agentslim

agentslim 🪶

Make your AI agents leaner, faster, and cheaper.

agentslim is a zero-dependency Python toolkit that reduces token consumption in LLM-powered agents — without sacrificing reasoning quality.

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Why?

Every token counts — literally. When building agents you routinely waste tokens on:

agentslim solves all four with one clean API.

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Install

pip install agentslim

For accurate token counting (uses tiktoken under the hood):

pip install agentslim[tiktoken]

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Quick Start

from agentslim import Compressor, AgentMemory, ToolMinifier, CodeContext

# 1 ── Compress any content before sending to LLM
c = Compressor()
slim = c.compress(raw_html_or_json_or_text)   # auto-detects format

# 2 ── Smart context window with auto-summarization
mem = AgentMemory(max_tokens=6000)
mem.add("user", "Build me a FastAPI app")
mem.add("assistant", "Sure! Here's the plan...")
messages = mem.get_messages()   # ready for openai.chat.completions.create()

# 3 ── Minify tool schemas
slim_tools = ToolMinifier.minify(my_tools)          # shorter descriptions
hint_str   = ToolMinifier.to_compact_str(my_tools)  # one-liner per tool

# 4 ── Send only the relevant code chunk, not the whole file
snippet = CodeContext.extract_function("app.py", "handle_request")
outline = CodeContext.outline("app.py")   # class/function map

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Modules

🗜️ Compressor — Text / HTML / JSON compressor

Strips noise from content before it hits your LLM.

from agentslim import Compressor
from agentslim.compressor import CompressorConfig

# Defaults — safe for most use cases
c = Compressor()

# Fine-grained control
c = Compressor(config=CompressorConfig(
    strip_html=True,
    remove_decorative_html=True,   # drops <script>, <style>, <nav>, etc.
    collapse_whitespace=True,
    remove_filler_phrases=True,    # "Certainly! As an AI language model..."
    compact_json=True,
    remove_python_comments=False,  # keep comments by default
))

clean = c.compress(raw_content)          # auto-detects JSON / HTML / text
clean = c.compress_html(html_string)
clean = c.compress_json(json_string)
clean = c.compress_text(plain_text)
clean = c.compress_code(source, language="python")  # or "js" / "ts"

Savings report:

from agentslim.utils import tokens_saved_report

report = tokens_saved_report(original, compressed, model="gpt-4o")
# {
#   'original_tokens': 1842,
#   'compressed_tokens': 612,
#   'tokens_saved': 1230,
#   'percent_saved': 66.8,
#   'cost_saved_usd': 0.003075
# }

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🧠 AgentMemory — Smart sliding-window context manager

Instead of naively cutting old messages (which breaks reasoning), AgentMemory auto-summarizes the oldest messages into a compact system note.

from agentslim import AgentMemory

mem = AgentMemory(
    max_tokens=6000,       # soft limit on the active window
    archive_ratio=0.4,     # archive the oldest 40% when limit is hit
    summarize_fn=None,     # optional: plug in your LLM for better summaries
)

mem.add("system", "You are a helpful assistant.")
mem.add("user", "Hello!")
mem.add("assistant", "Hi! How can I help?")

messages = mem.get_messages()   # list[dict] — pass directly to any OpenAI-compatible API
print(mem.stats())
# MemoryStats(active_messages=3, archived=0, active_tokens=24, ...)

With a real LLM summarizer:

import openai

def gpt_summarize(messages):
    history = "\n".join(f"{m.role}: {m.content}" for m in messages)
    resp = openai.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {"role": "system", "content": "Summarize in 3 sentences."},
            {"role": "user",   "content": history},
        ],
    )
    return resp.choices[0].message.content

mem = AgentMemory(max_tokens=8000, summarize_fn=gpt_summarize)

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🛠️ ToolMinifier — Tool schema minifier

OpenAI function schemas are JSON-heavy. ToolMinifier cuts them down.

from agentslim import ToolMinifier

# Option A: minify but keep JSON format (for the API)
slim_tools = ToolMinifier.minify(tools, max_desc=80)

# Option B: ultra-compact one-liner hint for system prompts
print(ToolMinifier.to_compact_str(tools))
# get_weather(location:string, unit:string?) -> Any  # Get current weather…
# send_email(to:string, subject:string, body:string) -> Any

# Option C: auto-generate schemas from Python functions
def search(query: str, max_results: int) -> str:
    """Search the web for real-time info."""
    ...

tools = ToolMinifier.from_python_functions(search)

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📄 CodeContext — Code-aware chunk extractor

Don't send 500-line files to your coding agent — send only what it needs.

from agentslim import CodeContext

# Extract a single function (+ N lines of context)
snippet = CodeContext.extract_function("app.py", "process_payment", context_lines=3)

# Extract a class skeleton (signatures only)
skeleton = CodeContext.extract_class("service.py", "PaymentService", methods_only=True)

# Outline: class/function map of the whole file
outline = CodeContext.outline("app.py")
# ['class PaymentService (L12)', 'def charge (L28)', 'def refund (L45)']

# Folded view: function bodies replaced with '...'
folded = CodeContext.folded("large_module.py")

# Extract specific line range
chunk = CodeContext.extract_lines("app.py", start_line=120, end_line=145, context_lines=5)

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📊 utils — Token counting & cost estimation

from agentslim.utils import count_tokens, estimate_cost

tokens = count_tokens("Hello, world!")  # uses tiktoken if available

cost = estimate_cost(input_tokens=1000, output_tokens=200, model="gpt-4o")
# {'input_usd': 0.0025, 'output_usd': 0.002, 'total_usd': 0.0045}

Supported models: gpt-4o, gpt-4o-mini, gpt-4-turbo, gpt-3.5-turbo, claude-3-5-sonnet, claude-3-haiku, gemini-1.5-pro, gemini-1.5-flash.

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Compatibility

agentslim is framework-agnostic. It works with anything that accepts a list of {"role": ..., "content": ...} dicts:

• ✅ OpenAI Python SDK
• ✅ LangChain / LangGraph
• ✅ LlamaIndex
• ✅ Anthropic SDK
• ✅ Google Generative AI SDK
• ✅ Any custom agent framework

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Running tests

pip install -e ".[dev]"
pytest

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Contributing

PRs and issues welcome! See CONTRIBUTING.md.

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License

MIT © agentslim contributors