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ASTra MCP is an open-source MCP server that builds a permanent AST knowledge graph of your codebase, so AI coding assistants like Claude Code, Cursor, and Codex get surgical context — not entire files. 98.9% fewer tokens. Zero cloud. Runs fully local.

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🔥 The Problem Your AI assistant reads entire files to understand your codebase. On a 100k-line repo that's 500k+ tokens per session. ⏱️ Slow, expensive responses 💸 Burns API credits fast 🚫 Hits context window limits 🎯 Misses cross-file connections 🔁 Re-reads the same code every session

⚡ The Fix ASTra builds a permanent knowledge graph of your codebase. Every AI task gets only the 5–25 most relevant functions — not 50 whole files. 🚀 Sub-second context injection 💰 ~99% reduction in tokens ♾️ Never hits context limits 🧠 Understands cross-file structure 💾 Memory persists across sessions

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📊 Real Numbers

💡 50 AI tasks/day × 10 engineers = roughly $5,000/month saved.

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🎬 See It In Action

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🚀 Quickstart

# 1. Install
pip install astra-mcp

# 2. Index your project (one-time, ~60s)
cd ~/your-project
astra init

# 3. Start live daemon (keeps graph hot in memory)
astra daemon start

# 4. Connect your AI assistant (2 min setup)
# → Claude Code:  add to ~/.claude/mcp.json  (or use Plugin)
# → Cursor:       Settings → Features → MCP Servers
# → Windsurf:     Settings → MCP
# → Continue.dev: ~/.continue/config.json
# Full per-assistant instructions: see "Integrate With Your AI Assistant" below

# 5. Optional: open the visual dashboard
astra dashboard
# → http://localhost:7865

That's it. Your AI assistant now has permanent structural memory of your codebase.

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🧠 How It Works

YOUR CODEBASE
      │
      ▼
┌─────────────────────────────────────────────────────────────┐
│  PHASE 1 — INDEX  (one-time, ~60s)                          │
│                                                             │
│  tree-sitter  →  AST parse every .py/.js/.ts/.go/.rs/.java │
│       ↓                                                     │
│  Extract symbols: functions, classes, methods, imports      │
│       ↓                                                     │
│  all-MiniLM-L6-v2  →  embed each symbol → 384-dim vector   │
│       ↓                                                     │
│  SQLite  →  store nodes + edges + embeddings                │
└─────────────────────────────────────────────────────────────┘
      │
      ▼
┌─────────────────────────────────────────────────────────────┐
│  PHASE 2 — LIVE DAEMON  (background process)                │
│                                                             │
│  watchdog  →  detects file saves → re-index changed file    │
│  Unix socket  →  any tool queries the live in-memory graph  │
│  Incremental PageRank  →  updates subgraph only (10× faster)│
└─────────────────────────────────────────────────────────────┘
      │
      ▼
┌─────────────────────────────────────────────────────────────┐
│  PHASE 3 — QUERY  (per AI task, <100ms)                     │
│                                                             │
│  embed(task)  →  cosine similarity  →  top-5 seed nodes     │
│       ↓                                                     │
│  Personalized PageRank from seeds  →  expand to top-25      │
│       ↓                                                     │
│  Serialize signatures only  →  fit token budget             │
│       ↓                                                     │
│  Inject into AI assistant via MCP protocol                  │
└─────────────────────────────────────────────────────────────┘

Full query trace example:

You type:  "Add 2FA to the login flow"
                    ↓
           ASTra embeds → 384-dim vector
                    ↓
           Cosine top-5 seeds:
             • login()        0.81
             • auth_check()   0.78
             • User.verify()  0.74
             • session_new()  0.71
             • hash_pw()      0.68
                    ↓
           PageRank expands to 25 nodes:
             JWT helpers, session store,
             rate limiter, middleware...
                    ↓
           1,254 tokens injected  (was 112,000)
                    ↓
           Claude writes focused 2FA code
           using only the 25 relevant symbols.

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🔬 Intelligence Layers

ASTra ships with 5 analysis engines beyond basic context retrieval:

⚡ Live Daemon

astra daemon start          # persistent background process
astra daemon status         # live graph stats
astra daemon query "auth"   # query hot in-memory graph (~10ms)
astra daemon stop

The daemon keeps the knowledge graph loaded in memory. No cold-start per query. Broadcasts graph deltas to all subscribers (dashboard auto-refreshes).

