# Code-Graph-RAG Roadmap: 2026–2027 Development Plan

> Explore the Code-Graph-RAG roadmap through mid-2027. Discover how this open-source system transforms source code into a queryable knowledge graph for enhanced development.

- Repository: [Vitali Avagyan/code-graph-rag](https://github.com/vitali87/code-graph-rag)
- Tags: roadmap
- Published: 2026-08-14

---

**Code-Graph-RAG is a two-component open-source system that converts source code into a knowledge graph and enables natural-language querying, with a public roadmap outlining seven major goals through mid-2027.**

This article breaks down the official [Code-Graph-RAG roadmap](https://github.com/vitali87/code-graph-rag/blob/main/docs/roadmap.md) maintained in the `vitali87/code-graph-rag` repository. You'll learn what improvements are planned, which architectural components are affected, and what deliberate constraints keep the project focused on local-first, CLI-driven code intelligence.

## How Code-Graph-RAG Works Today

Before examining future plans, understanding the current architecture clarifies why each roadmap item matters.

The system splits cleanly into two layers:

- **Multi-Language Parser** — A Tree-sitter-based ingestion pipeline that parses source code and stores structured AST data in Memgraph (`codebase_rag/parsers/`).
- **RAG System (`codebase_rag/`)** — A CLI and Python SDK that translates natural language into Cypher queries, retrieves relevant code snippets, and supports AI-driven edits.

The `ask()` function in [`codebase_rag/cli.py`](https://github.com/vitali87/code-graph-rag/blob/main/codebase_rag/cli.py) and the real-time `cgr-updater` command both depend on this pipeline, as shown in the usage examples below.

## Seven Priority Goals (Mid-2026 to Mid-2027)

The roadmap organizes development into seven concrete objectives, each tied to specific files and measurable outcomes.

### 1. Deepen Analysis Quality for Existing Languages

**Better call-resolution, type inference, and data-flow edges across all supported languages.**

This improves the AST extraction logic in `codebase_rag/parsers/` and enriches the Memgraph schema documented in [[`docs/architecture/graph-schema.md`](https://github.com/vitali87/code-graph-rag/blob/main/docs/architecture/graph-schema.md)](https://github.com/vitali87/code-graph-rag/blob/main/docs/architecture/graph-schema.md). Rather than adding surface-level language support, the focus is making existing extractions more precise—especially for cross-file references and implicit type flows.

### 2. Improve Dead-Code Detection Precision

**Reduce false positives by validating against large open-source repositories.**

The dead-code analyzer in [`codebase_rag/dead_code.py`](https://github.com/vitali87/code-graph-rag/blob/main/codebase_rag/dead_code.py) will be stress-tested against real-world codebases. Findings from this process feed back into both the analysis heuristics and the evaluation harness that measures accuracy.

### 3. Maintain and Extend the Evaluation Harness

**Keep per-language AST oracles current and expand test coverage.**

The `evals/` directory—particularly files like [`evals/static_calls.py`](https://github.com/vitali87/code-graph-rag/blob/main/evals/static_calls.py)—provides ground-truth validation for parser output. This roadmap item ensures new analysis features ship with rigorous, automated correctness checks.

### 4. Grow the Configuration-Driven Language Tier

**Enable new languages via YAML configuration instead of custom extractors.**

Code-Graph-RAG already supports a hybrid architecture: hand-written Tree-sitter extractors for complex languages, and `ast-grep` YAML rules for simpler cases. This goal extends [`codebase_rag/language_spec.py`](https://github.com/vitali87/code-graph-rag/blob/main/codebase_rag/language_spec.py) to make YAML-driven additions the default path, lowering the barrier for community contributions.

### 5. Stabilize the SDK and MCP Server

**Maintain API stability under the pre-1.0 versioning policy.**

Three public interfaces fall under this umbrella:

- The **Typer-based CLI** ([`codebase_rag/cli.py`](https://github.com/vitali87/code-graph-rag/blob/main/codebase_rag/cli.py)) — `ask` and `cgr-updater` commands
- The **generated Python SDK** (`codebase_rag/sdk/`)
- The **Model-Context-Protocol (MCP) server** integration

Breaking changes will be documented, but full semantic-versioning guarantees wait until a 1.0 release.

