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No hidden state machines, no forced abstractions—just typed agents, composable graphs, and complete control in a complete toolkit.\n\n**[Documentation](https://docs.agention.ai/)** • **[Examples](https://docs.agention.ai/guide/examples)** • **[GitHub](https://github.com/laurentzuijdwijk/agention-lib)** • **[npm](https://www.npmjs.com/package/@agentionai/agents)**\n\n## Quick Start\n\n### 1. Install\n\nInstall only what you need with selective imports:\n\n```bash\n# Install core library + Claude SDK\nnpm install @agentionai/agents @anthropic-ai/sdk\n```\n\n### 2. Get API Key\n\nGet an API key from your chosen provider:\n\n- **Claude**: [console.anthropic.com](https://console.anthropic.com/)\n- **OpenAI**: [platform.openai.com](https://platform.openai.com/api-keys)\n- **Gemini**: [aistudio.google.com](https://aistudio.google.com/app/apikey)\n- **Mistral**: [console.mistral.ai](https://console.mistral.ai/)\n- **OpenRouter**: [openrouter.ai/keys](https://openrouter.ai/keys) — one key, dozens of upstream providers\n- **Ollama** / **llama.cpp**: no API key needed — run models locally (see [Agents guide](https://docs.agention.ai/guide/agents))\n\nSet it as an environment variable:\n\n```bash\nexport ANTHROPIC_API_KEY=your-key-here\n```\n\n### 3. Create Your First Agent\n\n```typescript\n// Import only Claude - no other agent SDKs required!\nimport { ClaudeAgent } from '@agentionai/agents/claude';\n\nconst agent = new ClaudeAgent({\n  apiKey: process.env.ANTHROPIC_API_KEY,\n  id: 'assistant',\n  name: 'Assistant',\n  description: 'You are a helpful assistant.',\n  model: 'claude-sonnet-4-5',\n});\n\nconst response = await agent.execute('What can you help me with?');\nconsole.log(response);\n```\n\n### Selective Imports\n\nImport only the agents you need:\n\n```typescript\nimport { ClaudeAgent } from '@agentionai/agents/claude';     // Requires @anthropic-ai/sdk\nimport { OpenAiAgent } from '@agentionai/agents/openai';     // Requires openai\nimport { GeminiAgent } from '@agentionai/agents/gemini';     // Requires @google/generative-ai\nimport { MistralAgent } from '@agentionai/agents/mistral';   // Requires @mistralai/mistralai\nimport { OpenRouterAgent } from '@agentionai/agents/openrouter'; // Requires @openrouter/sdk\nimport { OllamaAgent } from '@agentionai/agents/ollama';     // Requires ollama (local, no API key)\nimport { LlamaCppAgent } from '@agentionai/agents/llamacpp'; // Requires openai (local, no API key)\n```\n\nOr import everything (requires all SDKs):\n\n```typescript\nimport { ClaudeAgent, OpenAiAgent } from '@agentionai/agents';\n```\n\n\n## Features\n\n- **Multi-Provider, No Lock-in** - Claude, OpenAI, Gemini, Mistral, OpenRouter (one key, dozens of upstream providers), plus local models via Ollama and llama.cpp—same interface. Switch models with one line. `listModels()` asks any provider what it currently offers, in one shared shape.\n- **Composable Context Management** - Tool result masking (lossless, free) + rolling summarization (auto-firing) + sub-agent delegation (token isolation by architecture).\n- **Streaming** - `executeStream()` on Claude, OpenAI, OpenRouter, and all OpenAI-compatible agents. Yields `{ type: \"text\" | \"reasoning\" }` chunks; tool calls handled transparently.\n- **Built-In Tools** - Use provider-defined server-side tools (e.g. Anthropic's web search, bash, text editor) alongside your own.\n- **Composable, Not Magical** - Agents are objects. Pipelines are arrays. No hidden state, no surprises.\n- **Multimodal / Vision** - Send images alongside text with a unified `MessageContent[]` API across all providers.\n- **Full Observability** - Per-call token counts, execution timing, pipeline structure visualization.\n- **TypeScript-Native** - Strict typing, interfaces, and generics from the ground up.\n- **RAG Ready** - LanceDB vector store, token-aware chunking, ingestion pipeline out of the box.