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💥 Impact Analyzer

astra impact get_context get_node
# Impact Analysis
#   Changed nodes  : 2
#   Blast radius   : 278 functions affected
#   Risk score     : 91/100
#   ⚠ Untested high-risk: build_context, search_symbols, ...

Before you change a function, know what breaks. Reverse BFS over the call graph, weighted by PageRank. Parses git diff directly for pre-commit hooks.

# Wire into pre-commit
git diff HEAD | astra impact --diff --project .

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🔍 Semantic Drift Detector

astra audit .
# Found 134 drift warnings
# dashboard   drift=0.96   calls: start, _resolve_dirs
# _migrate    drift=0.95   calls: commit, commit
# recall      drift=0.95   calls: embed_text, top_k_similar

Detects functions whose name/docstring doesn't match their actual behavior (what they call). recall() that calls embed_text() is doing semantic search — that's drift. Uses existing embeddings, zero new ML.

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📅 Temporal Knowledge Graph

astra timeline . --max-commits 200
# Top volatile nodes:
# login        changes: 7   volatility: 0.035
# auth_check   changes: 5   volatility: 0.025

Replays git history through the AST parser. Builds a 4D graph (nodes + edges + time). Reveals which functions are volatile (high churn = high risk), when dependencies appeared, which files always break together.

pip install gitpython   # one-time dependency
astra timeline . --max-commits 50

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🌐 Cross-Repo Federation

astra federate ./service-auth ./service-api ./service-payments
# Added repo: service-auth
# Added repo: service-api
# Cross-repo edges: 47 found
# validate_token   EXPORT   service-auth → service-api   conf=0.90

Links boundary nodes across repos: __init__.py exports, API endpoints, shared function names. Builds a unified graph spanning your entire microservices fleet. PageRank runs across the full federation.

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🛠 Command Reference

Core

Command	Description
astra init [path]	Index codebase (one-time setup, ~60s)
astra init --force	Force full re-index
astra status	Show index health: nodes, edges, files
astra watch	Start MCP server + file watcher
astra query "task"	Test context retrieval
astra bench "task"	Benchmark token savings vs naive read
astra dashboard	Launch web dashboard on :7865

Intelligence

Command	Description
astra daemon start	Start live background daemon
astra daemon stop	Stop daemon
astra daemon status	Show daemon stats
astra daemon query "task"	Query hot in-memory graph
astra impact [fn1] [fn2]	Blast radius analysis
astra impact --diff	Impact from git diff stdin
astra audit	Semantic drift scan
astra audit --file path.py	Scan one file
astra timeline	Build temporal graph from git history
astra federate repo1 repo2	Link repos into federated graph

MCP Tools (AI assistant calls these automatically)

Tool	Description
astra_get_context	Main: task → minimal relevant context
astra_search	Semantic symbol search
astra_get_callers	Who calls this function
astra_get_callees	What this function calls
astra_get_file_map	Symbol signatures for a file
astra_session_memory	Recall past sessions
astra_index_status	Index health check
astra_impact_analysis	Blast radius before editing
astra_semantic_audit	Drift scan
astra_get_volatility	Temporal risk data
astra_trace_cross_repo	Follow calls across repos

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📥 Installation

Step 1 — Install ASTra

# From PyPI
pip install astra-mcp

# Or from source
git clone https://github.com/Charan-place/ASTra-MCP.git
cd ASTra-MCP && bash install.sh

Step 2 — Index your project

cd ~/your-project
astra init                  # one-time, ~60s for large repos
astra daemon start          # keep graph hot in memory

Step 3 — Connect to your AI assistant

Pick your assistant below. Each takes under 2 minutes.

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🔌 Integrate With Your AI Assistant

Claude Code

Option A — Plugin (zero config, recommended)

Claude Code → Settings → Manage Plugins → search "astra" → Install

Done. ASTra activates automatically for every project.

Option B — Manual MCP config

Find your Claude Code config file:

# macOS / Linux
~/.claude/mcp.json

# Or per-project (takes priority)
/your-project/.mcp.json

Add ASTra:

{
  "mcpServers": {
    "astra": {
      "command": "python3",
      "args": ["-m", "astra.mcp.server"],
      "env": {
        "ASTRA_PROJECT": "/absolute/path/to/your-project",
        "ASTRA_DATA_DIR": "/absolute/path/to/your-project/.astra"
      }
    }
  }
}

Restart Claude Code. You'll see astra in the MCP server list (green dot = connected).

