Memory that learns what works. So you can do more of it.

Q: How is this different from cloud AI memory (ChatGPT, Claude, etc.)?

Cloud AI stores what you say. Roampal learns what works. Three key differences: 1) It learns from results - when something helps you, Roampal remembers. When it doesn't work, it learns from that too. 2) Local-first privacy - your data stays on your machine. No cloud servers, no recurring subscriptions. 3) True ownership - you own your data, your models, your memory. Export everything anytime.

Q: What models does it support?

roampal-core works with whatever model your AI tool already uses (Claude, GPT, etc.) - memory and scoring are model-agnostic. Roampal Desktop supports local models via Ollama or LM Studio including Llama, Qwen, and Mixtral.

Q: Does it work offline?

roampal-core stores and retrieves memory fully locally. Your AI tool (Claude Code, OpenCode) still connects to its own API provider as usual. Roampal Desktop works fully offline after downloading models.

Q: What are the system requirements?

roampal-core requires Python 3.10+ and minimal disk space - runs on anything that runs Python. Roampal Desktop requires minimum 8GB RAM, 10GB free disk space, with 16GB RAM and a GPU recommended for faster local model responses.

Q: How do I export my data?

roampal-core stores all data locally in your data directory (e.g. %APPDATA%/Roampal/data on Windows) - copy the folder to backup. Roampal Desktop has a Settings → Export option to download a zip file.

Q: Why charge if it's open source?

roampal-core is 100% free - install with pip install roampal and use it forever. Roampal Desktop ($9.99 one-time) gives you a pre-packaged executable with bundled dependencies, tested build, and lifetime updates. You can also support Core development with an optional subscription.

Q: Is Roampal's advice always reliable?

No. Roampal uses AI models that can generate incorrect information, hallucinate facts, or reflect training biases. Always verify critical information from authoritative sources. Do not rely on AI-generated content for medical, legal, or financial advice, safety-critical systems, or production code without thorough review.

Q: What are the MCP tools and what do they do?

roampal-core (Claude Code and OpenCode) provides 6 MCP tools: search_memory, add_to_memory_bank, update_memory, delete_memory, score_memories, and record_response. Roampal Desktop provides 6 MCP tools: get_context_insights, search_memory, add_to_memory_bank, update_memory, archive_memory, and record_response. Desktop includes get_context_insights for manual context retrieval and archive_memory for soft-deletes, while Core automates context injection via hooks and uses delete_memory for hard removal. Core's score_memories is a separate tool enforced by hooks; Desktop bundles scoring into record_response.

Your AI sees what helped before. Automatically.
Not just similarity—what actually worked. All stored locally on your machine.

"Built this because I wanted my AI to actually be mine — to learn from me, run on my terms, and keep my data where it belongs."

For Claude Code & OpenCode

            $ pip install roampal
          
            $ roampal init

Auto-detects tools. Or target one: --claude-code --opencode

Get Core (Free) → Desktop App

Core: Works with Claude Code & OpenCode. Memories stored locally.
Desktop: 100% local with Ollama/LM Studio. GUI app + MCP tools. Buy on Gumroad

You type:
fix the auth bug
LLM 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

Open Source (Apache 2.0) Works with Claude Code & OpenCode

Similarity Search Isn't Enough

Most AI memory finds things that sound similar to your question.
But sounding similar doesn't mean it helped you before.

Say you ask:

"This function is broken, fix it"

Your AI remembers two approaches from last week:

Memory A:

"Rewrote the whole function to fix the edge case"

Tried this — introduced 3 new bugs

Memory B:

"Fixed just the one line that was actually broken"

Clean fix — worked first try

Regular AI picks Memory A because "function" and "fix" are closer keyword matches.
Roampal picks Memory B because it knows that approach actually worked.

How does it know what worked?

Just talk naturally. The AI reads your response, determines if you were satisfied, and scores the memory in Roampal. No buttons to click, no ratings to give - it learns from the conversation itself.

