ctxgraph 0.3.2

97% cheaper AI coding. ctx init → ctx build → ctx ask. Knowledge graphs, token-efficient capsules, query history, skills, and multi-provider LLM answers (Ollama, Claude, OpenAI, Azure). Cut context costs from cents to fractions of a cent.

ctxgraph — AI Context Engine for Python

Slash your LLM token costs by 97%. Stop dumping entire files into AI context. ctxgraph builds a multi-layer knowledge graph from your Python codebase and generates compact context capsules — delivering only what your AI needs, not every line of code.

pip install ctxgraph

ctx init                           # Scaffold .ctxgraph with config + default skills
ctx build                          # Build knowledge graph (AST analysis → SQLite)
ctx ask "how does JWT auth work"   # Ask questions with automatic token savings
ctx capsule "fix JWT expiry"       # 92-99% fewer tokens vs raw code
ctx history --stats                # Track total tokens saved across all queries
ccg "fix the login redirect bug"   # Launch Claude with context pre-loaded
ctx view                           # Interactive D3.js visualization (or --svg for static)

---

Quick Start

# 1. Initialize project
ctx init                           # Creates .ctxgraph/config.toml + default skills

# 2. Build the knowledge graph
ctx build                          # AST analysis → SQLite graph

# 3. Ask questions (requires Ollama or other LLM provider)
ctx ask "how does authentication work"

# 4. Or generate a capsule for your AI tool
ctx capsule "fix login rate limiter" --savings

---

Why ctxgraph?

Sending entire files to an AI is wasteful. ctxgraph analyzes your code with AST-based static analysis, stores the result in a queryable SQLite graph, and retrieves only the relevant nodes — compressed into a token-efficient DSL format.

Without ctxgraph	With ctxgraph	Savings
All files dumped to context	Targeted capsule (10-40 nodes)	97% fewer tokens
JSON-formatted metadata	Custom DSL format	4.7× less than JSON
Model guesses filenames	Graph provides exact paths	+16.7pp answer coverage

---

How It Works

Repository (.py files)
    │
    ▼
┌─────────────────────────────────────────────────────────┐
│  ctx build                                               │
│                                                          │
│  1. importer.py (AST)  →  import edges (file→file)       │
│  2. symbols.py (AST)   →  classes, functions, methods    │
│  3. semantic.py        →  docstring summaries            │
│                                                          │
│  Store: SQLite (nodes + edges)                           │
└────────────────────────┬────────────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────────────┐
│  ctx capsule "<query>"                                  │
│                                                          │
│  1. Tokenize query → keyword search                      │
│  2. Score: name matches (2x), text (0.5x)                │
│  3. BFS neighborhood expansion (depth=1-3)               │
│  4. Render token-efficient DSL → AI-ready capsule        │
└─────────────────────────────────────────────────────────┘

---

Token Efficiency

The DSL Advantage

ctxgraph's compact format uses 79% fewer tokens than JSON for the same data:

JSON: 426 tokens                        DSL: 143 tokens
─────                                     ────
{                                         [CTX]calculator expression parsing
  "nodes": [
    {                                     [F]calc/parser.py
      "id": "file:calc/parser.py",         D:Tokenize and parse math expressions
      "type": "file",                      S:tokenize, parse, Expression
      "name": "parser.py",               [F]calc/core.py
      ...                                  D:Core math operations
    }                                     [C]Calculator
  ],                                       D:Main calculator class
  "edges": [...]                          [DEP]
}                                           parser.py → core.py
                                            parser.py → plugins.py

4.7× compression ratio vs equivalent JSON — tested across all benchmark projects.

Capsule vs Raw Files

Project	Files	Raw Tokens	Avg Capsule	Savings	Build Time
tiny_app	7	1,558	~112	92.8%	~82ms
web_api	23	6,567	~136	97.9%	~474ms
microsvc	22	10,587	~63	99.4%	~916ms
dataflow	35	~12,500	~78	~99.4%	~560ms

97.0% average token reduction across 4 projects, 42 benchmark runs. The larger the project, the greater the savings.

