Vibecode Harvey
track this build5 steps, step by step0%NOT REALLY · don't bother
You can absolutely build a local RAG assistant over your own PDFs in an afternoon, and for reading your lease or a vendor contract that is genuinely enough. Harvey is not sold on that loop. It is sold on trained-and-evaluated legal workflows, curated case law and regulatory sources under license, deployment that survives a law firm's security review, and the ability to put a name behind an output that a partner will bill against. The thing you cannot one-shot is the confidence to rely on the answer, which in legal work is the entire product. A personal replacement is fine for personal stakes and dangerous the moment money or a filing depends on it.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of Harvey. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # Harvey indie build ## Goal Build the smallest trustworthy replacement for the core Harvey workflow for one developer or a tiny team. ## Scope Indexes a folder of your own contracts and PDFs locally, then answers questions about them with quotes and page citations so you can check every claim yourself. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - Licensed primary law: case law, statutes, filings and regulatory corpora you cannot legally scrape together - Workflow products that have been evaluated by actual lawyers: diligence checklists, redline review, deposition prep - Firm-grade deployment: SSO, data residency, retention controls, audit logs, security questionnaires answered - Anyone to blame. Your hallucination is your malpractice exposure - Integration into the systems legal work actually lives in: DMS, iManage, Word, the review platform If those capabilities are essential, use Harvey instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build a local document Q&A tool for my own contracts and PDFs. Python 3.11, single project, no web framework, no accounts, no telemetry. Stack, no alternatives: - CLI with Typer - pypdf for text extraction, page numbers preserved - SQLite with the sqlite-vec extension for vector storage - OpenAI API for embeddings and answers, key from .env via python-dotenv, plus an --ollama flag that swaps to a local model at http://localhost:11434 Commands: - ingest PATH: walk a folder, extract text per page, chunk to roughly 800 tokens with 100 overlap, store chunk text plus doc name plus page number, embed and index. Skip files already ingested unless --force. - ask "QUESTION": retrieve top 12 chunks, then answer with an LLM that is instructed to answer only from the provided chunks and to say "not in these documents" when the answer is absent. Every claim must carry an inline citation like [contract.pdf p.4]. - sources "QUESTION": print the retrieved chunks verbatim with file and page, no LLM, so I can read the raw text. - docs: list ingested files, page counts, chunk counts. In scope: local-only storage in ./index.db, deterministic chunking, a --model flag, plain text output. Out of scope: web UI, multi-user, cloud sync, any bundled case law or statute data, any attempt to cite external legal sources. Print a one-line disclaimer after every answer: this is a reading aid over my own files, not legal advice, verify each citation. Include a README with setup, .env.example, and a short section explaining that answers are only as good as the documents I ingested. ## Required capabilities - An LLM API key, or a local model via Ollama if you would rather nothing leaves the machine - Your own documents: no licensed case law, no statute databases, no primary sources - Python 3.11 and a willingness to read the cited passage rather than trust the summary ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a lean indie version of Harvey. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # Harvey indie build ## Goal Build the smallest trustworthy replacement for the core Harvey workflow for one developer or a tiny team. ## Scope Indexes a folder of your own contracts and PDFs locally, then answers questions about them with quotes and page citations so you can check every claim yourself. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - Licensed primary law: case law, statutes, filings and regulatory corpora you cannot legally scrape together - Workflow products that have been evaluated by actual lawyers: diligence checklists, redline review, deposition prep - Firm-grade deployment: SSO, data residency, retention controls, audit logs, security questionnaires answered - Anyone to blame. Your hallucination is your malpractice exposure - Integration into the systems legal work actually lives in: DMS, iManage, Word, the review platform If those capabilities are essential, use Harvey instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build a local document Q&A tool for my own contracts and PDFs. Python 3.11, single project, no web framework, no accounts, no telemetry. Stack, no alternatives: - CLI with Typer - pypdf for text extraction, page numbers preserved - SQLite with the sqlite-vec extension for vector storage - OpenAI API for embeddings and answers, key from .env via python-dotenv, plus an --ollama flag that swaps to a local model at http://localhost:11434 Commands: - ingest PATH: walk a folder, extract text per page, chunk to roughly 800 tokens with 100 overlap, store chunk text plus doc name plus page number, embed and index. Skip files already ingested unless --force. - ask "QUESTION": retrieve top 12 chunks, then answer with an LLM that is instructed to answer only from the provided chunks and to say "not in these documents" when the answer is absent. Every claim must carry an inline citation like [contract.pdf p.4]. - sources "QUESTION": print the retrieved chunks verbatim with file and page, no