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Agent Integration

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fastclaw-ai
agent-integration

Integrate an application's backend with FastClaw cloud agents over the FastClaw API. Use when a project needs to chat with one or more existing FastClaw agents (agent-scope API key), or to create, update, delete and chat with agents on demand (user-scope API key) — e.g. "connect this app to FastClaw", "call my FastClaw agent from the backend", "let each user create their own agent".

Overview

Publisherfastclaw-ai
Repositoryfastclaw
Skill nameagent-integration
Stars
1.4K
Forks
227
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by fastclaw-ai on GitHub. Read the source before you install it.

Installation

Install the Agent Integration AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/fastclaw-ai/fastclaw/tree/dev/skills/agent-integration
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/fastclaw-ai/fastclaw.git /tmp/fastclaw
mkdir -p .claude/skills
cp -r /tmp/fastclaw/skills/agent-integration .claude/skills/agent-integration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Integration in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agent Integration on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agent Integration is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

FastClaw Agent Integration

FastClaw is an agent runtime. It stores agent configuration, runs the agent loop with its tools and sandbox, keeps sessions and memory, and meters usage. Your application owns everything about its users: sign-in, which user may use which agent, chat records, billing. Your backend calls FastClaw; FastClaw never calls your app back and never knows your users.

Follow this guide over guesses from other FastClaw code (the dashboard's /api/* endpoints are internal and change without notice). Integrations use /v1/* only.

1. Configure the connection first

The integration needs two values. Never hardcode them; read them on the server side from configuration (environment variables or the app's secret store):

SettingWhat it isWhere the user finds it
FASTCLAW_BASE_URLThe FastClaw address, e.g. https://claw.example.com (no trailing slash).The address the user opens the FastClaw console at. The console's Integration page shows it.
FASTCLAW_API_KEYA FastClaw API key (fc_...), sent as Authorization: Bearer <key>.FastClaw console → API Keys → Add API key. The token is shown once.

If either is missing, stop and ask the user for it — tell them where to find it (table above) and which key type the integration needs (section 2). Add both to the project's environment template (.env.example or similar) without real values.

Then verify the connection:

bash
curl -s "$FASTCLAW_BASE_URL/v1/agents" -H "Authorization: Bearer $FASTCLAW_API_KEY"

A 200 lists the agents the key may use; 401 unauthorized means a wrong or revoked key.

Never expose the key to browsers or mobile apps. Clients talk to your backend; your backend talks to FastClaw.

2. Pick the scenario

A. Fixed agentsB. Manage agents
Use whenThe app talks to agents that already exist in FastClaw — one or several (e.g. an image-editing agent, a support bot).The app creates agents itself, e.g. one per user or per workspace, and manages them over time.
Key typeAgent key, granted exactly those agents.User key.
CanList and read its granted agents, update their settings, chat with them.Create, list, read, update and delete agents; chat with any agent of the account.
CannotCreate or delete agents; see any other agent.—

A user key reaches every agent of its FastClaw account. For scenario B, use a FastClaw account dedicated to this integration (and one per environment, e.g. myapp-prod / myapp-dev) so the key can't touch agents used elsewhere.

Whichever scenario: your app must check "may this user use this agent?" before every call. FastClaw only isolates accounts and API keys from each other.

3. Scenario A — chat with fixed agents

  1. The user creates the agent(s) in the FastClaw console and issues an Agent API key granted to them.

  2. Find the agents the key may use:

    bash
    curl -s "$FASTCLAW_BASE_URL/v1/agents" -H "Authorization: Bearer $FASTCLAW_API_KEY"
    json
    { "agents": [ { "id": "agt_2834a7a1e660d509b83d", "display_name": "Image Editor", "description": "…", "model": "…", "metadata": {}, "created_at": "…", "updated_at": "…" } ], "has_more": false }
  3. Keep the agent ids in configuration (e.g. FASTCLAW_AGENT_ID, or a map from your feature to an agent id when there are several). With a single granted agent, chat requests may omit agent_id; name it anyway once the app may use more than one.

  4. Chat (section 5).

4. Scenario B — manage agents

Agents created with the API belong to the key's FastClaw account. FastClaw does not record which of your users an agent is for — keep that mapping in your database (and mirror it into metadata if you want to filter by it).

Create — POST /v1/agents

bash
curl -s "$FASTCLAW_BASE_URL/v1/agents" \
  -H "Authorization: Bearer $FASTCLAW_API_KEY" -H "Content-Type: application/json" \
  -d '{
    "name": "Weekly Report Assistant",
    "description": "Summarizes the team every Friday",
    "instructions": "You write concise weekly reports.",
    "model": "anthropic/claude-sonnet-5-5",
    "metadata": { "app_user": "user-123" }
  }'
json
{ "agent": { "id": "agt_2834a7a1e660d509b83d", "display_name": "Weekly Report Assistant", "name": "agt_2834a7a1e660d509b83d", "description": "Summarizes the team every Friday", "model": "anthropic/claude-sonnet-5-5", "metadata": { "app_user": "user-123" }, "created_at": "2026-10-04T08:00:00Z", "updated_at": "2026-10-04T08:00:00Z" } }
  • Only name is required. instructions becomes the agent's SOUL.md (persona). model is <provider>/<model>; omit it for the account default.
  • metadata: your own string key/values — at most 16 keys, keys ≤ 64 characters, values ≤ 512. Stored and filterable, never used for authorization.
  • Show display_name to people. name equals id (kept for old clients).

List — GET /v1/agents

Newest first. Query: limit (1–100; omit for all), cursor (the previous page's next_cursor), metadata[key]=value filters (ANDed; also metadata.key=value).

bash
curl -s "$FASTCLAW_BASE_URL/v1/agents?limit=20&metadata%5Bapp_user%5D=user-123" \
  -H "Authorization: Bearer $FASTCLAW_API_KEY"

Read — GET /v1/agents/{id}

Update — PATCH /v1/agents/{id}

Any of name, description, instructions, model, metadata; omitted fields are unchanged. model: "" clears the override. metadata merges; a key set to null or "" is removed.

Identity files — PUT /v1/agents/{id}/system-files/{name}

Body {"content": "..."}. name is one of SOUL.md, IDENTITY.md, AGENTS.md, BOOTSTRAP.md, TOOLS.md, HEARTBEAT.md, KNOWLEDGE.md (max 256 KB). Shared by every end-user of the agent.

Delete — DELETE /v1/agents/{id}

A second delete returns 404 agent_not_found; treat it as "already deleted". Also remove your own mapping.

Agent keys may also read and update (PATCH, identity files) the agents granted to them; only user keys create and delete.

5. Chat (both scenarios) — POST /v1/chat/completions

OpenAI-compatible, with FastClaw extensions.

bash
curl -sN "$FASTCLAW_BASE_URL/v1/chat/completions" \
  -H "Authorization: Bearer $FASTCLAW_API_KEY" -H "Content-Type: application/json" \
  -H "X-Fastclaw-Session-Key: myapp:conv_42" \
  -d '{
    "agent_id": "agt_2834a7a1e660d509b83d",
    "user": "user-123",
    "stream": true,
    "messages": [{ "role": "user", "content": "Summarize today'\''s discussion" }]
  }'
  • agent_id (or header X-Fastclaw-Agent-ID; the body wins) picks the agent and is strict: an agent the key may not use is 404 agent_not_found — never a different agent. Omitted, FastClaw uses the key's only agent (or the account's only agent).
  • X-Fastclaw-Session-Key selects the conversation history. Always send it, deterministic per conversation and prefixed with your app: myapp:<conversation>[:<topic>]. Omitted, every call starts a new session.
  • user (or header X-Fastclaw-End-User; the body wins) is your user's stable id (not an email or display name). It keeps that user's session history and personal memory (USER.md / MEMORY.md) private to them. Omit it when all callers should share the agent's memory (e.g. a user's own agent, or a group chat).
  • params: per-turn structured context shown to the agent, not persisted. In group chats send the current speaker as "params": {"speaker": {"id": "user-456", "name": "Bob"}}.
  • Files: images (image URLs or data URLs, shown to vision models) and attachments ([{"url", "name"}], any file type) land in the agent's workspace for this turn. A message's content may also be an array of parts in the OpenAI or Anthropic shape — {"type": "text", "text"}, {"type": "image_url", "image_url": {"url"}}, {"type": "image", "source": {"type": "base64", "media_type", "data"}} — and its images are treated like images.
  • project_id (optional) files the conversation under one of the agent's projects so it shares that project's workspace.
  • Only the last user message is sent to the agent — FastClaw keeps the history itself, keyed by the session key. Don't resend old turns.

