase at this [wiki](https://github.com/Significant-Gravitas/Nexus/wiki)
+# Contributing to Auto-GPT
 
-We would like to say "We value all contributions". After all, we are an open-source project, so we should say something fluffy like this, right?
+First of all, thank you for considering contributing to our project! We appreciate your time and effort, and we value any contribution, whether it's reporting a bug, suggesting a new feature, or submitting a pull request.
 
-However the reality is that some contributions are SUPER-valuable, while others create more trouble than they are worth and actually _create_ work for the core team.
+This document provides guidelines and best practices to help you contribute effectively.
 
-If you wish to contribute, please look through the wiki [contributing](https://github.com/Significant-Gravitas/Nexus/wiki/Contributing) page.
+## Code of Conduct
 
-If you wish to involve with the project (beyond just contributing PRs), please read the wiki [catalyzing](https://github.com/Significant-Gravitas/Nexus/wiki/Catalyzing) page.
+By participating in this project, you agree to abide by our [Code of Conduct]. Please read it to understand the expectations we have for everyone who contributes to this project.
 
-In fact, why not just look through the whole wiki (it's only a few pages) and hop on our discord (you'll find it in the wiki).
+[Code of Conduct]: https://significant-gravitas.github.io/Auto-GPT/code-of-conduct.md
 
-❤️ & 🔆
-The team @ Auto-GPT
+## 📢 A Quick Word
+Right now we will not be accepting any Contributions that add non-essential commands to Auto-GPT.
+
+However, you absolutely can still add these commands to Auto-GPT in the form of plugins.
+Please check out this [template](https://github.com/Significant-Gravitas/Auto-GPT-Plugin-Template).
+
+## Getting Started
+
+1. Fork the repository and clone your fork.
+2. Create a new branch for your changes (use a descriptive name, such as `fix-bug-123` or `add-new-feature`).
+3. Make your changes in the new branch.
+4. Test your changes thoroughly.
+5. Commit and push your changes to your fork.
+6. Create a pull request following the guidelines in the [Submitting Pull Requests](#submitting-pull-requests) section.
+
+## How to Contribute
+
+### Reporting Bugs
+
+If you find a bug in the project, please create an issue on GitHub with the following information:
+
+- A clear, descriptive title for the issue.
+- A description of the problem, including steps to reproduce the issue.
+- Any relevant logs, screenshots, or other supporting information.
+
+### Suggesting Enhancements
+
+If you have an idea for a new feature or improvement, please create an issue on GitHub with the following information:
+
+- A clear, descriptive title for the issue.
+- A detailed description of the proposed enhancement, including any benefits and potential drawbacks.
+- Any relevant examples, mockups, or supporting information.
+
+### Submitting Pull Requests
+
+When submitting a pull request, please ensure that your changes meet the following criteria:
+
+- Your pull request should be atomic and focus on a single change.
+- Your pull request should include tests for your change. We automatically enforce this with [CodeCov](https://docs.codecov.com/docs/commit-status)
+- You should have thoroughly tested your changes with multiple different prompts.
+- You should have considered potential risks and mitigations for your changes.
+- You should have documented your changes clearly and comprehensively.
+- You should not include any unrelated or "extra" small tweaks or changes.
+
+## Style Guidelines
+
+### Code Formatting
+
+We use the `black` and `isort` code formatters to maintain a consistent coding style across the project. Please ensure that your code is formatted properly before submitting a pull request.
+
+To format your code, run the following commands in the project's root directory:
+
+```bash
+python -m black .
+python -m isort .
+```
+
+Or if you have these tools installed globally:
+```bash
+black .
+isort .
+```
+
+### Pre-Commit Hooks
+
+We use pre-commit hooks to ensure that code formatting and other checks are performed automatically before each commit. To set up pre-commit hooks for this project, follow these steps:
+
+Install the pre-commit package using pip:
+```bash
+pip install pre-commit
+```
+
+Run the following command in the project's root directory to install the pre-commit hooks:
+```bash
+pre-commit install
+```
+
+Now, the pre-commit hooks will run automatically before each commit, checking your code formatting and other requirements.
+
+If you encounter any issues or have questions, feel free to reach out to the maintainers or open a new issue on GitHub. We're here to help and appreciate your efforts to contribute to the project.
+
+Happy coding, and once again, thank you for your contributions!
+
+Maintainers will look at PR that have no merge conflicts when deciding what to add to the project. Make sure your PR shows up here:
+https://github.com/Significant-Gravitas/Auto-GPT/pulls?q=is%3Apr+is%3Aopen+-label%3Aconflicts
+
+## Testing your changes
+
+If you add or change code, make sure the updated code is covered by tests.
+To increase coverage if necessary, [write tests using pytest].
+
+For more info on running tests, please refer to ["Running tests"](https://significant-gravitas.github.io/Auto-GPT/testing/).
+
+[write tests using pytest]: https://realpython.com/pytest-python-testing/
+
+### API-dependent tests
+
+To run tests that involve making calls to the OpenAI API, we use VCRpy. It caches known
+requests and matching responses in so-called *cassettes*, allowing us to run the tests
+in CI without needing actual API access.
+
+When changes cause a test prompt to be generated differently, it will likely miss the
+cache and make a request to the API, updating the cassette with the new request+response.
+*Be sure to include the updated cassette in your PR!*
+
+When you run Pytest locally:
+
+- If no prompt change: you will not consume API tokens because there are no new OpenAI calls required.
+- If the prompt changes in a way that the cassettes are not reusable:
+    - If no API key, the test fails. It requires a new cassette. So, add an API key to .env.
+    - If the API key is present, the tests will make a real call to OpenAI.
+        - If the test ends up being successful, your prompt changes didn't introduce regressions. This is good. Commit your cassettes to your PR.
+        - If the test is unsuccessful:
+            - Either: Your change made Auto-GPT less capable, in that case, you have to change your code.
+            - Or: The test might be poorly written. In that case, you can make suggestions to change the test.
+
+In our CI pipeline, Pytest will use the cassettes and not call paid API providers, so we need your help to record the replays that you break.
+
+
+### Community Challenges
+Challenges are goals we need Auto-GPT to achieve. 
+To pick the challenge you like, go to the tests/integration/challenges folder and select the areas you would like to work on. 
+- a challenge is new if level_currently_beaten is None
+- a challenge is in progress if level_currently_beaten is greater or equal to 1
+- a challenge is beaten if level_currently_beaten = max_level
+
+Here is an example of how to run the memory challenge A and attempt to beat level 3.
+
+pytest -s tests/integration/challenges/memory/test_memory_challenge_a.py --level=3
+
+To beat a challenge, you're not allowed to change anything in the tests folder, you have to add code in the autogpt folder
+
+Challenges use cassettes. Cassettes allow us to replay your runs in our CI pipeline.
+Don't hesitate to delete the cassettes associated to the challenge you're working on if you need to. Otherwise it will keep replaying the last run.
+
+Once you've beaten a new level of a challenge, please create a pull request and we will analyze how you changed Auto-GPT to beat the challenge.

Dockerfile

@@ -6,13 +6,11 @@ FROM python:3.10-slim AS autogpt-base
 
 # Install browsers
 RUN apt-get update && apt-get install -y \
-    chromium-driver firefox-esr ca-certificates \
-    && apt-get clean && rm -rf /var/lib/apt/lists/*
+    chromium-driver firefox-esr \
+    ca-certificates
 
 # Install utilities
-RUN apt-get update && apt-get install -y \
-    curl jq wget git \
-    && apt-get clean && rm -rf /var/lib/apt/lists/*
+RUN apt-get install -y curl jq wget git
 
 # Set environment variables
 ENV PIP_NO_CACHE_DIR=yes \
@@ -24,7 +22,7 @@ ENV PATH="$PATH:/root/.local/bin"
 COPY requirements.txt .
 
 # Set the entrypoint
-ENTRYPOINT ["python", "-m", "autogpt", "--install-plugin-deps"]
+ENTRYPOINT ["python", "-m", "autogpt"]
 
 # dev build -> include everything
 FROM autogpt-base as autogpt-dev
@@ -38,9 +36,5 @@ RUN sed -i '/Items below this point will not be included in the Docker Image/,$d
 	pip install --no-cache-dir -r requirements.txt
 WORKDIR /app
 ONBUILD COPY autogpt/ ./autogpt
-ONBUILD COPY scripts/ ./scripts
-ONBUILD COPY plugins/ ./plugins
-ONBUILD COPY prompt_settings.yaml ./prompt_settings.yaml
-ONBUILD RUN mkdir ./data
 
 FROM autogpt-${BUILD_TYPE} AS auto-gpt

autogpt/agent/__init__.py

@@ -0,0 +1,4 @@
+from autogpt.agent.agent import Agent
+from autogpt.agent.agent_manager import AgentManager
+
+__all__ = ["Agent", "AgentManager"]

autogpt/agent/agent.py

@@ -0,0 +1,290 @@
+from colorama import Fore, Style
+
+from autogpt.app import execute_command, get_command
+from autogpt.config import Config
+from autogpt.json_utils.json_fix_llm import fix_json_using_multiple_techniques
+from autogpt.json_utils.utilities import LLM_DEFAULT_RESPONSE_FORMAT, validate_json
+from autogpt.llm import chat_with_ai, create_chat_completion, create_chat_message
+from autogpt.logs import logger, print_assistant_thoughts
+from autogpt.speech import say_text
+from autogpt.spinner import Spinner
+from autogpt.utils import clean_input
+from autogpt.workspace import Workspace
+
+
+class Agent:
+    """Agent class for interacting with Auto-GPT.
+
+    Attributes:
+        ai_name: The name of the agent.
+        memory: The memory object to use.
+        full_message_history: The full message history.
+        next_action_count: The number of actions to execute.
+        system_prompt: The system prompt is the initial prompt that defines everything
+          the AI needs to know to achieve its task successfully.
+        Currently, the dynamic and customizable information in the system prompt are
+          ai_name, description and goals.
+
+        triggering_prompt: The last sentence the AI will see before answering.
+            For Auto-GPT, this prompt is:
+            Determine which next command to use, and respond using the format specified
+              above:
+            The triggering prompt is not part of the system prompt because between the
+              system prompt and the triggering
+            prompt we have contextual information that can distract the AI and make it
+              forget that its goal is to find the next task to achieve.
+            SYSTEM PROMPT
+            CONTEXTUAL INFORMATION (memory, previous conversations, anything relevant)
+            TRIGGERING PROMPT
+
+        The triggering prompt reminds the AI about its short term meta task
+        (defining the next task)
+    """
+
+    def __init__(
+        self,
+        ai_name,
+        memory,
+        full_message_history,
+        next_action_count,
+        command_registry,
+        config,
+        system_prompt,
+        triggering_prompt,
+        workspace_directory,
+    ):
+        cfg = Config()
+        self.ai_name = ai_name
+        self.memory = memory
+        self.summary_memory = (
+            "I was created."  # Initial memory necessary to avoid hilucination
+        )
+        self.last_memory_index = 0
+        self.full_message_history = full_message_history
+        self.next_action_count = next_action_count
+        self.command_registry = command_registry
+        self.config = config
+        self.system_prompt = system_prompt
+        self.triggering_prompt = triggering_prompt
+        self.workspace = Workspace(workspace_directory, cfg.restrict_to_workspace)
+
+    def start_interaction_loop(self):
+        # Interaction Loop
+        cfg = Config()
+        loop_count = 0
+        command_name = None
+        arguments = None
+        user_input = ""
+
+        while True:
+            # Discontinue if continuous limit is reached
+            loop_count += 1
+            if (
+                cfg.continuous_mode
+                and cfg.continuous_limit > 0
+                and loop_count > cfg.continuous_limit
+            ):
+                logger.typewriter_log(
+                    "Continuous Limit Reached: ", Fore.YELLOW, f"{cfg.continuous_limit}"
+                )
+                break
+            # Send message to AI, get response
+            with Spinner("Thinking... "):
+                assistant_reply = chat_with_ai(
+                    self,
+                    self.system_prompt,
+                    self.triggering_prompt,
+                    self.full_message_history,
+                    self.memory,
+                    cfg.fast_token_limit,
+                )  # TODO: This hardcodes the model to use GPT3.5. Make this an argument
+
+            assistant_reply_json = fix_json_using_multiple_techniques(assistant_reply)
+            for plugin in cfg.plugins:
+                if not plugin.can_handle_post_planning():
+                    continue
+                assistant_reply_json = plugin.post_planning(self, assistant_reply_json)
+
+            # Print Assistant thoughts
+            if assistant_reply_json != {}:
+                validate_json(assistant_reply_json, LLM_DEFAULT_RESPONSE_FORMAT)
+                # Get command name and arguments
+                try:
+                    print_assistant_thoughts(
+                        self.ai_name, assistant_reply_json, cfg.speak_mode
+                    )
+                    command_name, arguments = get_command(assistant_reply_json)
+                    if cfg.speak_mode:
+                        say_text(f"I want to execute {command_name}")
+
+                    arguments = self._resolve_pathlike_command_args(arguments)
+
+                except Exception as e:
+                    logger.error("Error: \n", str(e))
+
+            if not cfg.continuous_mode and self.next_action_count == 0:
+                # ### GET USER AUTHORIZATION TO EXECUTE COMMAND ###
+                # Get key press: Prompt the user to press enter to continue or escape
+                # to exit
+                self.user_input = ""
+                logger.typewriter_log(
+                    "NEXT ACTION: ",
+                    Fore.CYAN,
+                    f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL}  "
+                    f"ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
+                )
+
+                logger.info(
+                    "Enter 'y' to authorise command, 'y -N' to run N continuous commands, 's' to run self-feedback commands"
+                    "'n' to exit program, or enter feedback for "
+                    f"{self.ai_name}..."
+                )
+                while True:
+                    if cfg.chat_messages_enabled:
+                        console_input = clean_input("Waiting for your response...")
+                    else:
+                        console_input = clean_input(
+                            Fore.MAGENTA + "Input:" + Style.RESET_ALL
+                        )
+                    if console_input.lower().strip() == cfg.authorise_key:
+                        user_input = "GENERATE NEXT COMMAND JSON"
+                        break
+                    elif console_input.lower().strip() == "s":
+                        logger.typewriter_log(
+                            "-=-=-=-=-=-=-= THOUGHTS, REASONING, PLAN AND CRITICISM WILL NOW BE VERIFIED BY AGENT -=-=-=-=-=-=-=",
+                            Fore.GREEN,
+                            "",
+                        )
+                        thoughts = assistant_reply_json.get("thoughts", {})
+                        self_feedback_resp = self.get_self_feedback(
+                            thoughts, cfg.fast_llm_model
+                        )
+                        logger.typewriter_log(
+                            f"SELF FEEDBACK: {self_feedback_resp}",
+                            Fore.YELLOW,
+                            "",
+                        )
+                        if self_feedback_resp[0].lower().strip() == cfg.authorise_key:
+                            user_input = "GENERATE NEXT COMMAND JSON"
+                        else:
+                            user_input = self_feedback_resp
+                        break
+                    elif console_input.lower().strip() == "":
+                        logger.warn("Invalid input format.")
+                        continue
+                    elif console_input.lower().startswith(f"{cfg.authorise_key} -"):
+                        try:
+                            self.next_action_count = abs(
+                                int(console_input.split(" ")[1])
+                            )
+                            user_input = "GENERATE NEXT COMMAND JSON"
+                        except ValueError:
+                            logger.warn(
+                                "Invalid input format. Please enter 'y -n' where n is"
+                                " the number of continuous tasks."
+                            )
+                            continue
+                        break
+                    elif console_input.lower() == cfg.exit_key:
+                        user_input = "EXIT"
+                        break
+                    else:
+                        user_input = console_input
+                        command_name = "human_feedback"
+                        break
+
+                if user_input == "GENERATE NEXT COMMAND JSON":
+                    logger.typewriter_log(
+                        "-=-=-=-=-=-=-= COMMAND AUTHORISED BY USER -=-=-=-=-=-=-=",
+                        Fore.MAGENTA,
+                        "",
+                    )
+                elif user_input == "EXIT":
+                    logger.info("Exiting...")
+                    break
+            else:
+                # Print command
+                logger.typewriter_log(
+                    "NEXT ACTION: ",
+                    Fore.CYAN,
+                    f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL}"
+                    f"  ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
+                )
+
+            # Execute command
+            if command_name is not None and command_name.lower().startswith("error"):
+                result = (
+                    f"Command {command_name} threw the following error: {arguments}"
+                )
+            elif command_name == "human_feedback":
+                result = f"Human feedback: {user_input}"
+            else:
+                for plugin in cfg.plugins:
+                    if not plugin.can_handle_pre_command():
+                        continue
+                    command_name, arguments = plugin.pre_command(
+                        command_name, arguments
+                    )
+                command_result = execute_command(
+                    self.command_registry,
+                    command_name,
+                    arguments,
+                    self.config.prompt_generator,
+                )
+                result = f"Command {command_name} returned: " f"{command_result}"
+
+                for plugin in cfg.plugins:
+                    if not plugin.can_handle_post_command():
+                        continue
+                    result = plugin.post_command(command_name, result)
+                if self.next_action_count > 0:
+                    self.next_action_count -= 1
+
+            # Check if there's a result from the command append it to the message
+            # history
+            if result is not None:
+                self.full_message_history.append(create_chat_message("system", result))
+                logger.typewriter_log("SYSTEM: ", Fore.YELLOW, result)
+            else:
+                self.full_message_history.append(
+                    create_chat_message("system", "Unable to execute command")
+                )
+                logger.typewriter_log(
+                    "SYSTEM: ", Fore.YELLOW, "Unable to execute command"
+                )
+
+    def _resolve_pathlike_command_args(self, command_args):
+        if "directory" in command_args and command_args["directory"] in {"", "/"}:
+            command_args["directory"] = str(self.workspace.root)
+        else:
+            for pathlike in ["filename", "directory", "clone_path"]:
+                if pathlike in command_args:
+                    command_args[pathlike] = str(
+                        self.workspace.get_path(command_args[pathlike])
+                    )
+        return command_args
+
+    def get_self_feedback(self, thoughts: dict, llm_model: str) -> str:
+        """Generates a feedback response based on the provided thoughts dictionary.
+        This method takes in a dictionary of thoughts containing keys such as 'reasoning',
+        'plan', 'thoughts', and 'criticism'. It combines these elements into a single
+        feedback message and uses the create_chat_completion() function to generate a
+        response based on the input message.
+        Args:
+            thoughts (dict): A dictionary containing thought elements like reasoning,
+            plan, thoughts, and criticism.
+        Returns:
+            str: A feedback response generated using the provided thoughts dictionary.
+        """
+        ai_role = self.config.ai_role
+
+        feedback_prompt = f"Below is a message from an AI agent with the role of {ai_role}. Please review the provided Thought, Reasoning, Plan, and Criticism. If these elements accurately contribute to the successful execution of the assumed role, respond with the letter 'Y' followed by a space, and then explain why it is effective. If the provided information is not suitable for achieving the role's objectives, please provide one or more sentences addressing the issue and suggesting a resolution."
+        reasoning = thoughts.get("reasoning", "")
+        plan = thoughts.get("plan", "")
+        thought = thoughts.get("thoughts", "")
+        criticism = thoughts.get("criticism", "")
+        feedback_thoughts = thought + reasoning + plan + criticism
+        return create_chat_completion(
+            [{"role": "user", "content": feedback_prompt + feedback_thoughts}],
+            llm_model,
+        )

autogpt/agent/agent_manager.py

@@ -0,0 +1,145 @@
+"""Agent manager for managing GPT agents"""
+from __future__ import annotations
+
+from typing import List
+
+from autogpt.config.config import Config
+from autogpt.llm import Message, create_chat_completion
+from autogpt.singleton import Singleton
+
+
+class AgentManager(metaclass=Singleton):
+    """Agent manager for managing GPT agents"""
+
+    def __init__(self):
+        self.next_key = 0
+        self.agents = {}  # key, (task, full_message_history, model)
+        self.cfg = Config()
+
+    # Create new GPT agent
+    # TODO: Centralise use of create_chat_completion() to globally enforce token limit
+
+    def create_agent(self, task: str, prompt: str, model: str) -> tuple[int, str]:
+        """Create a new agent and return its key
+
+        Args:
+            task: The task to perform
+            prompt: The prompt to use
+            model: The model to use
+
+        Returns:
+            The key of the new agent
+        """
+        messages: List[Message] = [
+            {"role": "user", "content": prompt},
+        ]
+        for plugin in self.cfg.plugins:
+            if not plugin.can_handle_pre_instruction():
+                continue
+            if plugin_messages := plugin.pre_instruction(messages):
+                messages.extend(iter(plugin_messages))
+        # Start GPT instance
+        agent_reply = create_chat_completion(
+            model=model,
+            messages=messages,
+        )
+
+        messages.append({"role": "assistant", "content": agent_reply})
+
+        plugins_reply = ""
+        for i, plugin in enumerate(self.cfg.plugins):
+            if not plugin.can_handle_on_instruction():
+                continue
+            if plugin_result := plugin.on_instruction(messages):
+                sep = "\n" if i else ""
+                plugins_reply = f"{plugins_reply}{sep}{plugin_result}"
+
+        if plugins_reply and plugins_reply != "":
+            messages.append({"role": "assistant", "content": plugins_reply})
+        key = self.next_key
+        # This is done instead of len(agents) to make keys unique even if agents
+        # are deleted
+        self.next_key += 1
+
+        self.agents[key] = (task, messages, model)
+
+        for plugin in self.cfg.plugins:
+            if not plugin.can_handle_post_instruction():
+                continue
+            agent_reply = plugin.post_instruction(agent_reply)
+
+        return key, agent_reply
+
+    def message_agent(self, key: str | int, message: str) -> str:
+        """Send a message to an agent and return its response
+
+        Args:
+            key: The key of the agent to message
+            message: The message to send to the agent
+
+        Returns:
+            The agent's response
+        """
+        task, messages, model = self.agents[int(key)]
+
+        # Add user message to message history before sending to agent
+        messages.append({"role": "user", "content": message})
+
+        for plugin in self.cfg.plugins:
+            if not plugin.can_handle_pre_instruction():
+                continue
+            if plugin_messages := plugin.pre_instruction(messages):
+                for plugin_message in plugin_messages:
+                    messages.append(plugin_message)
+
+        # Start GPT instance
+        agent_reply = create_chat_completion(
+            model=model,
+            messages=messages,
+        )
+
+        messages.append({"role": "assistant", "content": agent_reply})
+
+        plugins_reply = agent_reply
+        for i, plugin in enumerate(self.cfg.plugins):
+            if not plugin.can_handle_on_instruction():
+                continue
+            if plugin_result := plugin.on_instruction(messages):
+                sep = "\n" if i else ""
+                plugins_reply = f"{plugins_reply}{sep}{plugin_result}"
+        # Update full message history
+        if plugins_reply and plugins_reply != "":
+            messages.append({"role": "assistant", "content": plugins_reply})
+
+        for plugin in self.cfg.plugins:
+            if not plugin.can_handle_post_instruction():
+                continue
+            agent_reply = plugin.post_instruction(agent_reply)
+
+        return agent_reply
+
+    def list_agents(self) -> list[tuple[str | int, str]]:
+        """Return a list of all agents
+
+        Returns:
+            A list of tuples of the form (key, task)
+        """
+
+        # Return a list of agent keys and their tasks
+        return [(key, task) for key, (task, _, _) in self.agents.items()]
+
+    def delete_agent(self, key: str | int) -> bool:
+        """Delete an agent from the agent manager
+
+        Args:
+            key: The key of the agent to delete
+
+        Returns:
+            True if successful, False otherwise
+        """
+
+        try:
+            del self.agents[int(key)]
+            return True
+        except KeyError:
+            return False

autogpt/agents/__init__.py

@@ -1,3 +0,0 @@
-from .agent import Agent
-
-__all__ = ["Agent"]

autogpt/agents/agent.py

@@ -1,398 +0,0 @@
-import json
-import signal
-import sys
-from datetime import datetime
-from pathlib import Path
-
-from colorama import Fore, Style
-
-from autogpt.config import Config
-from autogpt.config.ai_config import AIConfig
-from autogpt.json_utils.utilities import extract_json_from_response, validate_json
-from autogpt.llm import ChatModelResponse
-from autogpt.llm.chat import chat_with_ai
-from autogpt.llm.providers.openai import OPEN_AI_CHAT_MODELS
-from autogpt.llm.utils import count_string_tokens
-from autogpt.logs import (
-    FULL_MESSAGE_HISTORY_FILE_NAME,
-    NEXT_ACTION_FILE_NAME,
-    USER_INPUT_FILE_NAME,
-    LogCycleHandler,
-    logger,
-    print_assistant_thoughts,
-    remove_ansi_escape,
-)
-from autogpt.memory.message_history import MessageHistory
-from autogpt.memory.vector import VectorMemory
-from autogpt.models.command_registry import CommandRegistry
-from autogpt.speech import say_text
-from autogpt.spinner import Spinner
-from autogpt.utils import clean_input
-from autogpt.workspace import Workspace
-
-
-class Agent:
-    """Agent class for interacting with Auto-GPT.
-
-    Attributes:
-        ai_name: The name of the agent.
-        memory: The memory object to use.
-        next_action_count: The number of actions to execute.
-        system_prompt: The system prompt is the initial prompt that defines everything
-          the AI needs to know to achieve its task successfully.
-        Currently, the dynamic and customizable information in the system prompt are
-          ai_name, description and goals.
-
-        triggering_prompt: The last sentence the AI will see before answering.
-            For Auto-GPT, this prompt is:
-            Determine exactly one command to use, and respond using the format specified
-              above:
-            The triggering prompt is not part of the system prompt because between the
-              system prompt and the triggering
-            prompt we have contextual information that can distract the AI and make it
-              forget that its goal is to find the next task to achieve.
-            SYSTEM PROMPT
-            CONTEXTUAL INFORMATION (memory, previous conversations, anything relevant)
-            TRIGGERING PROMPT
-
-        The triggering prompt reminds the AI about its short term meta task
-        (defining the next task)
-    """
-
-    def __init__(
-        self,
-        ai_name: str,
-        memory: VectorMemory,
-        next_action_count: int,
-        command_registry: CommandRegistry,
-        ai_config: AIConfig,
-        system_prompt: str,
-        triggering_prompt: str,
-        workspace_directory: str | Path,
-        config: Config,
-    ):
-        self.ai_name = ai_name
-        self.memory = memory
-        self.history = MessageHistory.for_model(config.smart_llm, agent=self)
-        self.next_action_count = next_action_count
-        self.command_registry = command_registry
-        self.config = config
-        self.ai_config = ai_config
-        self.system_prompt = system_prompt
-        self.triggering_prompt = triggering_prompt
-        self.workspace = Workspace(workspace_directory, config.restrict_to_workspace)
-        self.created_at = datetime.now().strftime("%Y%m%d_%H%M%S")
-        self.cycle_count = 0
-        self.log_cycle_handler = LogCycleHandler()
-        self.smart_token_limit = OPEN_AI_CHAT_MODELS.get(config.smart_llm).max_tokens
-
-    def start_interaction_loop(self):
-        # Interaction Loop
-        self.cycle_count = 0
-        command_name = None
-        arguments = None
-        user_input = ""
-
-        # Signal handler for interrupting y -N
-        def signal_handler(signum, frame):
-            if self.next_action_count == 0:
-                sys.exit()
-            else:
-                print(
-                    Fore.RED
-                    + "Interrupt signal received. Stopping continuous command execution."
-                    + Style.RESET_ALL
-                )
-                self.next_action_count = 0
-
-        signal.signal(signal.SIGINT, signal_handler)
-
-        while True:
-            # Discontinue if continuous limit is reached
-            self.cycle_count += 1
-            self.log_cycle_handler.log_count_within_cycle = 0
-            self.log_cycle_handler.log_cycle(
-                self.ai_config.ai_name,
-                self.created_at,
-                self.cycle_count,
-                [m.raw() for m in self.history],
-                FULL_MESSAGE_HISTORY_FILE_NAME,
-            )
-            if (
-                self.config.continuous_mode
-                and self.config.continuous_limit > 0
-                and self.cycle_count > self.config.continuous_limit
-            ):
-                logger.typewriter_log(
-                    "Continuous Limit Reached: ",
-                    Fore.YELLOW,
-                    f"{self.config.continuous_limit}",
-                )
-                break
-            # Send message to AI, get response
-            with Spinner("Thinking... ", plain_output=self.config.plain_output):
-                assistant_reply = chat_with_ai(
-                    self.config,
-                    self,
-                    self.system_prompt,
-                    self.triggering_prompt,
-                    self.smart_token_limit,
-                    self.config.smart_llm,
-                )
-
-            try:
-                assistant_reply_json = extract_json_from_response(
-                    assistant_reply.content
-                )
-                validate_json(assistant_reply_json, self.config)
-            except json.JSONDecodeError as e:
-                logger.error(f"Exception while validating assistant reply JSON: {e}")
-                assistant_reply_json = {}
-
-            for plugin in self.config.plugins:
-                if not plugin.can_handle_post_planning():
-                    continue
-                assistant_reply_json = plugin.post_planning(assistant_reply_json)
-
-            # Print Assistant thoughts
-            if assistant_reply_json != {}:
-                # Get command name and arguments
-                try:
-                    print_assistant_thoughts(
-                        self.ai_name, assistant_reply_json, self.config
-                    )
-                    command_name, arguments = extract_command(
-                        assistant_reply_json, assistant_reply, self.config
-                    )
-                    if self.config.speak_mode:
-                        say_text(f"I want to execute {command_name}", self.config)
-
-                except Exception as e:
-                    logger.error("Error: \n", str(e))
-            self.log_cycle_handler.log_cycle(
-                self.ai_config.ai_name,
-                self.created_at,
-                self.cycle_count,
-                assistant_reply_json,
-                NEXT_ACTION_FILE_NAME,
-            )
-
-            # First log new-line so user can differentiate sections better in console
-            logger.typewriter_log("\n")
-            logger.typewriter_log(
-                "NEXT ACTION: ",
-                Fore.CYAN,
-                f"COMMAND = {Fore.CYAN}{remove_ansi_escape(command_name)}{Style.RESET_ALL}  "
-                f"ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
-            )
-
-            if not self.config.continuous_mode and self.next_action_count == 0:
-                # ### GET USER AUTHORIZATION TO EXECUTE COMMAND ###
-                # Get key press: Prompt the user to press enter to continue or escape
-                # to exit
-                self.user_input = ""
-                logger.info(
-                    f"Enter '{self.config.authorise_key}' to authorise command, "
-                    f"'{self.config.authorise_key} -N' to run N continuous commands, "
-                    f"'{self.config.exit_key}' to exit program, or enter feedback for "
-                    f"{self.ai_name}..."
-                )
-                while True:
-                    if self.config.chat_messages_enabled:
-                        console_input = clean_input(
-                            self.config, "Waiting for your response..."
-                        )
-                    else:
-                        console_input = clean_input(
-                            self.config, Fore.MAGENTA + "Input:" + Style.RESET_ALL
-                        )
-                    if console_input.lower().strip() == self.config.authorise_key:
-                        user_input = "GENERATE NEXT COMMAND JSON"
-                        break
-                    elif console_input.lower().strip() == "":
-                        logger.warn("Invalid input format.")
-                        continue
-                    elif console_input.lower().startswith(
-                        f"{self.config.authorise_key} -"
-                    ):
-                        try:
-                            self.next_action_count = abs(
-                                int(console_input.split(" ")[1])
-                            )
-                            user_input = "GENERATE NEXT COMMAND JSON"
-                        except ValueError:
-                            logger.warn(
-                                f"Invalid input format. Please enter '{self.config.authorise_key} -n' "
-                                "where n is the number of continuous tasks."
-                            )
-                            continue
-                        break
-                    elif console_input.lower() == self.config.exit_key:
-                        user_input = "EXIT"
-                        break
-                    else:
-                        user_input = console_input
-                        command_name = "human_feedback"
-                        self.log_cycle_handler.log_cycle(
-                            self.ai_config.ai_name,
-                            self.created_at,
-                            self.cycle_count,
-                            user_input,
-                            USER_INPUT_FILE_NAME,
-                        )
-                        break
-
-                if user_input == "GENERATE NEXT COMMAND JSON":
-                    logger.typewriter_log(
-                        "-=-=-=-=-=-=-= COMMAND AUTHORISED BY USER -=-=-=-=-=-=-=",
-                        Fore.MAGENTA,
-                        "",
-                    )
-                elif user_input == "EXIT":
-                    logger.info("Exiting...")
-                    break
-            else:
-                # First log new-line so user can differentiate sections better in console
-                logger.typewriter_log("\n")
-                # Print authorized commands left value
-                logger.typewriter_log(
-                    f"{Fore.CYAN}AUTHORISED COMMANDS LEFT: {Style.RESET_ALL}{self.next_action_count}"
-                )
-
-            # Execute command
-            if command_name is not None and command_name.lower().startswith("error"):
-                result = f"Could not execute command: {arguments}"
-            elif command_name == "human_feedback":
-                result = f"Human feedback: {user_input}"
-            else:
-                for plugin in self.config.plugins:
-                    if not plugin.can_handle_pre_command():
-                        continue
-                    command_name, arguments = plugin.pre_command(
-                        command_name, arguments
-                    )
-                command_result = execute_command(
-                    command_name=command_name,
-                    arguments=arguments,
-                    agent=self,
-                )
-                result = f"Command {command_name} returned: " f"{command_result}"
-
-                result_tlength = count_string_tokens(
-                    str(command_result), self.config.smart_llm
-                )
-                memory_tlength = count_string_tokens(
-                    str(self.history.summary_message()), self.config.smart_llm
-                )
-                if result_tlength + memory_tlength + 600 > self.smart_token_limit:
-                    result = f"Failure: command {command_name} returned too much output. \
-                        Do not execute this command again with the same arguments."
-
-                for plugin in self.config.plugins:
-                    if not plugin.can_handle_post_command():
-                        continue
-                    result = plugin.post_command(command_name, result)
-                if self.next_action_count > 0:
-                    self.next_action_count -= 1
-
-            # Check if there's a result from the command append it to the message
-            # history
-            if result is not None:
-                self.history.add("system", result, "action_result")
-                logger.typewriter_log("SYSTEM: ", Fore.YELLOW, result)
-            else:
-                self.history.add("system", "Unable to execute command", "action_result")
-                logger.typewriter_log(
-                    "SYSTEM: ", Fore.YELLOW, "Unable to execute command"
-                )
-
-
-def extract_command(
-    assistant_reply_json: dict, assistant_reply: ChatModelResponse, config: Config
-):
-    """Parse the response and return the command name and arguments
-
-    Args:
-        assistant_reply_json (dict): The response object from the AI
-        assistant_reply (ChatModelResponse): The model response from the AI
-        config (Config): The config object
-
-    Returns:
-        tuple: The command name and arguments
-
-    Raises:
-        json.decoder.JSONDecodeError: If the response is not valid JSON
-
-        Exception: If any other error occurs
-    """
-    if config.openai_functions:
-        if assistant_reply.function_call is None:
-            return "Error:", "No 'function_call' in assistant reply"
-        assistant_reply_json["command"] = {
-            "name": assistant_reply.function_call.name,
-            "args": json.loads(assistant_reply.function_call.arguments),
-        }
-    try:
-        if "command" not in assistant_reply_json:
-            return "Error:", "Missing 'command' object in JSON"
-
-        if not isinstance(assistant_reply_json, dict):
-            return (
-                "Error:",
-                f"The previous message sent was not a dictionary {assistant_reply_json}",
-            )
-
-        command = assistant_reply_json["command"]
-        if not isinstance(command, dict):
-            return "Error:", "'command' object is not a dictionary"
-
-        if "name" not in command:
-            return "Error:", "Missing 'name' field in 'command' object"
-
-        command_name = command["name"]
-
-        # Use an empty dictionary if 'args' field is not present in 'command' object
-        arguments = command.get("args", {})
-
-        return command_name, arguments
-    except json.decoder.JSONDecodeError:
-        return "Error:", "Invalid JSON"
-    # All other errors, return "Error: + error message"
-    except Exception as e:
-        return "Error:", str(e)
-
-
-def execute_command(
-    command_name: str,
-    arguments: dict[str, str],
-    agent: Agent,
-):
-    """Execute the command and return the result
-
-    Args:
-        command_name (str): The name of the command to execute
-        arguments (dict): The arguments for the command
-        agent (Agent): The agent that is executing the command
-
-    Returns:
-        str: The result of the command
-    """
-    try:
-        # Execute a native command with the same name or alias, if it exists
-        if command := agent.command_registry.get_command(command_name):
-            return command(**arguments, agent=agent)
-
-        # Handle non-native commands (e.g. from plugins)
-        for command in agent.ai_config.prompt_generator.commands:
-            if (
-                command_name == command["label"].lower()
-                or command_name == command["name"].lower()
-            ):
-                return command["function"](**arguments)
-
-        raise RuntimeError(
-            f"Cannot execute '{command_name}': unknown command."
-            " Do not try to use this command again."
-        )
-    except Exception as e:
-        return f"Error: {str(e)}"

