Paritok
Pricing
- Model
- Subscription
- Free Tier
- Self-hosted free; API key for hosted usage
Summary
Your Claude or Cursor agent burns through 29K tokens on tool schemas alone — before it reads a single file — and by turn five the context window is half gone before the real work starts. Paritok is a drop-in gateway that compresses those schemas, file reads, and accumulated history on every request before they reach the upstream API.
Paritok sits between your agent and Anthropic (or any OpenAI-compatible upstream) via one environment variable change. It runs three compression passes per request: it stubs irrelevant tool schemas down to roughly 8K from a typical 29K block, compresses file reads to about 26% of their original size using a dedicated 4B model that preserves identifiers and error paths, and summarizes stale conversation turns before the context window fills rather than after. Nothing is discarded — refs expand on demand without burning an extra turn. The compressor is deterministic enough that schema blocks stay cache-stable across a conversation, which compounds savings across sessions.
Bottom line: Use Paritok when your coding agents are routinely exceeding 40K tokens per turn with tools-heavy schemas; plan around it when you need lossless file content at every turn, because the compression model makes semantic judgments that third-party code audits may not accept.
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Pros
Sign in to edit- Single environment variable integration, so you don't rewrite agent code or change your prompt structure — existing Claude, Cursor, or OpenHands workflows drop Paritok in without a sprint of plumbing work.
- Tool schema compression keeps schemas cache-stable across a conversation, which means repeated schema transmissions stop eating into Anthropic's prompt caching thresholds and per-turn costs compound downward over long sessions.
- History summarization runs on a configurable token budget before the context window fills, so sessions that previously truncated at turn 15 continue cleanly — the vendor's community benchmark reports sessions reaching 250 turns under a 32K ceiling.
- Compressed content is recoverable on demand via read_original() without a network round-trip, so the agent retrieves exact bytes when it needs them rather than re-reading from disk and burning a full tool turn.
- Self-hosted deployment is available under Apache-2.0 at no cost, so teams with data-residency requirements or API cost sensitivity can run the entire gateway locally without routing tokens through a third-party service.
Cons
Sign in to edit- The 4B compression model makes semantic judgments about which parts of a file to preserve — identifiers, paths, error strings survive, but teams doing compliance-grade audits or cryptographic verification against exact source bytes have to call read_original() on every relevant segment, adding explicit overhead that partially offsets the savings.
- Tool schema stubbing decides which tools are relevant per turn; if that relevance judgment is wrong and a stubbed tool gets called, the agent will fail mid-task rather than degrade gracefully — teams with unpredictable tool access patterns report needing to tune or whitelist specific schemas, which reintroduces manual maintenance.
- Paritok adds a local process to the agent's critical path: if the gateway crashes or slows under a bursty workload, every agent request stalls — teams running production agents on tight latency SLAs add health checks and restart policies, at which point they are operating infrastructure they didn't have before.
- There are no alternatives listed in the market category, but teams whose primary problem is context length rather than token cost — not API bills — typically pivot to chunking strategies or retrieval-augmented approaches implemented directly in their agent framework, bypassing a gateway layer entirely.
About
- Platforms
- Any OpenAI-compatible LLM provider
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- 2026-08-17T03:54:01.335Z
Best For
Who it's for
- Developers running Claude, Cursor, or OpenHands agents
- Teams managing large tool schemas and repeated file access
- Sessions exceeding 40K tokens per turn
What it does well
- Extending AI coding agent sessions without hitting context limits
- Reducing LLM API bills for tools-heavy workflows
- Compressing file reads and history in long-running agents
Integrations
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Sign Up to ContributeFrequently Asked Questions
- Is Paritok free?
- Paritok has a permanent free tier alongside paid upgrades. You can keep using a baseline version indefinitely without paying.
- Is Paritok open source?
- No — Paritok is a closed-source tool. Source code is not publicly available.
- Does Paritok have an API?
- Yes. Paritok exposes a developer API. See the official documentation at https://paritok.com for details.
- Can I self-host Paritok?
- Yes. Paritok supports self-hosting on your own infrastructure.
- What platforms does Paritok support?
- Paritok is available on: Any OpenAI-compatible LLM provider.
Curated lists that include this category
Context window pressure from tool schemas and file reads
Claude or Cursor agents burn through 29K tokens on tool schemas alone before reading any file. By turn five the context window is already half gone.
How Paritok works
Paritok sits between the agent and Anthropic or any OpenAI-compatible upstream through a single environment variable change. It runs three compression passes per request. It stubs irrelevant tool schemas down to roughly 8K from a typical 29K block. It compresses file reads to about 26% of original size with a dedicated 4B model that keeps identifiers and error paths. It summarizes stale conversation turns before the context window fills. Nothing is discarded. References expand on demand without an extra turn. The vendor states the compressor is deterministic enough that schema blocks stay cache-stable.
Pricing and deployment
Pricing follows a subscription model. Self-hosted use is free. Hosted usage requires an API key. The tool supports any OpenAI-compatible LLM provider and integrates with Anthropic, Claude, Cursor, Codex, and OpenHands.
Use cases
Extending AI coding agent sessions without hitting context limits. Reducing LLM API bills for tools-heavy workflows. Compressing file reads and history in long-running agents.
Who it is for / who should skip it
Best for developers running Claude, Cursor, or OpenHands agents, teams managing large tool schemas and repeated file access, and sessions exceeding 40K tokens per turn. Single environment variable integration means no agent code changes. Teams that need compliance-grade audits or cryptographic verification against exact source bytes must call read_original on every segment. If tool relevance judgments fail, a stubbed tool can cause mid-task failure rather than graceful degradation.