Paritok-official/paritok-4b-v1
Non-destructive compression gateway for AI coding agents. Cuts token bills 25% on turn 1 to past 85% in long or saturated sessions, and fits ~3× more turns in the same context window. Powered by our open-source code-native 4B model. Drop-in for Claude Code, Cursor, Codex, OpenHands, and any BASE_URL agent. observed · 2026-08-28
Health v2 · maintenance only
57/100
- Activity 99
- Release rhythm 35
- Longevity 3
Flags: no_releases young
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 49
- days_rel: n/a
- days_push: 9
- n_releases_24m: 0
Adoption not part of the score
1449 stars · 138 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
Paritok is a non-destructive compression gateway that sits as a drop-in proxy between AI coding agents (Claude Code, Cursor, Codex, OpenHands, or any BASE_URL-configured agent) and their LLM upstream. It uses an open-source 4B model (Qwen3-4B backbone) to strip tool-schema bloat, compress tool results and file reads, and summarize stale history, cutting input-token bills ~25% on turn one to past 85% in long sessions while allowing exact originals to be pulled back on demand.
Use cases
- reduce LLM API token costs for AI coding agents
- fit more turns into a fixed context window
- compress tool schemas and file reads in agent sessions
- self-host a proxy to cut Claude Code or Cursor token bills
- summarize stale conversation history without losing recoverable originals
- recover exact original file content compressed by the gateway
- filter irrelevant tool definitions per request to shrink prompts
When to choose
- you run coding agents like Claude Code, Cursor, Codex, or OpenHands and want lower token bills without changing agent code
- your agent sessions hit context-window saturation and you need ~3x more turns in the same window
- you want a self-hosted, Apache-2.0 compression layer with on-demand recovery of original content
When to avoid
- you need zero added latency or cannot run a local 4B model / proxy hop
- your workflow requires byte-exact prompts upstream at all times with no rewriting
- you only make short, single-turn LLM calls where compression savings are minimal
Facets
service · maturity active
llm-inference proxy middleware machine-learning developer-tools large-language-models developer-tools infrastructure-as-code self-hosted python self-hosted cross-platform llm-gateway token-optimization context-compression coding-agents drop-in-proxy cost-reduction qwen3-4b prompt-compression ai-agents docker
2 sources
- readme: https://github.com/Paritok-official/paritok-4b-v1 · fetched 2026-08-28 · 1798284054a5
- homepage: https://www.paritok.com/ · fetched 2026-08-29 · 87382babe42b
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| Paritok-official/paritok-4b-v1 | main | 57 |
For agents
markdown · JSON · MCP: product_card(name="Paritok-official/paritok-4b-v1")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem