contexttrim 0.1.0

Pluggable context window management strategies for LLM agents. Zero dependencies.

contexttrim

Pluggable context-window management for LLM agents.

When a conversation exceeds the model's token limit, most code drops the oldest messages (FIFO) — often dropping a critical system prompt while keeping ten redundant tool results. contexttrim lets you choose what gets dropped and why, with swappable strategies. Zero dependencies, no ML, no tokenizer required.

from contexttrim import ContextManager
from contexttrim.strategies import ImportanceWeighted

ctx = ContextManager(token_budget=8_000, strategy=ImportanceWeighted())
ctx.add({"role": "system", "content": "You are a helpful assistant."})
ctx.add({"role": "user", "content": "Find me flights to NYC."})
ctx.add({"role": "tool", "content": "<very long search result>"})

trimmed = ctx.fit()              # a new list that fits the budget
report = ctx.last_fit_report()   # what was dropped and why
print(report.dropped, report.tokens_used)

Why contexttrim?

Naive FIFO truncation throws away the wrong things. contexttrim gives you a ContextManager plus a set of strategies that make deliberate, inspectable decisions — and it's pure Python stdlib, so it adds nothing to your dependency tree.

Installation

pip install contexttrim

Requires Python 3.9+. No other dependencies, ever.

Strategies

Import from contexttrim.strategies:

Strategy	What it does
RecencyDrop	Drop the oldest messages first. Fast, simple, often wrong.
MiddleDrop	Drop from the middle — models attend least there ("lost in the middle"). Head and tail preserved longest.
RoleWeighted	Score by role; drop lowest-scored first. system pinned by default.
ImportanceWeighted	role_weight × recency_decay^age ÷ (1 + length_penalty·tokens). Keeps short, recent, high-role messages.
ToolResultMerge	Merge redundant adjacent tool results (dedup), then truncate the largest if still over budget — no conversational context dropped.
SemanticCluster	Drop messages least topically relevant to the recent conversation, via TF-IDF cosine similarity (pure stdlib, no ML). system pinned.

from contexttrim.strategies import RoleWeighted

RoleWeighted(
    role_weights={"system": 10.0, "user": 2.0, "assistant": 1.0, "tool": 0.5},
    pin_roles=frozenset({"system"}),   # never dropped
)

The fit report

Every fit() records what happened:

trimmed = ctx.fit()
report = ctx.last_fit_report()

report.kept           # the messages that survived
report.dropped        # list of Dropped(message, reason)
report.tokens_used    # total tokens of the kept messages
report.tokens_budget  # the budget you set
report.fits           # False only if pinned messages alone exceed the budget

for d in report.dropped:
    print(d.reason, "->", d.message["role"])

Token counting

By default, contexttrim estimates tokens with a deterministic ~4-characters-per-token heuristic — zero dependencies. Inject your own counter for exact counts (e.g. tiktoken):

import tiktoken
enc = tiktoken.encoding_for_model("gpt-4o")

ctx = ContextManager(
    token_budget=8_000,
    strategy=ImportanceWeighted(),
    token_counter=lambda m: len(enc.encode(m.get("content", "") or "")),
)

Message format

Messages are plain dicts in the common OpenAI/Anthropic shape — {"role": ..., "content": ...}. contexttrim never mutates them; fit() returns a new list. Non-string content (e.g. Anthropic content blocks) is serialized for token counting.

Writing your own strategy

Subclass Strategy and return (kept, dropped):

from contexttrim import Strategy, Dropped

class DropAssistant(Strategy):
    def fit(self, messages, budget, count):
        kept, dropped = [], []
        for msg in messages:
            if msg.get("role") == "assistant":
                dropped.append(Dropped(msg, "assistant messages disabled"))
            else:
                kept.append(msg)
        return kept, dropped

Contributing

See CONTRIBUTING.md.

License

MIT — see LICENSE.

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Part of the aenealabs AI agent toolkit.