Flaiwheel

Self-hosted memory & governance layer for AI coding agents. Turn every bug fix into permanent knowledge. Zero cloud. Zero lock-in.

🚀 Why Flaiwheel Exists

AI coding agents forget everything between sessions. That leads to repeated bugs, lost architectural decisions, and knowledge decay.

Flaiwheel ensures:

• Agents search before coding
• Agents document after fixing
• Commits automatically capture knowledge
• Memory compounds over time

Every bug fixed makes the next bug cheaper.

🧠 How Flaiwheel Is Different

• Persistent AI Memory That Compounds — knowledge doesn't reset between sessions.
• Git-Native Automation — commits automatically become structured knowledge.
• Governance, Not Just Storage — quality gates + enforced documentation.
• Hybrid Search + Reranking — high-precision context for real codebases.
• Fully Self-Hosted — single Docker container, no external infrastructure.
• Zero Lock-In — all knowledge stored as structured flat files in Git.

✅ Who Flaiwheel Is For

• Engineering teams using AI coding assistants in real projects
• Codebases where repeated bugs are expensive
• Teams requiring full data control
• AI-native development environments

❌ Not For

• Small hobby projects under a few thousand lines
• Developers who just want better autocomplete
• Pure SaaS workflows with no interest in self-hosting

🆚 Where Flaiwheel Fits

• AI coding tools generate code.
• RAG tools retrieve documents.
• Flaiwheel governs and compounds structured engineering knowledge inside your own infrastructure.

It does not replace your AI assistant. It makes it reliable at scale.

📄 Whitepaper (PDF) — Vision, architecture, and design in depth.

⚙️ Key Technical Features

Flaiwheel is a self-contained Docker service that operates on three levels: Pull — agents search before they code (search_docs, get_file_context)
Push — agents document as they work (write_bugfix_summary, write_architecture_doc, …)
Capture — git commits auto-capture knowledge via a post-commit hook, even without an AI agent

• Indexes your project documentation (.md, .pdf, .html, .docx, .rst, .txt, .json, .yaml, .csv) into a vector database
• Provides an MCP server that AI agents (Cursor, Claude Code, VS Code Copilot) connect to
• Hybrid search — combines semantic vector search with BM25 keyword search via Reciprocal Rank Fusion (RRF) for best-of-both-worlds retrieval
• Cross-encoder reranker — optional reranking step that rescores candidates with a cross-encoder model for significantly higher precision on vocabulary-mismatch queries
• Behavioral Directives — AI agents silently search Flaiwheel before every response, auto-document after every task, and reuse before recreating — all without being asked
• get_file_context(filename) — pre-loads spatial knowledge for any file the agent is about to edit (complements get_recent_sessions for full temporal + spatial context)
• post-commit git hook — captures every fix:, feat:, refactor:, perf:, docs: commit as a structured knowledge doc automatically
• Living Architecture — AI agents are instructed to maintain self-updating Mermaid.js diagrams for system components and flows
• Executable Test Flows — test scenarios are documented in machine-readable BDD/Gherkin format (Given, When, Then) for QA automation
• Learns from bugfixes — agents write bugfix summaries that are instantly indexed
• Structured write tools — 7 category-specific tools (bugfix, architecture, API, best-practice, setup, changelog, test case) that enforce quality at the source
• Structured relations (v1) — relations() and timeline() derive a per-project knowledge graph from optional YAML frontmatter on existing docs (id, replaces, depends_on, fixes, implements, status). No second store — markdown stays canonical and Git history is the validity window
• Pre-commit validation — validate_doc() checks freeform markdown before it enters the knowledge base, including unknown-relation-key warnings
• Ingest quality gate — files with critical issues are automatically skipped during indexing (never deleted — you own your files)
• Auto-syncs via Git — pulls AND pushes to a dedicated knowledge repo
• Tool telemetry (persistent) — tracks every MCP call per project (searches, writes, misses, patterns), detects knowledge gaps, and nudges agents to document — persisted across restarts and visible in the Web UI
• Impact metrics API — /api/impact-metrics computes estimated time saved + regressions avoided; CI pipelines can post guardrail outcomes to /api/telemetry/ci-guardrail-report
• Proactive quality checks — automatically validates knowledge base after every reindex
• Knowledge Bootstrap — "This is the Way": analyse messy repos, classify files, detect duplicates, propose a cleanup plan, execute with user approval (never deletes files)
• Cold-Start Codebase Analyzer — analyze_codebase(path) scans a source code directory entirely server-side (zero tokens, zero cloud). Uses Python's built-in ast module for Python, regex for TypeScript/JavaScript, the existing MiniLM embedding model for classification and duplicate detection. Returns a single bootstrap_report.md with language distribution, category map, top 20 files to document first ranked by documentability score, duplicate pairs, and coverage gaps. Reduces cold-start token cost by ~90% on legacy codebases.
• Multi-project support — one container manages multiple knowledge repos with per-project isolation
• Includes a Web UI for configuration, monitoring, and testing