autogpt/app.py

@@ -0,0 +1,255 @@
+""" Command and Control """
+import json
+from typing import Dict, List, NoReturn, Union
+
+from autogpt.agent.agent_manager import AgentManager
+from autogpt.commands.command import CommandRegistry, command
+from autogpt.commands.web_requests import scrape_links, scrape_text
+from autogpt.config import Config
+from autogpt.logs import logger
+from autogpt.memory import get_memory
+from autogpt.processing.text import summarize_text
+from autogpt.prompts.generator import PromptGenerator
+from autogpt.speech import say_text
+from autogpt.url_utils.validators import validate_url
+
+CFG = Config()
+AGENT_MANAGER = AgentManager()
+
+
+def is_valid_int(value: str) -> bool:
+    """Check if the value is a valid integer
+
+    Args:
+        value (str): The value to check
+
+    Returns:
+        bool: True if the value is a valid integer, False otherwise
+    """
+    try:
+        int(value)
+        return True
+    except ValueError:
+        return False
+
+
+def get_command(response_json: Dict):
+    """Parse the response and return the command name and arguments
+
+    Args:
+        response_json (json): The response from the AI
+
+    Returns:
+        tuple: The command name and arguments
+
+    Raises:
+        json.decoder.JSONDecodeError: If the response is not valid JSON
+
+        Exception: If any other error occurs
+    """
+    try:
+        if "command" not in response_json:
+            return "Error:", "Missing 'command' object in JSON"
+
+        if not isinstance(response_json, dict):
+            return "Error:", f"'response_json' object is not dictionary {response_json}"
+
+        command = response_json["command"]
+        if not isinstance(command, dict):
+            return "Error:", "'command' object is not a dictionary"
+
+        if "name" not in command:
+            return "Error:", "Missing 'name' field in 'command' object"
+
+        command_name = command["name"]
+
+        # Use an empty dictionary if 'args' field is not present in 'command' object
+        arguments = command.get("args", {})
+
+        return command_name, arguments
+    except json.decoder.JSONDecodeError:
+        return "Error:", "Invalid JSON"
+    # All other errors, return "Error: + error message"
+    except Exception as e:
+        return "Error:", str(e)
+
+
+def map_command_synonyms(command_name: str):
+    """Takes the original command name given by the AI, and checks if the
+    string matches a list of common/known hallucinations
+    """
+    synonyms = [
+        ("write_file", "write_to_file"),
+        ("create_file", "write_to_file"),
+        ("search", "google"),
+    ]
+    for seen_command, actual_command_name in synonyms:
+        if command_name == seen_command:
+            return actual_command_name
+    return command_name
+
+
+def execute_command(
+    command_registry: CommandRegistry,
+    command_name: str,
+    arguments,
+    prompt: PromptGenerator,
+):
+    """Execute the command and return the result
+
+    Args:
+        command_name (str): The name of the command to execute
+        arguments (dict): The arguments for the command
+
+    Returns:
+        str: The result of the command
+    """
+    try:
+        cmd = command_registry.commands.get(command_name)
+
+        # If the command is found, call it with the provided arguments
+        if cmd:
+            return cmd(**arguments)
+
+        # TODO: Remove commands below after they are moved to the command registry.
+        command_name = map_command_synonyms(command_name.lower())
+
+        if command_name == "memory_add":
+            return get_memory(CFG).add(arguments["string"])
+
+        # TODO: Change these to take in a file rather than pasted code, if
+        # non-file is given, return instructions "Input should be a python
+        # filepath, write your code to file and try again
+        elif command_name == "task_complete":
+            shutdown()
+        else:
+            for command in prompt.commands:
+                if (
+                    command_name == command["label"].lower()
+                    or command_name == command["name"].lower()
+                ):
+                    return command["function"](**arguments)
+            return (
+                f"Unknown command '{command_name}'. Please refer to the 'COMMANDS'"
+                " list for available commands and only respond in the specified JSON"
+                " format."
+            )
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+
+@command(
+    "get_text_summary", "Get text summary", '"url": "<url>", "question": "<question>"'
+)
+@validate_url
+def get_text_summary(url: str, question: str) -> str:
+    """Return the results of a Google search
+
+    Args:
+        url (str): The url to scrape
+        question (str): The question to summarize the text for
+
+    Returns:
+        str: The summary of the text
+    """
+    text = scrape_text(url)
+    summary = summarize_text(url, text, question)
+    return f""" "Result" : {summary}"""
+
+
+@command("get_hyperlinks", "Get text summary", '"url": "<url>"')
+@validate_url
+def get_hyperlinks(url: str) -> Union[str, List[str]]:
+    """Return the results of a Google search
+
+    Args:
+        url (str): The url to scrape
+
+    Returns:
+        str or list: The hyperlinks on the page
+    """
+    return scrape_links(url)
+
+
+def shutdown() -> NoReturn:
+    """Shut down the program"""
+    logger.info("Shutting down...")
+    quit()
+
+
+@command(
+    "start_agent",
+    "Start GPT Agent",
+    '"name": "<name>", "task": "<short_task_desc>", "prompt": "<prompt>"',
+)
+def start_agent(name: str, task: str, prompt: str, model=CFG.fast_llm_model) -> str:
+    """Start an agent with a given name, task, and prompt
+
+    Args:
+        name (str): The name of the agent
+        task (str): The task of the agent
+        prompt (str): The prompt for the agent
+        model (str): The model to use for the agent
+
+    Returns:
+        str: The response of the agent
+    """
+    # Remove underscores from name
+    voice_name = name.replace("_", " ")
+
+    first_message = f"""You are {name}.  Respond with: "Acknowledged"."""
+    agent_intro = f"{voice_name} here, Reporting for duty!"
+
+    # Create agent
+    if CFG.speak_mode:
+        say_text(agent_intro, 1)
+    key, ack = AGENT_MANAGER.create_agent(task, first_message, model)
+
+    if CFG.speak_mode:
+        say_text(f"Hello {voice_name}. Your task is as follows. {task}.")
+
+    # Assign task (prompt), get response
+    agent_response = AGENT_MANAGER.message_agent(key, prompt)
+
+    return f"Agent {name} created with key {key}. First response: {agent_response}"
+
+
+@command("message_agent", "Message GPT Agent", '"key": "<key>", "message": "<message>"')
+def message_agent(key: str, message: str) -> str:
+    """Message an agent with a given key and message"""
+    # Check if the key is a valid integer
+    if is_valid_int(key):
+        agent_response = AGENT_MANAGER.message_agent(int(key), message)
+    else:
+        return "Invalid key, must be an integer."
+
+    # Speak response
+    if CFG.speak_mode:
+        say_text(agent_response, 1)
+    return agent_response
+
+
+@command("list_agents", "List GPT Agents", "")
+def list_agents() -> str:
+    """List all agents
+
+    Returns:
+        str: A list of all agents
+    """
+    return "List of agents:\n" + "\n".join(
+        [str(x[0]) + ": " + x[1] for x in AGENT_MANAGER.list_agents()]
+    )
+
+
+@command("delete_agent", "Delete GPT Agent", '"key": "<key>"')
+def delete_agent(key: str) -> str:
+    """Delete an agent with a given key
+
+    Args:
+        key (str): The key of the agent to delete
+
+    Returns:
+        str: A message indicating whether the agent was deleted or not
+    """
+    result = AGENT_MANAGER.delete_agent(key)
+    return f"Agent {key} deleted." if result else f"Agent {key} does not exist."

autogpt/cli.py

@@ -1,6 +1,4 @@
 """Main script for the autogpt package."""
-from typing import Optional
-
 import click
 
 
@@ -17,11 +15,6 @@ import click
     "-C",
     help="Specifies which ai_settings.yaml file to use, will also automatically skip the re-prompt.",
 )
-@click.option(
-    "--prompt-settings",
-    "-P",
-    help="Specifies which prompt_settings.yaml file to use.",
-)
 @click.option(
     "-l",
     "--continuous-limit",
@@ -67,29 +60,12 @@ import click
     is_flag=True,
     help="Installs external dependencies for 3rd party plugins.",
 )
-@click.option(
-    "--ai-name",
-    type=str,
-    help="AI name override",
-)
-@click.option(
-    "--ai-role",
-    type=str,
-    help="AI role override",
-)
-@click.option(
-    "--ai-goal",
-    type=str,
-    multiple=True,
-    help="AI goal override; may be used multiple times to pass multiple goals",
-)
 @click.pass_context
 def main(
     ctx: click.Context,
     continuous: bool,
     continuous_limit: int,
     ai_settings: str,
-    prompt_settings: str,
     skip_reprompt: bool,
     speak: bool,
     debug: bool,
@@ -101,9 +77,6 @@ def main(
     skip_news: bool,
     workspace_directory: str,
     install_plugin_deps: bool,
-    ai_name: Optional[str],
-    ai_role: Optional[str],
-    ai_goal: tuple[str],
 ) -> None:
     """
     Welcome to AutoGPT an experimental open-source application showcasing the capabilities of the GPT-4 pushing the boundaries of AI.
@@ -118,7 +91,6 @@ def main(
             continuous,
             continuous_limit,
             ai_settings,
-            prompt_settings,
             skip_reprompt,
             speak,
             debug,
@@ -130,9 +102,6 @@ def main(
             skip_news,
             workspace_directory,
             install_plugin_deps,
-            ai_name,
-            ai_role,
-            ai_goal,
         )
 
 

autogpt/command_decorator.py

@@ -1,57 +0,0 @@
-import functools
-from typing import Any, Callable, Optional, TypedDict
-
-from autogpt.config import Config
-from autogpt.models.command import Command, CommandParameter
-
-# Unique identifier for auto-gpt commands
-AUTO_GPT_COMMAND_IDENTIFIER = "auto_gpt_command"
-
-
-class CommandParameterSpec(TypedDict):
-    type: str
-    description: str
-    required: bool
-
-
-def command(
-    name: str,
-    description: str,
-    parameters: dict[str, CommandParameterSpec],
-    enabled: bool | Callable[[Config], bool] = True,
-    disabled_reason: Optional[str] = None,
-    aliases: list[str] = [],
-) -> Callable[..., Any]:
-    """The command decorator is used to create Command objects from ordinary functions."""
-
-    def decorator(func: Callable[..., Any]) -> Command:
-        typed_parameters = [
-            CommandParameter(
-                name=param_name,
-                description=parameter.get("description"),
-                type=parameter.get("type", "string"),
-                required=parameter.get("required", False),
-            )
-            for param_name, parameter in parameters.items()
-        ]
-        cmd = Command(
-            name=name,
-            description=description,
-            method=func,
-            parameters=typed_parameters,
-            enabled=enabled,
-            disabled_reason=disabled_reason,
-            aliases=aliases,
-        )
-
-        @functools.wraps(func)
-        def wrapper(*args, **kwargs) -> Any:
-            return func(*args, **kwargs)
-
-        wrapper.command = cmd
-
-        setattr(wrapper, AUTO_GPT_COMMAND_IDENTIFIER, True)
-
-        return wrapper
-
-    return decorator

autogpt/commands/analyze_code.py

@@ -0,0 +1,31 @@
+"""Code evaluation module."""
+from __future__ import annotations
+
+from autogpt.commands.command import command
+from autogpt.llm import call_ai_function
+
+
+@command(
+    "analyze_code",
+    "Analyze Code",
+    '"code": "<full_code_string>"',
+)
+def analyze_code(code: str) -> list[str]:
+    """
+    A function that takes in a string and returns a response from create chat
+      completion api call.
+
+    Parameters:
+        code (str): Code to be evaluated.
+    Returns:
+        A result string from create chat completion. A list of suggestions to
+            improve the code.
+    """
+
+    function_string = "def analyze_code(code: str) -> list[str]:"
+    args = [code]
+    description_string = (
+        "Analyzes the given code and returns a list of suggestions for improvements."
+    )
+
+    return call_ai_function(function_string, args, description_string)

autogpt/commands/audio_text.py

@@ -0,0 +1,61 @@
+"""Commands for converting audio to text."""
+import json
+
+import requests
+
+from autogpt.commands.command import command
+from autogpt.config import Config
+
+CFG = Config()
+
+
+@command(
+    "read_audio_from_file",
+    "Convert Audio to text",
+    '"filename": "<filename>"',
+    CFG.huggingface_audio_to_text_model,
+    "Configure huggingface_audio_to_text_model.",
+)
+def read_audio_from_file(filename: str) -> str:
+    """
+    Convert audio to text.
+
+    Args:
+        filename (str): The path to the audio file
+
+    Returns:
+        str: The text from the audio
+    """
+    with open(filename, "rb") as audio_file:
+        audio = audio_file.read()
+    return read_audio(audio)
+
+
+def read_audio(audio: bytes) -> str:
+    """
+    Convert audio to text.
+
+    Args:
+        audio (bytes): The audio to convert
+
+    Returns:
+        str: The text from the audio
+    """
+    model = CFG.huggingface_audio_to_text_model
+    api_url = f"https://api-inference.huggingface.co/models/{model}"
+    api_token = CFG.huggingface_api_token
+    headers = {"Authorization": f"Bearer {api_token}"}
+
+    if api_token is None:
+        raise ValueError(
+            "You need to set your Hugging Face API token in the config file."
+        )
+
+    response = requests.post(
+        api_url,
+        headers=headers,
+        data=audio,
+    )
+
+    text = json.loads(response.content.decode("utf-8"))["text"]
+    return f"The audio says: {text}"

autogpt/commands/command.py

@@ -1,10 +1,44 @@
+import functools
 import importlib
 import inspect
-from typing import Any
+from typing import Any, Callable, Optional
 
-from autogpt.command_decorator import AUTO_GPT_COMMAND_IDENTIFIER
-from autogpt.logs import logger
-from autogpt.models.command import Command
+# Unique identifier for auto-gpt commands
+AUTO_GPT_COMMAND_IDENTIFIER = "auto_gpt_command"
+
+
+class Command:
+    """A class representing a command.
+
+    Attributes:
+        name (str): The name of the command.
+        description (str): A brief description of what the command does.
+        signature (str): The signature of the function that the command executes. Defaults to None.
+    """
+
+    def __init__(
+        self,
+        name: str,
+        description: str,
+        method: Callable[..., Any],
+        signature: str = "",
+        enabled: bool = True,
+        disabled_reason: Optional[str] = None,
+    ):
+        self.name = name
+        self.description = description
+        self.method = method
+        self.signature = signature if signature else str(inspect.signature(self.method))
+        self.enabled = enabled
+        self.disabled_reason = disabled_reason
+
+    def __call__(self, *args, **kwargs) -> Any:
+        if not self.enabled:
+            return f"Command '{self.name}' is disabled: {self.disabled_reason}"
+        return self.method(*args, **kwargs)
+
+    def __str__(self) -> str:
+        return f"{self.name}: {self.description}, args: {self.signature}"
 
 
 class CommandRegistry:
@@ -15,15 +49,8 @@ class CommandRegistry:
     directory.
     """
 
-    commands: dict[str, Command]
-    commands_aliases: dict[str, Command]
-
     def __init__(self):
         self.commands = {}
-        self.commands_aliases = {}
-
-    def __contains__(self, command_name: str):
-        return command_name in self.commands or command_name in self.commands_aliases
 
     def _import_module(self, module_name: str) -> Any:
         return importlib.import_module(module_name)
@@ -32,27 +59,13 @@ class CommandRegistry:
         return importlib.reload(module)
 
     def register(self, cmd: Command) -> None:
-        if cmd.name in self.commands:
-            logger.warn(
-                f"Command '{cmd.name}' already registered and will be overwritten!"
-            )
         self.commands[cmd.name] = cmd
 
-        if cmd.name in self.commands_aliases:
-            logger.warn(
-                f"Command '{cmd.name}' will overwrite alias with the same name of "
-                f"'{self.commands_aliases[cmd.name]}'!"
-            )
-        for alias in cmd.aliases:
-            self.commands_aliases[alias] = cmd
-
-    def unregister(self, command: Command) -> None:
-        if command.name in self.commands:
-            del self.commands[command.name]
-            for alias in command.aliases:
-                del self.commands_aliases[alias]
+    def unregister(self, command_name: str):
+        if command_name in self.commands:
+            del self.commands[command_name]
         else:
-            raise KeyError(f"Command '{command.name}' not found in registry.")
+            raise KeyError(f"Command '{command_name}' not found in registry.")
 
     def reload_commands(self) -> None:
         """Reloads all loaded command plugins."""
@@ -63,17 +76,14 @@ class CommandRegistry:
             if hasattr(reloaded_module, "register"):
                 reloaded_module.register(self)
 
-    def get_command(self, name: str) -> Command | None:
-        if name in self.commands:
-            return self.commands[name]
-
-        if name in self.commands_aliases:
-            return self.commands_aliases[name]
+    def get_command(self, name: str) -> Callable[..., Any]:
+        return self.commands[name]
 
     def call(self, command_name: str, **kwargs) -> Any:
-        if command := self.get_command(command_name):
-            return command(**kwargs)
-        raise KeyError(f"Command '{command_name}' not found in registry")
+        if command_name not in self.commands:
+            raise KeyError(f"Command '{command_name}' not found in registry.")
+        command = self.commands[command_name]
+        return command(**kwargs)
 
     def command_prompt(self) -> str:
         """
@@ -112,3 +122,35 @@ class CommandRegistry:
             ):
                 cmd_instance = attr()
                 self.register(cmd_instance)
+
+
+def command(
+    name: str,
+    description: str,
+    signature: str = "",
+    enabled: bool = True,
+    disabled_reason: Optional[str] = None,
+) -> Callable[..., Any]:
+    """The command decorator is used to create Command objects from ordinary functions."""
+
+    def decorator(func: Callable[..., Any]) -> Command:
+        cmd = Command(
+            name=name,
+            description=description,
+            method=func,
+            signature=signature,
+            enabled=enabled,
+            disabled_reason=disabled_reason,
+        )
+
+        @functools.wraps(func)
+        def wrapper(*args, **kwargs) -> Any:
+            return func(*args, **kwargs)
+
+        wrapper.command = cmd
+
+        setattr(wrapper, AUTO_GPT_COMMAND_IDENTIFIER, True)
+
+        return wrapper
+
+    return decorator

autogpt/commands/decorators.py

@@ -1,64 +0,0 @@
-import functools
-from pathlib import Path
-from typing import Callable
-
-from autogpt.agents.agent import Agent
-from autogpt.logs import logger
-
-
-def sanitize_path_arg(arg_name: str):
-    def decorator(func: Callable):
-        # Get position of path parameter, in case it is passed as a positional argument
-        try:
-            arg_index = list(func.__annotations__.keys()).index(arg_name)
-        except ValueError:
-            raise TypeError(
-                f"Sanitized parameter '{arg_name}' absent or not annotated on function '{func.__name__}'"
-            )
-
-        # Get position of agent parameter, in case it is passed as a positional argument
-        try:
-            agent_arg_index = list(func.__annotations__.keys()).index("agent")
-        except ValueError:
-            raise TypeError(
-                f"Parameter 'agent' absent or not annotated on function '{func.__name__}'"
-            )
-
-        @functools.wraps(func)
-        def wrapper(*args, **kwargs):
-            logger.debug(f"Sanitizing arg '{arg_name}' on function '{func.__name__}'")
-            logger.debug(f"Function annotations: {func.__annotations__}")
-
-            # Get Agent from the called function's arguments
-            agent = kwargs.get(
-                "agent", len(args) > agent_arg_index and args[agent_arg_index]
-            )
-            logger.debug(f"Args: {args}")
-            logger.debug(f"KWArgs: {kwargs}")
-            logger.debug(f"Agent argument lifted from function call: {agent}")
-            if not isinstance(agent, Agent):
-                raise RuntimeError("Could not get Agent from decorated command's args")
-
-            # Sanitize the specified path argument, if one is given
-            given_path: str | Path | None = kwargs.get(
-                arg_name, len(args) > arg_index and args[arg_index] or None
-            )
-            if given_path:
-                if given_path in {"", "/"}:
-                    sanitized_path = str(agent.workspace.root)
-                else:
-                    sanitized_path = str(agent.workspace.get_path(given_path))
-
-                if arg_name in kwargs:
-                    kwargs[arg_name] = sanitized_path
-                else:
-                    # args is an immutable tuple; must be converted to a list to update
-                    arg_list = list(args)
-                    arg_list[arg_index] = sanitized_path
-                    args = tuple(arg_list)
-
-            return func(*args, **kwargs)
-
-        return wrapper
-
-    return decorator

autogpt/commands/execute_code.py

@@ -4,82 +4,17 @@ import subprocess
 from pathlib import Path
 
 import docker
-from docker.errors import DockerException, ImageNotFound
-from docker.models.containers import Container as DockerContainer
+from docker.errors import ImageNotFound
 
-from autogpt.agents.agent import Agent
-from autogpt.command_decorator import command
+from autogpt.commands.command import command
 from autogpt.config import Config
 from autogpt.logs import logger
 
-from .decorators import sanitize_path_arg
-
-ALLOWLIST_CONTROL = "allowlist"
-DENYLIST_CONTROL = "denylist"
+CFG = Config()
 
 
-@command(
-    "execute_python_code",
-    "Creates a Python file and executes it",
-    {
-        "code": {
-            "type": "string",
-            "description": "The Python code to run",
-            "required": True,
-        },
-        "name": {
-            "type": "string",
-            "description": "A name to be given to the python file",
-            "required": True,
-        },
-    },
-)
-def execute_python_code(code: str, name: str, agent: Agent) -> str:
-    """Create and execute a Python file in a Docker container and return the STDOUT of the
-    executed code. If there is any data that needs to be captured use a print statement
-
-    Args:
-        code (str): The Python code to run
-        name (str): A name to be given to the Python file
-
-    Returns:
-        str: The STDOUT captured from the code when it ran
-    """
-    ai_name = agent.ai_config.ai_name
-    code_dir = agent.workspace.get_path(Path(ai_name, "executed_code"))
-    os.makedirs(code_dir, exist_ok=True)
-
-    if not name.endswith(".py"):
-        name = name + ".py"
-
-    # The `name` arg is not covered by @sanitize_path_arg,
-    # so sanitization must be done here to prevent path traversal.
-    file_path = agent.workspace.get_path(code_dir / name)
-    if not file_path.is_relative_to(code_dir):
-        return "Error: 'name' argument resulted in path traversal, operation aborted"
-
-    try:
-        with open(file_path, "w+", encoding="utf-8") as f:
-            f.write(code)
-
-        return execute_python_file(str(file_path), agent)
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-
-@command(
-    "execute_python_file",
-    "Executes an existing Python file",
-    {
-        "filename": {
-            "type": "string",
-            "description": "The name of te file to execute",
-            "required": True,
-        },
-    },
-)
-@sanitize_path_arg("filename")
-def execute_python_file(filename: str, agent: Agent) -> str:
+@command("execute_python_file", "Execute Python File", '"filename": "<filename>"')
+def execute_python_file(filename: str) -> str:
     """Execute a Python file in a Docker container and return the output
 
     Args:
@@ -88,36 +23,23 @@ def execute_python_file(filename: str, agent: Agent) -> str:
     Returns:
         str: The output of the file
     """
-    logger.info(
-        f"Executing python file '{filename}' in working directory '{agent.config.workspace_path}'"
-    )
+    logger.info(f"Executing file '{filename}'")
 
     if not filename.endswith(".py"):
         return "Error: Invalid file type. Only .py files are allowed."
 
-    file_path = Path(filename)
-    if not file_path.is_file():
-        # Mimic the response that you get from the command line so that it's easier to identify
-        return (
-            f"python: can't open file '{filename}': [Errno 2] No such file or directory"
-        )
+    if not os.path.isfile(filename):
+        return f"Error: File '{filename}' does not exist."
 
     if we_are_running_in_a_docker_container():
-        logger.debug(
-            f"Auto-GPT is running in a Docker container; executing {file_path} directly..."
-        )
         result = subprocess.run(
-            ["python", str(file_path)],
-            capture_output=True,
-            encoding="utf8",
-            cwd=agent.config.workspace_path,
+            f"python {filename}", capture_output=True, encoding="utf8", shell=True
         )
         if result.returncode == 0:
             return result.stdout
         else:
             return f"Error: {result.stderr}"
 
-    logger.debug("Auto-GPT is not running in a Docker container")
     try:
         client = docker.from_env()
         # You can replace this with the desired Python image/version
@@ -126,10 +48,10 @@ def execute_python_file(filename: str, agent: Agent) -> str:
         image_name = "python:3-alpine"
         try:
             client.images.get(image_name)
-            logger.debug(f"Image '{image_name}' found locally")
+            logger.warn(f"Image '{image_name}' found locally")
         except ImageNotFound:
             logger.info(
-                f"Image '{image_name}' not found locally, pulling from Docker Hub..."
+                f"Image '{image_name}' not found locally, pulling from Docker Hub"
             )
             # Use the low-level API to stream the pull response
             low_level_client = docker.APIClient()
@@ -141,13 +63,11 @@ def execute_python_file(filename: str, agent: Agent) -> str:
                     logger.info(f"{status}: {progress}")
                 elif status:
                     logger.info(status)
-
-        logger.debug(f"Running {file_path} in a {image_name} container...")
-        container: DockerContainer = client.containers.run(
+        container = client.containers.run(
             image_name,
-            ["python", str(file_path.relative_to(agent.workspace.root))],
+            f"python {Path(filename).relative_to(CFG.workspace_path)}",
             volumes={
-                agent.config.workspace_path: {
+                CFG.workspace_path: {
                     "bind": "/workspace",
                     "mode": "ro",
                 }
@@ -156,7 +76,7 @@ def execute_python_file(filename: str, agent: Agent) -> str:
             stderr=True,
             stdout=True,
             detach=True,
-        )  # type: ignore
+        )
 
         container.wait()
         logs = container.logs().decode("utf-8")
@@ -167,7 +87,7 @@ def execute_python_file(filename: str, agent: Agent) -> str:
 
         return logs
 
-    except DockerException as e:
+    except docker.errors.DockerException as e:
         logger.warn(
             "Could not run the script in a container. If you haven't already, please install Docker https://docs.docker.com/get-docker/"
         )
@@ -177,43 +97,16 @@ def execute_python_file(filename: str, agent: Agent) -> str:
         return f"Error: {str(e)}"
 
 
-def validate_command(command: str, config: Config) -> bool:
-    """Validate a command to ensure it is allowed
-
-    Args:
-        command (str): The command to validate
-        config (Config): The config to use to validate the command
-
-    Returns:
-        bool: True if the command is allowed, False otherwise
-    """
-    if not command:
-        return False
-
-    command_name = command.split()[0]
-
-    if config.shell_command_control == ALLOWLIST_CONTROL:
-        return command_name in config.shell_allowlist
-    else:
-        return command_name not in config.shell_denylist
-
-
 @command(
     "execute_shell",
-    "Executes a Shell Command, non-interactive commands only",
-    {
-        "command_line": {
-            "type": "string",
-            "description": "The command line to execute",
-            "required": True,
-        }
-    },
-    enabled=lambda config: config.execute_local_commands,
-    disabled_reason="You are not allowed to run local shell commands. To execute"
+    "Execute Shell Command, non-interactive commands only",
+    '"command_line": "<command_line>"',
+    CFG.execute_local_commands,
+    "You are not allowed to run local shell commands. To execute"
     " shell commands, EXECUTE_LOCAL_COMMANDS must be set to 'True' "
-    "in your config file: .env - do not attempt to bypass the restriction.",
+    "in your config. Do not attempt to bypass the restriction.",
 )
-def execute_shell(command_line: str, agent: Agent) -> str:
+def execute_shell(command_line: str) -> str:
     """Execute a shell command and return the output
 
     Args:
@@ -222,14 +115,11 @@ def execute_shell(command_line: str, agent: Agent) -> str:
     Returns:
         str: The output of the command
     """
-    if not validate_command(command_line, agent.config):
-        logger.info(f"Command '{command_line}' not allowed")
-        return "Error: This Shell Command is not allowed."
 
     current_dir = Path.cwd()
     # Change dir into workspace if necessary
-    if not current_dir.is_relative_to(agent.config.workspace_path):
-        os.chdir(agent.config.workspace_path)
+    if not current_dir.is_relative_to(CFG.workspace_path):
+        os.chdir(CFG.workspace_path)
 
     logger.info(
         f"Executing command '{command_line}' in working directory '{os.getcwd()}'"
@@ -246,20 +136,14 @@ def execute_shell(command_line: str, agent: Agent) -> str:
 
 @command(
     "execute_shell_popen",
-    "Executes a Shell Command, non-interactive commands only",
-    {
-        "query": {
-            "type": "string",
-            "description": "The search query",
-            "required": True,
-        }
-    },
-    lambda config: config.execute_local_commands,
+    "Execute Shell Command, non-interactive commands only",
+    '"command_line": "<command_line>"',
+    CFG.execute_local_commands,
     "You are not allowed to run local shell commands. To execute"
     " shell commands, EXECUTE_LOCAL_COMMANDS must be set to 'True' "
     "in your config. Do not attempt to bypass the restriction.",
 )
-def execute_shell_popen(command_line, agent: Agent) -> str:
+def execute_shell_popen(command_line) -> str:
     """Execute a shell command with Popen and returns an english description
     of the event and the process id
 
@@ -269,14 +153,11 @@ def execute_shell_popen(command_line, agent: Agent) -> str:
     Returns:
         str: Description of the fact that the process started and its id
     """
-    if not validate_command(command_line, agent.config):
-        logger.info(f"Command '{command_line}' not allowed")
-        return "Error: This Shell Command is not allowed."
 
     current_dir = os.getcwd()
     # Change dir into workspace if necessary
-    if agent.config.workspace_path not in current_dir:
-        os.chdir(agent.config.workspace_path)
+    if CFG.workspace_path not in current_dir:
+        os.chdir(CFG.workspace_path)
 
     logger.info(
         f"Executing command '{command_line}' in working directory '{os.getcwd()}'"

autogpt/commands/file_operations.py

@@ -1,144 +1,83 @@
 """File operations for AutoGPT"""
 from __future__ import annotations
 
-import contextlib
-import hashlib
 import os
 import os.path
-from pathlib import Path
-from typing import Generator, Literal
+from typing import Generator
 
-from autogpt.agents.agent import Agent
-from autogpt.command_decorator import command
+import requests
+from colorama import Back, Fore
+from requests.adapters import HTTPAdapter, Retry
+
+from autogpt.commands.command import command
+from autogpt.config import Config
 from autogpt.logs import logger
-from autogpt.memory.vector import MemoryItem, VectorMemory
+from autogpt.spinner import Spinner
+from autogpt.utils import readable_file_size
 
-from .decorators import sanitize_path_arg
-from .file_operations_utils import read_textual_file
-
-Operation = Literal["write", "append", "delete"]
+CFG = Config()
 
 
-def text_checksum(text: str) -> str:
-    """Get the hex checksum for the given text."""
-    return hashlib.md5(text.encode("utf-8")).hexdigest()
-
-
-def operations_from_log(
-    log_path: str,
-) -> Generator[tuple[Operation, str, str | None], None, None]:
-    """Parse the file operations log and return a tuple containing the log entries"""
-    try:
-        log = open(log_path, "r", encoding="utf-8")
-    except FileNotFoundError:
-        return
-
-    for line in log:
-        line = line.replace("File Operation Logger", "").strip()
-        if not line:
-            continue
-        operation, tail = line.split(": ", maxsplit=1)
-        operation = operation.strip()
-        if operation in ("write", "append"):
-            try:
-                path, checksum = (x.strip() for x in tail.rsplit(" #", maxsplit=1))
-            except ValueError:
-                logger.warn(f"File log entry lacks checksum: '{line}'")
-                path, checksum = tail.strip(), None
-            yield (operation, path, checksum)
-        elif operation == "delete":
-            yield (operation, tail.strip(), None)
-
-    log.close()
-
-
-def file_operations_state(log_path: str) -> dict[str, str]:
-    """Iterates over the operations log and returns the expected state.
-
-    Parses a log file at config.file_logger_path to construct a dictionary that maps
-    each file path written or appended to its checksum. Deleted files are removed
-    from the dictionary.
-
-    Returns:
-        A dictionary mapping file paths to their checksums.
-
-    Raises:
-        FileNotFoundError: If config.file_logger_path is not found.
-        ValueError: If the log file content is not in the expected format.
-    """
-    state = {}
-    for operation, path, checksum in operations_from_log(log_path):
-        if operation in ("write", "append"):
-            state[path] = checksum
-        elif operation == "delete":
-            del state[path]
-    return state
-
-
-@sanitize_path_arg("filename")
-def is_duplicate_operation(
-    operation: Operation, filename: str, agent: Agent, checksum: str | None = None
-) -> bool:
-    """Check if the operation has already been performed
+def check_duplicate_operation(operation: str, filename: str) -> bool:
+    """Check if the operation has already been performed on the given file
 
     Args:
-        operation: The operation to check for
-        filename: The name of the file to check for
-        agent: The agent
-        checksum: The checksum of the contents to be written
+        operation (str): The operation to check for
+        filename (str): The name of the file to check for
 
     Returns:
-        True if the operation has already been performed on the file
+        bool: True if the operation has already been performed on the file
     """
-    # Make the filename into a relative path if possible
-    with contextlib.suppress(ValueError):
-        filename = str(Path(filename).relative_to(agent.workspace.root))
-
-    state = file_operations_state(agent.config.file_logger_path)
-    if operation == "delete" and filename not in state:
-        return True
-    if operation == "write" and state.get(filename) == checksum:
-        return True
-    return False
+    log_content = read_file(CFG.file_logger_path)
+    log_entry = f"{operation}: {filename}\n"
+    return log_entry in log_content
 
 
-@sanitize_path_arg("filename")
-def log_operation(
-    operation: Operation, filename: str, agent: Agent, checksum: str | None = None
-) -> None:
+def log_operation(operation: str, filename: str) -> None:
     """Log the file operation to the file_logger.txt
 
     Args:
-        operation: The operation to log
-        filename: The name of the file the operation was performed on
-        checksum: The checksum of the contents to be written
+        operation (str): The operation to log
+        filename (str): The name of the file the operation was performed on
     """
-    # Make the filename into a relative path if possible
-    with contextlib.suppress(ValueError):
-        filename = str(Path(filename).relative_to(agent.workspace.root))
-
-    log_entry = f"{operation}: {filename}"
-    if checksum is not None:
-        log_entry += f" #{checksum}"
-    logger.debug(f"Logging file operation: {log_entry}")
-    append_to_file(
-        agent.config.file_logger_path, f"{log_entry}\n", agent, should_log=False
-    )
+    log_entry = f"{operation}: {filename}\n"
+    append_to_file(CFG.file_logger_path, log_entry, should_log=False)
 
 
-@command(
-    "read_file",
-    "Read an existing file",
-    {
-        "filename": {
-            "type": "string",
-            "description": "The path of the file to read",
-            "required": True,
-        }
-    },
-)
-@sanitize_path_arg("filename")
-def read_file(filename: str, agent: Agent) -> str:
+def split_file(
+    content: str, max_length: int = 4000, overlap: int = 0
+) -> Generator[str, None, None]:
+    """
+    Split text into chunks of a specified maximum length with a specified overlap
+    between chunks.
+
+    :param content: The input text to be split into chunks
+    :param max_length: The maximum length of each chunk,
+        default is 4000 (about 1k token)
+    :param overlap: The number of overlapping characters between chunks,
+        default is no overlap
+    :return: A generator yielding chunks of text
+    """
+    start = 0
+    content_length = len(content)
+
+    while start < content_length:
+        end = start + max_length
+        if end + overlap < content_length:
+            chunk = content[start : end + overlap - 1]
+        else:
+            chunk = content[start:content_length]
+
+            # Account for the case where the last chunk is shorter than the overlap, so it has already been consumed
+            if len(chunk) <= overlap:
+                break
+
+        yield chunk
+        start += max_length - overlap
+
+
+@command("read_file", "Read file", '"filename": "<filename>"')
+def read_file(filename: str) -> str:
     """Read a file and return the contents
 
     Args:
@@ -148,63 +87,49 @@ def read_file(filename: str, agent: Agent) -> str:
         str: The contents of the file
     """
     try:
-        content = read_textual_file(filename, logger)
-
-        # TODO: invalidate/update memory when file is edited
-        file_memory = MemoryItem.from_text_file(content, filename, agent.config)
-        if len(file_memory.chunks) > 1:
-            return file_memory.summary
-
+        with open(filename, "r", encoding="utf-8") as f:
+            content = f.read()
         return content
     except Exception as e:
         return f"Error: {str(e)}"
 
 
 def ingest_file(
-    filename: str,
-    memory: VectorMemory,
+    filename: str, memory, max_length: int = 4000, overlap: int = 200
 ) -> None:
     """
     Ingest a file by reading its content, splitting it into chunks with a specified
     maximum length and overlap, and adding the chunks to the memory storage.
 
