Give your AI agent a real browser, and turn the traffic it sees into API docs and clients.

A local-first Firefox + Chrome extension and MCP server for Claude Code, OpenAI Codex and any other MCP client.

Chrome, Edge and Brave use the Chrome Web Store build. Then install the small local program: on Windows run the installer above (it brings its own Node.js; Windows may warn because it is not code-signed yet: More info, then Run anyway), elsewhere see Quick start.

The approval step (12 s). Watch the full 73-second demo (MP4): fill a form, approve the submit, turn traffic into an OpenAPI spec, open the dashboard. Recorded on the demo site in this repo; a script plays the agent. Re-record it with npm run demo:record.

AI agents are good at deciding what to do on a website and bad at being trusted with your accounts. BrowserForge closes that gap: the agent works in your real browser through an extension, a small local program carries its requests, and you approve anything consequential (submit, send, pay, passwords, uploads) with a click the agent cannot answer. What the agent sees and learns stays on your computer and is turned into reusable things: replayable flows, shareable skills, and API specs and clients built from the traffic it observed. The longer story, how this differs from headless automation and other browser bridges, and where the project is going: docs/VISION.md.

Real browser, real session A WebExtension, not a headless clone: your tabs, your logins, your cookies.

Local-first Everything runs on 127.0.0.1. No cloud service, no API credits.

Reverse-engineer APIs Capture traffic, then get endpoints, schemas, auth and pagination, an OpenAPI 3.1 spec and Python / TypeScript / JavaScript / PHP clients.

Human in the loop Submitting forms, purchases, sending, accepting terms, passwords and uploads wait for a click in the extension popup that the agent cannot answer. A red STOP halts the agent until you resume.

Honest output Every inferred API fact is labelled observed, inferred or hypothesis with confidence and evidence. Per-browser capture limits are documented in docs/LIMITATIONS.md.

Secure by default Token-authenticated loopback transports, Origin/Host checks, strict schemas, secret redaction, path-safe file access. See docs/SECURITY.md.

Quick start

Requires Node.js 22 or newer, and Chrome, Edge, Brave or Firefox, on Linux, macOS or Windows (Linux is the one it has been tested on; see docs/INSTALL.md).

From npm (no build): npm install -g @el-makina-ai/browserforge-mcp then: browserforge setup

From a checkout:

npm install && npm run build node apps/mcp-server/dist/cli.js serve # prints endpoints, creates tokens in ~/.config/browserforge/ node apps/mcp-server/dist/cli.js tokens # shows the extension token

Install the extension from the store:

Working from a checkout? Load your own build instead:

Connect it. Click the toolbar icon, paste the extension token, press Connect.

Connect your agent. Guides: Codex · Claude Code

codex mcp add browserforge --url http://127.0.0.1:7345/mcp --bearer-token-env-var BROWSERFORGE_MCP_TOKEN claude mcp add --transport http browserforge http://127.0.0.1:7345/mcp --header "Authorization: Bearer $(cat ~/.config/browserforge/mcp-token)"

Try it. Start the demo site with node tests/fixtures/demo-site/server.mjs, then ask your agent:

Call system_status, then open http://127.0.0.1:8099/apply and describe the form.

Full instructions: docs/INSTALL.md · guides in the wiki · what is planned: the roadmap board. Problems: docs/TROUBLESHOOTING.md.

"Open this site, click through the checkout flow, and tell me which API calls it makes."

"Analyze the captured traffic and generate an OpenAPI spec and a Python client for the endpoints I used."

"Fill in the application form from my saved profile, show me a preview, and wait for my approval before submitting."

"Export the capture as a HAR file with secrets redacted."

From a website to a working API client, in one conversation: the agent browses the shop, BrowserForge analyses the requests it saw (by code, not a model), writes an OpenAPI spec and a Python client that passes its own tests, and the client fetches real data:

Watch the 41-second API demo (MP4). Recorded on the demo site in this repo with npm run demo:record -- --scene api; a script plays the agent.

More end-to-end examples: docs/WORKFLOWS.md.

Namespace Tools

browser browser_get_tabs, browser_get_active_tab, browser_select_tab, browser_close_tab, browser_navigate, browser_click, browser_type, browser_fill_field, browser_select_option, browser_scroll, browser_press_key, browser_get_page_text, browser_get_dom_snapshot, browser_get_interactive_elements, browser_take_screenshot, browser_wait_for_element, browser_get_forms, browser_extract_table, browser_verify, browser_fill_form, browser_get_downloads, browser_get_console_errors, browser_solve_captcha, browser_pending_requests, browser_pause, browser_resume, browser_stop

network network_list_requests, network_get_request, network_get_request_details, network_search_requests, network_list_captures, network_configure_capture, network_export_har, network_import_har, network_clear_capture

api (reverse engineering) api_analyze_traffic, api_list_endpoints, api_get_endpoint_details, api_generate_openapi, api_generate_client

form form_profile_save, form_profile_list, form_profile_delete, form_discover, form_fill_from_profile, form_validate, form_preview_submission, form_prepare_cover_letter

goal (optional, local model) browser_run_goal, browser_goal_feedback

knowledge knowledge_add_document, knowledge_search, knowledge_list, knowledge_remove, knowledge_export_dataset

flows flow_list, flow_get, flow_save, flow_delete, flow_record, flow_export, flow_run, flow_consolidate, flow_to_skill

sites site_info, site_note, site_list

skills skill_list, skill_get, skill_run_workflow (plus one prompt per skill)

plugins whatever the plugins you install add, e.g. cv_* from the RenderCV plugin

workflow workflow_create, workflow_run, workflow_pause, workflow_resume, workflow_get_status

system system_status, system_diagnostics

Read-only tools carry readOnlyHint. Every tool has a strict JSON schema (unknown arguments are rejected) and bounded output.

browser_run_goal is an optional fast path: local models pick one routine step at a time from a compact table of the page. By default Qwen (via Ollama) decides; --decider cascade puts Laya (via Ollaya) first with Qwen as backup. The run hands back to your agent when the models are unsure, and every step says which model decided. It only types values you pass in, never navigates to other sites, and goes through the same approval gate as every other action.

