Skip to main content

Slash LLM costs with intelligent context compression, smart routing, and cost tracking

Project description

TokenPak — Cut your LLM token spend by 30–50%, zero config

PyPI version Python 3.10+ License: Apache 2.0

TokenPak is a local proxy that compresses your LLM context before it hits the API — fewer tokens, lower cost, same results. No code changes, no cloud, no credentials stored.

Status: early preview. Core compression engine and proxy are in place. Per-client auto-integration (the tokenpak integrate command) is not yet shipped — configure your client manually by pointing it at http://127.0.0.1:8766. See QUICKSTART at https://github.com/tokenpak/docs (rendered at tokenpak.ai/quickstart).


Quick start

pip install tokenpak
tokenpak start                      # start the local proxy at 127.0.0.1:8766

Point your LLM client at the proxy. For example, the Anthropic SDK:

export ANTHROPIC_BASE_URL=http://127.0.0.1:8766

Or for OpenAI-compatible clients:

export OPENAI_BASE_URL=http://127.0.0.1:8766

Then use your client normally. TokenPak compresses requests on the way out and logs savings to a local SQLite ledger.

See QUICKSTART at https://github.com/tokenpak/docs (rendered at tokenpak.ai/quickstart) for per-client setup (Claude Code, Cursor, Aider, and others).


What savings look like

After a few proxied requests, tokenpak savings reports the cumulative reduction:

┌──────────────────────────────────────────────────────┐
│  TokenPak — Savings                                  │
├──────────────────────────────────────────────────────┤
│  Sample scenario       DevOps agent (config + logs)  │
│  Savings drivers                      dedup + alias  │
├──────────────────────────────────────────────────────┤
│  Original                                747 tokens  │
│  Compressed                              502 tokens  │
│  Saved                          245 tokens  (32.8%)  │
│  Cost saved (est.)                $0.00073 per call  │
├──────────────────────────────────────────────────────┤
│  Stages: dedup, alias, segmentize, directives        │
└──────────────────────────────────────────────────────┘

Actual numbers depend on your workload. Agent-style prompts with lots of repeated context see the biggest gains.


Works with

Any LLM client that respects a custom base URL:

Claude Code · Cursor · Cline · Continue.dev · Aider · OpenAI SDK · Anthropic SDK · LiteLLM · Codex

Per-client configuration steps are in QUICKSTART at https://github.com/tokenpak/docs (rendered at tokenpak.ai/quickstart). Auto-wiring via a single tokenpak integrate <client> command is tracked for a future release.


Install

pip install tokenpak

TokenPak's runtime dependencies include anthropic, openai, fastapi, flask, litellm, llmlingua, pandas, pydantic, requests, rich, scipy, sentence-transformers, tree-sitter-languages, watchdog, and a few others — all installed automatically. Note that sentence-transformers and scipy are large (several hundred MB of dependencies); expect pip install to take a few minutes on first install.

Requires Python 3.10+.

See QUICKSTART at https://github.com/tokenpak/docs (rendered at tokenpak.ai/quickstart) for virtual-env setup and first-run details.


What's included

  • Context compression — deterministic pipeline (dedup → alias → segmentize → directives); typical 30–50% token reduction on agent workloads.
  • Local proxy — runs at 127.0.0.1:8766; zero cloud component.
  • Model routing — configurable rules with fallback chains.
  • Cost & savings tracking — per model, per session, per agent; local SQLite (~/.tokenpak/monitor.db).
  • Dashboard — local web UI for visualizing savings (tokenpak dashboard).
  • Vault indexing + semantic search — index a directory; search without an LLM call.
  • A/B testing and request replay — compare compression configs; re-run past requests.
  • 50 built-in compression recipes — YAML, customizable.

See QUICKSTART at https://github.com/tokenpak/docs (rendered at tokenpak.ai/quickstart) and API reference at https://github.com/tokenpak/docs (rendered at tokenpak.ai/api) to get started.


Current limitations

Honest about what isn't ready yet:

  • No tokenpak integrate <client> auto-wire command — configure clients by env var as shown above. Auto-wire is planned.
  • No published CI/CD — releases are manual; automation is tracked in the release-workflow standards.
  • tokenpak demo is a compression-recipes demo (shows recipes applied to a sample input), not the decorated savings panel above. The panel shows what tokenpak savings output can look like after real usage.

We'd rather ship an honest preview than an advertised product that doesn't match install-time reality.


Support


License

Apache 2.0. See LICENSE.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

tokenpak-1.2.2.tar.gz (1.2 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

tokenpak-1.2.2-py3-none-any.whl (1.6 MB view details)

Uploaded Python 3

File details

Details for the file tokenpak-1.2.2.tar.gz.

File metadata

  • Download URL: tokenpak-1.2.2.tar.gz
  • Upload date:
  • Size: 1.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.3

File hashes

Hashes for tokenpak-1.2.2.tar.gz
Algorithm Hash digest
SHA256 8835e6d99098a063e057c941514acd30fdab48fcecc52df2b3c063dbbe1d0ce6
MD5 0365dea7b4fb77e5997288b68ded01d6
BLAKE2b-256 a317e1315117d80bb14b8c34f2faa4ddf718c5f7623952f8445552196022a79e

See more details on using hashes here.

File details

Details for the file tokenpak-1.2.2-py3-none-any.whl.

File metadata

  • Download URL: tokenpak-1.2.2-py3-none-any.whl
  • Upload date:
  • Size: 1.6 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.3

File hashes

Hashes for tokenpak-1.2.2-py3-none-any.whl
Algorithm Hash digest
SHA256 7690ea4524dcc1c27f194bb564fc9e0aa82315a4ffff6103fff95d95a664bae3
MD5 c69e772de420dd4347c397d2be675b3c
BLAKE2b-256 8434324ad4891028bd79126277291cb787d4beaeb1edcb235a581b5e12cfe54c

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page