navegador

mcp
Guvenlik Denetimi
Uyari
Health Uyari
  • License — License: MIT
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Low visibility — Only 7 GitHub stars
Code Gecti
  • Code scan — Scanned 12 files during light audit, no dangerous patterns found
Permissions Gecti
  • Permissions — No dangerous permissions requested
Purpose

This tool parses your source code into a property graph (AST) and layers your team's documentation, decisions, and rules on top. It acts as an MCP server to feed this structured context to AI coding agents instead of raw file dumps.

Security Assessment

Overall Risk: Medium. The light code scan of 12 files found no dangerous patterns, hardcoded secrets, or dangerous permission requests. It operates locally using SQLite, though it notes FalkorDB and Redis for production environments. However, there are two notable points to consider. First, it reads and ingests your local source files, meaning it will access whatever repositories you point it at. Second, the MCP integration provides a `query_graph` tool that allows raw Cypher query passthrough. Even though the documentation mentions security hardening, Cypher injection is a known risk with graph databases, so administrators should ensure proper input sanitization if used in shared environments.

Quality Assessment

The project is very new and has low visibility, currently sitting at only 7 GitHub stars. Despite the low community trust and lack of extensive testing across different CLIs, it is actively maintained (last push was today). It uses the permissive MIT license and has a clear description, automated CI, and thorough documentation.

Verdict

Use with caution — it is actively maintained and safe for local trials, but its low community adoption and raw database query capabilities warrant careful review before deploying in sensitive production environments.
SUMMARY

AST + knowledge graph context engine for AI coding agents

README.md

Navegador

Your codebase + everything your team knows about it — in one queryable graph.

Navegador parses your source code into a property graph and layers your team's knowledge on top: decisions, concepts, rules, people, wiki pages, and meeting outputs. AI coding agents get structured, precise context instead of raw file dumps.

navegador — Spanish for navigator / sailor

CI
PyPI
Python
License: MIT
Docs


Two layers, one graph

┌─────────────────────────────────────────────────────────────────┐
│  KNOWLEDGE LAYER                                                │
│  Concepts · Rules · Decisions · WikiPages · People · Domains    │
│                                                                 │
│         ↕  GOVERNS / IMPLEMENTS / DOCUMENTS / ANNOTATES         │
│                                                                 │
│  CODE LAYER                                                     │
│  Repository · File · Module · Class · Function · Method         │
│  Variable · Import · Decorator · (call graphs, hierarchies)     │
└─────────────────────────────────────────────────────────────────┘
              stored in FalkorDB  (SQLite local · Redis prod)

The code layer is built automatically by ingesting source trees. The knowledge layer is populated by your team — manually, via wiki ingestion, or from PlanOpticon meeting analysis output.


Quick start

pip install navegador

# Ingest your repo
navegador ingest ./myrepo

# Load context for a file
navegador context src/auth.py

# Search across code + knowledge
navegador search "rate limit" --all

# Explain a symbol
navegador explain AuthService

# Check graph stats
navegador stats

MCP integration

Add to your Claude / Cursor / Gemini MCP config:

{
  "mcpServers": {
    "navegador": {
      "command": "navegador",
      "args": ["mcp", "--db", ".navegador/graph.db"]
    }
  }
}

Available MCP tools:

Tool Description
ingest_repo Parse and load a repo into the graph
load_file_context All symbols in a file + their relationships
load_function_context What a function calls and what calls it
load_class_context Class methods, inheritance, subclasses
search_symbols Fuzzy search for functions/classes by name
query_graph Raw Cypher passthrough (with security hardening)
graph_stats Node and edge counts
get_rationale Decision rationale, alternatives, and status
find_owners People assigned to any node
search_knowledge Search concepts, rules, decisions, wiki
blast_radius Impact analysis — what's affected by a change

Knowledge layer

Beyond code structure, navegador stores what your team knows:

# Record an architectural decision
navegador add decision "Use FalkorDB for graph storage" \
  --rationale "Cypher queries, SQLite-backed zero-infra mode"