We tested this:

Questions designed to trick the AI into giving the wrong answer.

Approach	Got It Right	Improvement
Standard AI search	10%	-
+ Smarter ranking	20%	+10 pts
Roampal (ranking + outcomes)	60% at maturity	+50 pts

Smarter ranking helps a little. At maturity, outcome tracking helps 5× more (+50 pts vs +10 pts).

It gets better the more you use it

We measured how well it finds the right answer over time.

10%

Cold start (0 uses)

60%

At maturity (20 uses)

5×

Better than ranking alone

The more you use it, the better it gets at finding what actually helps you.

Why this matters:

Better answers with less noise. Roampal only retrieves what's proven helpful, not everything that sounds related. That means lower API costs, faster responses, and more accurate answers.

Roampal vs pure vector search

30 adversarial scenarios designed to trick similarity search. Real embeddings (all-mpnet-base-v2).

Vector DB accuracy

0/30 correct on adversarial queries

40%

Roampal accuracy

12/30 correct — p=0.000135

When the semantically similar answer is wrong and the right answer doesn't sound related, vector search fails every time. Outcome learning finds what actually worked.

Technical details for researchers

4-way comparison: 200 tests (10 scenarios × 5 levels × 4 conditions) • Learning curve: 50 tests • Token efficiency: 100 adversarial scenarios
Full methodology & reproducible benchmarks

Your AI Gets Smarter Over Time

Traditional AI memory gets fuller. Roampal gets smarter.

Remembers What Worked

Auto

Good advice rises to the top. Bad advice sinks. Automatically.

Organized Memory

Smart

Short-term, long-term, permanent - your memories organized by how often you need them

Learns Your Patterns

Adapts

Learns which tools work, which topics connect, and what advice helps you specifically

Instant Responses

Fast

Local search — no round-trip to a cloud server. All on your machine.

100% Privacy

Local

All memory data stays on your machine. No cloud. No tracking. Your AI tool connects to its own API as usual.

How It Works

Handles Confusing Situations

Even when there's lots of similar-sounding info, outcome scoring surfaces what actually worked instead of just what sounds similar.

Knows Where to Look

Learns which memories are useful for which questions. Ask about recipes? Checks your food notes. Ask about work? Checks your project notes.

Remembers What Worked

When something helps you, it remembers. When something doesn't, it learns from that too. Next time, helpful stuff comes first.

All of this is tested and verified. Want to see the details?

View Benchmark Source Code

How It Learns

Bad Advice Fails

This function is broken, fix it

I'll rewrite the whole thing to handle the edge case properly.

That introduced 3 new bugs, had to revert.

Recording: FAILED Bad advice sinks ↓

Good Advice Works

Can you try a smaller fix?

Found it — just this one line was wrong. Here's the fix.

That worked, clean fix.

Recording: WORKED Good advice rises ↑

Next Time It Remembers

This function is broken again

Past Experience

Full rewrite: Introduced new bugs
Targeted one-line fix: Worked first try

Last time a targeted fix worked better than a rewrite. Let me find the specific line that's broken.

Why Not Just Use CLAUDE.md?

Learns From Results

CLAUDE.md is static — you write it, it stays the same. Roampal tracks what actually helped and what didn't. Bad advice gets demoted automatically.

AI suggests a fix → you say "that broke it" → Roampal scores it as failed
Next time → that approach gets deprioritized automatically

Zero Workflow Changes

No files to maintain. No prompts to write. Relevant context is injected before every message automatically. You just code.

pip install roampal && roampal init
That's it. Context injection, scoring, and memory management happen in the background.

Remembers Across Sessions

Your preferences, your architecture decisions, what worked last week — it's all there when you start a new session. No re-explaining.

