
characterfile
A simple file format for character data
Stars: 302

The Characterfile project aims to create a simple format for generating and transmitting character files, compatible with Eliza and other LLM agents. Users can convert their Twitter archive into a character file using the provided scripts. The project also includes examples, JSON schema, and TypeScript types for the character file. Scripts like tweets2character, folder2knowledge, and knowledge2character facilitate the conversion of tweets, documents, and knowledge files into character files for use with AI agents.
README:
The goal of this project is to create a simple, easy-to-use format for generating and transmitting character files. You can use these character files out of the box with Eliza or other LLM agents.
- Open Terminal. On Mac, you can press Command + Spacebar and search for "Terminal". If you're using Windows, use WSL2
- Type
npx tweets2character
and run it. If you get an error about npx not existing, you'll need to install Node.js - If you need to install node, you can do that by pasting
curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.1/install.sh | bash
into your terminal to install Node Version Manager (nvm) - Once that runs, make a new terminal window (the old one will not have the new software linked) and run
nvm install node
followed bynvm use node
- Now copy and paste
npx tweets2character
into your terminal again. - NOTE: You will need to get a Claude or OpenAI API key. Paste that in when prompted
- You will need to get the path of your Twitter archive. If it's in your Downloads folder on a Mac, that's ~/Downloads/.zip
- If everything is correct, you'll see a loading bar as the script processes your tweets and generates a character file. This will be output at character.json in the directory where you run
npx tweets2character
. If you run the commandcd
in the terminal before or after generating the file, you should see where you are.
The JSON schema for the character file is here. This also matches the expected format for OpenAI function calling.
Typescript types for the character file are here.
Basic example of a character file, with values that are instructional examples/example.character.json
Read the example character file and print the contents examples/example.py
Read the example character file and validate it against the JSON schema examples/validate.py
Read the example character file and print the contents examples/example.mjs
Read the example character file and validate it against the JSON schema examples/validate.mjs
You can use the scripts the generate a character file from your tweets, convert a folder of documents into a knowledge file, and add knowledge to your character file.
Most of these scripts require an OpenAI or Anthropic API key.
Convert your twitter archive into a .character.json
First, download your Twitter archive here: https://help.x.com/en/managing-your-account/how-to-download-your-x-archive
You can run tweets2character directly from your command line with no downloads:
npx tweets2character
Note: you will need node.js installed. The easiest way is with nvm.
Then clone this repo and run these commands:
npm install
node scripts/tweets2character.js twitter-2024-07-22-aed6e84e05e7976f87480bc36686bd0fdfb3c96818c2eff2cebc4820477f4da3.zip # path to your zip archive
Note that the arguments are optional and will be prompted for if not provided.
Convert a folder of images and videos into a .knowledge file which you can use with Eliza. Will convert text, markdown and PDF into normalized text in JSON format.
You can run folder2knowledge directly from your command line with no downloads:
npx folder2knowledge <path/to/folder>
npm install
node scripts/folder2knowledge.js path/to/folder # path to your folder
Note that the arguments are optional and will be prompted for if not provided.
Add knowledge to your .character file from a generated knowledge.json file.
You can run knowledge2character directly from your command line with no downloads:
npx knowledge2character <path/to/character.character> <path/to/knowledge.knowledge>
npm install
node scripts/knowledge2character.js path/to/character.character path/to/knowledge.knowledge # path to your character file and knowledge file
Note that the arguments are optional and will be prompted for if not provided.
Process WhatsApp chat exports to create character profiles.
You can run chats2character directly from your command line with no downloads:
npx chats2character -f path/to/chat.txt -u "Username" npx chats2character -d path/to/chats/dir -u "John Doe"
Or if you have cloned the repo:
npm install node scripts/chats2character.js -f path/to/chat.txt -u "Username" node scripts/chats2character.js -d path/to/chats/dir -u "John Doe"
Options: -u, --user Target username as it appears in chats (use quotes for names with spaces) -f, --file Path to single chat export file -d, --dir Path to directory containing chat files -i, --info Path to JSON file containing additional user information -l, --list List all users found in chats --openai [api_key] Use OpenAI model (optionally provide API key) --claude [api_key] Use Claude model (default, optionally provide API key)
Examples:
npx chats2character -d whatsapp/chats --openai sk-... npx chats2character -d whatsapp/chats --claude sk-...
npx chats2character -d whatsapp/chats --openai npx chats2character -d whatsapp/chats --claude
The script will look for API keys in the following order:
- Command line argument if provided
- Environment variables (OPENAI_API_KEY or CLAUDE_API_KEY)
- Cached keys in ~/.eliza/.env
- Prompt for key if none found
Example user info file (info.txt): The user is a mother of two, currently living in Madrid. She works as a high school teacher and has been teaching mathematics for over 15 years. She's very active in the school's parent association and often organizes educational events. In her free time, she enjoys gardening and cooking traditional Spanish recipes.
The file should be a plain text file with descriptive information about the user. This information helps provide context to better understand and analyze the chat messages.
The script will:
- Extract messages from the specified user
- Process content in chunks
- Generate a character profile
- Save results to character.json
Note: WhatsApp chat exports should be in .txt format with standard WhatsApp export formatting: [timestamp] Username: message
For usernames with spaces, make sure to use quotes: [timestamp] John Doe: message
The license is the MIT license, with slight modifications so that users are not required to include the full license in their own software. See LICENSE for more details.
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for characterfile
Similar Open Source Tools

