
typedai
TypeScript AI platform with AI chat, Autonomous agents, Software developer agents, chatbots and more
Stars: 1007

TypedAI is a TypeScript-first AI platform designed for developers to create and run autonomous AI agents, LLM based workflows, and chatbots. It offers advanced autonomous agents, software developer agents, pull request code review agent, AI chat interface, Slack chatbot, and supports various LLM services. The platform features configurable Human-in-the-loop settings, functional callable tools/integrations, CLI and Web UI interface, and can be run locally or deployed on the cloud with multi-user/SSO support. It leverages the Python AI ecosystem through executing Python scripts/packages and provides flexible run/deploy options like single user mode, Firestore & Cloud Run deployment, and multi-user SSO enterprise deployment. TypedAI also includes UI examples, code examples, and automated LLM function schemas for seamless development and execution of AI workflows.
README:
The TypeScript-first AI platform for developers
Autonomous AI agents and LLM based workflows
Home | Setup | Observability | Function calling | Autonomous AI Agent | AI Software Engineer | AI Code reviews | Tools/Integrations | Roadmap
Features | UI Examples | Code examples | Contributing
TypedAI is a full-featured platform for developing and running agents, LLM based workflows and chatbots.
Included are capable software engineering agents, which have assisted building the platform.
- Advanced Autonomous agents
- Software developer agents
- Pull request code review agent
- AI chat interface
- Slack chatbot
- Supports many LLM services - OpenAI, Anthropic (native & Vertex), Gemini, Groq, Fireworks, Together.ai, DeepSeek, Ollama, Cerebras, X.ai
- Multi-agent extend-reasoning implementations of the LLM interface
- Configurable Human-in-the-loop settings
- Functional callable tools/integrations (Filesystem, Jira, Slack, Perplexity, Google Cloud, Gitlab, GitHub etc)
- CLI and Web UI interface
- Run locally or deployed on the cloud with multi-user/SSO
- OpenTelemetry based observability
- Leverages the extensive Python AI ecosystem through executing Python scripts/packages
- Reasoning/planning inspired from Google's Self-Discover and other papers
- Memory and function call history for complex workflows
- Iterative planning with hierarchical task decomposition
- Sandboxed execution of generated code for multi-step function calling and logic
- LLM function schemas auto-generated from source code
- Human-in-the-loop for budget control, agent initiated questions and error handling
More details at the Autonomous agent docs
- Code Editing Agent for local repositories
- Auto-detection of project initialization, compile, test and lint
- Task file selection agent selects the relevant files
- Design agent creates the implementation plan.
- Code editing loop with compile, lint, test, fix (editing delegates to Aider)
- Compile error analyser can search online, add additional files and packages
- Final review of the changes with an additional code editing loop if required.
- Software Engineer Agent (For ticket to Pull Request workflow):
- Find the appropriate repository from GitLab/GitHub
- Clone and create branch
- Call the Code Editing Agent
- Create merge request
- Code Review agent:
- Configurable code review guidelines
- Posts comments on GitLab merge requests at the appropriate line with suggested changes
- Repository ad hoc query agent
- Codebase awareness - optional index creation used by the task file selection agent
More details at the Software developer agents docs.
- Run from the repository or the provided Dockerfile in single user mode.
- CLI interface
- Web interface
- Scale-to-zero deployment on Firestore & Cloud Run
- Multi-user SSO enterprise deployment (with Google Cloud IAP)
- Terraform, infra scripts and more authentication options coming soon.
Default values can also be set from environment variables.
TypedAI doesn't use LangChain, for many reasons that you can read online
The scope of the TypedAI platform covers functionality found in LangChain and LangSmith.
Let's compare the LangChain document example for Multiple Chains to the equivalent TypedAI implementation.
import { PromptTemplate } from "@langchain/core/prompts";
import { RunnableSequence } from "@langchain/core/runnables";
import { StringOutputParser } from "@langchain/core/output_parsers";
import { ChatAnthropic } from "@langchain/anthropic";
const prompt1 = PromptTemplate.fromTemplate(
`What is the city {person} is from? Only respond with the name of the city.`
);
const prompt2 = PromptTemplate.fromTemplate(
`What country is the city {city} in? Respond in {language}.`
);
const model = new ChatAnthropic({});
const chain = prompt1.pipe(model).pipe(new StringOutputParser());
const combinedChain = RunnableSequence.from([
{
city: chain,
language: (input) => input.language,
},
prompt2,
model,
new StringOutputParser(),
]);
const result = await combinedChain.invoke({
person: "Obama",
language: "German",
});
console.log(result);
import { runAgentWorkflow } from '#agent/agentWorkflowRunner';
import { anthropicLLMs } from '#llms/anthropic'
const cityFromPerson = (person: string) => `What is the city ${person} is from? Only respond with the name of the city.`;
const countryFromCity = (city: string, language: string) => `What country is the city ${city} in? Respond in ${language}.`;
runAgentWorkflow({ llms: anthropicLLMs() }, async () => {
const city = await llms().easy.generateText(cityFromPerson('Obama'));
const country = await llms().easy.generateText(countryFromCity(city, 'German'));
console.log(country);
});
The TypedAI code also has the advantage of static typing with the prompt arguments, enabling you to refactor with ease. Using simple control flow allows easy debugging with breakpoints/logging.
To run a fully autonomous agent:
startAgent({
agentName: 'Create ollama',
initialPrompt: 'Research how to use ollama using node.js and create a new implementation under the llm folder. Look at a couple of the other files in that folder for the style which must be followed',
functions: [FileSystem, Perplexity, CodeEditinAgent],
llms,
});
LLM function calling schema are automatically generated by having the @func
decorator on class methods, avoiding the
definition duplication using zod or JSON.
@funcClass(__filename)
export class Jira {
instance: AxiosInstance | undefined;
/**
* Gets the description of a JIRA issue
* @param {string} issueId - the issue id (e.g. XYZ-123)
* @returns {Promise<string>} the issue description
*/
@func()
async getJiraDescription(issueId: string): Promise<string> {
if (!issueId) throw new Error('issueId is required');
const response = await this.axios().get(`issue/${issueId}`);
return response.data.fields.description;
}
}
We warmly welcome contributions to the project through issues, pull requests or discussions
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for typedai
Similar Open Source Tools

