codedb

Code intelligence server for AI agents. Zig core. MCP native. Zero dependencies.

Structural indexing · Trigram search · Word index · Dependency graph · File watching · MCP + HTTP

Status · Install · Quick Start · MCP Tools · Benchmarks · Architecture · Data & Privacy · Building

Status

Alpha software — API is stabilizing but may change

codedb works and is used daily in production AI workflows, but:

Language support — Zig, Python, TypeScript/JavaScript (more planned)

No auth — HTTP server binds to localhost only

Snapshot format may change between versions

MCP protocol is JSON-RPC 2.0 over stdio (stable)

What works today	What's in progress
12 MCP tools for full codebase intelligence	Additional language parsers
Trigram-accelerated full-text search	WASM target for Cloudflare Workers
O(1) inverted word index for identifier lookup	Incremental snapshot updates
Structural outlines (functions, structs, imports)	Multi-project support
Reverse dependency graph	Remote indexing over SSH
Atomic line-range edits with version tracking
Auto-registration in Claude, Codex, Gemini, Cursor
Polling file watcher with filtered directory walker
Portable snapshot for instant MCP startup
Multi-agent support with file locking + heartbeats
Codesigned + notarized macOS binaries
Cross-platform: macOS (ARM/x86), Linux (ARM/x86)

⚡ Install

curl -fsSL https://codedb.codegraff.com/install.sh | sh

Downloads the binary for your platform and auto-registers codedb as an MCP server in Claude Code, Codex, Gemini CLI, and Cursor.

Platform	Binary	Signed
macOS ARM64 (Apple Silicon)	`codedb-darwin-arm64`	✅ codesigned + notarized
macOS x86_64 (Intel)	`codedb-darwin-x86_64`	✅ codesigned + notarized
Linux ARM64	`codedb-linux-arm64`	—
Linux x86_64	`codedb-linux-x86_64`	—

Or install manually from GitHub Releases.

⚡ Quick Start

As an MCP server (recommended)

After installing, codedb is automatically registered. Just open a project and the 12 MCP tools are available to your AI agent.

# Manual MCP start (auto-configured by install script)
codedb mcp /path/to/your/project

As an HTTP server

codedb serve /path/to/your/project
# listening on localhost:7719

CLI

codedb tree /path/to/project          # file tree with symbol counts
codedb outline src/main.zig           # symbols in a file
codedb find AgentRegistry             # find symbol definitions
codedb search "handleAuth"            # full-text search (trigram-accelerated)
codedb word Store                     # exact word lookup (inverted index, O(1))
codedb hot                            # recently modified files

🔧 MCP Tools

12 tools over the Model Context Protocol (JSON-RPC 2.0 over stdio):

Tool	Description
`codedb_tree`	Full file tree with language, line counts, symbol counts
`codedb_outline`	Symbols in a file: functions, structs, imports, with line numbers
`codedb_symbol`	Find where a symbol is defined across the codebase
`codedb_search`	Trigram-accelerated full-text search
`codedb_word`	O(1) inverted index word lookup
`codedb_hot`	Most recently modified files
`codedb_deps`	Reverse dependency graph (which files import this file)
`codedb_read`	Read file content
`codedb_edit`	Apply line-range edits (atomic writes)
`codedb_changes`	Changed files since a sequence number
`codedb_status`	Index status (file count, current sequence)
`codedb_snapshot`	Full pre-rendered JSON snapshot of the codebase

Example: agent explores a codebase

# 1. Get the file tree
curl localhost:7719/tree
# → src/main.zig      (zig, 55L, 4 symbols)
#   src/store.zig     (zig, 156L, 12 symbols)
#   src/agent.zig     (zig, 135L, 8 symbols)

# 2. Drill into a file
curl "localhost:7719/outline?path=src/store.zig"
# → L20: struct_def Store
#   L30: function init
#   L55: function recordSnapshot

# 3. Find a symbol across the codebase
curl "localhost:7719/symbol?name=AgentRegistry"
# → {"path":"src/agent.zig","line":30,"kind":"struct_def"}

# 4. Full-text search
curl "localhost:7719/search?q=handleAuth&max=10"

# 5. Check what changed
curl "localhost:7719/changes?since=42"

📊 Benchmarks

Measured on Apple M4 Pro, 48GB RAM. MCP = pre-indexed warm queries (20 iterations avg). CLI/external tools include process startup (3 iterations avg). Ground truth verified against Python reference implementation.

