# FlowState-QMD
**The fastest, most trustworthy memory layer for coding agents.**
FlowState-QMD turns local markdown knowledge into shared project memory for Codex-, Claude-, Cursor-, and MCP-style agents. It combines a durable markdown memory store with a FlowState anticipatory cache so agents can pull relevant context before they fall into a reactive search loop.
[](https://opensource.org/licenses/MIT)
[](https://github.com/tobi/qmd)
[](#)
## Why This Wins
- **Anticipatory memory, not just stored memory.** FlowState prefetches context into `~/.cache/qmd/intuition.json` so agents can start from the right documents instead of deciding to search after the fact.
- **Built for coding agents.** It works best on docs, ADRs, RFCs, notes, runbooks, changelogs, migration logs, and benchmark writeups.
- **Local-first and inspectable.** Everything runs on your machine with SQLite, `sqlite-vec`, and local GGUF models via `node-llama-cpp`.
- **MCP-native.** The default experience is a clean MCP server plus a packaged agent skill.
- **Trust over magic.** Results keep file paths, doc IDs, snippets, and explain traces so the agent can show its work.
## Memory Model
- **Durable knowledge**: indexed markdown files in named collections
- **Working memory**: the FlowState anticipatory cache at `~/.cache/qmd/intuition.json`
- **Context overlays**: human-authored collection/path summaries added with `qmd context add`
## 90-Second Quickstart
```bash
git clone https://github.com/amanning3390/flowstate-qmd.git
cd flowstate-qmd
bun install
# Verify the host, recommend a profile, and emit wrapper configs
qmd init --target all
# Inspect readiness any time
qmd doctor
# Index your repo memory
qmd collection add ./docs --name docs
qmd collection add ./notes --name notes
qmd embed
# Start the coding-agent memory server
qmd mcp
```
If you want FlowState anticipatory memory too:
```bash
# Watch the current agent session log and keep the anticipatory cache warm
qmd flow ~/.codex/sessions/current.log --lite
# Compatibility alias also supported:
qmd flow start --watch ~/.codex/sessions/current.log --lite
```
## Demo Path
The best live demo for judges or teammates:
1. Index a repo's `docs/`, `notes/`, `CHANGELOG.md`, and ADRs.
2. Start `qmd flow` on an active coding-agent session log.
3. Ask a question like:
- "Why did we roll back the auth migration?"
- "What changed after the database incident?"
- "What did we decide about the API contract in the ADR?"
4. Have the agent call `fetch_anticipatory_context` before a regular search.
5. Show that the returned memories already include the ADR, changelog, or meeting note the agent needed.
See [docs/DEMO.md](docs/DEMO.md) for an exact script.
## What Makes FlowState Different
Traditional RAG for agents looks like this:
```text
user asks โ agent decides to search โ tool call โ wait โ result processing โ answer
```
FlowState-QMD changes the loop:
```text
user asks โ anticipatory context already exists โ agent answers or deepens with query/get
```
That difference matters most in coding workflows where the missing context is often already in:
- design docs
- changelogs
- migration notes
- incident writeups
- RFCs / ADRs
- benchmark reports
## Core Workflow
### 1. Index project memory
```bash
qmd collection add . --name repo-memory --mask '**/*.md'
# Compatibility alias
qmd index . --name repo-memory --mask '**/*.md'
```
### 2. Add context overlays
```bash
qmd context add qmd://repo-memory/docs "Project documentation and architecture notes"
qmd context add qmd://repo-memory/adr "Architecture decision records and tradeoff history"
qmd context add / "Answer as a coding agent using the repo's documented decisions"
```
### 3. Generate embeddings
```bash
qmd embed
```
### 4. Query project memory
```bash
# Auto-expand + rerank
qmd query "why was the rollout reverted"
# Structured search for better control
qmd query $'lex: "auth rollback" migration\nvec: why did we revert the auth migration?'
# Inspect why memories were chosen
qmd query --json --explain "performance regression in auth service"
```
### 5. Fetch exact evidence
```bash
qmd get "#abc123"
qmd multi-get "docs/**/*.md,CHANGELOG.md"
```
### 6. Run as MCP memory for agents
```bash
qmd mcp
```
The flagship tool order for coding agents is:
1. `fetch_anticipatory_context`
2. `query`
3. `get` / `multi_get`
4. `status`
## MCP Setup
### Claude Code / Codex-style local agents
```json
{
"mcpServers": {
"qmd": { "command": "qmd", "args": ["mcp"] }
}
}
```
### One-command wrapper bootstrap
```bash
qmd init --target hermes
qmd init --target gemini,kiro,vscode
qmd doctor --json
```
`qmd init` now:
- profiles the host and recommends `standard` or `lite`
- writes a bootstrap report to `~/.cache/qmd/bootstrap-report.json`
- emits or installs config for Hermes, Claude Code, Codex, Gemini CLI, Kiro, VS Code, OpenClaw, and pi
- keeps the canonical `qmd` MCP namespace and tool order across every client
### Wrapper targets
- `hermes`: installs `~/.hermes/config.yaml`
- `claude-code`: installs `~/.claude.json`
- `codex`: installs `~/.codex/config.toml`
- `gemini`: installs `~/.gemini/settings.json`
- `kiro`: installs `.kiro/settings/mcp.json`
- `vscode`: installs `.vscode/mcp.json`
- `openclaw`, `pi`: emits ready-to-apply artifacts under `~/.cache/qmd/targets/`
### Install the packaged skill
```bash
qmd skill install --global --yes
```
The packaged skill is optimized for coding-agent memory and teaches the agent to:
- use `fetch_anticipatory_context` first
- use `query` for deeper retrieval
- use `get` / `multi_get` for exact evidence
## Anticipatory Memory Tool
`fetch_anticipatory_context` prefers the FlowState cache and falls back to a live project-memory query when needed.
