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.
276 repositories · updated October 4, 2026
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.
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.
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.
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.
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.
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.
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.
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.
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.
aport-spec: Define Pre-Action Authorization for AI Agents
aport-spec defines the Open Agent Passport (OAP), a draft specification for verifying and authorizing AI agent actions before they occur. It is aimed at platform builders and developers implementing runtime policies, signed decisions, and interoperable agent credentials.
a3m-router: Route LLM Requests Across Providers
A3M Router is a self-hostable gateway that routes OpenAI-compatible requests among language model providers. It offers heuristic and ML-based routing, provider failover, semantic caching, and optional parallel ensembles to help manage cost and availability.
Curie: Automate Scientific Experiments with AI Agents
Curie is a Python framework that uses AI agents to plan, run, analyze, and report scientific experiments. It is aimed at researchers and ML practitioners who want reproducible experimentation across their own datasets and code.