Distill

CI PyPI Python License: Apache 2.0 Python 3.12+ Ruff pre-commit

Point Distill at a research goal. It finds papers, talks, and pages from operator-trusted sites, captures supplied repos, podcasts, feeds, posts, and local files, analyzes each into structured insights with source receipts, verifies claims before write, and synthesizes a plain-Markdown corpus on your disk. Browse it in Obsidian, query it over MCP, ask cited questions that can re-enter the corpus, and refresh on a cadence instead of going stale.

PyPI package distillr; CLI is distill (plus distill-mcp).

Install and first run

uv tool install distillr
distill --cost-mode no-metered init
distill --cost-mode no-metered papers "temporal knowledge graph" --topic tkg --limit 5 --preview

no-metered only allows routes Distill can prove are not API-billed. Preview builds a current arXiv shortlist without ingesting. On a local Ollama or LM Studio route that spend is $0.00 and the budget is wall clock: a two-paper ingest can take an hour on a laptop, and that is the control, not a hang. Run distill bench once so preview can print how long the full ingest will take on this machine. If local inference is up, use it to ingest more and stay more current: ad hoc commands at the keyboard, distill profile refresh --max-hours 6 --yes after hours so many topics keep feeding the markdown wiki. When API spend is OK, the same pipeline goes wide and fast. When the shortlist looks right:

Hard dollar budgets cover registered token-priced calls. Gemini Deep Research has no provider request-side dollar ceiling, so Distill refuses that agent before remote setup whenever a hard workflow or MCP budget is active.

distill --cost-mode paid-ok papers "temporal knowledge graph" --topic tkg --limit 20
distill --cost-mode paid-ok papers "temporal knowledge graph" --topic tkg --limit 20 --workers 3

Paid-ok is how you do a lot quickly: preview the shortlist, then ingest a large set in minutes instead of hours. Paper analysis stays one-at-a-time by default. After reviewing the projected total, --workers 2 or --workers 3 analyzes independent papers in a small bounded group. Discovery, artifact writes, verification, synthesis, and report sections remain serialized.

distill papers CLI demo with synthetic paper titles, progress lines, cost summary, and Markdown corpus artifacts

Illustrative demo (synthetic titles and paths). Real runs use current arXiv results and your configured model route. Distill estimates the selected route before spend; actual cost and duration depend on source size and model output.

Alternate installers, keys, local models, and updates: docs/install.md. Full command reference: docs/usage.md.

What you get

One local library/ of plain Markdown: no database, no cloud lock-in. Same pipeline shape for every source (capture → analyze → verify → synthesize), with a write-time verify gate.

Source Entry point
YouTube distill latest, distill video, distill discover
Websites distill site, distill site-batch
arXiv distill papers
X, repos, podcasts, newsletters, local files distill ingest <url-or-path>

Plus distill ask (cited answers from the corpus), distill audit (free trust report), and distill report (a corpus-first sequential report by default, with explicit accordion and Deep Research profiles). MCP and recurring profiles expose the same durable corpus to agents. Artifact layout and samples: docs/outputs.md. Real example corpus: examples/.

Agent distribution uses one canonical Agent Skill plus an Agent Plugins 1.0.0 portable package and separate client compatibility surfaces. The specification is currently a Working Draft. distill export <topic> --format okf produces an OKF v0.2 projection with portable provenance, bounded receipt copies, lifecycle fields, and digest-bound machine-verification events. The native library/ remains the source of truth. Exact standards boundaries and update policy: docs/interoperability.md.

How Distill differs from Deep Research tools, notebooks, and Markdown wikis: docs/positioning.md.

Docs

Doc For
docs/README.md Full documentation index
docs/usage.md Commands, flags, first recipes
docs/install.md Install, providers, local models
docs/cost.md Cost model and guardrails
docs/mcp.md MCP tools and agent paths
docs/outputs.md What every artifact contains
docs/architecture.md Data flow and routing
docs/invariants.md Design charter
docs/interoperability.md Agent Plugins and OKF baselines
docs/SECURITY.md Trust boundaries and disclosure
docs/CONTRIBUTING.md Dev setup and quality gates
docs/CHANGELOG.md What shipped
ROADMAP.md What is next

Status

Active beta with a broad working surface (sources, discovery, verify, synthesis, ask, audit, MCP, dashboard, profiles, deferred workers). Every change still clears the same release gate (95% branch coverage, ruff, pyright, import-linter, bandit, pip-audit, supported Python matrix, build provenance). Covered v1 contract snapshots are freeze-ready under the published compatibility policy; uncovered pre-1.0 surfaces can still improve additively. Pin versions if you integrate on MCP schemas or frontmatter. 1.0 is a future stability commitment, not a calendar date: ROADMAP.md.

License

Apache 2.0. See LICENSE.

Project links

Data verified by PyPI on Aug 20, 2026
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  • Released:
Newer release available (0.19.74)

1 maintainer

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Avatar for blisspixel from gravatar.com blisspixel

Credits

Author: Nick Seal

GitHub Statistics

Data verified by PyPI on Aug 20, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

Apache-2.0
View SPDX License List

Requires

Python >=3.12

Provides Extra

entailment

Tags

research intelligence synthesis arxiv youtube rag knowledge-base mcp

Classifiers

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