tempograph 0.7.3

Code graph context engine for AI coding agents — structural dependency graph with adaptive injection

Tempograph

Your AI agent finds the right files. Every time.

Tempograph builds a dependency graph of your codebase and gives your AI coding agent exactly the files it needs before making changes. One tool call. No guessing.

The Problem

AI coding agents guess which files to look at. They search by filename, grep for keywords, and hope for the best. In large codebases, they miss critical dependencies, break things downstream, and waste tokens reading irrelevant code.

The Fix

pip install tempograph

Add to your MCP config (Claude Code, Cursor, Windsurf, or any MCP client):

{
  "mcpServers": {
    "tempograph": {
      "command": "tempograph-server",
      "args": []
    }
  }
}

Your agent calls prepare_context with a task description. Tempograph returns the exact files that matter — based on real dependency analysis, not text matching.

Does It Work?

Tested on real PRs from django, flask, httpx, fastapi, requests, and pydantic. Task: predict which files need to change.

Model	Without Tempograph	With Tempograph	Improvement
GPT-4o	21.7% F1	27.5% F1	+27%
GPT-4o-mini	19.2% F1	24.5% F1	+28%
qwen2.5-coder:32b	—	—	+18.6% (p=0.049)

Consistent improvement across every model. 2-3x more tasks helped than hurt. No other code context tool publishes retrieval benchmarks with statistical significance.

How It Works

your repo ──→ tree-sitter parse ──→ symbols + edges ──→ SQLite graph
                                                            │
                    AI agent calls prepare_context ─────────┘
                                                            │
                              ◄── KEY FILES + callers + callees + risk signals

• Parses your code with tree-sitter into a structural dependency graph
• Content-hashed and stored in SQLite — only changed files get re-parsed
• Warm queries in ~21ms. Branch switching doesn't trigger a rebuild
• Knows when NOT to inject context (adaptive gating avoids harming diffuse commits)

What Else Can It Do?

Beyond prepare_context, Tempograph exposes 24 MCP tools for deeper analysis when your agent needs it:

Tool	When to use it
blast_radius	"What breaks if I change this file?"
focus	"Show me everything related to auth"
hotspots	"Which files are riskiest to change?"
dead_code	"What can I safely delete?"
diff_context	"What's the impact of my current changes?"
overview	"Orient me in this new codebase"

All 24 tools

Tool	What it does
prepare_context	One-shot context for a task — the primary tool
overview	Repository orientation: size, languages, entry points
focus	Connected subgraph around a symbol — callers, callees
blast_radius	What breaks if you change this file or symbol
diff_context	Impact analysis of changed files
hotspots	Ranked risk list — complexity x coupling x size
dead_code	Unreferenced symbols — cleanup candidates
lookup	"Where is X?", "What calls X?"
dependencies	Circular imports, dependency layers
architecture	Module-level dependency view
symbols	Full symbol inventory
file_map	File tree with top symbols per file
search_semantic	Hybrid keyword + vector + structural search
cochange_context	Files that historically change together
suggest_next	Predicts the next useful tool call
run_kit	Composable multi-tool workflows
stats	Token budget estimates
get_patterns	Codebase conventions and idioms
report_feedback	Log whether output was useful
learn_recommendation	Suggestions from feedback history
index_repo	Build or rebuild the graph
watch_repo / unwatch_repo	Live incremental updates
embed_repo	Generate vector embeddings

CLI

# Orient in a new repo
tempograph ./my-project --mode overview

# What's connected to auth?
tempograph ./my-project --mode focus --query "authentication"

# What breaks if I touch db.ts?
tempograph ./my-project --mode blast --file src/lib/db.ts

# Find dead code to clean up
tempograph ./my-project --mode dead

Python API

from tempograph import build_graph

graph = build_graph("./my-project")
results = graph.search_symbols("handleLogin")
importers = graph.importers_of("src/lib/db.ts")
dead = graph.find_dead_code()

Languages

Python, TypeScript, JavaScript, Rust, Go, Java, C#, and Ruby get deep extraction (custom tree-sitter handlers). 170+ additional languages are supported via generic handler. pip install tempograph[full] for everything.

Support & Sponsorship

If TempoGraph saves you time, consider sponsoring the project. Sponsors get early access to new features.

Commercial Licensing

TempoGraph is AGPL-3.0 — free to use, modify, and distribute. If you use TempoGraph in a network service (SaaS, hosted IDE, AI coding platform), AGPL requires you to open-source your service code. If that doesn't work for you, commercial licenses are available.

Contact [e-mail] for commercial licensing terms.

License

AGPL-3.0 — free to use. Network service use requires source disclosure, or a commercial license.