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A curated map of open-source AI & agent tools — and what actually pairs with what.
Every connection is a typed, human-reviewed relationship — with the why written down. How we curate →
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239 repos · 17 topics · curated by ShipWithAI · new: Acontext, Agent-Reach, code-graph-rag →
new: your agent can query the map — npx -y @ship-with-ai/stackmap-mcpconnect →

Skill memory layer for agents: auto-captures learnings from runs into plain Markdown skill files you can read, edit, git and share across frameworks — memory without an opaque store.

One CLI gives agents read/search access to Twitter, Reddit, YouTube, GitHub, Bilibili and XiaoHongShu with zero API fees — multi-backend routing the maintainers repair when platforms break.

Parses a polyglot monorepo with Tree-sitter into a Memgraph knowledge graph: query it in plain English (NL→Cypher), trace data flow, find dead code, edit via AST-surgical patches.

KG-guided synthetic SFT data: builds a knowledge graph from source text, finds the LLM's knowledge gaps via calibration error, and generates targeted long-tail QA pairs. ACL-published.

LLM-driven Python scraping: describe what you want and graph pipelines extract structured data from websites or local docs (HTML, XML, JSON, Markdown). 29k stars; cloud API upsell.

Multiplayer agent harness for startups: every employee gets a scoped workspace — memory, files, keychain, crons, sandbox — in Slack and web, with Pi/OpenCode/Codex/Claude Code swappable underneath.

Rust framework for LLM apps: an agent harness, compile-time-typed task graphs, and streaming RAG pipelines — MCP toolboxes, human-in-the-loop approval, tracing with Langfuse support.

Context layer for large codebases: a graph of plain-English markdown nodes — no embeddings, no index — agents read like any repo file. Claude Code hooks + MCP; 42% fewer tokens in its bench.
LobeChat's 80k-star pivot: from chat UI to 'Chief Agent Operator' — hire, schedule and supervise a team of agents running 7×24, self-hosted via Docker or Vercel, plugin ecosystem carried over.

Local-first cost ledger for AI coding: reads the session files 36 tools already write and breaks every token and dollar down by task, model, project. TUI, web, desktop, menubar — no proxy, no keys.

Persistent memory for coding agents on the iii engine: MCP server with 53 tools, 12 auto-capture hooks, hybrid search + knowledge graph, zero external DBs. Claims 95% R@5 and 92% token cuts.
Turns your coding CLI into a job-search command center: scans Greenhouse/Ashby/Lever, scores listings A-F into a 1-5 rubric, tailors ATS-ready CVs, tracks applications. Claude Code, Codex, OpenCode+.
Single-file memory layer for agents: data, embeddings, index and metadata in one portable .mv2 — append-only Smart Frames, time-travel queries, sub-5ms recall, no server. Rust core, Node/Python SDKs.
Rust CLI proxy compressing dev-command output 60-90% before your agent reads it — git, tests, linters, docker, 100+ commands; hooks auto-rewrite bash calls. Single binary, <10ms overhead.
137k-star roster of specialist agent personas — engineering, design, marketing and ops divisions — installable into Claude Code, Cursor, Codex and 13+ tools via scripts or a native desktop app.
Claude Code config pack + optional local harness: TDD-enforcing hooks, 67 skills, quality gates, persistent memory — plus a dashboard routing each task across 13 models by cost-aware blast score.

Spec, task and memory layer that lives in your repo: .trellis/ holds conventions, PRDs and journals, auto-injected each session — one workflow across 20 coding-agent platforms.

YC-backed multi-agent harness for production: state an objective and the runtime compiles a graph DAG of specialized agents — role-based memory, crash recovery, cost limits, human-in-the-loop.

Rust coding-agent harness built for footprint: ~28MB per session vs 140-390MB for Codex/Claude Code, instant boot, optional local embeddings — made for running many sessions in parallel.

Runtime security for AI agents: watches actions AND reasoning traces to catch prompt injection, tool poisoning and out-of-remit behavior — blocking before the action lands. SDKs + Claude Code plugin.

LLM-driven platform turning unstructured documents into structured data: a no-code Prompt Studio to define extractions, then deploy as APIs or ETL pipelines. Self-hosted, AGPL + enterprise.
Graph engineering as a Claude skill: SEU's 9-stage knowledge-graph course (translated) plus task-graph orchestration patterns — with a teaching mode and nine paste-ready /kg workflows.

Self-hosted OSINT terminal: 27 open feeds — satellite fires, flights, radiation, sanctions, markets, conflict data — polled in parallel onto one Jarvis-style dashboard. LLM turns it two-way.

The first open foundation model for financial candlesticks: trained on K-lines from 45 global exchanges, AAAI 2026, weights on Hugging Face with fine-tuning scripts for your own tasks.

Aider woven into Emacs: AI pair programming with intelligent model selection, Ediff for reviewing AI changes, and file management that stays true to Emacs workflows. MELPA-packaged.

HKUDS's skill lifecycle layer for agents: retrieve the right skill per task, evaluate which ones actually work from real outcomes, share across agents and teammates, evolve with every run.

Hierarchical agent loops: nodes iterate toward a goal in their own git worktree and spawn children for subtasks — the tree grows to fit the problem. Hard caps, SQLite run log, live TUI.
Hugging Face's Python port of Pi's minimalist coding agent: a real terminal agent with TUI, sessions and skills — built to be READ, with a clean brain/environment/frontend separation.
Tokenization at GB/s: ~1000x faster than HuggingFace tokenizers with drop-in compatibility modes for HF and tiktoken — Rust reading your files directly. pip install gigatoken.

Agent multiplexer for your terminal: every Claude Code/Codex session in real panes — blocked/working/done at a glance, detach and reattach over SSH, plus a socket API agents drive themselves.

Runnable course: a production OCR pipeline on Kubernetes — Rust ingestion, Qwen 3.5 (4B) served by vLLM at 1.86 pages/s, Redis queues, KEDA autoscaling. Deploy it on AKS/GKE, not a notebook.

OpenAI's GitHub Action for CI hardening: bounded egress filtering and runner lockdown — the fence that keeps supply-chain attacks out and your CI-resident coding agent in.
Vectorless, reasoning-based RAG — builds a hierarchical tree index from long documents so an LLM retrieves by relevance instead of similarity. No chunking, no embeddings, no vector DB.

Rust memory layer for AI agents with Git-style version control — snapshot, branch, merge and rollback over MatrixOne's copy-on-write engine, plus hybrid vector + full-text retrieval.

From-scratch headless browser in Zig for AI agents and automation — CDP-compatible (Puppeteer/Playwright connect as-is) at ~9x the speed and ~16x less memory than headless Chrome.

Small Go HTTP/MCP server that gives AI agents direct control over Chrome — stealth CDP injection, multi-instance orchestration and a real-time dashboard. Local-first, single binary.
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