REQL

REQL is a local repository context and working-memory layer for coding agents and developer tools. It compiles source files and supported documents into a property graph, then answers bounded queries over code, symbols, tests, documents, dependencies, findings, and provenance.

In the intended coding-agent integration, the user does not treat REQL as a separate manual workflow. After the assistant instructions or skill are installed for Codex, Claude, Gemini, Cursor, or another agent environment, the agent uses REQL while it works: it compiles or refreshes the repository graph, retrieves compact source-backed context, records task-local notes and decisions, and reconstructs operational history and plans after context loss. Repository facts remain exclusively in the canonical project graph.

Token and reasoning budget: REQL helps coding agents spend fewer tokens on repository discovery and more tokens on the actual change. Bounded retrieval returns the files, symbols, relationships, and source spans that matter for the current task, while Agent Workspace preserves the task map, decisions, risks, and finish messages needed to reason through complex or large implementations across context windows.

The important part is that REQL gives the agent deterministic repository memory before and during edits:

• project compile scans the project, fingerprints artifacts, parses supported code and documents, and writes graph nodes, edges, cache records, compilation runs, and deltas;
• application surface files are linked deterministically when the graph can infer relationships, such as controller render calls to templates and shared template/CSS/JS identifiers;
• retrieval commands such as query_context, query_explore, query_graph, and query_memories find lexical seed nodes, expand a bounded graph neighborhood, rank the result, and return compact source-backed context;
• Python, CLI, and MCP query_context calls share one typed request/result service, so scopes, budgets, confidence, and revision metadata have identical semantics at every provider boundary;
• every result can point back to paths, line ranges, relationships, evidence, and graph provenance instead of relying on broad source dumps;
• reql agent maintains per-agent working memory for plans, findings, decisions, tasks, risks, and finish messages without changing the canonical project graph;
• compact context and saved working maps reduce repeated source reading, which helps preserve token budget and keeps large, multi-step tasks coherent;
• incremental compile and watch mode keep the graph current without rebuilding unchanged files.

REQL is deterministic by default. Compilation, storage, query, retrieval, analysis, reports, and MCP access work locally without mandatory LLM calls, accounts, hosted services, or an external graph database. Optional semantic adapters can exist at integration boundaries, but the core memory system remains usable on its own.

Quick Start

REQL requires Python 3.10 or newer. Install the package with pip:

python -m pip install reql

For local development from a checkout, install it in editable mode:

python -m pip install -e .

Install assistant instructions for the coding-agent environment:

reql install codex

Replace codex with another supported agent platform, or let interactive install auto-detect one. The installed instructions make REQL part of the agent's normal repository workflow. The generated SKILL.md is a concise coding workflow: REQL bounds discovery, while the checked-out source and tests remain authoritative. Bootstrap, query, update, reporting, document, and Agent Workspace details stay in routed references/ files loaded only when their situation occurs. The commands below are the operations the agent integration uses to bootstrap context, retrieve focused evidence, and keep working memory:

reql project compile
reql project explain --focus "payment workflow"
reql project pipeline
reql project pipeline --code
reql query_context --query "payment service"
reql query_memories --query "payment service" --limit 8 --json
reql query_explore --query "payment service serialization" --view owners --view code
reql query_explore --query "profile template" --structural-duplicates-only

For coding-agent tasks, the normal query_context --code output shows at most eight paths: five to eight source files when available, reduced as needed to reserve room for up to three associated tests. Each source row includes its owner symbols and best bounded line range. Detailed graph metadata and planning fields remain available from query_context --json for programmatic consumers; structured results include contract schema_version, a deterministic graph_revision, and typed confidence metadata. When the highest ranked score is below 0.25, query_context short-circuits with Confidence: insufficient and explicitly allows one targeted rg fallback using the user's exact symbol, path, or error terms.

Agent Workspace commands store the agent's session-scoped working state while it implements, reviews, or documents a repository:

reql agent init --name "Focused implementation pass"
reql agent dashboard
reql agent note "Read src/memory/cli.py and found the argparse command surface"
reql agent note --public "Parser API now returns a document result"
reql agent note --agent agent:reviewer "Check the updated parser return type"
reql agent task add "Implement the reset behavior"
reql agent task list
reql agent task done TASK_ID "Reset behavior implemented and tests pass"
reql agent finish "Implementation notes ready for master review"
reql agent list
reql agent terminate agent:stale-worker
reql agent search "parser return type"

reql agent init creates or resumes the selected private dashboard and marks the agent active. Explicit reql agent --agent AGENT_ID ... or REQL_AGENT_ID=AGENT_ID takes precedence over a stable activity or thread id. Use reql agent dashboard for the public dashboard and the selected private dashboard; use reql agent --agent "agent:AGENT_ID" dashboard to view another agent's private dashboard when permitted.

