Projects sharing features with Evolver

Book-to-skill converts technical books and other documents into structured skills for Claude Code and other coding assistants. Instead of producing a plain summary, it distills a PDF, a folder of documents, or a set of sources into frameworks, decision rules, anti-patterns, a glossary, and per-chapter reference files. All extraction and analysis runs on the user's own machine, so source documents are never uploaded. The structuring cost is paid once during conversion. When a question comes up later, only the relevant chapter file is loaded into context, which keeps answers grounded in the sou

CodeGen is a trained large language model and program synthesis model designed to generate functional source code. It utilizes a neural network architecture to synthesize executable code from natural language descriptions or partial code snippets. The model enables automated program synthesis and AI-assisted coding by predicting and filling in missing sections of code within a program. It transforms natural language descriptions into functional programming logic to automate the creation of boilerplate and logic.

This repository is a practical written guide, structured as a 21-section tutorial book, to an open-source AI agent framework called Hermes Agent. It walks readers from a first introduction through advanced, real-world usage, covering how to set up and use the framework rather than shipping the framework itself. The guide centers on what distinguishes the framework: a built-in learning loop that turns completed tasks into reusable skills, refines them through use, and prunes what is not worth keeping. It also covers a three-layer memory design combined with session search, so an agent can reca

Granite Code Models is a family of transformer-based foundational models designed for software engineering and logical reasoning tasks. These models are trained on high-quality programming datasets to interpret natural language prompts and generate functional source code, explain complex logic, repair code defects, and produce technical documentation. The project distinguishes itself through specialized training methodologies that align model behavior with complex programming instructions and mathematical problem-solving. By utilizing chain-of-thought reasoning and instruction-tuned parameter

GenericAgent is an LLM agent framework and autonomous system controller designed to manage local systems, web browsers, and hardware interfaces through action and observation loops. It functions as a tool orchestrator that routes model calls to local executors, enabling the automation of complex tasks on a host machine. The project is distinguished by its self-evolving AI agent capabilities, which convert successful execution paths into reusable procedural scripts and skill trees to reduce future reasoning overhead. It employs a context optimization engine that utilizes layered memory hierarc

JiuwenSwarm is a Python runtime and orchestration framework for large-language-model agents. A single agent can plan its work, call tools, use reusable skills, and delegate to helper agents. Alternatively, a leader agent can split a complex task into subtasks, assign them to specialized member agents, and merge their results into one summary. Each conversation runs in an execution mode made up of a category, a work profile, and a plan variant, and the mode can be switched mid-chat with slash commands. What sets it apart is the combination of team scale and human control. Leader and teammate a

MiroThinker is an autonomous research system that uses large language models to perform deep research and predictions through iterative reasoning. It functions as a web-search AI framework capable of retrieving real-time internet data and scraping web content to provide verifiable sources for complex queries. The system includes a multimodal content processor that converts images, audio, and video into text descriptions for analysis by text-based models. To ensure computational accuracy, it utilizes a sandboxed code executor for running Python code and data analysis. Performance is managed th

ms-agent is an LLM agent framework and multi-agent orchestration system designed to build autonomous entities that combine large language models with tool calling and structured workflows. It serves as a tool integration platform and workflow engine for executing complex tasks through the coordination of specialized agents. The project distinguishes itself through a multimodal agent workflow engine capable of automating the production of text, images, and video. It features a sandboxed code execution environment for running generated code and quantitative data analysis in isolated containers,

PraisonAI is an autonomous AI agent platform that coordinates multiple LLM-powered agents for research, planning, and execution of complex workflows. It functions as a multi-agent orchestration framework, a workflow builder, and a Model Context Protocol server, while also providing retrieval-augmented generation through vector knowledge bases. Agents can interact via CLI, web, or standardized protocols with sandboxed code execution. The platform distinguishes itself with a rich set of agent communication protocols, including A2A, REST, WebSocket, voice and telephony integration, and MCP, allo

