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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.

Talk to DEV.coDiscuss Implementation
Source: GitHub — github.com/ianarawjo/ChainForge
3k
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.

FieldValue
Repositoryianarawjo/ChainForge
Ownerianarawjo
Primary languageTypeScript
LicenseMIT — OSI-approved
Stars3k
Forks257
Open issues69
Latest releasev0.3.6 (2025-05-11)
Last updated2026-06-10
Sourcehttps://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
git clone https://github.com/ianarawjo/ChainForge.gitcd ChainForge# 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.

SignalAssessment
MaintenanceActive
DocumentationStrong
License clarityClear
Deployment complexityLow
DEV.co fitStrong
Assessment confidenceHigh
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

Related open-source tools

Surfaced by semantic similarity across the DEV.co open-source index.

chainlitpromptfoopromptflow

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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

Evaluating ChainForge is the easy part; running it is not. DEV.co offers software development services and AI software development services for ai frameworks teams that need a partner past the proof of concept.

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.

Talk to DEV.coSchedule a Consultation