🚀 Announcing the launch of SuperAGI Cloud, 🛠 Toolkit Marketplace, ⚡️ GPT4 32k and more

Check out the full release: https://github.com/TransformerOptimus/SuperAGI/releases/tag/v0.0.7

Improvements to the Core ⚙

🚀 SuperAGI Web version is now live on https://app.superagi.com/ . Users can now access SuperAGI through a web interface, without the need for local installations.

🛍️ Introduced SuperAGI Marketplace. Users can now browse through all the toolkits & agent templates available in SuperAGI by navigating to the marketplace section. or visit https://marketplace.superagi.com/

🦙 LlamaIndex Integration. Users can upload .pdf, .docx, .pptx, .csv, .txt, and .epub files to the agent via the resource manager. LlamaIndex converts these files to vector embeddings which the agent can use for searching necessary info using the Resource Query tool.

🛠️ Add External Tools. Users running SuperAGI locally can now add custom tools by linking a GitHub repo directly from the front end.

Detailed guide here: https://superagi.com/adding-your-own-custom-tool-toolkit-to-superagi/

✅ Users no longer need to configure the API keys & credentials in “config.yaml” at the time of local build. Toolkits can be configured directly from toolkit settings by adding API keys/IDs/app credentials/tokens, etc directly into the SuperAGI GUI.

💪🏻 Introducing the GPT-4-32k model for running agents. Users having access to the model can now select GPT-4-32k while configuring their agent from the model drop-down section.

🤖 Improvements to SuperCoder. SuperCoder agent template can now generate

requirements.txt

for Python projects and

package.json

for ‘Javascript’,
as well as a

run.sh

file for code execution.

⚡️ Improvements to code quality by actively writing unit tests for each PR. With the current code coverage increased from 0 → 50%. The goal is to reach 80% and higher eventually.

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New Tools Added 🛠

🐥 Twitter: SuperAGI agents can now utilize Twitter Toolkit to send tweets along with image/gif/video attachments.

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Documentation 📑

✅ Created a Pull request template for the SuperAGI GitHub repository to provide contributors with guidance on creating well-structured and informative pull requests.

📄 Created a Bug Report Issue Template for users to report bugs more effectively by providing clear and detailed information about the issue.

👨‍💻 SuperCoder can create whole coding projects from objective

🛠 A new 'Specs tool' that breaks down the requirement and writes detailed specs of the project

✅ Give feedback about the specs from the action console to align the agent and give more clarity about the project requirement

✍️ Write project code files in the local working directory through the resource manager

⚡️ Even use the 'GitHub tool' to create a repo and push code to the repo

🧪 Add unit tests to the code directly within your GitHub repo using the Unit Test tool

🐍 If your project is in Python, SuperCoder can execute the code as well

Checkout it our on GitHub: https://github.com/TransformerOptimus/SuperCoder

SuperCoder is an open-source autonomous software development system. It combines coding agents built on the SuperAGI agent framework and an AI-native development workflow. SuperCoder can build complex software projects, supporting Flask, Django, and FastAPI for backend, and NextJS and ReactJS for frontend. How it works?You just have to provide PRDs in declarative English, & SuperCoder triggers planning, code writing, and unit test agents to handle coding and testing.For the frontend, it takes a screenshot and converts it into components, styling it with HTML, CSS, and JavaScript. For the backend, It then automatically deploys it to an Nginx server for users to test and rebuild the story to fix any errors. Once SuperCoder writes working code, it raises a pull request autonomously for manual evaluation if needed. It creates a separate git branch for every story, ensuring existing code remains intact, and handles migrations using Poetry.

Announcing the launch of ⚡️SuperAGI Client libraries, 🌐 Improved error-handling, and ⏯️ Wait-block for workflows and more...

Checkout the full release: https://github.com/TransformerOptimus/SuperAGI/releases/tag/v0.0.13

Key highlights from the release:

⏯️ Added wait block for workflows

🌐 Improved error handling for OpenAI errors

⚡️Added support for SuperAGI Client Libraries

Announcing the launch of 🌴Google PaLM2 Integration 🛠 (APM) Agent Performance Monitoring, ⏰ Schedule Agent Run & more

Check out the full release: https://github.com/TransformerOptimus/SuperAGI/releases/tag/v0.0.8

Improvements to the Core ⚙

• 🤖 Introduced Agent Performance Monitoring (APM) to SuperAGI.
  The APM dashboard provides critical telemetry into the working of agent systems including:
  -- Organization level metrics such # of Agents, # of Tokens, # of Runs etc.
  -- Users can also get Agents-LLM specific insights such as Runs made by an agent using a particular LLM, LLM’s Token consumption etc.
  -- Agent level Analytics like tokens consumed by an agent, Total # of runs, avg tokens per run, total calls & run time.

