Caveman / Comparisons
Compare the tools.
Keep what works.
Caveman comparisons for LLM gateways, prompt compression, model routing, and agent observability. See where each tool fits and what changes when you add Caveman.
AI gateways
Caveman vs Bifrost: fast AI gateway, fewer tool tokens
Bifrost adds microseconds to each LLM call. Caveman Middleware cuts the tool-result tokens inside the call. What each does and how to run both together.
Caveman vs Cloudflare AI Gateway: add compression
Cloudflare AI Gateway gives you caching, rate limits and logs at the edge. Caveman Middleware compresses agent tool output before it gets there. Use both.
Caveman vs Helicone: LLM logs plus token compression
Helicone logs, caches and routes LLM calls through its AI gateway. Caveman Middleware shrinks tool output before the call leaves your app. How the two fit.
Caveman vs LiteLLM: tool output compression in LiteLLM
Keep LiteLLM. Caveman Middleware runs inside it as a native callback and shrinks large tool results before LiteLLM sends them, with originals recoverable.
Caveman vs OpenRouter: compression and auto routing
OpenRouter gives one API for hundreds of models plus an Auto Router. Caveman Middleware cuts the tool-output tokens you send through it. What each one does.
Caveman vs Portkey: AI gateway plus tool-token compression
Portkey governs and observes your LLM traffic. Caveman Middleware shrinks large tool results before requests reach it. How they fit, and when to add it.
Caveman vs Vercel AI Gateway: fewer tokens per call
Vercel AI Gateway gives one key, budgets and fallbacks across 200+ models. Caveman Middleware wraps the AI SDK model to shrink tool output. How they fit.
Context compression
Caveman vs Headroom: context compression compared
Headroom and Caveman both compress agent context by content type and keep originals recoverable. Compare recovery, store lifetime, integrations and license.
Caveman vs LLMLingua: prompt compression compared
LLMLingua prunes low-value tokens with a small model. Caveman compresses agent tool output by format and keeps every original recoverable. When to use each.
Caveman vs RTK (Rust Token Killer): which to use
RTK filters shell command output before your coding agent reads it. Caveman compresses every tool result and keeps originals recoverable. Where each one wins.
Model routing
Caveman vs Martian: model gateway, routing, compression
Martian Gateway gives one API over 200+ models, backed by routing research. See how it compares with Caveman Router and how Caveman compression runs through it.
Caveman vs Not Diamond: AI model router comparison
Not Diamond routes each prompt or agent step to the best-value model. Compare it with Caveman Router, and see how Caveman compression cuts tokens on any route.
Caveman vs RouteLLM: open-source LLM router compared
RouteLLM is an open-source framework that routes between a strong and a weak model. Compare it with Caveman Router, and add Caveman compression to routed calls.
Agent observability
Caveman vs AgentOps: agent replays plus token compression
AgentOps records and replays agent sessions and tracks cost. Caveman Middleware shrinks tool output before each model call. How they fit and what a span shows.
Caveman vs Arize Phoenix: traces, evals and compression
Arize Phoenix traces and evaluates LLM apps. Caveman Middleware shrinks tool output before the model call. How they fit, and which copy a Phoenix span records.
Caveman vs Braintrust: evals plus token compression
Braintrust scores and traces your AI app. Caveman Middleware shrinks the tool output your model calls send. How they fit and how to prove quality held.
Caveman vs Langfuse: tracing plus token compression
Langfuse traces, evaluates and prices your LLM calls. Caveman Middleware shrinks the tool output those calls send. How to run both and check quality held.
Caveman vs LangSmith: traces, evals, fewer tokens
LangSmith traces and evaluates LangChain and other agents. Caveman Middleware cuts the tool-output tokens they send. What each does and how they work together.
Caveman vs PromptLayer: prompt registry plus compression
PromptLayer versions, ships and evaluates prompts. Caveman Middleware shrinks the tool output around them. How they fit, and which copy PromptLayer logs.
Agent frameworks
Caveman vs LangGraph: compress tool results in LangGraph
Keep LangGraph and add Caveman Middleware through the LangChain adapter: shrink large tool results in create_agent graphs, keep originals, recover on demand.
Caveman vs Mastra: token compression for Mastra agents
Keep Mastra and add Caveman Middleware: withCavemanMastra wraps your agent, shrinks large tool results before each model call, and keeps originals recoverable.
Caveman vs Pydantic AI: token compression for agents
Keep Pydantic AI and add Caveman Middleware as a capability: CavemanCapability shrinks large tool returns before each model request, originals recoverable.
Caveman vs Vercel AI SDK: tool-result compression
Keep the Vercel AI SDK and add Caveman Middleware: a native withCaveman adapter that shrinks large tool results before each model call, originals recoverable.