Token Savings Display

ctx capsule "user authentication" --savings
# ┌──────────────────────────┬──────────────┐
# │ Metric                   │ Value        │
# ├──────────────────────────┼──────────────┤
# │ Raw Project .py Files    │ 10,587 tokens│
# │ Capsule DSL              │ 132 tokens   │
# │ JSON Equivalent          │ 490 tokens   │
# │ Savings vs Raw           │ 98.8%        │
# │ DSL vs JSON              │ 73.1%        │
# └──────────────────────────┴──────────────┘

ctx ask shows this automatically on every query. See how many tokens you save with each question.

---

Commands

ctx init — Scaffold project

ctx init                              # Default: current directory
ctx init /path/to/project             # Specific path

Creates a .ctxgraph/ directory with everything you need:

.ctxgraph/
├── config.toml          # API provider, model, context settings
├── history.jsonl        # Query history (auto-created, auto-pruned to 1000)
└── skills/
    ├── project-style.toml   # Default skill: project conventions
    └── field-guide.toml     # Default skill: field guide

Idempotent — safe to run on existing projects. Existing files are never overwritten.

ctx build — Build knowledge graph

ctx build                        # Current directory
ctx build /path/to/project       # Specific repo
ctx build --exclude "vendor/*"   # Custom exclude patterns
ctx build --provider claude      # Set LLM provider for later use
ctx build --model gpt-4o         # Set LLM model for later use

ctx ask <query> — Ask questions via LLM

The marquee command. Generates a context capsule, sends it to your LLM provider, and shows token savings.

ctx ask "how does JWT auth work"                       # Uses configured provider (default: Ollama)
ctx ask "fix login bug" --provider claude --model claude-sonnet-4-20250514
ctx ask "refactor payment flow" --skill project-style   # Activate a skill as system prompt
ctx ask "find auth code" --graph                        # Show graph search results table
ctx ask "deep dive" --mode deep                         # Use deep graph context (40 nodes)
ctx ask "explain" --skill field-guide --graph            # Combine skill + graph results

Flags:

Flag	Description
--provider	LLM provider: ollama, claude, openai, azure, custom
--model	Model name (e.g. gpt-4o, claude-sonnet-4-20250514)
--skill	Skill name to activate (e.g. project-style, field-guide)
--graph	Show ranked graph search results alongside the answer
--mode	Graph context mode: fast, balanced (default), deep

Requirements:

• A built graph (run ctx build first)
• A running LLM provider (Ollama by default at http://localhost:11434)

Example output:

$ ctx ask "how does the greeting system work"

# (Token savings table shown automatically)
┌──────────────────────────┬──────────────┐
│ Metric                   │ Value        │
├──────────────────────────┼──────────────┤
│ Raw Project .py Files    │ 1,234 tokens │
│ Capsule DSL              │ 45 tokens    │
│ JSON Equivalent          │ 180 tokens   │
│ Savings vs Raw           │ 96.4%        │
│ DSL vs JSON              │ 75.0%        │
└──────────────────────────┴──────────────┘

Answer:
The greeting system is implemented in `src/greet.py`. The `greet()` function takes a name
and returns a formatted greeting string. The `Greeter` class extends this with a configurable
prefix. Both use type hints and follow the project conventions.

ctx capsule <query> — Generate context capsule

Generate a token-efficient DSL capsule for use with any AI tool.

ctx capsule "fix JWT token validation"              # Balanced (default: 20 nodes, depth 2)
ctx capsule "fix JWT token validation" --mode fast  # Fast (10 nodes, depth 1)
ctx capsule "fix JWT token validation" --mode deep  # Deep (40 nodes, depth 3)
ctx capsule --overview                              # Project architecture overview
ctx capsule "fix auth" --savings                    # Show token savings table
ctx capsule "fix auth" --skill project-style         # Prepends skill context

