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gollm is a Go library that provides a unified interface to interact with multiple LLM providers (OpenAI, Anthropic, Groq, Ollama, etc.) with built-in support for prompt engineering, structured output, and advanced features like chain-of-thought reasoning and prompt optimization.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | teilomillet/gollm |
| Owner | teilomillet |
| Primary language | Go |
| License | Apache-2.0 — OSI-approved |
| Stars | 671 |
| Forks | 65 |
| Open issues | 6 |
| Latest release | Unknown |
| Last updated | 2026-03-21 |
| Source | https://github.com/teilomillet/gollm |
What gollm is
A Go package wrapping heterogeneous LLM APIs behind a consistent interface, supporting provider abstraction, prompt templating, JSON schema validation, retry logic, memory management, and optional features like prompt caching and model fallbacks via OpenRouter.
Get the gollm source
Clone the repository and explore it locally.
git clone https://github.com/teilomillet/gollm.gitcd gollm# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- API keys must be managed securely (environment variables or config files); no built-in secret management is documented—use external vaults for production.
- Retry logic is configurable (SetMaxRetries, SetRetryDelay); understand backoff behavior and rate-limit handling for each provider before production deployment.
- Structured output validation relies on JSON schema generation; validate schemas match your use case to avoid silent validation failures or unexpected rejections.
- Memory retention is supported but unbounded context management is not explicitly discussed; be cautious with long-running stateful sessions to avoid token bloat.
- Provider features (e.g., caching, reasoning tokens via OpenRouter) are optional; check provider-specific options before relying on advanced capabilities.
When to avoid it — and what to weigh
- Non-Go backend stack — If your primary backend is Python, Node.js, or another language, consider native SDKs or language-specific wrappers instead of calling gollm through FFI or network boundaries.
- Extreme latency sensitivity — Streaming responses and token-level control are not explicitly documented; batching and retry logic may add unpredictable latency for real-time chat or live inference.
- Single provider lock-in acceptable — If you only need one provider (e.g., OpenAI exclusively) and want minimal overhead, direct provider SDKs may be simpler and have fewer dependencies.
- Edge deployment or minimal footprint — The library's scope and dependencies are not disclosed; bundling with Go binaries targeting embedded or serverless environments requires validation.
License & commercial use
Apache License 2.0 (Apache-2.0): permissive open-source license allowing commercial use, modification, and distribution with attribution and liability disclaimer.
Apache-2.0 explicitly permits commercial use without restrictions or royalty obligations. No secondary licensing, proprietary commercial support, or locked commercial features are evident. Suitable for closed-source and open-source commercial products. Verify compliance with internal policies; no commercial entity endorsement or SLA is mentioned in available data.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
API keys and secrets are passed as options and environment variables; use secure credential management (e.g., vaults, secrets managers) and never embed keys in code or configs. Rate-limit and retry logic can mask DDoS or poisoned-input scenarios; monitor unusual API behavior. JSON schema validation is applied but doesn't prevent injection attacks in prompt content—sanitize user inputs. No explicit audit logging, encryption, or compliance features are documented.
Alternatives to consider
Langchain (Go port) or Python Langchain with go-python
Mature multi-language LLM orchestration with broader integrations (RAG, memory, chains); heavier but more feature-rich for complex workflows.
Native provider SDKs (OpenAI SDK-go, go-anthropic, etc.)
Tighter API parity with latest provider features, fewer abstractions, and lower dependency footprint if single-provider is acceptable.
LiteLLM (Python/Node backend wrapper)
Language-agnostic LLM abstraction with stronger provider parity, load-balancing, and logging; requires API gateway or microservice wrapper for Go.
Build on gollm with DEV.co software developers
Evaluate gollm for your multi-provider AI backend. Check documentation, review API coverage, and test provider failover strategies in staging before production rollout.
Talk to DEV.coRelated on DEV.co
Explore the category and the services that help you build with it.
gollm FAQ
Can I use gollm with Ollama for local models?
Does gollm support streaming responses?
What happens if a model/provider fails during Generate()?
Is gollm suitable for production chat applications?
Software developers & web developers for hire
Our engineers ship ai frameworks software for a living. DEV.co provides software development services, web development services, and ongoing support for teams standardizing on tools like gollm.
Ready to unify your LLM integration in Go?
Evaluate gollm for your multi-provider AI backend. Check documentation, review API coverage, and test provider failover strategies in staging before production rollout.