Kiln is an LLM development workbench and evaluation framework designed for designing, testing, and optimizing prompts and AI agents. It functions as a multi-agent orchestrator and a RAG optimization tool, providing a visual interface for the iterative development of AI systems. The project distinguishes itself through a comprehensive fine-tuning pipeline that supports zero-code model training and reasoning distillation. It enables the creation of hierarchical multi-agent systems where specialized actors coordinate via tool calling, and it implements a Model Context Protocol server to expose t

Reef is a platform that lets AI agents keep improving from real usage. It serves live agent traffic through OpenAI- and Anthropic-style endpoints, records each interaction, and turns the resulting user feedback into better versions of the agent. Those versions are tested, versioned, and shipped again and again, without manual retraining cycles. Reef can improve an agent in two ways. It can evolve the agent's harness, meaning its prompts, rules, and skills, stored as a plain text-file tree. Candidate edits are tested on real tasks and the best-scoring ones are published as snapshots that clien

Memary is a memory-augmented agent framework that stores and retrieves contextual information from a knowledge graph to personalize responses and maintain long-term memory across interactions. It automatically captures all agent interactions and stores them as structured memories without requiring explicit instrumentation, then injects top-ranked user entities and themes into the active context window to tailor agent responses dynamically. The framework distinguishes itself through a multi-retriever memory search that combines COLBERT reranking with recursive graph queries across databases, e

Micro-agent is a framework for AI-driven agents focused on automated test-driven development, design-to-code conversion, and external tool orchestration. It utilizes agents that iteratively write, test, and refine source code based on natural language prompts and design files. The system transforms visual design tokens and components into type-safe, linted code by comparing live URLs against reference screenshots to ensure visual parity. It also provides a protocol for linking agents to external commerce, search, and asset management services to synchronize data and expand functional capabili

CodeLlama is a family of large language models derived from the Llama 2 architecture and specialized for producing, completing, and refactoring source code across multiple programming languages. It functions as a code generation model capable of synthesizing source code from natural language descriptions. The project includes specific model variants designed for different programming tasks. This includes instruction-tuned models trained to follow complex natural language directions and code infilling models that predict and insert missing code segments into existing files by analyzing surroun

This is an LLM agent framework and symbolic learning system designed for building self-evolving autonomous agents. It functions as a computational graph orchestrator that organizes agent interactions and tool sequences as a trainable graph of nodes. The framework focuses on data-centric agent optimization, allowing agent pipelines and prompts to be upgraded through data-driven training rather than manual engineering. It utilizes a symbolic learning process that applies language-based loss and textual reflections to refine the operational logic and symbolic components of an agent. The system

OpenManus is an autonomous agent framework designed to build intelligent software entities capable of executing complex, multi-step tasks through independent decision-making. It functions as a workflow orchestration engine that uses a central language model to interpret user goals, break them down into actionable steps, and manage the execution flow of agents. The system maintains coherence across tasks through a stateful execution context that tracks progress and intermediate data. The platform distinguishes itself through a dynamic capability discovery mechanism that inspects tool definitio

AIOS is an LLM agent operating system and orchestration kernel designed to manage memory, resource scheduling, and tool execution for multiple autonomous AI agents. It serves as a comprehensive framework for developing and deploying agents, featuring a dedicated resource manager that coordinates model backends, GPU memory, and isolated kernel instances. The system distinguishes itself through a semantic memory engine that uses vector search and autonomous clustering for long-term knowledge management, and a semantic file system that allows users to control computer files and system operations

Atmosphere is a Java-based framework for building and coordinating AI agents. It provides a real-time transport layer for streaming data via WebSockets, SSE, gRPC, and WebTransport, alongside a multi-agent orchestration framework for managing agent fleets through sequential, parallel, and graph-based execution workflows. The project features a durable workflow engine that persists agent state as snapshots, allowing long-running tasks to survive system restarts and incorporate human-in-the-loop approvals. It also implements Model Context Protocol servers to expose tools, resources, and prompt