-    Args:
-        filename: The name of the file to ingest
-        memory: An object with an add() method to store the chunks in memory
+    :param filename: The name of the file to ingest
+    :param memory: An object with an add() method to store the chunks in memory
+    :param max_length: The maximum length of each chunk, default is 4000
+    :param overlap: The number of overlapping characters between chunks, default is 200
     """
     try:
-        logger.info(f"Ingesting file {filename}")
+        logger.info(f"Working with file {filename}")
         content = read_file(filename)
+        content_length = len(content)
+        logger.info(f"File length: {content_length} characters")
 
-        # TODO: differentiate between different types of files
-        file_memory = MemoryItem.from_text_file(content, filename)
-        logger.debug(f"Created memory: {file_memory.dump(True)}")
-        memory.add(file_memory)
+        chunks = list(split_file(content, max_length=max_length, overlap=overlap))
 
-        logger.info(f"Ingested {len(file_memory.e_chunks)} chunks from {filename}")
-    except Exception as err:
-        logger.warn(f"Error while ingesting file '{filename}': {err}")
+        num_chunks = len(chunks)
+        for i, chunk in enumerate(chunks):
+            logger.info(f"Ingesting chunk {i + 1} / {num_chunks} into memory")
+            memory_to_add = (
+                f"Filename: {filename}\n" f"Content part#{i + 1}/{num_chunks}: {chunk}"
+            )
+
+            memory.add(memory_to_add)
+
+        logger.info(f"Done ingesting {num_chunks} chunks from {filename}.")
+    except Exception as e:
+        logger.info(f"Error while ingesting file '{filename}': {str(e)}")
 
 
-@command(
-    "write_to_file",
-    "Writes to a file",
-    {
-        "filename": {
-            "type": "string",
-            "description": "The name of the file to write to",
-            "required": True,
-        },
-        "text": {
-            "type": "string",
-            "description": "The text to write to the file",
-            "required": True,
-        },
-    },
-    aliases=["write_file", "create_file"],
-)
-@sanitize_path_arg("filename")
-def write_to_file(filename: str, text: str, agent: Agent) -> str:
+@command("write_to_file", "Write to file", '"filename": "<filename>", "text": "<text>"')
+def write_to_file(filename: str, text: str) -> str:
     """Write text to a file
 
     Args:
@@ -214,40 +139,23 @@ def write_to_file(filename: str, text: str, agent: Agent) -> str:
     Returns:
         str: A message indicating success or failure
     """
-    checksum = text_checksum(text)
-    if is_duplicate_operation("write", filename, agent, checksum):
+    if check_duplicate_operation("write", filename):
         return "Error: File has already been updated."
     try:
         directory = os.path.dirname(filename)
         os.makedirs(directory, exist_ok=True)
         with open(filename, "w", encoding="utf-8") as f:
             f.write(text)
-        log_operation("write", filename, agent, checksum)
+        log_operation("write", filename)
         return "File written to successfully."
-    except Exception as err:
-        return f"Error: {err}"
+    except Exception as e:
+        return f"Error: {str(e)}"
 
 
 @command(
-    "append_to_file",
-    "Appends to a file",
-    {
-        "filename": {
-            "type": "string",
-            "description": "The name of the file to write to",
-            "required": True,
-        },
-        "text": {
-            "type": "string",
-            "description": "The text to write to the file",
-            "required": True,
-        },
-    },
+    "append_to_file", "Append to file", '"filename": "<filename>", "text": "<text>"'
 )
-@sanitize_path_arg("filename")
-def append_to_file(
-    filename: str, text: str, agent: Agent, should_log: bool = True
-) -> str:
+def append_to_file(filename: str, text: str, should_log: bool = True) -> str:
     """Append text to a file
 
     Args:
@@ -261,32 +169,19 @@ def append_to_file(
     try:
         directory = os.path.dirname(filename)
         os.makedirs(directory, exist_ok=True)
-        with open(filename, "a", encoding="utf-8") as f:
+        with open(filename, "a") as f:
             f.write(text)
 
         if should_log:
-            with open(filename, "r", encoding="utf-8") as f:
-                checksum = text_checksum(f.read())
-            log_operation("append", filename, agent, checksum=checksum)
+            log_operation("append", filename)
 
         return "Text appended successfully."
-    except Exception as err:
-        return f"Error: {err}"
+    except Exception as e:
+        return f"Error: {str(e)}"
 
 
-@command(
-    "delete_file",
-    "Deletes a file",
-    {
-        "filename": {
-            "type": "string",
-            "description": "The name of the file to delete",
-            "required": True,
-        }
-    },
-)
-@sanitize_path_arg("filename")
-def delete_file(filename: str, agent: Agent) -> str:
+@command("delete_file", "Delete file", '"filename": "<filename>"')
+def delete_file(filename: str) -> str:
     """Delete a file
 
     Args:
@@ -295,30 +190,19 @@ def delete_file(filename: str, agent: Agent) -> str:
     Returns:
         str: A message indicating success or failure
     """
-    if is_duplicate_operation("delete", filename, agent):
+    if check_duplicate_operation("delete", filename):
         return "Error: File has already been deleted."
     try:
         os.remove(filename)
-        log_operation("delete", filename, agent)
+        log_operation("delete", filename)
         return "File deleted successfully."
-    except Exception as err:
-        return f"Error: {err}"
+    except Exception as e:
+        return f"Error: {str(e)}"
 
 
-@command(
-    "list_files",
-    "Lists Files in a Directory",
-    {
-        "directory": {
-            "type": "string",
-            "description": "The directory to list files in",
-            "required": True,
-        }
-    },
-)
-@sanitize_path_arg("directory")
-def list_files(directory: str, agent: Agent) -> list[str]:
-    """lists files in a directory recursively
+@command("search_files", "Search Files", '"directory": "<directory>"')
+def search_files(directory: str) -> list[str]:
+    """Search for files in a directory
 
     Args:
         directory (str): The directory to search in
@@ -333,8 +217,56 @@ def list_files(directory: str, agent: Agent) -> list[str]:
             if file.startswith("."):
                 continue
             relative_path = os.path.relpath(
-                os.path.join(root, file), agent.config.workspace_path
+                os.path.join(root, file), CFG.workspace_path
             )
             found_files.append(relative_path)
 
     return found_files
+
+
+@command(
+    "download_file",
+    "Download File",
+    '"url": "<url>", "filename": "<filename>"',
+    CFG.allow_downloads,
+    "Error: You do not have user authorization to download files locally.",
+)
+def download_file(url, filename):
+    """Downloads a file
+    Args:
+        url (str): URL of the file to download
+        filename (str): Filename to save the file as
+    """
+    try:
+        directory = os.path.dirname(filename)
+        os.makedirs(directory, exist_ok=True)
+        message = f"{Fore.YELLOW}Downloading file from {Back.LIGHTBLUE_EX}{url}{Back.RESET}{Fore.RESET}"
+        with Spinner(message) as spinner:
+            session = requests.Session()
+            retry = Retry(total=3, backoff_factor=1, status_forcelist=[502, 503, 504])
+            adapter = HTTPAdapter(max_retries=retry)
+            session.mount("http://", adapter)
+            session.mount("https://", adapter)
+
+            total_size = 0
+            downloaded_size = 0
+
+            with session.get(url, allow_redirects=True, stream=True) as r:
+                r.raise_for_status()
+                total_size = int(r.headers.get("Content-Length", 0))
+                downloaded_size = 0
+
+                with open(filename, "wb") as f:
+                    for chunk in r.iter_content(chunk_size=8192):
+                        f.write(chunk)
+                        downloaded_size += len(chunk)
+
+                        # Update the progress message
+                        progress = f"{readable_file_size(downloaded_size)} / {readable_file_size(total_size)}"
+                        spinner.update_message(f"{message} {progress}")
+
+            return f'Successfully downloaded and locally stored file: "{filename}"! (Size: {readable_file_size(downloaded_size)})'
+    except requests.HTTPError as e:
+        return f"Got an HTTP Error whilst trying to download file: {e}"
+    except Exception as e:
+        return "Error: " + str(e)

autogpt/commands/file_operations_utils.py

@@ -1,161 +0,0 @@
-import json
-import os
-
-import charset_normalizer
-import docx
-import markdown
-import PyPDF2
-import yaml
-from bs4 import BeautifulSoup
-from pylatexenc.latex2text import LatexNodes2Text
-
-from autogpt import logs
-from autogpt.logs import logger
-
-
-class ParserStrategy:
-    def read(self, file_path: str) -> str:
-        raise NotImplementedError
-
-
-# Basic text file reading
-class TXTParser(ParserStrategy):
-    def read(self, file_path: str) -> str:
-        charset_match = charset_normalizer.from_path(file_path).best()
-        logger.debug(f"Reading '{file_path}' with encoding '{charset_match.encoding}'")
-        return str(charset_match)
-
-
-# Reading text from binary file using pdf parser
-class PDFParser(ParserStrategy):
-    def read(self, file_path: str) -> str:
-        parser = PyPDF2.PdfReader(file_path)
-        text = ""
-        for page_idx in range(len(parser.pages)):
-            text += parser.pages[page_idx].extract_text()
-        return text
-
-
-# Reading text from binary file using docs parser
-class DOCXParser(ParserStrategy):
-    def read(self, file_path: str) -> str:
-        doc_file = docx.Document(file_path)
-        text = ""
-        for para in doc_file.paragraphs:
-            text += para.text
-        return text
-
-
-# Reading as dictionary and returning string format
-class JSONParser(ParserStrategy):
-    def read(self, file_path: str) -> str:
-        with open(file_path, "r") as f:
-            data = json.load(f)
-            text = str(data)
-        return text
-
-
-class XMLParser(ParserStrategy):
-    def read(self, file_path: str) -> str:
-        with open(file_path, "r") as f:
-            soup = BeautifulSoup(f, "xml")
-            text = soup.get_text()
-        return text
-
-
-# Reading as dictionary and returning string format
-class YAMLParser(ParserStrategy):
-    def read(self, file_path: str) -> str:
-        with open(file_path, "r") as f:
-            data = yaml.load(f, Loader=yaml.FullLoader)
-            text = str(data)
-        return text
-
-
-class HTMLParser(ParserStrategy):
-    def read(self, file_path: str) -> str:
-        with open(file_path, "r") as f:
-            soup = BeautifulSoup(f, "html.parser")
-            text = soup.get_text()
-        return text
-
-
-class MarkdownParser(ParserStrategy):
-    def read(self, file_path: str) -> str:
-        with open(file_path, "r") as f:
-            html = markdown.markdown(f.read())
-            text = "".join(BeautifulSoup(html, "html.parser").findAll(string=True))
-        return text
-
-
-class LaTeXParser(ParserStrategy):
-    def read(self, file_path: str) -> str:
-        with open(file_path, "r") as f:
-            latex = f.read()
-        text = LatexNodes2Text().latex_to_text(latex)
-        return text
-
-
-class FileContext:
-    def __init__(self, parser: ParserStrategy, logger: logs.Logger):
-        self.parser = parser
-        self.logger = logger
-
-    def set_parser(self, parser: ParserStrategy) -> None:
-        self.logger.debug(f"Setting Context Parser to {parser}")
-        self.parser = parser
-
-    def read_file(self, file_path) -> str:
-        self.logger.debug(f"Reading file {file_path} with parser {self.parser}")
-        return self.parser.read(file_path)
-
-
-extension_to_parser = {
-    ".txt": TXTParser(),
-    ".csv": TXTParser(),
-    ".pdf": PDFParser(),
-    ".docx": DOCXParser(),
-    ".json": JSONParser(),
-    ".xml": XMLParser(),
-    ".yaml": YAMLParser(),
-    ".yml": YAMLParser(),
-    ".html": HTMLParser(),
-    ".htm": HTMLParser(),
-    ".xhtml": HTMLParser(),
-    ".md": MarkdownParser(),
-    ".markdown": MarkdownParser(),
-    ".tex": LaTeXParser(),
-}
-
-
-def is_file_binary_fn(file_path: str):
-    """Given a file path load all its content and checks if the null bytes is present
-
-    Args:
-        file_path (_type_): _description_
-
-    Returns:
-        bool: is_binary
-    """
-    with open(file_path, "rb") as f:
-        file_data = f.read()
-    if b"\x00" in file_data:
-        return True
-    return False
-
-
-def read_textual_file(file_path: str, logger: logs.Logger) -> str:
-    if not os.path.isfile(file_path):
-        raise FileNotFoundError(
-            f"read_file {file_path} failed: no such file or directory"
-        )
-    is_binary = is_file_binary_fn(file_path)
-    file_extension = os.path.splitext(file_path)[1].lower()
-    parser = extension_to_parser.get(file_extension)
-    if not parser:
-        if is_binary:
-            raise ValueError(f"Unsupported binary file format: {file_extension}")
-        # fallback to txt file parser (to support script and code files loading)
-        parser = TXTParser()
-    file_context = FileContext(parser, logger)
-    return file_context.read_file(file_path)

autogpt/commands/git_operations.py

@@ -1,35 +1,22 @@
 """Git operations for autogpt"""
-
 from git.repo import Repo
 
-from autogpt.agents.agent import Agent
-from autogpt.command_decorator import command
+from autogpt.commands.command import command
+from autogpt.config import Config
 from autogpt.url_utils.validators import validate_url
 
-from .decorators import sanitize_path_arg
+CFG = Config()
 
 
 @command(
     "clone_repository",
-    "Clones a Repository",
-    {
-        "url": {
-            "type": "string",
-            "description": "The URL of the repository to clone",
-            "required": True,
-        },
-        "clone_path": {
-            "type": "string",
-            "description": "The path to clone the repository to",
-            "required": True,
-        },
-    },
-    lambda config: bool(config.github_username and config.github_api_key),
+    "Clone Repository",
+    '"url": "<repository_url>", "clone_path": "<clone_path>"',
+    CFG.github_username and CFG.github_api_key,
     "Configure github_username and github_api_key.",
 )
-@sanitize_path_arg("clone_path")
 @validate_url
-def clone_repository(url: str, clone_path: str, agent: Agent) -> str:
+def clone_repository(url: str, clone_path: str) -> str:
     """Clone a GitHub repository locally.
 
     Args:
@@ -40,11 +27,7 @@ def clone_repository(url: str, clone_path: str, agent: Agent) -> str:
         str: The result of the clone operation.
     """
     split_url = url.split("//")
-    auth_repo_url = (
-        f"//{agent.config.github_username}:{agent.config.github_api_key}@".join(
-            split_url
-        )
-    )
+    auth_repo_url = f"//{CFG.github_username}:{CFG.github_api_key}@".join(split_url)
     try:
         Repo.clone_from(url=auth_repo_url, to_path=clone_path)
         return f"""Cloned {url} to {clone_path}"""

autogpt/commands/google_search.py

@@ -2,30 +2,17 @@
 from __future__ import annotations
 
 import json
-import time
-from itertools import islice
 
-from duckduckgo_search import DDGS
+from duckduckgo_search import ddg
 
-from autogpt.agents.agent import Agent
-from autogpt.command_decorator import command
+from autogpt.commands.command import command
+from autogpt.config import Config
 
-DUCKDUCKGO_MAX_ATTEMPTS = 3
+CFG = Config()
 
 
-@command(
-    "web_search",
-    "Searches the web",
-    {
-        "query": {
-            "type": "string",
-            "description": "The search query",
-            "required": True,
-        }
-    },
-    aliases=["search"],
-)
-def web_search(query: str, agent: Agent, num_results: int = 8) -> str:
+@command("google", "Google Search", '"query": "<query>"', not CFG.google_api_key)
+def google_search(query: str, num_results: int = 8) -> str:
     """Return the results of a Google search
 
     Args:
@@ -36,20 +23,15 @@ def web_search(query: str, agent: Agent, num_results: int = 8) -> str:
         str: The results of the search.
     """
     search_results = []
-    attempts = 0
+    if not query:
+        return json.dumps(search_results)
 
-    while attempts < DUCKDUCKGO_MAX_ATTEMPTS:
-        if not query:
-            return json.dumps(search_results)
+    results = ddg(query, max_results=num_results)
+    if not results:
+        return json.dumps(search_results)
 
-        results = DDGS().text(query)
-        search_results = list(islice(results, num_results))
-
-        if search_results:
-            break
-
-        time.sleep(1)
-        attempts += 1
+    for j in results:
+        search_results.append(j)
 
     results = json.dumps(search_results, ensure_ascii=False, indent=4)
     return safe_google_results(results)
@@ -58,19 +40,11 @@ def web_search(query: str, agent: Agent, num_results: int = 8) -> str:
 @command(
     "google",
     "Google Search",
-    {
-        "query": {
-            "type": "string",
-            "description": "The search query",
-            "required": True,
-        }
-    },
-    lambda config: bool(config.google_api_key)
-    and bool(config.google_custom_search_engine_id),
-    "Configure google_api_key and custom_search_engine_id.",
-    aliases=["search"],
+    '"query": "<query>"',
+    bool(CFG.google_api_key),
+    "Configure google_api_key.",
 )
-def google(query: str, agent: Agent, num_results: int = 8) -> str | list[str]:
+def google_official_search(query: str, num_results: int = 8) -> str | list[str]:
     """Return the results of a Google search using the official Google API
 
     Args:
@@ -86,8 +60,8 @@ def google(query: str, agent: Agent, num_results: int = 8) -> str | list[str]:
 
     try:
         # Get the Google API key and Custom Search Engine ID from the config file
-        api_key = agent.config.google_api_key
-        custom_search_engine_id = agent.config.google_custom_search_engine_id
+        api_key = CFG.google_api_key
+        custom_search_engine_id = CFG.custom_search_engine_id
 
         # Initialize the Custom Search API service
         service = build("customsearch", "v1", developerKey=api_key)
@@ -126,7 +100,7 @@ def google(query: str, agent: Agent, num_results: int = 8) -> str | list[str]:
 
 def safe_google_results(results: str | list) -> str:
     """
-        Return the results of a Google search in a safe format.
+        Return the results of a google search in a safe format.
 
     Args:
         results (str | list): The search results.
@@ -136,7 +110,7 @@ def safe_google_results(results: str | list) -> str:
     """
     if isinstance(results, list):
         safe_message = json.dumps(
-            [result.encode("utf-8", "ignore").decode("utf-8") for result in results]
+            [result.encode("utf-8", "ignore") for result in results]
         )
     else:
         safe_message = results.encode("utf-8", "ignore").decode("utf-8")

autogpt/commands/image_gen.py

@@ -1,7 +1,5 @@
 """ Image Generation Module for AutoGPT."""
 import io
-import json
-import time
 import uuid
 from base64 import b64decode
 
@@ -9,25 +7,15 @@ import openai
 import requests
 from PIL import Image
 
-from autogpt.agents.agent import Agent
-from autogpt.command_decorator import command
+from autogpt.commands.command import command
+from autogpt.config import Config
 from autogpt.logs import logger
 
+CFG = Config()
 
-@command(
-    "generate_image",
-    "Generates an Image",
-    {
-        "prompt": {
-            "type": "string",
-            "description": "The prompt used to generate the image",
-            "required": True,
-        },
-    },
-    lambda config: bool(config.image_provider),
-    "Requires a image provider to be set.",
-)
-def generate_image(prompt: str, agent: Agent, size: int = 256) -> str:
+
+@command("generate_image", "Generate Image", '"prompt": "<prompt>"', CFG.image_provider)
+def generate_image(prompt: str, size: int = 256) -> str:
     """Generate an image from a prompt.
 
     Args:
@@ -37,21 +25,21 @@ def generate_image(prompt: str, agent: Agent, size: int = 256) -> str:
     Returns:
         str: The filename of the image
     """
-    filename = f"{agent.config.workspace_path}/{str(uuid.uuid4())}.jpg"
+    filename = f"{CFG.workspace_path}/{str(uuid.uuid4())}.jpg"
 
     # DALL-E
-    if agent.config.image_provider == "dalle":
-        return generate_image_with_dalle(prompt, filename, size, agent)
+    if CFG.image_provider == "dalle":
+        return generate_image_with_dalle(prompt, filename, size)
     # HuggingFace
-    elif agent.config.image_provider == "huggingface":
-        return generate_image_with_hf(prompt, filename, agent)
+    elif CFG.image_provider == "huggingface":
+        return generate_image_with_hf(prompt, filename)
     # SD WebUI
-    elif agent.config.image_provider == "sdwebui":
-        return generate_image_with_sd_webui(prompt, filename, agent, size)
+    elif CFG.image_provider == "sdwebui":
+        return generate_image_with_sd_webui(prompt, filename, size)
     return "No Image Provider Set"
 
 
-def generate_image_with_hf(prompt: str, filename: str, agent: Agent) -> str:
+def generate_image_with_hf(prompt: str, filename: str) -> str:
     """Generate an image with HuggingFace's API.
 
     Args:
@@ -61,57 +49,35 @@ def generate_image_with_hf(prompt: str, filename: str, agent: Agent) -> str:
     Returns:
         str: The filename of the image
     """
-    API_URL = f"https://api-inference.huggingface.co/models/{agent.config.huggingface_image_model}"
-    if agent.config.huggingface_api_token is None:
+    API_URL = (
+        f"https://api-inference.huggingface.co/models/{CFG.huggingface_image_model}"
+    )
+    if CFG.huggingface_api_token is None:
         raise ValueError(
             "You need to set your Hugging Face API token in the config file."
         )
     headers = {
-        "Authorization": f"Bearer {agent.config.huggingface_api_token}",
+        "Authorization": f"Bearer {CFG.huggingface_api_token}",
         "X-Use-Cache": "false",
     }
 
-    retry_count = 0
-    while retry_count < 10:
-        response = requests.post(
-            API_URL,
-            headers=headers,
-            json={
-                "inputs": prompt,
-            },
-        )
+    response = requests.post(
+        API_URL,
+        headers=headers,
+        json={
+            "inputs": prompt,
+        },
+    )
 
-        if response.ok:
-            try:
-                image = Image.open(io.BytesIO(response.content))
-                logger.info(f"Image Generated for prompt:{prompt}")
-                image.save(filename)
-                return f"Saved to disk:{filename}"
-            except Exception as e:
-                logger.error(e)
-                break
-        else:
-            try:
-                error = json.loads(response.text)
-                if "estimated_time" in error:
-                    delay = error["estimated_time"]
-                    logger.debug(response.text)
-                    logger.info("Retrying in", delay)
-                    time.sleep(delay)
-                else:
-                    break
-            except Exception as e:
-                logger.error(e)
-                break
+    image = Image.open(io.BytesIO(response.content))
+    logger.info(f"Image Generated for prompt:{prompt}")
 
-        retry_count += 1
+    image.save(filename)
 
-    return f"Error creating image."
+    return f"Saved to disk:{filename}"
 
 
-def generate_image_with_dalle(
-    prompt: str, filename: str, size: int, agent: Agent
-) -> str:
+def generate_image_with_dalle(prompt: str, filename: str, size: int) -> str:
     """Generate an image with DALL-E.
 
     Args:
@@ -136,7 +102,7 @@ def generate_image_with_dalle(
         n=1,
         size=f"{size}x{size}",
         response_format="b64_json",
-        api_key=agent.config.openai_api_key,
+        api_key=CFG.openai_api_key,
     )
 
     logger.info(f"Image Generated for prompt:{prompt}")
@@ -152,7 +118,6 @@ def generate_image_with_dalle(
 def generate_image_with_sd_webui(
     prompt: str,
     filename: str,
-    agent: Agent,
     size: int = 512,
     negative_prompt: str = "",
     extra: dict = {},
@@ -169,19 +134,19 @@ def generate_image_with_sd_webui(
     """
     # Create a session and set the basic auth if needed
     s = requests.Session()
-    if agent.config.sd_webui_auth:
-        username, password = agent.config.sd_webui_auth.split(":")
+    if CFG.sd_webui_auth:
+        username, password = CFG.sd_webui_auth.split(":")
         s.auth = (username, password or "")
 
     # Generate the images
     response = requests.post(
-        f"{agent.config.sd_webui_url}/sdapi/v1/txt2img",
+        f"{CFG.sd_webui_url}/sdapi/v1/txt2img",
         json={
             "prompt": prompt,
             "negative_prompt": negative_prompt,
             "sampler_index": "DDIM",
             "steps": 20,
-            "config_scale": 7.0,
+            "cfg_scale": 7.0,
             "width": size,
             "height": size,
             "n_iter": 1,

autogpt/commands/improve_code.py

@@ -0,0 +1,35 @@
+from __future__ import annotations
+
+import json
+
+from autogpt.commands.command import command
+from autogpt.llm import call_ai_function
+
+
+@command(
+    "improve_code",
+    "Get Improved Code",
+    '"suggestions": "<list_of_suggestions>", "code": "<full_code_string>"',
+)
+def improve_code(suggestions: list[str], code: str) -> str:
+    """
+    A function that takes in code and suggestions and returns a response from create
+      chat completion api call.
+
+    Parameters:
+        suggestions (list): A list of suggestions around what needs to be improved.
+        code (str): Code to be improved.
+    Returns:
+        A result string from create chat completion. Improved code in response.
+    """
+
+    function_string = (
+        "def generate_improved_code(suggestions: list[str], code: str) -> str:"
+    )
+    args = [json.dumps(suggestions), code]
+    description_string = (
+        "Improves the provided code based on the suggestions"
+        " provided, making no other changes."
+    )
+
+    return call_ai_function(function_string, args, description_string)

autogpt/commands/task_statuses.py

@@ -1,33 +0,0 @@
-"""Task Statuses module."""
-from __future__ import annotations
-
-from typing import NoReturn
-
-from autogpt.agents.agent import Agent
-from autogpt.command_decorator import command
-from autogpt.logs import logger
-
-
-@command(
-    "goals_accomplished",
-    "Goals are accomplished and there is nothing left to do",
-    {
-        "reason": {
-            "type": "string",
-            "description": "A summary to the user of how the goals were accomplished",
-            "required": True,
-        }
-    },
-)
-def task_complete(reason: str, agent: Agent) -> NoReturn:
-    """
-    A function that takes in a string and exits the program
-
-    Parameters:
-        reason (str): A summary to the user of how the goals were accomplished.
-    Returns:
-        A result string from create chat completion. A list of suggestions to
-            improve the code.
-    """
-    logger.info(title="Shutting down...\n", message=reason)
-    quit()

autogpt/commands/twitter.py

@@ -0,0 +1,41 @@
+"""A module that contains a command to send a tweet."""
+import os
+
+import tweepy
+
+from autogpt.commands.command import command
+
+
+@command(
+    "send_tweet",
+    "Send Tweet",
+    '"tweet_text": "<tweet_text>"',
+)
+def send_tweet(tweet_text: str) -> str:
+    """
+      A function that takes in a string and returns a response from create chat
+        completion api call.
+
+    Args:
+      tweet_text (str): Text to be tweeted.
+
+      Returns:
+          A result from sending the tweet.
+    """
+    consumer_key = os.environ.get("TW_CONSUMER_KEY")
+    consumer_secret = os.environ.get("TW_CONSUMER_SECRET")
+    access_token = os.environ.get("TW_ACCESS_TOKEN")
+    access_token_secret = os.environ.get("TW_ACCESS_TOKEN_SECRET")
+    # Authenticate to Twitter
+    auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
+    auth.set_access_token(access_token, access_token_secret)
+
+    # Create API object
+    api = tweepy.API(auth)
+
+    # Send tweet
+    try:
+        api.update_status(tweet_text)
+        return "Tweet sent successfully!"
+    except tweepy.TweepyException as e:
+        return f"Error sending tweet: {e.reason}"

autogpt/commands/web_playwright.py

@@ -0,0 +1,82 @@
+"""Web scraping commands using Playwright"""
+from __future__ import annotations
+
+from autogpt.logs import logger
+
+try:
+    from playwright.sync_api import sync_playwright
+except ImportError:
+    logger.info(
+        "Playwright not installed. Please install it with 'pip install playwright' to use."
+    )
+from bs4 import BeautifulSoup
+
+from autogpt.processing.html import extract_hyperlinks, format_hyperlinks
+
+
+def scrape_text(url: str) -> str:
+    """Scrape text from a webpage
+
+    Args:
+        url (str): The URL to scrape text from
+
+    Returns:
+        str: The scraped text
+    """
+    with sync_playwright() as p:
+        browser = p.chromium.launch()
+        page = browser.new_page()
+
+        try:
+            page.goto(url)
+            html_content = page.content()
+            soup = BeautifulSoup(html_content, "html.parser")
+
+            for script in soup(["script", "style"]):
+                script.extract()
+
+            text = soup.get_text()
+            lines = (line.strip() for line in text.splitlines())
+            chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
+            text = "\n".join(chunk for chunk in chunks if chunk)
+
+        except Exception as e:
+            text = f"Error: {str(e)}"
+
+        finally:
+            browser.close()
+
+    return text
+
+
+def scrape_links(url: str) -> str | list[str]:
+    """Scrape links from a webpage
+
+    Args:
+        url (str): The URL to scrape links from
+
+    Returns:
+        Union[str, List[str]]: The scraped links
+    """
+    with sync_playwright() as p:
+        browser = p.chromium.launch()
+        page = browser.new_page()
+
+        try:
+            page.goto(url)
+            html_content = page.content()
+            soup = BeautifulSoup(html_content, "html.parser")
+
+            for script in soup(["script", "style"]):
+                script.extract()
+
+            hyperlinks = extract_hyperlinks(soup, url)
+            formatted_links = format_hyperlinks(hyperlinks)
+
+        except Exception as e:
+            formatted_links = f"Error: {str(e)}"
+
+        finally:
+            browser.close()
+
+    return formatted_links

autogpt/commands/web_requests.py

@@ -0,0 +1,112 @@
+"""Browse a webpage and summarize it using the LLM model"""
+from __future__ import annotations
+
+import requests
+from bs4 import BeautifulSoup
+from requests import Response
+
+from autogpt.config import Config
+from autogpt.processing.html import extract_hyperlinks, format_hyperlinks
+from autogpt.url_utils.validators import validate_url
+
+CFG = Config()
+
+session = requests.Session()
+session.headers.update({"User-Agent": CFG.user_agent})
+
+
+@validate_url
+def get_response(
+    url: str, timeout: int = 10
+) -> tuple[None, str] | tuple[Response, None]:
+    """Get the response from a URL
+
+    Args:
+        url (str): The URL to get the response from
+        timeout (int): The timeout for the HTTP request
+
+    Returns:
+        tuple[None, str] | tuple[Response, None]: The response and error message
+
+    Raises:
+        ValueError: If the URL is invalid
+        requests.exceptions.RequestException: If the HTTP request fails
+    """
+    try:
+        response = session.get(url, timeout=timeout)
+
+        # Check if the response contains an HTTP error
+        if response.status_code >= 400:
+            return None, f"Error: HTTP {str(response.status_code)} error"
+
+        return response, None
+    except ValueError as ve:
+        # Handle invalid URL format
+        return None, f"Error: {str(ve)}"
+
+    except requests.exceptions.RequestException as re:
+        # Handle exceptions related to the HTTP request
+        #  (e.g., connection errors, timeouts, etc.)
+        return None, f"Error: {str(re)}"
+
+
+def scrape_text(url: str) -> str:
+    """Scrape text from a webpage
+
+    Args:
+        url (str): The URL to scrape text from
+
+    Returns:
+        str: The scraped text
+    """
+    response, error_message = get_response(url)
+    if error_message:
+        return error_message
+    if not response:
+        return "Error: Could not get response"
+
+    soup = BeautifulSoup(response.text, "html.parser")
+
+    for script in soup(["script", "style"]):
+        script.extract()
+
+    text = soup.get_text()
+    lines = (line.strip() for line in text.splitlines())
+    chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
+    text = "\n".join(chunk for chunk in chunks if chunk)
+
+    return text
+
+
+def scrape_links(url: str) -> str | list[str]:
+    """Scrape links from a webpage
+
+    Args:
+        url (str): The URL to scrape links from
+
+    Returns:
+       str | list[str]: The scraped links
+    """
+    response, error_message = get_response(url)
+    if error_message:
+        return error_message
+    if not response:
+        return "Error: Could not get response"
+    soup = BeautifulSoup(response.text, "html.parser")
+
+    for script in soup(["script", "style"]):
+        script.extract()
+
+    hyperlinks = extract_hyperlinks(soup, url)
+
+    return format_hyperlinks(hyperlinks)
+
+
+def create_message(chunk, question):
+    """Create a message for the user to summarize a chunk of text"""
+    return {
+        "role": "user",
+        "content": f'"""{chunk}""" Using the above text, answer the following'
+        f' question: "{question}" -- if the question cannot be answered using the'
+        " text, summarize the text.",
+    }

autogpt/commands/web_selenium.py

@@ -4,55 +4,37 @@ from __future__ import annotations
 import logging
 from pathlib import Path
 from sys import platform
-from typing import Optional, Type
 
 from bs4 import BeautifulSoup
+from selenium import webdriver
 from selenium.common.exceptions import WebDriverException
 from selenium.webdriver.chrome.options import Options as ChromeOptions
-from selenium.webdriver.chrome.service import Service as ChromeDriverService
-from selenium.webdriver.chrome.webdriver import WebDriver as ChromeDriver
 from selenium.webdriver.common.by import By
-from selenium.webdriver.edge.options import Options as EdgeOptions
-from selenium.webdriver.edge.service import Service as EdgeDriverService
-from selenium.webdriver.edge.webdriver import WebDriver as EdgeDriver
 from selenium.webdriver.firefox.options import Options as FirefoxOptions
-from selenium.webdriver.firefox.service import Service as GeckoDriverService
-from selenium.webdriver.firefox.webdriver import WebDriver as FirefoxDriver
 from selenium.webdriver.remote.webdriver import WebDriver
 from selenium.webdriver.safari.options import Options as SafariOptions
-from selenium.webdriver.safari.webdriver import WebDriver as SafariDriver
 from selenium.webdriver.support import expected_conditions as EC
 from selenium.webdriver.support.wait import WebDriverWait
 from webdriver_manager.chrome import ChromeDriverManager
 from webdriver_manager.firefox import GeckoDriverManager
-from webdriver_manager.microsoft import EdgeChromiumDriverManager as EdgeDriverManager
 
-from autogpt.agents.agent import Agent
-from autogpt.command_decorator import command
-from autogpt.logs import logger
-from autogpt.memory.vector import MemoryItem, get_memory
+import autogpt.processing.text as summary
+from autogpt.commands.command import command
+from autogpt.config import Config
 from autogpt.processing.html import extract_hyperlinks, format_hyperlinks
 from autogpt.url_utils.validators import validate_url
 
-BrowserOptions = ChromeOptions | EdgeOptions | FirefoxOptions | SafariOptions
-
 FILE_DIR = Path(__file__).parent.parent
+CFG = Config()
 
 
 @command(
     "browse_website",
-    "Browses a Website",
-    {
-        "url": {"type": "string", "description": "The URL to visit", "required": True},
-        "question": {
-            "type": "string",
-            "description": "What you want to find on the website",
-            "required": True,
-        },
-    },
+    "Browse Website",
+    '"url": "<url>", "question": "<what_you_want_to_find_on_website>"',
 )
 @validate_url
-def browse_website(url: str, question: str, agent: Agent) -> str:
+def browse_website(url: str, question: str) -> tuple[str, WebDriver]:
     """Browse a website and return the answer and links to the user
 
     Args:
@@ -63,25 +45,25 @@ def browse_website(url: str, question: str, agent: Agent) -> str:
         Tuple[str, WebDriver]: The answer and links to the user and the webdriver
     """
     try:
-        driver, text = scrape_text_with_selenium(url, agent)
+        driver, text = scrape_text_with_selenium(url)
     except WebDriverException as e:
         # These errors are often quite long and include lots of context.
         # Just grab the first line.
         msg = e.msg.split("\n")[0]
-        return f"Error: {msg}"
+        return f"Error: {msg}", None
 
     add_header(driver)
-    summary = summarize_memorize_webpage(url, text, question, agent, driver)
+    summary_text = summary.summarize_text(url, text, question, driver)
     links = scrape_links_with_selenium(driver, url)
 
     # Limit links to 5
     if len(links) > 5:
         links = links[:5]
     close_browser(driver)
-    return f"Answer gathered from website: {summary}\n\nLinks: {links}"
+    return f"Answer gathered from website: {summary_text} \n \n Links: {links}", driver
 
 
-def scrape_text_with_selenium(url: str, agent: Agent) -> tuple[WebDriver, str]:
+def scrape_text_with_selenium(url: str) -> tuple[WebDriver, str]:
     """Scrape text from a website using selenium
 
     Args:
@@ -92,49 +74,44 @@ def scrape_text_with_selenium(url: str, agent: Agent) -> tuple[WebDriver, str]:
     """
     logging.getLogger("selenium").setLevel(logging.CRITICAL)
 
-    options_available: dict[str, Type[BrowserOptions]] = {
+    options_available = {
         "chrome": ChromeOptions,
-        "edge": EdgeOptions,
-        "firefox": FirefoxOptions,
         "safari": SafariOptions,
+        "firefox": FirefoxOptions,
     }
 
-    options: BrowserOptions = options_available[agent.config.selenium_web_browser]()
+    options = options_available[CFG.selenium_web_browser]()
     options.add_argument(
         "user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.5615.49 Safari/537.36"
     )
 
-    if agent.config.selenium_web_browser == "firefox":
-        if agent.config.selenium_headless:
+    if CFG.selenium_web_browser == "firefox":
+        if CFG.selenium_headless:
             options.headless = True
             options.add_argument("--disable-gpu")
-        driver = FirefoxDriver(
-            service=GeckoDriverService(GeckoDriverManager().install()), options=options
+        driver = webdriver.Firefox(
+            executable_path=GeckoDriverManager().install(), options=options
         )
-    elif agent.config.selenium_web_browser == "edge":
-        driver = EdgeDriver(
-            service=EdgeDriverService(EdgeDriverManager().install()), options=options
-        )
-    elif agent.config.selenium_web_browser == "safari":
+    elif CFG.selenium_web_browser == "safari":
         # Requires a bit more setup on the users end
         # See https://developer.apple.com/documentation/webkit/testing_with_webdriver_in_safari
-        driver = SafariDriver(options=options)
+        driver = webdriver.Safari(options=options)
     else:
         if platform == "linux" or platform == "linux2":
             options.add_argument("--disable-dev-shm-usage")
             options.add_argument("--remote-debugging-port=9222")
 
         options.add_argument("--no-sandbox")
-        if agent.config.selenium_headless:
+        if CFG.selenium_headless:
             options.add_argument("--headless=new")
             options.add_argument("--disable-gpu")
 
         chromium_driver_path = Path("/usr/bin/chromedriver")
 
-        driver = ChromeDriver(
-            service=ChromeDriverService(str(chromium_driver_path))
+        driver = webdriver.Chrome(
+            executable_path=chromium_driver_path
             if chromium_driver_path.exists()
-            else ChromeDriverService(ChromeDriverManager().install()),
+            else ChromeDriverManager().install(),
             options=options,
         )
     driver.get(url)
@@ -198,40 +175,4 @@ def add_header(driver: WebDriver) -> None:
     Returns:
         None
     """
-    try:
-        with open(f"{FILE_DIR}/js/overlay.js", "r") as overlay_file:
-            overlay_script = overlay_file.read()
-        driver.execute_script(overlay_script)
-    except Exception as e:
-        print(f"Error executing overlay.js: {e}")
-
-
-def summarize_memorize_webpage(
-    url: str,
-    text: str,
-    question: str,
-    agent: Agent,
-    driver: Optional[WebDriver] = None,
-) -> str:
-    """Summarize text using the OpenAI API
-
-    Args:
-        url (str): The url of the text
-        text (str): The text to summarize
-        question (str): The question to ask the model
-        driver (WebDriver): The webdriver to use to scroll the page
-
-    Returns:
-        str: The summary of the text
-    """
-    if not text:
-        return "Error: No text to summarize"
-
-    text_length = len(text)
-    logger.info(f"Text length: {text_length} characters")
-
-    memory = get_memory(agent.config)
-
-    new_memory = MemoryItem.from_webpage(text, url, agent.config, question=question)
-    memory.add(new_memory)
-    return new_memory.summary
+    driver.execute_script(open(f"{FILE_DIR}/js/overlay.js", "r").read())

autogpt/commands/write_tests.py

@@ -0,0 +1,37 @@
+"""A module that contains a function to generate test cases for the submitted code."""
+from __future__ import annotations
+
+import json
+
+from autogpt.commands.command import command
+from autogpt.llm import call_ai_function
+
+
+@command(
+    "write_tests",
+    "Write Tests",
+    '"code": "<full_code_string>", "focus": "<list_of_focus_areas>"',
+)
+def write_tests(code: str, focus: list[str]) -> str:
+    """
+    A function that takes in code and focus topics and returns a response from create
+      chat completion api call.
+
+    Parameters:
+        focus (list): A list of suggestions around what needs to be improved.
+        code (str): Code for test cases to be generated against.
+    Returns:
+        A result string from create chat completion. Test cases for the submitted code
+          in response.
+    """
+
+    function_string = (
+        "def create_test_cases(code: str, focus: Optional[str] = None) -> str:"
+    )
+    args = [code, json.dumps(focus)]
+    description_string = (
+        "Generates test cases for the existing code, focusing on"
+        " specific areas if required."
+    )
+
+    return call_ai_function(function_string, args, description_string)

autogpt/config/__init__.py

@@ -1,12 +1,11 @@
 """
 This module contains the configuration classes for AutoGPT.
 """
-from .ai_config import AIConfig
-from .config import Config, ConfigBuilder, check_openai_api_key
+from autogpt.config.ai_config import AIConfig
+from autogpt.config.config import Config, check_openai_api_key
 
 __all__ = [
     "check_openai_api_key",
     "AIConfig",
     "Config",
-    "ConfigBuilder",
 ]

autogpt/config/ai_config.py

@@ -7,14 +7,12 @@ from __future__ import annotations
 import os
 import platform
 from pathlib import Path
-from typing import TYPE_CHECKING, Optional
+from typing import Any, Optional, Type
 
 import distro
 import yaml
 
-if TYPE_CHECKING:
-    from autogpt.models.command_registry import CommandRegistry
-    from autogpt.prompts.generator import PromptGenerator
+from autogpt.prompts.generator import PromptGenerator
 
 # Soon this will go in a folder where it remembers more stuff about the run(s)
 SAVE_FILE = str(Path(os.getcwd()) / "ai_settings.yaml")
@@ -35,7 +33,7 @@ class AIConfig:
         self,
         ai_name: str = "",
         ai_role: str = "",
-        ai_goals: list[str] = [],
+        ai_goals: list | None = None,
         api_budget: float = 0.0,
     ) -> None:
         """
@@ -49,30 +47,33 @@ class AIConfig:
         Returns:
             None
         """
+        if ai_goals is None:
+            ai_goals = []
         self.ai_name = ai_name
         self.ai_role = ai_role
         self.ai_goals = ai_goals
         self.api_budget = api_budget
-        self.prompt_generator: PromptGenerator | None = None
-        self.command_registry: CommandRegistry | None = None
+        self.prompt_generator = None
+        self.command_registry = None
 
     @staticmethod
-    def load(ai_settings_file: str = SAVE_FILE) -> "AIConfig":
+    def load(config_file: str = SAVE_FILE) -> "AIConfig":
         """
-        Returns class object with parameters (ai_name, ai_role, ai_goals, api_budget)
-        loaded from yaml file if yaml file exists, else returns class with no parameters.
+        Returns class object with parameters (ai_name, ai_role, ai_goals, api_budget) loaded from
+          yaml file if yaml file exists,
+        else returns class with no parameters.
 
         Parameters:
-            ai_settings_file (int): The path to the config yaml file.
-              DEFAULT: "../ai_settings.yaml"
+           config_file (int): The path to the config yaml file.
+             DEFAULT: "../ai_settings.yaml"
 
         Returns:
             cls (object): An instance of given cls object
         """
 
         try:
-            with open(ai_settings_file, encoding="utf-8") as file:
-                config_params = yaml.load(file, Loader=yaml.FullLoader) or {}
+            with open(config_file, encoding="utf-8") as file:
+                config_params = yaml.load(file, Loader=yaml.FullLoader)
         except FileNotFoundError:
             config_params = {}
 
@@ -88,12 +89,12 @@ class AIConfig:
         # type: Type[AIConfig]
         return AIConfig(ai_name, ai_role, ai_goals, api_budget)
 
-    def save(self, ai_settings_file: str = SAVE_FILE) -> None:
+    def save(self, config_file: str = SAVE_FILE) -> None:
         """
         Saves the class parameters to the specified file yaml file path as a yaml file.
 
         Parameters:
-            ai_settings_file(str): The path to the config yaml file.
+            config_file(str): The path to the config yaml file.
               DEFAULT: "../ai_settings.yaml"
 
         Returns:
@@ -106,11 +107,11 @@ class AIConfig:
             "ai_goals": self.ai_goals,
             "api_budget": self.api_budget,
         }
-        with open(ai_settings_file, "w", encoding="utf-8") as file:
+        with open(config_file, "w", encoding="utf-8") as file:
             yaml.dump(config, file, allow_unicode=True)
 
     def construct_full_prompt(
-        self, config, prompt_generator: Optional[PromptGenerator] = None
+        self, prompt_generator: Optional[PromptGenerator] = None
     ) -> str:
         """
         Returns a prompt to the user with the class information in an organized fashion.
@@ -130,20 +131,22 @@ class AIConfig:
             ""
         )
 
+        from autogpt.config import Config
         from autogpt.prompts.prompt import build_default_prompt_generator
 
+        cfg = Config()
         if prompt_generator is None:
-            prompt_generator = build_default_prompt_generator(config)
+            prompt_generator = build_default_prompt_generator()
         prompt_generator.goals = self.ai_goals
         prompt_generator.name = self.ai_name
         prompt_generator.role = self.ai_role
         prompt_generator.command_registry = self.command_registry
-        for plugin in config.plugins:
+        for plugin in cfg.plugins:
             if not plugin.can_handle_post_prompt():
                 continue
             prompt_generator = plugin.post_prompt(prompt_generator)
 
-        if config.execute_local_commands:
+        if cfg.execute_local_commands:
             # add OS info to prompt
             os_name = platform.system()
             os_info = (
@@ -161,5 +164,5 @@ class AIConfig:
         if self.api_budget > 0.0:
             full_prompt += f"\nIt takes money to let you run. Your API budget is ${self.api_budget:.3f}"
         self.prompt_generator = prompt_generator
-        full_prompt += f"\n\n{prompt_generator.generate_prompt_string(config)}"
+        full_prompt += f"\n\n{prompt_generator.generate_prompt_string()}"
         return full_prompt

autogpt/config/config.py

@@ -1,332 +1,174 @@
 """Configuration class to store the state of bools for different scripts access."""
-from __future__ import annotations
-
-import contextlib
 import os
-import re
-from typing import Any, Dict, Optional, Union
+from typing import List
 
+import openai
 import yaml
 from auto_gpt_plugin_template import AutoGPTPluginTemplate
 from colorama import Fore
-from pydantic import Field, validator
 
-from autogpt.core.configuration.schema import Configurable, SystemSettings
-from autogpt.plugins.plugins_config import PluginsConfig
-
-AZURE_CONFIG_FILE = os.path.join(os.path.dirname(__file__), "../..", "azure.yaml")
-PLUGINS_CONFIG_FILE = os.path.join(
-    os.path.dirname(__file__), "../..", "plugins_config.yaml"
-)
-GPT_4_MODEL = "gpt-4"
-GPT_3_MODEL = "gpt-3.5-turbo"
+from autogpt.singleton import Singleton
 
 
-class Config(SystemSettings, arbitrary_types_allowed=True):
-    name: str = "Auto-GPT configuration"
-    description: str = "Default configuration for the Auto-GPT application."
-    ########################
-    # Application Settings #
-    ########################
-    skip_news: bool = False
-    skip_reprompt: bool = False
-    authorise_key: str = "y"
-    exit_key: str = "n"
-    debug_mode: bool = False
-    plain_output: bool = False
-    chat_messages_enabled: bool = True
-    # TTS configuration
-    speak_mode: bool = False
-    text_to_speech_provider: str = "gtts"
-    streamelements_voice: str = "Brian"
-    elevenlabs_voice_id: Optional[str] = None
+class Config(metaclass=Singleton):
+    """
+    Configuration class to store the state of bools for different scripts access.
+    """
 
-    ##########################
-    # Agent Control Settings #
-    ##########################
-    # Paths
-    ai_settings_file: str = "ai_settings.yaml"
-    prompt_settings_file: str = "prompt_settings.yaml"
-    workspace_path: Optional[str] = None
-    file_logger_path: Optional[str] = None
-    # Model configuration
-    fast_llm: str = "gpt-3.5-turbo"
-    smart_llm: str = "gpt-4"
-    temperature: float = 0
-    openai_functions: bool = False
-    embedding_model: str = "text-embedding-ada-002"
-    browse_spacy_language_model: str = "en_core_web_sm"
-    # Run loop configuration
-    continuous_mode: bool = False
-    continuous_limit: int = 0
-
-    ##########
-    # Memory #
-    ##########
-    memory_backend: str = "json_file"
-    memory_index: str = "auto-gpt-memory"
-    redis_host: str = "localhost"
-    redis_port: int = 6379
-    redis_password: str = ""
-    wipe_redis_on_start: bool = True
-
-    ############
-    # Commands #
-    ############
-    # General
-    disabled_command_categories: list[str] = Field(default_factory=list)
-    # File ops
-    restrict_to_workspace: bool = True
-    allow_downloads: bool = False
-    # Shell commands
-    shell_command_control: str = "denylist"
-    execute_local_commands: bool = False
-    shell_denylist: list[str] = Field(default_factory=lambda: ["sudo", "su"])
-    shell_allowlist: list[str] = Field(default_factory=list)
-    # Text to image
-    image_provider: Optional[str] = None
-    huggingface_image_model: str = "CompVis/stable-diffusion-v1-4"
-    sd_webui_url: Optional[str] = "http://localhost:7860"
-    image_size: int = 256
-    # Audio to text
-    audio_to_text_provider: str = "huggingface"
-    huggingface_audio_to_text_model: Optional[str] = None
-    # Web browsing
-    selenium_web_browser: str = "chrome"
-    selenium_headless: bool = True
-    user_agent: 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"
-
-    ###################
-    # Plugin Settings #
-    ###################
-    plugins_dir: str = "plugins"
-    plugins_config_file: str = PLUGINS_CONFIG_FILE
-    plugins_config: PluginsConfig = Field(
-        default_factory=lambda: PluginsConfig(plugins={})
-    )
-    plugins: list[AutoGPTPluginTemplate] = Field(default_factory=list, exclude=True)
-    plugins_allowlist: list[str] = Field(default_factory=list)
-    plugins_denylist: list[str] = Field(default_factory=list)
-    plugins_openai: list[str] = Field(default_factory=list)
-
-    ###############
-    # Credentials #
-    ###############
-    # OpenAI
-    openai_api_key: Optional[str] = None
-    openai_api_type: Optional[str] = None
-    openai_api_base: Optional[str] = None
-    openai_api_version: Optional[str] = None
-    openai_organization: Optional[str] = None
-    use_azure: bool = False
-    azure_config_file: Optional[str] = AZURE_CONFIG_FILE
-    azure_model_to_deployment_id_map: Optional[Dict[str, str]] = None
-    # Elevenlabs
-    elevenlabs_api_key: Optional[str] = None
-    # Github
-    github_api_key: Optional[str] = None
-    github_username: Optional[str] = None
-    # Google
-    google_api_key: Optional[str] = None
-    google_custom_search_engine_id: Optional[str] = None
-    # Huggingface
-    huggingface_api_token: Optional[str] = None
-    # Stable Diffusion
-    sd_webui_auth: Optional[str] = None
-
-    @validator("plugins", each_item=True)
-    def validate_plugins(cls, p: AutoGPTPluginTemplate | Any):
-        assert issubclass(
-            p.__class__, AutoGPTPluginTemplate
-        ), f"{p} does not subclass AutoGPTPluginTemplate"
-        assert (
-            p.__class__.__name__ != "AutoGPTPluginTemplate"
-        ), f"Plugins must subclass AutoGPTPluginTemplate; {p} is a template instance"
-        return p
-
-    def get_openai_credentials(self, model: str) -> dict[str, str]:
-        credentials = {
-            "api_key": self.openai_api_key,
-            "api_base": self.openai_api_base,
-            "organization": self.openai_organization,
-        }
-        if self.use_azure:
-            azure_credentials = self.get_azure_credentials(model)
-            credentials.update(azure_credentials)
-        return credentials
-
-    def get_azure_credentials(self, model: str) -> dict[str, str]:
-        """Get the kwargs for the Azure API."""
-
-        # Fix --gpt3only and --gpt4only in combination with Azure
-        fast_llm = (
-            self.fast_llm
-            if not (
-                self.fast_llm == self.smart_llm
-                and self.fast_llm.startswith(GPT_4_MODEL)
-            )
-            else f"not_{self.fast_llm}"
-        )
-        smart_llm = (
-            self.smart_llm
-            if not (
-                self.smart_llm == self.fast_llm
-                and self.smart_llm.startswith(GPT_3_MODEL)
-            )
-            else f"not_{self.smart_llm}"
-        )
-
-        deployment_id = {
-            fast_llm: self.azure_model_to_deployment_id_map.get(
-                "fast_llm_deployment_id",
-                self.azure_model_to_deployment_id_map.get(
-                    "fast_llm_model_deployment_id"  # backwards compatibility
-                ),
-            ),
-            smart_llm: self.azure_model_to_deployment_id_map.get(
-                "smart_llm_deployment_id",
-                self.azure_model_to_deployment_id_map.get(
-                    "smart_llm_model_deployment_id"  # backwards compatibility
-                ),
-            ),
-            self.embedding_model: self.azure_model_to_deployment_id_map.get(
-                "embedding_model_deployment_id"
-            ),
-        }.get(model, None)
-
-        kwargs = {
-            "api_type": self.openai_api_type,
-            "api_base": self.openai_api_base,
-            "api_version": self.openai_api_version,
-        }
-        if model == self.embedding_model:
-            kwargs["engine"] = deployment_id
-        else:
-            kwargs["deployment_id"] = deployment_id
-        return kwargs
-
-
-class ConfigBuilder(Configurable[Config]):
-    default_settings = Config()
-
-    @classmethod
-    def build_config_from_env(cls) -> Config:
+    def __init__(self) -> None:
         """Initialize the Config class"""
-        config_dict = {
-            "authorise_key": os.getenv("AUTHORISE_COMMAND_KEY"),
-            "exit_key": os.getenv("EXIT_KEY"),
-            "plain_output": os.getenv("PLAIN_OUTPUT", "False") == "True",
-            "shell_command_control": os.getenv("SHELL_COMMAND_CONTROL"),
-            "ai_settings_file": os.getenv("AI_SETTINGS_FILE"),
-            "prompt_settings_file": os.getenv("PROMPT_SETTINGS_FILE"),
-            "fast_llm": os.getenv("FAST_LLM", os.getenv("FAST_LLM_MODEL")),
-            "smart_llm": os.getenv("SMART_LLM", os.getenv("SMART_LLM_MODEL")),
-            "embedding_model": os.getenv("EMBEDDING_MODEL"),
-            "browse_spacy_language_model": os.getenv("BROWSE_SPACY_LANGUAGE_MODEL"),
-            "openai_api_key": os.getenv("OPENAI_API_KEY"),
-            "use_azure": os.getenv("USE_AZURE") == "True",
-            "azure_config_file": os.getenv("AZURE_CONFIG_FILE", AZURE_CONFIG_FILE),
-            "execute_local_commands": os.getenv("EXECUTE_LOCAL_COMMANDS", "False")
-            == "True",
-            "restrict_to_workspace": os.getenv("RESTRICT_TO_WORKSPACE", "True")
-            == "True",
-            "openai_functions": os.getenv("OPENAI_FUNCTIONS", "False") == "True",
-            "elevenlabs_api_key": os.getenv("ELEVENLABS_API_KEY"),
-            "streamelements_voice": os.getenv("STREAMELEMENTS_VOICE"),
-            "text_to_speech_provider": os.getenv("TEXT_TO_SPEECH_PROVIDER"),
-            "github_api_key": os.getenv("GITHUB_API_KEY"),
-            "github_username": os.getenv("GITHUB_USERNAME"),
-            "google_api_key": os.getenv("GOOGLE_API_KEY"),
-            "image_provider": os.getenv("IMAGE_PROVIDER"),
-            "huggingface_api_token": os.getenv("HUGGINGFACE_API_TOKEN"),
-            "huggingface_image_model": os.getenv("HUGGINGFACE_IMAGE_MODEL"),
-            "audio_to_text_provider": os.getenv("AUDIO_TO_TEXT_PROVIDER"),
-            "huggingface_audio_to_text_model": os.getenv(
-                "HUGGINGFACE_AUDIO_TO_TEXT_MODEL"
-            ),
-            "sd_webui_url": os.getenv("SD_WEBUI_URL"),
-            "sd_webui_auth": os.getenv("SD_WEBUI_AUTH"),
-            "selenium_web_browser": os.getenv("USE_WEB_BROWSER"),
-            "selenium_headless": os.getenv("HEADLESS_BROWSER", "True") == "True",
-            "user_agent": os.getenv("USER_AGENT"),
-            "memory_backend": os.getenv("MEMORY_BACKEND"),
-            "memory_index": os.getenv("MEMORY_INDEX"),
-            "redis_host": os.getenv("REDIS_HOST"),
-            "redis_password": os.getenv("REDIS_PASSWORD"),
-            "wipe_redis_on_start": os.getenv("WIPE_REDIS_ON_START", "True") == "True",
-            "plugins_dir": os.getenv("PLUGINS_DIR"),
-            "plugins_config_file": os.getenv("PLUGINS_CONFIG_FILE"),
-            "chat_messages_enabled": os.getenv("CHAT_MESSAGES_ENABLED") == "True",
-        }
+        self.workspace_path = None
+        self.file_logger_path = None
 
-        config_dict["disabled_command_categories"] = _safe_split(
-            os.getenv("DISABLED_COMMAND_CATEGORIES")
+        self.debug_mode = False
+        self.continuous_mode = False
+        self.continuous_limit = 0
+        self.speak_mode = False
+        self.skip_reprompt = False
+        self.allow_downloads = False
+        self.skip_news = False
+
+        self.authorise_key = os.getenv("AUTHORISE_COMMAND_KEY", "y")
+        self.exit_key = os.getenv("EXIT_KEY", "n")
+        self.ai_settings_file = os.getenv("AI_SETTINGS_FILE", "ai_settings.yaml")
+        self.fast_llm_model = os.getenv("FAST_LLM_MODEL", "gpt-3.5-turbo")
+        self.smart_llm_model = os.getenv("SMART_LLM_MODEL", "gpt-4")
+        self.fast_token_limit = int(os.getenv("FAST_TOKEN_LIMIT", 4000))
+        self.smart_token_limit = int(os.getenv("SMART_TOKEN_LIMIT", 8000))
+        self.browse_chunk_max_length = int(os.getenv("BROWSE_CHUNK_MAX_LENGTH", 3000))
+        self.browse_spacy_language_model = os.getenv(
+            "BROWSE_SPACY_LANGUAGE_MODEL", "en_core_web_sm"
         )
 
-        config_dict["shell_denylist"] = _safe_split(
-            os.getenv("SHELL_DENYLIST", os.getenv("DENY_COMMANDS"))
+        self.openai_api_key = os.getenv("OPENAI_API_KEY")
+        self.temperature = float(os.getenv("TEMPERATURE", "0"))
+        self.use_azure = os.getenv("USE_AZURE") == "True"
+        self.execute_local_commands = (
+            os.getenv("EXECUTE_LOCAL_COMMANDS", "False") == "True"
         )
-        config_dict["shell_allowlist"] = _safe_split(
-            os.getenv("SHELL_ALLOWLIST", os.getenv("ALLOW_COMMANDS"))
+        self.restrict_to_workspace = (
+            os.getenv("RESTRICT_TO_WORKSPACE", "True") == "True"
         )
 
-        config_dict["google_custom_search_engine_id"] = os.getenv(
-            "GOOGLE_CUSTOM_SEARCH_ENGINE_ID", os.getenv("CUSTOM_SEARCH_ENGINE_ID")
+        if self.use_azure:
+            self.load_azure_config()
+            openai.api_type = self.openai_api_type
+            openai.api_base = self.openai_api_base
+            openai.api_version = self.openai_api_version
+
+        self.elevenlabs_api_key = os.getenv("ELEVENLABS_API_KEY")
+        self.elevenlabs_voice_1_id = os.getenv("ELEVENLABS_VOICE_1_ID")
+        self.elevenlabs_voice_2_id = os.getenv("ELEVENLABS_VOICE_2_ID")
+
+        self.use_mac_os_tts = False
+        self.use_mac_os_tts = os.getenv("USE_MAC_OS_TTS")
+
+        self.chat_messages_enabled = os.getenv("CHAT_MESSAGES_ENABLED") == "True"
+
+        self.use_brian_tts = False
+        self.use_brian_tts = os.getenv("USE_BRIAN_TTS")
+
+        self.github_api_key = os.getenv("GITHUB_API_KEY")
+        self.github_username = os.getenv("GITHUB_USERNAME")
+
+        self.google_api_key = os.getenv("GOOGLE_API_KEY")
+        self.custom_search_engine_id = os.getenv("CUSTOM_SEARCH_ENGINE_ID")
+
+        self.pinecone_api_key = os.getenv("PINECONE_API_KEY")
+        self.pinecone_region = os.getenv("PINECONE_ENV")
+
+        self.weaviate_host = os.getenv("WEAVIATE_HOST")
+        self.weaviate_port = os.getenv("WEAVIATE_PORT")
+        self.weaviate_protocol = os.getenv("WEAVIATE_PROTOCOL", "http")
+        self.weaviate_username = os.getenv("WEAVIATE_USERNAME", None)
+        self.weaviate_password = os.getenv("WEAVIATE_PASSWORD", None)
+        self.weaviate_scopes = os.getenv("WEAVIATE_SCOPES", None)
+        self.weaviate_embedded_path = os.getenv("WEAVIATE_EMBEDDED_PATH")
+        self.weaviate_api_key = os.getenv("WEAVIATE_API_KEY", None)
+        self.use_weaviate_embedded = (
+            os.getenv("USE_WEAVIATE_EMBEDDED", "False") == "True"
         )
 
-        config_dict["elevenlabs_voice_id"] = os.getenv(
-            "ELEVENLABS_VOICE_ID", os.getenv("ELEVENLABS_VOICE_1_ID")
+        # milvus or zilliz cloud configuration.
+        self.milvus_addr = os.getenv("MILVUS_ADDR", "localhost:19530")
+        self.milvus_username = os.getenv("MILVUS_USERNAME")
+        self.milvus_password = os.getenv("MILVUS_PASSWORD")
+        self.milvus_collection = os.getenv("MILVUS_COLLECTION", "autogpt")
+        self.milvus_secure = os.getenv("MILVUS_SECURE") == "True"
+
+        self.image_provider = os.getenv("IMAGE_PROVIDER")
+        self.image_size = int(os.getenv("IMAGE_SIZE", 256))
+        self.huggingface_api_token = os.getenv("HUGGINGFACE_API_TOKEN")
+        self.huggingface_image_model = os.getenv(
+            "HUGGINGFACE_IMAGE_MODEL", "CompVis/stable-diffusion-v1-4"
         )
-        elevenlabs_api_key = os.getenv("ELEVENLABS_API_KEY")
-        if os.getenv("USE_MAC_OS_TTS"):
-            default_tts_provider = "macos"
-        elif elevenlabs_api_key:
-            default_tts_provider = "elevenlabs"
-        elif os.getenv("USE_BRIAN_TTS"):
-            default_tts_provider = "streamelements"
+        self.huggingface_audio_to_text_model = os.getenv(
+            "HUGGINGFACE_AUDIO_TO_TEXT_MODEL"
+        )
+        self.sd_webui_url = os.getenv("SD_WEBUI_URL", "http://localhost:7860")
+        self.sd_webui_auth = os.getenv("SD_WEBUI_AUTH")
+
+        # Selenium browser settings
+        self.selenium_web_browser = os.getenv("USE_WEB_BROWSER", "chrome")
+        self.selenium_headless = os.getenv("HEADLESS_BROWSER", "True") == "True"
+
+        # User agent header to use when making HTTP requests
+        # Some websites might just completely deny request with an error code if
+        # no user agent was found.
+        self.user_agent = os.getenv(
+            "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",
+        )
+
+        self.redis_host = os.getenv("REDIS_HOST", "localhost")
+        self.redis_port = os.getenv("REDIS_PORT", "6379")
+        self.redis_password = os.getenv("REDIS_PASSWORD", "")
+        self.wipe_redis_on_start = os.getenv("WIPE_REDIS_ON_START", "True") == "True"
+        self.memory_index = os.getenv("MEMORY_INDEX", "auto-gpt")
+        # Note that indexes must be created on db 0 in redis, this is not configurable.
+
+        self.memory_backend = os.getenv("MEMORY_BACKEND", "local")
+
+        self.plugins_dir = os.getenv("PLUGINS_DIR", "plugins")
+        self.plugins: List[AutoGPTPluginTemplate] = []
+        self.plugins_openai = []
+
+        plugins_allowlist = os.getenv("ALLOWLISTED_PLUGINS")
+        if plugins_allowlist:
+            self.plugins_allowlist = plugins_allowlist.split(",")
         else:
-            default_tts_provider = "gtts"
-        config_dict["text_to_speech_provider"] = default_tts_provider
+            self.plugins_allowlist = []
+        self.plugins_denylist = []
 
-        config_dict["plugins_allowlist"] = _safe_split(os.getenv("ALLOWLISTED_PLUGINS"))
-        config_dict["plugins_denylist"] = _safe_split(os.getenv("DENYLISTED_PLUGINS"))
+    def get_azure_deployment_id_for_model(self, model: str) -> str:
+        """
+        Returns the relevant deployment id for the model specified.
 
-        with contextlib.suppress(TypeError):
-            config_dict["image_size"] = int(os.getenv("IMAGE_SIZE"))
-        with contextlib.suppress(TypeError):
-            config_dict["redis_port"] = int(os.getenv("REDIS_PORT"))
-        with contextlib.suppress(TypeError):
-            config_dict["temperature"] = float(os.getenv("TEMPERATURE"))
+        Parameters:
+            model(str): The model to map to the deployment id.
 
