contexel 0.1.17

Deterministic, dependency-free context-economy stages for code-writing agents.

The context element. Deterministic, dependency-free context shaping for code-writing agents — the same transform, every run.

Why this exists

An agent's tools return big lists of records: search hits, file reads, API responses. Before they reach the model's context window they must be cut down — and today that cutting is usually code the model improvises fresh each run, which drifts: different field choices, different truncation, different survivors, every time. contexel replaces the improvisation with vetted, versioned stages at the tool → context boundary. Same input, byte-identical context, on every run — a context contract instead of a nightly rewrite.

What you get

Eight deterministic pipeline stages plus a merge combiner, over plain list[dict] — the shape tools already return — with composition and observability:

• select — project to the fields you need (lossless)
• dedupe — drop duplicates by key, type-qualified, order preserved (lossless)
• allowlist — provenance gate: refuse records from unapproved sources (fail closed; the strong injection control)
• quarantine — tripwire for literal injection markers; drop or flag (paraphrase passes — not a security boundary)
• rescore — BM25-style lexical relevance derived from the records' own text, so ranking doesn't have to trust the tool's score
• rank — stable sort, missing-field records last
• truncate_field — clip a text field to a token cap, never exceeding it
• trim_to_budget — keep the top records that fit a token budget
• merge — unify differently-shaped tool outputs into one schema
• pipeline([...]) / @shaped / trace() — compose (each pipeline carries a stable policy fingerprint), pin at the tool boundary (async tools are awaited, then shaped), and inspect what every stage removed — trace(id_field=...) + t.audit() record exactly which records were dropped, per stage

Token counting is pluggable twice over: the tokenizer (a dependency-free estimate by default; tokens.use_tiktoken() for exact) and the serializer (budgets are priced in the encoding your boundary actually emits — JSON by default, or e.g. an ISON table).

No embeddings, no model calls, zero dependencies — pure Python 3.9+.

Install

pip install contexel              # pure Python, zero dependencies
pip install "contexel[accurate]"  # + tiktoken, for exact token counts

Building agents in TypeScript/JavaScript or C#/.NET instead? npm install contexel and dotnet add package Contexel ship the same stages with golden-vector-enforced parity to this package (details).

Sixty seconds

from contexel import select, dedupe, rescore, truncate_field, rank, trim_to_budget, trace

raw = search_tool(query=user_query)          # a batch of verbose results

with trace() as t:
    r = select(raw, ["title", "url", "snippet", "published"])
    r = dedupe(r, key="url")
    r = rescore(r, query=user_query, fields=("title", "snippet"))
    r = truncate_field(r, "snippet", max_tokens=120)
    r = rank(r, by="score", desc=True)
    r = trim_to_budget(r, max_tokens=1500)

print(t.report())    # per-stage: records in -> out, tokens before -> after
context = r          # only this distilled result enters the model's context

Or pin the policy at the tool boundary, so the model never sees raw output:

from contexel import shaped, stage, select, dedupe, trim_to_budget

@shaped([
    stage(select, fields=["path", "line", "snippet"]),
    stage(dedupe, key=["path", "line"]),
    stage(trim_to_budget, max_tokens=2000),
])
def search_code(query: str) -> list[dict]:
    return repo.search(query)     # shaped identically, every call

Measured, not asserted

Benchmarked on the full CodeSearchNet python/test split (22,176 real functions → 28,047 search-tool records) with 100 ground-truth episodes: contexel is the only implementation among the near-neighbours (hand-written glue, toolz, langchain-core, llama-index-core) that covers all the operations natively, holds a 1,500-token budget in 100% of episodes, keeps the needed record in 100% of them under both a strong and a weak retrieval signal, and spends 100% of context tokens on unique, needed fields — at 0 dependencies and a sub-20 ms import. It is also the slowest budget-compliant row, because it does the most work per record — and the limits are stated with the same care: recall@budget is bounded by query specificity for any shaper, and rescore is lexical, not semantic.

Determinism is proven, not promised: the suite hashes the same contract's output across fresh interpreters with different PYTHONHASHSEED values — same SHA-256 every time, even when raw tool output carries fresh request ids.

Two limits, stated plainly: hard token limits require an exact tokenizer (contexel[accurate], exact where tiktoken covers the model — other providers plug their own counter via set_tokenizer; the built-in estimate is for soft budgeting only), and shaping is not an injection defense — rescore ranks textual relevance. contexel ships allowlist (provenance gate) and quarantine (pattern tripwire) as deterministic boundary controls, but content authentication still belongs upstream. Treat every shaped record as untrusted data, never as instructions.

Full method and tables: benchmarks/COMPARISON.md · benchmarks/RESULTS.md

Where it fits

Inside agent-written code (programmatic tool calling), as a @shaped tool wrapper, inside an MCP server, under LangChain/LlamaIndex via a thin adapter, or ahead of a token-efficient encoding — each with a runnable example in examples/, plus a complete no-API-key reference agent (python -m reference_agent).

Links

• Docs: https://maheshvaikri-code.github.io/contexel/
• Source: https://github.com/maheshvaikri-code/contexel
• TypeScript package: https://www.npmjs.com/package/contexel
• C# package: https://www.nuget.org/packages/Contexel
• Changelog: https://github.com/maheshvaikri-code/contexel/blob/main/CHANGELOG.md
• License: MIT