Mode	Max Nodes	BFS Depth	When to Use
fast	10	1	Quick questions, small fixes
balanced (default)	20	2	General development
deep	40	3	Complex refactoring, architecture

ctx query <search> — Search graph

ctx query "user auth"
ctx query "payment gateway" --mode deep

Returns ranked nodes with relevance scores, displaying type, name, path, and score.

ctx view — Visualize graph

ctx view                              # Opens interactive D3.js HTML in browser
ctx view --output graph.html          # Save to custom path
ctx view --svg                        # Generate static SVG
ctx view --no-open                    # Generate without opening browser

ctx history — Query history

Review past questions, token savings, and provider usage.

ctx history                           # Last 10 queries
ctx history -n 20                     # Last 20
ctx history --filter "auth"           # Filter by keyword in query text
ctx history --stats                   # Aggregate statistics

Example output:

$ ctx history
┌────────────┬──────────────────────────────────────────────────┬─────────┬──────────┬──────────┐
│ Time       │ Query                                            │ Savings │ Provider │ Skill    │
├────────────┼──────────────────────────────────────────────────┼─────────┼──────────┼──────────┤
│ 2026-06-06 │ how does the greeting system work                │ 96.4%   │ ollama   │ -        │
│ 2026-06-06 │ fix login rate limiter                           │ 97.1%   │ ollama   │ -        │
│ 2026-06-06 │ refactor payment module with proper error handl… │ 95.8%   │ claude   │ project-style │
└────────────┴──────────────────────────────────────────────────┴─────────┴──────────┴──────────┘

$ ctx history --stats
┌──────────────────────┬───────────┐
│ Metric               │ Value     │
├──────────────────────┼───────────┤
│ Total Queries        │ 24        │
│ Total Raw Tokens     │ 254,088   │
│ Total Tokens Saved   │ 246,465   │
│ Avg Savings          │ 96.5%     │
│   Provider: ollama   │ 18        │
│   Provider: claude   │ 6         │
└──────────────────────┴───────────┘

History is stored as JSONL in .ctxgraph/history.jsonl. Auto-prunes to 1000 entries (oldest dropped).

ctx skill — Manage skills

Skills are reusable system prompts that prepend project knowledge to your capsule context.

ctx skill list                         # Show all available skills
ctx skill show project-style           # Display a skill's contents

Example:

$ ctx skill list
┌─────────┬─────────────────┬────────────────────────────────────────────────────┐
│ Source  │ Name            │ Preview                                            │
├─────────┼─────────────────┼────────────────────────────────────────────────────┤
│ builtin │ project-style   │ # Project Style Guide — default ctxgraph skill     │
│ builtin │ field-guide     │ # Project Field Guide — default ctxgraph skill     │
└─────────┴─────────────────┴────────────────────────────────────────────────────┘

$ ctx skill show project-style
Skill: project-style

# Project Style Guide — default ctxgraph skill
# Activate with: ctx ask --skill project-style "..."

[about]
name = "Project Style Guide"
description = "Enforces project conventions, code style, and naming patterns"

[rules]
import_style = "absolute imports, grouped: stdlib, third-party, local"
naming = "snake_case for functions/variables, PascalCase for classes, UPPER_CASE for constants"
...

Activating a skill:

ctx ask "refactor payment flow" --skill project-style
ctx capsule "fix auth" --skill field-guide

When activated, the skill contents are prepended as a ## Project Knowledge section before the capsule DSL.