Monday: "Always use snake_case in this project"
Thursday: AI already knows — no need to remind it

How Memories Are Organized

How Memories Move From New to Permanent

Working (24h)

Current context

Used successfully twice

↓

History (30d)

Recent conversations

Helped you many times

↓

Patterns (∞)

Proven solutions

Permanent storage

Permanent Collections (No Promotion)

Memory Bank

Your preferences & context

AI adds these automatically

Books

Reference docs

User-uploaded

Outcome Detection Algorithm

Roampal uses LLM to detect outcomes from natural language:

Positive signals: "that worked!", "perfect", "exactly what I needed"
Negative signals: "didn't work", "that failed", "not helpful"
Implicit signals: Topic changes, abandoning approach, asking similar question

# LLM analyzes conversation and detects outcome
if outcome == "worked":
    score += 0.2  # Positive outcome
elif outcome == "failed":
    score -= 0.3  # Negative outcome
elif outcome == "partial":
    score += 0.05  # Partial success

Learns Your Patterns

Roampal builds 3 interconnected Knowledge Graphs that learn different aspects of your memory:

1. Routing KG - "Where to Look"

Learns which memory collections answer which types of questions.

You ask: "What books did I read about investing?"

System learned: Questions about "books" → books collection (85% success)

Result: Searches books first, skips less relevant collections → faster answers

2. Content KG - "What's Connected"

Extracts people, places, tools, and concepts from your memories and tracks relationships.

Your memories mention: "Sarah Chen works at TechCorp as an engineer"

System extracts: Sarah Chen → TechCorp → engineer (with quality scores)

Result: "Who works at TechCorp?" → finds Sarah even without exact keyword match

3. Action-Effectiveness KG - "What Works"

Tracks which tools and actions succeed or fail in different contexts.

During a quiz: AI uses create_memory() to answer questions

System tracks: create_memory in "recall_test" → 5% success (hallucinating!)

System tracks: search_memory in "recall_test" → 85% success

Result: After 3+ failures, warns AI: "create_memory has 5% success here - use search_memory instead"

Together: Routing KG finds the right place, Content KG finds connected information, Action-Effectiveness KG prevents mistakes. All three learn from real outcomes.

Privacy Architecture

All data stored locally:

Vector DB: ChromaDB (local files)
Outcomes: stored as vector metadata in ChromaDB
Knowledge graph: JSON file
Desktop: Ollama or LM Studio (local inference)
Core: uses your existing AI tool's LLM (Claude, etc.)

Zero telemetry. Your data never leaves your machine. Minimal network: PyPI version check on startup, embedding model download on first run.

Built for Developers

"Remembers my entire stack. Never suggests Python when I use Rust."

✓ Learns debugging patterns that work with YOU
✓ Recalls past solutions
✓ Builds knowledge of your architecture

Works with any project — from solo side projects to production codebases.

Persistent Memory for AI Coding Tools

Two commands. Your AI coding assistant gets persistent memory. Works with Claude Code and OpenCode.

Works with Claude Code & OpenCode

              $ 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

Automatic Context Injection

Other memory tools wait for you to ask. Roampal injects context automatically — before every prompt, after every response. No manual calls. No workflow changes.

Before You Prompt

Context injected automatically. Claude Code uses hooks, OpenCode uses a plugin — same result. Your AI sees what worked before.

After Each Response

The exchange is captured and scoring is enforced — not optional. Both Claude Code hooks and OpenCode plugin handle this automatically.

Memory Tools via MCP

Core: 6 tools | Desktop: 6 tools

search_memory add_to_memory_bank update_memory delete_memory record_response score_memories

Scoring is automatic — Claude Code uses hooks to prompt score_memories, OpenCode uses an independent sidecar so the model never self-scores.

Desktop uses get_context_insights + archive_memory instead of Core's delete_memory + score_memories, and bundles scoring into record_response.

Core (Claude Code & OpenCode) vs Desktop MCP

	Core	Desktop MCP
Context injection	Automatic (hooks & plugin)	Manual (prompt LLM)
Outcome scoring	Enforced	Opt-in
Learning	Every exchange	When you remember to score
Best for	Zero-friction workflow	Multi-tool power users

Using Desktop MCP? Tips for better results:

Add to system prompt: "Check search_memory for context before answering"
Remind mid-conversation: "Check memory for what we discussed about X"
Record outcomes: "Record in Roampal - this worked" or "...failed"

With roampal-core (Claude Code & OpenCode), this happens automatically — hooks and plugin inject context so the AI knows to use memory tools.

