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 fixes this with outcome-based memory. Good advice gets promoted. Bad advice gets demoted. Your AI gets smarter every exchange, across every session.

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

roampal init demo

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 prompted via hooks	Main LLM prompted + independent sidecar fallback
Self-healing	Hooks auto-restart server on failure	Plugin auto-restarts server on failure

Both platforms prompt the main LLM to score each exchange. OpenCode adds an independent sidecar call (using free models) as a fallback — sidecar only runs if the main LLM doesn't call score_response, so memories are never double-scored.

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

You type a message
Roampal injects relevant context automatically (hooks in Claude Code, plugin in OpenCode)
AI responds with full awareness of your history, preferences, and what worked before
Outcome scored — good advice gets promoted, bad advice gets demoted
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 start               # Start the HTTP server manually
roampal stop                # Stop the HTTP server
roampal status              # Check if server is running
roampal stats               # View memory statistics
roampal doctor              # Diagnose installation issues
roampal ingest <file>       # Add documents to books collection
roampal books               # List all ingested books
roampal remove <title>      # Remove a book by title

MCP Tools

Your AI gets 7 memory tools:

Tool	Description
`get_context_insights`	Quick topic lookup — user profile + relevant memories
`search_memory`	Deep search across all collections
`add_to_memory_bank`	Store permanent facts (identity, preferences, goals)
`update_memory`	Correct or update existing memories
`delete_memory`	Remove outdated info
`score_response`	Score previous exchange — prompted automatically by hooks
`record_response`	Store key takeaways from significant exchanges

What's Different?

Without Roampal	With Roampal
Forgets everything between sessions	Remembers you, your preferences, what worked
You repeat context every time	Context injected automatically
No learning from mistakes	Outcomes tracked — bad advice gets demoted
No document memory	Ingest docs, searchable forever

Benchmarks

Tested across 10 adversarial scenarios designed to trick similarity search (200 total tests):

Condition	Top-1 Accuracy
RAG baseline (vector search only)	10%
+ Cross-encoder reranking	20%
Full Roampal (outcomes + reranking)	10% → 60% at maturity

Outcome learning provides a 5x improvement over reranking alone (+50 pts vs +10 pts). Roampal vs plain vector DB: 40% vs 0% accuracy on adversarial queries (p=0.000135).

Full benchmark data: dev/benchmarks/results/

How Roampal Compares

Feature	Roampal Core	.cursorrules / CLAUDE.md	Mem0
Learns from outcomes	Yes — bad advice demoted, good advice promoted	No	No
Zero-config context injection	Yes — hooks inject automatically	Manual file editing	API calls required
Works across sessions	Yes — 5 memory collections with promotion	Per-project static files	Yes
Fully local / private	Yes — all data on your machine	Yes	Cloud or self-hosted
Open source	Apache 2.0	N/A	Apache 2.0

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          │
│    → Server returns context + scoring prompt            │
│    → AI sees context, scores previous, responds         │
│    → Exchange stored for next turn                      │
└─────────────────────────────────────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────────────┐
│  Single-Writer Backend                                  │
│    FastAPI → UnifiedMemorySystem → ChromaDB             │
│    All clients share one server, isolated by session    │
└─────────────────────────────────────────────────────────┘

See ARCHITECTURE.md for full technical details.

Requirements

Python 3.10+
One of: Claude Code or OpenCode
Platforms: Windows, macOS, Linux (primarily developed and tested on Windows)

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, hooks automatically restart it and retry.

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

License

Apache 2.0

roampal-core