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waggle-dance

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agi-merge/waggle-dance

Knowledge work automation with AI agents

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Last push
TypeScript MITCreated Apr 23, 2023

This repository is archived on GitHub and is no longer actively maintained.

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Maintenance
Archived (936d since push)
As of today · Source: github_public_v1
Provenance
Not a fork · Organization account
As of today · Source: github_public_v1
Security (OSV)
No lockfile
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Overview

Knowledge work automation with AI agents

Capability facts

Deploy
Self-host

Source: dockerfile:Dockerfile · Jul 11, 2026

Docker
Dockerfile present

Source: dockerfile:Dockerfile · Jul 11, 2026

CLI
CLI entrypoint

Source: package.json:bin|scripts · Jul 11, 2026

MCP server
No MCP server detected

Source: repo_scan · Jul 11, 2026

Languages
typescript, javascript

Source: github.language+package.json · Jul 11, 2026

Categories

AI AgentsVector DatabasesLLM Frameworks

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Jul 11, 2026)

ity_, and _simplicity_. Additionally, many other agentic systems are written in Python, so this project acts as a small counter-balance, and is accessible to the larg
Source link

Tags

autogptllmainextjslangchain-jslangchainagent-based-frameworkagent

README

Quick Start

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You can also build and deploy yourself! However, you must configure your environment.

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waggledance.ai is an experimental application focused on achieving user-specified goals. It provides a friendly but opinionated user interface for building agent-based systems. The project focuses on explainability, observability, concurrent generation, and exploration. Currently in pre-alpha, the development philosophy prefers experimentation over stability as goal-solving and Agent systems are rapidly evolving.

waggledance.ai takes a goal and passes it to a Planner Agent which streams an execution graph for sub-tasks. Each sub-task is executed as concurrently as possible by Execution Agents. To reduce poor results and hallucinations, sub-results are reviewed by Criticism Agents. Eventually, the Human in the loop (you!) will be able to chat with individual Agents and provide course-corrections if needed.

It was originally inspired by Auto-GPT, and has concurrency features similar to those found in gpt-researcher. Therefore, core tenets of the project include speed, accuracy, observability, and simplicity. Additionally, many other agentic systems are written in Python, so this project acts as a small counter-balance, and is accessible to the large number of Javascript developers.

An (unstable) API is also available via tRPC as well an API implemented within Next.js. The client-side is mostly responsible for orchestrating and rendering the agent executions, while the API and server-side executes the agents and stores the results. This architecture is likely to be adjusted in the future.