CareerWeaver — Free, Open-Source AI-Apply Alternative

The free, local CLI alternative to AI-Apply / LazyApply ($200-$400/mo). One canonical fact graph, six persona lenses, one CLI that turns them into tailored resumes — then fills the application form for you, without inventing a thing you didn't do.

$0 · MIT · Local-first · Agent-driven — No subscription. No hosted data. Your resume, fact graph, and every tailored doc stay on your machine. Bring your own LLM (any OpenAI-compatible endpoint) or run deterministic with no key.

Free AI-Apply / LazyApply replacement · ATS auto-apply · Greenhouse / Lever / Workable / Ashby · Resume tailor · Cover letter

Everything runs on your machine. Your resume, your profile, and your fact graph never leave it except to reach the LLM provider you configure — or the job site you explicitly apply to. No server, no account, no telemetry.

weaver init            # blank profile template — yours to fill
weaver seed-import     # your old resumes → a canonical fact graph
weaver tailor          # facts + a lens    → a tailored resume (md/docx/pdf)
weaver apply           # a resume + a job  → a filled form (human sends)

Agent-first. Every command emits clean JSON when piped. Any agent — Claude Code, Codex, OpenCode, Hermes, GLM — can drive the full find → tailor → apply loop from the CLI. See Agent Setup and docs/agents.md.

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Why CareerWeaver — the free AI-Apply alternative

	AI-Apply / LazyApply / Simplify / Teal / Huntr / JobCopilot / Sonara / LoopCV (hosted, $20-$400/mo)	CareerWeaver
Price	$200-$400/mo subscription	$0 · MIT open-source
Where your data lives	Their cloud (resume, PII, docs)	Your machine only (data/ gitignored)
Model	Locked to their provider	Bring your own — any OpenAI-compatible endpoint, or deterministic with no key
Resume tailoring	Black-box rewrite	Fact graph + persona lenses — same facts, different emphasis, unverified_mentions never shipped
Auto-apply	Hosted browser, auto-submit	Local Playwright Chrome — verifies every field landed, parks at held for your audit, you press send
ATS coverage	Often limited / scraping	Greenhouse + Lever + Ashby + Workable (public APIs, no scraping)
Agent / automation	Web UI only	CLI-first, JSON when piped — any agent can drive find → tailor → apply
Telemetry	Vendor analytics	Zero — no server, no account, no telemetry

• One resume is wrong. The same career reads differently for a Design Engineer vs. an SRE. Weaver keeps one verified fact graph and re-tells it through persona lenses — same facts, different emphasis.
• LLMs are good at re-weighting, bad at truth. The guardrail is non-negotiable: every claim in a generated resume must trace to a fact in your graph. Unbacked claims surface as unverified_mentions, not shipped.
• Applying is the grind. Weaver drives a real Chrome via Playwright, verifies every field actually landed, and parks at held for your audit — you clear CAPTCHA and press send.
• Bring your own model. Any OpenAI-compatible /chat/completions endpoint. No LLM key? Every command degrades to a deterministic local path — the test suite runs this way.

Search keywords: ai-apply alternative, lazyapply alternative, simplify alternative, teal alternative, huntr alternative, jobcopilot alternative, sonara alternative, loopcv alternative, applyhero alternative, careerflow alternative, jobright alternative — free open-source job application automation, ATS auto-apply, Greenhouse Lever Workable Ashby Workday iCIMS Taleo BambooHR, resume tailoring, cover letter generator, autofill, auto-apply bot**

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Install

Requires Python 3.11+.

git clone https://github.com/idea-torx/CareerWeaver.git
cd CareerWeaver

# with uv (recommended)
uv sync
uv run playwright install chromium   # apply engine

# or with pip
pip install -e .
playwright install chromium

uv run weaver --version      # or `weaver` once the venv is active
# every example below also works as `uv run weaver ...`

pip install -e . installs a weaver console script; uv sync keeps it inside .venv.

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60-second quickstart

# 1. Profile (blank template, nothing ships with real details)
weaver init --interactive
# or non-interactively:
weaver init --email you@example.com --location "Portland, OR" \
            --links "linkedin.com/in/you,you.example" \
            --target-roles "Design Engineer,Full Stack Engineer"
# env-var alternative:
WEAVER_PROFILE_EMAIL=you@example.com weaver init

# 2. Fact graph — import every resume you've ever written
weaver seed-import --dir ./seed_resumes   # reads .md/.txt/.docx/.pdf
weaver stats                               # facts_total, verified %, lenses, jobs

# 3. Tailor — lens or job auto-picks the lens
weaver lens list
weaver tailor graph --lens fde --format docx
weaver tailor graph --job 3   # lens chosen to fit the posting

# 4. Apply — dry-run first, then fill held for audit
weaver preflight 3
weaver apply 7 --dry-run
weaver apply 7 --visible --hold --tab   # one shared window, trace survives kills
weaver apps list
weaver serve   # read-only ledger viewer at http://127.0.0.1:8787

Re-running weaver init never blanks a field you already set. data/ (config, DB, traces, renders) is gitignored by construction.