typedai
TypedAI is a TypeScript-first AI platform designed for developers to create and run autonomous AI agents, LLM based workflows, and chatbots. It offers advanced autonomous agents, software developer agents, pull request code review agent, AI chat interface, Slack chatbot, and supports various LLM services. The platform features configurable Human-in-the-loop settings, functional callable tools/integrations, CLI and Web UI interface, and can be run locally or deployed on the cloud with multi-user/SSO support. It leverages the Python AI ecosystem through executing Python scripts/packages and provides flexible run/deploy options like single user mode, Firestore & Cloud Run deployment, and multi-user SSO enterprise deployment. TypedAI also includes UI examples, code examples, and automated LLM function schemas for seamless development and execution of AI workflows.

sophia
Sophia is an open-source TypeScript platform designed for autonomous AI agents and LLM based workflows. It aims to automate processes, review code, assist with refactorings, and support various integrations. The platform offers features like advanced autonomous agents, reasoning/planning inspired by Google's Self-Discover paper, memory and function call history, adaptive iterative planning, and more. Sophia supports multiple LLMs/services, CLI and web interface, human-in-the-loop interactions, flexible deployment options, observability with OpenTelemetry tracing, and specific agents for code editing, software engineering, and code review. It provides a flexible platform for the TypeScript community to expand and support various use cases and integrations.

inferable
Inferable is an open source platform that helps users build reliable LLM-powered agentic automations at scale. It offers a managed agent runtime, durable tool calling, zero network configuration, multiple language support, and is fully open source under the MIT license. Users can define functions, register them with Inferable, and create runs that utilize these functions to automate tasks. The platform supports Node.js/TypeScript, Go, .NET, and React, and provides SDKs, core services, and bootstrap templates for various languages.

composio
Composio is a production-ready toolset for AI agents that enables users to integrate AI agents with various agentic tools effortlessly. It provides support for over 100 tools across different categories, including popular softwares like GitHub, Notion, Linear, Gmail, Slack, and more. Composio ensures managed authorization with support for six different authentication protocols, offering better agentic accuracy and ease of use. Users can easily extend Composio with additional tools, frameworks, and authorization protocols. The toolset is designed to be embeddable and pluggable, allowing for seamless integration and consistent user experience.

