AI Agent

galdor

A Go-native framework for building, orchestrating, and observing AI agents with native OpenTelemetry observability and a self-hosted dashboard.

What is galdor?

galdor is an open-source Go framework for building AI agents, featuring native OpenTelemetry observability, an embedded dashboard, multi-agent patterns, MCP and A2A protocol support, and a single-binary deployment.

How to use galdor?

  1. 1Install the core module and providers: go get github.com/YasserCR/galdor@v1.0.0
  2. 2Import the provider (e.g., anthropic) and agent package.
  3. 3Create a provider with API key.
  4. 4Run an agent with agent.Run().
  5. 5Optionally use the CLI for observability: galdor ui --db ./traces.db

galdor Key Features

  • Native OpenTelemetry observability with embedded SQLite trace store and dashboard
  • Type-safe tools with reflection-derived JSON schemas
  • Multi-agent supervision (Supervisor and Swarm patterns) built-in
  • MCP client and server (stdio, SSE, Streamable HTTP)
  • A2A protocol support (Google spec)
  • Deterministic replay from recorded fixtures
  • Human-in-the-loop with InterruptBefore and Resume
  • Self-hosted embeddings via HTTP
  • Production hardening: retry/backoff, timeouts, panic recovery
  • Providers for Anthropic, OpenAI, Google Gemini, AWS Bedrock

galdor Use Cases

  • Building single or multi-agent AI applications in Go
  • Auditable agent workflows with telemetry and replay
  • Exposing tools via MCP to Claude Desktop and other clients
  • Cross-agent A2A communication
  • Compliant or air-gapped deployments needing self-hosted observability

galdor Pricing & Free Credits

galdor currently operates on a Free model.

Open Source

Free

Apache 2.0 license, free to use, modify, and distribute.

galdor Pros & Cons

Pros

  • Full self-hosted observability with embedded dashboard
  • Go-native, single binary deployment
  • Strong type safety with generics and reflection
  • Built-in multi-agent patterns and protocol support (MCP, A2A)
  • Deterministic replay for testing and debugging

Cons

  • Smaller ecosystem than LangChain Python
  • Limited provider coverage (4 LLM providers) compared to some alternatives
  • Relatively new project with smaller community

What is galdor best for?

  • Go developers building production-grade AI agents
  • Teams needing self-hosted, auditable agent frameworks
  • Projects requiring MCP server or A2A interop in Go
  • Environments with compliance or air-gap constraints

galdor FAQ

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