Announcing the Chat Completion API: Build Agentic Apps with Meta LLAMA & Claude

# Announcing the Chat Completion API: Build Agentic Apps with Meta LLAMA & Claude **Published**: January 6, 2026 **Author**: AINative Studio Engineering Team **Tags**: #API #LLM #Agentic #MetaLLAMA #Claude #DeveloperTools --- We're excited to announce the launch of our **Chat Completion API** – a powerful new endpoint that brings agentic tool calling to Meta LLAMA and Anthropic Claude models. Whether you're building an AI-powered IDE, intelligent assistant, or autonomous agent, this API makes it simple to combine the reasoning power of LLMs with **30+ production-ready tools** from the AINative ecosystem. ## 🎯 What Makes This Special? ### 1. **True Agentic Workflows** Unlike traditional chat APIs that just return text, our endpoint enables **multi-iteration agentic loops** where the LLM can: - Request tool execution (database queries, git operations, file management) - Process tool results - Make decisions based on outcomes - Continue iterating until the task is complete ```python # The LLM can automatically orchestrate multi-step workflows response = await chat_with_tools( message="Create a table, insert 3 users, then query them all", tools=[create_table_tool, insert_rows_tool, query_table_tool], max_iterations=5 ) # LLM handles: create → insert → query → final answer ``` ### 2. **Bring Your Own Key (BYOK)** You control your costs and data. Use your own Meta LLAMA or Anthropic API keys – we just provide the infrastructure and tool definitions: ```python # Set your provider keys export META_API_KEY="your-meta-key" export ANTHROPIC_API_KEY="your-anthropic-key" # Start building - no AINative credits required ``` ### 3. **30+ Tool Definitions Ready to Go** We've documented **30+ production-ready tool schemas** across: - **ZeroDB**: Database operations, vector search, AI memory - **DevOps**: Git, Docker, deployment, testing - **Code**: File operations, shell commands, package management - **AI**: Embeddings, semantic search, RLHF feedback The LLM sees these tool definitions and knows when to call them. Your client executes the actual tools. --- ## 🚀 Real-World Use Cases ### AI-Powered IDE Build a coding assistant that understands context and takes action: ```python # Your AI assistant can: # 1. Read existing code files # 2. Generate new implementations # 3. Write tests automatically # 4. Run the test suite # 5. Commit changes to git tools = [file_tool, bash_tool, testing_tool, git_tool] response = await chat( message="Implement the fibonacci function, test it, and commit if tests pass", tools=tools ) ``` **Result**: The LLM orchestrates reading existing code, writing the implementation, creating tests, running them, and making a git commit – all autonomously. ### Intelligent Data Analyst Create an agent that queries databases and generates insights: ```python tools = [ zerodb_query_table, zerodb_semantic_search, zerodb_create_event, zerodb_generate_embeddings ] response = await chat( message="Analyze user behavior patterns from the users table and store insights", tools=tools ) ``` **Result**: The agent queries data, performs semantic analysis, identifies patterns, and stores findings – without manual intervention. ### Memory-Backed Assistant Build a conversational agent with persistent memory: ```python tools = [zerodb_store_memory, zerodb_search_memory] # First conversation await agent.chat("My favorite color is blue and I love jazz music") # Later (different session) await agent.chat("What music do I like?") # Agent searches its memory: "You mentioned you love jazz music!" ``` **Result**: Context persists across sessions using ZeroDB memory operations. --- ## 💡 How It Works ### The Agentic Loop 1. **Send Request**: User message + tool definitions 2. **LLM Analyzes**: Determines which tools to call 3. **Tool Calls Returned**: LLM responds with `tool_calls` in the message 4. **Client Executes**: Your code runs the requested tools 5. **Send Results**: Tool results sent back to LLM 6. **Repeat**: Loop continues until `finish_reason: "stop"` ```python # Example: Multi-iteration workflow POST /v1/chat/completions { "provider": "meta_llama", "model": "Llama-3.3-70B-Instruct", "messages": [ {"role": "user", "content": "What's the weather in SF? Then calculate 15 * 23."} ], "tools": [weather_tool, calculator_tool], "max_iterations": 5 } # Response after iteration 1: { "finish_reason": "tool_calls", "message": { "tool_calls": [ {"function": {"name": "get_weather", "arguments": {"location": "SF"}}}, {"function": {"name": "calculate", "arguments": {"expression": "15 * 23"}}} ] } } # Client executes tools, sends results back # ... # Response after iteration 2: { "finish_reason": "stop", "message": { "content": "The weather in SF is 72°F and sunny. 15 * 23 equals 345." }, "iterations": 2, "total_tokens_used": 736 } ``` --- ## 🛠️ Getting Started in 5 Minutes ### Step 1: Set Your API Keys ```bash export META_API_KEY="LLM|your-meta-key" export ANTHROPIC_API_KEY="sk-ant-your-anthropic-key" ``` ### Step 2: Install Dependencies ```bash pip install httpx asyncio ``` ### Step 3: Make Your First Request ```python import httpx import asyncio async def hello_llm(): async with httpx.AsyncClient() as client: response = await client.post( "https://api.ainative.studio/v1/chat/completions", headers={"Content-Type": "application/json"}, json={ "provider": "meta_llama", "model": "Llama-3.3-70B-Instruct", "messages": [ {"role": "user", "content": "Hello! Explain async programming in one sentence."