Why outcome learning beats regular memory:

Your AI Remembers Everything. But Does It Learn? → Your AI Keeps Forgetting What You Told It →

View on PyPI GitHub Docs

roampal-core is free and open source. Support development →

Connect via Roampal Desktop

Roampal Desktop can connect to any MCP-compatible AI tool via its Settings panel. Note: Desktop MCP provides memory tools but does not include hooks-based context injection or automatic scoring — for that, use roampal-core with Claude Code or OpenCode.

STEP 1

Open Settings

Click the settings icon in Roampal's sidebar to access configuration options.

STEP 2

Navigate to Integrations

Go to the Integrations tab. Roampal automatically detects MCP-compatible tools installed on your system.

Auto-detects: Any tool with an MCP config file (Claude Code, OpenCode, Cline, and more)

STEP 3

Connect to Your Tool

Click Connect next to any detected tool. Roampal automatically configures the MCP integration.

Don't see your tool?

Click Add Custom MCP Client to manually specify a config file path.

🔌

Desktop Memory Tools (6):
get_context_insights search_memory add_to_memory_bank update_memory archive_memory record_response

STEP 4

Restart Your Tool

Close and reopen your connected tool. Memory tools will be available immediately.

Auto-Discovery

No manual JSON editing required

50+ Languages

Bundled paraphrase-multilingual-mpnet-base-v2, no Ollama needed

Cross-Tool Memory

Memories shared across all connected tools

Works Offline

Fully local after initial setup

Need Help?

Connection not working? Try disconnecting and reconnecting in Settings → Integrations, or reach out to the community for support.

Join Discord Community

Releases

roampal-core

v0.4.4 - Async Parallelization

Released March 2026

LATEST

Performance release — all independent operations now run concurrently via asyncio.gather. Same behavior, same results, measurably faster. Memory metadata now fully visible to the LLM for informed decisions.

Parallel Everything

Collection searches, context injection, per-memory scoring, background KG updates, adapter initialization, and startup cleanup all run concurrently. Saves 100–200ms per search call.

Full Memory Metadata

KNOWN CONTEXT now shows wilson:N% reliability, used:Nx retrieval count, and last:outcome for every scored memory. The LLM can make informed decisions about memory trustworthiness.

View Full Details

Parallel collection searches via asyncio.gather with per-collection exception handling
Context injection: 3 sequential searches → 1 concurrent gather
Parallel collection adapter initialization (3–5x faster cold start)
Parallel per-memory scoring and background KG updates
Parallel startup cleanup (working memory + history cleanup run concurrently)
Added negative example to record_response tool description to prevent misuse
Updated ARCHITECTURE.md: dependencies, ONNX embedding docs, version reference

Full Release Notes

pip install roampal View on GitHub

View Previous Releases (v0.4.3 and earlier)

v0.4.3 - Lightweight Install: Drop PyTorch, Go Pure ONNX

March 2026 — Replaces PyTorch + sentence-transformers with direct ONNX Runtime inference. Install drops from ~2.5GB to ~200MB.

Release Notes →

v0.4.2 - Hook Reliability, Embedding Performance & OpenCode Plugin Fixes

March 2026 — Embedding cache fixes hook timeouts, OpenCode scoring fixes, ONNX groundwork.

Release Notes →

v0.4.1 - Linux Stability, Performance & Sidecar-Only Scoring

March 2026 — Linux reliability fixes, event loop unblocking, performance caps, sidecar-only scoring on OpenCode.

Release Notes →

v0.4.0 - Cross-Platform Audit & Data Integrity

March 2026 - Full cross-platform audit, backend data integrity fixes, standardized path handling across Windows/macOS/Linux.

Release Notes →

v0.3.9 - Scoring Truncation Fix & Safety Cap

March 2026 - Fixed scoring truncation bug, added memory storage safety cap to prevent unbounded growth.

Release Notes →

v0.3.8 - Memory Bank Transparency & Docker Support

March 2026 - Memory_bank scoring transparency, thread-safety fix, version string fix, Docker support.

Release Notes →

v0.3.7 - Sidecar Setup & Cold Start Recovery

February 2026 - Sidecar-only scoring for OpenCode with one-command setup. Cold start injects recent exchanges. ~280 MB deps removed. Security audit.

Release Notes →

v0.3.6 - Retrieval Fairness & Token Optimization

February 2026 - 78% token reduction via exchange summarization. Retrieval rebalanced so memory_bank no longer dominates. Wilson scores carry through promotion. Platform-split scoring — main LLM for Claude Code, sidecar for OpenCode.

Release Notes →

v0.3.5 - Precision Scoring & Security

February 2026 - Lean scoring prompts save ~60K tokens over 30 turns. Rewritten tool descriptions with memory hygiene and verification discipline. Security hardening: CORS, input validation, process management.

Release Notes →

v0.3.4 - OpenCode Scoring Fixes

February 2026 - Deep-clone fix for garbled UI, deferred sidecar scoring to prevent double-scoring, scoring prompt now asks for both exchange outcome and per-memory scores.

Release Notes →

v0.3.3 - OpenCode Plugin Packaging

February 2026 - Fixes OpenCode packaging bug, smart email collection marker, memory awareness preamble, robust tool detection for fresh installs, relative timestamps in KNOWN CONTEXT.

Release Notes →

v0.3.2 - Multi-Client Support

February 2026 - Claude Code + OpenCode via shared single-writer server. 4-slot context injection, self-healing hooks, OpenCode TypeScript plugin.

Release Notes →

v0.3.1 - Reserved Working Memory Slot

January 2026 - Guarantees recent session context always surfaces in automatic injection. 1 reserved working slot + 2 from other collections.

Release Notes →

v0.3.0 - Resilience

January 2026 - Fixes silent hook server crashes, health check tests embeddings, auto-restart on corruption

Release Notes →

v0.2.9 - Natural Selection for Memory

Wilson scoring for memory_bank, stricter promotion requirements, archive cleanup.

Release Notes →

v0.2.8 - Per-Memory Scoring

Score each cached memory individually. Wilson score fix, FastAPI lifecycle management.

Release Notes →

v0.2.7 - Cold Start Fix + Identity Injection

Critical fixes for identity injection and cold start bloat.

Release Notes →

v0.2.6 - Identity Prompt + Profile-Only Cold Start

January 2026 - Identity detection prompts, profile-only cold start, recency sort fix

Release Notes →

v0.2.5 - MCP Config Location Fix

January 2026 - Critical fix: roampal init now writes to ~/.claude.json instead of invalid ~/.claude/.mcp.json

Release Notes →

v0.2.4 - Scoring Reliability Fix

January 2026 - Fixed related parameter handling in score_response fallback path

Release Notes →

v0.2.3 - Outcome Scoring Speed

January 2026 - Critical performance fix: score_response latency 10s → <500ms

Release Notes →

v0.2.2 - Cursor IDE Support (currently broken due to Cursor bug)

December 2025 - Cursor MCP support (non-functional due to Cursor-side bug), always-inject identity, ghost registry, roampal doctor command

Release Notes →

v0.2.1 - MCP Tool Loading Hotfix

December 2025 - Emergency fix for fresh installs failing to load MCP tools

Release Notes →

v0.2.0 - Action KG Sync Fix

December 2025 - Fixed critical "reading your own writes" bug in knowledge graph

Release Notes →

v0.1.10 - Update Notifications in MCP

December 2025 - Claude Code users now see update notifications via MCP hook

Release Notes →

v0.1.9 - ChromaDB Collection Fix

December 2025 - Fixed critical bug where MCP connected to wrong ChromaDB collection