The public dashboard contains the agent roster, coordination-safe active-task summaries, shared context, and drill information. A private dashboard contains the complete task list, private notes, and directed external notes. Public notes, task-completion messages, and finish messages are all timestamped shared context. Private history remains intact after finish or terminate.

reql agent finish "<outcome>" closes the current session, marks the agent finished, and publishes its supplied message as shared context. reql agent terminate AGENT_ID performs the same lifecycle cleanup for a stale agent, while preserving all task, note, and dashboard history.

reql agent list returns active agents by default; add --all for finished and terminated agents. reql agent search QUERY searches public context and permitted private dashboard history, returning timestamp, agent id, and enough context to explain each match.

Use agent note TEXT for a private self-note, agent note --agent AGENT_ID TEXT to send a private note to another agent, and agent note --public TEXT to publish shared context. Tasks remain private to their owner; only a task's completion message becomes public.

reql agent reset discards the selected agent's dashboard history. It never reads or modifies the canonical project graph.

Context results use schema version 2. Alongside the query-specific graph_revision, they report the committed source_revision and freshness state (current, refreshing, stale, or unknown).

From a source checkout, python cli.py ... exposes the same command surface without requiring an editable install:

python cli.py project compile
python cli.py query_context --query "payment service"

Use the Python API directly:

from reql import MemoryGraph

graph = MemoryGraph.open(".reql/memory.reql")

try:
    graph.compile_project(".")

    context = graph.query_context("payment service")
    print(context)
finally:
    graph.close()

Start MCP when an integration needs a tool server:

reql-mcp --read-only

Project/cache commands and reql storage clear default storage to ./.reql/memory.reql; other commands default to ./.reql/memory.reql. storage clear rebuilds that store from the current project tree and discards historical or archived graph state. Use --json for automation. See docs/CLI.md for the complete command reference, query modes, install behavior, MCP startup, config lookup, reports, exports, and maintenance workflows.

Use reql config set OPTION VALUE to add or update a validated setting in the local ./reql.conf, for example reql config set retention.agent_sessions 30.

Automatic project maintenance uses retention.commits from reql.conf (default 20). A REQL commit is a successful compilation that changes the project manifest and creates a ProjectRevision; clean compile invocations do not advance retention. When the limit is exceeded, REQL removes history, archived records, and project-owned usage entries older than the oldest retained commit while preserving the active graph. Agent coordination records retain the latest retention.agent_sessions completed sessions (default 20), and finish preserves the completed agent's private dashboard history.

Features

• Local project compilation into a property graph with explicit provenance.
• Retrieval with lexical seed nodes, bounded graph expansion, and chain-aware ranking.
• Compact query_context, query_explore, query_graph, and query_memories outputs for coding-agent workflows, including owner symbols, bounded source ranges, and associated test targets.
• Separate reql agent dashboard state for sessions, private tasks, private and external notes, public notes, completion messages, and finish messages. It contains no copied project, file, symbol, or canonical graph records.
• Local block-file persistence with fixed-size pages, compressed records, reader/writer lock diagnostics, safe stale-lock recovery, transactions, compaction, and atomic clean rebuilds.
• Incremental compilation cache with persistent compilation runs and graph deltas.
• Artifact document parsing for Markdown, plain text, and PDF with graceful fallbacks.
• Code artifact recognition for Python, TS/JS, Go, Rust, Java, C/C++, Ruby, C#, Kotlin, Scala, PHP, Swift, Lua, Zig, PowerShell, Elixir, Julia, Verilog, Fortran, Bash, SQL, Terraform, Apex, Pascal, Razor, and related extensions, with Tree-sitter AST graph extraction for recognized languages.
• Static-analysis findings for cleanup-oriented queries, including aggregated orphan-directory candidates so a detached folder is suggested once instead of file by file.
• Deterministic community detection, hub analysis, and bridge analysis with generic-node penalties.
• Markdown reports, JSON export, standalone graph.html, interactive project pipeline HTML and Mermaid export, guided launcher, installable CLI, typed Python API, and optional dependency-free MCP server.

