FlowIndex

Behavior-first repository indexing for AI coding agents.

FlowIndex maps how a codebase behaves: entrypoints, call paths, tests, runtime traces, and git history. It helps AI coding agents understand impact before editing code.

Most coding-agent tools index files, chunks, symbols, or embeddings. FlowIndex indexes behavior.

It answers questions like:

• What code path handles this feature?
• What will break if I change this function?
• Which tests should run for this patch?
• Which previous bug fixes touched this area?
• What minimal context should an agent receive before editing?

Why behavior-first?

File trees and embedding search tell you what exists. They do not tell you what runs, what breaks, or what matters when you change a shared module.

FlowIndex builds a local, deterministic behavior graph:

• Entrypoints — API routes, webhooks, pages, CLI commands
• Call paths — function-to-function relationships from static analysis
• Tests — pytest, Jest/Vitest detection linked to symbols
• Git history — co-change patterns and bug-fix commit signals
• Impact — transparent risk scoring before you edit

No vector database. No LLM calls. No SaaS. Inspectable SQLite.

How it differs

Installation

pip install flowindex

MCP support for Cursor / Claude Code:

pip install "flowindex[mcp]"

From source:

git clone https://github.com/adu3110/flowIndex.git
cd flowIndex
pip install -e ".[dev]"

Quickstart

cd your-project
flowindex init          # use --here inside nested example dirs
flowindex scan
flowindex overview
flowindex explain "POST /api/payments"
flowindex impact src/services/ledger.py
flowindex tests-for update_ledger
flowindex context "fix duplicate payments when webhook retries"

Demo

cd examples/python_fastapi_app
flowindex init --here
flowindex scan
flowindex context "fix duplicate payments when webhook retries"

Record a GIF locally: brew install vhs && ./scripts/record-demo.sh

See docs/demo-output.txt for captured terminal output.

CLI examples

# Initialize index in current repo
flowindex init

# Scan and build behavior graph
flowindex scan

# Explain an entrypoint flow
flowindex explain "POST /payments"

# Analyze change impact
flowindex impact services/ledger.py

# Suggest tests for a change
flowindex tests-for services/ledger.py

# Generate agent context pack
flowindex context "fix webhook retry duplicate ledger entries"

MCP usage (Cursor)

Add to Cursor MCP settings (~/.cursor/mcp.json or project settings):

{
  "mcpServers": {
    "flowindex": {
      "command": "flowindex",
      "args": ["mcp"],
      "cwd": "/absolute/path/to/your/repo"
    }
  }
}

Run flowindex init && flowindex scan in that repo first.

Start the server manually:

flowindex mcp

Tools: get_repo_overview, explain_entrypoint, get_change_impact, suggest_tests, make_context_pack, and more — see docs/mcp.md.

Architecture

flowchart LR
  subgraph ingest [Ingest]
    Scan[File Scanner]
    Py[Python Parser]
    TS[TS/JS Parser]
    Git[Git Analyzer]
  end

  subgraph index [Local Index]
    DB[(SQLite)]
    Graph[Behavior Graph]
  end

  subgraph out [Outputs]
    CLI[CLI Commands]
    MCP[MCP Server]
    Pack[Context Packs]
  end

  Scan --> Py
  Scan --> TS
  Py --> Graph
  TS --> Graph
  Git --> Graph
  Graph --> DB
  DB --> CLI
  DB --> MCP
  DB --> Pack

Example context pack

flowindex context "fix duplicate payments when webhook retries"

# FlowIndex Context Pack

## Task
fix duplicate payments when webhook retries

## Likely Relevant Entrypoints
- POST /payments
- POST /stripe/webhook

## Likely Relevant Files
- main.py
- services/ledger.py
- services/payments.py

## High-Risk Symbols
- update_ledger()
- handle_stripe_webhook()

## Tests to Run
- tests/test_payments.py

## Caution
- services/ledger.py has high change risk.
- update_ledger() is shared by refunds and payments.

Roadmap

• Tree-sitter parsers for TS/JS and richer call resolution
• Runtime trace ingestion (OpenTelemetry, test coverage)
• Cross-repo dependency indexing
• Patch-aware incremental scan
• Language servers: Go, Rust, Java

Research questions

• How much agent error reduction comes from behavior graphs vs embeddings?
• What is the minimal context pack size that preserves patch correctness?
• Can co-change graphs predict test selection better than import graphs alone?
• Which entrypoint classes correlate most with production incidents?

Contributing

1. Fork and clone the repository
2. pip install -e ".[dev]"
3. Make changes with tests
4. ruff check . && mypy flowindex && pytest
5. Open a pull request

See docs/ for concepts, CLI reference, and examples.

License

MIT — see LICENSE.