Latency — codedb MCP vs codedb CLI vs ast-grep vs ripgrep vs grep

codedb2 repo (20 files, 12.6k lines):

Query	codedb MCP	codedb CLI	ast-grep	ripgrep	grep	MCP speedup
File tree	0.04 ms	52.9 ms	—	—	—	1,253x vs CLI
Symbol search (`init`)	0.10 ms	54.1 ms	3.2 ms	6.3 ms	6.5 ms	549x vs CLI
Full-text search (`allocator`)	0.05 ms	60.7 ms	3.2 ms	5.3 ms	6.6 ms	1,340x vs CLI
Word index (`self`)	0.04 ms	59.7 ms	n/a	7.2 ms	6.5 ms	1,404x vs CLI
Structural outline	0.05 ms	53.5 ms	3.1 ms	—	2.4 ms	1,143x vs CLI
Dependency graph	0.05 ms	2.2 ms	n/a	n/a	n/a	45x vs CLI

merjs repo (100 files, 17.3k lines):

Query	codedb MCP	codedb CLI	ast-grep	ripgrep	grep	MCP speedup
File tree	0.05 ms	54.0 ms	—	—	—	1,173x vs CLI
Symbol search (`init`)	0.07 ms	54.4 ms	3.4 ms	6.3 ms	3.6 ms	758x vs CLI
Full-text search (`allocator`)	0.03 ms	54.1 ms	2.9 ms	5.1 ms	3.7 ms	1,554x vs CLI
Word index (`self`)	0.04 ms	54.7 ms	n/a	6.3 ms	4.2 ms	1,518x vs CLI
Structural outline	0.04 ms	54.9 ms	3.4 ms	—	2.5 ms	1,243x vs CLI
Dependency graph	0.05 ms	1.9 ms	n/a	n/a	n/a	41x vs CLI

Token Efficiency

codedb returns structured, relevant results — not raw line dumps. For AI agents, this means dramatically fewer tokens per query:

Repo	codedb MCP	ripgrep / grep	Reduction
codedb2 (search `allocator`)	~20 tokens	~32,564 tokens	1,628x fewer
merjs (search `allocator`)	~20 tokens	~4,007 tokens	200x fewer

Indexing Speed

codedb builds all indexes on startup (outlines, trigram, word, dependency graph) — not just a parse tree:

Repo	Files	Lines	Cold start	Per file
codedb2	20	12.6k	17 ms	0.85 ms
merjs	100	17.3k	16 ms	0.16 ms
openclaw/openclaw	11,281	2.29M	75 s	6.66 ms
vitessio/vitess	5,028	2.18M	50 s	9.95 ms
Indexes are built once on startup. After that, the file watcher keeps them updated incrementally (single-file re-index: <2ms). Queries never re-scan the filesystem.

Why codedb is fast

MCP server indexes once on startup → all queries hit in-memory data structures (O(1) hash lookups)
CLI pays ~55ms process startup + full filesystem scan on every invocation
ast-grep re-parses all files through tree-sitter on every call (~3ms)
ripgrep/grep brute-force scan every file on every call (~5-7ms)
The MCP advantage: index once, query thousands of times at sub-millisecond latency

Feature Matrix

Feature	codedb MCP	codedb CLI	ast-grep	ripgrep	grep	ctags
Structural parsing	✅	✅	✅	❌	❌	✅
Trigram search index	✅	✅	❌	❌	❌	❌
Inverted word index	✅	✅	❌	❌	❌	❌
Dependency graph	✅	✅	❌	❌	❌	❌
Version tracking	✅	✅	❌	❌	❌	❌
Multi-agent locking	✅	✅	❌	❌	❌	❌
Pre-indexed (warm)	✅	❌	❌	❌	❌	❌
No process startup	✅	❌	❌	❌	❌	❌
MCP protocol	✅	❌	❌	❌	❌	❌
Full-text search	✅	✅	✅	✅	✅	❌
Atomic file edits	✅	✅	✅	❌	❌	❌
File watcher	✅	✅	❌	❌	❌	❌

codedb = tree-sitter + search index + dependency graph + agent runtime. Zero external dependencies. Pure Zig. Single binary.