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LLM provider

Weaver speaks any OpenAI-compatible /chat/completions endpoint. Nothing is baked in beyond a default pair you are free to replace.

Variable	Meaning	Default
WEAVER_API_KEY (or OPENAI_API_KEY)	provider key	—
WEAVER_BASE_URL (or OPENAI_BASE_URL)	endpoint root	https://api.openai.com/v1
WEAVER_MODEL	model id — required when you change the base URL	gpt-4o-mini
WEAVER_TEMPERATURE	sampling temperature	0.3
WEAVER_TIMEOUT	seconds per request	60
WEAVER_USER_AGENT / WEAVER_HTTP_REFERER / WEAVER_APP_TITLE	attribution headers (some relays 403 without them)	CareerWeaver/0.1

export WEAVER_API_KEY=sk-...
# a different provider — set both, or weaver refuses loudly:
export WEAVER_BASE_URL=https://your-provider.example/v1
export WEAVER_MODEL=their-model-id

WEAVER_BASE_URL without WEAVER_MODEL is a configuration error: weaver reports it on stderr, in weaver stats --json (llm.config_error), and in every fallback reason, then degrades to the deterministic path rather than shipping an OpenAI model id to someone else's endpoint.

No key is a supported mode. Every command falls back to deterministic local assembly — resumes still get written, just without LLM re-wording.

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Your profile

weaver init writes data/config.json — the only place your personal details live:

• Name, email, phone, location, links (LinkedIn/GitHub/portfolio)
• Target roles & skills (drives find scoring and lens ranking)
• Work authorization, visa, availability / earliest start date
• ATS-consent and "how did you hear about us" defaults
• Voluntary self-ID fields (empty = "I do not wish to disclose" — exactly what the agent answers)
• avoid — topics the model must never mention (e.g. an employer you don't want surfaced)

Prefer flags or env vars over prompts for scripting:

weaver init --phone "+1 (503) 555-0148" --availability "Immediately" \
            --how-did-you-hear "Company careers page"

data/ is gitignored. Real resumes belong in seed_resumes/ (also gitignored); samples/ ships only synthetic fixtures (mira@halloway.example, +1 (503) 555-01xx — reserved .example names).

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The three lanes

1. Find — collect the postings

weaver jobs add https://boards.example.com/acme/jobs/42
weaver jobs add ./posting.txt            # file
pbpaste | weaver jobs add -              # stdin
weaver jobs add https://... --fetch      # also fetch the posting text now
weaver jobs list

weaver find                              # orgs in config.json find.orgs
weaver find --wide --limit 10            # sweep the whole board registry
weaver find --wide --max-boards 60 --delay 1.2
weaver boards list                       # what --wide sweeps
weaver boards add acme lever             # extend it (writes <data-dir>/boards.json)

jobs add <url> records the URL and does not touch the network unless --fetch is passed. --wide walks the committed registry of public Greenhouse/Lever/Ashby/Workable boards — product companies and creative agencies — one board at a time with a delay between requests, capped at 60 boards per sweep. Postings already in the ledger are reported as N already applied, skipped.

Ashby URLs require --fetch (resolved via the public Ashby API). Auto-lens, preflight, and fit scoring are blind on a job with no posting text.

2. Adapt — build the fact graph, then the resume

weaver seed-import --dir seed_resumes     # every resume you have ever written
weaver seed-import --dir seed_resumes --no-llm  # deterministic, no network
weaver stats                              # what the graph knows
weaver lens list
weaver lens show fde
weaver lens create --name platform \
  --lead-domains sre_cloud,fullstack_engineering \
  --titles "Platform Engineer;Infrastructure Engineer"

weaver tailor graph --lens fde --format docx
weaver tailor graph --lens multimedia --format md --out ./out/resume.md
weaver tailor graph --job 3               # picks the lens that fits the posting
weaver tailor graph --job 3 --json        # structured JSON + unverified_mentions

seed-import reads .md/.txt/.docx/.pdf, merges into one fact graph, and marks a fact verified when ≥2 seed documents agree on it. A lens re-tells those same facts for a different audience; it never adds a new one. Rendered output is diffed back against the graph — claims with no supporting fact surface as unverified_mentions.

A do-not-mention guard also runs: any fact whose topic is in profile.avoid is stripped from the prompt and refused if the model tries to mention it.

