Stop grepping for concepts. Start searching for meaning.

opencode-codebase-index brings semantic understanding to your OpenCode workflow — and now to any MCP-compatible client like Cursor, Claude Code, and Windsurf. Instead of guessing function names or grepping for keywords, ask your codebase questions in plain English.

• 🧠 Semantic Search: Finds "user authentication" logic even if the function is named check_creds.
• ⚡ Blazing Fast Indexing: Powered by a Rust native module using tree-sitter and usearch. Incremental updates take milliseconds.
• 🌿 Branch-Aware: Seamlessly handles git branch switches — reuses embeddings, filters stale results.
• 🔒 Privacy Focused: Your vector index is stored locally in your project.
• 🔌 Model Agnostic: Works out-of-the-box with GitHub Copilot, OpenAI, Gemini, or local Ollama models.
• 🌐 MCP Server: Use with Cursor, Claude Code, Windsurf, or any MCP-compatible client — index once, search from anywhere.

1. Install the plugin

   npm install opencode-codebase-index

2. Add to opencode.json

   {
     "plugin": ["opencode-codebase-index"]
   }

3. Index your codebase Run /index or ask the agent to index your codebase. This only needs to be done once — subsequent updates are incremental.

4. Start Searching Ask:

   "Find the function that handles credit card validation errors"

Use the same semantic search from any MCP-compatible client. Index once, search from anywhere.

1. Install dependencies

   npm install opencode-codebase-index @modelcontextprotocol/sdk zod

2. Configure your MCP client

   Cursor (.cursor/mcp.json):

   {
     "mcpServers": {
       "codebase-index": {
         "command": "npx",
         "args": ["opencode-codebase-index-mcp", "--project", "/path/to/your/project"]
       }
     }
   }

   Claude Code (claude_desktop_config.json):

   {
     "mcpServers": {
       "codebase-index": {
         "command": "npx",
         "args": ["opencode-codebase-index-mcp", "--project", "/path/to/your/project"]
       }
     }
   }

3. CLI options

   npx opencode-codebase-index-mcp --project /path/to/repo    # specify project root
   npx opencode-codebase-index-mcp --config /path/to/config   # custom config file
   npx opencode-codebase-index-mcp                            # uses current directory

The MCP server exposes all 8 tools (codebase_search, codebase_peek, find_similar, index_codebase, index_status, index_health_check, index_metrics, index_logs) and 4 prompts (search, find, index, status).

The MCP dependencies (@modelcontextprotocol/sdk, zod) are optional peer dependencies — they're only needed if you use the MCP server.

Scenario: You're new to a codebase and need to fix a bug in the payment flow.

Without Plugin (grep):

• grep "payment" . → 500 results (too many)
• grep "card" . → 200 results (mostly UI)
• grep "stripe" . → 50 results (maybe?)

With opencode-codebase-index: You ask: "Where is the payment validation logic?"

Plugin returns:

src/services/billing.ts:45  (Class PaymentValidator)
src/utils/stripe.ts:12      (Function validateCardToken)
src/api/checkout.ts:89      (Route handler for /pay)

Rule of thumb: codebase_peek to find locations → Read to examine → grep for precision.

In our testing across open-source codebases (axios, express), we observed up to 90% reduction in token usage for conceptual queries like "find the error handling middleware".

• Without plugin: Agent explores files, reads code, backtracks, explores more
• With plugin: Semantic search returns relevant code immediately → less exploration

1. Significant savings possible: Up to 90% reduction in the best cases
2. Results vary: Savings depend on query type, codebase structure, and agent behavior
3. Best for discovery: Conceptual queries benefit most; exact identifier lookups should use grep
4. Complements existing tools: Provides a faster initial signal, doesn't replace grep/explore

• Conceptual queries: "Where is the authentication logic?" (no keywords to grep for)
• Unfamiliar codebases: You don't know what to search for yet
• Large codebases: Semantic search scales better than exhaustive exploration

graph TD
    subgraph Indexing
    A[Source Code] -->|Tree-sitter| B[Semantic Chunks]
    B -->|Embedding Model| C[Vectors]
    C -->|uSearch| D[(Vector Store)]
    C -->|SQLite| G[(Embeddings DB)]
    B -->|BM25| E[(Inverted Index)]
    B -->|Branch Catalog| G
    end

    subgraph Searching
    Q[User Query] -->|Embedding Model| V[Query Vector]
    V -->|Cosine Similarity| D
    Q -->|BM25| E
    G -->|Branch Filter| F
    D --> F[Hybrid Fusion]
    E --> F
    F --> R[Ranked Results]
    end

1. Parsing: We use tree-sitter to intelligently parse your code into meaningful blocks (functions, classes, interfaces). JSDoc comments and docstrings are automatically included with their associated code.