\n\n\n### Agent with Tools\n\n```typescript\nimport { GeminiAgent, Tool } from '@agentionai/agents/gemini';\n\nconst weatherTool = new Tool({\n  name: 'get_weather',\n  description: 'Get the current weather for a location',\n  inputSchema: {\n    type: 'object',\n    properties: {\n      location: { type: 'string', description: 'City name' },\n    },\n    required: ['location'],\n  },\n  execute: async ({ location }) => {\n    // In production, call a weather API\n    return JSON.stringify({\n      location,\n      temperature: 22,\n      conditions: 'Sunny',\n    });\n  },\n});\n\nconst agent = new GeminiAgent({\n  apiKey: process.env.GEMINI_API_KEY,\n  id: 'weather-agent',\n  name: 'Weather Agent',\n  description: 'You are a weather assistant.',\n  model: 'gemini-flash-lite-latest',\n  tools: [weatherTool],\n});\n\nconst response = await agent.execute(\"What's the weather in Paris?\");\n```\n\n### Context Management\n\nEvery agent conversation grows — tool results pile up, turns accumulate, tokens compound. Agention's history plugins keep the context window lean automatically, without manual bookkeeping.\n\n```typescript\nimport { toolResultMaskingPlugin, compressionPlugin } from '@agentionai/agents/history/plugins';\nimport { History } from '@agentionai/agents/history';\n\n// Mask old tool results at read time — sync, free, lossless\nconst maskingPlugin = toolResultMaskingPlugin({ keepRecentResults: 2 });\n\n// Compress old turns into a rolling summary — auto-fires past a token budget\nconst history = new History([], { maxTokens: 50000 })\n  .use(maskingPlugin)\n  .use(compressionPlugin(summaryAgent, { autoReduceWhen: { maxTokens: 8000 } }));\n\nconst agent = new ClaudeAgent({\n  apiKey: process.env.ANTHROPIC_API_KEY,\n  id: 'researcher',\n  name: 'Researcher',\n  description: 'Research topics thoroughly.',\n  model: 'claude-sonnet-4-6',\n  tools: [searchTool, maskingPlugin.retrieveTool],\n}, history);\n```\n\n| Strategy | Cost | Data loss | Trigger |\n|---|---|---|---|\n| `toolResultMaskingPlugin` | Zero — sync, no LLM calls | None — full content always retrievable | Every `getEntries()` call |\n| `compressionPlugin` | LLM tokens (use a cheap model) | Yes — detail traded for brevity | `autoReduceWhen` threshold or manual `history.reduce()` |\n| `Tool.fromAgent()` | None — structural choice | None in main context (sub-agent history is independent) | Automatic — just wrap an agent as a tool |\n\n[Context Management guide →](https://docs.agention.ai/guide/context-management) · [History API →](https://docs.agention.ai/guide/history)\n\n### Local Models (Ollama / llama.cpp / OpenAI-compatible servers)\n\nRun models on your own machine — no API key required. Same agent interface as every other provider:\n\n```typescript\nimport { OllamaAgent } from '@agentionai/agents/ollama';\nimport { LlamaCppAgent } from '@agentionai/agents/llamacpp';\n\n// Ollama (https://ollama.com) — pull a model first: `ollama pull qwen2.5`\nconst ollama = new OllamaAgent({\n  id: 'local-ollama',\n  name: 'Local Assistant',\n  description: 'You are a helpful assistant.',\n  model: 'qwen2.5',\n  apiKey: '',\n});\n\n// llama.cpp server (`llama-server -m model.gguf`) — OpenAI-compatible API\nconst llamaCpp = new LlamaCppAgent({\n  id: 'local-llamacpp',\n  name: 'Local Assistant',\n  description: 'You are a helpful assistant.',\n  apiKey: '',\n  baseURL: 'http://localhost:8080/v1',\n});\n\nconst response = await ollama.execute('What can you run locally?');\n\n// Discover which models are available on the server\nconst models = await ollama.listModels();\n```\n\n**Custom OpenAI-compatible server** (vLLM, LM Studio, Together AI, Groq, …): extend `OpenAICompatibleAgent` directly:\n\n```typescript\nimport { OpenAICompatibleAgent, OpenAICompatibleConfig } from '@agentionai/agents/llamacpp';\n\nclass VLLMAgent extends OpenAICompatibleAgent {\n  constructor(config: Omit<OpenAICompatibleConfig, 'vendor'>) {\n    super({ ...config, vendor: 'llamacpp', baseURL: config.baseURL ?? 