# LinkedIn Post — MemoTrail v1.0.0 Launch

> Asagidaki 3 farkli format: kisa post, uzun makale, ve teknik odakli post.

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## Option 1: Kisa Post (Viral format)

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Your AI coding assistant forgets everything. Every new session starts from zero.

I built MemoTrail to fix this — an open-source memory layer for AI coding assistants. Today it hits v1.0.0.

What it does: records every AI conversation, indexes it with embeddings, and gives your AI semantic search over its own past. It remembers decisions, tracks how they evolved, and surfaces relevant context automatically.

What's in v1.0.0:

- 5 platforms in 1 memory — Claude Code, Cursor, Copilot, Windsurf, Aider
- Knowledge graph — extracts libraries, frameworks, services and their relationships from conversations
- Auto context injection — relevant past decisions and memories are assembled within a token budget, no manual search needed
- Web dashboard — browse sessions, search, manage memories, visualize the knowledge graph with D3.js
- REST API — full /api/v1/ for CI/CD integration and automation
- Temporal tracking — see when and why any decision changed over time
- Memory scopes — global, project-specific, or session-level memories
- Export/import — move your AI memory between machines in one command

249 tests. 100% local. No API keys. MIT licensed.

pip install memotrail
GitHub: github.com/HalilHopa-Datatent/memotrail

#OpenSource #AI #DeveloperTools #MCP #ClaudeCode

GitHub: github.com/HalilHopa-Datatent/memotrail
PyPI: pip install memotrail

#OpenSource #AI #DeveloperTools #MCP #ClaudeCode #CodingAssistant

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## Option 2: Uzun Makale (LinkedIn Article)

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### Title: I Built a Memory Layer for AI Coding Assistants — Here's What I Learned

Every AI coding assistant — Claude Code, Cursor, Copilot, Windsurf, Aider — has the same fundamental problem: amnesia.

Start a new session, and it's like meeting a stranger. All that context from yesterday? Gone. The architectural decisions from last week? Vanished. The three failed approaches you tried before finding the right one? Your AI will happily suggest them again.

I've been building MemoTrail for the past few months to solve this. Today it hits v1.0.0, and I want to share what I learned along the way.

**The core insight**

AI assistants don't need to be retrained. They need to be reminded. If you can surface the right 500 tokens of past context at the right moment, the quality of AI responses improves dramatically.

MemoTrail does exactly this: it records every AI coding session, indexes them with embeddings, and provides semantic search so your AI can "remember" past conversations.

**How it works**

1. A collector parses your AI tool's conversation logs (Claude Code JSONL, Cursor/Copilot SQLite databases, Aider markdown files)
2. Conversations are chunked and embedded using all-MiniLM-L6-v2 (runs locally, no API keys)
3. Decisions and entities are automatically extracted
4. Everything is stored in ChromaDB (vectors) + SQLite (metadata + knowledge graph)
5. An MCP server exposes 12 tools that your AI can call to search, recall, and learn from the past

**What's in v1.0.0**

When I started, MemoTrail had 7 tools and supported one platform. Here's where it is now:

- **5 platforms**: Claude Code, Cursor, GitHub Copilot, Windsurf, Aider
- **Knowledge graph**: Every library, framework, file, and service mentioned in your conversations becomes a node with relationships
- **Automatic context injection**: Instead of searching manually, MemoTrail assembles a curated context package within a token budget
- **Web dashboard**: Browse sessions, search conversations, manage memories, and visualize your knowledge graph with D3.js
- **REST API**: Full `/api/v1/` for CI/CD, dashboards, and automation
- **Temporal tracking**: See how any decision evolved — "We chose PostgreSQL on Jan 15, then discussed migration to Redis on Feb 3"
- **Memory scopes**: Global memories (always apply), project-specific memories (only in that project), and session-level notes
- **Data export/import**: Move your memory between machines with a single command
- **249 tests**, MIT licensed, 100% local

**The technical challenge: consolidation**

The hardest problem wasn't search — it was avoiding information rot. If you tell your AI "use PostgreSQL" in January and "switch to Redis" in March, both facts exist in memory. Without consolidation, your AI gets confused.

MemoTrail uses embedding similarity + regex-based contradiction detection to automatically:
- Skip duplicates (NOOP)
- Update existing facts with richer versions (UPDATE)
- Detect contradictions like "switched from X to Y" and clean up stale records (DELETE)

**What's next**

Team memory sharing is the obvious next step. Imagine onboarding a new developer and their AI already knows every architectural decision the team has made.

If this sounds useful, give it a try:

```
pip install memotrail
claude mcp add memotrail -- memotrail serve
```

GitHub: github.com/HalilHopa-Datatent/memotrail

I'd love to hear how you handle AI context across sessions. What's your current approach?

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## Option 3: Teknik Odakli Post

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Shipped MemoTrail v1.0.0 — an open-source MCP memory server for AI coding assistants.

The architecture, for those interested:

Storage layer:
- ChromaDB for vector embeddings (all-MiniLM-L6-v2, ~80MB, CPU)
- SQLite for metadata, knowledge graph, fact history, and schema migrations
- BM25 index for keyword search + Reciprocal Rank Fusion for hybrid results

Knowledge graph:
- Regex-based entity extraction (libraries, frameworks, files, services)
- Relation extraction (uses, depends_on, replaces, part_of)
- BFS traversal for multi-hop queries
- Stored in SQLite with proper indexing

Context packing:
- Token-budgeted assembly of decisions + memories + session summaries
- Proportional allocation across sections
- Uses tiktoken (cl100k_base) for accurate counting

Platform collectors:
- Claude Code: JSONL parser
- Cursor/Copilot/Windsurf: SQLite state.vscdb parser
- Aider: Markdown history parser
- Registry pattern for easy extension

Web stack:
- FastAPI + Jinja2 + HTMX (no JS build step)
- D3.js force-directed graph
- Pico CSS dark theme
- REST API at /api/v1/

The part I'm most proud of: memory consolidation. It uses cosine similarity to find near-duplicates, then regex patterns to detect contradictions ("switched from X", "no longer using Y"). The system automatically decides ADD/UPDATE/DELETE/NOOP for every new piece of information.

249 tests. Zero external API calls required. Everything runs locally.

pip install memotrail
GitHub: github.com/HalilHopa-Datatent/memotrail

#OpenSource #MCP #AI #Python #DeveloperTools
