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agent runtime for AntSeed — monetize knowledge with read-only, knowledge-augmented AI agents","maintainers":[{"name":"shahafan","email":"santwarg@gmail.com"}],"readme":"# @antseed/bound-agent\n\nBound agent runtime for AntSeed. Lets seed runners monetize their knowledge by wrapping any provider with a persona, guardrails, on-demand knowledge modules, and custom tools.\n\nA bound agent is a **read-only, knowledge-augmented AI service**. It doesn't act on the user's behalf — it answers questions using curated expertise that the creator packages and maintains.\n\n## How it works\n\n1. Creator defines an agent: persona + guardrails + knowledge modules (markdown files)\n2. On each buyer request, persona, guardrails, and an `antseed_load_knowledge` tool are injected\n3. The LLM decides when to load knowledge — it calls the tool with a module name and receives the content\n4. The agent loop executes internal tool calls and re-prompts until the LLM produces a final text response\n5. The buyer gets a clean streamed response — no internal tools or loop artifacts\n\nWhen no knowledge modules are defined, no tools are injected and it's a single-call injection (persona + guardrails only).\n\n## Agent directory structure\n\n```\nmy-agent/\n  agent.json           # manifest\n  persona.md           # who the agent is\n  knowledge/           # knowledge modules\n    linkedin-posting.md\n    content-strategy.md\n  tools/               # custom tool scripts (optional)\n    fetch-trends.js\n```\n\n### agent.json\n\n```json\n{\n  \"name\": \"social-media-advisor\",\n  \"persona\": \"./persona.md\",\n  \"guardrails\": [\n    \"Never write posts without explicit request\",\n    \"Always disclose AI-generated content when asked\"\n  ],\n  \"knowledge\": [\n    {\n      \"name\": \"linkedin-posting\",\n      \"description\": \"Creating and optimizing LinkedIn posts\",\n      \"file\": \"./knowledge/linkedin-posting.md\"\n    },\n    {\n      \"name\": \"content-strategy\",\n      \"description\": \"Content calendars and strategy frameworks\",\n      \"file\": \"./knowledge/content-strategy.md\"\n    }\n  ],\n  \"tools\": [\n    {\n      \"name\": \"fetch_trends\",\n      \"description\": \"Fetch trending topics for a platform\",\n      \"parameters\": {\n        \"type\": \"object\",\n        \"properties\": { \"platform\": { \"type\": \"string\" } },\n        \"required\": [\"platform\"]\n      },\n      \"execute\": \"./tools/fetch-trends.js\"\n    }\n  ]\n}\n```\n\n| Field | Required | Description |\n|---|---|---|\n| `name` | Yes | Agent name |\n| `persona` | No | Path to markdown file with the agent's system prompt / personality |\n| `guardrails` | No | Array of hard rules the agent must follow |\n| `knowledge` | No | Array of knowledge modules for on-demand loading |\n| `knowledge[].name` | Yes | Unique identifier for the module |\n| `knowledge[].description` | Yes | Short description — shown to the LLM to decide relevance |\n| `knowledge[].file` | Yes | Path to the markdown file with the full content |\n| `tools` | No | Array of custom tools the LLM can call |\n| `tools[].name` | Yes | Tool name (auto-prefixed with `antseed_`) |\n| `tools[].description` | Yes | Description shown to the LLM |\n| `tools[].parameters` | No | JSON Schema for tool parameters (defaults to empty object) |\n| `tools[].execute` | Yes | Path to a JS file that exports a default function |\n| `confidentialityPrompt` | No | Custom confidentiality instruction (has a sensible default) |\n\n## Usage\n\n### Programmatic\n\n```typescript\nimport { loadBoundAgent, BoundAgentProvider } from '@antseed/bound-agent';\n\n// Load agent (knowledge + tools from agent.json)\nconst agent = await loadBoundAgent('./my-agent');\nconst boundProvider = new BoundAgentProvider(innerProvider, agent);\n\n// Per-service agents\nconst socialAgent = await loadBoundAgent('./social-agent');\nconst codingAgent = await loadBoundAgent('./coding-agent');\nconst boundProvider = new BoundAgentProvider(innerProvider, {\n  'social-model-v1': socialAgent,\n  