If you follow me, you know I'm a big fan of AI coding assistants. I use them daily, GitHub Copilot, Cursor, Claude, and they've genuinely transformed how I write code. But there's one thing that has frustrated me for months: these assistants have no memory.
Every single time I start a new session, my AI assistant has to re-learn my codebase. It scans files, asks me the same questions, and burns through tokens just to understand context it already knew yesterday. It's like working with a brilliant colleague who gets amnesia every night.
So I built Nexus-Dev, an open-source local RAG system that gives AI coding agents persistent memory.
What is RAG? (For Those New to AI)
Before diving in, let's clarify some terms:
RAG (Retrieval-Augmented Generation) is a technique where, instead of feeding an entire document to an AI, you store information in a searchable database and retrieve only the relevant pieces when needed. Think of it like giving the AI a smart index to your codebase rather than making it read everything.
MCP (Model Context Protocol) is an open standard introduced by Anthropic that allows AI agents to connect to external tools and data sources. If you've used GitHub Copilot, Cursor, or Claude with plugins, you're already using MCP.
Embeddings are numerical representations of text that capture meaning. Similar texts have similar embeddings, which enables semantic search, finding content by meaning, not just keywords.
The Problem: AI Agents Have Amnesia
No Memory Between Sessions
When you close your IDE and reopen it the next day, your AI assistant forgets everything. It doesn't remember the architecture decisions you made, the bugs you fixed together, or the patterns your codebase uses.
Token Consumption Adds Up
Every session, the AI needs to "warm up" by reading your codebase again. This consumes tokens, and tokens cost money. Research shows that giving AI agents more context can actually make them perform worse. Accuracy can drop significantly (from 87% to 54%) due to context overload.
Existing Solutions Are Cloud-Based
Several solutions address this problem:
But I wanted something local-first (my code never leaves my machine), open-source (I control the stack), and cross-project (knowledge learned in one project helps others).
The Solution: How Nexus-Dev Works
The diagram shows the two main flows:
Indexing (top flow): Source code → Chunker → Embeddings → LanceDB Search (bottom flow): Query → Embeddings → LanceDB → Relevant Results
Step 1: Language-Aware Chunking
The first insight is that naive text splitting doesn't work for code. Cutting a function in half destroys its meaning.
Instead, Nexus-Dev uses tree-sitter to parse code into an Abstract Syntax Tree (AST) and extract semantic units: functions, classes, and methods.
@dataclass
class CodeChunk:
content: str
chunk_type: ChunkType
name: str
docstring: str | None
signature: str | None
start_line: int
end_line: int
Supported languages: Python, JavaScript/TypeScript, Java, and Markdown/RST for documentation.
Step 2: Multi-Provider Embeddings
Embeddings convert code chunks into vectors that capture meaning. Nexus-Dev supports multiple providers:
| Provider | Best For |
| OpenAI | Easy setup, general purpose |
| Ollama | Privacy, offline, free |
| Google/AWS | Enterprise environments |
| Voyage AI | Best RAG quality |
⚠️ Important: Embeddings aren't portable between providers. Switching requires re-indexing.
For privacy-focused teams, Ollama runs entirely locally:
{
"embedding_provider": "ollama",
"embedding_model": "nomic-embed-text"
}
Step 3: LanceDB Vector Storage
LanceDB is a local vector database, no server to run, just a file on disk.
results = database.search(
query="authentication middleware",
doc_type=DocumentType.CODE,
limit=5
)
Step 4: The Secret Weapon: Lessons Learned
After fixing a tricky bug, record it:
record_lesson(
problem="JWT validation fails with special characters",
solution="Use base64url decode instead of base64",
code_snippet="claims = base64url.decode(token.split('.')[1])"
)
Next time you encounter a similar issue, the AI finds it automatically. This creates institutional memory that survives team changes.
Getting Started
pip install nexus-dev
cd your-project
nexus-init --project-name "my-project" --embedding-provider openai
export OPENAI_API_KEY="sk-..."
nexus-index src/ docs/ -r
nexus-status
Add to your IDE's MCP configuration:
For Cursor (.cursor/mcp.json):
{
"mcpServers": {
"nexus-dev": { "command": "nexus-dev" }
}
}
For VS Code (.vscode/mcp.json):
{
"mcpServers": {
"nexus-dev": { "command": "nexus-dev" }
}
}
| Tool | What It Does |
search_code | Find functions, classes by meaning |
search_docs | Search documentation |
search_lessons | Find past solutions |
search_knowledge | Search everything |
index_file | Add files to the knowledge base |
record_lesson | Save debugging insights |
get_project_context | View project stats |
Real Example
You: "I need to add authentication to the API"
AI: Let me search the codebase...
[Calls search_code("authentication middleware")]
Found 3 relevant results:
1. auth_middleware.py:15-45 - JWTAuthMiddleware class
2. user_service.py:23-67 - authenticate_user function
No more reading through entire files. The AI finds exactly what's relevant.
Results
Since deploying Nexus-Dev:
Faster session starts: AI immediately has context
Reduced token usage: Only fetching what's needed
Cross-project learning: Lessons from one project help others
All local: No cloud, no API costs for storage
Nexus-Dev is open source: github.com/mmornati/nexus-dev