} ], "temperature": 0.7 } ) result = response.json() print(result["choices"][0]["message"]["content"]) asyncio.run(hello_llm()) ``` **Output**: > "Async programming allows multiple operations to run concurrently without blocking, enabling efficient handling of I/O-bound tasks in Python." --- ## 🎨 Code Example: Full Agentic Workflow Here's a complete example showing the power of agentic tool calling: ```python import httpx import asyncio async def agentic_code_assistant(): """AI assistant that writes, tests, and commits code""" tools = [ { "type": "function", "function": { "name": "file_write", "description": "Write content to a file", "parameters": { "type": "object", "properties": { "path": {"type": "string"}, "content": {"type": "string"} }, "required": ["path", "content"] } } }, { "type": "function", "function": { "name": "bash_execute", "description": "Run shell commands", "parameters": { "type": "object", "properties": { "command": {"type": "string"} }, "required": ["command"] } } } ] async with httpx.AsyncClient() as client: response = await client.post( "https://api.ainative.studio/v1/chat/completions", headers={"Content-Type": "application/json"}, json={ "provider": "meta_llama", "model": "Llama-3.3-70B-Instruct", "messages": [ { "role": "system", "content": "You are a coding assistant. Write clean, tested code." }, { "role": "user", "content": """Create a Python script that: 1. Defines a function to check if a number is prime 2. Includes a test for the function 3. Runs the test""" } ], "tools": tools, "max_iterations": 6, "temperature": 0.7 }, timeout=120.0 ) result = response.json() print(f"✅ Task completed in {result['iterations']} iterations") print(f"📊 Token usage: {result['total_tokens_used']}") print(f"\n💬 Final message:\n{result['choices'][0]['message']['content']}") asyncio.run(agentic_code_assistant()) ``` **What the LLM Does Autonomously**: 1. **Iteration 1**: Calls `file_write` to create `prime_checker.py` 2. **Iteration 2**: Calls `file_write` to create `test_prime.py` 3. **Iteration 3**: Calls `bash_execute("pytest test_prime.py")` to run tests 4. **Iteration 4**: Returns final summary with test results All without manual orchestration! 🎯 --- ## 📊 Supported Models ### Meta LLAMA - **Llama-4-Maverick-17B** (400B params) - Flagship model - **Llama-3.3-70B-Instruct** (70B params) - Balanced performance - **Llama-3.3-8B-Instruct** (8B params) - Fast, cost-effective ### Anthropic Claude - **Claude Sonnet 4.5** - Best reasoning - **Claude Opus 4** - Maximum capabilities All models support **128K-200K context windows** for handling large codebases and documents. --- ## 🔧 Available Tool Categories ### **ZeroDB Tools (15 tools)** - Database operations (create tables, insert, query) - Vector search & semantic similarity - AI memory (store/search agent memories) - Event logging & RLHF feedback - SQL execution ### **Agent Framework Tools (15+ tools)** - **Code**: File operations, bash commands - **Git**: Commits, branches, push/pull - **DevOps**: Docker, deployment automation - **Testing**: pytest, jest, coverage - **Security**: Vulnerability scans - **UI**: Component generation, styling See the [full tool catalog](https://docs.ainative.studio/chat-api/tools) for complete reference. --- ## 💰 Pricing Model ### BYOK Mode (This Endpoint) ✅ **No AINative charges** - You're billed directly by Meta/Anthropic ✅ Use your own API keys ✅ Full control over costs ✅ No rate limits from AINative **Provider Pricing** (approximate): - Meta LLAMA: ~$0.60 input / ~$1.80 output per 1M tokens - Anthropic Claude: ~$3.00 input / ~$15.00 output per 1M tokens --- ## 🎓 Learn More ### Documentation - **Developer Guide**: `docs/development-guides/CHAT_COMPLETION_API_DEVELOPER_GUIDE.md` - **Tool Reference**: Complete schemas for all 30+ tools - **Code Examples**: Real-world patterns and use cases - **API Reference**: https://api.ainative.studio/docs-enhanced ### Resources - **GitHub**: https://github.com/AINative-Studio - **Support**: support@ainative.studio --- ## 🚦 What's Next? ### Roadmap (Q1 2026) **✅ Shipped**: - Multi-provider support (Meta LLAMA, Anthropic) - Agentic tool calling with client-side execution - 30+ tool definitions - Token tracking across iterations **🔄 In Progress**: - **Server-side tool execution** - Tools run automatically on our servers - **Streaming responses** - Real-time token streaming - **Vision support** - Image analysis with tool calling - **Function calling enforcement** - Guaranteed tool use with `tool_choice: required` **📋 Planned**: - Multi-modal tool calling (images + text) - Custom tool deployment - Fine-tuned models for specific tools - Team collaboration features --- ## 💬 Community Feedback We'd love to hear what you build with this API! Share your projects: - **GitHub Discussions**: Show off your agentic apps - **Twitter**: Tag @AINativeStudio with your creations --- ## 🎉 Start Building Today The Chat Completion API is live and ready for production use. Whether you're building an AI coding assistant, intelligent data analyst, or autonomous agent, we've provided the infrastructure and tool definitions to make it happen. ```bash # Get started in 30 seconds export META_API_KEY="your-key" curl -X POST https://api.ainative.studio/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "provider": "meta_llama", "model": "Llama-3.3-70B-Instruct", "messages": [{"role": "user", "content": "Hello, LLM!"}] }' ``` **Happy building!** 🚀 --- **Questions?** Drop us a line at support@ainative.studio --- *The Chat Completion API is part of AINative Studio's mission to democratize AI development. Built by developers, for developers.* **Version**: 1.0.0 **Published**: January 6, 2026

Announcing the Chat Completion API: Build Agentic Apps with Meta LLAMA & Claude

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