-        if config_dict["use_azure"]:
-            azure_config = cls.load_azure_config(config_dict["azure_config_file"])
-            config_dict.update(azure_config)
+        Returns:
+            The matching deployment id if found, otherwise an empty string.
+        """
+        if model == self.fast_llm_model:
+            return self.azure_model_to_deployment_id_map[
+                "fast_llm_model_deployment_id"
+            ]  # type: ignore
+        elif model == self.smart_llm_model:
+            return self.azure_model_to_deployment_id_map[
+                "smart_llm_model_deployment_id"
+            ]  # type: ignore
+        elif model == "text-embedding-ada-002":
+            return self.azure_model_to_deployment_id_map[
+                "embedding_model_deployment_id"
+            ]  # type: ignore
+        else:
+            return ""
 
-        elif os.getenv("OPENAI_API_BASE_URL"):
-            config_dict["openai_api_base"] = os.getenv("OPENAI_API_BASE_URL")
+    AZURE_CONFIG_FILE = os.path.join(os.path.dirname(__file__), "../..", "azure.yaml")
 
-        openai_organization = os.getenv("OPENAI_ORGANIZATION")
-        if openai_organization is not None:
-            config_dict["openai_organization"] = openai_organization
-
-        config_dict_without_none_values = {
-            k: v for k, v in config_dict.items() if v is not None
-        }
-
-        config = cls.build_agent_configuration(config_dict_without_none_values)
-
-        # Set secondary config variables (that depend on other config variables)
-
-        config.plugins_config = PluginsConfig.load_config(
-            config.plugins_config_file,
-            config.plugins_denylist,
-            config.plugins_allowlist,
-        )
-
-        return config
-
-    @classmethod
-    def load_azure_config(cls, config_file: str = AZURE_CONFIG_FILE) -> Dict[str, str]:
+    def load_azure_config(self, config_file: str = AZURE_CONFIG_FILE) -> None:
         """
         Loads the configuration parameters for Azure hosting from the specified file
           path as a yaml file.
@@ -335,55 +177,106 @@ class ConfigBuilder(Configurable[Config]):
             config_file(str): The path to the config yaml file. DEFAULT: "../azure.yaml"
 
         Returns:
-            Dict
+            None
         """
         with open(config_file) as file:
-            config_params = yaml.load(file, Loader=yaml.FullLoader) or {}
+            config_params = yaml.load(file, Loader=yaml.FullLoader)
+        self.openai_api_type = config_params.get("azure_api_type") or "azure"
+        self.openai_api_base = config_params.get("azure_api_base") or ""
+        self.openai_api_version = (
+            config_params.get("azure_api_version") or "2023-03-15-preview"
+        )
+        self.azure_model_to_deployment_id_map = config_params.get("azure_model_map", {})
 
-        return {
-            "openai_api_type": config_params.get("azure_api_type", "azure"),
-            "openai_api_base": config_params.get("azure_api_base", ""),
-            "openai_api_version": config_params.get(
-                "azure_api_version", "2023-03-15-preview"
-            ),
-            "azure_model_to_deployment_id_map": config_params.get(
-                "azure_model_map", {}
-            ),
-        }
+    def set_continuous_mode(self, value: bool) -> None:
+        """Set the continuous mode value."""
+        self.continuous_mode = value
+
+    def set_continuous_limit(self, value: int) -> None:
+        """Set the continuous limit value."""
+        self.continuous_limit = value
+
+    def set_speak_mode(self, value: bool) -> None:
+        """Set the speak mode value."""
+        self.speak_mode = value
+
+    def set_fast_llm_model(self, value: str) -> None:
+        """Set the fast LLM model value."""
+        self.fast_llm_model = value
+
+    def set_smart_llm_model(self, value: str) -> None:
+        """Set the smart LLM model value."""
+        self.smart_llm_model = value
+
+    def set_fast_token_limit(self, value: int) -> None:
+        """Set the fast token limit value."""
+        self.fast_token_limit = value
+
+    def set_smart_token_limit(self, value: int) -> None:
+        """Set the smart token limit value."""
+        self.smart_token_limit = value
+
+    def set_browse_chunk_max_length(self, value: int) -> None:
+        """Set the browse_website command chunk max length value."""
+        self.browse_chunk_max_length = value
+
+    def set_openai_api_key(self, value: str) -> None:
+        """Set the OpenAI API key value."""
+        self.openai_api_key = value
+
+    def set_elevenlabs_api_key(self, value: str) -> None:
+        """Set the ElevenLabs API key value."""
+        self.elevenlabs_api_key = value
+
+    def set_elevenlabs_voice_1_id(self, value: str) -> None:
+        """Set the ElevenLabs Voice 1 ID value."""
+        self.elevenlabs_voice_1_id = value
+
+    def set_elevenlabs_voice_2_id(self, value: str) -> None:
+        """Set the ElevenLabs Voice 2 ID value."""
+        self.elevenlabs_voice_2_id = value
+
+    def set_google_api_key(self, value: str) -> None:
+        """Set the Google API key value."""
+        self.google_api_key = value
+
+    def set_custom_search_engine_id(self, value: str) -> None:
+        """Set the custom search engine id value."""
+        self.custom_search_engine_id = value
+
+    def set_pinecone_api_key(self, value: str) -> None:
+        """Set the Pinecone API key value."""
+        self.pinecone_api_key = value
+
+    def set_pinecone_region(self, value: str) -> None:
+        """Set the Pinecone region value."""
+        self.pinecone_region = value
+
+    def set_debug_mode(self, value: bool) -> None:
+        """Set the debug mode value."""
+        self.debug_mode = value
+
+    def set_plugins(self, value: list) -> None:
+        """Set the plugins value."""
+        self.plugins = value
+
+    def set_temperature(self, value: int) -> None:
+        """Set the temperature value."""
+        self.temperature = value
+
+    def set_memory_backend(self, name: str) -> None:
+        """Set the memory backend name."""
+        self.memory_backend = name
 
 
-def check_openai_api_key(config: Config) -> None:
+def check_openai_api_key() -> None:
     """Check if the OpenAI API key is set in config.py or as an environment variable."""
-    if not config.openai_api_key:
+    cfg = Config()
+    if not cfg.openai_api_key:
         print(
             Fore.RED
             + "Please set your OpenAI API key in .env or as an environment variable."
             + Fore.RESET
         )
         print("You can get your key from https://platform.openai.com/account/api-keys")
-        openai_api_key = input(
-            "If you do have the key, please enter your OpenAI API key now:\n"
-        )
-        key_pattern = r"^sk-\w{48}"
-        openai_api_key = openai_api_key.strip()
-        if re.search(key_pattern, openai_api_key):
-            os.environ["OPENAI_API_KEY"] = openai_api_key
-            config.openai_api_key = openai_api_key
-            print(
-                Fore.GREEN
-                + "OpenAI API key successfully set!\n"
-                + Fore.YELLOW
-                + "NOTE: The API key you've set is only temporary.\n"
-                + "For longer sessions, please set it in .env file"
-                + Fore.RESET
-            )
-        else:
-            print("Invalid OpenAI API key!")
-            exit(1)
-
-
-def _safe_split(s: Union[str, None], sep: str = ",") -> list[str]:
-    """Split a string by a separator. Return an empty list if the string is None."""
-    if s is None:
-        return []
-    return s.split(sep)
+        exit(1)

autogpt/config/prompt_config.py

@@ -1,47 +0,0 @@
-# sourcery skip: do-not-use-staticmethod
-"""
-A module that contains the PromptConfig class object that contains the configuration
-"""
-import yaml
-from colorama import Fore
-
-from autogpt import utils
-from autogpt.logs import logger
-
-
-class PromptConfig:
-    """
-    A class object that contains the configuration information for the prompt, which will be used by the prompt generator
-
-    Attributes:
-        constraints (list): Constraints list for the prompt generator.
-        resources (list): Resources list for the prompt generator.
-        performance_evaluations (list): Performance evaluation list for the prompt generator.
-    """
-
-    def __init__(self, prompt_settings_file: str) -> None:
-        """
-        Initialize a class instance with parameters (constraints, resources, performance_evaluations) loaded from
-          yaml file if yaml file exists,
-        else raises error.
-
-        Parameters:
-            constraints (list): Constraints list for the prompt generator.
-            resources (list): Resources list for the prompt generator.
-            performance_evaluations (list): Performance evaluation list for the prompt generator.
-        Returns:
-            None
-        """
-        # Validate file
-        (validated, message) = utils.validate_yaml_file(prompt_settings_file)
-        if not validated:
-            logger.typewriter_log("FAILED FILE VALIDATION", Fore.RED, message)
-            logger.double_check()
-            exit(1)
-
-        with open(prompt_settings_file, encoding="utf-8") as file:
-            config_params = yaml.load(file, Loader=yaml.FullLoader)
-
-        self.constraints = config_params.get("constraints", [])
-        self.resources = config_params.get("resources", [])
-        self.performance_evaluations = config_params.get("performance_evaluations", [])

autogpt/configurator.py

@@ -1,27 +1,19 @@
 """Configurator module."""
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
 import click
 from colorama import Back, Fore, Style
 
 from autogpt import utils
-from autogpt.config.config import GPT_3_MODEL, GPT_4_MODEL
-from autogpt.llm.utils import check_model
+from autogpt.config import Config
 from autogpt.logs import logger
-from autogpt.memory.vector import get_supported_memory_backends
+from autogpt.memory import get_supported_memory_backends
 
-if TYPE_CHECKING:
-    from autogpt.config import Config
+CFG = Config()
 
 
 def create_config(
-    config: Config,
     continuous: bool,
     continuous_limit: int,
     ai_settings_file: str,
-    prompt_settings_file: str,
     skip_reprompt: bool,
     speak: bool,
     debug: bool,
@@ -38,7 +30,6 @@ def create_config(
         continuous (bool): Whether to run in continuous mode
         continuous_limit (int): The number of times to run in continuous mode
         ai_settings_file (str): The path to the ai_settings.yaml file
-        prompt_settings_file (str): The path to the prompt_settings.yaml file
         skip_reprompt (bool): Whether to skip the re-prompting messages at the beginning of the script
         speak (bool): Whether to enable speak mode
         debug (bool): Whether to enable debug mode
@@ -49,13 +40,13 @@ def create_config(
         allow_downloads (bool): Whether to allow Auto-GPT to download files natively
         skips_news (bool): Whether to suppress the output of latest news on startup
     """
-    config.debug_mode = False
-    config.continuous_mode = False
-    config.speak_mode = False
+    CFG.set_debug_mode(False)
+    CFG.set_continuous_mode(False)
+    CFG.set_speak_mode(False)
 
     if debug:
         logger.typewriter_log("Debug Mode: ", Fore.GREEN, "ENABLED")
-        config.debug_mode = True
+        CFG.set_debug_mode(True)
 
     if continuous:
         logger.typewriter_log("Continuous Mode: ", Fore.RED, "ENABLED")
@@ -66,13 +57,13 @@ def create_config(
             " cause your AI to run forever or carry out actions you would not usually"
             " authorise. Use at your own risk.",
         )
-        config.continuous_mode = True
+        CFG.set_continuous_mode(True)
 
         if continuous_limit:
             logger.typewriter_log(
                 "Continuous Limit: ", Fore.GREEN, f"{continuous_limit}"
             )
-            config.continuous_limit = continuous_limit
+            CFG.set_continuous_limit(continuous_limit)
 
     # Check if continuous limit is used without continuous mode
     if continuous_limit and not continuous:
@@ -80,26 +71,15 @@ def create_config(
 
     if speak:
         logger.typewriter_log("Speak Mode: ", Fore.GREEN, "ENABLED")
-        config.speak_mode = True
+        CFG.set_speak_mode(True)
 
-    # Set the default LLM models
     if gpt3only:
         logger.typewriter_log("GPT3.5 Only Mode: ", Fore.GREEN, "ENABLED")
-        # --gpt3only should always use gpt-3.5-turbo, despite user's FAST_LLM config
-        config.fast_llm = GPT_3_MODEL
-        config.smart_llm = GPT_3_MODEL
-    elif (
-        gpt4only
-        and check_model(GPT_4_MODEL, model_type="smart_llm", config=config)
-        == GPT_4_MODEL
-    ):
+        CFG.set_smart_llm_model(CFG.fast_llm_model)
+
+    if gpt4only:
         logger.typewriter_log("GPT4 Only Mode: ", Fore.GREEN, "ENABLED")
-        # --gpt4only should always use gpt-4, despite user's SMART_LLM config
-        config.fast_llm = GPT_4_MODEL
-        config.smart_llm = GPT_4_MODEL
-    else:
-        config.fast_llm = check_model(config.fast_llm, "fast_llm", config=config)
-        config.smart_llm = check_model(config.smart_llm, "smart_llm", config=config)
+        CFG.set_fast_llm_model(CFG.smart_llm_model)
 
     if memory_type:
         supported_memory = get_supported_memory_backends()
@@ -110,13 +90,13 @@ def create_config(
                 Fore.RED,
                 f"{supported_memory}",
             )
-            logger.typewriter_log("Defaulting to: ", Fore.YELLOW, config.memory_backend)
+            logger.typewriter_log("Defaulting to: ", Fore.YELLOW, CFG.memory_backend)
         else:
-            config.memory_backend = chosen
+            CFG.memory_backend = chosen
 
     if skip_reprompt:
         logger.typewriter_log("Skip Re-prompt: ", Fore.GREEN, "ENABLED")
-        config.skip_reprompt = True
+        CFG.skip_reprompt = True
 
     if ai_settings_file:
         file = ai_settings_file
@@ -129,24 +109,11 @@ def create_config(
             exit(1)
 
         logger.typewriter_log("Using AI Settings File:", Fore.GREEN, file)
-        config.ai_settings_file = file
-        config.skip_reprompt = True
-
-    if prompt_settings_file:
-        file = prompt_settings_file
-
-        # Validate file
-        (validated, message) = utils.validate_yaml_file(file)
-        if not validated:
-            logger.typewriter_log("FAILED FILE VALIDATION", Fore.RED, message)
-            logger.double_check()
-            exit(1)
-
-        logger.typewriter_log("Using Prompt Settings File:", Fore.GREEN, file)
-        config.prompt_settings_file = file
+        CFG.ai_settings_file = file
+        CFG.skip_reprompt = True
 
     if browser_name:
-        config.selenium_web_browser = browser_name
+        CFG.selenium_web_browser = browser_name
 
     if allow_downloads:
         logger.typewriter_log("Native Downloading:", Fore.GREEN, "ENABLED")
@@ -161,7 +128,7 @@ def create_config(
             Fore.YELLOW,
             f"{Back.RED + Style.BRIGHT}ALWAYS REMEMBER TO NEVER OPEN FILES YOU AREN'T SURE OF!{Style.RESET_ALL}",
         )
-        config.allow_downloads = True
+        CFG.allow_downloads = True
 
     if skip_news:
-        config.skip_news = True
+        CFG.skip_news = True

autogpt/core/ARCHITECTURE_NOTES.md

@@ -1,272 +0,0 @@
-# Re-architecture Notes
-
-## Key Documents
-
-- [Planned Agent Workflow](https://whimsical.com/agent-workflow-v2-NmnTQ8R7sVo7M3S43XgXmZ)
-- [Original Architecture Diagram](https://www.figma.com/file/fwdj44tPR7ArYtnGGUKknw/Modular-Architecture?type=whiteboard&node-id=0-1) - This is sadly well out of date at this point.
-- [Kanban](https://github.com/orgs/Significant-Gravitas/projects/1/views/1?filterQuery=label%3Are-arch)
-
-## The Motivation
-
-The `master` branch of Auto-GPT is an organically grown amalgamation of many thoughts 
-and ideas about agent-driven autonomous systems.  It lacks clear abstraction boundaries, 
-has issues of global state and poorly encapsulated state, and is generally just hard to 
-make effective changes to.  Mainly it's just a system that's hard to make changes to.  
-And research in the field is moving fast, so we want to be able to try new ideas 
-quickly.  
-
-## Initial Planning
-
-A large group of maintainers and contributors met do discuss the architectural 
-challenges associated with the existing codebase. Many much-desired features (building 
-new user interfaces, enabling project-specific agents, enabling multi-agent systems) 
-are bottlenecked by the global state in the system. We discussed the tradeoffs between 
-an incremental system transition and a big breaking version change and decided to go 
-for the breaking version change. We justified this by saying:
-
-- We can maintain, in essence, the same user experience as now even with a radical 
-  restructuring of the codebase
-- Our developer audience is struggling to use the existing codebase to build 
-  applications and libraries of their own, so this breaking change will largely be 
-  welcome.
-
-## Primary Goals
-
-- Separate the AutoGPT application code from the library code.
-- Remove global state from the system
-- Allow for multiple agents per user (with facilities for running simultaneously)
-- Create a serializable representation of an Agent
-- Encapsulate the core systems in abstractions with clear boundaries.
-
-## Secondary goals
-
-- Use existing tools to ditch any unneccesary cruft in the codebase (document loading, 
-  json parsing, anything easier to replace than to port).
-- Bring in the [core agent loop updates](https://whimsical.com/agent-workflow-v2-NmnTQ8R7sVo7M3S43XgXmZ)
-  being developed simultaneously by @Pwuts 
-
-# The Agent Subsystems
-
-## Configuration
-
-We want a lot of things from a configuration system. We lean heavily on it in the 
-`master` branch to allow several parts of the system to communicate with each other.  
-[Recent work](https://github.com/Significant-Gravitas/Auto-GPT/pull/4737) has made it 
-so that the config is no longer a singleton object that is materialized from the import 
-state, but it's still treated as a 
-[god object](https://en.wikipedia.org/wiki/God_object) containing all information about
-the system and _critically_ allowing any system to reference configuration information 
-about other parts of the system.  
-
-### What we want
-
-- It should still be reasonable to collate the entire system configuration in a 
-  sensible way.
-- The configuration should be validatable and validated.
-- The system configuration should be a _serializable_ representation of an `Agent`.
-- The configuration system should provide a clear (albeit very low-level) contract 
-  about user-configurable aspects of the system.
-- The configuration should reasonably manage default values and user-provided overrides.
-- The configuration system needs to handle credentials in a reasonable way.
-- The configuration should be the representation of some amount of system state, like 
-  api budgets and resource usage.  These aspects are recorded in the configuration and 
-  updated by the system itself.
-- Agent systems should have encapsulated views of the configuration.  E.g. the memory 
-  system should know about memory configuration but nothing about command configuration.
-
-## Workspace
-
-There are two ways to think about the workspace:
-
-- The workspace is a scratch space for an agent where it can store files, write code, 
-  and do pretty much whatever else it likes.
-- The workspace is, at any given point in time, the single source of truth for what an 
-  agent is.  It contains the serializable state (the configuration) as well as all 
-  other working state (stored files, databases, memories, custom code).  
-
-In the existing system there is **one** workspace.  And because the workspace holds so 
-much agent state, that means a user can only work with one agent at a time.
-
-## Memory
-
-The memory system has been under extremely active development. 
-See [#3536](https://github.com/Significant-Gravitas/Auto-GPT/issues/3536) and 
-[#4208](https://github.com/Significant-Gravitas/Auto-GPT/pull/4208) for discussion and 
-work in the `master` branch.  The TL;DR is 
-that we noticed a couple of months ago that the `Agent` performed **worse** with 
-permanent memory than without it.  Since then the knowledge storage and retrieval 
-system has been [redesigned](https://whimsical.com/memory-system-8Ae6x6QkjDwQAUe9eVJ6w1) 
-and partially implemented in the `master` branch.
-
-## Planning/Prompt-Engineering
-
-The planning system is the system that translates user desires/agent intentions into
-language model prompts.  In the course of development, it has become pretty clear 
-that `Planning` is the wrong name for this system
-
-### What we want
-
-- It should be incredibly obvious what's being passed to a language model, when it's
-  being passed, and what the language model response is. The landscape of language 
-  model research is developing very rapidly, so building complex abstractions between 
-  users/contributors and the language model interactions is going to make it very 
-  difficult for us to nimbly respond to new research developments.
-- Prompt-engineering should ideally be exposed in a parameterizeable way to users. 
-- We should, where possible, leverage OpenAI's new  
-  [function calling api](https://openai.com/blog/function-calling-and-other-api-updates) 
-  to get outputs in a standard machine-readable format and avoid the deep pit of 
-  parsing json (and fixing unparsable json).
-
-### Planning Strategies
-
-The [new agent workflow](https://whimsical.com/agent-workflow-v2-NmnTQ8R7sVo7M3S43XgXmZ) 
-has many, many interaction points for language models.  We really would like to not 
-distribute prompt templates and raw strings all through the system. The re-arch solution 
-is to encapsulate language model interactions into planning strategies. 
-These strategies are defined by 
-
-- The `LanguageModelClassification` they use (`FAST` or `SMART`)
-- A function `build_prompt` that takes strategy specific arguments and constructs a 
-  `LanguageModelPrompt` (a simple container for lists of messages and functions to
-  pass to the language model)
-- A function `parse_content` that parses the response content (a dict) into a better 
-  formatted dict.  Contracts here are intentionally loose and will tighten once we have 
-  at least one other language model provider.
-
-## Resources
-
-Resources are kinds of services we consume from external APIs.  They may have associated 
-credentials and costs we need to manage.  Management of those credentials is implemented 
-as manipulation of the resource configuration.  We have two categories of resources 
-currently
-
-- AI/ML model providers (including language model providers and embedding model providers, ie OpenAI)
-- Memory providers (e.g. Pinecone, Weaviate, ChromaDB, etc.)
-
-### What we want
-
-- Resource abstractions should provide a common interface to different service providers 
-  for a particular kind of service.  
-- Resource abstractions should manipulate the configuration to manage their credentials 
-  and budget/accounting.
-- Resource abstractions should be composable over an API (e.g. I should be able to make 
-  an OpenAI provider that is both a LanguageModelProvider and an EmbeddingModelProvider
-  and use it wherever I need those services).
-
-## Abilities
-
-Along with planning and memory usage, abilities are one of the major augmentations of 
-augmented language models.  They allow us to expand the scope of what language models
-can do by hooking them up to code they can execute to obtain new knowledge or influence
-the world.  
-
-### What we want
-
-- Abilities should have an extremely clear interface that users can write to.
-- Abilities should have an extremely clear interface that a language model can 
-  understand
-- Abilities should be declarative about their dependencies so the system can inject them
-- Abilities should be executable (where sensible) in an async run loop.
-- Abilities should be not have side effects unless those side effects are clear in 
-  their representation to an agent (e.g. the BrowseWeb ability shouldn't write a file,
-  but the WriteFile ability can).
-
-## Plugins
-
-Users want to add lots of features that we don't want to support as first-party. 
-Or solution to this is a plugin system to allow users to plug in their functionality or
-to construct their agent from a public plugin marketplace.  Our primary concern in the
-re-arch is to build a stateless plugin service interface and a simple implementation 
-that can load plugins from installed packages or from zip files.  Future efforts will 
-expand this system to allow plugins to load from a marketplace or some other kind 
-of service.
-
-### What is a Plugin
-
-Plugins are a kind of garbage term.  They refer to a number of things.
-
-- New commands for the agent to execute.  This is the most common usage.
-- Replacements for entire subsystems like memory or language model providers
-- Application plugins that do things like send emails or communicate via whatsapp
-- The repositories contributors create that may themselves have multiple plugins in them.
-
-### Usage in the existing system
-
-The current plugin system is _hook-based_.  This means plugins don't correspond to 
-kinds of objects in the system, but rather to times in the system at which we defer 
-execution to them.  The main advantage of this setup is that user code can hijack 
-pretty much any behavior of the agent by injecting code that supercedes the normal 
-agent execution.  The disadvantages to this approach are numerous:
-
-- We have absolutely no mechanisms to enforce any security measures because the threat 
-  surface is everything.
-- We cannot reason about agent behavior in a cohesive way because control flow can be
-  ceded to user code at pretty much any point and arbitrarily change or break the
-  agent behavior
-- The interface for designing a plugin is kind of terrible and difficult to standardize
-- The hook based implementation means we couple ourselves to a particular flow of 
-  control (or otherwise risk breaking plugin behavior).  E.g. many of the hook targets
-  in the [old workflow](https://whimsical.com/agent-workflow-VAzeKcup3SR7awpNZJKTyK) 
-  are not present or mean something entirely different in the 
-  [new workflow](https://whimsical.com/agent-workflow-v2-NmnTQ8R7sVo7M3S43XgXmZ).
-- Etc.
-
-### What we want
-
-- A concrete definition of a plugin that is narrow enough in scope that we can define 
-  it well and reason about how it will work in the system.
-- A set of abstractions that let us define a plugin by its storage format and location 
-- A service interface that knows how to parse the plugin abstractions and turn them 
-  into concrete classes and objects.
-
-
-## Some Notes on how and why we'll use OO in this project
-
-First and foremost, Python itself is an object-oriented language. It's 
-underlying [data model](https://docs.python.org/3/reference/datamodel.html) is built 
-with object-oriented programming in mind. It offers useful tools like abstract base 
-classes to communicate interfaces to developers who want to, e.g., write plugins, or 
-help work on implementations. If we were working in a different language that offered 
-different tools, we'd use a different paradigm.
-
-While many things are classes in the re-arch, they are not classes in the same way. 
-There are three kinds of things (roughly) that are written as classes in the re-arch:
-1.  **Configuration**:  Auto-GPT has *a lot* of configuration.  This configuration 
-    is *data* and we use **[Pydantic](https://docs.pydantic.dev/latest/)** to manage it as 
-    pydantic is basically industry standard for this stuff. It provides runtime validation 
-    for all the configuration and allows us to easily serialize configuration to both basic 
-    python types (dicts, lists, and primatives) as well as serialize to json, which is 
-    important for us being able to put representations of agents 
-    [on the wire](https://en.wikipedia.org/wiki/Wire_protocol) for web applications and 
-    agent-to-agent communication. *These are essentially 
-    [structs](https://en.wikipedia.org/wiki/Struct_(C_programming_language)) rather than 
-    traditional classes.*
-2.  **Internal Data**: Very similar to configuration, Auto-GPT passes around boatloads 
-    of internal data.  We are interacting with language models and language model APIs 
-    which means we are handling lots of *structured* but *raw* text.  Here we also 
-    leverage **pydantic** to both *parse* and *validate* the internal data and also to 
-    give us concrete types which we can use static type checkers to validate against 
-    and discover problems before they show up as bugs at runtime. *These are 
-    essentially [structs](https://en.wikipedia.org/wiki/Struct_(C_programming_language)) 
-    rather than traditional classes.*
-3.  **System Interfaces**: This is our primary traditional use of classes in the 
-    re-arch.  We have a bunch of systems. We want many of those systems to have 
-    alternative implementations (e.g. via plugins). We use abstract base classes to 
-    define interfaces to communicate with people who might want to provide those 
-    plugins. We provide a single concrete implementation of most of those systems as a 
-    subclass of the interface. This should not be controversial.
-
-The approach is consistent with 
-[prior](https://github.com/Significant-Gravitas/Auto-GPT/issues/2458)
-[work](https://github.com/Significant-Gravitas/Auto-GPT/pull/2442) done by other 
-maintainers in this direction.
-
-From an organization standpoint, OO programming is by far the most popular programming 
-paradigm (especially for Python). It's the one most often taught in programming classes
-and the one with the most available online training for people interested in 
-contributing.   
-
-Finally, and importantly, we scoped the plan and initial design of the re-arch as a 
-large group of maintainers and collaborators early on. This is consistent with the 
-design we chose and no-one offered alternatives.
- 

