sponge-ai 0.1.1

Cost-optimal AI agent harness — use the best model, pay the least

Sponge 🧽

Same model. Same quality. Fraction of the tokens.

Sponge wraps your LLM calls with architecture-level cost compression — not by switching to cheaper models, not by bolt-on caching. Every layer of the agent loop is designed to minimize token consumption.

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Quick Start

pip install sponge-ai[deepseek]       # or [anthropic] or [openai] or [openrouter]
export SPONGE_DEEPSEEK_API_KEY=sk-...  # or SPONGE_ANTHROPIC_API_KEY etc.
sponge run "explain the CAP theorem in one sentence"

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How It Saves Tokens

Layer	What It Does	Cost
Plugin Routing	File ops, code search, shell commands → handled locally	$0
Exact Cache	Identical tasks return cached result (SHA256 match)	$0
Semantic Cache	Similar tasks match via Jaccard similarity	$0
Self-Tuning	Detects waste (TTL too short, budget too loose) → proposes fixes	5-20%
Context Compression	Old conversation turns summarized, not re-sent	2-5×
Task Decomposition	Complex tasks split into focused sub-tasks	5-10×
LLM Call	Only when nothing else works	Full price

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Commands

sponge run "task"          # Execute a task
  --model, -m MODEL        # Override the model
  --json                   # JSON output
  --auto-approve           # Allow write/delete/shell operations

sponge benchmark           # Run benchmark fixtures against a real provider
  --fixture, -f NAME       # Run a single fixture
  --output, -o FILE.json   # Save results

sponge cost session        # Cost breakdown for latest session
sponge cost total --days 30 # Total cost over N days
sponge cost stats          # Overall efficiency statistics

sponge tune report         # Detect optimization opportunities
sponge tune apply ID       # Activate a tuning proposal
sponge tune review         # Evaluate active experiments

sponge session start       # Start a multi-turn conversation
sponge session chat "msg"  # Send a message
sponge session resume ID   # Resume a saved session
sponge session list        # List all sessions

sponge memory add "rule"   # Add project convention
sponge memory list         # List all conventions
sponge memory remove N     # Remove a convention

sponge config show         # Show current configuration
sponge config set KEY=VAL  # Change a setting

sponge --version           # Show version

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Supported Providers

Provider	Install	Env Var
Anthropic (Claude)	[anthropic]	SPONGE_ANTHROPIC_API_KEY
OpenAI (GPT-4o)	[openai]	SPONGE_OPENAI_API_KEY
DeepSeek (V4)	[deepseek]	SPONGE_DEEPSEEK_API_KEY
OpenRouter (200+ models)	[openrouter]	SPONGE_OPENROUTER_API_KEY

Switch via SPONGE_PROVIDER env var or sponge config set provider=deepseek.

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Project Memory

Create .sponge/memory.toml in your project root (or use sponge memory add):

[memory]
rules = [
    "Never modify tests/fixtures/ without asking",
    "Use httpx instead of requests",
]

These are injected into every LLM call as system instructions.

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Cost Transparency

Every LLM call is logged to ~/.sponge/telemetry/fingerprints.db:

sponge cost stats
# Sponge Efficiency Stats
#   Total calls:      42
#   Cache hits:       23 (55%)
#   Plugin calls:     8 ($0)
#   LLM calls:        11
#   Total cost:       $0.002347
#   Naive cost:       $0.005120
#   Total saved:      $0.002773 (54.2%)

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Development

git clone https://github.com/your/sponge.git
cd sponge
pip install -e ".[dev]"
pytest                  # 106 tests, zero API calls required
ruff check src/         # lint
mypy src/sponge/        # type check

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License

MIT