starwhale
Starwhale is an MLOps/LLMOps platform that brings efficiency and standardization to machine learning operations. It streamlines the model development lifecycle, enabling teams to optimize workflows around key areas like model building, evaluation, release, and fine-tuning. Starwhale abstracts Model, Runtime, and Dataset as first-class citizens, providing tailored capabilities for common workflow scenarios including Models Evaluation, Live Demo, and LLM Fine-tuning. It is an open-source platform designed for clarity and ease of use, empowering developers to build customized MLOps features tailored to their needs.

nous
Nous is an open-source TypeScript platform for autonomous AI agents and LLM based workflows. It aims to automate processes, support requests, review code, assist with refactorings, and more. The platform supports various integrations, multiple LLMs/services, CLI and web interface, human-in-the-loop interactions, flexible deployment options, observability with OpenTelemetry tracing, and specific agents for code editing, software engineering, and code review. It offers advanced features like reasoning/planning, memory and function call history, hierarchical task decomposition, and control-loop function calling options. Nous is designed to be a flexible platform for the TypeScript community to expand and support different use cases and integrations.

bee-agent-framework
The Bee Agent Framework is an open-source tool for building, deploying, and serving powerful agentic workflows at scale. It provides AI agents, tools for creating workflows in Javascript/Python, a code interpreter, memory optimization strategies, serialization for pausing/resuming workflows, traceability features, production-level control, and upcoming features like model-agnostic support and a chat UI. The framework offers various modules for agents, llms, memory, tools, caching, errors, adapters, logging, serialization, and more, with a roadmap including MLFlow integration, JSON support, structured outputs, chat client, base agent improvements, guardrails, and evaluation.

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.

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.

TornadoVM
TornadoVM is a plug-in to OpenJDK and GraalVM that allows programmers to automatically run Java programs on heterogeneous hardware. TornadoVM targets OpenCL, PTX and SPIR-V compatible devices which include multi-core CPUs, dedicated GPUs (Intel, NVIDIA, AMD), integrated GPUs (Intel HD Graphics and ARM Mali), and FPGAs (Intel and Xilinx).

scikit-llm
Scikit-LLM is a tool that seamlessly integrates powerful language models like ChatGPT into scikit-learn for enhanced text analysis tasks. It allows users to leverage large language models for various text analysis applications within the familiar scikit-learn framework. The tool simplifies the process of incorporating advanced language processing capabilities into machine learning pipelines, enabling users to benefit from the latest advancements in natural language processing.

beyondllm
Beyond LLM offers an all-in-one toolkit for experimentation, evaluation, and deployment of Retrieval-Augmented Generation (RAG) systems. It simplifies the process with automated integration, customizable evaluation metrics, and support for various Large Language Models (LLMs) tailored to specific needs. The aim is to reduce LLM hallucination risks and enhance reliability.

Eco2AI
Eco2AI is a python library for CO2 emission tracking that monitors energy consumption of CPU & GPU devices and estimates equivalent carbon emissions based on regional emission coefficients. Users can easily integrate Eco2AI into their Python scripts by adding a few lines of code. The library records emissions data and device information in a local file, providing detailed session logs with project names, experiment descriptions, start times, durations, power consumption, CO2 emissions, CPU and GPU names, operating systems, and countries.

LocalAI
LocalAI is a free and open-source OpenAI alternative that acts as a drop-in replacement REST API compatible with OpenAI (Elevenlabs, Anthropic, etc.) API specifications for local AI inferencing. It allows users to run LLMs, generate images, audio, and more locally or on-premises with consumer-grade hardware, supporting multiple model families and not requiring a GPU. LocalAI offers features such as text generation with GPTs, text-to-audio, audio-to-text transcription, image generation with stable diffusion, OpenAI functions, embeddings generation for vector databases, constrained grammars, downloading models directly from Huggingface, and a Vision API. It provides a detailed step-by-step introduction in its Getting Started guide and supports community integrations such as custom containers, WebUIs, model galleries, and various bots for Discord, Slack, and Telegram. LocalAI also offers resources like an LLM fine-tuning guide, instructions for local building and Kubernetes installation, projects integrating LocalAI, and a how-tos section curated by the community. It encourages users to cite the repository when utilizing it in downstream projects and acknowledges the contributions of various software from the community.