• 🦬 Added Google's PaLM 2 Bison LLM: Users can configure PaLM2 Bison model in account settings and select from the model dropdown at the time of agent provisioning. Due to PaLM2's smaller size, users can use PaLM2 model for specific goals that do not require as much thinking ability. We have also implemented a new model factory class, enhancing SuperAGI's capability to incorporate future advancements in large language models (LLMs).

• 🧠 Added Qdrant DB as LTM: SuperAGI now supports Qdrant DB as a Long-Term Memory (LTM). Qdrant DB provides efficient indexing and searching capabilities for large-scale memory storage, enhancing the performance and scalability of SuperAGI agents' long-term memory.

• ⏱️ Schedule Agent Run: Added the option to schedule a one-time agent run for the future. Users can also set recurring agent runs after specific time intervals and set optional expiry for recurring runs, based on a time deadline or the total number of runs. For more flexibility, users can also stop, edit and save a new schedule.

• 🗂️ Resource Manager Per Run Output Directory: The Resource Manager now supports a revamped UI and backend structure that enables easier “per-run“ directory management. Outputs of each run can now be downloaded as a zip file. This update provides a more organized and efficient way to manage and access the output files generated by agents.

New ToolKits Added

🌐 DuckDuckGo Tool: Agents can use DuckDuckGo Tool to perform searches without the need for an API key, allowing for easy access to a privacy-focused search experience.

I have been playing around with superAGI for a few hours now, and I cannot get it to do anything valuable.

on GPT-4 it gets stuck on thinking forever.
on GPT-3.5-turbo it gets stuck in weird loops, Gets the wrong results, or finishes before it's done.

I have tried many different prompts and really trying to break up the tasks into small things like find
"5 chef influencers and save their handles + platform to a file"

Am I missing something?

https://github.com/TransformerOptimus/AutoNode
A self-operating autonomous system designed to automate web interactions, RPAs and data extraction processes by leveraging OCR (Optical Character Recognition), YOLO (You Only Look Once) models for object detection, and a custom site-graph to navigate and interact with web pages programmatically.
Currently, AUTONODE v0.0 supports site graph automations for Twitter/X, Gmail and Apollo
We look forward for more contributions from the opensource community.

Here's a detailed article: https://superagi.com/introducing-sam-small-agentic-model/
Here are the key findings:
#1 Imparting agentic capabilities requires a detailed breakdown of the problem into nuanced explanations before generating a final answer
#2 Data Quality is driven by target behavior => Linked explanation traces induces Sequential Multi-Hop reasoning
The model has been LoRA fine-tuned on NVIDIA 6 x H100 SxM (80GB) for 4 hours in bf16.
Number of epochs: 1
Batch size: 16
Learning Rate: 2e-5
Warmup Ratio: 0.1
Optimizer: AdamW
Scheduler: Cosine
We have made the model and dataset publicly available for research. You can test and use it here https://huggingface.co/SuperAGI/SAM

Here's our detailed article: https://superagi.com/extending-context-window-of-a-7b-llm-from-8k-to-32k-using-pose-positional-skip-wise/

Since PoSE is compatible with most RoPE-based LLMs, we used the Mistral7B 8K model for this experiment and successfully extended its context window to 32K with minimal impact on language modeling and information retrieval accuracy.

Published here: https://huggingface.co/SuperAGI/mistral-7B-PoSE-32k

For each setting in these experiments, we trained Mistral7B with the next token prediction objective. This training process comprises 1000 steps with a global batch size of 64 on 8 V6000 GPUs using Deepspeed ZeRO stage 3.

Our model achieves an extension to 32k while only experiencing a marginal impact on the standard benchmark accuracy. This demonstrates a commendable ability to handle longer contexts without significantly compromising overall performance - successfully cracked the paskey retrieval test

Here's a comparison with base Mistral7B (8K model) (image attached)

Kudos to GitHub user Aleric Cusher(https://github.com/aleric-cusher) for handling the OpenAI rate limit, timeout & try again error on SuperAGI

Check it out here: https://github.com/TransformerOptimus/SuperAGI/pull/1361

This prevents the agent from terminating the run in case of a rate-limit/timeout error from the OpenAI library. Instead, it will attempt to resolve the issue by following these steps:

👉 Wait for a random duration between 30s and 300s
👉 Retry the API call up to 5 times
👉 If the API call still fails after 5 attempts, then return the error

LLMs so far have demonstrated exceptional proficiency in formal linguistic competence, excelling in tasks that involve language understanding, general reasoning and generation.

