In agentic workflows, raw command outputs and state recollection after compaction contribute to major token waste. Agents often pull raw terminal output into context even when they only need a few anchors, then pay the same cost again later when compaction forces them to reread files or rerun commands.
CtxSift was built to cut that loop down to two operations: keep only the signal that matters now, then recover it later without rebuilding the whole state trail.
It was inspired by the original Distill project and extends that direction toward local execution, file rereads, and read-after-compression state recovery for coding agents.
How it works
With CtxSift, your agents use two steps to keep minimal token footprint:
1. Extract and cache only what they need from raw outputs
2. Look up context later instead of repeatedly re-running commands or dragging raw terminal output back into the session.
That's it. Unlike other token savers, which can get heavy can confuse the agent with multiple tools, CtxSift keeps it simple and light. No multiple tools, MCP servers or sandbox spin-up dependencies.
Show only high-signal tokens to the agent after command runs and context compactions.
In agentic workflows, raw command outputs and state recollection after compaction contribute to major token waste. Agents often pull raw terminal output into context even when they only need a few anchors, then pay the same cost again later when compaction forces them to reread files or rerun commands.
CtxSift was built to cut that loop down to two operations: keep only the signal that matters now, then recover it later without rebuilding the whole state trail.
It was inspired by the original Distill project and extends that direction toward local execution, file rereads, and read-after-compression state recovery for coding agents.
Use local models on CPU/GPU or remotely hosted LLMs for compression.
By default, CtxSift starts with a small GGUF model on local CPU. If you have CUDA available, local compression can use normal Hugging Face text-generation models instead. If you prefer hosted inference, remote compression works through LiteLLM-compatible endpoints.
Recall embeddings stay local and separate from compression, so the retrieval path remains the same whether compression is local or remote.
Start with the runtime path that matches your machine and workflow.
Use local CPU for the simplest default path, local GPU when you want faster local inference, and remote provider mode when you want hosted models through a LiteLLM-compatible endpoint.
Benchmarked model comparisons live on their own page. Use the benchmark guide when you want tested CPU and GPU recommendations rather than setup instructions.