autogpt/core/README.md

@@ -1,94 +0,0 @@
-# Auto-GPT Core
-
-This subpackage contains the ongoing work for the 
-[Auto-GPT Re-arch](https://github.com/Significant-Gravitas/Auto-GPT/issues/4770). It is 
-a work in progress and is not yet feature complete.  In particular, it does not yet
-have many of the Auto-GPT commands implemented and is pending ongoing work to 
-[re-incorporate vector-based memory and knowledge retrieval](https://github.com/Significant-Gravitas/Auto-GPT/issues/3536).
-
-## [Overview](ARCHITECTURE_NOTES.md)
-
-The Auto-GPT Re-arch is a re-implementation of the Auto-GPT agent that is designed to be more modular,
-more extensible, and more maintainable than the original Auto-GPT agent.  It is also designed to be
-more accessible to new developers and to be easier to contribute to. The re-arch is a work in progress
-and is not yet feature complete.  It is also not yet ready for production use.
-
-## Running the Re-arch Code
-
-There are two client applications for Auto-GPT included. 
-
-Unlike the main version of Auto-GPT, the re-arch requires you to actually install Auto-GPT in your python 
-environment to run this application.  To do so, run
-
-```
-pip install -e REPOSITORY_ROOT
-```
-
-where `REPOSITORY_ROOT` is the root of the Auto-GPT repository on your machine. The `REPOSITORY_ROOT` 
-is the directory that contains the `setup.py` file and is the main, top-level directory of the repository 
-when you clone it.
-
-## CLI Application
-
-:star2: **This is the reference application I'm working with for now** :star2: 
-
-The first app is a straight CLI application.  I have not done anything yet to port all the friendly display stuff from the `logger.typewriter_log` logic.  
-
-- [Entry Point](https://github.com/Significant-Gravitas/Auto-GPT/blob/master/autogpt/core/runner/cli_app/cli.py)
-- [Client Application](https://github.com/Significant-Gravitas/Auto-GPT/blob/master/autogpt/core/runner/cli_app/main.py)
-
-You'll then need a settings file.  Run
-
-```
- python REPOSITORY_ROOT/autogpt/core/runner/cli_app/cli.py make-settings
- ```
-
-This will write a file called `default_agent_settings.yaml` with all the user-modifiable 
-configuration keys to `~/auto-gpt/default_agent_settings.yml` and make the `auto-gpt` directory 
-in your user directory if it doesn't exist). Your user directory is located in different places 
-depending on your operating system:
-
-- On Linux, it's `/home/USERNAME`
-- On Windows, it's `C:\Users\USERNAME`
-- On Mac, it's `/Users/USERNAME`
-
-At a bare minimum, you'll need to set `openai.credentials.api_key` to your OpenAI API Key to run 
-the model.
-
-You can then run Auto-GPT with 
-
-```
-python REPOSITORY_ROOT/autogpt/core/runner/cli_app/cli.py run
-```
-
-to launch the interaction loop.
-
-### CLI Web App
-
-:warning: I am not actively developing this application.  I am primarily working with the traditional CLI app
-described above.  It is a very good place to get involved if you have web application design experience and are 
-looking to get involved in the re-arch.
-
-The second app is still a CLI, but it sets up a local webserver that the client application talks to
-rather than invoking calls to the Agent library code directly.  This application is essentially a sketch 
-at this point as the folks who were driving it have had less time (and likely not enough clarity) to proceed.
-
-- [Entry Point](https://github.com/Significant-Gravitas/Auto-GPT/blob/master/autogpt/core/runner/cli_web_app/cli.py)
-- [Client Application](https://github.com/Significant-Gravitas/Auto-GPT/blob/master/autogpt/core/runner/cli_web_app/client/client.py)
-- [Server API](https://github.com/Significant-Gravitas/Auto-GPT/blob/master/autogpt/core/runner/cli_web_app/server/api.py)
-
-To run, you still need to generate a default configuration.  You can do 
-
-```
-python REPOSITORY_ROOT/autogpt/core/runner/cli_web_app/cli.py make-settings
-```
-
-It invokes the same command as the bare CLI app, so follow the instructions above about setting your API key.
-
-To run, do 
-
-```
-python REPOSITORY_ROOT/autogpt/core/runner/cli_web_app/cli.py client
-```
-
-This will launch a webserver and then start the client cli application to communicate with it.

autogpt/core/ability/__init__.py

@@ -1,4 +0,0 @@
-"""The command system provides a way to extend the functionality of the AI agent."""
-from autogpt.core.ability.base import Ability, AbilityRegistry
-from autogpt.core.ability.schema import AbilityResult
-from autogpt.core.ability.simple import AbilityRegistrySettings, SimpleAbilityRegistry

autogpt/core/ability/base.py

@@ -1,92 +0,0 @@
-import abc
-from pprint import pformat
-from typing import ClassVar
-
-import inflection
-from pydantic import Field
-
-from autogpt.core.ability.schema import AbilityResult
-from autogpt.core.configuration import SystemConfiguration
-from autogpt.core.planning.simple import LanguageModelConfiguration
-
-
-class AbilityConfiguration(SystemConfiguration):
-    """Struct for model configuration."""
-
-    from autogpt.core.plugin.base import PluginLocation
-
-    location: PluginLocation
-    packages_required: list[str] = Field(default_factory=list)
-    language_model_required: LanguageModelConfiguration = None
-    memory_provider_required: bool = False
-    workspace_required: bool = False
-
-
-class Ability(abc.ABC):
-    """A class representing an agent ability."""
-
-    default_configuration: ClassVar[AbilityConfiguration]
-
-    @classmethod
-    def name(cls) -> str:
-        """The name of the ability."""
-        return inflection.underscore(cls.__name__)
-
-    @classmethod
-    @abc.abstractmethod
-    def description(cls) -> str:
-        """A detailed description of what the ability does."""
-        ...
-
-    @classmethod
-    @abc.abstractmethod
-    def arguments(cls) -> dict:
-        """A dict of arguments in standard json schema format."""
-        ...
-
-    @classmethod
-    def required_arguments(cls) -> list[str]:
-        """A list of required arguments."""
-        return []
-
-    @abc.abstractmethod
-    async def __call__(self, *args, **kwargs) -> AbilityResult:
-        ...
-
-    def __str__(self) -> str:
-        return pformat(self.dump)
-
-    def dump(self) -> dict:
-        return {
-            "name": self.name(),
-            "description": self.description(),
-            "parameters": {
-                "type": "object",
-                "properties": self.arguments(),
-                "required": self.required_arguments(),
-            },
-        }
-
-
-class AbilityRegistry(abc.ABC):
-    @abc.abstractmethod
-    def register_ability(
-        self, ability_name: str, ability_configuration: AbilityConfiguration
-    ) -> None:
-        ...
-
-    @abc.abstractmethod
-    def list_abilities(self) -> list[str]:
-        ...
-
-    @abc.abstractmethod
-    def dump_abilities(self) -> list[dict]:
-        ...
-
-    @abc.abstractmethod
-    def get_ability(self, ability_name: str) -> Ability:
-        ...
-
-    @abc.abstractmethod
-    def perform(self, ability_name: str, **kwargs) -> AbilityResult:
-        ...

autogpt/core/ability/builtins/__init__.py

@@ -1,6 +0,0 @@
-from autogpt.core.ability.builtins.create_new_ability import CreateNewAbility
-from autogpt.core.ability.builtins.query_language_model import QueryLanguageModel
-
-BUILTIN_ABILITIES = {
-    QueryLanguageModel.name(): QueryLanguageModel,
-}

autogpt/core/ability/builtins/create_new_ability.py

@@ -1,102 +0,0 @@
-import logging
-
-from autogpt.core.ability.base import Ability, AbilityConfiguration
-from autogpt.core.ability.schema import AbilityResult
-from autogpt.core.plugin.simple import PluginLocation, PluginStorageFormat
-
-
-class CreateNewAbility(Ability):
-    default_configuration = AbilityConfiguration(
-        location=PluginLocation(
-            storage_format=PluginStorageFormat.INSTALLED_PACKAGE,
-            storage_route="autogpt.core.ability.builtins.CreateNewAbility",
-        ),
-    )
-
-    def __init__(
-        self,
-        logger: logging.Logger,
-        configuration: AbilityConfiguration,
-    ):
-        self._logger = logger
-        self._configuration = configuration
-
-    @classmethod
-    def description(cls) -> str:
-        return "Create a new ability by writing python code."
-
-    @classmethod
-    def arguments(cls) -> dict:
-        return {
-            "ability_name": {
-                "type": "string",
-                "description": "A meaningful and concise name for the new ability.",
-            },
-            "description": {
-                "type": "string",
-                "description": "A detailed description of the ability and its uses, including any limitations.",
-            },
-            "arguments": {
-                "type": "array",
-                "items": {
-                    "type": "object",
-                    "properties": {
-                        "name": {
-                            "type": "string",
-                            "description": "The name of the argument.",
-                        },
-                        "type": {
-                            "type": "string",
-                            "description": "The type of the argument. Must be a standard json schema type.",
-                        },
-                        "description": {
-                            "type": "string",
-                            "description": "A detailed description of the argument and its uses.",
-                        },
-                    },
-                },
-                "description": "A list of arguments that the ability will accept.",
-            },
-            "required_arguments": {
-                "type": "array",
-                "items": {
-                    "type": "string",
-                    "description": "The names of the arguments that are required.",
-                },
-                "description": "A list of the names of the arguments that are required.",
-            },
-            "package_requirements": {
-                "type": "array",
-                "items": {
-                    "type": "string",
-                    "description": "The of the Python package that is required to execute the ability.",
-                },
-                "description": "A list of the names of the Python packages that are required to execute the ability.",
-            },
-            "code": {
-                "type": "string",
-                "description": "The Python code that will be executed when the ability is called.",
-            },
-        }
-
-    @classmethod
-    def required_arguments(cls) -> list[str]:
-        return [
-            "ability_name",
-            "description",
-            "arguments",
-            "required_arguments",
-            "package_requirements",
-            "code",
-        ]
-
-    async def __call__(
-        self,
-        ability_name: str,
-        description: str,
-        arguments: list[dict],
-        required_arguments: list[str],
-        package_requirements: list[str],
-        code: str,
-    ) -> AbilityResult:
-        raise NotImplementedError

autogpt/core/ability/builtins/file_operations.py

@@ -1,167 +0,0 @@
-import logging
-import os
-
-from autogpt.core.ability.base import Ability, AbilityConfiguration
-from autogpt.core.ability.schema import AbilityResult, ContentType, Knowledge
-from autogpt.core.workspace import Workspace
-
-
-class ReadFile(Ability):
-    default_configuration = AbilityConfiguration(
-        packages_required=["unstructured"],
-        workspace_required=True,
-    )
-
-    def __init__(
-        self,
-        logger: logging.Logger,
-        workspace: Workspace,
-    ):
-        self._logger = logger
-        self._workspace = workspace
-
-    @property
-    def description(self) -> str:
-        return "Read and parse all text from a file."
-
-    @property
-    def arguments(self) -> dict:
-        return {
-            "filename": {
-                "type": "string",
-                "description": "The name of the file to read.",
-            },
-        }
-
-    def _check_preconditions(self, filename: str) -> AbilityResult | None:
-        message = ""
-        try:
-            pass
-        except ImportError:
-            message = "Package charset_normalizer is not installed."
-
-        try:
-            file_path = self._workspace.get_path(filename)
-            if not file_path.exists():
-                message = f"File {filename} does not exist."
-            if not file_path.is_file():
-                message = f"{filename} is not a file."
-        except ValueError as e:
-            message = str(e)
-
-        if message:
-            return AbilityResult(
-                ability_name=self.name(),
-                ability_args={"filename": filename},
-                success=False,
-                message=message,
-                data=None,
-            )
-
-    def __call__(self, filename: str) -> AbilityResult:
-        if result := self._check_preconditions(filename):
-            return result
-
-        from unstructured.partition.auto import partition
-
-        file_path = self._workspace.get_path(filename)
-        try:
-            elements = partition(str(file_path))
-            # TODO: Lots of other potentially useful information is available
-            #   in the partitioned file. Consider returning more of it.
-            new_knowledge = Knowledge(
-                content="\n\n".join([element.text for element in elements]),
-                content_type=ContentType.TEXT,
-                content_metadata={"filename": filename},
-            )
-            success = True
-            message = f"File {file_path} read successfully."
-        except IOError as e:
-            new_knowledge = None
-            success = False
-            message = str(e)
-
-        return AbilityResult(
-            ability_name=self.name(),
-            ability_args={"filename": filename},
-            success=success,
-            message=message,
-            new_knowledge=new_knowledge,
-        )
-
-
-class WriteFile(Ability):
-    default_configuration = AbilityConfiguration(
-        packages_required=["unstructured"],
-        workspace_required=True,
-    )
-
-    def __init__(
-        self,
-        logger: logging.Logger,
-        workspace: Workspace,
-    ):
-        self._logger = logger
-        self._workspace = workspace
-
-    @property
-    def description(self) -> str:
-        return "Write text to a file."
-
-    @property
-    def arguments(self) -> dict:
-        return {
-            "filename": {
-                "type": "string",
-                "description": "The name of the file to write.",
-            },
-            "contents": {
-                "type": "string",
-                "description": "The contents of the file to write.",
-            },
-        }
-
-    def _check_preconditions(
-        self, filename: str, contents: str
-    ) -> AbilityResult | None:
-        message = ""
-        try:
-            file_path = self._workspace.get_path(filename)
-            if file_path.exists():
-                message = f"File {filename} already exists."
-            if len(contents):
-                message = f"File {filename} was not given any content."
-        except ValueError as e:
-            message = str(e)
-
-        if message:
-            return AbilityResult(
-                ability_name=self.name(),
-                ability_args={"filename": filename, "contents": contents},
-                success=False,
-                message=message,
-                data=None,
-            )
-
-    def __call__(self, filename: str, contents: str) -> AbilityResult:
-        if result := self._check_preconditions(filename, contents):
-            return result
-
-        file_path = self._workspace.get_path(filename)
-        try:
-            directory = os.path.dirname(file_path)
-            os.makedirs(directory)
-            with open(filename, "w", encoding="utf-8") as f:
-                f.write(contents)
-            success = True
-            message = f"File {file_path} written successfully."
-        except IOError as e:
-            success = False
-            message = str(e)
-
-        return AbilityResult(
-            ability_name=self.name(),
-            ability_args={"filename": filename},
-            success=success,
-            message=message,
-        )

autogpt/core/ability/builtins/query_language_model.py

@@ -1,78 +0,0 @@
-import logging
-
-from autogpt.core.ability.base import Ability, AbilityConfiguration
-from autogpt.core.ability.schema import AbilityResult
-from autogpt.core.planning.simple import LanguageModelConfiguration
-from autogpt.core.plugin.simple import PluginLocation, PluginStorageFormat
-from autogpt.core.resource.model_providers import (
-    LanguageModelMessage,
-    LanguageModelProvider,
-    MessageRole,
-    ModelProviderName,
-    OpenAIModelName,
-)
-
-
-class QueryLanguageModel(Ability):
-    default_configuration = AbilityConfiguration(
-        location=PluginLocation(
-            storage_format=PluginStorageFormat.INSTALLED_PACKAGE,
-            storage_route="autogpt.core.ability.builtins.QueryLanguageModel",
-        ),
-        language_model_required=LanguageModelConfiguration(
-            model_name=OpenAIModelName.GPT3,
-            provider_name=ModelProviderName.OPENAI,
-            temperature=0.9,
-        ),
-    )
-
-    def __init__(
-        self,
-        logger: logging.Logger,
-        configuration: AbilityConfiguration,
-        language_model_provider: LanguageModelProvider,
-    ):
-        self._logger = logger
-        self._configuration = configuration
-        self._language_model_provider = language_model_provider
-
-    @classmethod
-    def description(cls) -> str:
-        return "Query a language model. A query should be a question and any relevant context."
-
-    @classmethod
-    def arguments(cls) -> dict:
-        return {
-            "query": {
-                "type": "string",
-                "description": "A query for a language model. A query should contain a question and any relevant context.",
-            },
-        }
-
-    @classmethod
-    def required_arguments(cls) -> list[str]:
-        return ["query"]
-
-    async def __call__(self, query: str) -> AbilityResult:
-        messages = [
-            LanguageModelMessage(
-                content=query,
-                role=MessageRole.USER,
-            ),
-        ]
-        model_response = await self._language_model_provider.create_language_completion(
-            model_prompt=messages,
-            functions=[],
-            model_name=self._configuration.language_model_required.model_name,
-            completion_parser=self._parse_response,
-        )
-        return AbilityResult(
-            ability_name=self.name(),
-            ability_args={"query": query},
-            success=True,
-            message=model_response.content["content"],
-        )
-
-    @staticmethod
-    def _parse_response(response_content: dict) -> dict:
-        return {"content": response_content["content"]}

autogpt/core/ability/schema.py

@@ -1,30 +0,0 @@
-import enum
-from typing import Any
-
-from pydantic import BaseModel
-
-
-class ContentType(str, enum.Enum):
-    # TBD what these actually are.
-    TEXT = "text"
-    CODE = "code"
-
-
-class Knowledge(BaseModel):
-    content: str
-    content_type: ContentType
-    content_metadata: dict[str, Any]
-
-
-class AbilityResult(BaseModel):
-    """The AbilityResult is a standard response struct for an ability."""
-
-    ability_name: str
-    ability_args: dict[str, str]
-    success: bool
-    message: str
-    new_knowledge: Knowledge = None
-
-    def summary(self) -> str:
-        kwargs = ", ".join(f"{k}={v}" for k, v in self.ability_args.items())
-        return f"{self.ability_name}({kwargs}): {self.message}"

autogpt/core/ability/simple.py

@@ -1,96 +0,0 @@
-import logging
-
-from autogpt.core.ability.base import Ability, AbilityConfiguration, AbilityRegistry
-from autogpt.core.ability.builtins import BUILTIN_ABILITIES
-from autogpt.core.ability.schema import AbilityResult
-from autogpt.core.configuration import Configurable, SystemConfiguration, SystemSettings
-from autogpt.core.memory.base import Memory
-from autogpt.core.plugin.simple import SimplePluginService
-from autogpt.core.resource.model_providers import (
-    LanguageModelProvider,
-    ModelProviderName,
-)
-from autogpt.core.workspace.base import Workspace
-
-
-class AbilityRegistryConfiguration(SystemConfiguration):
-    """Configuration for the AbilityRegistry subsystem."""
-
-    abilities: dict[str, AbilityConfiguration]
-
-
-class AbilityRegistrySettings(SystemSettings):
-    configuration: AbilityRegistryConfiguration
-
-
-class SimpleAbilityRegistry(AbilityRegistry, Configurable):
-    default_settings = AbilityRegistrySettings(
-        name="simple_ability_registry",
-        description="A simple ability registry.",
-        configuration=AbilityRegistryConfiguration(
-            abilities={
-                ability_name: ability.default_configuration
-                for ability_name, ability in BUILTIN_ABILITIES.items()
-            },
-        ),
-    )
-
-    def __init__(
-        self,
-        settings: AbilityRegistrySettings,
-        logger: logging.Logger,
-        memory: Memory,
-        workspace: Workspace,
-        model_providers: dict[ModelProviderName, LanguageModelProvider],
-    ):
-        self._configuration = settings.configuration
-        self._logger = logger
-        self._memory = memory
-        self._workspace = workspace
-        self._model_providers = model_providers
-        self._abilities = []
-        for (
-            ability_name,
-            ability_configuration,
-        ) in self._configuration.abilities.items():
-            self.register_ability(ability_name, ability_configuration)
-
-    def register_ability(
-        self, ability_name: str, ability_configuration: AbilityConfiguration
-    ) -> None:
-        ability_class = SimplePluginService.get_plugin(ability_configuration.location)
-        ability_args = {
-            "logger": self._logger.getChild(ability_name),
-            "configuration": ability_configuration,
-        }
-        if ability_configuration.packages_required:
-            # TODO: Check packages are installed and maybe install them.
-            pass
-        if ability_configuration.memory_provider_required:
-            ability_args["memory"] = self._memory
-        if ability_configuration.workspace_required:
-            ability_args["workspace"] = self._workspace
-        if ability_configuration.language_model_required:
-            ability_args["language_model_provider"] = self._model_providers[
-                ability_configuration.language_model_required.provider_name
-            ]
-        ability = ability_class(**ability_args)
-        self._abilities.append(ability)
-
-    def list_abilities(self) -> list[str]:
-        return [
-            f"{ability.name()}: {ability.description()}" for ability in self._abilities
-        ]
-
-    def dump_abilities(self) -> list[dict]:
-        return [ability.dump() for ability in self._abilities]
-
-    def get_ability(self, ability_name: str) -> Ability:
-        for ability in self._abilities:
-            if ability.name() == ability_name:
-                return ability
-        raise ValueError(f"Ability '{ability_name}' not found.")
-
-    async def perform(self, ability_name: str, **kwargs) -> AbilityResult:
-        ability = self.get_ability(ability_name)
-        return await ability(**kwargs)

autogpt/core/agent/__init__.py

@@ -1,3 +0,0 @@
-"""The Agent is an autonomouos entity guided by a LLM provider."""
-from autogpt.core.agent.base import Agent
-from autogpt.core.agent.simple import AgentSettings, SimpleAgent

autogpt/core/agent/base.py

@@ -1,26 +0,0 @@
-import abc
-import logging
-from pathlib import Path
-
-
-class Agent(abc.ABC):
-    @abc.abstractmethod
-    def __init__(self, *args, **kwargs):
-        ...
-
-    @classmethod
-    @abc.abstractmethod
-    def from_workspace(
-        cls,
-        workspace_path: Path,
-        logger: logging.Logger,
-    ) -> "Agent":
-        ...
-
-    @abc.abstractmethod
-    async def determine_next_ability(self, *args, **kwargs):
-        ...
-
-    @abc.abstractmethod
-    def __repr__(self):
-        ...

autogpt/core/agent/simple.py

@@ -1,391 +0,0 @@
-import logging
-from datetime import datetime
-from pathlib import Path
-from typing import Any
-
-from pydantic import BaseModel
-
-from autogpt.core.ability import (
-    AbilityRegistrySettings,
-    AbilityResult,
-    SimpleAbilityRegistry,
-)
-from autogpt.core.agent.base import Agent
-from autogpt.core.configuration import Configurable, SystemConfiguration, SystemSettings
-from autogpt.core.memory import MemorySettings, SimpleMemory
-from autogpt.core.planning import PlannerSettings, SimplePlanner, Task, TaskStatus
-from autogpt.core.plugin.simple import (
-    PluginLocation,
-    PluginStorageFormat,
-    SimplePluginService,
-)
-from autogpt.core.resource.model_providers import OpenAIProvider, OpenAISettings
-from autogpt.core.workspace.simple import SimpleWorkspace, WorkspaceSettings
-
-
-class AgentSystems(SystemConfiguration):
-    ability_registry: PluginLocation
-    memory: PluginLocation
-    openai_provider: PluginLocation
-    planning: PluginLocation
-    workspace: PluginLocation
-
-
-class AgentConfiguration(SystemConfiguration):
-    cycle_count: int
-    max_task_cycle_count: int
-    creation_time: str
-    name: str
-    role: str
-    goals: list[str]
-    systems: AgentSystems
-
-
-class AgentSystemSettings(SystemSettings):
-    configuration: AgentConfiguration
-
-
-class AgentSettings(BaseModel):
-    agent: AgentSystemSettings
-    ability_registry: AbilityRegistrySettings
-    memory: MemorySettings
-    openai_provider: OpenAISettings
-    planning: PlannerSettings
-    workspace: WorkspaceSettings
-
-    def update_agent_name_and_goals(self, agent_goals: dict) -> None:
-        self.agent.configuration.name = agent_goals["agent_name"]
-        self.agent.configuration.role = agent_goals["agent_role"]
-        self.agent.configuration.goals = agent_goals["agent_goals"]
-
-
-class SimpleAgent(Agent, Configurable):
-    default_settings = AgentSystemSettings(
-        name="simple_agent",
-        description="A simple agent.",
-        configuration=AgentConfiguration(
-            name="Entrepreneur-GPT",
-            role=(
-                "An AI designed to autonomously develop and run businesses with "
-                "the sole goal of increasing your net worth."
-            ),
-            goals=[
-                "Increase net worth",
-                "Grow Twitter Account",
-                "Develop and manage multiple businesses autonomously",
-            ],
-            cycle_count=0,
-            max_task_cycle_count=3,
-            creation_time="",
-            systems=AgentSystems(
-                ability_registry=PluginLocation(
-                    storage_format=PluginStorageFormat.INSTALLED_PACKAGE,
-                    storage_route="autogpt.core.ability.SimpleAbilityRegistry",
-                ),
-                memory=PluginLocation(
-                    storage_format=PluginStorageFormat.INSTALLED_PACKAGE,
-                    storage_route="autogpt.core.memory.SimpleMemory",
-                ),
-                openai_provider=PluginLocation(
-                    storage_format=PluginStorageFormat.INSTALLED_PACKAGE,
-                    storage_route="autogpt.core.resource.model_providers.OpenAIProvider",
-                ),
-                planning=PluginLocation(
-                    storage_format=PluginStorageFormat.INSTALLED_PACKAGE,
-                    storage_route="autogpt.core.planning.SimplePlanner",
-                ),
-                workspace=PluginLocation(
-                    storage_format=PluginStorageFormat.INSTALLED_PACKAGE,
-                    storage_route="autogpt.core.workspace.SimpleWorkspace",
-                ),
-            ),
-        ),
-    )
-
-    def __init__(
-        self,
-        settings: AgentSystemSettings,
-        logger: logging.Logger,
-        ability_registry: SimpleAbilityRegistry,
-        memory: SimpleMemory,
-        openai_provider: OpenAIProvider,
-        planning: SimplePlanner,
-        workspace: SimpleWorkspace,
-    ):
-        self._configuration = settings.configuration
-        self._logger = logger
-        self._ability_registry = ability_registry
-        self._memory = memory
-        # FIXME: Need some work to make this work as a dict of providers
-        #  Getting the construction of the config to work is a bit tricky
-        self._openai_provider = openai_provider
-        self._planning = planning
-        self._workspace = workspace
-        self._task_queue = []
-        self._completed_tasks = []
-        self._current_task = None
-        self._next_ability = None
-
-    @classmethod
-    def from_workspace(
-        cls,
-        workspace_path: Path,
-        logger: logging.Logger,
-    ) -> "SimpleAgent":
-        agent_settings = SimpleWorkspace.load_agent_settings(workspace_path)
-        agent_args = {}
-
-        agent_args["settings"] = agent_settings.agent
-        agent_args["logger"] = logger
-        agent_args["workspace"] = cls._get_system_instance(
-            "workspace",
-            agent_settings,
-            logger,
-        )
-        agent_args["openai_provider"] = cls._get_system_instance(
-            "openai_provider",
-            agent_settings,
-            logger,
-        )
-        agent_args["planning"] = cls._get_system_instance(
-            "planning",
-            agent_settings,
-            logger,
-            model_providers={"openai": agent_args["openai_provider"]},
-        )
-        agent_args["memory"] = cls._get_system_instance(
-            "memory",
-            agent_settings,
-            logger,
-            workspace=agent_args["workspace"],
-        )
-
-        agent_args["ability_registry"] = cls._get_system_instance(
-            "ability_registry",
-            agent_settings,
-            logger,
-            workspace=agent_args["workspace"],
-            memory=agent_args["memory"],
-            model_providers={"openai": agent_args["openai_provider"]},
-        )
-
-        return cls(**agent_args)
-
-    async def build_initial_plan(self) -> dict:
-        plan = await self._planning.make_initial_plan(
-            agent_name=self._configuration.name,
-            agent_role=self._configuration.role,
-            agent_goals=self._configuration.goals,
-            abilities=self._ability_registry.list_abilities(),
-        )
-        tasks = [Task.parse_obj(task) for task in plan.content["task_list"]]
-
-        # TODO: Should probably do a step to evaluate the quality of the generated tasks,
-        #  and ensure that they have actionable ready and acceptance criteria
-
-        self._task_queue.extend(tasks)
-        self._task_queue.sort(key=lambda t: t.priority, reverse=True)
-        self._task_queue[-1].context.status = TaskStatus.READY
-        return plan.content
-
-    async def determine_next_ability(self, *args, **kwargs):
-        if not self._task_queue:
-            return {"response": "I don't have any tasks to work on right now."}
-
-        self._configuration.cycle_count += 1
-        task = self._task_queue.pop()
-        self._logger.info(f"Working on task: {task}")
-
-        task = await self._evaluate_task_and_add_context(task)
-        next_ability = await self._choose_next_ability(
-            task,
-            self._ability_registry.dump_abilities(),
-        )
-        self._current_task = task
-        self._next_ability = next_ability.content
-        return self._current_task, self._next_ability
-
-    async def execute_next_ability(self, user_input: str, *args, **kwargs):
-        if user_input == "y":
-            ability = self._ability_registry.get_ability(
-                self._next_ability["next_ability"]
-            )
-            ability_response = await ability(**self._next_ability["ability_arguments"])
-            await self._update_tasks_and_memory(ability_response)
-            if self._current_task.context.status == TaskStatus.DONE:
-                self._completed_tasks.append(self._current_task)
-            else:
-                self._task_queue.append(self._current_task)
-            self._current_task = None
-            self._next_ability = None
-
-            return ability_response.dict()
-        else:
-            raise NotImplementedError
-
-    async def _evaluate_task_and_add_context(self, task: Task) -> Task:
-        """Evaluate the task and add context to it."""
-        if task.context.status == TaskStatus.IN_PROGRESS:
-            # Nothing to do here
-            return task
-        else:
-            self._logger.debug(f"Evaluating task {task} and adding relevant context.")
-            # TODO: Look up relevant memories (need working memory system)
-            # TODO: Evaluate whether there is enough information to start the task (language model call).
-            task.context.enough_info = True
-            task.context.status = TaskStatus.IN_PROGRESS
-            return task
-
-    async def _choose_next_ability(self, task: Task, ability_schema: list[dict]):
-        """Choose the next ability to use for the task."""
-        self._logger.debug(f"Choosing next ability for task {task}.")
-        if task.context.cycle_count > self._configuration.max_task_cycle_count:
-            # Don't hit the LLM, just set the next action as "breakdown_task" with an appropriate reason
-            raise NotImplementedError
-        elif not task.context.enough_info:
-            # Don't ask the LLM, just set the next action as "breakdown_task" with an appropriate reason
-            raise NotImplementedError
-        else:
-            next_ability = await self._planning.determine_next_ability(
-                task, ability_schema
-            )
-            return next_ability
-
-    async def _update_tasks_and_memory(self, ability_result: AbilityResult):
-        self._current_task.context.cycle_count += 1
-        self._current_task.context.prior_actions.append(ability_result)
-        # TODO: Summarize new knowledge
-        # TODO: store knowledge and summaries in memory and in relevant tasks
-        # TODO: evaluate whether the task is complete
-
-    def __repr__(self):
-        return "SimpleAgent()"
-
-    ################################################################
-    # Factory interface for agent bootstrapping and initialization #
-    ################################################################
-
-    @classmethod
-    def build_user_configuration(cls) -> dict[str, Any]:
-        """Build the user's configuration."""
-        configuration_dict = {
-            "agent": cls.get_user_config(),
-        }
-
-        system_locations = configuration_dict["agent"]["configuration"]["systems"]
-        for system_name, system_location in system_locations.items():
-            system_class = SimplePluginService.get_plugin(system_location)
-            configuration_dict[system_name] = system_class.get_user_config()
-        configuration_dict = _prune_empty_dicts(configuration_dict)
-        return configuration_dict
-
-    @classmethod
-    def compile_settings(
-        cls, logger: logging.Logger, user_configuration: dict
-    ) -> AgentSettings:
-        """Compile the user's configuration with the defaults."""
-        logger.debug("Processing agent system configuration.")
-        configuration_dict = {
-            "agent": cls.build_agent_configuration(
-                user_configuration.get("agent", {})
-            ).dict(),
-        }
-
-        system_locations = configuration_dict["agent"]["configuration"]["systems"]
-
-        # Build up default configuration
-        for system_name, system_location in system_locations.items():
-            logger.debug(f"Compiling configuration for system {system_name}")
-            system_class = SimplePluginService.get_plugin(system_location)
-            configuration_dict[system_name] = system_class.build_agent_configuration(
-                user_configuration.get(system_name, {})
-            ).dict()
-
-        return AgentSettings.parse_obj(configuration_dict)
-
-    @classmethod
-    async def determine_agent_name_and_goals(
-        cls,
-        user_objective: str,
-        agent_settings: AgentSettings,
-        logger: logging.Logger,
-    ) -> dict:
-        logger.debug("Loading OpenAI provider.")
-        provider: OpenAIProvider = cls._get_system_instance(
-            "openai_provider",
-            agent_settings,
-            logger=logger,
-        )
-        logger.debug("Loading agent planner.")
-        agent_planner: SimplePlanner = cls._get_system_instance(
-            "planning",
-            agent_settings,
-            logger=logger,
-            model_providers={"openai": provider},
-        )
-        logger.debug("determining agent name and goals.")
-        model_response = await agent_planner.decide_name_and_goals(
-            user_objective,
-        )
-
-        return model_response.content
-
-    @classmethod
-    def provision_agent(
-        cls,
-        agent_settings: AgentSettings,
-        logger: logging.Logger,
-    ):
-        agent_settings.agent.configuration.creation_time = datetime.now().strftime(
-            "%Y%m%d_%H%M%S"
-        )
-        workspace: SimpleWorkspace = cls._get_system_instance(
-            "workspace",
-            agent_settings,
-            logger=logger,
-        )
-        return workspace.setup_workspace(agent_settings, logger)
-
-    @classmethod
-    def _get_system_instance(
-        cls,
-        system_name: str,
-        agent_settings: AgentSettings,
-        logger: logging.Logger,
-        *args,
-        **kwargs,
-    ):
-        system_locations = agent_settings.agent.configuration.systems.dict()
-
-        system_settings = getattr(agent_settings, system_name)
-        system_class = SimplePluginService.get_plugin(system_locations[system_name])
-        system_instance = system_class(
-            system_settings,
-            *args,
-            logger=logger.getChild(system_name),
-            **kwargs,
-        )
-        return system_instance
-
-
-def _prune_empty_dicts(d: dict) -> dict:
-    """
-    Prune branches from a nested dictionary if the branch only contains empty dictionaries at the leaves.
-
-    Args:
-        d: The dictionary to prune.
-
-    Returns:
-        The pruned dictionary.
-    """
-    pruned = {}
-    for key, value in d.items():
-        if isinstance(value, dict):
-            pruned_value = _prune_empty_dicts(value)
-            if (
-                pruned_value
-            ):  # if the pruned dictionary is not empty, add it to the result
-                pruned[key] = pruned_value
-        else:
-            pruned[key] = value
-    return pruned

autogpt/core/configuration/__init__.py

@@ -1,7 +0,0 @@
-"""The configuration encapsulates settings for all Agent subsystems."""
-from autogpt.core.configuration.schema import (
-    Configurable,
-    SystemConfiguration,
-    SystemSettings,
-    UserConfigurable,
-)

autogpt/core/configuration/schema.py

@@ -1,107 +0,0 @@
-import abc
-import typing
-from typing import Any, Generic, TypeVar
-
-from pydantic import BaseModel, Field
-
-
-def UserConfigurable(*args, **kwargs):
-    return Field(*args, **kwargs, user_configurable=True)
-
-
-class SystemConfiguration(BaseModel):
-    def get_user_config(self) -> dict[str, Any]:
-        return _get_user_config_fields(self)
-
-    class Config:
-        extra = "forbid"
-        use_enum_values = True
-
-
-class SystemSettings(BaseModel):
-    """A base class for all system settings."""
-
-    name: str
-    description: str
-
-    class Config:
-        extra = "forbid"
-        use_enum_values = True
-
-
-S = TypeVar("S", bound=SystemSettings)
-
-
-class Configurable(abc.ABC, Generic[S]):
-    """A base class for all configurable objects."""
-
-    prefix: str = ""
-    default_settings: typing.ClassVar[S]
-
-    @classmethod
-    def get_user_config(cls) -> dict[str, Any]:
-        return _get_user_config_fields(cls.default_settings)
-
-    @classmethod
-    def build_agent_configuration(cls, configuration: dict) -> S:
-        """Process the configuration for this object."""
-
-        defaults = cls.default_settings.dict()
-        final_configuration = deep_update(defaults, configuration)
-
-        return cls.default_settings.__class__.parse_obj(final_configuration)
-
-
-def _get_user_config_fields(instance: BaseModel) -> dict[str, Any]:
-    """
-    Get the user config fields of a Pydantic model instance.
-
-    Args:
-        instance: The Pydantic model instance.
-
-    Returns:
-        The user config fields of the instance.
-    """
-    user_config_fields = {}
-
-    for name, value in instance.__dict__.items():
-        field_info = instance.__fields__[name]