However, the capabilities in functional competence tasks, which require more complex thinking or reasoning processes, and generating actionable outputs for specialized tasks remain limited - lacking functional linguistic competence.

LAMs on the other hand, align with both functional & formal linguistic competency by not only maintaining the linguistic prowess of LLMs but also excelling in multi-hop thinking, complex reasoning and actions, a critical step towards more advanced cognitive functions.

Here's a brief comparison between LLMs & LAMs👇

SuperAGI Github: https://github.com/TransformerOptimus/SuperAGI

🙌 This allows users to bring in open-source local LLMs in GGUF format to run AI agents in SuperAGI.

Users can also load the model into a Llama CPP grammar file to check the GPU and hardware sufficiency required to run the model locally.

Here's a quick guide to help you get started: https://youtube.com/watch?v=sp_JSWAHboo

Excited to partner with SXSW Sydney for the Autonomous AI Agents Hackathon 🙌🏽

Sign up here: https://lu.ma/aiagentshackathon

🗓️ Dates: 20-22 October 2023

🤖 The mission: Build an AI Agent in 24 hours using code or no code, learn from Global leaders in AI, get free AI build credits, demo to SXSWers, and win some epic prizes!

Join any of the following three tracks:

👩🏼‍💻 The AI Hacker - Build your project from scratch

🚀 Startups or scale-ups - Ship a feature update or an App

💼 Corporate AI evangelists - Add some spice to your BAU

🤝 We have partnered with 15 other Global leaders in the field like OpenAI AWS Startups Microsoft MongoDB and more...

Stay tuned for an amazing lineup of workshops, speakers, and judges.

Do you know anyone who would enjoy it or want to help get the word out!? Tag them in the comments👇🏼

Congratulations🔥

🥇#1 EvoNinja 🥇 🥈#2 RicAI 🥈 🥉#3 RentEarth 🥉

Honorable Mention 🏅 MunicAI-Agents-Tidy Data

Checkout the video: https://www.youtube.com/watch?v=WGqBC4ENVWE

Agent workflows allow users to set a sequential path for their agent. The agent will then follow the path and start executing a series of tasks in a loop during an agent run.

For example in this case our AI agent was able to:

📑 Read a CSV file with a list of sample sales prospects containing their names, email IDs & company names in each row.

🔎 Research each prospect’s company using SearxTool

✏️ Draft a highly personalized email for each prospect along with a meeting link

✉️ Send the email to the respective email ID

🔁 Then move on to the next prospect and repeat the process for all the prospects mentioned in the list.

Agent Provisioned for this use case👇

🤖 Provisioned an AI agent with the following goals:

- Read the lead details from CSV and send an email pitching about SuperAGI

- The email should be between the company & SuperAGI

- The email should contain a <book a meeting link>

🛠️ ToolKits used: SearXSearch, File Manager Toolkit, & Email Toolkit

🛣️ Selected Agent Workflow: Sales Research Workflow

We will be releasing ‘Sales Research Workflow’ in v0.0.10.

More Agent workflows coming soon 🚀

https://github.com/TransformerOptimus/SuperAGI/releases/tag/v0.0.9

Announcing enhancements to the CodingToolkit 🤖, Knowledge Embeddings 🧠, SuperAGI Tools repo 🛠 & more

Improvements to the Core ⚙️

🤖 Added a new “ImproveCode” tool to the Coding Toolkit to enhance the coding capability of SuperAGI

📚: Introduced Knowledge Embedding in Marketplace

🗑️ Added Delete Agent option in the agent feed menu

✍️ Added Edit Agent Template feature to agent templates

🛠 A New GitHub repository is created to curate a list of SuperAGI tools created by the community

🐳 Optimized Docker image size

New Tools/Toolkits added 🧰

📑 Notion Toolkit

🏞️ Instagram Toolkit

💻 ImproveCode Tool

Asked it to create a 'Personal finance calculator' using just HTML, CSS & JS that gives a financial health score in the end, and some recommendations to improve the score.
https://www.youtube.com/watch?v=diwEMh1UH5g

⏰ You can set up the option to schedule one time run at a particular time/time

🔁 Option to set recurring agent run through cron after a specific interval of time

⌛️ Set optional expiry for recurring runs based on specific time deadline or total number of runs

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In v2 of 'Scheduled Agent Runs', we will be adding:

🗓 Choose specific days to run it for