MUR

The learning layer for AI coding tools.

MUR is the only system that captures patterns from your coding sessions, evolves them over time, and injects them into 16+ AI tools automatically. Unlike static rules files or generic memory APIs, MUR's patterns mature, decay, and adapt — so your AI assistants continuously get smarter.

Why MUR?

Every AI coding tool starts from scratch. You repeat the same instructions, lose hard-won discoveries, and maintain separate config files for each tool.

MUR closes the loop.

Session 1:  You correct the AI → MUR captures the pattern (Draft)
Session 5:  Pattern auto-promoted to Emerging (validated by usage)
Session 20: Pattern reaches Stable — injected with high confidence
Month 3:    Unused patterns decay. Active ones get promoted to Canonical.

How MUR compares

In short: Rules sync tools write config files but don't learn. Memory frameworks store data but don't evolve it. MUR does both — and connects them in a closed loop.

Agents (P0a — murmur)

mur agent ... spawns and manages long-running per-agent processes that speak A2A v0.3. Each agent has its own profile, prompt, MCP servers, skills, entitlements, and Unix socket; the runtime is one BusyBox-style binary symlinked once per agent (mur_agent_<name> → mur-agent-runtime). See mur-agent-runtime/README.md for the walkthrough (create / start / send / card / list / export).

Quick Start

# macOS / Linux — one-liner
curl -fsSL https://mur.run/install.sh | sh

# Interactive setup — configures embeddings, hooks, and sync targets
mur init

# Create your first pattern
mur new "prefer-swift-testing"

# Sync to all your AI tools (Claude Code, Gemini CLI, Cursor, etc.)
mur sync

# Windows (PowerShell)
irm https://mur.run/install.ps1 | iex

# macOS — signed installer
# Download mur-aarch64-apple-darwin.dmg from the Releases page and double-click.

# Homebrew (macOS arm64)
brew install mur-run/tap/mur

# crates.io
cargo install mur

# Keep up to date
mur update           # check + install latest
mur update --check   # check only, don't install

How It Works

 You use AI CLI normally
 $ claude "write tests for auth module"
              │
              ▼
 MUR hooks inject relevant patterns ──────────────────────┐
 [swift-testing, error-handling, auth-conventions]         │
              │                                            │
              ▼                                            │
 AI responds with your preferences                        │
              │                                            │
              ▼                                            │
 Post-session feedback loop ◄─────────────────────────────┘
 • Contradiction detection
 • Pattern reinforcement / decay
 • Cross-session emergence clustering

Patterns start as Draft, get promoted through Emerging → Stable → Canonical based on real usage, and automatically decay if unused. No junk accumulates.

Features

Dashboard

MUR includes a built-in web dashboard for visual management:

# Start the dashboard
mur serve

# Opens at http://localhost:3847

The dashboard provides:

• Pattern management — view, edit, filter by maturity/tier/tags, bulk operations
• Workflow editor — create, edit, and search workflows with semantic search
• Session review — browse recordings, extract workflows from sessions
• Statistics — confidence distribution, maturity breakdown, decay warnings
• Settings — data source config, export/import, theme toggle

Workflows

Workflows are reusable multi-step procedures extracted from your sessions:

# Record a session
mur session start
# ... do your work in an AI CLI ...
mur session stop

# Extract a workflow from the session (via dashboard)
mur serve
# → Sessions → Select session → "Extract Workflow"

# Or create manually
mur workflow new "deploy-staging"

# Search workflows (semantic + keyword)
mur workflow search "deploy process"

# Show workflow as AI-readable prompt
mur workflow show "deploy-staging" --md

# Run a workflow (outputs executable prompt for AI)
mur run "deploy"

Workflows support:

• Variables — string, url, path, number, bool, array types with defaults
• Tool detection — auto-detects agent-browser, MCP servers, etc.
• Smart extraction — heuristic title, description, and variable detection from session recordings

CLI Commands

mur
├── init               Interactive setup wizard
├── new <name>         Create a new pattern
├── search <query>     Semantic + keyword hybrid search
├── inject             Inject matching patterns into context
├── context            Preview what would be injected
├── sync               Sync patterns to all AI tools + auto-reindex
├── serve              Start the web dashboard
├── stats              Pattern library statistics
├── evolve             Run maturity promotion/demotion cycle
├── emerge             Detect cross-session emergence clusters
├── suggest            Composition / decomposition suggestions
├── feedback
│   ├── auto           Post-session contradiction detection
│   ├── helpful        Mark last injection as helpful
│   └── unhelpful      Mark last injection as unhelpful
├── workflow
│   ├── list           List all workflows
│   ├── show <name>    Show workflow details (--md for AI format)
│   ├── search <q>     Semantic search workflows
│   └── new <name>     Create a new workflow
├── run <query>        Find and output workflow as executable prompt
├── session
│   ├── start          Start recording a session
│   ├── stop           Stop recording
│   ├── record         Record an event
│   ├── status         Current session status
│   └── list           List past sessions
├── learn extract      Extract patterns from AI transcripts
├── exchange
│   ├── import         Import pattern file (MKEF format)
│   └── export         Export pattern to MKEF
├── pattern show       Show pattern details
├── gc                 Garbage collect expired patterns
├── pin / mute / boost Pattern management shortcuts
├── promote            Manually promote maturity
├── deprecate          Deprecate a pattern
├── reindex            Rebuild vector search index
├── links              Show pattern links
└── community          Browse community patterns