Supported Languages: TypeScript, JavaScript, Python, Rust, Go, Java, C#, Ruby, Bash, C, C++, JSON, TOML, YAML 2. Chunking: Large blocks are split with overlapping windows to preserve context across chunk boundaries. 3. Embedding: These blocks are converted into vector representations using your configured AI provider. 4. Storage: Embeddings are stored in SQLite (deduplicated by content hash) and vectors in usearch with F16 quantization for 50% memory savings. A branch catalog tracks which chunks exist on each branch. 5. Hybrid Search: Combines semantic similarity (vectors) with BM25 keyword matching, filtered by current branch.

Performance characteristics:

• Incremental indexing: ~50ms check time — only re-embeds changed files
• Smart chunking: Understands code structure to keep functions whole, with overlap for context
• Native speed: Core logic written in Rust for maximum performance
• Memory efficient: F16 vector quantization reduces index size by 50%
• Branch-aware: Automatically tracks which chunks exist on each git branch
• Provider validation: Detects embedding provider/model changes and requires rebuild to prevent garbage results

The plugin automatically detects git branches and optimizes indexing across branch switches.

When you switch branches, code changes but embeddings for unchanged content remain the same. The plugin:

1. Stores embeddings by content hash: Embeddings are deduplicated across branches
2. Tracks branch membership: A lightweight catalog tracks which chunks exist on each branch
3. Filters search results: Queries only return results relevant to the current branch

• Branch detection: Automatically reads from .git/HEAD
• Re-indexing on switch: Triggers when you switch branches (via file watcher)
• Legacy migration: Automatically migrates old indexes on first run
• Garbage collection: Health check removes orphaned embeddings and chunks

.opencode/index/
├── codebase.db           # SQLite: embeddings, chunks, branch catalog
├── vectors.usearch       # Vector index (uSearch)
├── inverted-index.json   # BM25 keyword index
└── file-hashes.json      # File change detection

The plugin exposes these tools to the OpenCode agent:

The primary tool. Searches code by describing behavior.

• Use for: Discovery, understanding flows, finding logic when you don't know the names.
• Example: "find the middleware that sanitizes input"

Writing good queries:

Token-efficient discovery. Returns only metadata (file, line, name, type) without code content.

• Use for: Finding WHERE code is before deciding what to read. Saves ~90% tokens vs codebase_search.
• Example output:

  [1] function "validatePayment" at src/billing.ts:45-67 (score: 0.92)
  [2] class "PaymentProcessor" at src/processor.ts:12-89 (score: 0.87)
  
  Use Read tool to examine specific files.
  

• Workflow: codebase_peek → find locations → Read specific files

Manually trigger indexing.

• Use for: Forcing a re-index or checking stats.
• Parameters: force (rebuild all), estimateOnly (check costs), verbose (show skipped files and parse failures).

Checks if the index is ready and healthy.

Maintenance tool to remove stale entries from deleted files and orphaned embeddings/chunks from the database.

Returns collected metrics about indexing and search performance. Requires debug.enabled and debug.metrics to be true.

• Metrics include: Files indexed, chunks created, cache hit rate, search timing breakdown, GC stats, embedding API call stats.

Returns recent debug logs with optional filtering.

• Parameters: category (optional: search, embedding, cache, gc, branch), level (optional: error, warn, info, debug), limit (default: 50).

The plugin automatically registers these slash commands:

Zero-config by default (uses auto mode). Customize in .opencode/codebase-index.json:

{
  "embeddingProvider": "auto",
  "scope": "project",
  "indexing": {
    "autoIndex": false,
    "watchFiles": true,
    "maxFileSize": 1048576,
    "maxChunksPerFile": 100,
    "semanticOnly": false,
    "autoGc": true,
    "gcIntervalDays": 7,
    "gcOrphanThreshold": 100,
    "requireProjectMarker": true
  },
  "search": {
    "maxResults": 20,
    "minScore": 0.1,
    "hybridWeight": 0.5,
    "contextLines": 0
  },
  "debug": {
    "enabled": false,
    "logLevel": "info",
    "metrics": false
  }
}

