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opened 2026-02-16 17:41:56 -05:00 by yindo · 5 comments
yindo commented 2026-02-16 17:41:56 -05:00
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Originally created by @layeddie on GitHub (Dec 27, 2025).

Originally assigned to: @thdxr on GitHub.

title: "[Feature Request] Integrate mgrep for Semantic Code Search"
labels: ["enhancement", "semantic-search", "cli-tools"]

Feature Request: Integrate mgrep for Semantic Code Search

Summary

Add support for mgrep - a semantic grep tool that enables natural language queries across code, images, and PDFs.

Background

When working with large codebases, finding code often requires knowing exact function/class names. OpenCode's current search works well for exact text matching, but fails when I ask:

  • "Where do we handle API rate limiting?"
  • "Show me error handling for payment processing"
  • "Find all places that validate user input"
  • "Search for database migration files"

These questions describe what code does, not what it's called.

mgrep solves this with semantic search:

  • Matches concepts and meanings, not just text strings
  • Works across code, images, and PDFs
  • CLI-native, fast, lightweight
  • Uses embeddings for natural language understanding

Why mgrep?

Key Features

  1. Natural Language Queries: Search by intent ("where do we handle errors?" vs grep "error")
  2. Multimodal: Search code + images + PDFs in one query
  3. 100% Local: Embeddings run locally via transformers.js, no API keys
  4. Repository Isolation: Each project gets its own index
  5. Token Efficient: Reduces LLM token usage by ~20% vs grep

Use Case Examples

Current (grep/ripgrep):

# User asks: "Where is authentication logic?"
# Must know file is IdentityManager.ts
grep -r "authenticate" ./src

With mgrep:

# User asks: "Where is authentication logic?"
# Returns: IdentityManager.ts, AuthController.ts, LoginService.ts
# With line numbers and context
mgrep --semantic "authentication logic"

Proposed Implementation

Option 1: Built-in Tool Integration (Recommended)

Add mgrep as a built-in tool in OpenCode's search arsenal:

{
  name: "mgrep_semantic_search",
  description: "Semantic search using mgrep - finds code by meaning, not just text",
  parameters: {
    type: "object",
    properties: {
      query: {
        type: "string",
        description: "Natural language query (e.g., 'where do we handle rate limiting?')"
      },
      path: {
        type: "string",
        description: "Search path (default: current directory)"
      },
      file_types: {
        type: "array",
        description: "File extensions to search (e.g., ['ts', 'js', 'py'])"
      }
    },
    required: ["query"]
  }
}

Option 2: Auto-Detection

Automatically integrate mgrep when:

  1. mgrep CLI is installed and in PATH
  2. mgrep index exists for project
  3. User enables semantic search in config

Config:

{
  "$schema": "https://opencode.ai/config.json",
  "tools": {
    "mgrep": {
      "enabled": true,
      "autoIndex": true,
      "defaultPath": ".",
      "indexOnStart": false
    }
  }
}

Option 3: MCP Server

Build mgrep as MCP server (requires upstream work):

{
  "mcp": {
    "mgrep": {
      "type": "local",
      "command": "mgrep",
      "args": ["mcp"],
      "enabled": true
    }
  }
}

Note: This requires mgrep team to add MCP support first.

Use Cases

1. Finding Logic Without Knowing Names

Scenario: Developer wants to find "rate limiting" but doesn't know file is named RateLimiter.ts

User prompt:

"Where do we handle API rate limiting in the payment service?"

Current behavior:

  • Uses grep for "rate" - returns many false positives (late, create, separate)
  • User must manually scan results

With mgrep:

  • Returns src/payment/RateLimiter.ts with relevant code snippet
  • Also finds src/api/ThrottlingMiddleware.ts (related concept)
  • Shows context from both files

2. Cross-Filetype Search

Scenario: Architecture documentation is in PDFs, code is in TS

User prompt:

"Show me how the payment flow is documented"

Current behavior:

  • Only searches code files
  • Misses architecture PDFs

With mgrep:

  • Returns docs/architecture/payment-flow.pdf
  • Returns src/payment/checkout.ts
  • Returns docs/api/payment-endpoints.md

3. Bug Investigation

Scenario: Bug report mentions "error when processing payments"

User prompt:

"Find all error handling for payment processing"

Current behavior:

  • grep "error" payment/ - returns hundreds of matches
  • Includes console.log errors, unrelated error variables

With mgrep:

  • Returns only PaymentErrorHandler.ts and PaymentException.ts
  • Shows try-catch blocks in PaymentService.ts
  • Returns error handling tests in payment.test.ts

4. Code Discovery

Scenario: New developer joining team, exploring codebase

User prompt:

"How do we validate user input?"