-        if "user_configurable" in field_info.field_info.extra:
-            user_config_fields[name] = value
-        elif isinstance(value, SystemConfiguration):
-            user_config_fields[name] = value.get_user_config()
-        elif isinstance(value, list) and all(
-            isinstance(i, SystemConfiguration) for i in value
-        ):
-            user_config_fields[name] = [i.get_user_config() for i in value]
-        elif isinstance(value, dict) and all(
-            isinstance(i, SystemConfiguration) for i in value.values()
-        ):
-            user_config_fields[name] = {
-                k: v.get_user_config() for k, v in value.items()
-            }
-
-    return user_config_fields
-
-
-def deep_update(original_dict: dict, update_dict: dict) -> dict:
-    """
-    Recursively update a dictionary.
-
-    Args:
-        original_dict (dict): The dictionary to be updated.
-        update_dict (dict): The dictionary to update with.
-
-    Returns:
-        dict: The updated dictionary.
-    """
-    for key, value in update_dict.items():
-        if (
-            key in original_dict
-            and isinstance(original_dict[key], dict)
-            and isinstance(value, dict)
-        ):
-            original_dict[key] = deep_update(original_dict[key], value)
-        else:
-            original_dict[key] = value
-    return original_dict

autogpt/core/memory/__init__.py

@@ -1,3 +0,0 @@
-"""The memory subsystem manages the Agent's long-term memory."""
-from autogpt.core.memory.base import Memory
-from autogpt.core.memory.simple import MemorySettings, SimpleMemory

autogpt/core/memory/base.py

@@ -1,13 +0,0 @@
-import abc
-
-
-class Memory(abc.ABC):
-    pass
-
-
-class MemoryItem(abc.ABC):
-    pass
-
-
-class MessageHistory(abc.ABC):
-    pass

autogpt/core/memory/simple.py

@@ -1,47 +0,0 @@
-import json
-import logging
-
-from autogpt.core.configuration import Configurable, SystemConfiguration, SystemSettings
-from autogpt.core.memory.base import Memory
-from autogpt.core.workspace import Workspace
-
-
-class MemoryConfiguration(SystemConfiguration):
-    pass
-
-
-class MemorySettings(SystemSettings):
-    configuration: MemoryConfiguration
-
-
-class MessageHistory:
-    def __init__(self, previous_message_history: list[str]):
-        self._message_history = previous_message_history
-
-
-class SimpleMemory(Memory, Configurable):
-    default_settings = MemorySettings(
-        name="simple_memory",
-        description="A simple memory.",
-        configuration=MemoryConfiguration(),
-    )
-
-    def __init__(
-        self,
-        settings: MemorySettings,
-        logger: logging.Logger,
-        workspace: Workspace,
-    ):
-        self._configuration = settings.configuration
-        self._logger = logger
-        self._message_history = self._load_message_history(workspace)
-
-    @staticmethod
-    def _load_message_history(workspace: Workspace):
-        message_history_path = workspace.get_path("message_history.json")
-        if message_history_path.exists():
-            with message_history_path.open("r") as f:
-                message_history = json.load(f)
-        else:
-            message_history = []
-        return MessageHistory(message_history)

autogpt/core/planning/__init__.py

@@ -1,10 +0,0 @@
-"""The planning system organizes the Agent's activities."""
-from autogpt.core.planning.schema import (
-    LanguageModelClassification,
-    LanguageModelPrompt,
-    LanguageModelResponse,
-    Task,
-    TaskStatus,
-    TaskType,
-)
-from autogpt.core.planning.simple import PlannerSettings, SimplePlanner

autogpt/core/planning/base.py

@@ -1,76 +0,0 @@
-import abc
-
-from autogpt.core.configuration import SystemConfiguration
-from autogpt.core.planning.schema import (
-    LanguageModelClassification,
-    LanguageModelPrompt,
-)
-
-# class Planner(abc.ABC):
-#     """Manages the agent's planning and goal-setting by constructing language model prompts."""
-#
-#     @staticmethod
-#     @abc.abstractmethod
-#     async def decide_name_and_goals(
-#         user_objective: str,
-#     ) -> LanguageModelResponse:
-#         """Decide the name and goals of an Agent from a user-defined objective.
-#
-#         Args:
-#             user_objective: The user-defined objective for the agent.
-#
-#         Returns:
-#             The agent name and goals as a response from the language model.
-#
-#         """
-#         ...
-#
-#     @abc.abstractmethod
-#     async def plan(self, context: PlanningContext) -> LanguageModelResponse:
-#         """Plan the next ability for the Agent.
-#
-#         Args:
-#             context: A context object containing information about the agent's
-#                        progress, result, memories, and feedback.
-#
-#
-#         Returns:
-#             The next ability the agent should take along with thoughts and reasoning.
-#
-#         """
-#         ...
-#
-#     @abc.abstractmethod
-#     def reflect(
-#         self,
-#         context: ReflectionContext,
-#     ) -> LanguageModelResponse:
-#         """Reflect on a planned ability and provide self-criticism.
-#
-#
-#         Args:
-#             context: A context object containing information about the agent's
-#                        reasoning, plan, thoughts, and criticism.
-#
-#         Returns:
-#             Self-criticism about the agent's plan.
-#
-#         """
-#         ...
-
-
-class PromptStrategy(abc.ABC):
-    default_configuration: SystemConfiguration
-
-    @property
-    @abc.abstractmethod
-    def model_classification(self) -> LanguageModelClassification:
-        ...
-
-    @abc.abstractmethod
-    def build_prompt(self, *_, **kwargs) -> LanguageModelPrompt:
-        ...
-
-    @abc.abstractmethod
-    def parse_response_content(self, response_content: dict) -> dict:
-        ...

autogpt/core/planning/schema.py

@@ -1,76 +0,0 @@
-import enum
-
-from pydantic import BaseModel, Field
-
-from autogpt.core.ability.schema import AbilityResult
-from autogpt.core.resource.model_providers.schema import (
-    LanguageModelFunction,
-    LanguageModelMessage,
-    LanguageModelProviderModelResponse,
-)
-
-
-class LanguageModelClassification(str, enum.Enum):
-    """The LanguageModelClassification is a functional description of the model.
-
-    This is used to determine what kind of model to use for a given prompt.
-    Sometimes we prefer a faster or cheaper model to accomplish a task when
-    possible.
-
-    """
-
-    FAST_MODEL: str = "fast_model"
-    SMART_MODEL: str = "smart_model"
-
-
-class LanguageModelPrompt(BaseModel):
-    messages: list[LanguageModelMessage]
-    functions: list[LanguageModelFunction] = Field(default_factory=list)
-
-    def __str__(self):
-        return "\n\n".join([f"{m.role.value}: {m.content}" for m in self.messages])
-
-
-class LanguageModelResponse(LanguageModelProviderModelResponse):
-    """Standard response struct for a response from a language model."""
-
-
-class TaskType(str, enum.Enum):
-    RESEARCH: str = "research"
-    WRITE: str = "write"
-    EDIT: str = "edit"
-    CODE: str = "code"
-    DESIGN: str = "design"
-    TEST: str = "test"
-    PLAN: str = "plan"
-
-
-class TaskStatus(str, enum.Enum):
-    BACKLOG: str = "backlog"
-    READY: str = "ready"
-    IN_PROGRESS: str = "in_progress"
-    DONE: str = "done"
-
-
-class TaskContext(BaseModel):
-    cycle_count: int = 0
-    status: TaskStatus = TaskStatus.BACKLOG
-    parent: "Task" = None
-    prior_actions: list[AbilityResult] = Field(default_factory=list)
-    memories: list = Field(default_factory=list)
-    user_input: list[str] = Field(default_factory=list)
-    supplementary_info: list[str] = Field(default_factory=list)
-    enough_info: bool = False
-
-
-class Task(BaseModel):
-    objective: str
-    type: str  # TaskType  FIXME: gpt does not obey the enum parameter in its schema
-    priority: int
-    ready_criteria: list[str]
-    acceptance_criteria: list[str]
-    context: TaskContext = Field(default_factory=TaskContext)
-
-
-# Need to resolve the circular dependency between Task and TaskContext once both models are defined.
-TaskContext.update_forward_refs()

autogpt/core/planning/simple.py

@@ -1,182 +0,0 @@
-import logging
-import platform
-import time
-
-import distro
-
-from autogpt.core.configuration import (
-    Configurable,
-    SystemConfiguration,
-    SystemSettings,
-    UserConfigurable,
-)
-from autogpt.core.planning import strategies
-from autogpt.core.planning.base import PromptStrategy
-from autogpt.core.planning.schema import (
-    LanguageModelClassification,
-    LanguageModelResponse,
-    Task,
-)
-from autogpt.core.resource.model_providers import (
-    LanguageModelProvider,
-    ModelProviderName,
-    OpenAIModelName,
-)
-from autogpt.core.workspace import Workspace
-
-
-class LanguageModelConfiguration(SystemConfiguration):
-    """Struct for model configuration."""
-
-    model_name: str = UserConfigurable()
-    provider_name: ModelProviderName = UserConfigurable()
-    temperature: float = UserConfigurable()
-
-
-class PromptStrategiesConfiguration(SystemConfiguration):
-    name_and_goals: strategies.NameAndGoalsConfiguration
-    initial_plan: strategies.InitialPlanConfiguration
-    next_ability: strategies.NextAbilityConfiguration
-
-
-class PlannerConfiguration(SystemConfiguration):
-    """Configuration for the Planner subsystem."""
-
-    models: dict[LanguageModelClassification, LanguageModelConfiguration]
-    prompt_strategies: PromptStrategiesConfiguration
-
-
-class PlannerSettings(SystemSettings):
-    """Settings for the Planner subsystem."""
-
-    configuration: PlannerConfiguration
-
-
-class SimplePlanner(Configurable):
-    """Manages the agent's planning and goal-setting by constructing language model prompts."""
-
-    default_settings = PlannerSettings(
-        name="planner",
-        description="Manages the agent's planning and goal-setting by constructing language model prompts.",
-        configuration=PlannerConfiguration(
-            models={
-                LanguageModelClassification.FAST_MODEL: LanguageModelConfiguration(
-                    model_name=OpenAIModelName.GPT3,
-                    provider_name=ModelProviderName.OPENAI,
-                    temperature=0.9,
-                ),
-                LanguageModelClassification.SMART_MODEL: LanguageModelConfiguration(
-                    model_name=OpenAIModelName.GPT4,
-                    provider_name=ModelProviderName.OPENAI,
-                    temperature=0.9,
-                ),
-            },
-            prompt_strategies=PromptStrategiesConfiguration(
-                name_and_goals=strategies.NameAndGoals.default_configuration,
-                initial_plan=strategies.InitialPlan.default_configuration,
-                next_ability=strategies.NextAbility.default_configuration,
-            ),
-        ),
-    )
-
-    def __init__(
-        self,
-        settings: PlannerSettings,
-        logger: logging.Logger,
-        model_providers: dict[ModelProviderName, LanguageModelProvider],
-        workspace: Workspace = None,  # Workspace is not available during bootstrapping.
-    ) -> None:
-        self._configuration = settings.configuration
-        self._logger = logger
-        self._workspace = workspace
-
-        self._providers: dict[LanguageModelClassification, LanguageModelProvider] = {}
-        for model, model_config in self._configuration.models.items():
-            self._providers[model] = model_providers[model_config.provider_name]
-
-        self._prompt_strategies = {
-            "name_and_goals": strategies.NameAndGoals(
-                **self._configuration.prompt_strategies.name_and_goals.dict()
-            ),
-            "initial_plan": strategies.InitialPlan(
-                **self._configuration.prompt_strategies.initial_plan.dict()
-            ),
-            "next_ability": strategies.NextAbility(
-                **self._configuration.prompt_strategies.next_ability.dict()
-            ),
-        }
-
-    async def decide_name_and_goals(self, user_objective: str) -> LanguageModelResponse:
-        return await self.chat_with_model(
-            self._prompt_strategies["name_and_goals"],
-            user_objective=user_objective,
-        )
-
-    async def make_initial_plan(
-        self,
-        agent_name: str,
-        agent_role: str,
-        agent_goals: list[str],
-        abilities: list[str],
-    ) -> LanguageModelResponse:
-        return await self.chat_with_model(
-            self._prompt_strategies["initial_plan"],
-            agent_name=agent_name,
-            agent_role=agent_role,
-            agent_goals=agent_goals,
-            abilities=abilities,
-        )
-
-    async def determine_next_ability(
-        self,
-        task: Task,
-        ability_schema: list[dict],
-    ):
-        return await self.chat_with_model(
-            self._prompt_strategies["next_ability"],
-            task=task,
-            ability_schema=ability_schema,
-        )
-
-    async def chat_with_model(
-        self,
-        prompt_strategy: PromptStrategy,
-        **kwargs,
-    ) -> LanguageModelResponse:
-        model_classification = prompt_strategy.model_classification
-        model_configuration = self._configuration.models[model_classification].dict()
-        self._logger.debug(f"Using model configuration: {model_configuration}")
-        del model_configuration["provider_name"]
-        provider = self._providers[model_classification]
-
-        template_kwargs = self._make_template_kwargs_for_strategy(prompt_strategy)
-        template_kwargs.update(kwargs)
-        prompt = prompt_strategy.build_prompt(**template_kwargs)
-
-        self._logger.debug(f"Using prompt:\n{prompt}\n\n")
-        response = await provider.create_language_completion(
-            model_prompt=prompt.messages,
-            functions=prompt.functions,
-            **model_configuration,
-            completion_parser=prompt_strategy.parse_response_content,
-        )
-        return LanguageModelResponse.parse_obj(response.dict())
-
-    def _make_template_kwargs_for_strategy(self, strategy: PromptStrategy):
-        provider = self._providers[strategy.model_classification]
-        template_kwargs = {
-            "os_info": get_os_info(),
-            "api_budget": provider.get_remaining_budget(),
-            "current_time": time.strftime("%c"),
-        }
-        return template_kwargs
-
-
-def get_os_info() -> str:
-    os_name = platform.system()
-    os_info = (
-        platform.platform(terse=True)
-        if os_name != "Linux"
-        else distro.name(pretty=True)
-    )
-    return os_info

autogpt/core/planning/strategies/__init__.py

@@ -1,12 +0,0 @@
-from autogpt.core.planning.strategies.initial_plan import (
-    InitialPlan,
-    InitialPlanConfiguration,
-)
-from autogpt.core.planning.strategies.name_and_goals import (
-    NameAndGoals,
-    NameAndGoalsConfiguration,
-)
-from autogpt.core.planning.strategies.next_ability import (
-    NextAbility,
-    NextAbilityConfiguration,
-)

autogpt/core/planning/strategies/initial_plan.py

@@ -1,190 +0,0 @@
-from autogpt.core.configuration import SystemConfiguration, UserConfigurable
-from autogpt.core.planning.base import PromptStrategy
-from autogpt.core.planning.schema import (
-    LanguageModelClassification,
-    LanguageModelPrompt,
-    Task,
-    TaskType,
-)
-from autogpt.core.planning.strategies.utils import json_loads, to_numbered_list
-from autogpt.core.resource.model_providers import (
-    LanguageModelFunction,
-    LanguageModelMessage,
-    MessageRole,
-)
-
-
-class InitialPlanConfiguration(SystemConfiguration):
-    model_classification: LanguageModelClassification = UserConfigurable()
-    system_prompt_template: str = UserConfigurable()
-    system_info: list[str] = UserConfigurable()
-    user_prompt_template: str = UserConfigurable()
-    create_plan_function: dict = UserConfigurable()
-
-
-class InitialPlan(PromptStrategy):
-    DEFAULT_SYSTEM_PROMPT_TEMPLATE = (
-        "You are an expert project planner. You're responsibility is to create work plans for autonomous agents. "
-        "You will be given a name, a role, set of goals for the agent to accomplish. Your job is to "
-        "break down those goals into a set of tasks that the agent can accomplish to achieve those goals. "
-        "Agents are resourceful, but require clear instructions. Each task you create should have clearly defined "
-        "`ready_criteria` that the agent can check to see if the task is ready to be started. Each task should "
-        "also have clearly defined `acceptance_criteria` that the agent can check to evaluate if the task is complete. "
-        "You should create as many tasks as you think is necessary to accomplish the goals.\n\n"
-        "System Info:\n{system_info}"
-    )
-
-    DEFAULT_SYSTEM_INFO = [
-        "The OS you are running on is: {os_info}",
-        "It takes money to let you run. Your API budget is ${api_budget:.3f}",
-        "The current time and date is {current_time}",
-    ]
-
-    DEFAULT_USER_PROMPT_TEMPLATE = (
-        "You are {agent_name}, {agent_role}\n" "Your goals are:\n" "{agent_goals}"
-    )
-
-    DEFAULT_CREATE_PLAN_FUNCTION = {
-        "name": "create_initial_agent_plan",
-        "description": "Creates a set of tasks that forms the initial plan for an autonomous agent.",
-        "parameters": {
-            "type": "object",
-            "properties": {
-                "task_list": {
-                    "type": "array",
-                    "items": {
-                        "type": "object",
-                        "properties": {
-                            "objective": {
-                                "type": "string",
-                                "description": "An imperative verb phrase that succinctly describes the task.",
-                            },
-                            "type": {
-                                "type": "string",
-                                "description": "A categorization for the task. ",
-                                "enum": [t.value for t in TaskType],
-                            },
-                            "acceptance_criteria": {
-                                "type": "array",
-                                "items": {
-                                    "type": "string",
-                                    "description": "A list of measurable and testable criteria that must be met for the task to be considered complete.",
-                                },
-                            },
-                            "priority": {
-                                "type": "integer",
-                                "description": "A number between 1 and 10 indicating the priority of the task relative to other generated tasks.",
-                                "minimum": 1,
-                                "maximum": 10,
-                            },
-                            "ready_criteria": {
-                                "type": "array",
-                                "items": {
-                                    "type": "string",
-                                    "description": "A list of measurable and testable criteria that must be met before the task can be started.",
-                                },
-                            },
-                        },
-                        "required": [
-                            "objective",
-                            "type",
-                            "acceptance_criteria",
-                            "priority",
-                            "ready_criteria",
-                        ],
-                    },
-                },
-            },
-        },
-    }
-
-    default_configuration = InitialPlanConfiguration(
-        model_classification=LanguageModelClassification.SMART_MODEL,
-        system_prompt_template=DEFAULT_SYSTEM_PROMPT_TEMPLATE,
-        system_info=DEFAULT_SYSTEM_INFO,
-        user_prompt_template=DEFAULT_USER_PROMPT_TEMPLATE,
-        create_plan_function=DEFAULT_CREATE_PLAN_FUNCTION,
-    )
-
-    def __init__(
-        self,
-        model_classification: LanguageModelClassification,
-        system_prompt_template: str,
-        system_info: list[str],
-        user_prompt_template: str,
-        create_plan_function: dict,
-    ):
-        self._model_classification = model_classification
-        self._system_prompt_template = system_prompt_template
-        self._system_info = system_info
-        self._user_prompt_template = user_prompt_template
-        self._create_plan_function = create_plan_function
-
-    @property
-    def model_classification(self) -> LanguageModelClassification:
-        return self._model_classification
-
-    def build_prompt(
-        self,
-        agent_name: str,
-        agent_role: str,
-        agent_goals: list[str],
-        abilities: list[str],
-        os_info: str,
-        api_budget: float,
-        current_time: str,
-        **kwargs,
-    ) -> LanguageModelPrompt:
-        template_kwargs = {
-            "agent_name": agent_name,
-            "agent_role": agent_role,
-            "os_info": os_info,
-            "api_budget": api_budget,
-            "current_time": current_time,
-            **kwargs,
-        }
-        template_kwargs["agent_goals"] = to_numbered_list(
-            agent_goals, **template_kwargs
-        )
-        template_kwargs["abilities"] = to_numbered_list(abilities, **template_kwargs)
-        template_kwargs["system_info"] = to_numbered_list(
-            self._system_info, **template_kwargs
-        )
-
-        system_prompt = LanguageModelMessage(
-            role=MessageRole.SYSTEM,
-            content=self._system_prompt_template.format(**template_kwargs),
-        )
-        user_prompt = LanguageModelMessage(
-            role=MessageRole.USER,
-            content=self._user_prompt_template.format(**template_kwargs),
-        )
-        create_plan_function = LanguageModelFunction(
-            json_schema=self._create_plan_function,
-        )
-
-        return LanguageModelPrompt(
-            messages=[system_prompt, user_prompt],
-            functions=[create_plan_function],
-            # TODO:
-            tokens_used=0,
-        )
-
-    def parse_response_content(
-        self,
-        response_content: dict,
-    ) -> dict:
-        """Parse the actual text response from the objective model.
-
-        Args:
-            response_content: The raw response content from the objective model.
-
-        Returns:
-            The parsed response.
-
-        """
-        parsed_response = json_loads(response_content["function_call"]["arguments"])
-        parsed_response["task_list"] = [
-            Task.parse_obj(task) for task in parsed_response["task_list"]
-        ]
-        return parsed_response

autogpt/core/planning/strategies/name_and_goals.py

@@ -1,139 +0,0 @@
-from autogpt.core.configuration import SystemConfiguration, UserConfigurable
-from autogpt.core.planning.base import PromptStrategy
-from autogpt.core.planning.schema import (
-    LanguageModelClassification,
-    LanguageModelPrompt,
-)
-from autogpt.core.planning.strategies.utils import json_loads
-from autogpt.core.resource.model_providers import (
-    LanguageModelFunction,
-    LanguageModelMessage,
-    MessageRole,
-)
-
-
-class NameAndGoalsConfiguration(SystemConfiguration):
-    model_classification: LanguageModelClassification = UserConfigurable()
-    system_prompt: str = UserConfigurable()
-    user_prompt_template: str = UserConfigurable()
-    create_agent_function: dict = UserConfigurable()
-
-
-class NameAndGoals(PromptStrategy):
-    DEFAULT_SYSTEM_PROMPT = (
-        "Your job is to respond to a user-defined task by invoking the `create_agent` function "
-        "to generate an autonomous agent to complete the task. You should supply a role-based "
-        "name for the agent, an informative description for what the agent does, and 1 to 5 "
-        "goals that are optimally aligned with the successful completion of its assigned task.\n\n"
-        "Example Input:\n"
-        "Help me with marketing my business\n\n"
-        "Example Function Call:\n"
-        "create_agent(name='CMOGPT', "
-        "description='A professional digital marketer AI that assists Solopreneurs in "
-        "growing their businesses by providing world-class expertise in solving "
-        "marketing problems for SaaS, content products, agencies, and more.', "
-        "goals=['Engage in effective problem-solving, prioritization, planning, and "
-        "supporting execution to address your marketing needs as your virtual Chief "
-        "Marketing Officer.', 'Provide specific, actionable, and concise advice to "
-        "help you make informed decisions without the use of platitudes or overly "
-        "wordy explanations.', 'Identify and prioritize quick wins and cost-effective "
-        "campaigns that maximize results with minimal time and budget investment.', "
-        "'Proactively take the lead in guiding you and offering suggestions when faced "
-        "with unclear information or uncertainty to ensure your marketing strategy "
-        "remains on track.'])"
-    )
-
-    DEFAULT_USER_PROMPT_TEMPLATE = "'{user_objective}'"
-
-    DEFAULT_CREATE_AGENT_FUNCTION = {
-        "name": "create_agent",
-        "description": ("Create a new autonomous AI agent to complete a given task."),
-        "parameters": {
-            "type": "object",
-            "properties": {
-                "agent_name": {
-                    "type": "string",
-                    "description": "A short role-based name for an autonomous agent.",
-                },
-                "agent_role": {
-                    "type": "string",
-                    "description": "An informative one sentence description of what the AI agent does",
-                },
-                "agent_goals": {
-                    "type": "array",
-                    "minItems": 1,
-                    "maxItems": 5,
-                    "items": {
-                        "type": "string",
-                    },
-                    "description": (
-                        "One to five highly effective goals that are optimally aligned with the completion of a "
-                        "specific task. The number and complexity of the goals should correspond to the "
-                        "complexity of the agent's primary objective."
-                    ),
-                },
-            },
-            "required": ["agent_name", "agent_role", "agent_goals"],
-        },
-    }
-
-    default_configuration = NameAndGoalsConfiguration(
-        model_classification=LanguageModelClassification.SMART_MODEL,
-        system_prompt=DEFAULT_SYSTEM_PROMPT,
-        user_prompt_template=DEFAULT_USER_PROMPT_TEMPLATE,
-        create_agent_function=DEFAULT_CREATE_AGENT_FUNCTION,
-    )
-
-    def __init__(
-        self,
-        model_classification: LanguageModelClassification,
-        system_prompt: str,
-        user_prompt_template: str,
-        create_agent_function: str,
-    ):
-        self._model_classification = model_classification
-        self._system_prompt_message = system_prompt
-        self._user_prompt_template = user_prompt_template
-        self._create_agent_function = create_agent_function
-
-    @property
-    def model_classification(self) -> LanguageModelClassification:
-        return self._model_classification
-
-    def build_prompt(self, user_objective: str = "", **kwargs) -> LanguageModelPrompt:
-        system_message = LanguageModelMessage(
-            role=MessageRole.SYSTEM,
-            content=self._system_prompt_message,
-        )
-        user_message = LanguageModelMessage(
-            role=MessageRole.USER,
-            content=self._user_prompt_template.format(
-                user_objective=user_objective,
-            ),
-        )
-        create_agent_function = LanguageModelFunction(
-            json_schema=self._create_agent_function,
-        )
-        prompt = LanguageModelPrompt(
-            messages=[system_message, user_message],
-            functions=[create_agent_function],
-            # TODO
-            tokens_used=0,
-        )
-        return prompt
-
-    def parse_response_content(
-        self,
-        response_content: dict,
-    ) -> dict:
-        """Parse the actual text response from the objective model.
-
-        Args:
-            response_content: The raw response content from the objective model.
-
-        Returns:
-            The parsed response.
-
-        """
-        parsed_response = json_loads(response_content["function_call"]["arguments"])
-        return parsed_response

autogpt/core/planning/strategies/next_ability.py

@@ -1,183 +0,0 @@
-from autogpt.core.configuration import SystemConfiguration, UserConfigurable
-from autogpt.core.planning.base import PromptStrategy
-from autogpt.core.planning.schema import (
-    LanguageModelClassification,
-    LanguageModelPrompt,
-    Task,
-)
-from autogpt.core.planning.strategies.utils import json_loads, to_numbered_list
-from autogpt.core.resource.model_providers import (
-    LanguageModelFunction,
-    LanguageModelMessage,
-    MessageRole,
-)
-
-
-class NextAbilityConfiguration(SystemConfiguration):
-    model_classification: LanguageModelClassification = UserConfigurable()
-    system_prompt_template: str = UserConfigurable()
-    system_info: list[str] = UserConfigurable()
-    user_prompt_template: str = UserConfigurable()
-    additional_ability_arguments: dict = UserConfigurable()
-
-
-class NextAbility(PromptStrategy):
-    DEFAULT_SYSTEM_PROMPT_TEMPLATE = "System Info:\n{system_info}"
-
-    DEFAULT_SYSTEM_INFO = [
-        "The OS you are running on is: {os_info}",
-        "It takes money to let you run. Your API budget is ${api_budget:.3f}",
-        "The current time and date is {current_time}",
-    ]
-
-    DEFAULT_USER_PROMPT_TEMPLATE = (
-        "Your current task is is {task_objective}.\n"
-        "You have taken {cycle_count} actions on this task already. "
-        "Here is the actions you have taken and their results:\n"
-        "{action_history}\n\n"
-        "Here is additional information that may be useful to you:\n"
-        "{additional_info}\n\n"
-        "Additionally, you should consider the following:\n"
-        "{user_input}\n\n"
-        "Your task of {task_objective} is complete when the following acceptance criteria have been met:\n"
-        "{acceptance_criteria}\n\n"
-        "Please choose one of the provided functions to accomplish this task. "
-        "Some tasks may require multiple functions to accomplish. If that is the case, choose the function that "
-        "you think is most appropriate for the current situation given your progress so far."
-    )
-
-    DEFAULT_ADDITIONAL_ABILITY_ARGUMENTS = {
-        "motivation": {
-            "type": "string",
-            "description": "Your justification for choosing choosing this function instead of a different one.",
-        },
-        "self_criticism": {
-            "type": "string",
-            "description": "Thoughtful self-criticism that explains why this function may not be the best choice.",
-        },
-        "reasoning": {
-            "type": "string",
-            "description": "Your reasoning for choosing this function taking into account the `motivation` and weighing the `self_criticism`.",
-        },
-    }
-
-    default_configuration = NextAbilityConfiguration(
-        model_classification=LanguageModelClassification.SMART_MODEL,
-        system_prompt_template=DEFAULT_SYSTEM_PROMPT_TEMPLATE,
-        system_info=DEFAULT_SYSTEM_INFO,
-        user_prompt_template=DEFAULT_USER_PROMPT_TEMPLATE,
-        additional_ability_arguments=DEFAULT_ADDITIONAL_ABILITY_ARGUMENTS,
-    )
-
-    def __init__(
-        self,
-        model_classification: LanguageModelClassification,
-        system_prompt_template: str,
-        system_info: list[str],
-        user_prompt_template: str,
-        additional_ability_arguments: dict,
-    ):
-        self._model_classification = model_classification
-        self._system_prompt_template = system_prompt_template
-        self._system_info = system_info
-        self._user_prompt_template = user_prompt_template
-        self._additional_ability_arguments = additional_ability_arguments
-
-    @property
-    def model_classification(self) -> LanguageModelClassification:
-        return self._model_classification
-
-    def build_prompt(
-        self,
-        task: Task,
-        ability_schema: list[dict],
-        os_info: str,
-        api_budget: float,
-        current_time: str,
-        **kwargs,
-    ) -> LanguageModelPrompt:
-        template_kwargs = {
-            "os_info": os_info,
-            "api_budget": api_budget,
-            "current_time": current_time,
-            **kwargs,
-        }
-
-        for ability in ability_schema:
-            ability["parameters"]["properties"].update(
-                self._additional_ability_arguments
-            )
-            ability["parameters"]["required"] += list(
-                self._additional_ability_arguments.keys()
-            )
-
-        template_kwargs["task_objective"] = task.objective
-        template_kwargs["cycle_count"] = task.context.cycle_count
-        template_kwargs["action_history"] = to_numbered_list(
-            [action.summary() for action in task.context.prior_actions],
-            no_items_response="You have not taken any actions yet.",
-            **template_kwargs,
-        )
-        template_kwargs["additional_info"] = to_numbered_list(
-            [memory.summary() for memory in task.context.memories]
-            + [info for info in task.context.supplementary_info],
-            no_items_response="There is no additional information available at this time.",
-            **template_kwargs,
-        )
-        template_kwargs["user_input"] = to_numbered_list(
-            [user_input for user_input in task.context.user_input],
-            no_items_response="There are no additional considerations at this time.",
-            **template_kwargs,
-        )
-        template_kwargs["acceptance_criteria"] = to_numbered_list(
-            [acceptance_criteria for acceptance_criteria in task.acceptance_criteria],
-            **template_kwargs,
-        )
-
-        template_kwargs["system_info"] = to_numbered_list(
-            self._system_info,
-            **template_kwargs,
-        )
-
-        system_prompt = LanguageModelMessage(
-            role=MessageRole.SYSTEM,
-            content=self._system_prompt_template.format(**template_kwargs),
-        )
-        user_prompt = LanguageModelMessage(
-            role=MessageRole.USER,
-            content=self._user_prompt_template.format(**template_kwargs),
-        )
-        functions = [
-            LanguageModelFunction(json_schema=ability) for ability in ability_schema
-        ]
-
-        return LanguageModelPrompt(
-            messages=[system_prompt, user_prompt],
-            functions=functions,
-            # TODO:
-            tokens_used=0,
-        )
-
-    def parse_response_content(
-        self,
-        response_content: dict,
-    ) -> dict:
-        """Parse the actual text response from the objective model.
-
-        Args:
-            response_content: The raw response content from the objective model.
-
-        Returns:
-            The parsed response.
-
-        """
-        function_name = response_content["function_call"]["name"]
-        function_arguments = json_loads(response_content["function_call"]["arguments"])
-        parsed_response = {
-            "motivation": function_arguments.pop("motivation"),
-            "self_criticism": function_arguments.pop("self_criticism"),
-            "reasoning": function_arguments.pop("reasoning"),
-            "next_ability": function_name,
-            "ability_arguments": function_arguments,
-        }
-        return parsed_response

autogpt/core/planning/strategies/utils.py

@@ -1,27 +0,0 @@
-import ast
-import json
-
-
-def to_numbered_list(
-    items: list[str], no_items_response: str = "", **template_args
-) -> str:
-    if items:
-        return "\n".join(
-            f"{i+1}. {item.format(**template_args)}" for i, item in enumerate(items)
-        )
-    else:
-        return no_items_response
-
-
-def json_loads(json_str: str):
-    # TODO: this is a hack function for now. Trying to see what errors show up in testing.
-    #   Can hopefully just replace with a call to ast.literal_eval (the function api still
-    #   sometimes returns json strings with minor issues like trailing commas).
-    try:
-        return ast.literal_eval(json_str)
-    except json.decoder.JSONDecodeError as e:
-        try:
-            print(f"json decode error {e}. trying literal eval")
-            return ast.literal_eval(json_str)
-        except Exception:
-            breakpoint()

autogpt/core/planning/templates.py

@@ -1,102 +0,0 @@
-# Rules of thumb:
-# - Templates don't add new lines at the end of the string.  This is the
-#   responsibility of the or a consuming template.
-
-####################
-# Planner defaults #
-####################
-
-
-USER_OBJECTIVE = (
-    "Write a wikipedia style article about the project: "
-    "https://github.com/significant-gravitas/Auto-GPT"
-)
-
-
-ABILITIES = (
-    'analyze_code: Analyze Code, args: "code": "<full_code_string>"',
-    'execute_python_file: Execute Python File, args: "filename": "<filename>"',
-    'append_to_file: Append to file, args: "filename": "<filename>", "text": "<text>"',
-    'delete_file: Delete file, args: "filename": "<filename>"',
-    'list_files: List Files in Directory, args: "directory": "<directory>"',
-    'read_file: Read a file, args: "filename": "<filename>"',
-    'write_to_file: Write to file, args: "filename": "<filename>", "text": "<text>"',
-    'google: Google Search, args: "query": "<query>"',
-    'improve_code: Get Improved Code, args: "suggestions": "<list_of_suggestions>", "code": "<full_code_string>"',
-    'browse_website: Browse Website, args: "url": "<url>", "question": "<what_you_want_to_find_on_website>"',
-    'write_tests: Write Tests, args: "code": "<full_code_string>", "focus": "<list_of_focus_areas>"',
-    'get_hyperlinks: Get hyperlinks, args: "url": "<url>"',
-    'get_text_summary: Get text summary, args: "url": "<url>", "question": "<question>"',
-    'task_complete: Task Complete (Shutdown), args: "reason": "<reason>"',
-)
-
-
-# Plan Prompt
-# -----------
-
-
-PLAN_PROMPT_CONSTRAINTS = (
-    "~4000 word limit for short term memory. Your short term memory is short, so "
-    "immediately save important information to files.",
-    "If you are unsure how you previously did something or want to recall past "
-    "events, thinking about similar events will help you remember.",
-    "No user assistance",
-    "Exclusively use the commands listed below e.g. command_name",
-)
-
-PLAN_PROMPT_RESOURCES = (
-    "Internet access for searches and information gathering.",
-    "Long-term memory management.",
-    "File output.",
-)
-
-PLAN_PROMPT_PERFORMANCE_EVALUATIONS = (
-    "Continuously review and analyze your actions to ensure you are performing to"
-    " the best of your abilities.",
-    "Constructively self-criticize your big-picture behavior constantly.",
-    "Reflect on past decisions and strategies to refine your approach.",
-    "Every command has a cost, so be smart and efficient. Aim to complete tasks in"
-    " the least number of steps.",
-    "Write all code to a file",
-)
-
-
-PLAN_PROMPT_RESPONSE_DICT = {
-    "thoughts": {
-        "text": "thought",
-        "reasoning": "reasoning",
-        "plan": "- short bulleted\n- list that conveys\n- long-term plan",
-        "criticism": "constructive self-criticism",
-        "speak": "thoughts summary to say to user",
-    },
-    "command": {"name": "command name", "args": {"arg name": "value"}},
-}
-
-PLAN_PROMPT_RESPONSE_FORMAT = (
-    "You should only respond in JSON format as described below\n"
-    "Response Format:\n"
-    "{response_json_structure}\n"
-    "Ensure the response can be parsed by Python json.loads"
-)
-
-PLAN_TRIGGERING_PROMPT = (
-    "Determine which next command to use, and respond using the format specified above:"
-)
-
-PLAN_PROMPT_MAIN = (
-    "{header}\n\n"
-    "GOALS:\n\n{goals}\n\n"
-    "Info:\n{info}\n\n"
-    "Constraints:\n{constraints}\n\n"
-    "Commands:\n{commands}\n\n"
-    "Resources:\n{resources}\n\n"
-    "Performance Evaluations:\n{performance_evaluations}\n\n"
-    "You should only respond in JSON format as described below\n"
-    "Response Format:\n{response_json_structure}\n"
-    "Ensure the response can be parsed by Python json.loads"
-)
-
-
-###########################
-# Parameterized templates #
-###########################

autogpt/core/plugin/__init__.py

@@ -1,2 +0,0 @@
-"""The plugin system allows the Agent to be extended with new functionality."""
-from autogpt.core.plugin.base import PluginService

autogpt/core/plugin/base.py

@@ -1,155 +0,0 @@
-import abc
-import enum
-from typing import TYPE_CHECKING, Type
-