The plugin automatically detects available credentials in this order:

1. GitHub Copilot (Free if you have it)
2. OpenAI (Standard Embeddings)
3. Google (Gemini Embeddings)
4. Ollama (Local/Private - requires nomic-embed-text)

Each provider has different rate limits. The plugin automatically adjusts concurrency and delays:

For large codebases, use Ollama locally to avoid rate limits:

# Install the embedding model
ollama pull nomic-embed-text

// .opencode/codebase-index.json
{
  "embeddingProvider": "ollama"
}

The plugin is built for speed with a Rust native module. Here are typical performance numbers (Apple M1):

Batch operations provide significant speedups:

Run benchmarks yourself: npx tsx benchmarks/run.ts

Use this decision tree to pick the right embedding provider:

                    ┌─────────────────────────┐
                    │ Do you have Copilot?    │
                    └───────────┬─────────────┘
                          ┌─────┴─────┐
                         YES          NO
                          │            │
              ┌───────────▼───────┐    │
              │ Codebase < 1k     │    │
              │ files?            │    │
              └─────────┬─────────┘    │
                  ┌─────┴─────┐        │
                 YES          NO       │
                  │            │       │
                  ▼            │       │
           ┌──────────┐        │       │
           │ Copilot  │        │       │
           │ (free)   │        │       │
           └──────────┘        │       │
                               ▼       ▼
                    ┌─────────────────────────┐
                    │ Need fastest indexing?  │
                    └───────────┬─────────────┘
                          ┌─────┴─────┐
                         YES          NO
                          │            │
                          ▼            ▼
                   ┌──────────┐ ┌──────────────┐
                   │ Ollama   │ │ OpenAI or    │
                   │ (local)  │ │ Google       │
                   └──────────┘ └──────────────┘

*Requires active Copilot subscription

Ollama (Recommended for large codebases)

ollama pull nomic-embed-text

{ "embeddingProvider": "ollama" }

OpenAI

export OPENAI_API_KEY=sk-...

{ "embeddingProvider": "openai" }

Google

export GOOGLE_API_KEY=...

{ "embeddingProvider": "google" }

GitHub Copilot No setup needed if you have an active Copilot subscription.

{ "embeddingProvider": "github-copilot" }

Be aware of these characteristics:

1. Build:

   npm run build

2. Register in Test Project (use file:// URL in opencode.json):

   {
     "plugin": [
       "file:///path/to/opencode-codebase-index"
     ]
   }

   This loads directly from your source directory, so changes take effect after rebuilding.

1. Fork the repository
2. Create a feature branch: git checkout -b feature/my-feature
3. Make your changes and add tests
4. Run checks: npm run build && npm run test:run && npm run lint
5. Commit: git commit -m "feat: add my feature"
6. Push and open a pull request

CI will automatically run tests and type checking on your PR.

├── src/
│   ├── index.ts              # Plugin entry point
│   ├── mcp-server.ts         # MCP server (Cursor, Claude Code, Windsurf)
│   ├── cli.ts                # CLI entry for MCP stdio transport
│   ├── config/               # Configuration schema
│   ├── embeddings/           # Provider detection and API calls
│   ├── indexer/              # Core indexing logic + inverted index
│   ├── git/                  # Git utilities (branch detection)
│   ├── tools/                # OpenCode tool definitions
│   ├── utils/                # File collection, cost estimation
│   ├── native/               # Rust native module wrapper
│   └── watcher/              # File/git change watcher
├── native/
│   └── src/                  # Rust: tree-sitter, usearch, xxhash, SQLite
├── tests/                    # Unit tests (vitest)
├── commands/                 # Slash command definitions
├── skill/                    # Agent skill guidance
└── .github/workflows/        # CI/CD (test, build, publish)

The Rust native module handles performance-critical operations:

• tree-sitter: Language-aware code parsing with JSDoc/docstring extraction
• usearch: High-performance vector similarity search with F16 quantization
• SQLite: Persistent storage for embeddings, chunks, and branch catalog
• BM25 inverted index: Fast keyword search for hybrid retrieval
• xxhash: Fast content hashing for change detection

Rebuild with: npm run build:native (requires Rust toolchain)

Pre-built native binaries are published for:

Windows builds use scalar distance functions instead of SIMD — functionally identical, marginally slower for very large indexes. This is due to MSVC lacking support for certain AVX-512 intrinsics used by simsimd.

MIT