Symdex-100 is a semantic code search and indexing tool for Python codebases that generates compact metadata "Cyphers" for rapid, intent-based code discovery. It enables sub-second natural language searches across large codebases while dramatically reducing token consumption for AI agents. Key capabilities: - Indexes Python functions into 20-byte semantic fingerprints (Cyphers) for ultra-fast lookups and structured code analysis - Natural language search that understands intent, returning precise file locations and code context - Security and domain-specific auditing to identify code patterns, potential vulnerabilities, and quality issues - High-level codebase overviews for understanding structure and patterns without reading thousands of lines - 10-50x token reduction for AI agents compared to grep or full-text search methods - Sub-second query performance even on large, complex codebases

Symdex-100

*Symdex-100 — your AI companion for code exploration*

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Semantic fingerprints for intent-based Python code search — 50–100x faster index lookups, 10–50x fewer tokens for AI agents.

Symdex-100 generates compact, structured metadata ("Cyphers") for every function in your Python codebase. Each Cypher is typically 20 bytes — a semantic fingerprint that enables sub-second, intent-based code search for developers and AI agents without reading thousands of lines of code.

# Your Python function → Indexed automatically
async def validate_user_token(token: str, user_id: int) -> bool:
    """Verify JWT token for a specific user."""
    # ... implementation ...

# Natural language search → Sub-second results
$ symdex search "where do we validate user tokens"
──────────────────────────────────────────────────────────────────────────────
  SYMDEX — 1 result in 0.0823 seconds
──────────────────────────────────────────────────────────────────────────────
  #1  validate_user_token  (Python)
  ────────────────────────────────────────────────────────────────────────────
    File   : /project/auth/tokens.py
    Lines  : 42–67
    Cypher : SEC:VAL_TOKEN--ASY
    Score  : 24.5
      42 │ async def validate_user_token(token: str, user_id: int) -> bool:
      43 │     """Verify JWT token for a specific user."""
      44 │     if not token:
      45 │         return False

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The Problem

Traditional code search methods scale poorly on large codebases:

| Approach | Limitation | Token Cost (AI agents) | |----------|-----------|------------------------| | grep | Keyword noise — finds "token" in comments, strings, variable names | 3,000+ tokens (read all matches, many false positives) | | Full-text search | No semantic understanding — can't distinguish intent | 5,000+ tokens (read 10 files, variable success) | | Embeddings | Opaque, expensive, query-time overhead | 2,000+ tokens (re-rank results, embedding index size) | | AST/LSP | Limited to structural queries (class/function names) | N/A (doesn't understand "what validates X") | | Symdex | Requires indexing step (one-time per codebase) | ~100–300 tokens (1–5 precise results with context) |

Result: Developers waste time reading irrelevant code. AI agents burn tokens on noise. Symdex reduces token usage by 10–50x for intent-based queries (vs reading multiple files) while providing sub-second index lookups.

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The Solution: Semantic Fingerprints

Symdex-100 solves this with Cypher-100, a structured metadata format that encodes function semantics in 20 bytes:

Anatomy of a Cypher-100 String

Each Cypher follows a strict four-slot hierarchy designed for both machine filtering and human readability:

┌─────────────────────────────────────────────────────────────┐
│                                                             │
│            DOM   :   ACT   _   OBJ   --   PAT               │
│              │        │         │           │               │
│         Domain   Action       Object        Pattern         │
│                                                             │
│   Where does     What does    What is       How does        │
│   this live?     it do?       the target?   it run?         │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Formal specification:

$$ \text{Cypher} = \text{DOM} : \text{ACT} \text{OBJ} \text{--} \text{PAT} $$

Where:

Example:

SEC:SCR_EMAIL--ASY

Translation: A security function that scrubs email data asynchronously.

Breakdown:

This 18-character string (or 30–40 chars with compound OBJ like RELATIONSHIPS+AUDIT) replaces 2,000+ characters of function body for search purposes — a 50–100:1 compression ratio with zero semantic loss. Compound OBJ improves ranking for multi-concept queries (e.g. "audit relations" → functions with `RELATIONSHIPS+AUD