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Basic Information

Solana Agent is a Python framework for building AI agents that integrate with the Solana ecosystem. It is designed to let developers define agents quickly using a small configuration, enabling single-agent or multi-agent swarms that can process text, audio, and images. The project provides built-in support for multiple AI model vendors, conversational memory and history, a knowledge base with PDF/text ingestion, observability via Pydantic Logfire, and optional persistent stores such as MongoDB, Zep Cloud, and Pinecone. It includes first-class tooling for Solana operations, Zapier MCP integrations, image generation and storage to S3-compatible services, and a CLI for local use. The README documents code examples for streaming inputs and outputs, plugin and inline tool development, guardrails for input/output moderation, and patterns for autonomous or event-driven agents triggered by schedulers or webhooks.

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Features
Key features include a minimal three-line setup and simple JSON-based agent definitions. The framework supports fast streaming responses across text, audio, and images and persistent conversational memory. Multi-vendor model support includes OpenAI, Grok, and Gemini model configurations and dedicated models for embeddings, TTS, transcription, and image generation. Tooling supports async inline tools and plugins such as Solana RPC tools, internet search, MCP/Zapier integration, image generation with S3 upload, and a Nemo Agent plugin for producing zips of generated code. Additional features include an integrated knowledge base with semantic search and PDF chunking, intelligent routing between agents, business-aligned value and voice configuration, input/output guardrails including a built-in PII scrubber, and observability/tracing integration.
Use Cases
Solana Agent helps teams and developers rapidly prototype and deploy AI-driven assistants and autonomous processes that interact with blockchain services and external systems. It reduces integration work by providing prebuilt connectors for Solana operations, MCP automation, knowledge bases, and cloud storage, while offering multi-modal streaming so agents can handle text, audio, and images in the same workflow. Guardrails and observability help maintain safety and traceability in production scenarios. The routing, persistent memory, and KB features improve answer relevance and context retention. Its plugin architecture and CLI permit customization, automation via external triggers, and extension with inline async tools, making it useful for customer support bots, research agents, on-chain utilities, and event-driven automation in Python environments.

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