🏗️ Architecture

┌─────────────┐     ┌─────────────┐
│  HTTP :7719 │     │  MCP stdio  │
│  server.zig │     │  mcp.zig    │
└──────┬──────┘     └──────┬──────┘
       │                   │
       └───────┬───────────┘
               │
    ┌──────────▼──────────┐
    │     Explorer        │
    │   explore.zig       │
    │  ┌───────────────┐  │
    │  │ WordIndex      │  │
    │  │ TrigramIndex   │  │
    │  │ Outlines       │  │
    │  │ Contents       │  │
    │  │ DepGraph       │  │
    │  └───────────────┘  │
    └──────────┬──────────┘
               │
    ┌──────────▼──────────┐
    │      Store          │──── data.log
    │    store.zig        │
    └──────────┬──────────┘
               │
    ┌──────────▼──────────┐
    │     Watcher         │ ← polls every 2s
    │   watcher.zig       │
    │  (FilteredWalker)   │
    └─────────────────────┘

No SQLite. No dependencies. Purpose-built data model:

Explorer — structural index engine. Parses Zig, Python, TypeScript/JavaScript. Maintains outlines, trigram index, inverted word index, content cache, and dependency graph behind a single mutex.
Store — append-only version log. Every mutation (snapshot, edit, delete) gets a monotonically increasing sequence number. Version history capped at 100 per file.
Watcher — polling file watcher (2s interval). FilteredWalker prunes .git, node_modules, zig-cache, __pycache__, etc. before descending.
Agents — first-class structs with cursors, heartbeats, and exclusive file locks. Stale agents reaped after 30s.

Threading Model

Thread	Role
Main	HTTP accept loop or MCP read loop
Watcher	Polls filesystem every 2s via `FilteredWalker`
ISR	Rebuilds snapshot when stale flag is set
Reap	Cleans up stale agents every 5s
Per-connection	HTTP server spawns a thread per connection

All threads share a shutdown: atomic.Value(bool) for graceful termination.

🔒 Data & Privacy

codedb keeps runtime data local by default. Telemetry, when enabled, is written to ~/.codedb/telemetry.ndjson on the same machine and is not uploaded automatically.

Location	Contents	Purpose
`~/.codedb/projects/<hash>/`	Trigram index, frequency table, data log	Persistent index cache
`~/.codedb/telemetry.ndjson`	Aggregate tool calls and startup stats	Local telemetry log
`./codedb.snapshot`	File tree, outlines, content, frequency table	Portable snapshot for instant MCP startup

Not stored: No source code is sent anywhere. No file contents, file paths, or search queries are collected in telemetry. Sensitive files auto-excluded (.env*, credentials.json, secrets.*, .pem, .key, SSH keys, AWS configs).

To disable the local telemetry log entirely, set CODEDB_NO_TELEMETRY=1.

To sync the local NDJSON file into Postgres for analysis or dashboards, use scripts/sync-telemetry.py with the schema in docs/telemetry/postgres-schema.sql. The data flow is documented in docs/telemetry.md.

rm -rf ~/.codedb/          # clear all cached indexes
rm -f codedb.snapshot      # remove snapshot from project

🔨 Building from Source

Requirements: Zig 0.15+

git clone https://github.com/justrach/codedb.git
cd codedb
zig build                              # debug build
zig build -Doptimize=ReleaseFast       # release build
zig build test                         # run tests
zig build bench                        # run benchmarks

Binary: zig-out/bin/codedb

Cross-compilation

zig build -Doptimize=ReleaseFast -Dtarget=x86_64-linux
zig build -Doptimize=ReleaseFast -Dtarget=aarch64-linux
zig build -Doptimize=ReleaseFast -Dtarget=x86_64-macos

Releasing

./release.sh 0.2.0              # build, codesign, notarize, upload to GitHub Releases
./release.sh 0.2.0 --dry-run    # preview without executing

License

See LICENSE for details.

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codedb

Code intelligence server for AI agents. Zig core. MCP native. Zero dependencies.

Status

⚡ Install

⚡ Quick Start

As an MCP server (recommended)

As an HTTP server

CLI

🔧 MCP Tools

Example: agent explores a codebase

📊 Benchmarks

Latency — codedb MCP vs codedb CLI vs ast-grep vs ripgrep vs grep

Token Efficiency

Indexing Speed

Why codedb is fast

Feature Matrix

🏗️ Architecture

Threading Model

🔒 Data & Privacy

🔨 Building from Source

Cross-compilation

Releasing

License

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README.md

Latest commit

History

README.md

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codedb

Code intelligence server for AI agents. Zig core. MCP native. Zero dependencies.

Status

⚡ Install

⚡ Quick Start

As an MCP server (recommended)

As an HTTP server

CLI

🔧 MCP Tools

Example: agent explores a codebase

📊 Benchmarks

Latency — codedb MCP vs codedb CLI vs ast-grep vs ripgrep vs grep

Token Efficiency

Indexing Speed

Why codedb is fast

Feature Matrix

🏗️ Architecture

Threading Model

🔒 Data & Privacy

🔨 Building from Source

Cross-compilation

Releasing

License