BigLaw

The BigLaw tool stack. Open. Free.

What Am Law 100 firms spend $2M/year on — consolidated into one open-source platform, free for solos, boutiques, and small firms.

The platform is a single static Go binary — it runs end-to-end on a Raspberry Pi with 4 GB of RAM, or entirely on local models (Ollama / LM Studio). Benchmarks vs the original TypeScript implementation: 1.25×–6.9× (methodology). Sections below that reference src/*.ts paths describe the architecture as originally implemented — the code now lives in biglaw-go/internal/, and the TypeScript original is preserved at the tag typescript-final.

⚠ Experimental — Security Notice

BigLaw is an experimental research project. It is not production-hardened software.

The goal of this project is to build the most comprehensive open legal AI platform possible — covering the widest breadth of legal workflows, integrations, agent types, and jurisdictions. Comprehensiveness of capability is the primary objective. Test coverage and security hardening, while taken seriously and continuously improved, are secondary to that goal.

What this means in practice:

• The platform handles credentials, client matter data, and privileged legal communications. Firms deploying it are responsible for their own threat model.
• The codebase receives ongoing security sweeps and bug fixes, but has not undergone a formal independent security audit.
• Before deploying in any environment where real client data is involved, you must engage an independent security professional (pen tester, security engineer, or FDE — Forward Deployed Engineer / Formal Deployment Expert) to review the deployment configuration and code.
• AUTH_ENABLED=false is the default for local development. Never expose the API on a public or shared network without enabling authentication.
• API keys, session secrets, and OAuth credentials must be treated as production secrets regardless of environment.

Independent security review is not optional for production deployments. It is a prerequisite.

This notice does not diminish what BigLaw is — it is the most capable open legal AI stack available. It does mean you should not deploy it like a SaaS product without the due diligence that any complex, credential-holding, client-data-processing system requires.

What BigLaw Is

BigLaw is a cross between a platform, an experiment, and an art project.

As a platform, it is the most comprehensive open legal AI stack that exists — spanning research, drafting, redlining, e-signatures, briefing, docketing, billing, and collaboration across a bench of 100+ agents in a structured multi-round debate architecture.

As an experiment, it is an ongoing attempt to answer a genuine engineering question: how much of the $50,000–150,000 per-lawyer-per-year legal tech stack can be replicated with open models, open protocols, and open code? The answer so far is: most of it.

As an art project, it is a provocation. The cost chart below is not a sales pitch. It is a statement about who gets access to tools and who doesn't, and what happens when that changes. It is deliberately maximalist, deliberately opinionated, and deliberately not finished.

You are not buying a product. You are picking up a thing that is still being built and deciding what to do with it.

Legal Notices and Disclaimers

Read these. They are not boilerplate. They describe real risks that apply to you.

No Legal Advice

BigLaw does not provide legal advice. Nothing produced by this software — no output, finding, draft, analysis, summary, headnote, redline, briefing, or synthesis — constitutes legal advice, and none of it should be relied upon as such.

BigLaw is a software tool that uses large language models to assist with legal research and document tasks. LLMs hallucinate. They misstate case holdings. They miss recent developments. They confuse jurisdictions. They produce authoritative-sounding text that is factually wrong. The debate and verification protocols in this system reduce these errors but do not eliminate them.

Every output of this system requires review by a licensed attorney before it is used in any legal matter. Relying on unreviewed AI output in client matters may constitute malpractice, regardless of how capable the underlying system appears.

If you are not a licensed attorney and you are using this software to answer legal questions about your own situation: please consult a lawyer. This software is not a substitute.

No Attorney-Client Relationship

Use of BigLaw does not create an attorney-client relationship of any kind — between you and Discover Legal, between you and any contributor to this project, or between you and any AI system operated through this software.

⚠ PRIVILEGE IS NOT GUARANTEED

Whether communications, outputs, or data processed through this system attract legal professional privilege (attorney-client privilege, legal advice privilege, litigation privilege, or equivalent) depends entirely on your jurisdiction, the specific facts of your deployment, how the system is configured, who has access to it, and how outputs are used.

Do not assume privilege applies. It may not.

To structure a deployment that maximises privilege protection for your jurisdiction — including network isolation, access controls, data residency, and workflow design — engage an independent FDE (Forward Deployed Engineer / Formal Deployment Expert) before handling any privileged matter.

Unauthorised Practice of Law

Depending on your jurisdiction, using AI tools to perform certain legal tasks — drafting court documents, providing legal advice to third parties, representing parties in legal proceedings — may constitute the unauthorised practice of law if performed by a non-attorney. The fact that the work is AI-assisted does not change this analysis. Know your jurisdiction's rules.

If you are a law firm deploying BigLaw, you remain responsible for supervising all AI-assisted work product under your professional responsibility obligations, including the duty of competence (understanding the technology), the duty of confidentiality (securing client data), and the duty of supervision (reviewing outputs before they leave the firm).

Confidentiality and Data Security

BigLaw processes whatever data you give it. If you feed it client communications, privileged documents, personally identifiable information, health records, financial data, or anything else that is sensitive or regulated, that data will flow through your configured model provider and may be stored locally. Where that data goes depends entirely on how you have deployed the system.

