🎯

ocr

🎯Skill

from trpc-group/trpc-agent-go

What it does

Extracts and recognizes text from images using optical character recognition (OCR) technology.

📦

Part of

trpc-group/trpc-agent-go(6 items)

ocr

Installation

git cloneClone repository

git clone https://github.com/trpc-group/trpc-agent-go.git

📖 Extracted from docs: trpc-group/trpc-agent-go

Need more details? View full documentation on GitHub →

2Installs

AddedFeb 4, 2026

View on GitHub Back to Skills

Skill Details

SKILL.md

Overview

English | [中文](README.zh_CN.md)

# tRPC-Agent-Go

[![Go Reference](https://pkg.go.dev/badge/trpc.group/trpc-go/trpc-agent-go.svg)](https://pkg.go.dev/trpc.group/trpc-go/trpc-agent-go)

[![Go Report Card](https://goreportcard.com/badge/github.com/trpc-group/trpc-agent-go)](https://goreportcard.com/report/github.com/trpc-group/trpc-agent-go)

[![LICENSE](https://img.shields.io/badge/license-Apache--2.0-green.svg)](https://github.com/trpc-group/trpc-agent-go/blob/main/LICENSE)

[![Releases](https://img.shields.io/github/release/trpc-group/trpc-agent-go.svg?style=flat-square)](https://github.com/trpc-group/trpc-agent-go/releases)

[![Tests](https://github.com/trpc-group/trpc-agent-go/actions/workflows/prc.yml/badge.svg)](https://github.com/trpc-group/trpc-agent-go/actions/workflows/prc.yml)

[![Coverage](https://codecov.io/gh/trpc-group/trpc-agent-go/branch/main/graph/badge.svg)](https://app.codecov.io/gh/trpc-group/trpc-agent-go/tree/main)

[![Documentation](https://img.shields.io/badge/Docs-Website-blue.svg)](https://trpc-group.github.io/trpc-agent-go/)

A powerful Go framework for building intelligent agent systems that transforms how you create AI applications. Build autonomous agents that think, remember, collaborate, and act with unprecedented ease.

Why tRPC-Agent-Go?

Intelligent Reasoning: Advanced hierarchical planners and multi-agent orchestration
Rich Tool Ecosystem: Seamless integration with external APIs, databases, and services
Persistent Memory: Long-term state management and contextual awareness
Multi-Agent Collaboration: Chain, parallel, and graph-based agent workflows
GraphAgent: Type-safe graph workflows with multi-conditional routing, functionally equivalent to LangGraph for Go
Agent Skills: Reusable SKILL.md workflows with safe execution
Artifacts: Versioned storage for files produced by agents and tools
Prompt Caching: Automatic cost optimization with 90% savings on cached content
Evaluation & Benchmarks: Eval sets + metrics to measure quality over time
UI & Server Integration: AG-UI (Agent-User Interaction),

and Agent-to-Agent (A2A) interoperability

Production Ready: Built-in telemetry, tracing, and enterprise-grade reliability
High Performance: Optimized for scalability and low latency

Use Cases

Perfect for building:

Customer Support Bots - Intelligent agents that understand context and solve complex queries
Data Analysis Assistants - Agents that query databases, generate reports, and provide insights
DevOps Automation - Smart deployment, monitoring, and incident response systems
Business Process Automation - Multi-step workflows with human-in-the-loop capabilities
Research & Knowledge Management - RAG-powered agents for document analysis and Q&A

Key Features

Multi-Agent Orchestration

```go

// Chain agents for complex workflows

pipeline := chainagent.New("pipeline",

chainagent.WithSubAgents([]agent.Agent{

analyzer, processor, reporter,

}))

// Or run them in parallel

parallel := parallelagent.New("concurrent",

parallelagent.WithSubAgents(tasks))

```

Advanced Memory System

```go

// Persistent memory with search

memory := memorysvc.NewInMemoryService()

agent := llmagent.New("assistant",

llmagent.WithTools(memory.Tools()),

llmagent.WithModel(model))

// Memory service managed at runner level

runner := runner.NewRunner("app", agent,

runner.WithMemoryService(memory))

// Agents remember context across sessions

```

Rich Tool Integration

```go

// Any function becomes a tool

calculator := function.NewFunctionTool(

calculate,

function.WithName("calculator"),

function.WithDescription("Math operations"))

// MCP protocol support

mcpTool := mcptool.New(serverConn)