characterfile
The Characterfile project aims to create a simple format for generating and transmitting character files, compatible with Eliza and other LLM agents. Users can convert their Twitter archive into a character file using the provided scripts. The project also includes examples, JSON schema, and TypeScript types for the character file. Scripts like tweets2character, folder2knowledge, and knowledge2character facilitate the conversion of tweets, documents, and knowledge files into character files for use with AI agents.

vector-vein
VectorVein is a no-code AI workflow software inspired by LangChain and langflow, aiming to combine the powerful capabilities of large language models and enable users to achieve intelligent and automated daily workflows through simple drag-and-drop actions. Users can create powerful workflows without the need for programming, automating all tasks with ease. The software allows users to define inputs, outputs, and processing methods to create customized workflow processes for various tasks such as translation, mind mapping, summarizing web articles, and automatic categorization of customer reviews.

mentat
Mentat is an AI tool designed to assist with coding tasks directly from the command line. It combines human creativity with computer-like processing to help users understand new codebases, add new features, and refactor existing code. Unlike other tools, Mentat coordinates edits across multiple locations and files, with the context of the project already in mind. The tool aims to enhance the coding experience by providing seamless assistance and improving edit quality.

FlowTest
FlowTestAI is the world’s first GenAI powered OpenSource Integrated Development Environment (IDE) designed for crafting, visualizing, and managing API-first workflows. It operates as a desktop app, interacting with the local file system, ensuring privacy and enabling collaboration via version control systems. The platform offers platform-specific binaries for macOS, with versions for Windows and Linux in development. It also features a CLI for running API workflows from the command line interface, facilitating automation and CI/CD processes.

llm-code-interpreter
The 'llm-code-interpreter' repository is a deprecated plugin that provides a code interpreter on steroids for ChatGPT by E2B. It gives ChatGPT access to a sandboxed cloud environment with capabilities like running any code, accessing Linux OS, installing programs, using filesystem, running processes, and accessing the internet. The plugin exposes commands to run shell commands, read files, and write files, enabling various possibilities such as running different languages, installing programs, starting servers, deploying websites, and more. It is powered by the E2B API and is designed for agents to freely experiment within a sandboxed environment.

conversational-agent-langchain
This repository contains a Rest-Backend for a Conversational Agent that allows embedding documents, semantic search, QA based on documents, and document processing with Large Language Models. It uses Aleph Alpha and OpenAI Large Language Models to generate responses to user queries, includes a vector database, and provides a REST API built with FastAPI. The project also features semantic search, secret management for API keys, installation instructions, and development guidelines for both backend and frontend components.

airbroke
Airbroke is an open-source error catcher tool designed for modern web applications. It provides a PostgreSQL-based backend with an Airbrake-compatible HTTP collector endpoint and a React-based frontend for error management. The tool focuses on simplicity, maintaining a small database footprint even under heavy data ingestion. Users can ask AI about issues, replay HTTP exceptions, and save/manage bookmarks for important occurrences. Airbroke supports multiple OAuth providers for secure user authentication and offers occurrence charts for better insights into error occurrences. The tool can be deployed in various ways, including building from source, using Docker images, deploying on Vercel, Render.com, Kubernetes with Helm, or Docker Compose. It requires Node.js, PostgreSQL, and specific system resources for deployment.

serverless-chat-langchainjs
This sample shows how to build a serverless chat experience with Retrieval-Augmented Generation using LangChain.js and Azure. The application is hosted on Azure Static Web Apps and Azure Functions, with Azure Cosmos DB for MongoDB vCore as the vector database. You can use it as a starting point for building more complex AI applications.

zep-python
Zep is an open-source platform for building and deploying large language model (LLM) applications. It provides a suite of tools and services that make it easy to integrate LLMs into your applications, including chat history memory, embedding, vector search, and data enrichment. Zep is designed to be scalable, reliable, and easy to use, making it a great choice for developers who want to build LLM-powered applications quickly and easily.

airgradient_esphome
ESPHome yaml files for AirGradient devices to maintain the research and accuracy of AirGradient sensors, while also gaining the benefits of ESPHome/HomeAssistant for easy to use switches, buttons, configurations, and dashboards. Maintains the ability to also send data to the AirGradient Dashboard, which can also be disabled/removed to keep all data local.