-from pydantic import BaseModel
-
-from autogpt.core.configuration import SystemConfiguration, UserConfigurable
-
-if TYPE_CHECKING:
-    from autogpt.core.ability import Ability, AbilityRegistry
-    from autogpt.core.memory import Memory
-    from autogpt.core.resource.model_providers import (
-        EmbeddingModelProvider,
-        LanguageModelProvider,
-    )
-
-    # Expand to other types as needed
-    PluginType = (
-        Type[Ability]  # Swappable now
-        | Type[AbilityRegistry]  # Swappable maybe never
-        | Type[LanguageModelProvider]  # Swappable soon
-        | Type[EmbeddingModelProvider]  # Swappable soon
-        | Type[Memory]  # Swappable now
-        #    | Type[Planner]  # Swappable soon
-    )
-
-
-class PluginStorageFormat(str, enum.Enum):
-    """Supported plugin storage formats.
-
-    Plugins can be stored at one of these supported locations.
-
-    """
-
-    INSTALLED_PACKAGE = "installed_package"  # Required now, loads system defaults
-    WORKSPACE = "workspace"  # Required now
-    # OPENAPI_URL = "open_api_url"           # Soon (requires some tooling we don't have yet).
-    # OTHER_FILE_PATH = "other_file_path"    # Maybe later (maybe now)
-    # GIT = "git"                            # Maybe later (or soon)
-    # PYPI = "pypi"                          # Maybe later
-    # AUTOGPT_PLUGIN_SERVICE = "autogpt_plugin_service"  # Long term solution, requires design
-    # AUTO = "auto"                          # Feature for later maybe, automatically find plugin.
-
-
-# Installed package example
-# PluginLocation(
-#     storage_format='installed_package',
-#     storage_route='autogpt_plugins.twitter.SendTwitterMessage'
-# )
-# Workspace example
-# PluginLocation(
-#     storage_format='workspace',
-#     storage_route='relative/path/to/plugin.pkl'
-#     OR
-#     storage_route='relative/path/to/plugin.py'
-# )
-# Git
-# PluginLocation(
-#     storage_format='git',
-#     Exact format TBD.
-#     storage_route='https://github.com/gravelBridge/AutoGPT-WolframAlpha/blob/main/autogpt-wolframalpha/wolfram_alpha.py'
-# )
-# PyPI
-# PluginLocation(
-#     storage_format='pypi',
-#     storage_route='package_name'
-# )
-
-
-# PluginLocation(
-#     storage_format='installed_package',
-#     storage_route='autogpt_plugins.twitter.SendTwitterMessage'
-# )
-
-
-# A plugin storage route.
-#
-# This is a string that specifies where to load a plugin from
-# (e.g. an import path or file path).
-PluginStorageRoute = str
-
-
-class PluginLocation(SystemConfiguration):
-    """A plugin location.
-
-    This is a combination of a plugin storage format and a plugin storage route.
-    It is used by the PluginService to load plugins.
-
-    """
-
-    storage_format: PluginStorageFormat = UserConfigurable()
-    storage_route: PluginStorageRoute = UserConfigurable()
-
-
-class PluginMetadata(BaseModel):
-    """Metadata about a plugin."""
-
-    name: str
-    description: str
-    location: PluginLocation
-
-
-class PluginService(abc.ABC):
-    """Base class for plugin service.
-
-    The plugin service should be stateless. This defines the interface for
-    loading plugins from various storage formats.
-
-    """
-
-    @staticmethod
-    @abc.abstractmethod
-    def get_plugin(plugin_location: PluginLocation) -> "PluginType":
-        """Get a plugin from a plugin location."""
-        ...
-
-    ####################################
-    # Low-level storage format loaders #
-    ####################################
-    @staticmethod
-    @abc.abstractmethod
-    def load_from_file_path(plugin_route: PluginStorageRoute) -> "PluginType":
-        """Load a plugin from a file path."""
-
-        ...
-
-    @staticmethod
-    @abc.abstractmethod
-    def load_from_import_path(plugin_route: PluginStorageRoute) -> "PluginType":
-        """Load a plugin from an import path."""
-        ...
-
-    @staticmethod
-    @abc.abstractmethod
-    def resolve_name_to_path(
-        plugin_route: PluginStorageRoute, path_type: str
-    ) -> PluginStorageRoute:
-        """Resolve a plugin name to a plugin path."""
-        ...
-
-    #####################################
-    # High-level storage format loaders #
-    #####################################
-
-    @staticmethod
-    @abc.abstractmethod
-    def load_from_workspace(plugin_route: PluginStorageRoute) -> "PluginType":
-        """Load a plugin from the workspace."""
-        ...
-
-    @staticmethod
-    @abc.abstractmethod
-    def load_from_installed_package(plugin_route: PluginStorageRoute) -> "PluginType":
-        """Load a plugin from an installed package."""
-        ...

autogpt/core/plugin/simple.py

@@ -1,74 +0,0 @@
-from importlib import import_module
-from typing import TYPE_CHECKING
-
-from autogpt.core.plugin.base import (
-    PluginLocation,
-    PluginService,
-    PluginStorageFormat,
-    PluginStorageRoute,
-)
-
-if TYPE_CHECKING:
-    from autogpt.core.plugin.base import PluginType
-
-
-class SimplePluginService(PluginService):
-    @staticmethod
-    def get_plugin(plugin_location: dict | PluginLocation) -> "PluginType":
-        """Get a plugin from a plugin location."""
-        if isinstance(plugin_location, dict):
-            plugin_location = PluginLocation.parse_obj(plugin_location)
-        if plugin_location.storage_format == PluginStorageFormat.WORKSPACE:
-            return SimplePluginService.load_from_workspace(
-                plugin_location.storage_route
-            )
-        elif plugin_location.storage_format == PluginStorageFormat.INSTALLED_PACKAGE:
-            return SimplePluginService.load_from_installed_package(
-                plugin_location.storage_route
-            )
-        else:
-            raise NotImplementedError(
-                f"Plugin storage format {plugin_location.storage_format} is not implemented."
-            )
-
-    ####################################
-    # Low-level storage format loaders #
-    ####################################
-    @staticmethod
-    def load_from_file_path(plugin_route: PluginStorageRoute) -> "PluginType":
-        """Load a plugin from a file path."""
-        # TODO: Define an on disk storage format and implement this.
-        #   Can pull from existing zip file loading implementation
-        raise NotImplemented("Loading from file path is not implemented.")
-
-    @staticmethod
-    def load_from_import_path(plugin_route: PluginStorageRoute) -> "PluginType":
-        """Load a plugin from an import path."""
-        module_path, _, class_name = plugin_route.rpartition(".")
-        return getattr(import_module(module_path), class_name)
-
-    @staticmethod
-    def resolve_name_to_path(
-        plugin_route: PluginStorageRoute, path_type: str
-    ) -> PluginStorageRoute:
-        """Resolve a plugin name to a plugin path."""
-        # TODO: Implement a discovery system for finding plugins by name from known
-        #   storage locations. E.g. if we know that path_type is a file path, we can
-        #   search the workspace for it. If it's an import path, we can check the core
-        #   system and the auto_gpt_plugins package.
-        raise NotImplemented("Resolving plugin name to path is not implemented.")
-
-    #####################################
-    # High-level storage format loaders #
-    #####################################
-
-    @staticmethod
-    def load_from_workspace(plugin_route: PluginStorageRoute) -> "PluginType":
-        """Load a plugin from the workspace."""
-        plugin = SimplePluginService.load_from_file_path(plugin_route)
-        return plugin
-
-    @staticmethod
-    def load_from_installed_package(plugin_route: PluginStorageRoute) -> "PluginType":
-        plugin = SimplePluginService.load_from_import_path(plugin_route)
-        return plugin

autogpt/core/resource/__init__.py

@@ -1,7 +0,0 @@
-from autogpt.core.resource.schema import (
-    ProviderBudget,
-    ProviderCredentials,
-    ProviderSettings,
-    ProviderUsage,
-    ResourceType,
-)

autogpt/core/resource/model_providers/__init__.py

@@ -1,44 +0,0 @@
-from autogpt.core.resource.model_providers.openai import (
-    OPEN_AI_MODELS,
-    OpenAIModelName,
-    OpenAIProvider,
-    OpenAISettings,
-)
-from autogpt.core.resource.model_providers.schema import (
-    Embedding,
-    EmbeddingModelProvider,
-    EmbeddingModelProviderModelInfo,
-    EmbeddingModelProviderModelResponse,
-    LanguageModelFunction,
-    LanguageModelMessage,
-    LanguageModelProvider,
-    LanguageModelProviderModelInfo,
-    LanguageModelProviderModelResponse,
-    MessageRole,
-    ModelProvider,
-    ModelProviderBudget,
-    ModelProviderCredentials,
-    ModelProviderModelInfo,
-    ModelProviderModelResponse,
-    ModelProviderName,
-    ModelProviderService,
-    ModelProviderSettings,
-    ModelProviderUsage,
-)
-
-__all__ = [
-    "ModelProvider",
-    "ModelProviderName",
-    "ModelProviderSettings",
-    "EmbeddingModelProvider",
-    "EmbeddingModelProviderModelResponse",
-    "LanguageModelProvider",
-    "LanguageModelProviderModelResponse",
-    "LanguageModelFunction",
-    "LanguageModelMessage",
-    "MessageRole",
-    "OpenAIModelName",
-    "OPEN_AI_MODELS",
-    "OpenAIProvider",
-    "OpenAISettings",
-]

autogpt/core/resource/model_providers/openai.py

@@ -1,373 +0,0 @@
-import enum
-import functools
-import logging
-import math
-import time
-from typing import Callable, ParamSpec, TypeVar
-
-import openai
-from openai.error import APIError, RateLimitError
-
-from autogpt.core.configuration import (
-    Configurable,
-    SystemConfiguration,
-    UserConfigurable,
-)
-from autogpt.core.resource.model_providers.schema import (
-    Embedding,
-    EmbeddingModelProvider,
-    EmbeddingModelProviderModelInfo,
-    EmbeddingModelProviderModelResponse,
-    LanguageModelFunction,
-    LanguageModelMessage,
-    LanguageModelProvider,
-    LanguageModelProviderModelInfo,
-    LanguageModelProviderModelResponse,
-    ModelProviderBudget,
-    ModelProviderCredentials,
-    ModelProviderName,
-    ModelProviderService,
-    ModelProviderSettings,
-    ModelProviderUsage,
-)
-
-OpenAIEmbeddingParser = Callable[[Embedding], Embedding]
-OpenAIChatParser = Callable[[str], dict]
-
-
-class OpenAIModelName(str, enum.Enum):
-    ADA = "text-embedding-ada-002"
-    GPT3 = "gpt-3.5-turbo-0613"
-    GPT3_16K = "gpt-3.5-turbo-16k-0613"
-    GPT4 = "gpt-4-0613"
-    GPT4_32K = "gpt-4-32k-0613"
-
-
-OPEN_AI_EMBEDDING_MODELS = {
-    OpenAIModelName.ADA: EmbeddingModelProviderModelInfo(
-        name=OpenAIModelName.ADA,
-        service=ModelProviderService.EMBEDDING,
-        provider_name=ModelProviderName.OPENAI,
-        prompt_token_cost=0.0004,
-        completion_token_cost=0.0,
-        max_tokens=8191,
-        embedding_dimensions=1536,
-    ),
-}
-
-
-OPEN_AI_LANGUAGE_MODELS = {
-    OpenAIModelName.GPT3: LanguageModelProviderModelInfo(
-        name=OpenAIModelName.GPT3,
-        service=ModelProviderService.LANGUAGE,
-        provider_name=ModelProviderName.OPENAI,
-        prompt_token_cost=0.0015,
-        completion_token_cost=0.002,
-        max_tokens=4096,
-    ),
-    OpenAIModelName.GPT3_16K: LanguageModelProviderModelInfo(
-        name=OpenAIModelName.GPT3,
-        service=ModelProviderService.LANGUAGE,
-        provider_name=ModelProviderName.OPENAI,
-        prompt_token_cost=0.003,
-        completion_token_cost=0.002,
-        max_tokens=16384,
-    ),
-    OpenAIModelName.GPT4: LanguageModelProviderModelInfo(
-        name=OpenAIModelName.GPT4,
-        service=ModelProviderService.LANGUAGE,
-        provider_name=ModelProviderName.OPENAI,
-        prompt_token_cost=0.03,
-        completion_token_cost=0.06,
-        max_tokens=8192,
-    ),
-    OpenAIModelName.GPT4_32K: LanguageModelProviderModelInfo(
-        name=OpenAIModelName.GPT4_32K,
-        service=ModelProviderService.LANGUAGE,
-        provider_name=ModelProviderName.OPENAI,
-        prompt_token_cost=0.06,
-        completion_token_cost=0.12,
-        max_tokens=32768,
-    ),
-}
-
-
-OPEN_AI_MODELS = {
-    **OPEN_AI_LANGUAGE_MODELS,
-    **OPEN_AI_EMBEDDING_MODELS,
-}
-
-
-class OpenAIConfiguration(SystemConfiguration):
-    retries_per_request: int = UserConfigurable()
-
-
-class OpenAIModelProviderBudget(ModelProviderBudget):
-    graceful_shutdown_threshold: float = UserConfigurable()
-    warning_threshold: float = UserConfigurable()
-
-
-class OpenAISettings(ModelProviderSettings):
-    configuration: OpenAIConfiguration
-    credentials: ModelProviderCredentials()
-    budget: OpenAIModelProviderBudget
-
-
-class OpenAIProvider(
-    Configurable,
-    LanguageModelProvider,
-    EmbeddingModelProvider,
-):
-    default_settings = OpenAISettings(
-        name="openai_provider",
-        description="Provides access to OpenAI's API.",
-        configuration=OpenAIConfiguration(
-            retries_per_request=10,
-        ),
-        credentials=ModelProviderCredentials(),
-        budget=OpenAIModelProviderBudget(
-            total_budget=math.inf,
-            total_cost=0.0,
-            remaining_budget=math.inf,
-            usage=ModelProviderUsage(
-                prompt_tokens=0,
-                completion_tokens=0,
-                total_tokens=0,
-            ),
-            graceful_shutdown_threshold=0.005,
-            warning_threshold=0.01,
-        ),
-    )
-
-    def __init__(
-        self,
-        settings: OpenAISettings,
-        logger: logging.Logger,
-    ):
-        self._configuration = settings.configuration
-        self._credentials = settings.credentials
-        self._budget = settings.budget
-
-        self._logger = logger
-
-        retry_handler = _OpenAIRetryHandler(
-            logger=self._logger,
-            num_retries=self._configuration.retries_per_request,
-        )
-
-        self._create_completion = retry_handler(_create_completion)
-        self._create_embedding = retry_handler(_create_embedding)
-
-    def get_token_limit(self, model_name: str) -> int:
-        """Get the token limit for a given model."""
-        return OPEN_AI_MODELS[model_name].max_tokens
-
-    def get_remaining_budget(self) -> float:
-        """Get the remaining budget."""
-        return self._budget.remaining_budget
-
-    async def create_language_completion(
-        self,
-        model_prompt: list[LanguageModelMessage],
-        functions: list[LanguageModelFunction],
-        model_name: OpenAIModelName,
-        completion_parser: Callable[[dict], dict],
-        **kwargs,
-    ) -> LanguageModelProviderModelResponse:
-        """Create a completion using the OpenAI API."""
-        completion_kwargs = self._get_completion_kwargs(model_name, functions, **kwargs)
-        response = await self._create_completion(
-            messages=model_prompt,
-            **completion_kwargs,
-        )
-        response_args = {
-            "model_info": OPEN_AI_LANGUAGE_MODELS[model_name],
-            "prompt_tokens_used": response.usage.prompt_tokens,
-            "completion_tokens_used": response.usage.completion_tokens,
-        }
-
-        parsed_response = completion_parser(
-            response.choices[0].message.to_dict_recursive()
-        )
-        response = LanguageModelProviderModelResponse(
-            content=parsed_response, **response_args
-        )
-        self._budget.update_usage_and_cost(response)
-        return response
-
-    async def create_embedding(
-        self,
-        text: str,
-        model_name: OpenAIModelName,
-        embedding_parser: Callable[[Embedding], Embedding],
-        **kwargs,
-    ) -> EmbeddingModelProviderModelResponse:
-        """Create an embedding using the OpenAI API."""
-        embedding_kwargs = self._get_embedding_kwargs(model_name, **kwargs)
-        response = await self._create_embedding(text=text, **embedding_kwargs)
-
-        response_args = {
-            "model_info": OPEN_AI_EMBEDDING_MODELS[model_name],
-            "prompt_tokens_used": response.usage.prompt_tokens,
-            "completion_tokens_used": response.usage.completion_tokens,
-        }
-        response = EmbeddingModelProviderModelResponse(
-            **response_args,
-            embedding=embedding_parser(response.embeddings[0]),
-        )
-        self._budget.update_usage_and_cost(response)
-        return response
-
-    def _get_completion_kwargs(
-        self,
-        model_name: OpenAIModelName,
-        functions: list[LanguageModelFunction],
-        **kwargs,
-    ) -> dict:
-        """Get kwargs for completion API call.
-
-        Args:
-            model: The model to use.
-            kwargs: Keyword arguments to override the default values.
-
-        Returns:
-            The kwargs for the chat API call.
-
-        """
-        completion_kwargs = {
-            "model": model_name,
-            **kwargs,
-            **self._credentials.unmasked(),
-        }
-        if functions:
-            completion_kwargs["functions"] = functions
-
-        return completion_kwargs
-
-    def _get_embedding_kwargs(
-        self,
-        model_name: OpenAIModelName,
-        **kwargs,
-    ) -> dict:
-        """Get kwargs for embedding API call.
-
-        Args:
-            model: The model to use.
-            kwargs: Keyword arguments to override the default values.
-
-        Returns:
-            The kwargs for the embedding API call.
-
-        """
-        embedding_kwargs = {
-            "model": model_name,
-            **kwargs,
-            **self._credentials.unmasked(),
-        }
-
-        return embedding_kwargs
-
-    def __repr__(self):
-        return "OpenAIProvider()"
-
-
-async def _create_embedding(text: str, *_, **kwargs) -> openai.Embedding:
-    """Embed text using the OpenAI API.
-
-    Args:
-        text str: The text to embed.
-        model_name str: The name of the model to use.
-
-    Returns:
-        str: The embedding.
-    """
-    return await openai.Embedding.acreate(
-        input=[text],
-        **kwargs,
-    )
-
-
-async def _create_completion(
-    messages: list[LanguageModelMessage], *_, **kwargs
-) -> openai.Completion:
-    """Create a chat completion using the OpenAI API.
-
-    Args:
-        messages: The prompt to use.
-
-    Returns:
-        The completion.
-
-    """
-    messages = [message.dict() for message in messages]
-    if "functions" in kwargs:
-        kwargs["functions"] = [function.json_schema for function in kwargs["functions"]]
-    return await openai.ChatCompletion.acreate(
-        messages=messages,
-        **kwargs,
-    )
-
-
-_T = TypeVar("_T")
-_P = ParamSpec("_P")
-
-
-class _OpenAIRetryHandler:
-    """Retry Handler for OpenAI API call.
-
-    Args:
-        num_retries int: Number of retries. Defaults to 10.
-        backoff_base float: Base for exponential backoff. Defaults to 2.
-        warn_user bool: Whether to warn the user. Defaults to True.
-    """
-
-    _retry_limit_msg = "Error: Reached rate limit, passing..."
-    _api_key_error_msg = (
-        "Please double check that you have setup a PAID OpenAI API Account. You can "
-        "read more here: https://docs.agpt.co/setup/#getting-an-api-key"
-    )
-    _backoff_msg = "Error: API Bad gateway. Waiting {backoff} seconds..."
-
-    def __init__(
-        self,
-        logger: logging.Logger,
-        num_retries: int = 10,
-        backoff_base: float = 2.0,
-        warn_user: bool = True,
-    ):
-        self._logger = logger
-        self._num_retries = num_retries
-        self._backoff_base = backoff_base
-        self._warn_user = warn_user
-
-    def _log_rate_limit_error(self) -> None:
-        self._logger.debug(self._retry_limit_msg)
-        if self._warn_user:
-            self._logger.warning(self._api_key_error_msg)
-            self._warn_user = False
-
-    def _backoff(self, attempt: int) -> None:
-        backoff = self._backoff_base ** (attempt + 2)
-        self._logger.debug(self._backoff_msg.format(backoff=backoff))
-        time.sleep(backoff)
-
-    def __call__(self, func: Callable[_P, _T]) -> Callable[_P, _T]:
-        @functools.wraps(func)
-        async def _wrapped(*args: _P.args, **kwargs: _P.kwargs) -> _T:
-            num_attempts = self._num_retries + 1  # +1 for the first attempt
-            for attempt in range(1, num_attempts + 1):
-                try:
-                    return await func(*args, **kwargs)
-
-                except RateLimitError:
-                    if attempt == num_attempts:
-                        raise
-                    self._log_rate_limit_error()
-
-                except APIError as e:
-                    if (e.http_status != 502) or (attempt == num_attempts):
-                        raise
-
-                self._backoff(attempt)
-
-        return _wrapped

autogpt/core/resource/model_providers/schema.py

@@ -1,219 +0,0 @@
-import abc
-import enum
-from typing import Callable, ClassVar
-
-from pydantic import BaseModel, Field, SecretStr, validator
-
-from autogpt.core.configuration import UserConfigurable
-from autogpt.core.resource.schema import (
-    Embedding,
-    ProviderBudget,
-    ProviderCredentials,
-    ProviderSettings,
-    ProviderUsage,
-    ResourceType,
-)
-
-
-class ModelProviderService(str, enum.Enum):
-    """A ModelService describes what kind of service the model provides."""
-
-    EMBEDDING: str = "embedding"
-    LANGUAGE: str = "language"
-    TEXT: str = "text"
-
-
-class ModelProviderName(str, enum.Enum):
-    OPENAI: str = "openai"
-
-
-class MessageRole(str, enum.Enum):
-    USER = "user"
-    SYSTEM = "system"
-    ASSISTANT = "assistant"
-
-
-class LanguageModelMessage(BaseModel):
-    role: MessageRole
-    content: str
-
-
-class LanguageModelFunction(BaseModel):
-    json_schema: dict
-
-
-class ModelProviderModelInfo(BaseModel):
-    """Struct for model information.
-
-    Would be lovely to eventually get this directly from APIs, but needs to be
-    scraped from websites for now.
-
-    """
-
-    name: str
-    service: ModelProviderService
-    provider_name: ModelProviderName
-    prompt_token_cost: float = 0.0
-    completion_token_cost: float = 0.0
-
-
-class ModelProviderModelResponse(BaseModel):
-    """Standard response struct for a response from a model."""
-
-    prompt_tokens_used: int
-    completion_tokens_used: int
-    model_info: ModelProviderModelInfo
-
-
-class ModelProviderCredentials(ProviderCredentials):
-    """Credentials for a model provider."""
-
-    api_key: SecretStr | None = UserConfigurable(default=None)
-    api_type: SecretStr | None = UserConfigurable(default=None)
-    api_base: SecretStr | None = UserConfigurable(default=None)
-    api_version: SecretStr | None = UserConfigurable(default=None)
-    deployment_id: SecretStr | None = UserConfigurable(default=None)
-
-    def unmasked(self) -> dict:
-        return unmask(self)
-
-    class Config:
-        extra = "ignore"
-
-
-def unmask(model: BaseModel):
-    unmasked_fields = {}
-    for field_name, field in model.__fields__.items():
-        value = getattr(model, field_name)
-        if isinstance(value, SecretStr):
-            unmasked_fields[field_name] = value.get_secret_value()
-        else:
-            unmasked_fields[field_name] = value
-    return unmasked_fields
-
-
-class ModelProviderUsage(ProviderUsage):
-    """Usage for a particular model from a model provider."""
-
-    completion_tokens: int = 0
-    prompt_tokens: int = 0
-    total_tokens: int = 0
-
-    def update_usage(
-        self,
-        model_response: ModelProviderModelResponse,
-    ) -> None:
-        self.completion_tokens += model_response.completion_tokens_used
-        self.prompt_tokens += model_response.prompt_tokens_used
-        self.total_tokens += (
-            model_response.completion_tokens_used + model_response.prompt_tokens_used
-        )
-
-
-class ModelProviderBudget(ProviderBudget):
-    total_budget: float = UserConfigurable()
-    total_cost: float
-    remaining_budget: float
-    usage: ModelProviderUsage
-
-    def update_usage_and_cost(
-        self,
-        model_response: ModelProviderModelResponse,
-    ) -> None:
-        """Update the usage and cost of the provider."""
-        model_info = model_response.model_info
-        self.usage.update_usage(model_response)
-        incremental_cost = (
-            model_response.completion_tokens_used * model_info.completion_token_cost
-            + model_response.prompt_tokens_used * model_info.prompt_token_cost
-        ) / 1000.0
-        self.total_cost += incremental_cost
-        self.remaining_budget -= incremental_cost
-
-
-class ModelProviderSettings(ProviderSettings):
-    resource_type = ResourceType.MODEL
-    credentials: ModelProviderCredentials
-    budget: ModelProviderBudget
-
-
-class ModelProvider(abc.ABC):
-    """A ModelProvider abstracts the details of a particular provider of models."""
-
-    defaults: ClassVar[ModelProviderSettings]
-
-    @abc.abstractmethod
-    def get_token_limit(self, model_name: str) -> int:
-        ...
-
-    @abc.abstractmethod
-    def get_remaining_budget(self) -> float:
-        ...
-
-
-####################
-# Embedding Models #
-####################
-
-
-class EmbeddingModelProviderModelInfo(ModelProviderModelInfo):
-    """Struct for embedding model information."""
-
-    model_service = ModelProviderService.EMBEDDING
-    embedding_dimensions: int
-
-
-class EmbeddingModelProviderModelResponse(ModelProviderModelResponse):
-    """Standard response struct for a response from an embedding model."""
-
-    embedding: Embedding = Field(default_factory=list)
-
-    @classmethod
-    @validator("completion_tokens_used")
-    def _verify_no_completion_tokens_used(cls, v):
-        if v > 0:
-            raise ValueError("Embeddings should not have completion tokens used.")
-        return v
-
-
-class EmbeddingModelProvider(ModelProvider):
-    @abc.abstractmethod
-    async def create_embedding(
-        self,
-        text: str,
-        model_name: str,
-        embedding_parser: Callable[[Embedding], Embedding],
-        **kwargs,
-    ) -> EmbeddingModelProviderModelResponse:
-        ...
-
-
-###################
-# Language Models #
-###################
-
-
-class LanguageModelProviderModelInfo(ModelProviderModelInfo):
-    """Struct for language model information."""
-
-    model_service = ModelProviderService.LANGUAGE
-    max_tokens: int
-
-
-class LanguageModelProviderModelResponse(ModelProviderModelResponse):
-    """Standard response struct for a response from a language model."""
-
-    content: dict = None
-
-
-class LanguageModelProvider(ModelProvider):
-    @abc.abstractmethod
-    async def create_language_completion(
-        self,
-        model_prompt: list[LanguageModelMessage],
-        functions: list[LanguageModelFunction],
-        model_name: str,
-        completion_parser: Callable[[dict], dict],
-        **kwargs,
-    ) -> LanguageModelProviderModelResponse:
-        ...

autogpt/core/resource/schema.py

@@ -1,57 +0,0 @@
-import abc
-import enum
-
-from pydantic import SecretBytes, SecretField, SecretStr
-
-from autogpt.core.configuration import (
-    SystemConfiguration,
-    SystemSettings,
-    UserConfigurable,
-)
-
-
-class ResourceType(str, enum.Enum):
-    """An enumeration of resource types."""
-
-    MODEL = "model"
-    MEMORY = "memory"
-
-
-class ProviderUsage(SystemConfiguration, abc.ABC):
-    @abc.abstractmethod
-    def update_usage(self, *args, **kwargs) -> None:
-        """Update the usage of the resource."""
-        ...
-
-
-class ProviderBudget(SystemConfiguration):
-    total_budget: float = UserConfigurable()
-    total_cost: float
-    remaining_budget: float
-    usage: ProviderUsage
-
-    @abc.abstractmethod
-    def update_usage_and_cost(self, *args, **kwargs) -> None:
-        """Update the usage and cost of the resource."""
-        ...
-
-
-class ProviderCredentials(SystemConfiguration):
-    """Struct for credentials."""
-
-    class Config:
-        json_encoders = {
-            SecretStr: lambda v: v.get_secret_value() if v else None,
-            SecretBytes: lambda v: v.get_secret_value() if v else None,
-            SecretField: lambda v: v.get_secret_value() if v else None,
-        }
-
-
-class ProviderSettings(SystemSettings):
-    resource_type: ResourceType
-    credentials: ProviderCredentials | None = None
-    budget: ProviderBudget | None = None
-
-
-# Used both by model providers and memory providers
-Embedding = list[float]

autogpt/core/runner/__init__.py

@@ -1,3 +0,0 @@
-"""
-This module contains the runner for the v2 agent server and client.
-"""

autogpt/core/runner/cli_app/cli.py

@@ -1,47 +0,0 @@
-from pathlib import Path
-
-import click
-import yaml
-
-from autogpt.core.runner.cli_app.main import run_auto_gpt
-from autogpt.core.runner.client_lib.shared_click_commands import (
-    DEFAULT_SETTINGS_FILE,
-    make_settings,
-)
-from autogpt.core.runner.client_lib.utils import coroutine, handle_exceptions
-
-
-@click.group()
-def autogpt():
-    """Temporary command group for v2 commands."""
-    pass
-
-
-autogpt.add_command(make_settings)
-
-
-@autogpt.command()
-@click.option(
-    "--settings-file",
-    type=click.Path(),
-    default=DEFAULT_SETTINGS_FILE,
-)
-@click.option(
-    "--pdb",
-    is_flag=True,
-    help="Drop into a debugger if an error is raised.",
-)
-@coroutine
-async def run(settings_file: str, pdb: bool) -> None:
-    """Run the Auto-GPT agent."""
-    click.echo("Running Auto-GPT agent...")
-    settings_file = Path(settings_file)
-    settings = {}
-    if settings_file.exists():
-        settings = yaml.safe_load(settings_file.read_text())
-    main = handle_exceptions(run_auto_gpt, with_debugger=pdb)
-    await main(settings)
-
-
-if __name__ == "__main__":
-    autogpt()

autogpt/core/runner/cli_app/main.py

@@ -1,110 +0,0 @@
-import click
-
-from autogpt.core.agent import AgentSettings, SimpleAgent
-from autogpt.core.runner.client_lib.logging import get_client_logger
-
-
-async def run_auto_gpt(user_configuration: dict):
-    """Run the Auto-GPT CLI client."""
-
-    client_logger = get_client_logger()
-    client_logger.debug("Getting agent settings")
-
-    agent_workspace = (
-        user_configuration.get("workspace", {}).get("configuration", {}).get("root", "")
-    )
-
-    if not agent_workspace:  # We don't have an agent yet.
-        #################
-        # Bootstrapping #
-        #################
-        # Step 1. Collate the user's settings with the default system settings.
-        agent_settings: AgentSettings = SimpleAgent.compile_settings(
-            client_logger,
-            user_configuration,
-        )
-
-        # Step 2. Get a name and goals for the agent.
-        # First we need to figure out what the user wants to do with the agent.
-        # We'll do this by asking the user for a prompt.
-        user_objective = click.prompt("What do you want Auto-GPT to do?")
-        # Ask a language model to determine a name and goals for a suitable agent.
-        name_and_goals = await SimpleAgent.determine_agent_name_and_goals(
-            user_objective,
-            agent_settings,
-            client_logger,
-        )
-        print(parse_agent_name_and_goals(name_and_goals))
-        # Finally, update the agent settings with the name and goals.
-        agent_settings.update_agent_name_and_goals(name_and_goals)
-
-        # Step 3. Provision the agent.
-        agent_workspace = SimpleAgent.provision_agent(agent_settings, client_logger)
-        print("agent is provisioned")
-
-    # launch agent interaction loop
-    agent = SimpleAgent.from_workspace(
-        agent_workspace,
-        client_logger,
-    )
-    print("agent is loaded")
-
-    plan = await agent.build_initial_plan()
-    print(parse_agent_plan(plan))
-
-    while True:
-        current_task, next_ability = await agent.determine_next_ability(plan)
-        print(parse_next_ability(current_task, next_ability))
-        user_input = click.prompt(
-            "Should the agent proceed with this ability?",
-            default="y",
-        )
-        ability_result = await agent.execute_next_ability(user_input)
-        print(parse_ability_result(ability_result))
-
-
-def parse_agent_name_and_goals(name_and_goals: dict) -> str:
-    parsed_response = f"Agent Name: {name_and_goals['agent_name']}\n"
-    parsed_response += f"Agent Role: {name_and_goals['agent_role']}\n"
-    parsed_response += "Agent Goals:\n"
-    for i, goal in enumerate(name_and_goals["agent_goals"]):
-        parsed_response += f"{i+1}. {goal}\n"
-    return parsed_response
-
-
-def parse_agent_plan(plan: dict) -> str:
-    parsed_response = f"Agent Plan:\n"
-    for i, task in enumerate(plan["task_list"]):
-        parsed_response += f"{i+1}. {task['objective']}\n"
-        parsed_response += f"Task type: {task['type']}  "
-        parsed_response += f"Priority: {task['priority']}\n"
-        parsed_response += f"Ready Criteria:\n"
-        for j, criteria in enumerate(task["ready_criteria"]):
-            parsed_response += f"    {j+1}. {criteria}\n"
-        parsed_response += f"Acceptance Criteria:\n"
-        for j, criteria in enumerate(task["acceptance_criteria"]):
-            parsed_response += f"    {j+1}. {criteria}\n"
-        parsed_response += "\n"
-
-    return parsed_response
-
-
-def parse_next_ability(current_task, next_ability: dict) -> str:
-    parsed_response = f"Current Task: {current_task.objective}\n"
-    ability_args = ", ".join(
-        f"{k}={v}" for k, v in next_ability["ability_arguments"].items()
-    )
-    parsed_response += f"Next Ability: {next_ability['next_ability']}({ability_args})\n"
-    parsed_response += f"Motivation: {next_ability['motivation']}\n"
-    parsed_response += f"Self-criticism: {next_ability['self_criticism']}\n"
-    parsed_response += f"Reasoning: {next_ability['reasoning']}\n"
-    return parsed_response
-
-
-def parse_ability_result(ability_result) -> str:
-    parsed_response = f"Ability: {ability_result['ability_name']}\n"
-    parsed_response += f"Ability Arguments: {ability_result['ability_args']}\n"
-    parsed_response = f"Ability Result: {ability_result['success']}\n"
-    parsed_response += f"Message: {ability_result['message']}\n"
-    parsed_response += f"Data: {ability_result['new_knowledge']}\n"
-    return parsed_response

autogpt/core/runner/cli_web_app/cli.py

@@ -1,101 +0,0 @@
-import contextlib
-import pathlib
-import shlex
-import subprocess
-import sys
-import time
-
-import click
-import requests
-import uvicorn
-import yaml
-
-from autogpt.core.runner.client_lib.shared_click_commands import (
-    DEFAULT_SETTINGS_FILE,
-    make_settings,
-    status,
-)
-from autogpt.core.runner.client_lib.utils import coroutine
-
-
-@click.group()
-def autogpt():
-    """Temporary command group for v2 commands."""
-    pass
-
-
-autogpt.add_command(make_settings)
-autogpt.add_command(status)
-
-
-@autogpt.command()
-@click.option(
-    "host",
-    "--host",
-    default="localhost",
-    help="The host for the webserver.",
-    type=click.STRING,
-)
-@click.option(
-    "port",
-    "--port",
-    default=8080,
-    help="The port of the webserver.",
-    type=click.INT,
-)
-def server(host: str, port: int) -> None:
-    """Run the Auto-GPT runner httpserver."""
-    click.echo("Running Auto-GPT runner httpserver...")
-    uvicorn.run(
-        "autogpt.core.runner.cli_web_app.server.api:app",
-        workers=1,
-        host=host,
-        port=port,
-        reload=True,
-    )
-
-
-@autogpt.command()
-@click.option(
-    "--settings-file",
-    type=click.Path(),
-    default=DEFAULT_SETTINGS_FILE,
-)
-@coroutine
-async def client(settings_file) -> None:
-    """Run the Auto-GPT runner client."""
-    settings_file = pathlib.Path(settings_file)
-    settings = {}
-    if settings_file.exists():
-        settings = yaml.safe_load(settings_file.read_text())
-
-    from autogpt.core.runner.cli_web_app.client.client import run
-
-    with autogpt_server():
-        run()
-
-
-@contextlib.contextmanager
-def autogpt_server():
-    host = "localhost"
-    port = 8080
-    cmd = shlex.split(
-        f"{sys.executable} autogpt/core/runner/cli_web_app/cli.py server --host {host} --port {port}"
-    )
-    server_process = subprocess.Popen(
-        args=cmd,
-    )
-    started = False
-
-    while not started:
-        try:
-            requests.get(f"http://{host}:{port}")
-            started = True
-        except requests.exceptions.ConnectionError:
-            time.sleep(0.2)
-    yield server_process
-    server_process.terminate()
-
-
-if __name__ == "__main__":
-    autogpt()

autogpt/core/runner/cli_web_app/client/client.py

@@ -1,16 +0,0 @@
-import json
-
-import requests
-
-
-def run():
-    body = json.dumps(
-        {"ai_name": "HelloBot", "ai_role": "test", "ai_goals": ["goal1", "goal2"]}
-    )
-
-    header = {"Content-Type": "application/json", "openai_api_key": "asdf"}
-    print("Sending: ", header, body)
-    response = requests.post(
-        "http://localhost:8080/api/v1/agents", data=body, headers=header
-    )
-    print(response.content.decode("utf-8"))

autogpt/core/runner/cli_web_app/server/api.py

@@ -1,48 +0,0 @@
-import uuid
-
-from fastapi import APIRouter, FastAPI, Request
-
-from autogpt.core.runner.cli_web_app.server.schema import InteractRequestBody
-
-router = APIRouter()
-
-
-@router.post("/agents")
-async def create_agent(request: Request):
-    """Create a new agent."""
-    agent_id = uuid.uuid4().hex
-    return {"agent_id": agent_id}
-
-
-@router.post("/agents/{agent_id}")
-async def interact(request: Request, agent_id: str, body: InteractRequestBody):
-    """Interact with an agent."""
-
-    # check headers
-
-    # check if agent_id exists
-
-    # get agent object from somewhere, e.g. a database/disk/global dict
-
-    # continue agent interaction with user input
-
-    return {
-        "thoughts": {
-            "thoughts": {
-                "text": "text",
-                "reasoning": "reasoning",
-                "plan": "plan",
-                "criticism": "criticism",
-                "speak": "speak",
-            },
-            "commands": {
-                "name": "name",
-                "args": {"arg_1": "value_1", "arg_2": "value_2"},
-            },
-        },
-        "messages": ["message1", agent_id],
-    }
-
-
-app = FastAPI()
-app.include_router(router, prefix="/api/v1")

autogpt/core/runner/cli_web_app/server/schema.py

@@ -1,36 +0,0 @@
-from uuid import UUID
-
-from pydantic import BaseModel, validator
-
-
-class AgentInfo(BaseModel):
-    id: UUID = None
-    objective: str = ""
-    name: str = ""
-    role: str = ""
-    goals: list[str] = []
-
-
-class AgentConfiguration(BaseModel):
-    """Configuration for creation of a new agent."""
-
-    # We'll want to get this schema from the configuration, so it needs to be dynamic.
-    user_configuration: dict
-    agent_goals: AgentInfo
-
-    @validator("agent_goals")
-    def only_objective_or_name_role_goals(cls, agent_goals):
-        goals_specification = [agent_goals.name, agent_goals.role, agent_goals.goals]
-        if agent_goals.objective and any(goals_specification):
-            raise ValueError("Cannot specify both objective and name, role, or goals")
-        if not agent_goals.objective and not all(goals_specification):
-            raise ValueError("Must specify either objective or name, role, and goals")
-
-
-class InteractRequestBody(BaseModel):
-    user_input: str = ""
-
-
-class InteractResponseBody(BaseModel):
-    thoughts: dict[str, str]  # TBD
-    messages: list[str]  # for example

autogpt/core/runner/cli_web_app/server/services/users.py

@@ -1,20 +0,0 @@
-import uuid
-
-from fastapi import Request
-
-
-class UserService:
-    def __init__(self):
-        self.users = {}
-
-    def get_user_id(self, request: Request) -> uuid.UUID:
-        # TODO: something real.  I don't know how this works.
-        hostname = request.client.host
-        port = request.client.port
-        user = f"{hostname}:{port}"
-        if user not in self.users:
-            self.users[user] = uuid.uuid4()
-        return self.users[user]
-
-
-USER_SERVICE = UserService()

autogpt/core/runner/client_lib/logging.py

@@ -1,20 +0,0 @@
-import logging
-
-
-def get_client_logger():
-    # Configure logging before we do anything else.
-    # Application logs need a place to live.
-    client_logger = logging.getLogger("autogpt_client_application")
-    client_logger.setLevel(logging.DEBUG)
-
-    formatter = logging.Formatter(
-        "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
-    )
-
-    ch = logging.StreamHandler()
-    ch.setLevel(logging.DEBUG)
-    ch.setFormatter(formatter)
-
-    client_logger.addHandler(ch)
-
-    return client_logger

autogpt/core/runner/client_lib/settings.py

@@ -1,14 +0,0 @@
-from pathlib import Path
-
-import yaml
-
-from autogpt.core.agent import SimpleAgent
-
-
-def make_user_configuration(settings_file_path: Path):
-    user_configuration = SimpleAgent.build_user_configuration()
-
-    settings_file_path.parent.mkdir(parents=True, exist_ok=True)
-    print("Writing settings to", settings_file_path)
-    with settings_file_path.open("w") as f:
-        yaml.safe_dump(user_configuration, f)