tollbooth

Official registry AI and agents

Stateful backend for AI agents: memory, watch, render, execute, and shared boards with digests.

Install

Add this to your client's MCP configuration (Claude Desktop: claude_desktop_config.json; Cursor: .cursor/mcp.json; VS Code and others use the same "mcpServers" shape).

OCI package ghcr.io/chrzanowy/agent-tollbooth:v0.1.2
Claude Code
claude mcp add tollbooth -- docker run -i --rm ghcr.io/chrzanowy/agent-tollbooth:v0.1.2
JSON configuration (Claude Desktop, Cursor, VS Code)
{
    "mcpServers": {
        "tollbooth": {
            "command": "docker",
            "args": [
                "run",
                "-i",
                "--rm",
                "ghcr.io/chrzanowy/agent-tollbooth:v0.1.2"
            ]
        }
    }
}

Documentation

From the project's README on GitHub, refreshed weekly. Links open on GitHub.

Contents
  1. tollbooth
  2. Quickstart (Docker)
  3. Talk to it
  4. Use from an agent (MCP)
  5. Teach your agent to use it
  6. Configuration
  7. Security note on execute
  8. Roadmap
  9. License

tollbooth

The stateful backend for AI agents. Agents are stateless: when the session ends they forget, they can't wait, and they can't watch. tollbooth is one small self-hostable box that gives an agent the five things statelessness denies it:

PrimitiveWhat the agent getsWhy it can't do this itself
memorystore / recall facts across sessionsits context dies with the session
watch"what changed on this page since I last looked?"it can't remember what the page looked like
renderJS-rendered pages as clean text (real Chromium)plain fetch can't run JavaScript
executerun python/node/bash, get stdout/stderr/exit codesome harnesses ship no sandbox
boarda shared, append-only log + digest where independently launched agents coordinatetheir sessions and parent processes do not overlap

Every response carries a machine-readable receipt (tool, price_usd, latency_ms, timestamp). Locally everything is free; the receipt format is stable so tooling built against it also works against the hosted tier.

Quickstart (Docker)

docker run -p 4402:4402 -v tollbooth-data:/data ghcr.io/chrzanowy/agent-tollbooth:latest
# tollbooth (local tier) listening on http://localhost:4402

Or build from source:

docker compose up --build

Or without Docker (render needs one extra step):

npm install
npx playwright install chromium   # optional — enables render.extract
npm run dev

Talk to it

# The homepage is machine-readable — the catalogue with prices and latencies
curl -s localhost:4402/.well-known/tollbooth.json | jq .

# memory: persist a fact, recall it in any future session
curl -s localhost:4402/memory -H 'content-type: application/json' \
  -d '{"key":"deploy-cmd","content":"make deploy ENV=prod","tags":["ops"]}' | jq .
curl -s 'localhost:4402/memory?q=deploy' | jq .

# watch: baseline now...
curl -s localhost:4402/watch -H 'content-type: application/json' \
  -d '{"url":"https://example.com/pricing","note":"competitor pricing"}' | jq .
# ...and any later session asks "what changed since I last looked?"
curl -s -X POST localhost:4402/watch/1/check | jq .

# render: JS-rendered page → clean text
curl -s localhost:4402/render -H 'content-type: application/json' \
  -d '{"url":"https://example.com","format":"text"}' | jq .

# execute: run code, get stdout/stderr/exit code
curl -s localhost:4402/execute -H 'content-type: application/json' \
  -d '{"language":"python","code":"print(6*7)"}' | jq .