Creating your own skills:

Skills are TOML files in .ctxgraph/skills/. Create a new file:

# .ctxgraph/skills/my-team-rules.toml

[about]
name = "Team Conventions"
description = "Custom team coding conventions"

[rules]
testing = "must write pytest tests for all new functions"
documentation = "every public API needs a docstring with Args and Returns"
branching = "prefer early returns over nested if-else"

Now it appears in ctx skill list and can be activated with --skill my-team-rules.

ctx info — Graph statistics

ctx info
# ┌────────────────────┬───────┐
# │ Total Nodes        │ 1090  │
# │ Total Edges        │ 1565  │
# │   files            │ 147   │
# │   classes          │ 45    │
# │   functions        │ 312   │
# └────────────────────┴───────┘

ctx serve — MCP server

pip install ctxgraph[mcp]
ctx serve

Claude Desktop config:

{
  "mcpServers": {
    "ctxgraph": {
      "command": "ctx",
      "args": ["serve"]
    }
  }
}

Tools: search_graph, get_context_capsule, get_file_dependencies, get_project_overview.

---

Claude Wrapper (ccg)

The ccg command launches Claude Code with ctxgraph context pre-loaded:

ccg "fix the JWT expiry bug in auth module"          # Single-shot
ccg --chat "refactor the payment flow"               # Interactive session
ccg --overview                                        # Project overview
ccg --mode deep "redesign the database schema"        # Deep mode

---

Configuration

.ctxgraph/config.toml is auto-created by ctx init:

[graph]
exclude = ["legacy/*", "vendor/*"]

[ai]
provider = "ollama"           # ollama, claude, openai, azure, custom
model = "qwen2.5-coder:7b"
endpoint = "http://localhost:11434"
api_key = ""

[context]
mode = "balanced"
max_nodes = 20
max_depth = 2

Environment variables

Variable	Overrides	Required For
CTXGRAPH_PROVIDER	ai.provider	—
CTXGRAPH_MODEL	ai.model	—
CTXGRAPH_ENDPOINT	ai.endpoint	—
ANTHROPIC_API_KEY	ai.api_key	Claude provider
OPENAI_API_KEY	ai.api_key	OpenAI provider
AZURE_OPENAI_API_KEY	ai.api_key	Azure provider

Provider examples

# Ollama (default — no env vars needed)
ctx ask "how does auth work"

# Claude
CTXGRAPH_PROVIDER=claude CTXGRAPH_MODEL=claude-sonnet-4-20250514 ctx ask "explain the architecture"

# OpenAI
CTXGRAPH_PROVIDER=openai CTXGRAPH_MODEL=gpt-4o ctx ask "find the bug"

# Azure OpenAI
CTXGRAPH_PROVIDER=azure \
  CTXGRAPH_MODEL=gpt-4o \
  CTXGRAPH_ENDPOINT=https://my-resource.openai.azure.com \
  AZURE_OPENAI_API_KEY=sk-... \
  ctx ask "refactor this"

# Custom (OpenAI-compatible)
CTXGRAPH_PROVIDER=custom CTXGRAPH_ENDPOINT=http://my-api/v1 ctx ask "explain"

# Per-command override (overrides both config and env vars)
ctx ask "how does this work" --provider claude --model claude-sonnet-4-20250514

Windows (PowerShell): Use $env: prefix instead:

$env:CTXGRAPH_PROVIDER = "azure"; $env:CTXGRAPH_MODEL = "gpt-4o"; ctx ask "query"

---

Use Cases

Debug a failing test

ctx build
ctx capsule "test_user_login is failing with auth error" --mode deep
# → [F]tests/test_auth.py
#   [F]src/auth/login.py
#   [C]AuthService
#   [DEP] auth/login.py → core/database.py, auth/session.py

# Or ask directly:
ctx ask "test_user_login is failing" --mode deep
# → Explains the issue with file references and suggests fixes

Understand a new codebase

ctx build
ctx capsule "project architecture" --overview
ccg --chat "explain the overall architecture and data flow"

# Or explore with skills active:
ctx ask "explain the architecture" --skill field-guide

Refactor across modules

ctx capsule "extract payment processing into separate module" --mode deep
ctx ask "plan the payment module extraction" --skill project-style --mode deep

Track your LLM usage

ctx history --stats
# Shows total queries, tokens saved, avg savings, provider breakdown

---

Framework Integrations

ctxgraph is a Python library first — the CLI is just a wrapper. This makes it easy to feed code context into LangChain, LangGraph, OpenAI Agents, or any LLM pipeline.

