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Basic Information

Palico AI is an opinionated tech stack and developer framework designed to build, iterate on, evaluate, and deploy LLM-powered applications. The README describes Palico as a toolkit for the iterative nature of LLM development that centralizes components such as agents, memory management, telemetry/tracing, experiments and evaluations, client SDKs, and deployment utilities. It provides primitives for implementing application logic (for example a Chat handler), local preview with a Playground UI, and first-party React support. The project emphasizes flexibility to choose models, prompts, retrieval datasets, and custom code while enabling systematic improvement cycles using test-cases, metrics, and experiment tooling. The repo includes documentation, quickstart commands, and examples to initialize projects and integrate with existing services and deployment pipelines. It targets developers building production LLM applications who need tooling for development, testing, observability, and rollout.

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Features
Palico lists a set of concrete features and integrations in the README. Core primitives include streaming of messages and intermediate steps, built-in conversation state and memory management, tool execution support for agents, feature flags to swap models and prompts at runtime, and comprehensive monitoring with logs and traces. It ships experiment and eval tooling with test-case definitions and evaluation metrics such as substring and Levenshtein checks and similarity metrics. Local development uses a Playground UI for instant previews. Integrations include many LLM providers and vector stores such as OpenAI, Anthropic, Cohere, Portkey, Azure, AWS Bedrock, GCP Vertex, LangChain, LlamaIndex, Pinecone, Postgres/PGVector and Chroma. The README also documents Docker-friendly production deployment, CI/CD guidance, client SDKs and a React hook for chat.
Use Cases
The repository is useful to developers creating and maturing production LLM applications by providing a structured, end-to-end workflow. It reduces boilerplate for common LLM concerns like conversation state, streaming responses, tool execution, and swapping model providers. The experiments and evaluations features help teams write reproducible test-cases and track metrics to iterate toward desired behavior. Observability features such as logs and traces assist debugging and performance tuning. Prebuilt integrations to popular models and vector databases simplify retrieval-augmented workflows. Local Playground previews accelerate developer feedback loops and first-party client SDKs including a React hook simplify frontend integration. Deployment guidance and Docker support help move apps from prototype to cloud with CI/CD and pull-request preview patterns.

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