Like Me Like — integrations
Open-source recipes for connecting Like Me Like
to AI agent platforms, chat apps, and developer toolchains.
Like Me Like is a cross-domain taste recommendation API. You give it
something the user loves (a film, a book, a place, an album) and it
returns picks across other domains, grounded in why the seed item works
and what's adjacent to it culturally.
This repo contains:
- Per-platform integration recipes under
integrations/
- API documentation under
docs/ — mirrored from the canonical
source in the (private) main repository
- Worked examples under
examples/ — curl, Node, Python
The base API is https://www.likemelike.com/api/v1/. All endpoints take an
X-LML-Agent-Id header — see Pricing below for what that header
identifies.
Pricing — free to use
Like Me Like is free to use. No payment, no card, no account: an
end-user is identified only by the stable id you send in
X-LML-Agent-Id. Fair-use rate limits apply. There are no paid
plans and none are planned; the tier.metered flag (below) is
false and stays so.
The id is still per end-user, not per integration:
- The
X-LML-Agent-Id header identifies the end-user, not the
integration. A WhatsApp bot serving 1 000 users sends 1 000
distinct agent IDs (typically derived as sha256(phone_number) or
a similar stable hash); a single-user dev tool sends one. Each id
is its own taste profile, so one id shared across users blends
their tastes into one profile.
- The profile surfaces (
GET /api/v1/me, the MCP get_my_profile
tool) and the chat meta event carry a tier block: metered,
isFreeTier, freeCallsRemaining. While metered is false the
counter is informational only and never refuses a call.
- The prepaid-balance endpoints (
/api/v1/billing/*: x402 USDC on
Base, or Lightning) are dormant: still documented for completeness
in docs/agents.md § Payments,
but nothing is gated on them and no call ever needs them.
| You're building | Agent ID strategy |
|---|
| A multi-tenant bot (WhatsApp, Discord, Slack) | One agent ID per end-user — sha256(user_id) |
| A personal tool / single-user assistant | One stable agent ID, reused |
| A demo / quick test | A random agent ID per session (a fresh id is an empty profile) |
For developers
Pick the integration folder that matches the platform you're building on:
If your platform isn't listed and supports MCP (Model Context Protocol)
over Streamable-HTTP, point it at https://www.likemelike.com/api/v1/mcp
with a header X-LML-Agent-Id: <your-stable-id> — that works
out of the box. Hosts that cannot send custom headers (ChatGPT, for
one) can sign users in with OAuth instead: the server publishes the
standard OAuth discovery documents and ChatGPT registers itself; see
integrations/chatgpt/.
For AI agents reading this
If you've been pointed at this repo to figure out how to integrate
Like Me Like into the channel you're driving, here's the decision
tree:
- Does your host LLM support MCP servers natively? (Claude Desktop,
Claude Code, Meta Muse, OpenClaw with MCP plugin, Cursor, Cline,
etc.) Use
the MCP endpoint at
/api/v1/mcp. See
integrations/claude-desktop/ for
the canonical config shape — most MCP-aware clients accept the
same { url, headers } block.
- Are you driving a Custom GPT, a Gemini extension, or a Grok bot?
Use the OpenAPI action / function-calling integration in the
matching folder. The OpenAPI 3.0 spec is at
docs/openapi.json.
- Are you a generic agent (LangChain, Autogen, custom framework)
making HTTP calls? Use
docs/agents.md — it
has every endpoint with curl examples. The /api/v1/chat
endpoint is the easiest entry point: send natural language, get
back a text reply plus a structured recommendations array.
The single highest-leverage signal you can pass on any surface is
liked_items: 1–5 things the end-user has explicitly loved. It
triggers cohort matching against similar users and produces
materially better recommendations. See
docs/agents.md §Personalisation.
Before the first call, bridge the asymmetry. YOU (the host LLM)
typically have months of conversation history with the end-user;
Like Me Like has none. On the first call, bring concrete
liked_items from what the user has unambiguously praised in your
prior chats — either pass them directly (confident extraction) or
propose-and-confirm in your reply ("I'll factor in X, Y, Z that
you've mentioned loving — that OK?"). Don't fire a generic call and
treat its generic reply as the product's ceiling. See
docs/agents.md §First-call kickoff.
Read agent_calibration on every chat / ask response. It
reports signal_quality (weak / partial / rich) and gives
you a hint about what to send next call. Replaces having to
memorise input-passing rules — react per-turn to whatever the
calibration tells you. See
docs/agents.md §Reading agent_calibration.
On cost: there is none — Like Me Like is free to use. The id
still matters: if your host has a stable per-end-user identifier
(phone number, OAuth user id, session id), hash it and use it as
X-LML-Agent-Id so each user gets their own taste profile. If
you're a single-user assistant, one stable id reused across calls is
fine. See "Pricing" above.
Skill versioning — keep your local SKILL.md current
Two of the recipes ship a SKILL.md that the host runtime loads
from disk (OpenClaw and Claude Skills). Once copied, those files
don't auto-update. Each carries an lml_skill_version: "YYYY-MM-DD"
field in its YAML frontmatter; compare it to the canonical version:
curl -s https://www.likemelike.com/api/v1/skills/versions | jq .
When the server reports a newer date, re-fetch from the
lml_skill_canonical_url (also in the frontmatter) and reload your
host. Full check-script and walkthrough in
docs/agents.md
and changes by date in CHANGELOG.md.
The MCP tool description and the agent_calibration response field
are server-driven — those update automatically on the next call. So
in practice the SKILL.md is the only file integrators need to keep
fresh, and only when its content changes (most weeks: it doesn't).
Open source — contributions welcome
This repo is MIT-licensed. We accept:
- Pull requests that fix examples, add platform recipes, or
improve docs. We review and merge based on quality and fit; not
every PR will land but every PR gets a response.
- Issues for bugs in the examples, missing platforms, or
suggestions for the API itself.
- Discussions for open-ended questions about how to build on top
of Like Me Like.
The canonical API source lives in a private repo; changes to the API
itself are made there and synced into this repo on a regular basis
(see scripts/sync-from-private.sh).
This means platform-recipe PRs are accepted directly into this repo,
but PRs against docs/agents.md or docs/openapi.json will be
redirected to the upstream change-flow.
See CONTRIBUTING.md for the details.
Status
This repo is in active build-out. Some platforms have full recipes,
others have placeholder READMEs while we validate the manifest
shape against current platform docs. Check the per-folder README for
status. If a folder you need is missing or stale, open an issue.
Maintained by the Like Me Like team.