Runtime Model

REQL works as a local repository index backed by a property graph:

• project compile scans the project with default ignores plus configured include/exclude rules;
• each artifact is fingerprinted, so unchanged files can be skipped on later compiles;
• supported code files are parsed into modules, symbols, imports, calls, dependencies, endpoints, config records, tests, and static-analysis findings;
• supported documents are split into source fragments, ranked document terms, raw observations, and document-to-code links when they explicitly name code symbols;
• graph records keep file paths, line ranges, evidence, confidence, and provenance so query results can be traced back to source;
• queries find lexical seed nodes, expand only a bounded graph neighborhood, rank the resulting records, and render compact context instead of dumping the repository;
• project explain projects technical code facts into business capabilities, architectural layers, multi-evidence semantic workflows with explicit implemented_by participants, and focus-specific change guidance without persisting another graph or requiring an LLM;
• project pipeline follows every detected entrypoint through project-local flow relations, collapses symbols into shared architectural components, and writes an interactive pipeline.html or Mermaid pipeline.mmd;
• project compile and watch mode reuse the same incremental compiler and write CompilationRun, GraphDelta, and cache records for changed or deleted artifacts.

The core path is deterministic and local. Optional semantic adapters can exist at integration boundaries, but project compilation, storage, retrieval, reports, analysis, and MCP tools do not require model calls.

Interactive Launcher

The project root launcher.py starts a guided terminal menu when run without arguments. It uses ./.reql/memory.reql in the current working directory by default and lets you choose actions interactively without writing command-line arguments:

python launcher.py

You can pass a storage path directly:

python launcher.py --storage .reql/memory.reql

The menu guides these workflows:

• create or open a graph storage file and initialize it;
• list available graph files under .reql/, open them, inspect them, or delete managed .reql files after an explicit name confirmation;
• retrieve ranked code records or compose bounded agent context;
• scan and incrementally compile projects;
• run predefined REQL queries or custom graph queries;
• inspect stats, communities, hubs, and graph analysis;
• export Markdown reports, JSON, and standalone graph.html;
• print Codex and Claude Desktop MCP configuration for reql-mcp.

For command-line automation, use python cli.py ... or reql:

reql project compile
reql query_memories --query "payment service" --limit 5 --json
reql query "FIND nodes WHERE type = 'Function' LIMIT 10"

Documentation

The README gives the project overview and common workflows. The focused documentation lives under docs/:

• Architecture: layers, storage port, compile flow, retrieval, maintenance, and deterministic analysis.
• Repository explanation: deterministic business capabilities, architecture, workflows, and change guidance derived from the code graph.
• CLI: command reference for compilation, retrieval, graph queries, exports, configuration, install helpers, and MCP startup.
• Configuration: reql.conf, defaults, overrides, scan rules, cache settings, document ingest, analysis toggles, and loader behavior.
• REQL language: first-class graph commands plus FIND, MATCH, PATH, SEARCH, RETRIEVE, EXPLAIN, filters, and examples.
• Schema: core node records, edge records, code types, and relations used in the graph.
• Storage: block adapter, data locality, compression, inspection, compaction, locking, transactions, and bounded operations.
• Artifact ingestion: supported document inputs, parser behavior, graph output, and graceful fallbacks.
• Code analysis: language support, Tree-sitter parsing, extracted symbols, static-analysis findings, and example queries.
• Incremental compilation: dirty planning, cache entries, deltas, deletion handling, and failure behavior.
• Graph analysis: deterministic communities, hubs, bridge signals, CLI usage, REQL examples, and known analysis limits.
• Reporting: generated Markdown reports and standalone HTML graph export.
• MCP server: stdio/HTTP server startup, tool descriptions, Codex and Claude configuration, workflow, and security notes.
• Extending: storage adapters, extractors, engines, and adding node or edge types.

Main Pipeline

REQL has a project compile pipeline. compile builds a technical graph for programming agents from scanning, AST/static analysis, document parsing, and deterministic document-to-code linking. It does not extract memories from chat or non-code prose.

