Pixelagent
Pixelagent is a declarative Python framework for building custom AI agents that unifies LLM capabilities, storage, and orchestration with build-your-own functionality for memory, tool-calling, and multimodal data handling.
https://github.com/pixeltable/pixelagent?ref=aipure
Product Information
Updated:May 20, 2025
What is Pixelagent
Pixelagent is an agent engineering blueprint built on top of Pixeltable's data infrastructure that enables developers to create and deploy custom AI agents. It provides a unified, type-safe Python interface for building agentic applications with native support for multiple AI models (like Anthropic, OpenAI, AWS Bedrock) and different data types including text, images, audio and video. The framework emphasizes a build-your-own philosophy while handling the complex data infrastructure needs of AI applications.
Key Features of Pixelagent
Pixelagent is an open-source agent engineering framework that unifies LLM, storage, and orchestration into a single declarative interface. It provides a comprehensive solution for building custom AI agents with built-in support for multimodal data, tool integration, memory management, and multiple provider compatibility, while handling all the underlying data infrastructure needs.
Unified Data Infrastructure: Built on Pixeltable's data infrastructure, providing seamless integration of storage, transformation, and orchestration capabilities in a declarative framework
Multimodal Support: Native handling of text, images, audio, and video data types, enabling creation of agents that can process and respond to various forms of input
Extensible Provider Integration: Support for multiple AI providers including Anthropic, OpenAI, and AWS Bedrock, allowing flexibility in model selection and implementation
Built-in State Management: Automatic persistence of agent memory and tool call history in tables, with customizable memory systems and semantic search capabilities
Use Cases of Pixelagent
Financial Analysis Assistant: Create AI agents that can analyze stock information, provide investment recommendations, and process financial data using integrated tools like yfinance
Multimodal Content Processing: Build agents that can handle and analyze multiple types of media content, perfect for content moderation or media analysis applications
Intelligent Conversation Systems: Develop chatbots with long-term memory and context awareness for customer service or educational applications
Research and Analysis Tools: Create agents that can perform step-by-step reasoning, planning, and analysis using ReAct patterns for complex problem-solving tasks
Pros
Comprehensive solution that handles both agent logic and data infrastructure
High flexibility with build-your-own functionality for customization
Strong support for multiple AI providers and multimodal data types
Cons
Requires understanding of Python and declarative programming concepts
Dependency on Pixeltable infrastructure might limit some deployment scenarios
How to Use Pixelagent
Install Pixelagent and dependencies: Run 'pip install pixelagent' followed by provider-specific dependencies like 'pip install anthropic' for Claude models or 'pip install openai' for GPT models
Import and create basic agent: Import agent class (e.g. 'from pixelagent.anthropic import Agent') and create agent instance with name and system prompt: agent = Agent(name='my_assistant', system_prompt='You are a helpful assistant.')
Basic chat interaction: Use agent.chat() method to interact with the agent: response = agent.chat('Hello, who are you?')
Add custom tools: Define tools as UDFs with @pxt.udf decorator, then create agent with tools parameter: agent = Agent(name='assistant', tools=pxt.tools(your_tool_function))
Use tool calling: Call tools through the agent using agent.tool_call() method with your query
Access conversation history: Get conversation memory from tables: memory = pxt.get_table('my_assistant.memory') and conversations = memory.collect()
Implement custom memory: Create agent with n_latest_messages parameter to customize memory: agent = Agent(name='conversation_agent', n_latest_messages=14)
Add advanced patterns: Implement ReAct patterns or other agentic strategies by defining custom system prompts and creating loop functions that handle step-by-step reasoning
Monitor tool usage: Access tool call history through tables: tools_log = pxt.get_table('assistant.tools') and tool_history = tools_log.collect()
Explore examples: Check provided example directories for implementations of reflection, planning, memory systems and other advanced patterns
Pixelagent FAQs
Pixelagent is an agent engineering framework built on Pixeltable that unifies LLM, storage, and orchestration into a single declarative framework. It allows engineers to build custom agentic applications with build-your-own functionality for memory, tool-calling, and more.
Pixelagent Video
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