Streaming returns OpenAI SSE chunks (data: {"choices":[{"delta":{"content":"..."}}]} … data: [DONE]); non-streaming returns an OpenAI chat.completion (choices[0].message.content). The request's model field doesn't change the agent's model — the agent's configuration decides it.

usage (non-streaming: top level; streaming: on the final chunk) sums every model call the agent made for this turn, tool loops included: prompt_tokens / completion_tokens / total_tokens as in OpenAI (prompt_tokens includes cached input, also in prompt_tokens_details.cached_tokens), plus input_tokens (uncached), cache_read_tokens, cache_creation_tokens and model_calls. Use it to attribute cost to your users per turn.

API conversations are never trusted with the FastClaw host: the agent's tools (shell, scripts) run in FastClaw's sandbox. If the FastClaw operator hasn't configured one, the agent can still answer but can't run commands.

Files the agent produces

Files the agent creates or changes during a turn — e.g. an edited image — come back with the reply in files (non-streaming: top level; streaming: on the final chunk, the one with finish_reason). Files the reply links as /workspace/<path> come first; your own uploads are not echoed back.

json
{
  "choices": [{ "message": { "role": "assistant", "content": "Done: ![edited](/workspace/edited.png)" } }],
  "files": [
    {
      "name": "edited.png",
      "path": "edited.png",
      "size": 77490,
      "content_type": "image/png",
      "url": "https://claw.example.com/v1/agents/agt_…/sessions/myapp%3Aconv_42/files/edited.png"
    }
  ]
}
  • Download url with the same Authorization header — and the same X-Fastclaw-End-User when the conversation had one: end-users can only read their own conversations' files. It is GET /v1/agents/{agent_id}/sessions/{session_key}/files/{path} (?project_id= for project conversations).
  • Send "return_files": "inline" to also get each file's bytes as a data_url (files up to 10 MB, 25 MB per reply) — simplest for one image.
  • Don't rely on the agent to put files anywhere else: FastClaw instructs it not to upload your users' files to third-party services.

6. Usage and quotas

  • GET /v1/usage — per-day token usage. Query: days (1–90, default 30), agent_id, end_user (your user id), scope=app (the whole account, all end-users). Each daily row has agentId and endUser; roll up by them to bill your users.
  • PUT|GET|DELETE /v1/quota — monthly token / request limits for an end-user (user_id from POST /v1/users with {"external_id": "<your user id>"}).

7. Errors

Every /v1 error is {"error": {"type", "code", "message"}}. Branch on code:

StatuscodeMeaning
400invalid_requestBad body, missing messages, metadata over limits, bad cursor.
401unauthorizedMissing or invalid API key.
403forbiddenThe key may not do this (e.g. an agent key creating agents).
403agent_quota_exceededThe account reached its agent quota.
404agent_not_foundNo such agent for this key.
402payment_requiredThe FastClaw account is on billing hold (its balance ran out). Tell your user the service is paused; retrying won't help until it's topped up.
404project_not_foundUnknown project_id.
429rate_limitedSlow down and retry with backoff.
503not_configuredThat feature isn't enabled on this FastClaw.

8. Checklist

  • FASTCLAW_BASE_URL and FASTCLAW_API_KEY read from server-side configuration, documented in the env template, verified with GET /v1/agents.
  • Right key type for the scenario (agent key for fixed agents, user key on a dedicated account for managed agents).
  • The app checks the user's access to an agent before every call.
  • Deterministic X-Fastclaw-Session-Key per conversation; user set when a user's history must stay private.
  • Streaming parsed as SSE; errors handled by error.code.
  • Produced files taken from files (download url or return_files: "inline").
  • No FastClaw key or URL in client-side code.

Frequently asked questions

What does the Agent Integration AI skill do?

Integrate an application's backend with FastClaw cloud agents over the FastClaw API. Use when a project needs to chat with one or more existing FastClaw agents (agent-scope API key), or to create, update, delete and chat with agents on demand (user-scope API key) — e.g. "connect this app to FastClaw", "call my FastClaw agent from the backend", "let each user create their own agent".

Why use Agent Integration on TypingMind?

Because you install it once and use it with any model. Agent Integration is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agent Integration in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/fastclaw-ai/fastclaw/tree/dev/skills/agent-integration. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Agent Integration?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agent Integration?

As many as you like. As long as a model supports skills, you can use Agent Integration with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agent Integration AI skill free?

It is published on GitHub by fastclaw-ai. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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