Example payload:
```json
{
"recent_conversation": "What changed in the auth rollback plan and why did we revert the migration?",
"refresh": false,
"lite_mode": false
}
```
Typical uses:
- current coding question with recent local context
- migration/debugging follow-up questions
- ADR/changelog lookups where the agent should feel preloaded
## CLI Highlights
```bash
qmd status
qmd collection list
qmd collection update-cmd docs 'git pull --rebase --ff-only'
qmd collection exclude archive
qmd ls repo-memory/docs
qmd cleanup
```
## Hardware Profiles
### Standard
- Qwen3 embedding + reranker models
- best quality
- recommended for Apple Silicon with 16GB+ RAM or comparable Linux hardware
### Lite
- **Models:** Qwen3-Embedding-0.6B + Qwen3-Reranker-0.6B (~1.2GB VRAM total)
- **Use case:** Laptops, CI environments, or demo setups with <8GB RAM
- **Behavior:** `hybridQuery()` caps results at 5, limits candidates to 15, and **skips LLM reranking**
- **Auto-engaged:** Systems with <8GB RAM automatically use Lite mode
Use:
```bash
qmd flow ~/.codex/sessions/current.log --lite
```
The `--lite` flag can also be passed to `qmd query` and `qmd embed` for consistent low-memory operation.
## Known Limits
- The best results come from markdown knowledge, not arbitrary binary project assets.
- Anticipatory memory depends on having an active session log to watch.
- First model-backed search can be slower while local models warm up.
- `qmd collection add`, `qmd embed`, and `qmd update` are intentionally manual operations; they are not auto-run by this repo's agents.
## Development
Use Bun in this repo.
```bash
bun install
npx vitest run --reporter=verbose test/
bun test --preload ./src/test-preload.ts test/
```
Important commands:
```bash
bun src/cli/qmd.ts <command>
qmd skill show
qmd mcp --http --daemon
qmd mcp stop
```
## Multi-Agent Idempotency
When multiple agents index the same project, FlowState-QMD prevents duplicate memories:
```bash
# Agent A indexes a doc about auth rollback
# Agent B tries to index a semantically identical doc
# โ QMD detects 0.94 cosine similarity (threshold: 0.90)
# โ Annotates existing memory instead of duplicating
```
Dedup stats are tracked via `getDedupStats()` and visible in `qmd status`.
## Cache Telemetry
FlowState tracks cache hit/miss rates and refresh latency in `~/.cache/qmd/telemetry.json`, surfaced via `qmd status` and the MCP `status` tool.
## Benchmarks
Reproducible benchmarks in `bench/`:
```bash
bun bench/latency.ts # FTS vs hybrid vs hybrid-lite search latency
bun bench/cache-hit.ts # Intuition cache read/parse latency (1000 rounds)
bun bench/tool-calls.ts # MCP tool round-trip simulation
```
All output JSON for programmatic consumption.
## Architecture
```
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ Agent (Hermes, Claude Code, Codex, ...) โ
โ โ โ
โ MCP tool calls โ
โ โผ โ
โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ
โ โ MCP Server (stdio / HTTP) โ โ
โ โ fetch_anticipatory_context โ query โ get โ status โ โ
โ โโโโโโโโโโโโโโโโฌโโโโโโโโโโโโโโโดโโโโโโโโดโโโโโโดโโโโโโโ โ
โ โ โ โ
โ โโโโโโโโโโโโโผโโโโโโโโโโโ โโโโโโโโโโผโโโโโโโโโ โ
โ โ FlowState Engine โ โ Store (SQLite) โ โ
โ โ fs.watch + debounce โ โ FTS5 + vec โ โ
โ โ intuition.json โ โ BM25 + cosine โ โ
โ โ telemetry.json โ โ RRF fusion โ โ
โ โโโโโโโโโโโโโโโโโโโโโโโโ โ LLM reranking โ โ
โ โ Idempotency โ โ
โ โโโโโโโโโโโโโโโโโโโโ โ
โ โ โ
โ โโโโโโโโโโโโผโโโโโโโโโโโ โ
โ โ node-llama-cpp โ โ
โ โ Qwen3 Embed + Rank โ โ
โ โโโโโโโโโโโโโโโโโโโโโโโ โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
```
- **Store**: SQLite + FTS5 + `sqlite-vec`
- **Retrieval**: BM25 + vector search + reciprocal rank fusion + reranking
- **FlowState**: event-driven watcher that keeps `intuition.json` fresh
- **Idempotency**: cosine similarity dedup at 0.90 threshold on document ingest
- **Telemetry**: cache hit/miss tracking persisted to `telemetry.json`
- **Interfaces**: CLI, MCP server, SDK, packaged agent skill
## Submission Assets
- Demo script: [docs/DEMO.md](docs/DEMO.md)
- Submission writeup: [SUBMISSION.md](SUBMISSION.md)
- Promo/demo video assets: [assets/video](assets/video)
## Why Ask When Your Agent Can Already Know?
FlowState-QMD is not trying to be every kind of memory system. It is trying to be the best coding-agent memory layer: fast, local, inspectable, shared across agents, and strong enough to make project context feel native.