BigLaw supports multiple inference backends — the data handling implications differ for each:

flowchart LR
    BL["BigLaw"]

    BL -->|"default

ANTHROPIC_API_KEY"| ANT["Anthropic API

<i>Haiku / Sonnet / Opus</i>

─────────────

Data leaves infrastructure

BAA: enterprise tier only

Review DPA before use"]

    BL -->|"OPENAI_API_KEY or

AZURE_OPENAI_*"| OAI["OpenAI / Azure OpenAI

<i>GPT-4o etc.</i>

─────────────

Data leaves infrastructure

BAA: ChatGPT Ent / Azure only

Azure has stronger DPA terms"]

    BL -->|"OLLAMA_ENABLED=true

LOCAL_INFERENCE_URL"| LOC["Local inference

<i>Ollama · LM Studio · vLLM</i>

─────────────

Data stays on your hardware

No BAA needed

Air-gap capable"]

    style LOC fill:#166534,color:#fff
    style ANT fill:#1e3a5f,color:#fff
    style OAI fill:#1e3a5f,color:#fff

• Anthropic API (default) — data is sent to Anthropic's servers subject to their data processing terms and usage policies. Review these before using with client data.
• OpenAI / Azure OpenAI — data is sent to OpenAI or Microsoft's servers subject to their respective terms. Azure OpenAI offers enterprise data handling commitments that the standard OpenAI API does not.
• Ollama / LM Studio / local inference (OLLAMA_ENABLED=true or LOCAL_INFERENCE_URL) — data never leaves your infrastructure. For air-gapped or maximally confidential deployments, local inference is the only option that gives you complete data control.

Regardless of backend, data may also be: - Stored in the local vector database (persists to disk at ./data/) - Written to the audit log (JSONL, also on disk) - Included in prompts that are cached by a cloud API provider

Regulatory obligations depend on your jurisdiction and the nature of the data:

• HIPAA (US) — if you process protected health information, you need a Business Associate Agreement (BAA) with your model provider. Anthropic offers BAAs on certain enterprise tiers only. OpenAI offers BAAs on ChatGPT Enterprise and Azure OpenAI. Standard API tiers typically do not include BAA coverage. If you cannot get a BAA, use local inference.
• GDPR (EU/EEA) — processing personal data of EU residents requires a lawful basis and, for cloud providers, appropriate Standard Contractual Clauses or equivalent transfer mechanisms. Data residency matters. Check where your provider processes and stores data.
• CCPA / US state privacy laws — obligations vary by state and the nature of the data.
• Bar association ethics rules — most jurisdictions now have guidance on cloud-based legal technology. Many require a reasonable investigation of the provider's security and privacy practices before using the service with client data.

The bottom line: your data handling obligations depend on your jurisdiction, your client base, the sensitivity of the data, and which inference backend you use. There is no universal answer. Engage qualified legal counsel and an independent FDE to map your specific obligations before deploying with real client data.

Deployment Liability

You deploy this software at your own risk. Discover Legal and the contributors to this project provide it under the AGPL-3.0 licence, which explicitly disclaims all warranties, including fitness for a particular purpose and non-infringement.

Specific risks that arise from misconfigured or insecure deployment include:

• Client data breach. If the API is exposed without authentication (AUTH_ENABLED=false on a network-accessible host), any client matter data ingested into the system is potentially accessible to anyone who can reach the endpoint. This would constitute a data breach under most applicable law and a serious professional responsibility violation.
• Credential exposure. API keys, OAuth tokens, and session secrets stored in .env files or accessible via a misconfigured server can be extracted and used to incur costs, access third-party systems, or impersonate your firm.
• Prompt injection. Malicious content in documents you ingest or queries you run through the system could potentially manipulate agent outputs. The system includes defences against this but they are not complete.
• Malpractice exposure. Using AI-generated output without adequate review in a client matter creates professional liability risk. This risk is yours, not ours.
• Regulatory exposure. Depending on your jurisdiction and practice area, use of AI tools in legal matters may trigger disclosure obligations to clients, adverse parties, or courts. Some courts require disclosure of AI use in filings. Check your local rules.

Jurisdiction

This software is designed to support legal work across multiple jurisdictions. It is not certified, approved, or validated for use in any jurisdiction. The agents, workflows, and outputs are not a substitute for jurisdiction-specific legal expertise.

Third-Party Services

BigLaw integrates with numerous third-party services — Anthropic, Microsoft Graph, Google Workspace, Slack, Clio, CourtListener, Westlaw, Everlaw, Ironclad, DocuSign, and others. Your use of those services through this software is governed by their own terms. BigLaw is not affiliated with, endorsed by, or a certified partner of any of these services.

Summary

You are using experimental software in one of the highest-stakes professional contexts that exists. The software is capable and the engineering is serious. It is also unaudited, incompletely tested, and built for comprehensiveness first. Use it with appropriate scepticism, appropriate oversight, and appropriate professional responsibility.

BigLaw isn't a chatbot with a legal prompt. It's an orchestration engine that replaces a stack of vendor contracts with a single open-source platform.

It runs DyTopo rounds of granular epistemic, conceptual, and writing agents over a RuVector native HNSW registry — and puts a debate + verification protocol between every finding and the page. Low-confidence or challenged findings stop at a human gate before they reach final synthesis.

Big Michael is the agent that lives inside your firm's collaboration channels. @-mention him in Teams or Slack and he dispatches tasks to BigLaw's bench, surfaces matter status and client briefings, and posts back when work is done — turning the platform into a conversational layer on top of everything else the firm already uses.

The cost chart

The tab in your browser you never click is a $300,000 invoice.

Am Law 100 firms