Drop-in SDK for Anthropic & OpenAI — compresses prompts transparently. CLI + REST API + Claude Code MCP.
Project description
Promptolian — Context Reliability Proxy for AI Agents
Proxy layer for AI agents — keeps context intact across long conversations and eliminates redundant token costs. One line to add, zero changes to your agent logic.
promptolian.com · Pricing · Dashboard · Docs
Install
pip install promptolian # core (rule-based, no deps)
pip install "promptolian[proxy]" # + transparent API proxy
pip install "promptolian[nlp]" # + NLP compression (spacy)
pip install "promptolian[mcp]" # + Claude Code MCP tool
pip install "promptolian[full]" # everything
Quick Start
# Transparent proxy at http://localhost:3002
python -m promptolian.proxy
# With automatic session reset at 70% of context window
python -m promptolian.proxy --reset-at 0.70
# With KV-sandwich compression (requires API key)
PROMPTOLIAN_API_KEY=your_key python -m promptolian.proxy --reset-at 0.70
Point your Anthropic or OpenAI client at http://localhost:3002 — no other changes needed.
What the proxy does
Tool Result Compression
In agentic workflows, the same files get read multiple times, bash outputs repeat, and API responses recur across tool calls. The proxy deduplicates these automatically:
- Exact repeat → replaced with
[TOOL_CACHE_REF: same as call #N](~5 tokens instead of thousands) - Similar content → replaced with a compact diff showing only what changed
Benchmark across 9 synthetic agentic sessions: 34.6% token savings on tool outputs, 99% fact retention.
Works transparently for both Anthropic (type=tool_result) and OpenAI (role=tool) message formats. No configuration needed — fires automatically on every request.
Response header: X-Promptolian-Tool-Tokens-Saved: <n>
Session Reset
The proxy tracks cumulative token usage per session. When usage approaches the model's context window limit, it automatically compresses the conversation history and starts a fresh session — injecting the compressed context as a system prompt. The provider never sees a context long enough to trigger their own native compression.
python -m promptolian.proxy --reset-at 0.70 # reset at 70% of context window
Two compression modes depending on your setup:
| Setup | Compression quality |
|---|---|
| No API key | Session reset disabled |
PROMPTOLIAN_API_KEY set |
KV-sandwich via cloud (4.26/5 quality score) |
| Run from source | KV-sandwich locally |
Response header: X-Promptolian-Reset: true when a reset occurs.
Tool Schema Caching
The proxy adds cache_control to tool schemas and re-injects them on subsequent calls. Anthropic bills cached tokens at 10%.
Without proxy: 500 calls/day × 5 tools × 120 tok × 30 days = 9M tok/mo → $27.00
With proxy: 9M tok × 10% cache rate = 900K tok/mo → $2.70
Context Engine (KV-sandwich)
KV-sandwich architecture — preserves what matters, compresses what doesn't:
- HEAD (first 2 turns): verbatim — session framing preserved
- MIDDLE: entity-encoded + compressed — redundant tokens removed
- TAIL (last 4 turns): verbatim — recent facts + task state intact
Benchmark results (25 sessions, Factory.ai 6-dimension scoring):
| Promptolian | Anthropic built-in | OpenAI built-in | |
|---|---|---|---|
| Quality score | 4.26 / 5 | 3.44 / 5 | 3.35 / 5 |
| Compression | 21.8% | 98.7% | 99.3% |
Requires a PROMPTOLIAN_API_KEY (cloud) or running from source.
Free vs Paid
| Feature | Free (no key) | Paid (API key) |
|---|---|---|
| Tool result REF/DIFF compression | Yes | Yes |
| Tool schema caching | Yes | Yes |
Rule-based compress_prompt (MCP) |
Yes | Yes |
| KV-sandwich context compression | — | Yes |
| Session reset with full quality | — | Yes |
Get a key at promptolian.com/pricing — Solo $9/mo, Team $49/mo.
Claude Code — MCP Integration
pip install "promptolian[mcp]"
Add to ~/.claude/settings.json:
{
"mcpServers": {
"promptolian": {
"command": "promptolian-mcp"
}
}
}
Restart Claude Code. Tools available: compress_prompt, compress_tools_schema, compression_stats.
Plans
| Plan | Price | Sessions | Keys |
|---|---|---|---|
| Free | $0 | SQLite · self-hosted | — |
| Solo | $9/mo | PostgreSQL · always-on | 1 |
| Team | $49/mo | PostgreSQL · always-on | Up to 10 |
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