LLM, so I can read the raw text. - docs: list ingested files, page counts, chunk counts. In scope: local-only storage in ./index.db, deterministic chunking, a --model flag, plain text output. Out of scope: web UI, multi-user, cloud sync, any bundled case law or statute data, any attempt to cite external legal sources. Print a one-line disclaimer after every answer: this is a reading aid over my own files, not legal advice, verify each citation. Include a README with setup, .env.example, and a short section explaining that answers are only as good as the documents I ingested. ## Required capabilities - An LLM API key, or a local model via Ollama if you would rather nothing leaves the machine - Your own documents: no licensed case law, no statute databases, no primary sources - Python 3.11 and a willingness to read the cited passage rather than trust the summary ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a production product version of Harvey. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== PRODUCT.md ===== # Harvey product brief ## Problem You can absolutely build a local RAG assistant over your own PDFs in an afternoon, and for reading your lease or a vendor contract that is genuinely enough. Harvey is not sold on that loop. It is sold on trained-and-evaluated legal workflows, curated case law and regulatory sources under license, deployment that survives a law firm's security review, and the ability to put a name behind an output that a partner will bill against. The thing you cannot one-shot is the confidence to rely on the answer, which in legal work is the entire product. A personal replacement is fine for personal stakes and dangerous the moment money or a filing depends on it. ## Product outcome Indexes a folder of your own contracts and PDFs locally, then answers questions about them with quotes and page citations so you can check every claim yourself. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - An LLM API key, or a local model via Ollama if you would rather nothing leaves the machine - Your own documents: no licensed case law, no statute databases, no primary sources - Python 3.11 and a willingness to read the cited passage rather than trust the summary ## Explicit non-goals for v1 - Licensed primary law: case law, statutes, filings and regulatory corpora you cannot legally scrape together - Workflow products that have been evaluated by actual lawyers: diligence checklists, redline review, deposition prep - Firm-grade deployment: SSO, data residency, retention controls, audit logs, security questionnaires answered - Anyone to blame. Your hallucination is your malpractice exposure - Integration into the systems legal work actually lives in: DMS, iManage, Word, the review platform ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees. ===== ARCHITECTURE.md ===== # Architecture ## Starting brief Build a local document Q&A tool for my own contracts and PDFs. Python 3.11, single project, no web framework, no accounts, no telemetry. Stack, no alternatives: - CLI with Typer - pypdf for text extraction, page numbers preserved - SQLite with the sqlite-vec extension for vector storage - OpenAI API for embeddings and answers, key from .env via python-dotenv, plus an --ollama flag that swaps to a local model at http://localhost:11434 Commands: - ingest PATH: walk a folder, extract text per page, chunk to roughly 800 tokens with 100 overlap, store chunk text plus doc name plus page number, embed and index. Skip files already ingested unless --force. - ask "QUESTION": retrieve top 12 chunks, then answer with an LLM that is instructed to answer only from the provided chunks and to say "not in these documents" when the answer is absent. Every claim must carry an inline citation like [contract.pdf p.4]. - sources "QUESTION": print the retrieved chunks verbatim with file and page, no LLM, so I can read the raw text. - docs: list ingested files, page counts, chunk counts. In scope: local-only storage in ./index.db, deterministic chunking, a --model flag, plain text output. Out of scope: web UI, multi-user, cloud sync, any bundled case law or statute data, any attempt to cite external legal sources. Print a one-line disclaimer after every answer: this is a reading aid over my own files, not legal advice, verify each citation. Include a README with setup, .env.example, and a short section explaining that answers are only as good as the documents I ingested. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it. ===== AGENTS.md ===== # Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone. ===== MILESTONES.md ===== # Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations. ===== OPERATIONS.md ===== # Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted Harvey capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
README.md
# Harvey indie build ## Goal Build the smallest trustworthy replacement for the core Harvey workflow for one developer or a tiny team. ## Scope Indexes a folder of your own contracts and PDFs locally, then answers questions about them with quotes and page citations so you can check every claim yourself. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - Licensed primary law: case law, statutes, filings and regulatory corpora you cannot legally scrape together - Workflow products that have been evaluated by actual lawyers: diligence checklists, redline review, deposition prep - Firm-grade deployment: SSO, data residency, retention controls, audit logs, security questionnaires answered - Anyone to blame. Your hallucination is your malpractice exposure - Integration into the systems legal work actually lives in: DMS, iManage, Word, the review platform If those capabilities are essential, use Harvey instead of pretending the gap is solved.