Release Notes →

v0.1.8 - Delete Memory (Hard Delete)

December 2025 - Renamed archive_memory → delete_memory, now actually removes memories

Release Notes →

v0.1.7 - Working Memory Cleanup

December 2025 - Fixed cleanup of old memories, added update notifications, archive_memory fix

Release Notes →

v0.1.6 - Score Response Fallback

December 2025 - Fixed score_response returning "0 memories updated", book management initialization

Release Notes →

v0.1.5 - DEV/PROD Isolation

December 2025 - Run dev and prod instances simultaneously without collision

Release Notes →

Roampal Desktop

v0.3.0 - Performance & Polish

Released January 2026

LATEST

TanStack Virtual migration for smoother scrolling, streaming fixes, message virtualization, and 50+ bug fixes. Security hardened with ZIP traversal, XSS, and query logging fixes.

TanStack Virtual

Smooth scrolling for conversations with 5000+ messages. No more lag or jank.

Streaming Fixes

Reliable token-by-token display. Fixed tool chaining and reconnection issues.

50+ Bug Fixes

Security hardened: ZIP traversal, XSS, query logging. Plus message virtualization and scrollbar fixes.

View Full Details

TanStack Virtual migration - smooth scrolling for 5000+ messages
Streaming fixes - text overlap, thinking icon, loading indicator
Tool interleaving - text and tools now display in correct order
Cancel button fix - actually stops generation now
Surfaced memories display - see what memories were used
Context overflow detection - warns before context limit is exceeded
Model switch race condition fixed
Security: ZIP path traversal, XSS in citations, query logging fixed
876 tests passing (509 frontend, 367 backend)

Full Release Notes

Download v0.3.0 - $9.99 View on GitHub

View Previous Releases (v0.2.12 and earlier)

v0.2.12 - Memory Attribution Scoring

January 2026 - LLM memory attribution, virtualization fix, selective scoring parity

Release Notes

v0.2.11 - Critical Performance Fixes

January 2026 - KG 25x faster, message virtualization, store subscription fix

Release Notes

v0.2.10 - ChromaDB 1.x + Query Timeouts + Ghost Entry Handling

December 2025 - ChromaDB upgraded to 1.x, query timeouts prevent UI freezes, ghost entry handling

Release Notes

v0.2.9 - Ghost Registry + Timeout Protection + Performance

December 2025 - Fixes book deletion ghost vectors, adds timeout protection, 3x faster startup

Release Notes →

v0.2.8 - MCP Security + No Truncation + Update Notifications

December 2025 - MCP security hardening, full memory content returned, in-app update notifications

Release Notes →

v0.2.7 - Architecture Refactoring

December 2025 - Monolithic file refactored into 9 focused, testable modules. 260 tests passing

Release Notes →

v0.2.6 - Unified Learning + Directive Insights + Contextual Embeddings

December 2025 - Internal LLM contributes to Action KG, actionable get_context_insights prompts, ~49% improved book retrieval

Release Notes →

v0.2.5 - MCP Client + Wilson Scoring + Multilingual Ranking

December 2025 - Connect to external MCP tool servers, statistical confidence scoring, 14-language smart sorting

Release Notes →

v0.2.1 - Action-Effectiveness KG + Enhanced Retrieval + Benchmarks

November 2025 - 3rd Knowledge Graph learns tool effectiveness, enhanced retrieval pipeline

Release Notes →

v0.2.0 - Learning-Based KG Routing + MCP Integration

November 2025 - Intelligent knowledge graph routing, enhanced MCP with semantic learning storage

Release Notes →

v0.1.6 - Hotfix Release

November 2025 - Critical fixes for collection-specific search and KG success rate accuracy

Release Notes →

v0.1.5 - Multi-Provider Support

October 2025 - Persistent memory bank, document upload (Books), 100% local with Ollama integration

Release Notes →

Frequently Asked Questions

How is this different from cloud AI memory (ChatGPT, Claude, etc.)?