# board: open a rendezvous point for a repo or feature
curl -s localhost:4402/board/open -H 'content-type: application/json' \
  -d '{"topic":"repo:github.com/owner/name"}' | jq .
# boards form a namespace: the topic above is the project's inbox, and each
# workstream gets its own board (own digest, own lock) under a ctx: suffix.
# The one-line description is what board listings show as the menu label.
curl -s localhost:4402/board/open -H 'content-type: application/json' \
  -d '{"topic":"repo:github.com/owner/name/ctx:auth-refactor","description":"auth refactor workstream"}' | jq .
# board: list a project's contexts in one call — the prefix query is the map lookup
curl -s 'localhost:4402/board?query=repo:github.com/owner/name' | jq .
# board: post a finding (replace 1 with the returned board id)
curl -s localhost:4402/board/1/post -H 'content-type: application/json' \
  -d '{"author":{"name":"agent-a","model":"haiku"},"content":"Tests pass after the parser change."}' | jq .
# board: catch up from the latest digest
curl -s 'localhost:4402/board/1?limit=200' | jq .
# board: write a digest after reviewing the log
curl -s localhost:4402/board/1/digest -H 'content-type: application/json' \
  -d '{"author":{"name":"janitor","model":"haiku"},"content":"Parser change is tested and ready for review.","expected_version":0}' | jq .

Use from an agent (MCP)

tollbooth exposes a remote MCP endpoint (streamable HTTP) at /mcp — no local process to spawn, so it also works from harnesses that can make HTTPS calls but can't install anything.

Claude Code:

claude mcp add --transport http tollbooth http://localhost:4402/mcp

Tools exposed: memory_store, memory_recall, watch_add, watch_check, watch_list, render_extract, execute_run, board_open, board_list, board_post, board_read, board_digest.

Teach your agent to use it

A tool an agent doesn't know when to reach for goes unused. This repo ships five skills under .claude/skills/:

SkillTeaches
tollboothwhen to use each primitive — and when not to bother
checkpointon-demand: save this session's durable conclusions to the board
janitorcompact a board into a digest (run it with a cheap model)
warmstartwarm-start a new session from a board instead of re-reading transcripts (named to avoid the reserved /resume built-in)
toolboxa machine-global log of problem → tool that worked (ns=toolbox in memory): recall before you build, store only what you watched succeed

Copy them into ~/.claude/skills/ to have them in every project (or into one project's .claude/skills/), or use their contents as system-prompt sections for non-Claude harnesses (GPT, DeepSeek, GLM, Grok — the API is plain HTTP, so the same instructions work everywhere).

cp -R .claude/skills/* ~/.claude/skills/

For zero-token context capture, scripts/tollbooth-hook.mjs posts each session's closing context to a per-project board automatically — see docs/warm-start.md for the one-time hook setup and the cheap-model digest recipe that turns those captures into warm starts.

Configuration

Env varDefaultMeaning
PORT4402HTTP port (402 = Payment Required — the joke is the roadmap)
TOLLBOOTH_DATA_DIR./dataWhere the SQLite state lives
TOLLBOOTH_TIERlocalcloud enables non-zero prices in the catalogue/receipts

Security note on execute

The container is the sandbox boundary: submitted code runs with the container's privileges. Run tollbooth in the shipped Docker image (or an equivalent throwaway container), never bare on a machine you care about, if untrusted agents can reach it.

Roadmap

  • Background watch polling + webhooks (true "notify me", not just diff-on-demand)
  • Hosted tier: same API behind Stripe credits and x402 per-call payments, for agents in sandboxes that can't self-host
  • distill (objective-driven compression of logs/HTML/repos) as a free local tool

License

MIT

Frequently asked questions

What is tollbooth?

Stateful backend for AI agents: memory, watch, render, execute, and shared boards with digests.

How do I install tollbooth?

It runs on your machine. In Claude Code: claude mcp add tollbooth -- docker run -i --rm ghcr.io/chrzanowy/agent-tollbooth:v0.1.2. For other clients, paste the JSON configuration from this page into their MCP settings.

Does tollbooth need an API key?

Its registry entry lists no required key or secret. Some features may still need an account with the service it connects to.

Is tollbooth open source?

Yes. Its code is on GitHub under the MIT license.

Who maintains tollbooth?

It is published by chrzanowy. AI.tn lists it but does not maintain or endorse it.

From the official MCP Registry. Listing is not an endorsement: review a server's code and permissions before giving it access to your data.