AgentLaw is legal research infrastructure where the primary consumer is an AI agent. Structured propositions with confidence scoring, authority hierarchy, and citation provenance — not document retrieval with an API wrapper.
Existing platforms give you ranked document lists. Every agent must then read, parse, assess authority, and repeat — burning tokens and time on work that should be done once.
| Document-Oriented | Proposition-Oriented | |
|---|---|---|
| Unit of information | A case or statute (document) | A legal assertion with provenance |
| Search model | Keywords → ranked document list | Structured query → graph traversal |
| Authority ranking | Human judgment at query time | Machine-readable hierarchy in every response |
| Citation checking | Visual signals (Shepard's / KeyCite) | Citation network as a queryable graph |
| Currency | "As of" dates on documents | Temporal validity on every node + conflict detection |
| Jurisdiction | Filters applied manually | First-class jurisdiction hierarchy |
Each layer builds on the one below. Agents consume the layer they need — from raw propositions to full regulatory compliance graphs.
Propositions as nodes. Typed edges (supports, contradicts, narrows, overrules). Authority hierarchy enforced at the data layer. Confidence scoring from four component signals.
Authority lookup, statute search, graph traversal, conflict detection, and temporal queries — exposed as API calls, not left to the agent to figure out.
Hierarchy resolution, Golsen rule, preemption mapping, and regulatory-body jurisdiction. Binding vs. persuasive authority resolved before it reaches your agent.
Current-law resolution accounting for amendments and sunsets. Point-in-time queries. Change webhooks when the law evolves.
Research contexts with cross-session continuity. Research chains for iterative refinement. Collaborative research so multiple agents avoid duplicating work.
Obligation mapping, compliance posture scoring, and enforcement pattern analysis — structured for automated regulatory monitoring.
When authorities conflict, the higher source controls. This is programmatically enforced in every API response — your agent never needs to implement hierarchy logic.
Every proposition carries a 0.0–1.0 confidence score computed from four signals: authority strength, recency, consistency, and novelty. Deterministic, auditable, re-scored when new cases arrive.
Propositions anchored to the same statute but at different authority levels are automatically flagged. Hierarchy resolution returns pre-grouped controlling, subordinate, and conflicting authorities.
Every proposition carries valid_from and valid_to dates. Superseded holdings don't silently pollute results — they're marked and traversable for historical research.
REST API with structured JSON responses. Every endpoint returns machine-readable authority metadata — no parsing required.
Keyword and statute-based search returning structured propositions with confidence scores, not document snippets.
Follow typed edges between propositions: supports, contradicts, narrows, interprets, overrules. Build reasoning chains programmatically.
Given a statute section, get pre-resolved results grouped by authority level — controlling, subordinate, conflicting. No hierarchy logic needed.
Every proposition is domain-expert verified before entering the graph. We start where we have the deepest expertise and expand from there.
248 verified propositions covering Collection Due Process hearings — deadlines, standards of review, collection alternatives, Tax Court procedure. Live at cdprights.com.
Deficiency proceedings, innocent spouse, passport revocation, penalty abatement. Same architecture, expanding the graph.
Regulatory obligations, filing requirements, financial regulation. Each domain follows the same pattern: public data → knowledge graph → expert validation → API.
Agents make thousands of queries. Per-query pricing aligns with how AI actually uses legal research.
AgentLaw is not a legal AI tool. It's the legal data infrastructure that legal AI tools query.
| Company | What they do | How AgentLaw differs |
|---|---|---|
| Harvey | AI assistant using legal databases | Harvey is the agent; AgentLaw is the database |
| Casetext / CoCounsel | AI on top of document stores | Propositions, not documents |
| Westlaw API | Document retrieval via API | Different data model entirely |
| CourtListener | Open legal documents | Adds the structured knowledge layer |
AgentLaw exposes a remote MCP server. Connect any MCP-compatible agent to query the legal knowledge graph with 8 tools.
search_propositions, get_proposition_details, get_by_statute, resolve_authority, graph traversal, topic search, review queue, list all.
Standard MCP SSE protocol. Works with Claude Desktop, Claude Code, and any MCP-compatible client.
Stop burning tokens on document retrieval. Query structured legal knowledge directly.