Six shipped lenses (from src/weaver/lenses.py):

Lens	Leads	For
fde	agentic_engineering, ai_expertise, fullstack_engineering, sre_cloud	Forward Deployed AI Engineer
fdc	ai_expertise, cgi_motion, video_multimedia, direction_pm	Forward Deployed Creative
design-engineer	design_engineering, graphics_brand, fullstack_engineering, ai_expertise	Design Engineer
multimedia	video_multimedia, cgi_motion, graphics_brand, direction_pm	Multimedia Production
creative	graphics_brand, cgi_motion, direction_pm	Brand / Creative Director
sre	sre_cloud, fullstack_engineering, agentic_engineering	SRE / Platform

3. Apply — fill the form

weaver preflight 3                        # can the facts answer this form? (no browser)
weaver apply 7 --dry-run                  # build + print the request, submit nothing
weaver apply 7 --visible --hold           # fill in a real browser, park for audit
weaver apply 7 --visible                  # fill AND submit, watched
weaver apply 7 --visible --hold --tab     # tab of the shared window (recommended)
weaver apps list | weaver apps show 12

Preflight fetches a posting's public questions (Greenhouse/Lever/Ashby/Workable expose them; Workable enumerates every field with its required flag) and checks each required one against your facts. If a required question has no supporting fact, the apply is refused before a browser opens — add the fact or pass --force.

One engine, on your machine: Playwright Chromium driven by the agent loop.

• --visible watches it work; closing the window stops that run.
• --hold fills everything but never submits — parks at audit_pending/held with the window open for you to review and press send.
• --tab opens concurrent runs as tabs of one shared window (weaver tab-host, auto-started) instead of a window per run; closing a tab ends only that run.
• Every real run streams its trace to data/traces/*.jsonl as it happens — a killed window never loses evidence.

Workable is real-Chrome-only. Turnstile at submit fails even a human inside the Playwright tab-host (Aug 2026). Workable fills auto-route to a real Chrome on a dedicated profile (CDP 127.0.0.1:9223 / WEAVER_REAL_CDP_PORT, WEAVER_CHROME_BIN overrides the binary). The hold semantics are identical — you answer Turnstile and press send. If Chrome can't start, only Workable entries fail, with remediation in their reason string.

Batch & cycle:

weaver batch 12 13 14                     # fill a queue as held tabs, one report at end
weaver cycle --count 5 --wide --json      # one-shot: find → adapt → batch (for cron)
weaver tab-host                           # pre-start the shared window

The agent stops and reports rather than guessing at a required question it has no fact for.

The morning flow

weaver jobs add <url> --fetch             # paste the postings worth a shot
weaver tailor graph --job <id>            # a resume per posting, lens auto-picked
weaver preflight <id>                     # cheap gate, no browser
weaver apply <resume-id> --visible --hold --tab
weaver apps list                          # what went out, and what stopped
weaver serve                              # read-only viewer at :8787

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How it works

seed resumes ──► extract ──► fact graph (sqlite) ──► lens ──► render ──► .md / .docx / .pdf
                              ▲                      ▲                     │
                        corroboration           job posting                ▼
                        (2+ seeds = verified)                       preflight → apply → ledger

• Fact graph — data/weaver.db (facts, skills, domains, lenses, jobs, resumes, applications). Facts carry kind, title, org, bullets, metrics, tags, source, verified.
• Lenses — ordered lead_domains, compress_domains, skills_order, summary_tone. Same facts, different emphasis.
• Guardrail — output diffed back against the graph; unverified_mentions are reported. The apply agent refuses to type a value that didn't come from your profile (PII guard).
• Ledger — every dry-run and real apply lands in applications — weaver apps list, weaver apps show <id>, weaver serve (/api/* JSON endpoints).

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Agent setup

Weaver is built for agents. Every command emits human text on a TTY, clean JSON when piped (--json forces JSON).

For any agent harness

# JSON mode (automatic when piped)
weaver stats --json | jq
weaver tailor graph --job 3 --json
weaver apply 7 --dry-run --json
weaver batch 12 13 --json          # one JSON doc on stdout, blocks on stderr
weaver cycle --count 5 --wide --json

• Credentials without a shell: put KEY=VALUE lines in <data-dir>/env (data/env by default, gitignored) — WEAVER_API_KEY, WEAVER_BASE_URL, WEAVER_MODEL, or WEAVER_LLM_CMD (a local CLI instead of an endpoint). Real env vars always win. export prefixes and quotes are tolerated.
• Absolute binary for launchers: <repo>/.venv/bin/weaver
• One argv, no shell: cycle is designed for harnesses that can only run a single command with a bare environment (cron, gated launchers) — no source, &&, or pipes required.
• WEAVER_PROFILE_* env vars set any profile field without a flag (e.g. WEAVER_PROFILE_EMAIL).
• Global --data-dir overrides where weaver.db/config.json live (default ./data).
• --visible --hold --tab is the safe default for agents: never auto-submits, tabs share one window, traces survive kills.