How the Python API works

The flow is always the same:

1. build_graph(path) → scans your code, stores a knowledge graph in path/.ctxgraph/graph.db
2. get_storage(path) → opens that SQLite database for queries (fast, no re-scanning)
3. render_capsule(storage, query) → searches the graph, returns a compact text capsule

from pathlib import Path
from ctxgraph.graph.builder import build_graph, get_storage
from ctxgraph.capsule.renderer import render_capsule
from ctxgraph.graph.query import search_relevant_nodes

# --- Step 1: Build (one-time, ~0.1-1s per project) ---
stats = build_graph(Path("/path/to/my_project"))
print(f"Built: {stats['total_nodes']} nodes, {stats['total_edges']} edges")

# --- Step 2: Use (instant — reads the .db file) ---
storage = get_storage(Path("/path/to/my_project"))

# Generate a capsule — a token-efficient DSL string
capsule = render_capsule(storage, "fix JWT token validation", max_nodes=20)
print(capsule)
# → [CTX]fix JWT token validation
#   [F]src/auth/jwt.py
#     D:JWT token creation and validation
#   [F]src/auth/middleware.py
#     D:Auth middleware for request validation
#   ...

# Or search for nodes programmatically
results = search_relevant_nodes(storage, "auth login", max_nodes=10, max_depth=2)
for node, score in results:
    print(f"  {node.type}:{node.name}  (score={score})")

Tip: build_graph is a one-time setup. In production, run ctx build during CI/deployment and let your app code only call get_storage + render_capsule.

Compose with skill context

from ctxgraph.skills import load_skill

storage = get_storage(Path("./my_project"))
skill_text = load_skill(Path("./my_project"), "project-style")
capsule = render_capsule(storage, "fix auth", max_nodes=20, skill_context=skill_text)
# Capsule now has "## Project Knowledge" section prepended

Compute token savings

from ctxgraph.capsule.savings import compute_savings

savings = compute_savings(Path("./my_project"), capsule_text)
print(f"Saved {savings['savings_pct']}% tokens")
print(f"DSL is {savings['dsl_vs_json']}% more efficient than JSON")

LangChain

Pass the capsule as context in your prompt template. The LLM gets exactly the files, classes, and dependencies it needs — no token waste.

from pathlib import Path
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from ctxgraph.graph.builder import get_storage
from ctxgraph.capsule.renderer import render_capsule

storage = get_storage(Path("./my_project"))
context = render_capsule(storage, "login rate limiter", max_nodes=15)

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a senior Python dev. Answer using the code context below.\n\n{context}"),
    ("user", "{question}"),
])

llm = ChatOpenAI(model="gpt-4o")
response = prompt | llm | (lambda msg: msg.content)

print(response.invoke({
    "context": context,
    "question": "Where is the rate limiter applied in the login flow?",
}))

LangGraph

Expose ctxgraph as a tool the agent calls on-demand.

from pathlib import Path
from langgraph.graph import StateGraph, MessagesState
from langgraph.prebuilt import ToolNode
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
from ctxgraph.graph.builder import get_storage
from ctxgraph.capsule.renderer import render_capsule

_storage = get_storage(Path("./my_project"))

