Julian Wiley

Building a Local RAG Coding Assistant with mem0

February 17, 2026· 2 min readAgentic Assistants

A deep dive into the local coding assistant example -- RAG over codebases, episodic memory with mem0, and Redis solution caching.

RAGCoding AssistantVector Databasemem0Redis

The Problem with Cloud Coding Assistants

Cloud-based coding assistants send your code to external servers. For many organizations, this is a non-starter -- proprietary codebases, compliance requirements, and the simple desire to keep intellectual property local.

The local coding assistant (examples/local-coding-assistant/) provides a fully offline alternative: RAG over your codebase, persistent memory, and solution caching, all running on your hardware.

Codebase Indexing

The first step is building a searchable index of your codebase. The indexer (src/agentic_assistants/indexing/) processes source files through a pipeline:

  1. Parse -- Extract functions, classes, and modules with AST-level understanding
  2. Chunk -- Split code into semantically meaningful segments (not arbitrary line counts)
  3. Embed -- Generate vector embeddings using Ollama's nomic-embed-text model
  4. Store -- Index into ChromaDB with rich metadata (file path, language, symbol type)
indexer = CodebaseIndexer(
    vector_store="chromadb",
    embedding_model="nomic-embed-text",
    chunk_strategy="semantic",
    languages=["python", "typescript", "yaml"]
)
indexer.index_directory("./src", recursive=True)

The semantic chunking is key -- it respects function and class boundaries rather than splitting mid-logic, which significantly improves retrieval quality.

RAG Pipeline

When you ask a question, the retrieval pipeline:

  1. Embeds the query using the same model used for indexing
  2. Retrieves the top-k most relevant code chunks from ChromaDB
  3. Ranks results using a combination of vector similarity and keyword overlap
  4. Constructs a prompt with the retrieved context and your question
  5. Sends the augmented prompt to Ollama for completion

Episodic Memory with mem0

The coding assistant uses mem0 for episodic memory -- it remembers context from previous conversations. If you discussed a specific architectural decision last week, the assistant can recall it without you re-explaining.

memory:
  provider: mem0
  config:
    vector_store: chromadb
    collection: coding_assistant_memory
    auto_summarize: true
    retention_days: 90

Memory entries are automatically summarized and tagged, so retrieval stays fast even as the memory store grows.

Redis Solution Cache

When the assistant generates a solution to a coding problem, it caches the question-answer pair in Redis. If a similar question comes up later (from you or a teammate), it can return the cached solution instantly rather than regenerating it.

The cache uses semantic similarity for lookup, not exact string matching. So "how do I read a CSV in pandas" and "loading CSV files with pandas" would hit the same cache entry.

Agent Workflows

The assistant supports multi-step workflows through CrewAI:

  • Code Review -- Analyze a diff, identify issues, suggest improvements
  • Documentation -- Generate docstrings and README sections from code
  • Refactoring -- Suggest and apply refactoring patterns
  • Bug Investigation -- Trace through code paths to diagnose reported issues

Each workflow is a crew of specialized agents that collaborate on the task.

Performance

On my development machine (RTX 3080, 32GB RAM), the assistant achieves sub-second retrieval and 2-5 second response times for most queries using Mistral 7B through Ollama. The codebase index for a ~50K line project takes about 3 minutes to build and under a second to query.

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