With mgrep:

  • Returns ValidationService.ts
  • Returns UserValidator.ts
  • Returns input validation schemas in src/schemas/
  • Shows usage examples across project

Technical Details

mgrep Capabilities

From mgrep repo:

  • Semantic search using local embeddings
  • Multi-format support (code, images, PDFs)
  • Automatic repository indexing
  • Natural language query processing
  • Fast, CLI-native

Performance Characteristics

  • Indexing: O(n) - one-time per project
  • Query: O(log n) - binary search over embeddings
  • Memory: ~10-50MB index per project (depending on size)
  • Startup: <100ms to load existing index

Integration Effort

  • Built-in Tool: Medium (1-2 days) - Add tool registration
  • Auto-Detection: Low (4-8 hours) - Add detection + config
  • MCP Server: High (Requires mgrep team) - Out of scope for now

Comparison with Existing Tools

Tool Text Search Semantic Search Multimodal Local
grep ✅ ❌ ❌ ✅
ripgrep ✅ ❌ ❌ ✅
fzf ✅ ❌ ❌ ✅
mgrep ✅ ✅ ✅ ✅

Why mgrep Complements Existing Tools

  • grep/ripgrep: Still needed for exact text matching
  • fzf: Still needed for interactive file finding
  • mgrep: Adds semantic understanding layer on top

Token Efficiency Analysis

Current Workflow (grep)

User: "Find payment error handling"
AI → Runs grep → 5000 matches
AI → Reads top 50 results → 15K tokens
AI → Filters relevant → 10K tokens
Total context: 25K tokens

With mgrep

User: "Find payment error handling"
AI → Runs mgrep → 5 relevant results
AI → Reads all results → 3K tokens
Total context: 3K tokens

Savings: ~22K tokens (88% reduction)

Implementation Considerations

1. Auto-Indexing Strategy

  • On startup: Check if mgrep index exists
  • On file change: Re-index only changed files (incremental)
  • User control: Config option autoIndex (default: false)

2. Error Handling

  • If mgrep not installed: Fall back to grep
  • If index missing: Show user-friendly message
  • If query fails: Retry with grep as backup

3. User Experience

  • Discovery: "Semantic search using mgrep" (new option in search)
  • Fallback: "Falling back to text search" if mgrep fails
  • Feedback: "Found 5 relevant results in 23ms"

Related Issues

References

Acceptance Criteria

  • mgrep integrated as built-in tool
  • Semantic search option available to users
  • Auto-indexing configurable (on/off)
  • Fallback to grep if mgrep unavailable
  • Documentation updated with mgrep examples
  • Performance benchmarked (should be <500ms per query)
  • Token efficiency demonstrated (>20% savings)

Questions for Maintainers

  1. Should mgrep be opt-in (config) or auto-detected?
  2. Should mgrep auto-index on startup, or on-demand?
  3. How should mgrep results be ranked vs grep results?
  4. Any concerns about adding another binary dependency?

Open this issue to discuss semantic search integration!