@tool
def code_context(task: str) -> str:
    """Fetch relevant source code for a development task."""
    return render_capsule(_storage, task, max_nodes=20)

tools = [code_context]
model = ChatOpenAI(model="gpt-4o", temperature=0).bind_tools(tools)

def agent_node(state: MessagesState):
    return {"messages": [model.invoke(state["messages"])]}

graph = StateGraph(MessagesState)
graph.add_node("agent", agent_node)
graph.add_node("tools", ToolNode(tools))
graph.set_entry_point("agent")
graph.add_conditional_edges(
    "agent",
    lambda s: "tools" if s["messages"][-1].tool_calls else "__end__",
)
graph.add_edge("tools", "agent")

app = graph.compile()

for chunk in app.stream({"messages": [("user", "How does payment retry work?")]}):
    for node, vals in chunk.items():
        msg = vals["messages"][0]
        if hasattr(msg, "content") and msg.content:
            print(f"[{node}]: {msg.content[:300]}")

OpenAI Agents SDK

from pathlib import Path
from agents import Agent, Runner, function_tool
from ctxgraph.graph.builder import get_storage
from ctxgraph.capsule.renderer import render_capsule

_storage = get_storage(Path("./my_project"))

@function_tool
def fetch_code_context(task_description: str) -> str:
    """Retrieve code context from the project's knowledge graph."""
    return render_capsule(_storage, task_description, max_nodes=20)

agent = Agent(
    name="Code Assistant",
    instructions="You help developers understand their codebase. Use fetch_code_context to get relevant files before answering.",
    model="gpt-4o",
    tools=[fetch_code_context],
)

result = Runner.run_sync(agent, "How does the notification system handle email vs SMS?")
print(result.final_output)

Azure OpenAI (direct client)

import os
from openai import AzureOpenAI
from pathlib import Path
from ctxgraph.graph.builder import get_storage
from ctxgraph.capsule.renderer import render_capsule

storage = get_storage(Path("./my_project"))
context = render_capsule(storage, "event bus architecture", max_nodes=25)

client = AzureOpenAI(
    api_version="2024-08-01-preview",
    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
    api_key=os.environ["AZURE_OPENAI_API_KEY"],
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": f"You are a senior developer. Code context:\n\n{context}"},
        {"role": "user", "content": "How do I add a new event handler?"},
    ],
)
print(response.choices[0].message.content)

---

Development

git clone https://github.com/shashi3070/ctxgraph.git
cd ctxgraph
pip install -e ".[dev]"
pytest                          # 88+ tests
python benchmarks/run_benchmarks.py
python benchmarks/run_ollama_comparison.py   # Requires local Ollama

Project Structure

src/ctxgraph/
├── cli/main.py              — Typer CLI (9 commands)
├── graph/
│   ├── models.py            — Node, Edge, Graph dataclasses
│   ├── storage.py           — SQLite persistence
│   ├── builder.py           — Graph build orchestrator
│   └── query.py             — Tokenizer + BFS + relevance scoring
├── capsule/
│   ├── renderer.py          — DSL context generation
│   └── savings.py           — Token savings computation
├── analyzers/python/
│   ├── importer.py          — AST import extraction
│   ├── symbols.py           — AST class/function/method analysis
│   └── semantic.py          — Docstring summarization
├── config/
│   ├── init.py              — Project scaffold (.ctxgraph dir)
│   ├── settings.py          — TOML/JSON/env config loading
│   └── providers.py         — Ollama, Claude, OpenAI clients
├── clients/models.py        — Mode enum (fast/balanced/deep)
├── exclude/patterns.py      — Exclusion pattern matching
├── view/visualizer.py       — D3.js HTML graph generator
├── wrapper/claude.py        — ccg Claude wrapper
├── mcp/server.py            — MCP protocol server
├── skills/
│   ├── __init__.py          — Skill discovery + loading
│   ├── project-style.toml   — Default skill: project conventions
│   └── field-guide.toml     — Default skill: field guide
└── history.py               — JSONL history append/query/stats

---

Limitations

• Python-only analysis — other languages get file-level nodes only
• Keyword-based search — no semantic/embedding matching (planned)
• No incremental rebuild — full rebuild on every ctx build (planned)
• MCP server — stdio mode only, SSE not yet supported

---

License

MIT