What’s New in v3.13.0 — Observability

• Flaiwheel now knows whether its knowledge repo is still connected to its remote. Everything before this reported on pushes that were attempted. The failure that hid 325 documents in a Docker volume for 2.5 months attempted nothing: the clone had drifted from its remote, so there was never anything to commit, so no push could fail, so nothing went red. check_divergence() compares HEAD against @{u} and classifies the result as synced / ahead / behind / diverged / no-upstream.
• The "nothing to push" path is where this matters. That branch used to return an unconditional "already in sync". It now verifies the claim. Divergence is also checked after every successful push (did the commit actually land?), after a rejected push (a rejection is the classic symptom — now named instead of leaving you to read a git error), and on every pull.
• A repo that indexes perfectly and pushes nothing is no longer "healthy". /health gains divergence_status, commits_ahead, commits_behind and last_divergence_at, and reports degraded on diverged, ahead or no-upstream. Being behind is the normal state between two pulls and deliberately does not alarm.
• The agent is told directly. write_* results append an explicit warning when the repo has diverged — including on "nothing to push". A warning in an endpoint nobody polls does not exist; the agent that just wrote the document is the one that needs to know it never left the machine.
• Tests: 316 → 335, against real temp repos including a force-pushed rewritten upstream — the real-world trigger, where a secret purge or a squash silently desynchronises every clone.
• This closes the 2026-08-19 incident completely. The one item that looked outstanding — "watcher path scoping" — was retracted as a misdiagnosis after checking the running container. Its only evidence was the log line knowledge: update flaiwheel/telemetry.json, read as one project's file being committed into all 11 repos. The real path is .flaiwheel/telemetry.json, with a leading dot: the signature of the porcelain off-by-one already fixed in v3.12.2. Every project owns an identically-named telemetry file, so all 11 watchers logged the same mangled string at once — uniformity caused by shared code, mistaken for shared state.

Previous: v3.12.3

• Every dependency is capped below the next major. Eleven requirements were unbounded >=X. That fails silently: the breaking release lands, existing installs keep working off a stale resolve, and it only bites on the next fresh install — CI, a Docker rebuild, a new contributor. Exactly how mcp 2.0.0 broke CI and the Docker build together three weeks after release while every dev machine stayed green. A clean install resolves to identical versions as before, so this constrains the future without moving anything today.
• Sustained push failure now degrades /health. HealthTracker kept only last_push_ok — a single boolean the next attempt overwrites — so one blip and a repo failing for weeks looked the same. push_failures_consecutive escalates past 3 consecutive failures. A single failure deliberately does not degrade; crying wolf on transients is how alerts get ignored.
• /health names the failing projects. Adds last_push_ok, last_push_error, push_failures_consecutive and degraded_projects — previously the endpoint could say degraded while showing only the default project's numbers, with no way to tell which repo was broken.
• Pre-deploy image smoke test documented. An image can build cleanly and still fail every import at runtime. The README now verifies from flaiwheel.server import create_mcp_server inside the image before starting a container, and renames rather than removes the previous container so rollback is instant.
• Tests: 308 → 316.