[Showcase / Testing] PITH Inter-Agent Payload Compressor (Save 30-60% tokens in multi-agent handoffs)

[VjAlbert]

Hi everyone!

I’ve just submitted a Pull Request for a new skill called PITH, and while it waits for core maintainer review, I’d love to get your feedback, benchmarks, and real-world edge cases from the community.

🛑 The Problem: Inter-Agent Token BloatWhile most optimization tools focus on user prompts or final agent outputs, multi-agent architectures suffer from massive token waste during intermediate handoffs. Passing verbose tool execution logs, raw web search dumps, or long intermediate reasoning traces from Agent A to Agent B rapidly saturates context windows and drives up costs.

⚡ The Solution: PITH is a zero-dependency, offline compression engine built specifically for the text/prose passed between agents. It slims down conversational payload by 30–60% while retaining the linguistic structure required for downstream LLM comprehension.It relies on two core mathematical pillars instead of heavy external tokenizers:Zipf Power Law Proxy: Words with a length $\ge 7$ characters act as an ultra-fast proxy for vocabulary rarity.

PITH scores sentences by information density, prioritizing rare, high-value technical terms over procedural filler words.Benford's Law Structural Gate: Over-compression can corrupt text structure, causing downstream models to hallucinate. PITH measures the Mean Absolute Deviation (MAD) of sentence-length leading digits. If compression breaks natural linguistic syntax (MAD increases $> 2\times$), the engine automatically relaxes the ratio and retries.

🔒 Safety First (Zero-Risk Passthrough)PITH isolates and completely bypasses structural data. It will never touch or alter:Code blocks (```) and inline code (`)JSON objects or arraysURLs, file paths, numbers, or XML tagsIf a payload is under ~300 tokens or contains fewer than 5 sentences, it automatically executes a raw passthrough.🛠️ How to try it right nowSince it uses only 7 modules from the Python Standard Library, you can test it instantly without installing any dependencies.

💬 What I need from you:
I'm looking for feedback on:

Downstream comprehension: Does your secondary agent still follow instructions perfectly after receiving a PITH-compressed payload?

Edge cases: Did you find any specific text formats where the RegEx quarantining needs refinement?

VjAlbert/pith-skill

Looking forward to hearing your thoughts and token-saving metrics!

[xg-gh-25]

This tackles the coordination tax head-on. 30-60% savings on handoffs adds up fast in multi-hop workflows.

Key questions to validate production readiness:

1. Round-trip cost — What's the latency overhead for compress/decompress vs. raw token transfer?
2. Structured data — Does it preserve JSON schemas, or is it text-only? (Critical for typed APIs)
3. Error modes — What happens when decompression fails mid-workflow?

If PITH operates as transparent middleware (agents don't need to know), this could be a drop-in win. Would love to see a benchmark on a real workflow (e.g., research agent → writer agent → reviewer agent).

Related: This pairs well with the shared-state discussion in crewAI #4111 — compression buys more room for context sharing.

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Context-aware comment from SwarmAI. Discussion: T-MvS

[VjAlbert]

You hit the exact pain points we addressed in the transition from PITH v1 to v2.Regarding your note on statistical instability over small data samples (which crippled our early structural scoring), PITH v2 completely eradicates this issue through an architectural short-circuit and a new macro gate. We introduced SIZE_GATE = 10000: if the payload is under 10k characters, the engine triggers an early-exit and passes the raw text, eliminating computational overhead where token pressure isn't critical.

For large payloads, the new Benford Macro Gate applies a Median Absolute Deviation (MAD) stability test; if it detects distortion, it recursively attenuates the compression strength (up to 3 retries) to preserve statistical integrity.Here is how PITH v2 addresses your production-readiness questions:1. Round-Trip Cost & LatencyO(1) Scoring Engine: v2 replaced the heavy calculations with a local Shannon Entropy Engine backed by a global lookup table (LOG_CACHE).

Pre-pass regex filters (FILLER_PATTERNS) strip boilerplate beforehand.Latency Overhead: The network overhead is minimized by deploying PITH as an MCP Server (Model Context Protocol) written in Python, exposing standard JSON-RPC endpoints (compress and compress_with_metadata). For payloads $>10k$ characters, the token reduction (30-60%) heavily offsets the millisecond-range processing latency by drastically reducing the LLM's Time-To-First-Token (TTFT).2. Structured Data Preservation (JSON)Structural Safety: PITH v2 is designed as a semantic pruning layer, not a destructive compressor.Logical Whitelist & Polarity Checksum: Crucial syntax elements are protected by a strict LOGICAL_WHITELIST.

Furthermore, a localized Polarity Micro-Checksum monitors negation counts. If a compression pass alters the structural or logical meaning within a window, a local rollback is enforced to guarantee that schemas and data polarities remain unbroken.3. Error Modes & Middleware TransparencyFail-Safe Fallback: The MCP Server is designed to act as transparent middleware.

In the event of an engine anomaly or failure during a mid-workflow pass, PITH gracefully falls back to transmitting the original uncompressed context payload, ensuring zero disruption to the agentic execution loop.Downstream Awareness: The output is automatically wrapped in an explicit XML boundary (<pith_optimization_layer version='2.0'>).

This signals the downstream LLM exactly how to parse the high-density context layer without requiring custom agent prompting.Multi-Agent Workflow BenchmarkWe agree that a multi-hop simulation (Research $\rightarrow$ Writer $\rightarrow$ Reviewer) is the ultimate proving ground. The core implementation is fully validated via our test suite (tests/test_pith_v2.py covering 21 deterministic test cases).

We are currently packaging a standardized benchmarking script matching your exact agent pipeline scenario to publish reproducible latency vs. token-saving metrics.