cendor-contextkit

Treat the context window like a packed suitcase, not a string you concatenate. Declare blocks with priorities and eviction rules; contextkit fits them to a token budget and tells you exactly what it kept, shrank, and dropped.

Every assembled prompt comes with a receipt.

PyPI license · pip install cendor-contextkit

from cendor.contextkit import Context, Block

ctx = Context(budget_tokens=8000, model="claude-opus-4-8", reserve_output=1000, order="attention")
ctx.add(Block(system_prompt, priority=10, pin=True, role="system"))
ctx.add(Block(retrieved_docs, priority=5, evict="compress"))        # uses squeeze if installed
ctx.add(Block(messages=chat_history, priority=3, evict="drop_oldest"))  # peels OLDEST turns
ctx.add(Block(user_msg, priority=9, pin=True, role="user"))

messages = ctx.assemble()      # provider-ready, guaranteed within budget — including framing
print(ctx.report())            # the receipt: kept / truncated / dropped + token math
preview = ctx.whatif(budget_tokens=4000)   # same inputs, tighter budget, no commit

Highlights

  • Token-budgeted packing — declare Blocks with priority and pin; assemble() fits them into the budget deterministically. Pinned blocks are never evicted (raises BudgetError if they alone overflow).
  • Per-block eviction — drop_oldest · truncate (keep head/tail, with a …[truncated] marker) · summarize (sync, or async via aassemble()) · compress (via squeeze — reversible: the receipt's BlockDecision.handle.expand() restores the original, and compression is sized against your model) · or any custom EvictionStrategy.
  • Real chat-history — Block(messages=[…]) peels the oldest turns to fit (a sliding window) — never mangling a turn.
  • An honest receipt — report().used == tokens.count(assemble(), model): packing charges the per-message framing providers add, so a "full" prompt never quietly overflows once sent.
  • Attention-aware ordering — order="default" · "attention" (lost-in-the-middle) · "cache" (stable prefix for prompt-cache hits).
  • Provider adapters & multimodal — for_anthropic() / for_gemini() / for_bedrock(); per-image image_tokens (int or resolution-aware callable); whatif(budget) previews; use_compressor() swaps the compression backend.

Inbound — call it before the model call; report() flows onto core's bus, so acttrace records what the model actually saw.

See docs/contextkit.md · CHANGELOG. Part of the Cendor stack — github.com/cendorhq/cendor-libs. Powered by PowerAI Labs. Apache-2.0; provided "as is", without warranty — use at your own risk (LICENSE §7–8).

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Credits

Author: Raghav Mishra

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Data verified by PyPI on Jul 5, 2026
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Apache-2.0
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Requires

Python >=3.11

Provides Extra

squeeze

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