flowindex

adu3110/flowIndex
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Install to Claude Code

This server doesn't publish a one-line install command. Follow the setup in the source repository.

Summary

Enables AI coding agents to analyze codebase behavior through entrypoints, call paths, and tests, providing impact analysis and context packs for code changes.

README.md

FlowIndex

![CI](https://github.com/adu3110/flowIndex/actions/workflows/ci.yml) ![PyPI version](https://pypi.org/project/flowindex/) ![License: MIT](LICENSE)

!FlowIndex demo

File trees and embeddings tell you what exists. FlowIndex tells you what runs, what breaks, and what matters.

Behavior-first repository indexing for AI coding agents. FlowIndex maps how a codebase behaves: entrypoints, call paths, tests, runtime traces, and git history — so 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

| Approach | FlowIndex | |----------|-----------| | Repo maps / file trees | Behavior graph with entrypoints and call edges | | Embeddings / RAG | Deterministic lexical + graph ranking | | Agent frameworks | Developer tool that feeds agents context | | Generic static analysis | Agent-oriented impact, tests-for, context packs |

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"

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.

Limitations

FlowIndex is a static-analysis tool, not a runtime tracer. Here is what works well and what is on the roadmap:

| Claim | Current reality | |-------|-----------------| | Behavior indexing | Static analysis + git co-change heuristics. Runtime traces not yet ingested. | | Call paths | Cross-file resolution via import graph + named symbol matching. Not a full compiler-grade call graph. | | Test selection | Name similarity, import edges, graph coverage links, co-change patterns. Not coverage-backed. | | Git history | Scoped to the indexed repo root even inside monorepos. Commit relevance is keyword-matched, not coverage-tracked. | | TS/JS support | Heuristic parser: functions, arrow functions, classes, class methods, qualified calls. Tree-sitter is on the roadmap. | | Context packs | Import-aware file ranking. May miss files with no keyword match in their name. |

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.

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