Analysis and code generation themselves are deterministic and use no LLM.

BrowserForge records what an agent does on each website as a flow: steps, several ways to find each element, and your typed values as named parameters (passwords are never kept). Replay a flow with no model (flow_run), or export it as a script for Playwright (TypeScript, Python), Selenium (Python, Java), Chrome DevTools Recorder / Puppeteer Replay, or neutral JSON:

browserforge flow list browserforge flow export <id> --format playwright-ts --out signup.ts FLOW_CONFIRM=1 FLOW_PARAM_FIRST_NAME=Grace npx tsx signup.ts

Exported scripts stop before submits, sends and uploads unless you say FLOW_CONFIRM=1, since outside BrowserForge there is no approval prompt. Details: docs/FLOWS.md.

Agent window: turn it on in the popup and the agent works in its own window instead of the tabs you are using; it can still use one of yours when it selects it on purpose. Take over stops the agent and shows its window so you can finish by hand; Hand back lets it carry on. Details: docs/AGENT_WINDOW.md.

Tables: browser_extract_table reads HTML tables and ARIA grids (also inside iframes) as column names and rows, with merged cells expanded, as JSON or CSV (spreadsheet formulas neutralised), or saved to a file so a big table does not fill the agent's context.

Memory that carries over

Site cards: navigating to a site returns what is known about it (obstacles, past results, flows to replay); agents add to it with site_note. See docs/SITES.md.

Automatic checks: after a submit, and at the end of browser_run_goal, the page is read for a confirmation or an error, and the run is labelled good or bad without waiting for anyone. browser_verify does it on demand.

Flows to skills: flow_consolidate merges many recordings of a task into one clean flow, and flow_to_skill turns it into a shareable skill that replays with no model.

Record me: an opt-in popup switch that also records your own clicks and fields as a flow and as training examples for the local model. Off by default, passwords never recorded, everything stays on your computer.

A dashboard for what is installed

browserforge dashboard opens a read-only page on your own computer: connected browsers, MCP sessions and every tool, the plugins and skills you installed (with the permissions you approved, in plain words), recorded flows and how the local models are doing. It changes nothing and has its own token. Details: docs/DASHBOARD.md.

Anyone can add to BrowserForge without touching the core:

browserforge plugin search cv # npm packages tagged "browserforge-plugin" browserforge plugin add ./plugins/rendercv browserforge plugin config rendercv cvDir=/cv rendercv=/venv/bin/rendercv

The first plugin is plugins/rendercv: tailor a RenderCV resume to each job (reorder and reword only; it rejects any number, link or tool name not already in your CV), render it, and keep an application log. Its companion skill skills/job-apply is a worked example of a skill. Contributing: CONTRIBUTING.md.

flowchart LR A["Your agent<br/>Claude Code · Codex · any MCP client"] -->|"MCP + token"| D subgraph D["browserforge daemon · 127.0.0.1"] T["Tools"] --- N["Traffic analyzer<br/>+ client generator"] T --- F["Flows"] T --- M["Memory<br/>site cards · traces"] T --- L["Local deciders<br/>Qwen · Laya"] end D -->|"WebSocket + token"| E["Extension<br/>Chrome · Firefox<br/>approvals · STOP"] N --> O["OpenAPI · API clients · HAR"] F --> S["Playwright · Selenium ·<br/>Chrome Recorder scripts"] F -->|"flow_to_skill"| K["Skills"] K --> A P["Plugins<br/>permission manifests"] --> T

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The extension, the daemon and the typed protocol between them are the core. Flows, skills and plugins are how it grows without touching the core, and the local models and site memory make repeated tasks cheaper. Each part, and where it is heading (a shared library of skills, local models that learn from your demonstrations, more plugins, more browsers and platforms), is in docs/VISION.md.

Claude Code / Codex ──MCP (HTTP + Bearer)──► browserforge daemon ──WebSocket (token + Origin)──► extension in Firefox / Chrome MCP tools · BrowserHub background · content script · popup NetworkStore · analyzer · generator approvals · STOP

A tool call is validated against its schema, then sent to the extension with a request id, timeout and cancellation.

The extension re-validates it, resolves the tab and element, and runs it in the page.

If the action looks consequential, it returns to the human for approval inside the browser before anything happens.

Results flow back to the agent as redacted, bounded, untrusted data.

Deeper dive: docs/ARCHITECTURE.md · docs/BROWSER_PROTOCOL.md · docs/RESEARCH.md.

Page content is data, never instructions. It is wrapped as untrusted before the agent sees it.

Approvals live in the browser. The agent cannot answer the popup.

Secrets are redacted from what the agent sees, from exports and from generated code. Revealing them requires starting the daemon with --allow-reveal.

Nothing is persisted unless you export it. Captured traffic stays in memory.

No anti-bot evasion and no arbitrary-JavaScript tool. CAPTCHA solving exists only as the opt-in browser_solve_captcha (local