Cloud AI stores what you say. Roampal learns what works.

Three key differences:

It learns from results: When something helps you, Roampal remembers. When it doesn't work, it learns from that too. Regular AI just stores what you said.
Local-first privacy: Your data stays on your machine. No cloud servers, no recurring subscriptions. roampal-core is free and open source. Desktop is a one-time $9.99 purchase.
True ownership: You own your data and your memory. Export everything anytime. No vendor lock-in.

What models does it support?

roampal-core: Works with whatever model your AI tool already uses (Claude, GPT, etc.). Memory and scoring are model-agnostic — just pip install roampal.

Roampal Desktop: Supports local models via Ollama or LM Studio:

Llama - Meta's free models
Qwen - Great for many languages
Mixtral - Fast and efficient

Install from Desktop Settings.

Does it work offline?

roampal-core: Memory storage and retrieval is fully local. Your AI tool (Claude Code, OpenCode) still connects to its own API provider as usual.

Roampal Desktop: Fully offline after downloading models. No internet required.

What are the system requirements?

roampal-core: Python 3.10+, minimal disk space. Runs on anything that runs Python.

Roampal Desktop: Minimum 8GB RAM, 10GB free disk space. Recommended: 16GB RAM and a GPU for faster local model responses.

How do I export my data?

roampal-core: All data is stored locally in your data directory (e.g. %APPDATA%/Roampal/data on Windows). Copy the folder to backup.

Roampal Desktop: Go to Settings → Export. Pick what you want to backup and download a zip file.

Why charge if it's open source?

roampal-core is 100% free. Install with pip install roampal and use it forever.

Roampal Desktop ($9.99 one-time) gets you:

Pre-packaged executable - No Python setup required
Bundled dependencies - Everything included
Tested build - Ready to run out of the box
Lifetime updates - All future releases included

You can also support Core development with an optional subscription.

Is Roampal's advice always reliable?

No. Roampal uses AI models that can generate incorrect information, hallucinate facts, or reflect training biases.

Always verify critical information from authoritative sources. Do not rely on AI-generated content for:

Medical, legal, or financial advice
Safety-critical systems or decisions
Production code without thorough review

What are the MCP tools and what do they do?

roampal-core (Claude Code & OpenCode) provides 6 MCP tools:

search_memory - Finds relevant memories based on what you're discussing
add_to_memory_bank - Stores permanent facts about you (preferences, identity, goals)
update_memory - Corrects or updates existing memories
delete_memory - Removes outdated information
score_memories - Scores cached memories from previous context (enforced automatically via hooks)
record_response - Stores key takeaways from significant exchanges

Roampal Desktop provides 6 MCP tools:

get_context_insights - Returns context about the user (Core handles this automatically via hooks)
search_memory, add_to_memory_bank, update_memory - Same as Core
archive_memory - Soft-deletes outdated information (vs Core's hard delete)
record_response - Combined outcome scoring + key takeaways in one tool

With roampal-core, context injection and scoring happen automatically via hooks. With Desktop, you may need to prompt the AI to use memory tools.

How does Roampal know when something worked?

Just talk naturally. The AI reads your response, determines if you were satisfied, and scores the memory in Roampal.

For example:

If you say "thanks, that fixed it" → the memory that helped gets scored higher
If you correct the AI → that memory gets scored lower
If you move on without comment → no strong signal either way

No buttons to click, no ratings to give. It learns from the conversation itself.

Why Roampal Exists

Roampal is built on a simple idea: software that respects you. No hidden fees, no data collection, no lock-in.

Open source from day one (Apache 2.0 License)
One-time payment, not subscription trap
Zero telemetry, zero tracking
Your data stays on your machine
Free to View on GitHub forever

Stop Re-Explaining Yourself

AI that grows with you. Learns from outcomes. Tracks what actually works. All data stays on your machine.

Get Core (Free) pip install roampal • Claude Code & OpenCode • Open source Desktop App - $9.99 Pre-built • Local models • MCP for any tool