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typedai

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Basic Information

TypedAI is a TypeScript-first platform for developers to build, run and operate autonomous AI agents, LLM-based workflows and chatbots. The repository provides a complete developer-focused toolkit including CLI and web UIs, agent orchestration, observability and deployment configurations. It includes prebuilt software engineering agents such as a code editing agent, a software engineer agent for ticket-to-pull-request workflows, and a configurable code review agent. The project emphasizes typed interfaces, automated LLM function schemas, multi-agent reasoning implementations and human-in-the-loop controls. It supports local execution and cloud deployment options with multi-user SSO and scale-to-zero patterns. The repo is intended for teams and developers who want to implement, extend or run LLM-driven workflows, integrate with developer tools and deploy agent services in production.

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1123
App URL
https://github.com/TrafficGuard/typedai
Github Repository
https://github.com/TrafficGuard/typedai/blob/main/README.md

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App Details

Features
The README highlights advanced autonomous agents with iterative planning, hierarchical task decomposition, memory and function call history, sandboxed execution of generated code, and automated LLM function schemas via decorators. It documents software developer agents for repository editing, branch creation and merge requests, plus a configurable code review agent that can post line-level comments. The platform supports many LLM providers and multi-agent extend-reasoning implementations. Integrations include callable tools for filesystem, Jira, Slack, Perplexity, Google Cloud, GitLab and GitHub. Other features are a CLI for automation, a Dockerized runtime, a web UI, OpenTelemetry-based observability, human-in-the-loop budget controls and the ability to run Python scripts and packages.
Use Cases
TypedAI helps developers automate complex workflows, create autonomous agents for software engineering tasks, and add LLM-driven features to their systems. It reduces manual work by automating repository tasks, running code edit cycles with compile, lint and test loops, and performing configurable code reviews that can comment on merge requests. The platform facilitates experimentation with many LLM providers and provides observability for agent traces and human approval flows. CLI tools enable quick queries, code edits and research interactions locally or in isolated Docker environments. Deployment options and SSO support make it suitable for teams moving from local prototypes to cloud-hosted multi-user deployments.

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