Verify it's working:

/mcp                         ← shows all connected servers
astra_index_status           ← call this tool to check node count

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Cursor

1. Open Cursor → Settings → Features → MCP Servers
2. Click + Add Server
3. Fill in:

Field	Value
Name	astra
Command	python3
Args	-m astra.mcp.server

Or edit ~/.cursor/mcp.json directly:

{
  "mcpServers": {
    "astra": {
      "command": "python3",
      "args": ["-m", "astra.mcp.server"],
      "env": {
        "ASTRA_PROJECT": "/absolute/path/to/your-project",
        "ASTRA_DATA_DIR": "/absolute/path/to/your-project/.astra"
      }
    }
  }
}

Restart Cursor. The ASTra tools appear in Cursor's tool list automatically.

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GitHub Copilot (VS Code)

Copilot supports MCP via the VS Code MCP extension.

1. Install: VS Code → Extensions → search "MCP Client" → install MCP Client for VS Code
2. Open settings.json (Cmd+Shift+P → Preferences: Open User Settings JSON)
3. Add:

{
  "mcp.servers": {
    "astra": {
      "command": "python3",
      "args": ["-m", "astra.mcp.server"],
      "env": {
        "ASTRA_PROJECT": "/absolute/path/to/your-project",
        "ASTRA_DATA_DIR": "/absolute/path/to/your-project/.astra"
      }
    }
  }
}

1. Restart VS Code → Copilot Chat will now call ASTra tools automatically.

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Windsurf (Codeium)

1. Open Windsurf → Settings → MCP
2. Add a new server entry:

{
  "mcpServers": {
    "astra": {
      "command": "python3",
      "args": ["-m", "astra.mcp.server"],
      "env": {
        "ASTRA_PROJECT": "/absolute/path/to/your-project",
        "ASTRA_DATA_DIR": "/absolute/path/to/your-project/.astra"
      }
    }
  }
}

1. Click Reload. ASTra appears in Windsurf's connected tools.

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OpenAI Codex / ChatGPT with Code Interpreter

Codex doesn't support MCP natively yet. Use the CLI bridge instead:

# Query ASTra from any terminal, pipe output to Codex
astra query "add rate limiting to auth middleware"
# Copy the output → paste into Codex chat as context

# Or use daemon for fast repeated queries
astra daemon start
astra daemon query "fix the payment flow"

For automation, use the JSON output flag:

astra query "task description" --no-tokens | jq '.context'

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Continue.dev

Edit ~/.continue/config.json:

{
  "mcpServers": [
    {
      "name": "astra",
      "command": "python3",
      "args": ["-m", "astra.mcp.server"],
      "env": {
        "ASTRA_PROJECT": "/absolute/path/to/your-project",
        "ASTRA_DATA_DIR": "/absolute/path/to/your-project/.astra"
      }
    }
  ]
}

Restart Continue. Tools appear under @astra in chat.

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Any MCP-Compatible Client

ASTra uses the standard Model Context Protocol over stdio. If your tool supports MCP, this config works:

{
  "mcpServers": {
    "astra": {
      "command": "python3",
      "args": ["-m", "astra.mcp.server"],
      "env": {
        "ASTRA_PROJECT": "/absolute/path/to/your-project",
        "ASTRA_DATA_DIR": "/absolute/path/to/your-project/.astra"
      }
    }
  }
}

Finding the right Python path (if python3 doesn't work):

which python3           # use this full path in "command"
# e.g. /usr/local/bin/python3  or  /opt/homebrew/bin/python3

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🔧 Troubleshooting Connection Issues

Server shows red / not connected

# 1. Verify astra is installed
python3 -m astra.mcp.server --help

# 2. Check paths are absolute (relative paths fail in MCP configs)
# ✅  /Users/you/project/.astra
# ❌  .astra

# 3. Check the crash log
cat ~/.astra-mcp/crash.log

Tools not appearing in assistant

# Confirm server starts successfully
python3 -m astra.mcp.server
# Should print: ASTra MCP server starting. project=...
# (Ctrl+C to stop)

Index is empty / no context returned

cd /your-project
astra init          # re-index
astra status        # should show nodes > 0

Wrong project being indexed

Set ASTRA_PROJECT explicitly in the MCP config env block to the absolute path of your repo root.