Semantic Search

Find patterns by meaning, not just keywords:

# With Ollama (free, local — recommended)
ollama pull qwen3-embedding:0.6b
mur reindex

# With OpenAI
export OPENAI_API_KEY=sk-...
mur reindex

# Search naturally
mur search "how to handle authentication errors"
# → error-handling-auth (0.84)
# → retry-with-backoff  (0.71)

Note: Semantic search requires an embedding provider (Ollama or OpenAI). Without one, MUR falls back to keyword search. Run mur init to configure.

Pattern Format

Patterns are YAML files in ~/.mur/patterns/:

schema: 2
name: swift-testing-macro
description: Prefer Swift Testing over XCTest
content:
  technical: |
    Use @Test macro instead of func test...()
    Use #expect() instead of XCTAssert
  principle: |
    Swift Testing is more expressive and supports async natively
tier: project
importance: 0.8
confidence: 0.7
maturity: stable
tags:
  languages: [swift]
  topics: [testing]
applies:
  languages: [swift]

Configuration

# ~/.mur/config.yaml
embedding:
  provider: ollama              # ollama | openai | gemini | anthropic
  model: qwen3-embedding:0.6b  # run mur init for options
  dimensions: 1024

llm:
  provider: anthropic
  model: claude-sonnet-4-20250514
  api_key_env: ANTHROPIC_API_KEY

ask:
  summarize_hits_enabled: true  # Enable abstractive compression in overflow cascade
  summarize_model: null         # Override model for Stage 1b (null → falls back to ask.model)

Run mur init for an interactive setup wizard.

Ask Configuration

The ask section controls behavior of mur ask queries:

• summarize_hits_enabled (default true) — When context is tight, mur ask runs an overflow cascade that compresses the longest hits using an abstractive summarization stage. This trades accuracy for context efficiency. Results cache per-hit at ~/.mur/conversations/cache/abstractive/ to amortize LLM cost. Summarization has a hardcoded 5s per-hit timeout and soft-fails silently on error. Set to false to restore pre-3.5 behavior (drop history first).

• summarize_model (default null) — Override the LLM model used for Stage 1b summarization. When null, falls back to ask.model. Pair with a faster model like qwen3:4b to trade summarization accuracy for speed on tight-budget queries.

CLI overrides (per-invocation)

Override the summarize_* config keys for a single mur ask invocation without editing ~/.mur/config.yaml:

• --no-summarize — Disable Stage 1b for this invocation. Overrides ask.summarize_hits_enabled. Useful for demos, benchmarks, or scripted comparisons.

• --summarize-model <model> — Override the Stage 1b model for this invocation. Overrides ask.summarize_model. Pair with a faster model like qwen3:4b to trade quality for speed on the summarize hop. The cache key includes the model name, so changing this flag produces a fresh cache entry.

The two flags are mutually exclusive: passing both errors at argument-parse time.

AI Tool Integration

MUR syncs patterns to your AI tools via their native skill/rules systems:

# Sync once
mur sync

# Auto-inject before each AI session (via shell hooks)
mur init --hooks

Privacy & Security

• 100% local — all patterns stored on your machine (~/.mur/)
• No telemetry — zero usage data collected
• Injection scanning — patterns checked for prompt injection attempts
• Content hashing — tamper detection on pattern files
• Trust levels — community patterns sandboxed from local ones

Architecture

mur-common/     Shared types (Pattern, Workflow, KnowledgeBase, Config)
mur-core/       CLI + server + all logic

~12,000 lines of Rust. YAML as source of truth with a rebuildable LanceDB vector index.

System Requirements

• macOS (Apple Silicon or Intel) — primary platform
• Linux — supported
• Optional: Ollama for local embeddings (recommended)
• Optional: OpenAI API key for cloud embeddings

Companion mode (Phase 1.1)

Companion mode adds a relationship-keyed warm voice into an agent's sys_prompt and an opt-in proactive outbox. The voice is composed once from onboarding answers and can be ejected to disk for manual editing; the outbox sends occasional context-aware check-ins based on time-of-day situation weights and a bandit-state picker — all fully local, no cloud required.

mur agent create my-buddy --provider stub
mur agent companion init my-buddy --answers <fixture>
mur agent companion proactive enable my-buddy   # opt in to occasional check-ins
mur agent companion inbox my-buddy --unread-only
mur agent companion ack <msg-id> --good

Contributing

Issues and PRs welcome.

git clone https://github.com/mur-run/mur.git
cd mur
cargo test --workspace

License

MIT