AGENTS.md
# Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs".
BUILD_PLAN.md
# Build plan ## Original build brief Build a local document Q&A tool for my own contracts and PDFs. Python 3.11, single project, no web framework, no accounts, no telemetry. Stack, no alternatives: - CLI with Typer - pypdf for text extraction, page numbers preserved - SQLite with the sqlite-vec extension for vector storage - OpenAI API for embeddings and answers, key from .env via python-dotenv, plus an --ollama flag that swaps to a local model at http://localhost:11434 Commands: - ingest PATH: walk a folder, extract text per page, chunk to roughly 800 tokens with 100 overlap, store chunk text plus doc name plus page number, embed and index. Skip files already ingested unless --force. - ask "QUESTION": retrieve top 12 chunks, then answer with an LLM that is instructed to answer only from the provided chunks and to say "not in these documents" when the answer is absent. Every claim must carry an inline citation like [contract.pdf p.4]. - sources "QUESTION": print the retrieved chunks verbatim with file and page, no LLM, so I can read the raw text. - docs: list ingested files, page counts, chunk counts. In scope: local-only storage in ./index.db, deterministic chunking, a --model flag, plain text output. Out of scope: web UI, multi-user, cloud sync, any bundled case law or statute data, any attempt to cite external legal sources. Print a one-line disclaimer after every answer: this is a reading aid over my own files, not legal advice, verify each citation. Include a README with setup, .env.example, and a short section explaining that answers are only as good as the documents I ingested. ## Required capabilities - An LLM API key, or a local model via Ollama if you would rather nothing leaves the machine - Your own documents: no licensed case law, no statute databases, no primary sources - Python 3.11 and a willingness to read the cited passage rather than trust the summary ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden.
.env.example
# Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
PRODUCT.md
# Harvey product brief ## Problem You can absolutely build a local RAG assistant over your own PDFs in an afternoon, and for reading your lease or a vendor contract that is genuinely enough. Harvey is not sold on that loop. It is sold on trained-and-evaluated legal workflows, curated case law and regulatory sources under license, deployment that survives a law firm's security review, and the ability to put a name behind an output that a partner will bill against. The thing you cannot one-shot is the confidence to rely on the answer, which in legal work is the entire product. A personal replacement is fine for personal stakes and dangerous the moment money or a filing depends on it. ## Product outcome Indexes a folder of your own contracts and PDFs locally, then answers questions about them with quotes and page citations so you can check every claim yourself. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - An LLM API key, or a local model via Ollama if you would rather nothing leaves the machine - Your own documents: no licensed case law, no statute databases, no primary sources - Python 3.11 and a willingness to read the cited passage rather than trust the summary ## Explicit non-goals for v1 - Licensed primary law: case law, statutes, filings and regulatory corpora you cannot legally scrape together - Workflow products that have been evaluated by actual lawyers: diligence checklists, redline review, deposition prep - Firm-grade deployment: SSO, data residency, retention controls, audit logs, security questionnaires answered - Anyone to blame. Your hallucination is your malpractice exposure - Integration into the systems legal work actually lives in: DMS, iManage, Word, the review platform ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees.
