llmflows
LLMFlows is a Python framework for building LLM applications (chatbots, Q&A systems, agents) with an emphasis on simplicity and transparency. It provides abstractions for LLM calls, prompt templates, and complex multi-step workflows without hidden logic.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | stoyan-stoyanov/llmflows |
| Owner | stoyan-stoyanov |
| Primary language | Python |
| License | MIT — OSI-approved |
| Stars | 706 |
| Forks | 35 |
| Open issues | 19 |
| Latest release | 0.2.1 (2023-10-08) |
| Last updated | 2025-02-20 |
| Source | https://github.com/stoyan-stoyanov/llmflows |
What llmflows is
LLMFlows wraps LLM APIs (OpenAI) via classes like OpenAI and OpenAIChat, manages conversation history via MessageHistory, and orchestrates multi-step dependencies through Flow and FlowStep with async support. Prompt templates support variable interpolation and are integrated into flow execution pipelines.
Get the llmflows source
Clone the repository and explore it locally.
git clone https://github.com/stoyan-stoyanov/llmflows.gitcd llmflows# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Requires valid OpenAI API key and active account; all LLM calls incur external costs.
- Flow dependency resolution is automatic but requires careful FlowStep design; incorrect connections can cause hanging or cascading failures.
- Message history and prompt templates must be managed by the application; no built-in persistence or session management.
- Async flows improve parallel execution but require understanding of Python asyncio semantics.
- Vector database integration (Pinecone mentioned) requires separate setup and credential management outside the framework.
When to avoid it — and what to weigh
- Proprietary or non-OpenAI LLM backends required — LLMFlows documentation focuses on OpenAI. Support for other providers (Anthropic, Cohere, local models) is not clearly stated in available data.
- Production-grade enterprise features needed — Project is 706 stars with minimal fork activity (35 forks). No data on enterprise support, SLA guarantees, or hardened production deployments.
- Real-time, low-latency streaming requirements — LLMFlows is designed for explicit, sequential workflows. No mention of streaming API support or optimizations for latency-critical applications.
- Advanced observability and tracing out of the box — While the framework emphasizes transparency, active integration with commercial observability platforms (DataDog, New Relic, etc.) is not documented.
License & commercial use
MIT License (permissive OSI license). Full rights to use, modify, and distribute with minimal restrictions.
MIT License permits commercial use without requiring attribution or sharing source code. However, this covers only LLMFlows itself; OpenAI API usage is governed by OpenAI's terms of service and pricing model, which must be reviewed separately for commercial applications.
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 | Good |
| Assessment confidence | High |
Framework passes API keys to OpenAI via environment or constructor; standard secret management practices apply. No mention of input validation, prompt injection prevention, or output sanitization. Applications must implement their own safeguards for user-supplied prompts. Vector store credentials require separate secure handling.
Alternatives to consider
LangChain
More mature ecosystem (100k+ stars), supports multiple LLM providers and tools, richer integrations, larger community. Higher complexity and learning curve.
Hugging Face Transformers + custom orchestration
For self-hosted LLM inference; avoids API costs and vendor lock-in but requires GPU infrastructure and lower-level implementation.
OpenAI SDK + plain Python
Minimal abstraction for simple use cases; maximum control but requires manual flow orchestration and error handling.
Build on llmflows with DEV.co software developers
LLMFlows is ideal if you need explicit control over prompts and multi-step orchestration. Evaluate fit with your LLM provider strategy and production requirements. Our AI development team can help architect and deploy LLM systems at scale.
Talk to DEV.coRelated open-source tools
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llmflows FAQ
Does LLMFlows support models other than OpenAI?
Is LLMFlows production-ready?
How do I handle errors and retries?
Can I persist conversation history or flow state?
Software development & web development with DEV.co
Adopting llmflows is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate vector databases software in production.
Ready to build transparent LLM applications?
LLMFlows is ideal if you need explicit control over prompts and multi-step orchestration. Evaluate fit with your LLM provider strategy and production requirements. Our AI development team can help architect and deploy LLM systems at scale.