langchain-ai/langchain
LangChain is a versatile framework for building and deploying applications powered by large language models, offering interoperability, real-time data integration, and advanced agent orchestration capabilities.
Awesome AI Agents › Language Agent Frameworks
trpc-group/trpc-agent-go
tRPC-Agent-Go is an agent development framework written in Go and aimed at services that need to run agents in production rather than in notebooks. It provides a Go-native runtime with streaming runners, context cancellation and service-friendly APIs, so agent code fits the concurrency and deployment model of ordinary Go servers. GraphAgent implements type-safe graph workflows with multi-conditional routing and is described as functionally equivalent to LangGraph for the Go ecosystem. Multi-agent collaboration is supported through chain, parallel and cycle-based composition, where sub-agents are passed to a chain or parallel constructor and executed as a pipeline or concurrently. The tool layer turns any Go function into a callable tool and also covers MCP tools, web search, code execution and custom services. State is persistent across turns: sessions, memory, artifacts and knowledge retrieval are exposed as services attached to the runner, so agents keep context between runs and can search their own memory. Agent Skills are folders containing a SKILL.md specification, loaded from a filesystem repository that also accepts multiple roots and remote archive URLs with local caching, and executed through dedicated load and run tools with a refresh call for long-lived processes. An evolution service performs asynchronous reviews of completed sessions, passes candidates through quality gates and publishes them back as managed skills for later turns. Prompt caching is applied automatically to reduce cost on repeated content. Evaluation sets and metrics allow quality to be measured over time, and observability is built on OpenTelemetry tracing and metrics with Langfuse examples. Protocol integration covers AG-UI for frontends, A2A for agent interoperability and MCP for tools. Typical uses given are support bots, data analysis assistants, DevOps automation, business process workflows and retrieval-based knowledge agents.
https://github.com/trpc-group/trpc-agent-go
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