# Define a business concept and link it to code
navegador add concept PaymentProcessing
navegador annotate PaymentProcessing --function process_charge

# Add a rule
navegador add rule "All writes must go through the service layer"

# Ingest your GitHub wiki
navegador wiki ingest --repo myorg/myrepo

# Import PlanOpticon meeting analysis
navegador planopticon ingest ./meeting-output/

Graph schema

Code nodes: Repository · File · Module · Class · Function · Method · Variable · Import · Decorator

Knowledge nodes: Concept · Rule · Decision · Person · Domain · WikiPage

Edges: CONTAINS · DEFINES · IMPORTS · CALLS · INHERITS · REFERENCES · DEPENDS_ON · GOVERNS · IMPLEMENTS · DOCUMENTS · ANNOTATES


Storage

Mode Backend When to use
Default falkordblite (SQLite) Local dev, zero infrastructure
Production Redis + FalkorDB module Shared deployments, agent swarms
from navegador.graph import GraphStore

store = GraphStore.sqlite(".navegador/graph.db")   # default
store = GraphStore.redis("redis://localhost:6379")  # production

Language support

Language Status
Python
TypeScript / JavaScript
Go
Rust
Java
Kotlin
C#
PHP
Ruby
Swift
C / C++

Framework enrichment

After ingesting code, navegador can promote generic AST nodes to framework-specific semantic types:

navegador enrich                          # auto-detect frameworks
navegador enrich --framework django       # target a specific framework

Supported frameworks: Django, FastAPI, React / Next.js, Express.js, React Native, Rails, Spring Boot, Laravel


Structural analysis

navegador impact AuthService --depth 3    # blast radius
navegador trace handle_request            # execution flow from entry point
navegador deadcode                        # unreachable functions/classes
navegador cycles                          # circular dependencies
navegador testmap                         # link tests to production code
navegador diff                            # map uncommitted changes to graph
navegador churn .                         # behavioural coupling from git history

Intelligence layer

navegador semantic-search "authentication flow"   # embedding-based search
navegador communities                              # detect code communities
navegador ask "what calls the payment service?"    # natural language queries
navegador docs src/auth.py                         # generate documentation

Requires an LLM provider: pip install navegador[llm]


Python SDK

from navegador import Navegador

nav = Navegador.sqlite(".navegador/graph.db")
nav.ingest("./myrepo")
nav.add_concept("Payment", description="Payment processing", domain="billing")

results = nav.search("auth")
bundle = nav.explain("AuthService")
owners = nav.find_owners("AuthService")

Cluster mode (agent swarms)

For multi-agent setups sharing a Redis-backed graph:

navegador init --redis redis://host:6379 --cluster

Features: shared graph with local snapshots, pub/sub notifications, task queues, distributed locking, session namespacing, checkpoints, agent messaging, observability dashboard.


Additional integrations

navegador codeowners ./myrepo             # parse CODEOWNERS → ownership graph
navegador adr ingest docs/decisions/      # Architecture Decision Records
navegador api ingest openapi.yaml         # OpenAPI / GraphQL schemas
navegador deps ingest package.json        # external dependency tracking
navegador pm ingest --github org/repo     # GitHub issues → knowledge graph
navegador editor setup claude-code        # generate MCP config for editors
navegador explore                         # browser-based graph visualization

Installation

PyPI

pip install navegador

Standalone binaries

No Python required — download prebuilt binaries from GitHub Releases:

Platform Binary
macOS (Apple Silicon) navegador-macos-arm64
macOS (Intel) navegador-macos-x86_64
Linux navegador-linux-x86_64
Windows navegador-windows-x86_64.exe

From source

git clone https://github.com/ConflictHQ/navegador.git
cd navegador
pip install -e ".[dev]"
pytest

Contributing

See CONTRIBUTING.md. Bug reports and feature requests welcome via GitHub Issues.


License

MIT — CONFLICT

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