honey
Bee is an ORM framework that provides easy and high-efficiency database operations, allowing developers to focus on business logic development. It supports various databases and features like automatic filtering, partial field queries, pagination, and JSON format results. Bee also offers advanced functionalities like sharding, transactions, complex queries, and MongoDB ORM. The tool is designed for rapid application development in Java, offering faster development for Java Web and Spring Cloud microservices. The Enterprise Edition provides additional features like financial computing support, automatic value insertion, desensitization, dictionary value conversion, multi-tenancy, and more.
For similar tasks

superagent-py
Superagent is an open-source framework that enables developers to integrate production-ready AI assistants into any application quickly and easily. It provides a Python SDK for interacting with the Superagent API, allowing developers to create, manage, and invoke AI agents. The SDK simplifies the process of building AI-powered applications, making it accessible to developers of all skill levels.

restai
RestAI is an AIaaS (AI as a Service) platform that allows users to create and consume AI agents (projects) using a simple REST API. It supports various types of agents, including RAG (Retrieval-Augmented Generation), RAGSQL (RAG for SQL), inference, vision, and router. RestAI features automatic VRAM management, support for any public LLM supported by LlamaIndex or any local LLM supported by Ollama, a user-friendly API with Swagger documentation, and a frontend for easy access. It also provides evaluation capabilities for RAG agents using deepeval.

rivet
Rivet is a desktop application for creating complex AI agents and prompt chaining, and embedding it in your application. Rivet currently has LLM support for OpenAI GPT-3.5 and GPT-4, Anthropic Claude Instant and Claude 2, [Anthropic Claude 3 Haiku, Sonnet, and Opus](https://www.anthropic.com/news/claude-3-family), and AssemblyAI LeMUR framework for voice data. Rivet has embedding/vector database support for OpenAI Embeddings and Pinecone. Rivet also supports these additional integrations: Audio Transcription from AssemblyAI. Rivet core is a TypeScript library for running graphs created in Rivet. It is used by the Rivet application, but can also be used in your own applications, so that Rivet can call into your own application's code, and your application can call into Rivet graphs.

ai2apps
AI2Apps is a visual IDE for building LLM-based AI agent applications, enabling developers to efficiently create AI agents through drag-and-drop, with features like design-to-development for rapid prototyping, direct packaging of agents into apps, powerful debugging capabilities, enhanced user interaction, efficient team collaboration, flexible deployment, multilingual support, simplified product maintenance, and extensibility through plugins.

foundationallm
FoundationaLLM is a platform designed for deploying, scaling, securing, and governing generative AI in enterprises. It allows users to create AI agents grounded in enterprise data, integrate REST APIs, experiment with large language models, centrally manage AI agents and assets, deploy scalable vectorization data pipelines, enable non-developer users to create their own AI agents, control access with role-based access controls, and harness capabilities from Azure AI and Azure OpenAI. The platform simplifies integration with enterprise data sources, provides fine-grain security controls, load balances across multiple endpoints, and is extensible to new data sources and orchestrators. FoundationaLLM addresses the need for customized copilots or AI agents that are secure, licensed, flexible, and suitable for enterprise-scale production.

learn-applied-generative-ai-fundamentals
This repository is part of the Certified Cloud Native Applied Generative AI Engineer program, focusing on Applied Generative AI Fundamentals. It covers prompt engineering, developing custom GPTs, and Multi AI Agent Systems. The course helps in building a strong understanding of generative AI, applying Large Language Models (LLMs) and diffusion models practically. It introduces principles of prompt engineering to work efficiently with AI, creating custom AI models and GPTs using OpenAI, Azure, and Google technologies. It also utilizes open source libraries like LangChain, CrewAI, and LangGraph to automate tasks and business processes.

shire
The Shire is an AI Coding Agent Language that facilitates communication between an LLM and control IDE for automated programming. It offers a straightforward approach to creating AI agents tailored to individual IDEs, enabling users to build customized AI-driven development environments. The concept of Shire originated from AutoDev, a subproject of UnitMesh, with DevIns as its precursor. The tool provides documentation and resources for implementing AI in software engineering projects.

