GoHector (gohector.dev)
by GoHector project
A declarative, production-first AI agent platform written in Go. Define agents with YAML, deploy single-binary agents, and orchestrate multi-agent systems using the A2A (Agent-to-Agent) protocol.
Quick summary
- GoHector is a zero-code/low-code agent framework that uses human-readable YAML to define agents, tools, reasoning strategies, and memory policies.
- Built in Go for production performance: single-binary deployment, low memory footprint, and multi-transport APIs (REST, SSE, WebSocket, gRPC).
- Native multi-agent orchestration via the A2A protocol; designed for composability and agent-to-agent communication.
- Includes features for advanced memory management, RAG (vector store + semantic search), tool integration (file, shell, web, custom plugins), and configurable reasoning engines.
- Open source (AGPL-3.0) and intended for teams that need production-ready agent deployments without heavy Python-based stacks.
Key features
- Declarative agent definitions (YAML): LLM selection, tools, reasoning engines, iteration limits, and memory strategies are all configured in YAML.
- Agent-to-Agent (A2A) protocol: first-class support for multi-agent communication and orchestration.
- High-performance Go implementation: single ~10MB binary, minimal runtime dependencies, suitable for constrained environments.
- Multi-transport APIs: REST, SSE, WebSocket, gRPC and JSON-RPC for flexible integration patterns.
- Built-in memory strategies: working memory (summary buffer), long-term memory options, and configurable retention policies.
- Retrieval-Augmented Generation (RAG): vector store integrations enabling semantic search over documents for grounded responses.
- Extensible tool ecosystem: native tools (search, file ops, command execution) plus MCP/plugin integrations and gRPC extension points.
- Reasoning controls: chain-of-thought and other engines with max-iteration guards to prevent runaway loops.
- Production security: JWT/API key support and agent-level permissioning for secure multi-tenant deployments.
Superpowers
- Rapidly convert ideas into deployed agents with YAML — no need to write scaffolding code.
- Production-first design: small binary and Go runtime make it easy to deploy, monitor, and scale in enterprise environments.
- Composable multi-agent systems: build specialized agents and orchestrate them together via A2A for complex workflows.
- Efficient memory & cost control: built-in memory strategies and reasoning iteration limits reduce token usage and inference cost.
Who this is for
- Engineering teams that need production-grade agent deployments with strong operational properties.
- Organizations building multi-agent orchestrations (e.g., analyst agents + executor agents + data agents).
- Developers who prefer declarative configuration and want to avoid Python dependency bloat in production.
Practical examples
- Coding assistant agent: define an LLM-backed agent with repo access, static analysis tools, and a test-runner tool to propose, patch, and test code changes.
- Customer support orchestration: use multiple agents (intent classifier, knowledge retriever, response generator) coordinated via A2A for robust ticket handling.
- Data-enriched agent: RAG agent that queries vector stores for product manuals and answers user queries with up-to-date documentation.
Deployment & integration notes
- Deploy as a single binary on Linux containers, systemd, or Kubernetes; works in low-memory environments.
- Integrate with existing infra via REST/gRPC or stream updates via SSE/WebSocket for live interactions.
- Extend functionality with custom plugins using gRPC or the MCP plugin system when native tools are insufficient.
Best practices
- Begin with conservative reasoning iteration limits and small memory windows, then increase as needed when behavior is validated.
- Start automation on a limited set of agents to validate safety and expected actions before scaling to production.
- Use RAG for domain-specific grounding rather than relying solely on LLM recall for factual accuracy.
- Apply agent-level permissions and API keys for multi-tenant or production deployments.
Limitations & considerations
- AGPL-3.0 license: suitability for commercial closed-source usage should be evaluated with legal counsel.
- Ecosystem maturity: compared to large Python ecosystems (LangChain, AutoGen), some integrations or community plugins may be fewer; evaluate plugin availability for your stack.
- LLM connectors: verify which LLM providers are supported out-of-the-box for your preferred model (open-source and hosted providers).
Tech stack & architecture
- Language: Go (single-binary distribution)
- Config: YAML declarative agent definitions
- Protocols: A2A (Agent-to-Agent), REST, SSE, WebSocket, gRPC, JSON-RPC
- Memory & RAG: built-in vector store integrations for semantic search
- Extensibility: MCP/plugin system and gRPC for custom tool integrations
Licensing & governance
- Open-source under AGPL-3.0. Contributions likely governed through a public repo; check project repo and LICENSE file for contribution process.
Sources & further reading (researched with Perplexity)
- Official site & docs: https://gohector.dev
- Project documentation and examples (YAML agent examples, CLI quickstart) — available from the site/docs pages
- Project license and OSS notes (AGPL-3.0) — referenced in the project documentation