agentic-stack

Keep one portable memory-and-skills layer across coding-agent harnesses, so switching tools doesn't reset how your agent works.

A portable .agent/ folder (memory + skills + protocols) that plugs into Claude Code, Cursor, Windsurf, OpenCode, OpenClaw, Hermes, Pi Coding Agent, or a DIY Python loop — and keeps its knowledge when you switch.

Made by https://x.com/Av1dlive

# tap + install (one-time — both lines required)
brew tap codejunkie99/agentic-stack https://github.com/codejunkie99/agentic-stack
brew install agentic-stack

# drop the brain into any project — the onboarding wizard runs automatically
cd your-project
agentic-stack claude-code
# or: cursor | windsurf | opencode | openclaw | hermes | pi | standalone-python | antigravity

# clone + run the native installer
git clone https://github.com/codejunkie99/agentic-stack.git
cd agentic-stack
.\install.ps1 claude-code C:\path\to\your-project

brew update && brew upgrade agentic-stack

git clone https://github.com/codejunkie99/agentic-stack.git
cd agentic-stack && ./install.sh claude-code         # mac / linux / git-bash
# or on Windows PowerShell: .\install.ps1 claude-code
# adapters: claude-code | cursor | windsurf | opencode | openclaw | hermes | pi | standalone-python | antigravity

After the adapter is installed, a terminal wizard populates .agent/memory/personal/PREFERENCES.md — the first file your AI reads at the start of every session — and writes a feature-toggle file at .agent/memory/.features.json.

Six preference questions (each skippable with Enter):

Plus one Optional features step (opt-in, off by default):

Flags:

agentic-stack claude-code --yes          # accept all defaults, beta off (CI/scripted)
agentic-stack claude-code --reconfigure  # re-run the wizard on an existing project

Edit .agent/memory/personal/PREFERENCES.md any time to refine your conventions, or .agent/memory/.features.json to flip feature toggles.

The nightly auto_dream.py cycle only stages candidate lessons. It does not mark anything accepted or modify semantic memory. Your host agent does the review in-session:

# list pending candidates, sorted by priority
python3 .agent/tools/list_candidates.py

# accept with rationale (required)
python3 .agent/tools/graduate.py <id> --rationale "evidence holds, matches PREFERENCES"

# reject with reason (required); preserves decision history
python3 .agent/tools/reject.py <id> --reason "too specific to generalize"

# requeue a previously-rejected candidate
python3 .agent/tools/reopen.py <id>

Graduated lessons land in semantic/lessons.jsonl (source of truth) and are rendered to semantic/LESSONS.md. Rejected candidates retain full decision history so recurring churn is visible, not fresh.

See docs/architecture.md for the full lifecycle.

Every guide shows the folder structure. This repo gives you the folder structure plus the files that actually go inside: a working portable brain with five seed skills, four memory layers, enforced permissions, a nightly staging cycle, host-agent review tools, and adapters for eight harnesses.

• Memory — working/, episodic/, semantic/, personal/. Each layer has its own retention policy. Query-aware retrieval (salience × relevance); nightly compression into reviewable candidates.
• Review protocol — auto_dream.py stages candidate lessons mechanically. Your host agent reviews them via CLI tools (graduate.py, reject.py, reopen.py) and commits decisions with a required rationale. No unattended reasoning, no provider coupling.
• Skills — progressive disclosure. A lightweight manifest always loads; full SKILL.md files only load when triggers match the task. Every skill ships with a self-rewrite hook.
• Protocols — typed tool schemas, a permissions.md that the pre-tool-call hook enforces, and a delegation contract for sub-agents.

Per-version release notes live in CHANGELOG.md. The latest release, what broke, what's new, upgrade path, all there.

Opt-in FTS5 keyword search over all memory documents:

# enable during onboarding (or set manually in .agent/memory/.features.json)
python3 .agent/memory/memory_search.py "deploy failure"
python3 .agent/memory/memory_search.py --status
python3 .agent/memory/memory_search.py --rebuild

Falls back to ripgrep (rg) if installed, then to grep — both restricted to .md / .jsonl so source files never pollute results. The index is stored at .agent/memory/.index/ and gitignored.

