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Elmoaid/TempoGraph

by Various

Your AI agent finds the right files. Every time. Code graph context engine with 24 MCP tools — 170+ languages, triple search, +27% F1 improvement.

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MCP

Elmoaid/TempoGraph

Added 1 June 2026

#ai-agents #code-analysis #code-context #code-graph #code-intelligence #context-engine #dependency-graph #developer-tools

Overview

TempoGraph is a code graph context engine that helps AI agents find the correct files in a codebase. It provides 24 MCP tools, supports over 170 programming languages, and uses a triple search method. The tool claims a 27% improvement in F1 score for file retrieval tasks.

Best for

Best for
Developers building AI agents that need precise file retrieval in polyglot codebases.

Use cases

  • Improving AI agent accuracy in locating relevant source files.
  • Enhancing code search across large multi-language repositories.
  • Integrating with MCP-based development workflows for context-aware assistance.

Notes

TempoGraph is a code graph context engine that helps AI agents find the correct files in a codebase. It provides 24 MCP tools, supports over 170 programming languages, and uses a triple search method. The tool claims a 27% improvement in F1 score for file retrieval tasks.

1 stars on GitHub. Last updated 2026-05-08. Licensed AGPL-3.0.

Use cases

  • Improving AI agent accuracy in locating relevant source files.
  • Enhancing code search across large multi-language repositories.
  • Integrating with MCP-based development workflows for context-aware assistance.

Pros

  • Supports a wide range of 170+ programming languages.
  • Provides 24 MCP tools for flexible integration.
  • Triple search method may improve retrieval accuracy.

Cons

  • Only 1 star on GitHub, indicating limited community adoption or early stage.
  • Requires Python environment and MCP setup.
  • Performance claims may not generalize to all codebases.

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Supports a wide range of 170+ programming languages.
  • Provides 24 MCP tools for flexible integration.
  • Triple search method may improve retrieval accuracy.

Cons

  • Only 1 star on GitHub, indicating limited community adoption or early stage.
  • Requires Python environment and MCP setup.
  • Performance claims may not generalize to all codebases.

Pairs with

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