ai-agents-masterclass
AI Agents Masterclass is a repository dedicated to teaching developers how to use AI agents to transform businesses and create powerful software. It provides weekly videos with accompanying code folders, guiding users on setting up Python environments, using environment variables, and installing necessary packages to run the code. The focus is on Large Language Models that can interact with the outside world to perform tasks like drafting emails, booking appointments, and managing tasks, enabling users to create innovative applications with minimal coding effort.
For similar jobs

goat
GOAT (Great Onchain Agent Toolkit) is an open-source framework designed to simplify the process of making AI agents perform onchain actions by providing a provider-agnostic solution that abstracts away the complexities of interacting with blockchain tools such as wallets, token trading, and smart contracts. It offers a catalog of ready-made blockchain actions for agent developers and allows dApp/smart contract developers to develop plugins for easy access by agents. With compatibility across popular agent frameworks, support for multiple blockchains and wallet providers, and customizable onchain functionalities, GOAT aims to streamline the integration of blockchain capabilities into AI agents.

typedai
TypedAI is a TypeScript-first AI platform designed for developers to create and run autonomous AI agents, LLM based workflows, and chatbots. It offers advanced autonomous agents, software developer agents, pull request code review agent, AI chat interface, Slack chatbot, and supports various LLM services. The platform features configurable Human-in-the-loop settings, functional callable tools/integrations, CLI and Web UI interface, and can be run locally or deployed on the cloud with multi-user/SSO support. It leverages the Python AI ecosystem through executing Python scripts/packages and provides flexible run/deploy options like single user mode, Firestore & Cloud Run deployment, and multi-user SSO enterprise deployment. TypedAI also includes UI examples, code examples, and automated LLM function schemas for seamless development and execution of AI workflows.

appworld
AppWorld is a high-fidelity execution environment of 9 day-to-day apps, operable via 457 APIs, populated with digital activities of ~100 people living in a simulated world. It provides a benchmark of natural, diverse, and challenging autonomous agent tasks requiring rich and interactive coding. The repository includes implementations of AppWorld apps and APIs, along with tests. It also introduces safety features for code execution and provides guides for building agents and extending the benchmark.

assistant-ui
assistant-ui is a set of React components for AI chat. It provides a collection of components that can be easily integrated into projects to create AI chat interfaces for Discord, websites, and demos. The components are designed to streamline the process of setting up AI chat functionality in React applications, making it easier for developers to incorporate AI chat features into their projects.

Simulator-Controller
Simulator Controller is a modular administration and controller application for Sim Racing, featuring a comprehensive plugin automation framework for external controller hardware. It includes voice chat capable Assistants like Virtual Race Engineer, Race Strategist, Race Spotter, and Driving Coach. The tool offers features for setup, strategy development, monitoring races, and more. Developed in AutoHotkey, it supports various simulation games and integrates with third-party applications for enhanced functionality.

RirikoBot
RirikoBot is a powerful AI-powered Discord bot with features like Twitch Live Notifier, Giveaways, OpenAI, Stable Diffusion, Moderations, Anime / Manga Finder, and more. It is based on Discord.js v14 and can be hosted on a PC or a Server. Users can interact with the bot through various commands to access different functionalities.

douyin-chatgpt-bot
Douyin ChatGPT Bot is an AI-driven system for automatic replies on Douyin, including comment and private message replies. It offers features such as comment filtering, customizable robot responses, and automated account management. The system aims to enhance user engagement and brand image on the Douyin platform, providing a seamless experience for managing interactions with followers and potential customers.

fridon-ai
FridonAI is an open-source project offering AI-powered tools for cryptocurrency analysis and blockchain operations. It includes modules like FridonAnalytics for price analysis, FridonSearch for technical indicators, FridonNotifier for custom alerts, FridonBlockchain for blockchain operations, and FridonChat as a unified chat interface. The platform empowers users to create custom AI chatbots, access crypto tools, and interact effortlessly through chat. The core functionality is modular, with plugins, tools, and utilities for easy extension and development. FridonAI implements a scoring system to assess user interactions and incentivize engagement. The application uses Redis extensively for communication and includes a Nest.js backend for system operations.