teamcache 0.1.2

Shared AI context cache for software teams — eliminate the cold start

TeamCache

Shared AI context cache for software teams. Eliminates the cold start — every developer after the first pays near-zero tokens for files that haven't changed.

The problem

10 developers. Same files. Read by AI every day. Nobody shares what was learned. Monthly token budget exhausted in 10–15 days instead of 20–22.

How it works

1. Static index — teamcache index parses every file in seconds with tree-sitter. No AI, no API key. Every file gets a structural summary immediately.
2. AI upgrade — When your AI tool reads a file, it calls cache_summary() to store a richer understanding. That summary is shared with your whole team via git.
3. No cold start — The next developer gets the AI summary instantly. They never read the raw file. Tokens saved.

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Quickstart (under 10 steps)

# 1. Install
pip install teamcache

# 2. Go to your repo
cd your-repo

# 3. Initialize
teamcache init

# 4. Index the whole repo (seconds, no AI, no API key)
teamcache index

# 5. Register with your AI tool
teamcache install                        # Claude Code (default)
teamcache install --agent cursor         # Cursor
teamcache install --agent codex          # OpenAI Codex CLI
teamcache install --agent windsurf       # Windsurf
teamcache install --agent aider          # Aider
teamcache install --agent opencode       # OpenCode

# 6. Commit the static index to share with your team
git add .teamcache/objects/
git commit -m "chore: add teamcache static index"
git push

That's it. Your AI tool now calls get_file_context() before reading any file and cache_summary() after. Every teammate gets the benefit.

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Commands

Command	What it does
teamcache init	Initialize in the current git repo
teamcache index	Parse all files, build static summaries
teamcache install [--agent NAME]	Register MCP server with your AI tool
teamcache serve	Start the MCP stdio server (called by AI tool)
teamcache changed [--since BRANCH]	Re-index files changed since a branch
teamcache sync	Rebuild local index from committed objects
teamcache invalidate [PATH|--stale|--all]	Mark entries as needing refresh
teamcache stats	Show AI vs static coverage, top contributors
teamcache report	Write .teamcache/reports/YYYY-MM.md
teamcache commit	git add .teamcache/objects/ && git commit
teamcache uninstall [--agent NAME]	Remove MCP registration and instructions

MCP tools

Your AI tool gets these tools via the MCP server:

Tool	What it does
repo_overview()	Directory tree, languages, entry points, coverage
get_file_context(path)	Returns AI or static summary; tells AI what to do
cache_summary(path, summary, lang)	AI writes its understanding back into the cache
find_relevant_files(task)	Semantic + keyword search across all summaries
get_symbols(path)	Functions, classes, imports for a file
find_by_symbol(name)	Where is UserService defined? Line number included.
get_changed_context(branch)	What changed since main, which need AI re-read

Architecture

.teamcache/
  objects/          ← git committed — shared with team
    summaries/      ← AI and static summary objects (immutable JSON)
    symbols/        ← tree-sitter symbol index objects
    repomap.json    ← cross-file import map
  config.yaml       ← schema_version: v1 (nothing else)
  local/            ← gitignored — rebuilt locally
    index.sqlite    ← fast lookup index
    embeddings.sqlite ← semantic search vectors

Cache key: sha256(sha256(file_bytes) + "|" + schema_version)
File changes → new key → old object ignored automatically.

Two-tier summaries:

• static — tree-sitter parse, runs in milliseconds, no AI, available from day one
• ai — written by the AI tool after it reads a file, much richer, preferred when available

No external calls from teamcache itself. The AI tool already running writes summaries via cache_summary(). teamcache never calls any AI API. No API key. No separate cost.

CI integration

GitHub Actions

.github/workflows/teamcache-sync.yml is included — keeps static index fresh on every merge to main.

GitLab CI

See .gitlab/teamcache-sync.yml. Include it in your pipeline:

include:
  - local: .gitlab/teamcache-sync.yml

Requirements

• Python 3.10+
• Git
• No API key required at any point

Optional (installed automatically with pip install teamcache[all]):

• tree-sitter — more accurate symbol extraction (falls back to regex without it)
• sentence-transformers — semantic search (falls back to keyword search without it)

License

MIT