Roampal — Outcome-Based Persistent Memory MCP Server

Two commands. Your AI coding assistant gets outcome-based memory.
Works with Claude Code and OpenCode.

Why?

AI coding assistants forget everything between sessions. You explain your architecture, your preferences, your conventions — again. When they give bad advice, there's no mechanism to learn from it.

Roampal is an MCP server that gives your AI persistent, outcome-based memory across every session. Good advice gets promoted. Bad advice gets demoted. Your AI learns what works and what doesn't — automatically, with zero workflow changes.

Quick Start

pip install roampal
roampal init

Auto-detects installed tools. Restart your editor and start chatting.

Target a specific tool: roampal init --claude-code or roampal init --opencode

The core loop is identical — both platforms inject context, capture exchanges, and score outcomes. The delivery mechanism differs:

Claude Code prompts the main LLM to score each exchange via the score_memories tool. OpenCode never self-scores — an independent sidecar (a separate API call) reviews each exchange as a third party, removing self-assessment bias. The score_memories tool is not registered on OpenCode. During roampal init or roampal sidecar setup, Roampal detects local models (Ollama, LM Studio, etc.) and lets you choose a scoring model. If configured, these take priority (Zen is skipped for privacy). A cheap or local model works great — scoring doesn't need a powerful model. Defaults to Zen free models (remote, best-effort) if you skip setup.

How It Works

When you type a message, Roampal automatically injects relevant context before your AI sees it:

You type:

fix the auth bug

Your AI sees:

═══ KNOWN CONTEXT ═══
• JWT refresh pattern fixed auth loop [id:patterns_a1b2] (3d, 90% proven, patterns)
• User prefers: never stage git changes [id:mb_c3d4] (memory_bank)
═══ END CONTEXT ═══

fix the auth bug

No manual calls. No workflow changes. It just works.

The Loop

1. You type a message
2. Roampal injects relevant context automatically (hooks in Claude Code, plugin in OpenCode)
3. AI responds with full awareness of your history, preferences, and what worked before
4. Outcome scored — good advice gets promoted, bad advice gets demoted
5. Repeat — the system gets smarter every exchange

Five Memory Collections

Commands

roampal init                # Auto-detect and configure installed tools
roampal init --claude-code  # Configure Claude Code explicitly
roampal init --opencode     # Configure OpenCode explicitly
roampal init --no-input     # Non-interactive setup (CI/scripts)
roampal start               # Start the HTTP server manually
roampal stop                # Stop the HTTP server
roampal status              # Check if server is running
roampal status --json       # Machine-readable status (for scripting)
roampal stats               # View memory statistics
roampal stats --json        # Machine-readable statistics (for scripting)
roampal doctor              # Diagnose installation issues
roampal summarize           # Summarize long memories (retroactive cleanup)
roampal score               # Score the last exchange (manual/testing)
roampal context             # Output recent exchange context
roampal ingest <file>       # Add documents to books collection
roampal books               # List all ingested books
roampal remove <title>      # Remove a book by title
roampal sidecar status      # Check scoring model configuration (OpenCode)
roampal sidecar setup       # Configure scoring model (OpenCode)
roampal sidecar test        # Test scoring model response format (OpenCode)
roampal sidecar disable     # Remove scoring model configuration (OpenCode)

MCP Tools

Your AI gets these memory tools:

How scoring works: Claude Code's hooks prompt the main LLM to call score_memories every turn. OpenCode uses an independent sidecar that scores silently in the background — the model never sees a scoring prompt and score_memories is not registered as a tool. If the sidecar is unavailable, a warning prompts the user to run roampal sidecar setup. Choose your scoring model during roampal init or via roampal sidecar setup.

What's Different?

Benchmarks

85.8% on LoCoMo (non-adversarial, end-to-end answer accuracy) — validated on 1,986 questions across 10 conversations with dual grading.

Benchmark pipeline runs on a single GPU with no cloud dependencies. Roampal itself runs on CPU — no GPU required. Full methodology, data, and evaluation scripts: roampal-labs

Paper: "Beyond Ingestion: What Conversational Memory Learning Reveals on a Corrected LoCoMo Benchmark" (Logan Teague, April 2026)

How Roampal Compares

┌─────────────────────────────────────────────────────────┐
│  pip install roampal && roampal init                    │
│    Claude Code: hooks + MCP → ~/.claude/                │
│    OpenCode:    plugin + MCP → ~/.config/opencode/      │
└─────────────────────────────────────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────────────┐
│  HTTP Hook Server (port 27182)                          │
│    Auto-started on first use, self-heals on failure     │
│    Manual control: roampal start / roampal stop         │
└─────────────────────────────────────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────────────┐
│  User types message                                     │
│    → Hook/plugin calls HTTP server for context          │
│    → AI sees relevant memories, responds                │
│    → Exchange stored, scored (hooks or sidecar)         │
└─────────────────────────────────────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────────────┐
│  Single-Writer Backend                                  │
│    FastAPI → UnifiedMemorySystem → ChromaDB             │
│    All clients share one server, isolated by session    │
└─────────────────────────────────────────────────────────┘

See dev/docs/ for full technical details.

Requirements

• Python 3.10+
• One of: Claude Code or OpenCode
• Platforms: Windows, macOS, Linux (primarily developed and tested on Windows)
• RAM: ~800MB available (cross-encoder reranker + embeddings + ChromaDB)
• Disk: ~500MB for models (multilingual embedding + reranker, downloaded automatically on first use)
• CPU: Any modern x86-64 processor with AVX2 (Intel Haswell 2013+ / AMD Excavator 2015+)
• GPU: Not required — all inference runs on CPU via ONNX Runtime

Troubleshooting

• Restart Claude Code (hooks load on startup)
• Check HTTP server: curl http://127.0.0.1:27182/api/health

• Verify ~/.claude.json has the roampal-core MCP entry with correct Python path
• Check Claude Code output panel for MCP errors

• Make sure you ran roampal init --opencode
• Check that the server auto-started: curl http://127.0.0.1:27182/api/health
• If not, start it manually: roampal start

This is expected. Roampal has self-healing -- if the HTTP server stops responding, it is automatically restarted and retried.

Still stuck? Ask your AI for help — it can read logs and debug Roampal issues directly.

Support

Roampal Core is completely free and open source.

• Support development: roampal.gumroad.com
• Feature ideas & feedback: Discord
• Bug reports: GitHub Issues
• Need help with AI memory? Reach out: [email protected] | LinkedIn

License

Apache 2.0