Code Context Engine

Code Context Engine

Index your codebase. AI searches instead of re-reading files. 94% token savings, reproducibly benchmarked.

Website · Docs · Why CCE? · Benchmark · GitHub

Python 3.11+ · macOS · Linux · Windows

One command. Auto-detects your editor. Zero cloud, zero config.

Talk: We Cut 94% of Our AI Coding Tokens — AI Engineer World's Fair 2026

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Use cases

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Quick start

One command. 30 seconds.

uvx --from "code-context-engine[local]" cce init    # install + index + configure, one shot

Or if you prefer a persistent install:

uv tool install "code-context-engine[local]"    # or: pipx install "code-context-engine[local]"

cd /path/to/your/project cce init

Restart your editor. Done. Every question now hits the index instead of re-reading files.

Agent Plugin support: Run cce init --plugin to generate a portable

Agent Plugin directory that works with

VS Code, Cursor, Copilot, Codex, ChatGPT, and Kiro. The plugin uses

uvx to launch CCE on demand, so users don't need to pre-install the

Python package. See Agent Plugin below.

Already have Ollama? Skip [local] and use uv tool install code-context-engine instead. CCE auto-detects Ollama at localhost:11434 and uses nomic-embed-text.

System requirements

Python 3.11+ and a C compiler (for tree-sitter grammars).

Tested on macOS, Linux, Windows with Python 3.11/3.12/3.13.

cce init auto-detects your editor and writes the right config. To target a specific agent, use --agent claude, --agent codex, --agent copilot, --agent pi, or --agent all.

Multiple editors in the same project? All get configured in one command.

Codex note: Codex CLI reads MCP servers from ~/.codex/config.toml only — it has no per-project config. cce init adds one [mcp_servers.cce- - ] section per project so multiple projects coexist; cce uninstall removes only the section for the current project.

Pi note: Pi does not support MCP natively. To use CCE with Pi, you need a pi MCP adapter extension (e.g. pi-mcp-adapter) that consumes the .mcp.json config and exposes CCE's tools to the Pi agent. cce init sets up both .mcp.json and AGENTS.md (Pi loads the latter automatically for startup instructions).

  my-project · 38 queries · last query 5m ago

⛁ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ 88% tokens saved

Input savings 1.9M tokens $27.78 Output savings 4.8k tokens $0.36 ────────────────────────────────────────── Total saved 1.9M tokens $28.15

Breakdown: retrieval 84% ▰▰▰▰▰▰▰▰▰▰ 1.8M $26.76 · 12 calls chunk compression 3% ▰▱▱▱▱▱▱▱▱▱ 68.5k $1.03 · 12 calls output compression* <1% ▰▱▱▱▱▱▱▱▱▱ 4.8k $0.36 · 12 calls

Cost estimate based on Opus pricing (input $15.0/1M, output $75.0/1M)

Supports Anthropic, OpenAI, and Google model pricing. Configure via pricing.model in ~/.cce/config.yaml.

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Why this matters

Input tokens are 85-95% of your Claude Code bill. CCE cuts them by 94% (benchmarked on FastAPI).

Without CCE:    Claude reads payments.py + shipping.py   = 45,000 tokens

With CCE: context_search "payment flow" = 800 tokens

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Benchmark: FastAPI (reproducible)

We benchmarked CCE against FastAPI (53 source files, 180K tokens) with 20 real coding questions. No cherry-picking, no synthetic queries.

Methodology: For each query, "without CCE" means reading the full content of every file the query touches. "With CCE" means the relevant chunks after compression.

Important baseline note: The 94% number is measured against full-file reads, not against what Claude Code actually does. In practice, Claude Code already uses grep, partial file reads, and targeted tools, so the real-world savings compared to normal Claude Code behavior will be lower than 94%. We use full-file as the baseline because it's reproducible and deterministic (no agent behavior variability). The benchmark measures CCE's retrieval efficiency, not a head-to-head comparison with Claude Code's built-in exploration.

Per-Layer Savings (each measured independently)

Output compression (reducing Claude's reply length) provides additional savings (~65% estimated) but is not included in the headline number above.

Multi-language benchmarks

Django (2,347 files, 5.4M tokens) shows CCE scales to large codebases with 0.95 recall. Go's shorter files reduce the retrieval headroom (smaller baseline). Monorepos dilute recall at top-10 (fiber). Middleware queries with one-feature-per-file hit R=1.00 consistently.

Reproduce it yourself:

pip install code-context-engine

python benchmarks/run_benchmark.py --repo https://github.com/fastapi/fastapi.git --source-dir fastapi python benchmarks/run_benchmark.py --repo https://github.com/go-chi/chi.git --source-dir .

Full results in benchmarks/results/. Queries and methodology in benchmarks/.

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What you get

11 MCP tools that Claude uses automatically:

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