### 6. Performance and Incremental Updates

**Reduce ingestion time for large repositories and accelerate real-time syncing.**

Two specific modules are targeted:

- [`codebase_rag/graph_updater.py`](https://github.com/vitali87/code-graph-rag/blob/main/codebase_rag/graph_updater.py) — handles live file watching and delta updates
- [`codebase_rag/embedder.py`](https://github.com/vitali87/code-graph-rag/blob/main/codebase_rag/embedder.py) — manages vector embedding and caching

Together, these changes make Code-Graph-RAG practical for monorepo-scale codebases without sacrificing the local-first architecture.

### 7. Security Posture

**Retain the OpenSSF Best Practices badge and pursue silver-level certification.**

This involves dependency hygiene tracked in [`pyproject.toml`](https://github.com/vitali87/code-graph-rag/blob/main/pyproject.toml) (which pins `tree-sitter`, `pymgclient`, `mcp`, and other critical packages) plus continuous security scanning via GitHub Workflows.

## What the Project Will *Not* Do

The roadmap explicitly defines three boundaries to maintain focus:

| Excluded Scope | Rationale |
|----------------|-----------|
| **No new first-class language extractors** without concrete user demand | Keeps maintenance burden manageable; YAML-tier expansion preferred |
| **No hosted SaaS offering** | Preserves local-first, privacy-respecting design |
| **No API stability guarantees before 1.0** | Allows iterative refinement based on real-world usage |

These constraints reinforce Code-Graph-RAG's identity as a maintainable, open-source CLI tool that runs entirely on the user's machine.

## Using Code-Graph-RAG Today

The following examples demonstrate the core workflows that the roadmap aims to improve.

### Python SDK: Load and Query

```python
from codebase_rag.graph_loader import load_graph
from codebase_rag.cli import ask

# Load a pre-indexed Python package

graph = load_graph("my_python_pkg")

# Ask natural-language questions; Cypher generation happens automatically

answer = ask("Where is the function that parses JSON?", graph=graph)
print(answer)

```

`load_graph()` is implemented in [`codebase_rag/graph_loader.py`](https://github.com/vitali87/code-graph-rag/blob/main/codebase_rag/graph_loader.py), while `ask()` wraps the RAG pipeline including Cypher translation and snippet retrieval.

### CLI: Real-Time Graph Updates

```bash

# Watch a repository and automatically refresh the knowledge graph on file changes

cgr-updater --path /path/to/repo --watch

```

This invokes the watcher logic in [`codebase_rag/graph_updater.py`](https://github.com/vitali87/code-graph-rag/blob/main/codebase_rag/graph_updater.py), which the roadmap targets for performance optimization.

## Summary

- **Code-Graph-RAG** combines Tree-sitter parsing with a Memgraph-backed RAG system for natural-language code exploration.
- The **2026–2027 roadmap** prioritizes analysis depth, dead-code accuracy, evaluation coverage, YAML-driven language expansion, SDK stability, performance, and security.
- **Key architectural files** include `codebase_rag/parsers/` (extraction), [`codebase_rag/graph_updater.py`](https://github.com/vitali87/code-graph-rag/blob/main/codebase_rag/graph_updater.py) (live updates), `evals/` (testing), and [`codebase_rag/cli.py`](https://github.com/vitali87/code-graph-rag/blob/main/codebase_rag/cli.py) (user interface).
- **Explicit exclusions** keep the project local-first, pre-1.0 flexible, and focused on configuration-based language growth rather than unlimited custom extractors.

## Frequently Asked Questions

### When will Code-Graph-RAG reach version 1.0?

Per the roadmap, no specific date is set. The project follows a "pre-1.0 versioning policy" that allows API iteration while documenting breaking changes. Stability guarantees arrive only after the SDK, MCP server, and CLI interfaces mature through real-world usage.

### How can I add support for a new programming language?

Use the YAML configuration tier in [`codebase_rag/language_spec.py`](https://github.com/vitali87/code-graph-rag/blob/main/codebase_rag/language_spec.py) with `ast-grep` rules for straightforward languages. First-class Tree-sitter extractors in `codebase_rag/parsers/` are reserved for cases with demonstrated demand and community commitment to maintenance.

### Is there a hosted or cloud version planned?

No. The roadmap explicitly excludes a SaaS offering. Code-Graph-RAG remains designed for local execution, keeping source code within the user's environment and avoiding external data transmission.

### What database does Code-Graph-RAG use for the knowledge graph?

**Memgraph** serves as the primary graph database. The [`codebase_rag/graph_loader.py`](https://github.com/vitali87/code-graph-rag/blob/main/codebase_rag/graph_loader.py) module handles connection and query execution, while `pymgclient` (listed in [`pyproject.toml`](https://github.com/vitali87/code-graph-rag/blob/main/pyproject.toml)) provides the Python driver.