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🏗 Architecture

Full deep-dive → ARCHITECTURE.md

astra/
├── daemon/         ← Live background process + Unix socket server
├── indexer/        ← tree-sitter parser + sentence-transformer embedder
├── graph/          ← SQLite store + NetworkX PageRank
├── query/          ← Semantic search + context serializer
├── impact/         ← Blast radius analyzer
├── semantics/      ← Drift detector
├── temporal/       ← Git history replay + volatility scoring
├── federation/     ← Cross-repo graph linker
├── mcp/            ← MCP stdio server + 11 tools
├── dashboard/      ← FastAPI + D3.js real-time dashboard
├── memory/         ← Session memory store
├── watcher/        ← watchdog file monitor
└── cli/            ← typer CLI

Stack:

• 🌳 tree-sitter — AST parsing (Python, JS, TS, JSX, TSX)
• 🤖 sentence-transformers — local embeddings (all-MiniLM-L6-v2, 384-dim)
• 🕸 NetworkX — Personalized PageRank over call graph
• 💾 SQLite — zero-dependency knowledge graph storage
• 🛰 MCP protocol — stdio interface for AI assistants
• 🌐 FastAPI + D3.js v7 — real-time knowledge graph dashboard

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🔐 Privacy & Security

✅	Local-first — code never leaves your machine
✅	No telemetry — ASTra doesn't phone home
✅	No API keys — embeddings model runs 100% locally
✅	Self-hosted dashboard — localhost only
✅	Open source — Apache 2.0, audit everything
✅	Delete anytime — rm -rf .astra removes all data

Safe for confidential codebases: medical, financial, defense, enterprise.

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🆚 vs. Alternatives

	ASTra	grep	Copilot RAG	Chroma RAG	tree-sitter
Semantic search	✅	❌	✅	✅	❌
Structural (AST)	✅	❌	❌	❌	✅
Call graph / PageRank	✅	❌	❌	❌	❌
Local / no cloud	✅	✅	❌	partial	✅
Auto-injects to AI	✅	❌	partial	manual	❌
Persistent memory	✅	❌	❌	❌	❌
Impact analysis	✅	❌	❌	❌	❌
Cross-repo tracing	✅	❌	❌	❌	❌

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❓ FAQ

Does this slow down my AI assistant?

No. Daemon queries take ~10ms. You save 10–15 seconds of file-reading per task.

How big is the index?

Roughly 1–3% of source size. A 50,000-line codebase produces a ~2MB SQLite file.

Languages supported?

Python, JavaScript, TypeScript, JSX, TSX, Go, Rust, Java.

What if my code changes constantly?

File watcher re-indexes changed files in <100ms. Daemon graph updates incrementally.

Does it work offline?

Yes. After first install, the embeddings model (~80MB) is cached locally. No internet needed.

How is this different from RAG?

RAG embeds raw text chunks. ASTra embeds parsed symbols with structural context — function signatures, docstrings, call relationships. Far higher signal density per token.

Does ASTra train on my code?

No. All computation is local. Nothing sent anywhere. Embeddings stored in .astra/graph.db.

Can I delete the index?

rm -rf .astra — rebuild with astra init.

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🗺 Roadmap

• Python, JS, TS parser
• Go, Rust, Java parsers
• Personalized PageRank
• MCP stdio protocol (11 tools)
• Real-time dashboard
• Live daemon + Unix socket
• Impact analyzer
• Semantic drift detector
• Temporal knowledge graph
• Cross-repo federation
• VS Code inline graph extension
• Team-shared index (S3/GCS backend)
• HNSW indexing for 100k+ symbol corpora
• Pre-commit hook installer

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🤝 Contributing

Read CONTRIBUTING.md for full setup instructions and guidelines.

PRs welcome. High-value areas:

• 🌐 New language parsers (C, C++, Ruby, ...) — astra/indexer/parser.py
• 📊 Benchmarks on diverse codebases — benchmarks/
• 🎨 Dashboard UX — astra/dashboard/
• 🧪 Test coverage — tests/

Please read our Code of Conduct before contributing.

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📜 License

	Apache 2.0 allows
✅	Commercial use, modification, distribution
✅	Patent grant from all contributors
✅	Private use without releasing changes
📌	Must: include LICENSE + NOTICE, state changes, keep copyright

Full text → LICENSE

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