MCP — Claude, Codex, Hermes

weaver-mcp exposes the CLI over MCP stdio. It runs on your machine: nothing is hosted, no account, and weaver.db never leaves the laptop. The fill step drives your own Chrome with your own logged-in ATS sessions, which is why a hosted server could not do this job.

Nine tools — weaver_stats, weaver_lens_list, weaver_jobs_add, weaver_jobs_list, weaver_preflight, weaver_tailor, weaver_apply_hold, weaver_apps_list, weaver_apps_show. There is no submit tool. weaver_apply_hold always passes --hold, so it fills and parks at audit_pending for you to review and send.

Claude Code — via the plugin (bundles the server config):

claude plugin marketplace add idea-torx/CareerWeaver

claude plugin install careerweaver@careerweaver

Or wire the server directly:

claude mcp add careerweaver --env WEAVER_DATA_DIR=$PWD/data -- weaver-mcp

Codex — ~/.codex/config.toml:

[mcp_servers.careerweaver]
command = "weaver-mcp"
env = { WEAVER_DATA_DIR = "/absolute/path/to/CareerWeaver/data" }

Claude Desktop — claude_desktop_config.json:

{
  "mcpServers": {
    "careerweaver": {
      "command": "weaver-mcp",
      "env": { "WEAVER_DATA_DIR": "/absolute/path/to/CareerWeaver/data" }
    }
  }
}

Hermes — hermes mcp install careerweaver once packaging/hermes/careerweaver/manifest.yaml is merged into its optional-mcps/ catalog.

Set WEAVER_DATA_DIR to an absolute path. The CLI defaults to ./data relative to the working directory, and an MCP client spawns the server with whatever cwd it likes — without it the server creates or reads an empty database wherever the client happened to start it (or errors outright, if that directory is not writable).

Run weaver init and weaver seed-import before first use — an empty fact graph has nothing to tailor from. If weaver-mcp is not on PATH, use the absolute path <repo>/.venv/bin/weaver-mcp.

Full guide

See docs/agents.md for the complete agent contract: JSON vs human output, exit codes, cycle one-shot, batch orchestration, preflight gate, single-flight lock, Workable/real-Chrome routing, harness constraints (launchd user agent vs system daemon), and the build/audit/test contributor loop.

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Privacy

Weaver runs on your machine. There is no CareerWeaver server, no telemetry, and no account. The database, your config, and every generated resume live in data/, which is gitignored. The only outbound requests weaver ever makes are:

1. Your LLM provider (only if WEAVER_API_KEY is set)
2. A posting URL you explicitly asked it to --fetch
3. On a real, non-dry-run apply — the job site itself

Run with no key at all and it makes zero LLM calls. Tests are built from samples/ (synthetic *.example fixtures); real seeds belong in seed_resumes/ (gitignored).

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CLI reference

weaver [--data-dir DIR] [--json] [--version]
  init [--interactive] [--email E --phone P --location L --links A,B ...]
  seed-import --dir DIR [--no-llm] [--overwrite-profile]
  lens list | lens show <name> | lens create --name N --lead-domains a,b --titles "A;B"
  tailor [graph|PATH] --lens <name> | --job <id> [--format md|docx|pdf] [--out PATH] [--json] [--no-llm]
    alias: adapt
  jobs add <url|path|-> [--fetch] | jobs list
  find [--wide] [--limit N] [--max-boards N] [--delay S]
  boards list | boards add <name> <type>
  preflight <job-id>
  apply <resume-id> [--dry-run] [--visible] [--hold] [--tab] [--force] [--max-actions N] [--resume-url URL]
  batch <resume-id>... [--force] [--max-actions N] [--notify/--no-notify]
  cycle [--count N] [--wide] [--force] [--format FMT] [--max-actions N] [--json]
  tab-host [--port P]
  apps list | apps show <id>    (alias: ledger)
  serve [--port 8787]
  stats [--json]

weaver apply and weaver batch/weaver cycle stream their loud completion block to stderr so it survives | jq and > file. Exit codes: 0 success / held+audit_pending (human to finish), 2 failed, 3 audit-pending (needs one field).

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Development

uv run pytest            # full suite, no keys, no network
uv run pytest -m live    # opt-in network tests

• samples/ is synthetic; seed_resumes/ is private and must not be committed (see .gitignore).
• AGENTS.md encodes the build/audit/test loop and hard rules (no invented facts, verify-before-trust, keep local_driver.py/local_agent.py single-editor).
• docs/batch-cycle-contract.md and docs/cron-and-harness-setup.md document orchestration and harness constraints.

Branding & scope

No web UI, no sales assets, no hosted deployment. Branding/PDF polish and D1/R2 port are later phases. Open-source from commit one — MIT.

License

MIT — see LICENSE.