Prerequisites¶
AWS Bedrock Access¶
Tokuye uses AWS Bedrock exclusively for LLM and embedding models. You need:
- An AWS account with Bedrock model access enabled
- IAM credentials with Bedrock permissions
Configure credentials via environment variables or AWS CLI profile:
# Option 1: Environment variables
export AWS_ACCESS_KEY_ID=your_key
export AWS_SECRET_ACCESS_KEY=your_secret
export AWS_DEFAULT_REGION=ap-northeast-1
# Option 2: AWS Profile
export AWS_PROFILE=your_profile
Python / uv (Binary install では不要)¶
If you install Tokuye via the binary installer (install.sh), Python and uv are not required.
Python 3.10+ and uv are only needed if you use uvx or uv tool install:
gh CLI (optional but recommended)¶
The GitHub CLI (gh) enables GitHub-integrated operations such as creating PRs, reviewing PRs, and browsing Issues directly from Tokuye. Without it, these features are unavailable.
⚠️ Important Notes¶
First-Time Execution¶
On first run, Tokuye builds a FAISS index for semantic code search. This may take some time depending on your project size.
Token Usage & Costs¶
- High Token Consumption: Tokuye reads and embeds repository code, which can consume significant tokens depending on project size.
- Bug Loop Risk: If bugs cause infinite loops or repeated operations, token usage will increase proportionally. Monitor your AWS Bedrock costs carefully.
- Cost Tracking: Real-time cost estimates are displayed in the UI (based on ap-northeast-1 pricing). Always verify actual costs in your AWS billing dashboard.
Best Practices¶
- Start with smaller projects to understand token consumption patterns
- Use
.tokuye/summary.ignoreto exclude large or irrelevant directories (see CLI Usage & Exclusions)