'http://localhost:8000/v1' });\n  }\n  protected getVendorName() { return 'vLLM'; }\n}\n```\n\n[Full guide →](https://docs.agention.ai/guide/agents#custom-openai-compatible-agents)\n\n### Built-In Tools\n\nUse a provider's own server-side tools (executed by the provider, not locally) alongside your custom tools:\n\n```typescript\nimport { ClaudeAgent, webSearchTool } from '@agentionai/agents/claude';\n\nconst agent = new ClaudeAgent({\n  apiKey: process.env.ANTHROPIC_API_KEY,\n  id: 'researcher',\n  name: 'Researcher',\n  description: 'You are a helpful research assistant with web access.',\n  model: 'claude-sonnet-4-6',\n  builtInTools: [webSearchTool({ maxUses: 5 })],\n});\n\nconst response = await agent.execute('What happened in the news today?');\n```\n\n### Streaming\n\nAll three major providers support streaming via `executeStream()`, which returns an `AsyncGenerator<StreamChunk>`. Each chunk is `{ type: \"text\" | \"reasoning\"; content: string }` — text for visible output, reasoning for internal thinking tokens (DeepSeek R1, Claude extended thinking, OpenAI o-series).\n\n```typescript\nimport { ClaudeAgent } from '@agentionai/agents/claude';\n\nconst agent = new ClaudeAgent({\n  apiKey: process.env.ANTHROPIC_API_KEY,\n  id: 'assistant',\n  name: 'Assistant',\n  description: 'You are a helpful assistant.',\n});\n\nfor await (const chunk of agent.executeStream('Tell me a story')) {\n  if (chunk.type === 'text') process.stdout.write(chunk.content);\n}\n```\n\nTool calls are handled transparently — the generator continues streaming after each round-trip. The same API works across `ClaudeAgent`, `OpenAiAgent`, and `LlamaCppAgent` / any `OpenAICompatibleAgent` subclass.\n\n### Multi-Agent Pipeline\n\nChain agents together with different providers and models:\n\n```typescript\nimport { ClaudeAgent } from '@agentionai/agents/claude';\nimport { OpenAiAgent } from '@agentionai/agents/openai';\nimport { Pipeline } from '@agentionai/agents/core';\n\nconst researcher = new OpenAiAgent({\n  apiKey: process.env.OPENAI_API_KEY,\n  id: 'researcher',\n  name: 'Researcher',\n  description: 'Research the given topic and provide key facts.',\n  model: 'gpt-4o',\n  tools: [searchTool],\n});\n\nconst writer = new ClaudeAgent({\n  apiKey: process.env.ANTHROPIC_API_KEY,\n  id: 'writer',\n  name: 'Writer',\n  description: 'Write a blog post based on the research provided.',\n  model: 'claude-sonnet-4-5',\n});\n\nconst pipeline = new Pipeline([researcher, writer]);\nconst result = await pipeline.execute('Renewable energy trends in 2024');\n```\n\n### Agent Delegation\n\nUse agents as tools for hierarchical workflows:\n\n```typescript\nimport { ClaudeAgent } from '@agentionai/agents/claude';\nimport { OpenAiAgent } from '@agentionai/agents/openai';\n\n// Research assistant (cheaper model for data gathering)\nconst researchAssistant = new OpenAiAgent({\n  apiKey: process.env.OPENAI_API_KEY,\n  id: 'research-assistant',\n  name: 'Research Assistant',\n  description: 'Search and summarize information on topics.',\n  model: 'gpt-4o-mini',\n  tools: [searchTool],\n});\n\n// Lead researcher delegates to assistant, synthesizes findings\nconst researcher = new ClaudeAgent({\n  apiKey: process.env.ANTHROPIC_API_KEY,\n  id: 'researcher',\n  name: 'Lead Researcher',\n  description: 'Research topics thoroughly using your assistant.',\n  model: 'claude-sonnet-4-5',\n  agents: [researchAssistant],  // Assistant available as a tool\n});\n\nconst result = await researcher.execute('Latest developments in quantum computing');\n```\n\n### Multimodal / Vision\n\nSend images alongside text using `imageUrl()` or `imageBase64()`. The same `MessageContent[]` interface works across all providers:\n\n```typescript\nimport { ClaudeAgent } from '@agentionai/agents/claude';\nimport { imageUrl, imageBase64 } from '@agentionai/agents/core';\nimport * as fs from 'fs';\n\nconst agent = new ClaudeAgent({\n  apiKey: process.env.ANTHROPIC_API_KEY,\n  id: 'vision-agent',\n  name: 'VisionAgent',\n  description: 'You analyze images.',\n  model: 'claude-opus-4-6',\n});\n\n// Remote image by URL\nconst response = await agent.execute([\n  imageUrl('https://example.com/chart.png'),\n  { type: 'text', text: 'Summarize this chart in one sentence.' },\n]);\n\n// Local image as base64\nconst data = fs.readFileSync('./photo.jpg').toString('base64');\nconst response2 = await agent.execute([\n  imageBase64(data, 'image/jpeg'),\n  { type: 'text', text: 'What plant is this?' },\n]);\n```\n\n| Provider | URL | Base64 |\n|----------|:---:|:------:|\n| Claude | ✅ | ✅ |\n| OpenAI | ✅ | ✅ |\n| Gemini | ✅ | ✅ |\n| Mistral | ✅ | ❌ |\n\n## Core Concepts\n\n### Agents\nUnified interface across Claude, OpenAI, Gemini, Mistral, and local models via Ollama and llama.cpp. Tools, history, and token tracking built-in.