ARCHITECTURE.md
# Architecture ## Starting brief Build a local document Q&A tool for my own contracts and PDFs. Python 3.11, single project, no web framework, no accounts, no telemetry. Stack, no alternatives: - CLI with Typer - pypdf for text extraction, page numbers preserved - SQLite with the sqlite-vec extension for vector storage - OpenAI API for embeddings and answers, key from .env via python-dotenv, plus an --ollama flag that swaps to a local model at http://localhost:11434 Commands: - ingest PATH: walk a folder, extract text per page, chunk to roughly 800 tokens with 100 overlap, store chunk text plus doc name plus page number, embed and index. Skip files already ingested unless --force. - ask "QUESTION": retrieve top 12 chunks, then answer with an LLM that is instructed to answer only from the provided chunks and to say "not in these documents" when the answer is absent. Every claim must carry an inline citation like [contract.pdf p.4]. - sources "QUESTION": print the retrieved chunks verbatim with file and page, no LLM, so I can read the raw text. - docs: list ingested files, page counts, chunk counts. In scope: local-only storage in ./index.db, deterministic chunking, a --model flag, plain text output. Out of scope: web UI, multi-user, cloud sync, any bundled case law or statute data, any attempt to cite external legal sources. Print a one-line disclaimer after every answer: this is a reading aid over my own files, not legal advice, verify each citation. Include a README with setup, .env.example, and a short section explaining that answers are only as good as the documents I ingested. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.
AGENTS.md
# Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone.
MILESTONES.md
# Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.
OPERATIONS.md
# Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted Harvey capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
$ choose a build depth, inspect the files, then open the complete pack in your agent · this prompt is generated from the build plan · improve it via PR
A law firm is not paying for text generation, it is paying for defensibility. The output has to be traceable to a licensed source, the deployment has to pass a security review that takes months, the workflows have to have been tested against how associates actually do diligence, and there has to be a vendor contract with indemnities when something goes wrong. Individual lawyers also cannot use a homemade tool on client matters without answering awkward questions about where the privileged data went. A local RAG box over your own files solves none of that and does not need to; it solves reading your own documents faster, which is a different and much smaller job.
xLicensed primary law: case law, statutes, filings and regulatory corpora you cannot legally scrape together
xWorkflow products that have been evaluated by actual lawyers: diligence checklists, redline review, deposition prep
xFirm-grade deployment: SSO, data residency, retention controls, audit logs, security questionnaires answered
xAnyone to blame. Your hallucination is your malpractice exposure
xIntegration into the systems legal work actually lives in: DMS, iManage, Word, the review platform
Nothing worth pointing at. That's why the prompt exists.
share on X ↗"I just replaced Harvey with one prompt"
Vibecode Harvey
Not really. Harvey's value is not the code: The moat is licensed primary law plus an enterprise security posture that took years to clear, and a name a partner can put in a risk memo. See the honest breakdown above.
How much does Harvey cost?
Harvey's pricing is usage-based or varies by plan · No published price anywhere on the site; every pricing CTA routes to 'Request a Demo'. harvey.ai/pricing returns 404..
What do I lose by replacing Harvey?
Honestly: Licensed primary law: case law, statutes, filings and regulatory corpora you cannot legally scrape together; Workflow products that have been evaluated by actual lawyers: diligence checklists, redline review, deposition prep; Firm-grade deployment: SSO, data residency, retention controls, audit logs, security questionnaires answered; Anyone to blame. Your hallucination is your malpractice exposure; Integration into the systems legal work actually lives in: DMS, iManage, Word, the review platform. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to Harvey?
No mature open-source alternative worth pointing at, which is exactly why the one-shot prompt on this page exists.