CLAP - Cognitive Layer Agent Package

What is CLAP - Cognitive Layer Agent Package?

CLAP is a Python framework for building sophisticated AI agents using modern agentic patterns (ReAct, Tool, Multi-Agent). It runs on Python 3.10+ and is built for developers who need modular, async‑capable agent workflows with integrated RAG and external tool support.

How to use CLAP - Cognitive Layer Agent Package?

Install via pip install clap-agents (optional extras for Qdrant, ChromaDB, Ollama). Write async Python code using provided agent classes (ToolAgent, ReactAgent, Agent), configure an LLM backend (Groq, Gemini, Ollama), and optionally connect to MCP servers or define custom tools with the @tool decorator.

Key features of CLAP - Cognitive Layer Agent Package

• Modular agent patterns: ReAct, Tool, and Multi-Agent Teams.
• Advanced tool integration: local Python functions and remote MCP tools (SSE).
• Pluggable LLM backends: Groq, Google Gemini, Ollama (local).
• Built‑in RAG: VectorStoreInterface for ChromaDB and Qdrant.
• Embedding wrappers: Sentence‑Transformers, Ollama, FastEmbed.
• Asynchronous core (asyncio) for efficient I/O.
• Strict tool argument validation and type coercion via jsonschema.

Use cases of CLAP - Cognitive Layer Agent Package

• Complex reasoning tasks: ReAct agent with thought‑prompting and native tool calling.
• Single‑step tool usage: Tool agent for simple function invocation or RAG.
• Collaborative task execution: Multi‑agent teams with sequential or parallel workflows.
• Retrieval‑Augmented Generation: Agents that query vector stores before responding.
• Integration with external systems: Connect to MCP servers to expose remote tools.

FAQ from CLAP - Cognitive Layer Agent Package

What does CLAP do that other agent frameworks don’t?

CLAP provides a modular, async‑native Python framework with built‑in RAG abstractions, pluggable LLM backends, and first‑class support for both local tools and remote MCP tools.

Which LLMs and platforms are supported?

Groq, Google Gemini (via OpenAI compatibility layer), and locally‑run Ollama models. The architecture is extensible to other providers.

How does MCP server integration work?

The MCPClientManager allows agents to discover and call tools exposed by MCP servers. Currently only SSE transport is supported.

Is CLAP free and open source?

Yes, it is licensed under Apache 2.0. No pricing or license restrictions beyond the license terms.

Are there any known limitations?

Performance for very large batch ingestions via the async FastEmbed wrapper may vary based on CPU. The project is actively developed; documentation and examples are provided.