AI Frameworks · ianarawjo

ChainForge

ChainForge is an open-source visual programming tool for testing and comparing LLM prompts across multiple models and parameter combinations. It enables rapid evaluation of prompt performance through a no-code interface with built-in support for major LLM providers and custom evaluation metrics.

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Source: GitHub — github.com/ianarawjo/ChainForge

GitHub stars

257

Forks

TypeScript

Primary language

MIT

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	ianarawjo/ChainForge
Owner	ianarawjo
Primary language	TypeScript
License	MIT — OSI-approved
Stars	3k
Forks	257
Open issues	69
Latest release	v0.3.6 (2025-05-11)
Last updated	2026-06-10
Source	https://github.com/ianarawjo/ChainForge

What ChainForge is

Built on ReactFlow (frontend) and Flask (backend) with TypeScript, ChainForge provides a dataflow-based environment for combinatorial prompt testing. It supports batch querying across OpenAI, Anthropic, Google Gemini, HuggingFace, Ollama, and other providers, with Python-based evaluation nodes and visualization capabilities.

Quickstart

Get the ChainForge source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/ianarawjo/ChainForge.git2cd ChainForge3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Prompt Engineering & Optimization

Systematically compare prompt variations across multiple LLMs to identify the highest-performing template for a specific task before deploying to production.

LLM Model Selection & Evaluation

Benchmark different models (GPT-4, Claude, Gemini, etc.) with identical prompts and parameters to determine the best fit for cost, latency, and accuracy requirements.

Evaluation Workflow Automation

Build reusable evaluation pipelines that combine LLM calls, custom Python scoring functions, and visualization to validate model behavior across datasets at scale.

Implementation considerations

Requires Python 3.8+ for local installation; Docker available for containerized deployment. API keys should be loaded via environment variables rather than hardcoded in UI.
Evaluation nodes require Python code authoring; non-technical users benefit from pre-built evaluation templates or AI-assisted code generation (advertised feature).
Batch querying can incur significant LLM API costs; implement rate limiting and cost tracking, especially when testing hundreds of prompt-model combinations.
Web-based version (chainforge.ai/play/) has limited feature set; local installation recommended for custom providers, Ollama integration, and persistent workflows.
Flows are exported as `.cforge` files; establish version control and archival practices for reproducible evaluation baselines.

When to avoid it — and what to weigh

Real-time Production Inference — ChainForge is designed for evaluation and testing, not production serving. Use dedicated inference platforms (Replicate, BentoML, vLLM) for high-throughput, low-latency deployments.
Sensitive Data / Compliance-Critical Workflows — Requires API keys and sends data to external LLM providers by design. Not suitable for HIPAA, PCI-DSS, or other regulated environments without private model hosting (e.g., on-premise Ollama).
Complex Multi-Modal Evaluation — Currently supports text-based evaluation. If you need to score images, audio, or structured complex outputs, augmenting capabilities or alternative tools may be needed.
Enterprise SSO / Advanced Access Control — The open-source version does not clearly document enterprise authentication, audit logging, or role-based access control features.

License & commercial use

MIT License (ianarawjo/ChainForge). Permissive OSI-approved license allowing commercial use, modification, and distribution with attribution.

MIT License explicitly permits commercial use without royalty or licensing restrictions. However, this is a development/evaluation tool; validate that your use case does not require liability indemnification or SLAs beyond the license terms. Consider contributing back improvements or supporting the maintainer if commercially benefiting.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Low
DEV.co fit	Strong
Assessment confidence	High

Security considerations

Requires handling of LLM API keys; recommend loading from environment variables, not hardcoding. Data sent to external LLM providers; review provider data handling policies. Local Ollama option enables air-gapped evaluation if needed. No documented vulnerability disclosure policy or security audit history. Self-hosted Flask backend; standard web security practices (HTTPS, input validation) recommended if deployed beyond localhost.

Alternatives to consider

Weights & Biases (Weave)

Provides prompt evaluation, model comparison, and experiment tracking with stronger enterprise features, but requires paid subscription for advanced features.

LangSmith (LangChain)

Integrated evaluation platform for LLM chains with tracing and debugging; tighter coupling to LangChain ecosystem; commercial licensing.

LlamaIndex Evaluation Module

Framework-native evaluation for RAG and LLM applications; lower barrier for users already in LlamaIndex ecosystem but less visual/interactive.

Software development agency

Build on ChainForge with DEV.co software developers

Start evaluating prompt performance today with ChainForge. Install locally, design evaluation flows, and compare results across models—no vendor lock-in. Learn more in the official documentation.

Talk to DEV.co

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ChainForge FAQ

Can I use ChainForge with my own locally-hosted LLM?

Yes. ChainForge supports Ollama for locally-run models. Set up Ollama, configure the endpoint in ChainForge, and query without external API costs or data transmission concerns.

Is ChainForge suitable for production LLM serving?

No. ChainForge is a development and evaluation tool. For production inference, use dedicated serving platforms (BentoML, Replicate, SageMaker, vLLM).

Can I share my evaluation flows with non-technical colleagues?

Via the web version (chainforge.ai/play/) Share button, yes—but flows are ephemeral and limited to 10 concurrent shares. Export important flows as `.cforge` files for archival and team sharing outside the web interface.

What is the cost impact of using ChainForge?

ChainForge itself is free (MIT). Costs come from LLM API calls (OpenAI, Anthropic, etc.). Batch testing hundreds of prompt-model combinations can incur significant charges; budget and monitor API usage.

Work with a software development agency

Adopting ChainForge 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 ai frameworks software in production.

Ready to Optimize Your LLM Prompts?

Start evaluating prompt performance today with ChainForge. Install locally, design evaluation flows, and compare results across models—no vendor lock-in. Learn more in the official documentation.

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