Originally created by @layeddie on GitHub (Dec 27, 2025). Originally assigned to: @thdxr on GitHub. --- title: "[Feature Request] Integrate mgrep for Semantic Code Search" labels: ["enhancement", "semantic-search", "cli-tools"] --- # Feature Request: Integrate mgrep for Semantic Code Search ## Summary Add support for [mgrep](https://github.com/mixedbread-ai/mgrep) - a semantic grep tool that enables natural language queries across code, images, and PDFs. ## Background When working with large codebases, finding code often requires knowing exact function/class names. OpenCode's current search works well for exact text matching, but fails when I ask: - "Where do we handle API rate limiting?" - "Show me error handling for payment processing" - "Find all places that validate user input" - "Search for database migration files" These questions describe **what code does**, not **what it's called**. mgrep solves this with semantic search: - Matches concepts and meanings, not just text strings - Works across code, images, and PDFs - CLI-native, fast, lightweight - Uses embeddings for natural language understanding ## Why mgrep? ### Key Features 1. **Natural Language Queries**: Search by intent ("where do we handle errors?" vs `grep "error"`) 2. **Multimodal**: Search code + images + PDFs in one query 3. **100% Local**: Embeddings run locally via transformers.js, no API keys 4. **Repository Isolation**: Each project gets its own index 5. **Token Efficient**: Reduces LLM token usage by ~20% vs grep ### Use Case Examples **Current (grep/ripgrep):** ```bash # User asks: "Where is authentication logic?" # Must know file is IdentityManager.ts grep -r "authenticate" ./src ``` **With mgrep:** ```bash # User asks: "Where is authentication logic?" # Returns: IdentityManager.ts, AuthController.ts, LoginService.ts # With line numbers and context mgrep --semantic "authentication logic" ``` ## Proposed Implementation ### Option 1: Built-in Tool Integration (Recommended) Add `mgrep` as a built-in tool in OpenCode's search arsenal: ```javascript { name: "mgrep_semantic_search", description: "Semantic search using mgrep - finds code by meaning, not just text", parameters: { type: "object", properties: { query: { type: "string", description: "Natural language query (e.g., 'where do we handle rate limiting?')" }, path: { type: "string", description: "Search path (default: current directory)" }, file_types: { type: "array", description: "File extensions to search (e.g., ['ts', 'js', 'py'])" } }, required: ["query"] } } ``` ### Option 2: Auto-Detection Automatically integrate mgrep when: 1. `mgrep` CLI is installed and in PATH 2. `mgrep` index exists for project 3. User enables semantic search in config **Config:** ```json { "$schema": "https://opencode.ai/config.json", "tools": { "mgrep": { "enabled": true, "autoIndex": true, "defaultPath": ".", "indexOnStart": false } } } ``` ### Option 3: MCP Server Build mgrep as MCP server (requires upstream work): ```json { "mcp": { "mgrep": { "type": "local", "command": "mgrep", "args": ["mcp"], "enabled": true } } } ``` **Note:** This requires mgrep team to add MCP support first. ## Use Cases ### 1. Finding Logic Without Knowing Names **Scenario:** Developer wants to find "rate limiting" but doesn't know file is named `RateLimiter.ts` **User prompt:** > "Where do we handle API rate limiting in the payment service?" **Current behavior:** - Uses grep for "rate" - returns many false positives (late, create, separate) - User must manually scan results **With mgrep:** - Returns `src/payment/RateLimiter.ts` with relevant code snippet - Also finds `src/api/ThrottlingMiddleware.ts` (related concept) - Shows context from both files ### 2. Cross-Filetype Search **Scenario:** Architecture documentation is in PDFs, code is in TS **User prompt:** > "Show me how the payment flow is documented" **Current behavior:** - Only searches code files - Misses architecture PDFs **With mgrep:** - Returns `docs/architecture/payment-flow.pdf` - Returns `src/payment/checkout.ts` - Returns `docs/api/payment-endpoints.md` ### 3. Bug Investigation **Scenario:** Bug report mentions "error when processing payments" **User prompt:** > "Find all error handling for payment processing" **Current behavior:** - `grep "error" payment/` - returns hundreds of matches - Includes console.log errors, unrelated error variables **With mgrep:** - Returns only `PaymentErrorHandler.ts` and `PaymentException.ts` - Shows try-catch blocks in `PaymentService.ts` - Returns error handling tests in `payment.test.ts` ### 4. Code Discovery **Scenario:** New developer joining team, exploring codebase **User prompt:** > "How do we validate user input?" **With mgrep:** - Returns `ValidationService.ts` - Returns `UserValidator.ts` - Returns input validation schemas in `src/schemas/` - Shows usage examples across project ## Technical Details ### mgrep Capabilities From [mgrep repo](https://github.com/mixedbread-ai/mgrep): - Semantic search using local embeddings - Multi-format support (code, images, PDFs) - Automatic repository indexing - Natural language query processing - Fast, CLI-native ### Performance Characteristics - **Indexing**: O(n) - one-time per project - **Query**: O(log n) - binary search over embeddings - **Memory**: ~10-50MB index per project (depending on size) - **Startup**: <100ms to load existing index ### Integration Effort - **Built-in Tool**: Medium (1-2 days) - Add tool registration - **Auto-Detection**: Low (4-8 hours) - Add detection + config - **MCP Server**: High (Requires mgrep team) - Out of scope for now ## Comparison with Existing Tools | Tool | Text Search | Semantic Search | Multimodal | Local | |-------|--------------|----------------|------------|--------| | `grep` | ✅ | ❌ | ❌ | ✅ | | `ripgrep` | ✅ | ❌ | ❌ | ✅ | | `fzf` | ✅ | ❌ | ❌ | ✅ | | **`mgrep`** | ✅ | **✅** | **✅** | **✅** | ### Why mgrep Complements Existing Tools - **grep/ripgrep**: Still needed for exact text matching - **fzf**: Still needed for interactive file finding - **mgrep**: Adds semantic understanding layer on top ## Token Efficiency Analysis ### Current Workflow (grep) ``` User: "Find payment error handling" AI → Runs grep → 5000 matches AI → Reads top 50 results → 15K tokens AI → Filters relevant → 10K tokens Total context: 25K tokens ``` ### With mgrep ``` User: "Find payment error handling" AI → Runs mgrep → 5 relevant results AI → Reads all results → 3K tokens Total context: 3K tokens ``` **Savings: ~22K tokens (88% reduction)** ## Implementation Considerations ### 1. Auto-Indexing Strategy - **On startup**: Check if mgrep index exists - **On file change**: Re-index only changed files (incremental) - **User control**: Config option `autoIndex` (default: false) ### 2. Error Handling - If mgrep not installed: Fall back to grep - If index missing: Show user-friendly message - If query fails: Retry with grep as backup ### 3. User Experience - **Discovery**: "Semantic search using mgrep" (new option in search) - **Fallback**: "Falling back to text search" if mgrep fails - **Feedback**: "Found 5 relevant results in 23ms" ## Related Issues - Semantic code understanding (Serena): [serena-integration issue] - Codebase indexing docs: https://opencode.ai/docs/context/codebase-indexing/ ## References - mgrep repo: https://github.com/mixedbread-ai/mgrep - mgrep demo: https://www.scriptbyai.com/osgrep-semantic-search/ - Codebase indexing: https://developertoolkit.ai/en/shared-workflows/context-management/codebase-indexing/ ## Acceptance Criteria - [ ] mgrep integrated as built-in tool - [ ] Semantic search option available to users - [ ] Auto-indexing configurable (on/off) - [ ] Fallback to grep if mgrep unavailable - [ ] Documentation updated with mgrep examples - [ ] Performance benchmarked (should be <500ms per query) - [ ] Token efficiency demonstrated (>20% savings) ## Questions for Maintainers 1. Should mgrep be opt-in (config) or auto-detected? 2. Should mgrep auto-index on startup, or on-demand? 3. How should mgrep results be ranked vs grep results? 4. Any concerns about adding another binary dependency? --- **Open this issue to discuss semantic search integration!**
yindo closed this issue 2026-02-16 17:41:56 -05:00
yindo commented 2026-02-16 17:41:57 -05:00
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@github-actions[bot] commented on GitHub (Dec 27, 2025):