langgraph-studio
LangGraph Studio is a specialized agent IDE that enables visualization, interaction, and debugging of complex agentic applications. It offers visual graphs and state editing to better understand agent workflows and iterate faster. Users can collaborate with teammates using LangSmith to debug failure modes. The tool integrates with LangSmith and requires Docker installed. Users can create and edit threads, configure graph runs, add interrupts, and support human-in-the-loop workflows. LangGraph Studio allows interactive modification of project config and graph code, with live sync to the interactive graph for easier iteration on long-running agents.

aws-bedrock-with-rag-and-react
This solution provides a low-code ReactJS application to prototype and vet business use cases for GenAI using Retrieval Augmented Generation (RAG). It includes a backend Flask application that uses LangChain to provide PDF data as embeddings to a text-gen model via Amazon Bedrock and a vector database with FAISS or Kendra Index. The solution utilizes Amazon Bedrock as the only cost-generating AWS service.

reai-ida
RevEng.AI IDA Pro Plugin is a tool that integrates with the RevEng.AI platform to provide various features such as uploading binaries for analysis, downloading analysis logs, renaming function names, generating AI summaries, synchronizing functions between local analysis and the platform, and configuring plugin settings. Users can upload files for analysis, synchronize function names, rename functions, generate block summaries, and explain function behavior using this plugin. The tool requires IDA Pro v8.0 or later with Python 3.9 and higher. It relies on the 'reait' package for functionality.

brokk
Brokk is a code assistant designed to understand code semantically, allowing LLMs to work effectively on large codebases. It offers features like agentic search, summarizing related classes, parsing stack traces, adding source for usages, and autonomously fixing errors. Users can interact with Brokk through different panels and commands, enabling them to manipulate context, ask questions, search codebase, run shell commands, and more. Brokk helps with tasks like debugging regressions, exploring codebase, AI-powered refactoring, and working with dependencies. It is particularly useful for making complex, multi-file edits with o1pro.

AirSane
AirSane is a SANE frontend and scanner server that supports Apple's AirScan protocol. It automatically detects scanners and publishes them through mDNS. Acquired images can be transferred in JPEG, PNG, and PDF/raster format. The tool is intended to be used with AirScan/eSCL clients such as Apple's Image Capture, sane-airscan on Linux, and the eSCL client built into Windows 10 and 11. It provides a simple web interface and encodes images on-the-fly to keep memory/storage demands low, making it suitable for devices like Raspberry Pi. Authentication and secure communication are supported in conjunction with a proxy server like nginx. AirSane has been reverse-engineered from Apple's AirScanScanner client communication protocol and offers a range of installation and configuration options for different operating systems.

lovelaice
Lovelaice is an AI-powered assistant for your terminal and editor. It can run bash commands, search the Internet, answer general and technical questions, complete text files, chat casually, execute code in various languages, and more. Lovelaice is configurable with API keys and LLM models, and can be used for a wide range of tasks requiring bash commands or coding assistance. It is designed to be versatile, interactive, and helpful for daily tasks and projects.
For similar tasks

characterfile
The Characterfile project aims to create a simple format for generating and transmitting character files, compatible with Eliza and other LLM agents. Users can convert their Twitter archive into a character file using the provided scripts. The project also includes examples, JSON schema, and TypeScript types for the character file. Scripts like tweets2character, folder2knowledge, and knowledge2character facilitate the conversion of tweets, documents, and knowledge files into character files for use with AI agents.

docling
Docling is a tool that bundles PDF document conversion to JSON and Markdown in an easy, self-contained package. It can convert any PDF document to JSON or Markdown format, understand detailed page layout, reading order, recover table structures, extract metadata such as title, authors, references, and language, and optionally apply OCR for scanned PDFs. The tool is designed to be stable, lightning fast, and suitable for macOS and Linux environments.

docling
Docling simplifies document processing, parsing diverse formats including advanced PDF understanding, and providing seamless integrations with the general AI ecosystem. It offers features such as parsing multiple document formats, advanced PDF understanding, unified DoclingDocument representation format, various export formats, local execution capabilities, plug-and-play integrations with agentic AI tools, extensive OCR support, and a simple CLI. Coming soon features include metadata extraction, visual language models, chart understanding, and complex chemistry understanding. Docling is installed via pip and works on macOS, Linux, and Windows environments. It provides detailed documentation, examples, integrations with popular frameworks, and support through the discussion section. The codebase is under the MIT license and has been developed by IBM.

markpdfdown
MarkPDFDown is a powerful tool that leverages multimodal large language models to transcribe PDF files into Markdown format. It simplifies the process of converting PDF documents into clean, editable Markdown text by accurately extracting text, preserving formatting, and handling complex document structures including tables, formulas, and diagrams.

datalore-localgen-cli
Datalore is a terminal tool for generating structured datasets from local files like PDFs, Word docs, images, and text. It extracts content, uses semantic search to understand context, applies instructions through a generated schema, and outputs clean, structured data. Perfect for converting raw or unstructured local documents into ready-to-use datasets for training, analysis, or experimentation, all without manual formatting.
For similar jobs

sweep
Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

teams-ai
The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

ai-guide
This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

classifai
Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

chatbot-ui
Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

BricksLLM
BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents
uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

griptape
Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.