Integuru is a system of AI-driven agents and frameworks designed to document undocumented APIs and convert network traffic into automation scripts. It functions as a headless API automation framework that replaces browser-based tools with direct HTTP requests to increase throughput and reliability. The project features an LLM-based reverse engineering agent that analyzes network traffic to discover internal APIs and a natural language integration engine that transforms text descriptions of workflows into sequences of valid API calls. It includes tools for extracting request and response forma

This project is a comprehensive educational curriculum and structured learning path covering the full lifecycle of large language models. It provides a guided progression through the theory, architecture, training, and deployment of these models. The curriculum includes specialized guides on transformer architecture, model training tutorials, and frameworks for designing autonomous agents. It also provides dedicated resources for studying model safety and ethics. The material covers a wide range of technical capabilities, including distributed training strategies, parameter-efficient fine-tu

This project is an LLM coding agent orchestrator and AI software engineering platform designed to manage fleets of agents that autonomously solve issues, handle pull requests, and fix CI failures. It functions as an agentic CI/CD automator and parallel workflow manager, coordinating the end-to-end development lifecycle from initial ticket tracking to final code merging. The system is distinguished by its modular plugin framework and isolated worktree management, which allow multiple agents to work on separate coding tasks simultaneously without file system conflicts. It utilizes role-based mo

Eino is an AI agent development kit and LLM application framework designed for building autonomous agents and orchestrating complex language model workflows. It serves as a multi-agent orchestration engine and workflow orchestrator, providing a graph-based execution model to route data between models, tools, and retrievers. The framework distinguishes itself through a robust set of multi-agent coordination patterns, including supervisor-led management, sequential flows, and autonomous reasoning loops like ReAct. It features advanced agent execution controls such as active turn preemption, che

DevOpsGPT is an LLM-driven DevOps automation platform and AI software development agent. It transforms natural language requirements into functional code and automated deployments by coordinating codebase analysis, code generation, and delivery pipelines. The system features an automated code generation engine and a task-based decomposition engine that analyze project structures to produce context-aware code extensions. It utilizes a pluggable model integration system to connect with private or professional language model deployments for domain-specific development tasks. The platform manage

Gop is a general purpose programming language and cross-language compiler designed to unify assets and libraries from multiple programming ecosystems into a single shared environment. It translates high-level source code into executable binaries using specialized backends tailored for different target environments. The project features a system for natural language programming, transforming human-readable instructions written in plain English into executable code. It also functions as a cross-language tool that imports and integrates external libraries and assets from different language ecosy

EvoAgentX is an agent platform that combines human-in-the-loop checkpoints, MCP tool integration, multi-agent workflow orchestration, and self-improvement capabilities. It functions as a self-improving agent framework that connects to MCP-compatible servers and orchestrates multi-agent workflows using natural-language goals, while also serving as a platform that discovers, configures, and manages tools from MCP servers for use in automated agent workflows. The platform distinguishes itself through a dual-memory agent architecture that maintains short-term and persistent memory stores, enablin

Code Llama is a large language model based on Llama 2 trained specifically for programming tasks and software development. It provides specialized model types optimized for general code generation, instruction following, and context-aware infilling. The project includes an instruction-tuned programming model for executing technical tasks via natural language prompts and a code infilling model that predicts missing sections based on surrounding source context. A large context code model is also provided to analyze extensive blocks of source code for improved coherence. The system covers capab

Vision-agent is an AI system and visual data extraction framework that translates natural language prompts into runnable Python scripts for analyzing images and video. It functions as a multi-model vision orchestrator, using large language models to plan and generate executable code for tasks such as object detection, counting, and video tracking. The system employs a plan-and-execute cycle that iteratively generates and tests code, using an error-correction loop to refine the implementation until a solution is validated. It is configuration-driven, allowing the underlying language model back