Top tokenmaxxing projects

An OpenAI-compatible gateway and SDK for calling many model providers with budgets, logging, load balancing, guardrails, and cost tracking.

The most direct tokenmaxxing fit: route calls, track spend, enforce budgets, and stop pretending every prompt deserves the priciest model.

Open-source LLM engineering platform for observability, traces, metrics, evals, prompt management, datasets, and playground workflows.

Turns token burn into something you can inspect: traces, costs, regressions, and evals instead of vibes and surprise invoices.

A data and document-agent framework for connecting LLM apps to files, structured data, retrieval systems, and agent workflows.

Good retrieval is tokenmaxxing in disguise: send the model the useful context, not a suitcase full of maybe-relevant text.

A framework for building resilient stateful agents with explicit graphs, persistence, human-in-the-loop flows, and controllable execution.

Stateful graphs help keep agents from wandering through expensive loops. Fewer accidental tool calls, more deliberate context.

A CLI and CI workflow for testing prompts, agents, and RAG systems across models, with evals and red-team style checks.

A bad prompt can spend tokens forever and still be wrong. Evals let you find the cheap-enough prompt before production does.

A framework for programming and optimizing language-model pipelines rather than hand-tuning one prompt at a time.

Optimization beats prompt superstition: measure the task, tune the pipeline, and spend tokens where they actually move quality.

A fast BPE tokenizer for OpenAI models, useful for counting and estimating token usage before requests go out.

You cannot manage what you do not count. Token counting is the basic meter that makes practical spend estimates possible.

A vector database and vector search engine for AI search, semantic retrieval, filtering, and hybrid-search applications.

Retrieval infrastructure helps swap bloated prompts for targeted context windows by sending the most relevant chunks first.

Search infrastructure for AI applications, commonly used as a retrieval layer for agents, RAG apps, and local prototypes.

A practical way to keep context nearby and queryable instead of force-feeding the model everything every turn.

An AI gateway for routing across LLMs with guardrails, provider abstraction, and an OpenAI-compatible API surface.

Model routing plus guardrails is the grown-up version of tokenmaxxing: pick the right route, then keep the call inside policy.

Open-source LLM observability for monitoring, evaluation, experimentation, latency, requests, and usage behavior.

A clean feedback loop for where tokens are going, which calls are slow, and which experiments are worth keeping.

A semantic cache for LLM applications, with integrations for LangChain and LlamaIndex-style workflows.

The cheapest token is the one you do not send twice. Semantic caching is the unglamorous cost killer.

A structured-output toolkit for constraining generation with formats like JSON, regex, and grammars.

Structured outputs reduce repair prompts and retry loops. Fewer malformed responses means fewer wasted follow-up calls.

Open-source observability for LLM and GenAI applications, built on OpenTelemetry conventions.

Useful for teams that already live in telemetry and want token behavior next to the rest of production reality.

A memory layer and integration collection for AI agents and knowledge-graph-backed language-model applications.

Agent memory is tokenmaxxing when it recalls the right prior fact instead of replaying the whole conversation.