Launched this week
ToolAhead
Make AI coding agents 28% faster
6 followers
Make AI coding agents 28% faster
6 followers
ToolAhead works while your model thinks: it learns your agent's recurring tool calls and starts them ahead — test runs, file reads, even your dev-server boot. Prepared results are served only if the exact call and workspace still match; otherwise everything just runs normally. Stale results are never served, and nothing runs from a cloned repo's config without a one-time approval. Local-only, MIT, works with Codex CLI, Claude Code, and MCP.
Coding agents are still painfully serial: reason, call a tool, wait, repeat. Annoying.
This is why I built ToolAhead to speculatively execute predictable tool calls while the model is still thinking. In some initial benchmarks, median end-to-end time dropped by 28.7%. Independent analysis and improvement ideas welcome!
Prediction and correctness are the two things I needed to handle. ToolAhead only returns prepared work if the exact request and workspace state match. If anything drifts, it falls back to normal execution. Stale results are never served.
Not only tool calls are predicted; it also pre-warms your dev servers. Declare your server once, and ToolAhead boots it the moment the agent edits a file.
To keep this safe, service commands require toolahead trust, which revokes automatically if the config changes.
Try it now and tell me what you think: uvx toolahead
Works with Codex CLI, Claude Code, and MCP. MIT license, no telemetry.
How can we further improve agents to make them faster? Any ideas?