llm-ctxpack

Pack the most important text into a fixed LLM context-window token budget.

Every RAG pipeline or agent eventually hits the same problem: you have more candidate context (retrieved chunks, chat history, tool output, file contents) than tokens to spend. llm-ctxpack solves the "what do I keep, what do I trim, what do I drop" decision for you — deterministically, and without pulling in a full LLM framework.

Install

pip install llm-ctxpack

# optional: exact token counts via tiktoken (otherwise a ~4-char/token estimate is used)
pip install "llm-ctxpack[tiktoken]"

Quickstart

from llm_ctxpack import ContextItem, pack

items = [
    ContextItem(id="system-notes", text=system_notes, priority=10, truncatable=False),
    ContextItem(id="retrieved-doc-1", text=doc1, priority=5),
    ContextItem(id="retrieved-doc-2", text=doc2, priority=3),
    ContextItem(id="chat-history", text=history, priority=1, truncate_strategy="head"),
]

result = pack(items, budget=6000)  # tokens

print(result.total_tokens, "/", result.budget, "tokens used")
print([i.id for i in result.items])   # what got kept
print(result.dropped_ids)             # what got dropped entirely
prompt_context = result.render()      # joined text, ready to drop into a prompt

How it works

1. Every item has a priority (higher = more important).
2. Items are considered highest-priority first. Ties are broken by "value density" — priority per token — so a small high-value item isn't starved by one large item of equal priority.
3. Each item is included whole if it fits in what's left of the budget.
4. If it doesn't fit and truncatable=True, it's trimmed to whatever budget remains, as long as the trimmed size clears min_tokens (otherwise trimming it further would produce useless mush, so it's dropped instead).
5. If truncatable=False, it's included whole or not at all.

Truncation can keep the "tail" (default — good for docs, keep the intro), the "head" (good for chat history — keep the most recent turns), or the "middle" (keep both ends, cut the middle — good for long files where the top and bottom carry the most signal).

Reserving room for a system prompt or expected output

result = pack(items, budget=8000, reserve_tokens=1500)  # only 6500 available to items

CLI

llm-ctxpack doc1.md doc2.md notes.txt --budget 4000 --stats
# earlier files = higher priority; --stats prints what was kept/trimmed/dropped to stderr

llm-ctxpack --help

Why not just truncate the whole prompt?

Naive truncation (cut the end of the final concatenated string) treats every token as equally disposable — you might cut the system prompt or the most relevant retrieved doc just because it happened to be assembled last. llm-ctxpack decides what to cut based on what you told it matters, before concatenation happens.

License

MIT