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Socratic is a framework for building reliable vertical AI agents by letting human experts teach agents interactively, tu
Socratic is a KnowledgeOps platform that helps teams build expert-level knowledge base for vertical, domain-specific agents. You point Socratic at your existing domain docs, and Socratic collaborates with you to build a curated and reliable knowledge base that you actually trust.
Socratic is a KnowledgeOps platform that helps teams build expert-level, trustworthy knowledge bases for vertical, domain-specific agents.
Point Socratic at your existing docs, and collaborate with Socratic to synthesize a curated, reliable, auditable knowledge base.
High-quality domain knowledge is critical for building high-value agents.
Today, most teams rely on "document dump" workflows (e.g., RAG): give the agent all the files and hope it interprets them correctly.
But this fails for the same reason it would with a human - you wouldn't onboard a new hire by handing them a folder of docs and saying "good luck."
| Traditional Document Dumps (e.g., RAG) | Socratic (KnowledgeOps) |
|---|---|
| ❌ "Hope-based" knowledge retrieval | ✅ Expert-controlled curation |
| ❌ Agent misinterprets raw docs | ✅ Guided, human-in-the-loop knowledge distillation |
| ❌ Opaque, “black box” knowledge | ✅ Clear, auditable knowledge base |
| ❌ Hard to detect contradictions | ✅ Surfaces ambiguities and inconsistencies |
Socratic rethinks how expert knowledge should be distilled. It treats knowledge building like teaching a human expert: capturing tacit rules, resolving ambiguities, surfacing edge cases, and clarifying domain intuitions.
Socratic does this by combining:
The result is a structured, trustworthy knowledge base, not an opaque embedding index giving you control over what your agent actually knows.
Using Socratic to build knowledge base to understand Socratic itself (3-min): https://youtu.be/R4YpbqQZlpU
Using Socratic to build knowledge base for Google Analytics SQL agent: https://youtu.be/L20vOB3whMs
Linux kernel >= 5.15 is required.
git clone https://github.com/kevins981/Socratic.git
cd socratic
# optional
conda create -n socratic python=3.10 -y
conda activate socratic
# install
pip install -e .
Install OpenAI Codex:
# might need sudo
npm install -g @openai/codex
For Web-UI:
# if npm is not installed
sudo apt install nodejs npm
cd web
npm install
Assume that the project name is airline_demo and relevant source files are located in examples/repos/tau_airline.
Web UI (recommended):
# 0. Create project
socratic-cli create --name airline_demo --input_dir examples/repos/tau_airline
# 1. Launch web UI
cd web
npm run dev:project -- --project airline_demo
Command line interface:
# Create project
socratic-cli create --name airline_demo --input_dir examples/repos/tau_airline
# Start working on your knowledge base!
socratic-cli synth --project airline_demo
# Digest model to look deeper into your existing knowledge base
socratic-cli digest --project airline_demo
# Export knowledge base (Agent.md)
socratic-cli export --project airline_demo
To configure the LLM model to use, create a .env file at the project root (see .env.example).
Socratic is powered by OpenAI Codex. If you already have access to Codex via a ChatGPT subscription, you can directly use it by setting PROVIDER=chatgpt in .env file. See .env.example for examples.
You can also bring your own OpenAI API key and use any OpenAI model available through the API.
To do this, specify PROVIDER=openai, BASE_URL=https://api.openai.com/v1, and ENV_KEY=OPENAI_API_KEY. Make sure OPENAI_API_KEY env var is set in your shell.
In addition, Socratic works with any LLM that exposes an OpenAI-compatible API, including locally hosted models. See .env.example for examples.
Some models do not work well with Socratic due to tool-calling issues. In practice:
gpt-5.2(-codex), gpt-5.1glm-4.6, grok-4In some cases, the model may encouter tool calling problems and therefore fail unexpectedly. E.g., gpt-oss-120 is known to be unstable. In general, smaller models tend to be more unstable.
Socratic relies on multi-step reasoning and tool operations, so the model must be sufficiently capable to run the full pipeline reliably.
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