roampal 0.4.9.3

Outcome-based persistent memory for AI coding tools (Claude Code, OpenCode)

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.

Benchmarks

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

Result	Score
Conversational learning vs raw ingestion	+23 points (76.6% vs 53.0%, p<0.0001)
Architecture vs model effect	Architecture ~10x larger contributor
Poison resilience (1,135 adversarial memories)	-2.6 to -4.2 points only
TagCascade retrieval (tags-first + CE rerank)	+1.9 Hit@1 vs pure CE (p<0.0001)

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)

---

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

Platform Differences

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

	Claude Code	OpenCode
Context injection	Hooks (stdout)	Plugin (system prompt)
Exchange capture	Stop hook	Plugin session.idle event
Scoring	Main LLM via score_memories tool	Independent sidecar (your chosen model > Zen free)
Self-healing	Hooks auto-restart server on failure	Plugin auto-restarts server on failure

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

Collection	Purpose	Lifetime
working	Current session context	24h — promotes if useful, deleted otherwise
history	Past conversations	30 days, outcome-scored
patterns	Proven solutions	Persistent while useful, promoted from history
memory_bank	Identity, preferences, goals	Permanent
books	Uploaded reference docs	Permanent

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 retag               # Re-extract tags on memories using sidecar LLM
roampal sidecar disable     # Remove scoring model configuration (OpenCode)

MCP Tools

Your AI gets these memory tools:

Tool	Description	Platforms
search_memory	Deep search across all collections	Both
add_to_memory_bank	Store permanent facts (identity, preferences, goals)	Both
update_memory	Correct or update existing memories	Both
delete_memory	Remove outdated info	Both
score_memories	Score previous exchange outcomes	Claude Code
record_response	Store key takeaways from significant exchanges	Both

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.

How Roampal Compares

Feature	Roampal Core	Claude Code built-in (CLAUDE.md / auto memory)	OpenCode built-in
Learns from outcomes	Yes — bad advice demoted, good advice promoted	No	No
Semantic retrieval	Yes — TagCascade + cross-encoder reranking	No — files loaded in full, no search	No memory system
Context injection	Automatic — relevant memories per query	Full CLAUDE.md every session, auto memory on demand	None
Atomic fact extraction	Yes — summaries + facts, two-lane retrieval	No — saves what Claude decides is useful	No
Works across projects	Yes — shared memory across all projects	Per-project only (per git repo)	No memory
Scales with history	Yes — 5 collections, promotion/demotion/decay	CLAUDE.md unbounded, auto memory first 200 lines	No memory
Fully local / private	Yes — ChromaDB on your machine	Yes	Yes

Architecture

┌─────────────────────────────────────────────────────────┐
│  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

Hooks not working? (Claude Code)

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

MCP not connecting? (Claude Code)

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

Context not appearing? (OpenCode)

• 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

Server crashes and recovers?

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: [e-mail] | LinkedIn

License

Apache 2.0