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Basic Information

LionAGI is an agentic intelligence SDK and orchestration framework designed to build and run multi-step AI workflows that combine multiple models, tools, and validations into coherent pipelines. It provides core primitives like Branch and iModel to manage conversation context and model instances, supports ReAct-style multi-step reasoning and tool invocation, and enforces structured, typed outputs via Pydantic. The project explicitly supports multiple providers (OpenAI, Anthropic, Perplexity and custom providers), Claude Code integration for persistent coding sessions, session auto-resume, concurrency strategies, streaming, and real-time observability. Optional extensions add reader tools for unstructured data, local inference via Ollama, database persistence for structured outputs, graph visualization, and rich console formatting. The README includes quick start examples, multi-model orchestration patterns, and cookbook-style usage for building parallelized fan-out/fan-in orchestrations and structured result handling.

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App Details

Features
Structured and typed LLM interactions using Pydantic for validated outputs. Multi-model orchestration allowing seamless routing and mid-flow switching between providers and models. ReAct support with verbose chain-of-thought and tool integrations so models can call external actions such as document readers. Observability features including real-time logging, message introspection, action logs, and a to_df export for debugging. Session management with auto-resume behavior and persistent sessions for Claude Code CLI. Support for streaming responses and fine-grained tool permissions. Built-in patterns for fan-out/fan-in parallel execution and utilities like alcall for concurrent task handling. Optional extras include reader, ollama, claude-code SDK bindings, rich formatting, schema-to-model persistence, Postgres and SQLite support, and graph visualization.
Use Cases
For developers building agentic systems, LionAGI centralizes control over complex AI workflows and reduces boilerplate for multi-step reasoning and tool use. Typed responses improve reliability when consuming model outputs and Pydantic schemas make results easier to persist and validate. Multi-model support and mid-flow switching let teams leverage the best model for each subtask. Observability, verbose ReAct traces, and action logs speed up debugging of tool calls and chain-of-thought. The framework simplifies integrating document reading, PDF summarization, and parallel research or coding tasks with fan-out/fan-in orchestration. Optional database and visualization plugins help store structured outputs and inspect complex workflows. Claude Code integration and session resume make it practical to build persistent coding and autonomous agent experiences.

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