\n\n[Learn more →](https://docs.agention.ai/guide/agents)\n\n### Tools\nJSON Schema + handler pattern. Unique capability: wrap any agent as a tool for delegation hierarchies. Also supports provider-defined built-in tools (e.g. Anthropic's web search, bash, text editor) that run server-side.\n\n[Learn more →](https://docs.agention.ai/guide/tools)\n\n### Context Management\nTool result masking (lossless, free) + rolling summarization (auto-firing) + sub-agent delegation (token isolation by architecture). Composable plugins keep the context window lean automatically.\n\n[Learn more →](https://docs.agention.ai/guide/context-management) · [History API →](https://docs.agention.ai/guide/history)\n\n### Multimodal / Vision\nUnified `MessageContent[]` interface for images across all providers. URL and base64 images, mix text and images freely in a single call.\n\n[Learn more →](https://docs.agention.ai/guide/multimodal)\n\n### Graph Pipelines\nCompose sequential, parallel, voting, routing, and nested graphs. Mix models and providers freely.\n\n[Learn more →](https://docs.agention.ai/guide/graph-pipelines)\n\n### RAG & Vector Stores\nLanceDB vector store, token-aware chunking, ingestion pipeline, and retrieval tools out of the box.\n\n[Learn more →](https://docs.agention.ai/guide/vector-stores)\n\n### Observability\nPer-call and per-node token counts, duration metrics, full execution visibility.\n\n[Learn more →](https://docs.agention.ai/guide/graph-pipelines#metrics--observability)\n\n## Documentation\n\n- **[Getting Started](https://docs.agention.ai/guide/getting-started)** - Installation and first agent\n- **[Context Management](https://docs.agention.ai/guide/context-management)** - Token budgets, masking, and compression\n- **[Agents](https://docs.agention.ai/guide/agents)** - Agent configuration and providers\n- **[Tools](https://docs.agention.ai/guide/tools)** - Adding capabilities and agent delegation\n- **[Multimodal / Vision](https://docs.agention.ai/guide/multimodal)** - Sending images across all providers\n- **[History](https://docs.agention.ai/guide/history)** - Conversation persistence and plugins\n- **[Graph Pipelines](https://docs.agention.ai/guide/graph-pipelines)** - Multi-agent workflows\n- **[Vector Stores](https://docs.agention.ai/guide/vector-stores)** - RAG and semantic search\n- **[Examples](https://docs.agention.ai/guide/examples)** - Real-world implementations\n- **[API Reference](https://docs.agention.ai/api)** - Full API documentation\n\n## Why Agention?\n\n|  | Raw SDKs | Heavy Frameworks | Agention |\n|---|---|---|---|\n| **Control** | Full | Limited | Full |\n| **Boilerplate** | High | Low | Low |\n| **Transparency** | Full | Limited | Full |\n| **Multi-provider** | Manual | Varies | Built-in |\n| **TypeScript** | Varies | Often partial | Native |\n\n- **Ship faster** — Stop rebuilding agent infrastructure for every project\n- **Stay flexible** — Swap providers, mix models, customize everything\n- **Keep control** — See exactly what's happening at every step\n- **Scale confidently** — Built-in metrics, token tracking, and observability\n\n## Examples\n\nCheck out the [examples](https://github.com/laurentzuijdwijk/agention-lib/tree/master/examples) directory for complete working examples:\n\n- Basic agents with different providers\n- Custom tools and agent delegation\n- Sequential, parallel, and voting pipelines\n- RAG applications with vector search\n- Document ingestion and chunking\n\n## Built with Agention\n\n- **[Marshall](https://marshall.agention.ai/)** — a coding agent for open weights. Runs a planner/coder/reviewer loop entirely on local hardware via llama.cpp or Ollama, no API key, account, or cloud required — with approval-gated file writes and shell commands, and support for mixing local and paid models across roles.\n\n## Contributing\n\nContributions are welcome! Please open an issue or submit a pull request.\n\n## Links\n\n- **[Documentation](https://docs.agention.ai/)**\n- **[GitHub](https://github.com/laurentzuijdwijk/agention-lib)**\n- **[npm](https://www.npmjs.com/package/@agentionai/agents)**\n- **[Issues](https://github.com/laurentzuijdwijk/agention-lib/issues)**\n","readmeFilename":"README.md"}