Open Source AI Projects
Artificial intelligence (AI) refers to systems that perform tasks such as recognizing patterns, generating content, making predictions, and interpreting language. AI tools can automate repetitive work, help people analyze large amounts of data, and add intelligent features to applications. They range from models that run locally to services and libraries that connect AI capabilities with software, devices, and creative workflows.
Open source AI resources include machine-learning frameworks, model runtimes, agent systems, coding assistants, and tools for image, audio, and text processing. When choosing one, consider its license, maintenance activity, documentation, hardware and software requirements, data handling, and compatibility with your existing stack. Some tools are intended for experimentation, while others support production use. They can be useful to developers, researchers, organizations, and individuals building or adapting AI systems.
262 repositories · updated October 15, 2026
tramAI: Govern AI Workflows on the JVM
TramAI is a Kotlin-first JVM runtime for typed AI workflows with configurable policy, provider routing, tool controls, approvals, and execution evidence. It suits teams that need governance around model use, not just a way to call an LLM.
NewHealth 45/100
ai-observer: Monitor AI Coding Assistant Usage Locally
AI Observer is a self-hosted OpenTelemetry backend and dashboard for tracking usage across local AI coding assistants. It brings token and cost data, traces, logs, and metrics together in DuckDB, with both OTLP ingestion and file-based import or watching for supported tools.
ActiveHealth 38/100
SwarmLLM: Run Local and Distributed AI Models
SwarmLLM runs open AI models on your computer and can pool resources with other computers to run larger models. It also provides OpenAI- and Anthropic-compatible APIs for local apps and agents.
ActiveHealth 46/100
ai-session-search: Search Local AI Coding Sessions
ai-session-search indexes local transcripts from multiple AI coding tools and lets you search sessions, messages, and edited files. Use it from a Rust-powered CLI, MCP server, Rust library, or Python API.
ActiveHealth 39/100
router: Route AI Requests to the Best Model
weave-os/router is a Go proxy that routes AI requests across configured model providers, while accepting Anthropic, OpenAI, and Gemini API formats. It suits developers who want model choice and routing behind one endpoint, including agent and coding-tool users.
NewHealth 57/100
llm-d-router: Route Inference Requests Intelligently
llm-d Router directs inference requests using model-serving signals such as KV-cache locality, load, and priority. It is for teams running LLM serving on Kubernetes that need proxy-integrated routing and request flow control.
ActiveHealth 75/100
llm_wiki: Turn Documents Into an Interlinked Knowledge Base
LLM Wiki is a cross-platform desktop app that uses an LLM to build and maintain a persistent, linked wiki from your documents. It suits people who want source-grounded knowledge they can explore and update, rather than answers assembled from scratch for each query.
ActiveHealth 59/100
guaardvark: Run a Local AI Studio on Your GPU
Guaardvark combines local AI media generation, coding agents, RAG, and automation in a self-hosted studio. It suits people who want these workflows on their own hardware and can meet its Python and GPU requirements.
ActiveHealth 63/100
maka: Run and Inspect AI Agent Workflows
Apache Maka is an agent workspace that records model messages, tool calls, and permission decisions as an append-only event log. It is aimed at people building or evaluating agents who want local session data and a recoverable execution history.
NewHealth 81/100
aport-agent-guardrails: Authorize AI Agent Actions Before Execution
APort adds policy checks before supported AI agent tools run, helping teams limit prompt-injection risks and unauthorized actions. It provides runtime integrations and GitHub repository checks, with local or hosted verification options.
ActiveHealth 53/100
ferret-mcp: Analyze Codebases Through MCP
Ferret MCP gives AI assistants static codebase analysis and optional LLM-powered reports about architecture, dependencies, patterns, and APIs. It suits developers exploring unfamiliar repositories, but its quiet, single-contributor project status is worth weighing before relying on it in a team workflow.
QuietHealth 20/100
a3m-router: Route LLM Requests Across Providers
A3M Router is a self-hosted gateway that routes OpenAI-compatible chat requests among multiple LLM providers based on request complexity and cost. It also offers provider failover, semantic caching, and parallel ensembles for teams seeking control over model choice and routing.
NewHealth 42/100