'coding-model-v1': codingAgent,\n  '*': socialAgent,\n});\n\nnode.registerProvider(boundProvider);\n```\n\n### CLI\n\nAdd `agentDir` to your antseed config. Use a string for a single agent (all services), or a map for per-service agents:\n\n```json\n{\n  \"seller\": {\n    \"agentDir\": \"./my-agent\"\n  }\n}\n```\n\nPer-service:\n\n```json\n{\n  \"seller\": {\n    \"agentDir\": {\n      \"social-model-v1\": \"./agents/social\",\n      \"coding-model-v1\": \"./agents/coding\",\n      \"*\": \"./agents/default\"\n    }\n  }\n}\n```\n\nThe `\"*\"` key is a fallback for services with no explicit agent. Services with no matching agent pass through unchanged.\n\nThen run `antseed seed` as usual.\n\n## Knowledge loading\n\nWhen knowledge modules are defined, the `antseed_load_knowledge` tool is injected into the request alongside any buyer tools. The tool description includes a catalog of available module names and descriptions.\n\nThe LLM decides which modules to load based on the conversation. It can:\n\n- Load one module, get the content, then respond\n- Load multiple modules across successive tool calls\n- Respond directly without loading any modules if the question doesn't require specialized knowledge\n\nThis keeps the context focused — only the knowledge the LLM judges relevant gets loaded. A buyer asking about LinkedIn won't get X/Twitter knowledge bloating the context.\n\n## Custom tools\n\nBeyond knowledge loading, creators can add custom tools that the LLM can call during the agent loop. Define tools in `agent.json` with an `execute` field pointing to a JS file:\n\n```json\n{\n  \"tools\": [{\n    \"name\": \"fetch_trends\",\n    \"description\": \"Fetch trending topics for a platform\",\n    \"parameters\": {\n      \"type\": \"object\",\n      \"properties\": { \"platform\": { \"type\": \"string\" } },\n      \"required\": [\"platform\"]\n    },\n    \"execute\": \"./tools/fetch-trends.js\"\n  }]\n}\n```\n\nThe JS file exports a default function that receives the tool arguments and returns a string:\n\n```js\n// tools/fetch-trends.js\nexport default async function(args) {\n  const res = await fetch(`https://api.example.com/trends/${args.platform}`);\n  return await res.text();\n}\n```\n\nTool names are automatically prefixed with `antseed_` when injected (e.g., `fetch_trends` becomes `antseed_fetch_trends`). The LLM sees all internal tools alongside buyer tools, with system prompt instructions to use `antseed_*` tools for context gathering and buyer tools only as requested.\n\nIf a tool's execute function throws, the error message is returned to the LLM as an error result so it can recover gracefully.\n\n### Programmatic tools\n\nTools can also be added programmatically via the options parameter (useful when tools need access to runtime state):\n\n```typescript\nimport { BoundAgentProvider, loadBoundAgent, type BoundAgentTool } from '@antseed/bound-agent';\n\nconst customTool: BoundAgentTool = {\n  name: 'lookup_user',\n  description: 'Look up user details',\n  parameters: { type: 'object', properties: { id: { type: 'string' } }, required: ['id'] },\n  execute: async (args) => db.users.findById(args.id),\n};\n\nconst agent = await loadBoundAgent('./my-agent');\nnew BoundAgentProvider(innerProvider, agent, { tools: [customTool] });\n```\n\nManifest tools and programmatic tools are merged together.\n\n## What gets injected\n\nThe system prompt is assembled in this order:\n\n1. **Persona** — the agent's identity and expertise\n2. **Tool-set instructions** — tells the LLM how to use `antseed_` tools vs buyer tools (only when knowledge modules exist)\n3. **Guardrails** — hard rules\n4. **Confidentiality prompt** — prevents the LLM from revealing injected content\n\nAdditionally, `antseed_*` tools are added to the request's tool list alongside any buyer-provided tools. This includes the built-in `antseed_load_knowledge` (when knowledge modules are defined) and any custom tools passed via options.\n\nThe buyer's own system prompt (if any) is preserved after the agent's content.\n","readmeFilename":"README.md"}