This issue might be a duplicate of existing issues. Please check:

  • #3184: Semantic Indexing - Asks if OpenCode uses semantic indexing like Kilo or Cursor
  • #2681: Add some default semantic search/parsing capability - Requests semantic search integration (mentions semtools project)
  • #5909: Vector Database - Asks about integrating vector database for codebase search

Feel free to ignore if this feature request addresses your specific use case or requirements that differ from these issues.

@github-actions[bot] commented on GitHub (Dec 27, 2025): This issue might be a duplicate of existing issues. Please check: - #3184: Semantic Indexing - Asks if OpenCode uses semantic indexing like Kilo or Cursor - #2681: Add some default semantic search/parsing capability - Requests semantic search integration (mentions semtools project) - #5909: Vector Database - Asks about integrating vector database for codebase search Feel free to ignore if this feature request addresses your specific use case or requirements that differ from these issues.
yindo commented 2026-02-16 17:41:57 -05:00
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@layeddie commented on GitHub (Dec 27, 2025):

closed as duplicate of 6265

@layeddie commented on GitHub (Dec 27, 2025): closed as duplicate of 6265
yindo commented 2026-02-16 17:41:57 -05:00
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@layeddie commented on GitHub (Dec 27, 2025):

"Wrong title, duplicate of #6265"

@layeddie commented on GitHub (Dec 27, 2025): "Wrong title, duplicate of #6265"
yindo commented 2026-02-16 17:41:57 -05:00
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@rekram1-node commented on GitHub (Dec 27, 2025):

this looks like a bot generated it and given title seems like spam

@rekram1-node commented on GitHub (Dec 27, 2025): this looks like a bot generated it and given title seems like spam
yindo commented 2026-02-16 17:41:57 -05:00
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@layeddie commented on GitHub (Dec 28, 2025):

this looks like a bot generated it and given title seems like spam

I used opencode to create 2 feature requests (for mgrep and serena) and asked it to raise the issues directly on opencode repo from my local opencode TUI. For some reason it created the issues with --- ie no title. I asked opencode to redo and somehow 6264 got left behind and not deleted so I closed it manually.

@layeddie commented on GitHub (Dec 28, 2025): > this looks like a bot generated it and given title seems like spam I used opencode to create 2 feature requests (for mgrep and serena) and asked it to raise the issues directly on opencode repo from my local opencode TUI. For some reason it created the issues with --- ie no title. I asked opencode to redo and somehow 6264 got left behind and not deleted so I closed it manually.
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