```

Production Observability

```go

// Start Langfuse integration

clean, _ := langfuse.Start(ctx)

defer clean(ctx)

runner := runner.NewRunner("app", agent)

// Run with Langfuse attributes

events, _ := runner.Run(ctx, "user-1", "session-1",

model.NewUserMessage("Hello"),

agent.WithSpanAttributes(

attribute.String("langfuse.user.id", "user-1"),

attribute.String("langfuse.session.id", "session-1"),

))

```

Agent Skills

```go

// Skills are folders with a SKILL.md spec.

repo, _ := skill.NewFSRepository("./skills")

// Let the agent load and run skills on demand.

tools := []tool.Tool{

skilltool.NewLoadTool(repo),

skilltool.NewRunTool(repo, localexec.New()),

}

```

NewFSRepository also accepts an HTTP(S) URL (for example, a .zip or

.tar.gz archive). The payload is downloaded and cached locally (set

SKILLS_CACHE_DIR to override the cache location).

Evaluation & Benchmarks

```go

evaluator, _ := evaluation.New("app", runner, evaluation.WithNumRuns(3))

defer evaluator.Close()

result, _ := evaluator.Evaluate(ctx, "math-basic")

_ = result.OverallStatus

```

[tRPC-Agent-Go](#trpc-agent-go)

- [Use Cases](#use-cases)

- [Key Features](#key-features)

- [Multi-Agent Orchestration](#multi-agent-orchestration)

- [Advanced Memory System](#advanced-memory-system)

- [Rich Tool Integration](#rich-tool-integration)

- [Production Observability](#production-observability)

- [Agent Skills](#agent-skills)

- [Evaluation \& Benchmarks](#evaluation--benchmarks)

- [Table of Contents](#table-of-contents)

- [Documentation](#documentation)

- [Quick Start](#quick-start)

- [Prerequisites](#prerequisites)

- [Run the Example](#run-the-example)

- [Basic Usage](#basic-usage)

- [Stop / Cancel a Run](#stop--cancel-a-run)

- [Examples](#examples)

- [1. Tool Usage](#1-tool-usage)

- [2. LLM-Only Agent](#2-llm-only-agent)

- [3. Multi-Agent Runners](#3-multi-agent-runners)

- [4. Graph Agent](#4-graph-agent)

- [5. Memory](#5-memory)

- [6. Knowledge](#6-knowledge)

- [7. Telemetry \& Tracing](#7-telemetry--tracing)

- [8. MCP Integration](#8-mcp-integration)

- [9. AG-UI Demo](#9-ag-ui-demo)

- [10. Evaluation](#10-evaluation)

- [11. Agent Skills](#11-agent-skills)

- [12. Artifacts](#12-artifacts)

- [13. A2A Interop](#13-a2a-interop)

- [Architecture Overview](#architecture-overview)

- [Execution Flow](#execution-flow)

- [Using Built-in Agents](#using-built-in-agents)

- [Multi-Agent Collaboration Example](#multi-agent-collaboration-example)

- [Contributing](#contributing)

- [Ways to Contribute](#ways-to-contribute)

- [Quick Contribution Setup](#quick-contribution-setup)

- [Acknowledgements](#acknowledgements)

- [Enterprise Validation](#enterprise-validation)

- [Open Source Inspiration](#open-source-inspiration)

- [Star History](#star-history)

- [License](#license)

- [Star us on GitHub • Report Issues • Join Discussions](#star-us-on-github--report-issues--join-discussions)

Documentation

Ready to dive into tRPC-Agent-Go? Our [documentation](https://trpc-group.github.io/trpc-agent-go/) covers everything from basic concepts to advanced techniques, helping you build powerful AI applications with confidence. Whether you're new to AI agents or an experienced developer, you'll find detailed guides, practical examples, and best practices to accelerate your development journey.

Quick Start

> See it in Action: _[Demo GIF placeholder - showing agent reasoning and tool usage]_

Prerequisites

Go 1.21 or later
LLM provider API key (OpenAI, DeepSeek, etc.)
5 minutes to build your first intelligent agent

Run the Example

Get started in 3 simple steps:

```bash

# 1. Clone and setup

git clone https://github.com/trpc-group/trpc-agent-go.git

cd trpc-agent-go

# 2. C

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user-file-ops🎯Skill

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whisper🎯Skill

Transcribes audio files to text using OpenAI's Whisper model, supporting multiple languages and generating accurate speech-to-text results

🎯

python-math🎯Skill

Performs advanced mathematical computations, statistical analysis, and numerical algorithms using Python's scientific computing libraries like NumPy and SciPy.

🎯

demo-skill🎯Skill

Demonstrates basic skill implementation and interaction patterns for tRPC Agent Go, serving as a reference template for custom skill development