.agent/                         # the portable brain (same across harnesses)
├── AGENTS.md                   # the map
├── harness/                    # conductor + hooks (standalone path)
│   └── hooks/
│       ├── claude_code_post_tool.py  # rich PostToolUse logging (v0.8+)
│       ├── pre_tool_call.py    # permissions enforcement
│       ├── post_execution.py   # log_execution() entry point
│       └── on_failure.py       # failure write + repeated-failure rewrite flag
├── memory/                     # working / episodic / semantic / personal
│   ├── auto_dream.py           # staging-only dream cycle
│   ├── cluster.py              # content clustering + pattern extraction
│   ├── promote.py              # stage candidates
│   ├── validate.py             # heuristic prefilter (length + exact duplicate)
│   ├── review_state.py         # candidate lifecycle + decision log
│   ├── render_lessons.py       # lessons.jsonl → LESSONS.md
│   └── memory_search.py        # [BETA] FTS5 search (opt-in)
├── skills/                     # _index.md + _manifest.jsonl + SKILL.md files
├── protocols/                  # permissions + tool schemas + delegation
│   └── hook_patterns.json      # user-owned high/medium-stakes regex (v0.8+)
└── tools/                      # host-agent CLI + memory_reflect + skill_loader
    ├── learn.py                # one-shot lesson teaching (stage + graduate)
    ├── recall.py               # surface lessons relevant to an intent
    ├── show.py                 # colorful brain-state dashboard
    ├── list_candidates.py
    ├── graduate.py
    ├── reject.py
    └── reopen.py

adapters/                       # one small shim per harness
├── claude-code/   (CLAUDE.md + settings.json hooks)
├── cursor/        (.cursor/rules/*.mdc)
├── windsurf/      (.windsurfrules)
├── opencode/      (AGENTS.md + opencode.json)
├── openclaw/      (system-prompt include)
├── hermes/        (AGENTS.md)
├── pi/            (AGENTS.md + .pi/skills symlink)
├── standalone-python/  (DIY conductor entrypoint)
└── antigravity/   (ANTIGRAVITY.md)

docs/                           # architecture, getting-started, per-harness
install.sh                      # mac / linux / git-bash installer
install.ps1                     # Windows PowerShell installer
CHANGELOG.md                    # per-version release notes (v0.1.0 onward)
onboard.py                      # onboarding wizard entry point
onboard_features.py             # .features.json read/write
onboard_ui.py                   # ANSI palette, banner, clack-style layout
onboard_widgets.py              # arrow-key prompts (text, select, confirm)
onboard_render.py               # answers → PREFERENCES.md content
onboard_write.py                # atomic file write with backup
test_claude_code_hook.py        # hook validation suite (54 checks)
verify_codex_fixes.py           # v0.8.0 regression checks (33 checks)

• skillforge — creates new skills from recurring patterns
• memory-manager — runs reflection cycles, surfaces candidate lessons
• git-proxy — all git ops, with safety constraints
• debug-investigator — reproduce → isolate → hypothesize → verify
• deploy-checklist — the fence between staging and production

1. Skills log every action to episodic memory.
2. auto_dream.py clusters recurring patterns into candidate lessons.
3. The host agent reviews candidates with graduate.py / reject.py.
4. Graduated lessons append to lessons.jsonl; LESSONS.md re-renders.
5. Future sessions load query-relevant accepted lessons automatically.
6. on_failure flags skills that fail 3+ times in 14 days for rewrite.
7. git log .agent/memory/ becomes the agent's autobiography.

crontab -e
0 3 * * * python3 /path/to/project/.agent/memory/auto_dream.py >> /path/to/project/.agent/memory/dream.log 2>&1

auto_dream.py resolves its paths absolutely and performs only mechanical file operations (cluster, stage, prefilter, decay). No git commits, no network, no reasoning — safe to run unattended.

Apache 2.0 — see LICENSE.

Based on the article "The Agentic Stack" by @AV1DLIVE — follow for updates and collabs. Coded using Minimax-M2.7 in the Claude Code harness; PR review by Macroscope and Codex. Patterns from Gstack, Claude Code's memory system, and conversations in the agent-engineering community. Built with the hypothesis that harness-agnosticism is the point.