Fenic
Semantic DataFrames for LLM-powered data processing
A sharp open-source framework for semantic data wrangling with LLMs. Caching and lineage make it more robust than ad-hoc scripts, but Python fluency is mandatory. Best for prototyping and exploration, not for real-time streaming or no-code teams.
- Data scientists processing messy text with LLMs
- AI engineers building semantic data pipelines
- Agent developers structuring unstructured sources
- Teams exploring LLM-powered data enrichment
- Real-time streaming data pipelines
- Users seeking a no-code or GUI-based tool
- Enterprise-scale production without custom orchestration
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In short
Fenic — Semantic DataFrames for LLM-powered data processing. Best for Data scientists processing messy text with LLMs, AI engineers building semantic data pipelines, Agent developers structuring unstructured sources. Free to use.
What's new in Fenic
Checked 11 days agoAcross the latest 3 updates: 1 launch and 2 changelog entries.
Semantic DataFrames: engineering notes, shipping logs, and release notes
Fenic 0.7.0: Gemini 3 Flash, four thinking levels, optional usage summary suppression, README overhaul.
fenic 0.7.0: Gemini 3 Flash, Granular Thinking Levels — plus session and docs improvements
Fenic 0.7.0 adds Gemini 3 Flash Preview with four thinking levels, optional usage summary suppression, and a use-case-first README overhaul.
fenic 0.6.0: LLM Caching, New Models, DataFrame Ops — plus PDF and Agent upgrades
Fenic 0.6.0 adds persistent LLM response caching, Claude 4.5 / GPT-5.1 / Gemini 3 Pro support, 20+ new DataFrame operations, and expands PDF parsing to OpenAI and OpenRouter.
Viability Score
How likely is Fenic to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Semantic DataFrames for unstructured text
- LLM-powered extraction, classification, embedding
- Join datasets by meaning
- Lineage tracking and explainability
- Cost tracking per operation
- Cached execution plans (rerun without redo)
- Promote data to tables or MCP tools
- Support for OpenAI, Anthropic, Google, OpenRouter
- PDF parsing with multiple backends
- 20+ new DataFrame operations (0.6.0)
- Persistent LLM response caching (0.6.0)
- Claude 4.5, GPT-5.1, Gemini 3 Pro support (0.6.0)
- Gemini 3 Flash with four thinking levels (0.7.0)
- Optional usage summary suppression (0.7.0)
- Python 3.10+ required
About Fenic
Fenic is an open-source Python framework that transforms messy unstructured data into typed, queryable Semantic DataFrames, enabling data scientists, AI engineers, and agent developers to extract, classify, embed, and join data using LLM-powered operations. Unlike traditional pandas workflows, Fenic lets you query by meaning alongside metadata, trace every operation via a lineage graph for explainability and cost tracking, and cache execution plans to avoid re-running expensive LLM calls. The latest 0.7.0 release adds Gemini 3 Flash with four granular thinking levels, optional usage summary suppression, and session improvements. Version 0.6.0 introduced persistent LLM response caching, support for Claude 4.5, GPT-5.1, and Gemini 3 Pro, plus over 20 new DataFrame operations and expanded PDF parsing with multiple backends. Fenic integrates directly with multiple LLM providers (OpenAI, Anthropic, Google, OpenRouter) and allows promoting results to production artifacts like tables or MCP tools, bridging exploration and deployment. While powerful for semantic enrichment, Fenic requires Python proficiency and is not a no-code solution — it excels at prototyping and exploration but may need custom orchestration for large-scale production use. Compared to ad-hoc scripts or traditional ETL, Fenic provides a structured, auditable pipeline with built-in cost visibility and caching.
Behind the Verdict
Fenic fills a clear gap: turning messy text into typed, queryable structures with LLMs while keeping costs and lineage visible. The caching introduced in 0.6.0 is a huge practical win — no more burning tokens on reruns. Gemini 3 Flash with thinking levels in 0.7.0 gives fine-grained control over reasoning depth. We'd reach for this when we need to prototype a semantic enrichment pipeline quickly, especially if we plan to promote results to a table or MCP tool later. Where it bites: you need Python fluency, and the framework is still early-stage — large-scale production may require custom orchestration around it. The closest alternative is probably writing ad-hoc scripts with LangChain or llamaindex, but Fenic gives you a DataFrame abstraction that feels more natural for data engineers. If you're comfortable with Jupyter notebooks and want to add LLM-powered columns to a dataset, this is one of the most direct ways to do it.
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Use Cases
- Extract structured fields like names, dates, emails from free-text documents.
- Classify customer feedback into sentiment categories with LLM.
- Join two datasets by semantic similarity of text columns.
- Embed product descriptions and query by similarity.
- Rerun a data processing pipeline without re-invoking LLMs thanks to caching.
- Promote a curated Semantic DataFrame to an MCP tool for agent consumption.
Models Under the Hood
as of 2026-07-16
Limitations
- Fenic is a Python library, not a managed service; users must handle their own compute and API keys.
- Caching reduces costs but still requires LLM API calls initially.
- No built-in GUI or web interface; all interactions are via CLI or code.
Integrations
Resources & Guides
Official links
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