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Rainman

Context-aware project memory for AI coding tools — zero LLM, zero tokens, runs locally

Rainman

About Rainman

Rainman is a persistent memory system that helps AI coding assistants like Claude Code remember project context, lessons learned, and solutions across sessions. It uses no LLM tokens and runs entirely on your local machine with just Python 3.10+.

Its smart retrieval system combines keyword matching, temporal recency using ACT-R power-law decay, category-based importance, and associative linking. Integrates via MCP server, Claude Code lifecycle hooks, or CLI. New memories automatically link when keyword overlap exceeds 25%.

Born from a real debugging scenario where Claude Code spent hours on a workaround when a 516-line solution already existed in the codebase. Rainman ensures that never happens again.

More Github

TokenScope
Github

TokenScope

Open-source token profiler for AI coding agents — see where your context budget goes

TokenScope bills itself as the first open-source token profiler for AI coding agents. It analyzes session logs, detects repeated content and bloated tool results, and shows you exactly where your context window budget is being spent — with concrete fixes and estimates. For anyone running Claude Code, Codex, or similar agents at scale, context bloat is a silent cost driver. TokenScope turns that invisible spend into a profile you can act on, trimming waste and keeping long agent sessions coherent and cheaper.

brain0
Github

brain0

The black box for AI-written code: provenance, drift detection, and MCP memory for coding agents

brain0 is a Rust-based "black box" for AI-written code. It builds a passive decision graph that links every commit back to the agent prompts behind it, giving you provenance, drift detection, and a DLP audit of exactly what your coding agents read and produced. It also ships MCP memory for coding agents plus signed, evidence-driven risk scoring. If you are shipping code written by Claude, Codex, or other agents, brain0 is a local-first way to answer "which prompt produced this change, and can I trust it?" — a governance layer builders increasingly need.

OpenScience
Github

OpenScience

The open-source AI workbench for scientific research

OpenScience is an open-source, agent-driven workbench built for scientific research. It packages a CLI, an LLM-backed "co-scientist" agent, and a set of research tools into a single TypeScript/Bun project so researchers and builders can run reproducible, AI-assisted investigations from the terminal. For AI builders, it is a strong reference architecture for domain-specific agents: it shows how to wire an LLM agent to real research workflows, structure tool use, and keep an experiment loop transparent and inspectable rather than a black box.

Godcoder
Github

Godcoder

Local-first, open-source coding agent — bring your own LLM key

Godcoder is a local-first, open-source coding agent for your desktop built with Rust and Tauri. Bring your own LLM key; your code stays on your machine and only ever leaves to the model provider you choose. The agent builds and manages its own harness.

ShipGenAI
Github

ShipGenAI

50 production-ready GenAI SaaS apps you can brand and ship

ShipGenAI is an MIT-licensed collection of 50 production-ready generative-AI SaaS apps. Each comes with Stripe billing, Google OAuth, and one-click Vercel deploy — brand them, ship them, and keep 100% of the revenue.