Retrieval:

query
  -> tokenization
  -> lexical seed-node search
  -> bounded graph expansion
  -> graph-aware ranking
  -> subgraph or context block

Maintenance:

activation and usage signals
  -> salience update
  -> sidecar retrieval usage updates
  -> provenance preservation

Compile-time project scan:

project directory
  -> recursive scanner
  -> default ignore rules and config include/exclude filtering
  -> file classification and SHA-256 fingerprints
  -> Project + Directory + File + SourceArtifact nodes
  -> CONTAINS edges

Project compile uses the same fingerprinting path and built-in default ignores. Put additional compile exclusions in the configured scan.exclude list.

Incremental compilation:

SourceArtifact fingerprints
  -> ArtifactCacheEntry comparison
  -> dirty and deleted artifact set
  -> deterministic compile graph updates
  -> CompilationRun
  -> GraphDelta

Artifact document parsing:

SourceArtifact bytes
  -> document parser
  -> DocumentFragment records
  -> SourceFragment nodes
  -> provenance and document relations

Code analysis:

code artifact
  -> AST/static parser when supported
  -> Module / Package / Class / Interface / Function / Method / useful Variable nodes
  -> Import / Dependency / Endpoint / Schema / Config / Test nodes
  -> CONTAINS / DEFINES / METHOD / IMPORTS / CALLS / REFERENCES / INHERITS edges
  -> DEPENDS_ON / IMPORTS_FROM / RE_EXPORTS / READS / WRITES / RETURNS edges
  -> RAISES / DECORATED_BY / HANDLES_ROUTE / HAS_FINDING edges
  -> StaticAnalysisFinding cleanup candidates for unused code

Graph analysis:

graph nodes and edges
  -> deterministic community detection
  -> specificity-aware hub scoring
  -> cross-community bridge edges
  -> Community nodes, BRIDGES_COMMUNITY edges, and hub properties

Project Structure

src/api/
|-- memory_graph.py         # Public facade
|-- __init__.py             # Public Python API exports
src/agents/
|-- install.py              # Agent skill/instruction installers
|-- __init__.py             # Agent installer exports
src/mcp/
|-- tools.py                # Dependency-free MCP tool handlers
`-- server.py               # stdio and HTTP JSON-RPC MCP transports
src/memory/
|-- domain/                 # Pure models, constants, ids, time, exceptions
|-- storage/                # Storage/extractor protocols and public exports
|   `-- adapters/           # Concrete adapters, including BlockGraphStore
|-- extraction/             # Deterministic query/source extraction and optional adapters
|-- artifacts/              # Project scanning, file classification, fingerprints
|-- engines/                # Activation and salience scoring
|   |-- activation.py       # Spreading activation
|   `-- salience.py         # Salience scoring
|-- services/               # Application orchestration
|   |-- retrieval/          # Search, expansion, context projections, renderers
|   |-- incremental_compilation.py
|   `-- project_watch.py
|-- query/                  # REQL lexer, parser, AST, and evaluator
|-- analysis/               # Communities, centrality, specificity, hubs, bridges
|-- reporting/              # Markdown reports
|-- config/                 # internal defaults, project models, and loader
`-- cli.py                  # Command-line interface

Public API

The main entry point is MemoryGraph. The canonical public import is from reql import MemoryGraph.

from reql import MemoryGraph

graph = MemoryGraph.open(".reql/memory.reql")

Main operations:

• retrieve(query)
• compose_context(query)
• query_context(query)
• query_context_result(QueryContextRequest(...))
• query_context_payload(query)
• query_explore(query)
• query_graph(query)
• query_memories(query)
• query_memories_payload(query)
• locate(path)
• inspect_node(node_id)
• export_json()
• query(statement)
• compile_project(path)
• update_project(path)
• watch_project(path)
• project_status(path)
• project_history(path)
• project_revision(revision_id)
• project_report(path, output_dir=...)
• project_pipeline(path)
• cache_status(path)
• clear_cache(path)
• list_deltas()
• show_delta(delta_id)
• detect_communities(project_id=...)
• analyze_hubs(project_id=..., limit=...)

The typed query-context API is available from the canonical package:

from reql import MemoryGraph, QueryContextRequest, QueryMode, RetrievalBudget

request = QueryContextRequest(
    text="payment service",
    mode=QueryMode.INFORMATIVE,
    scopes=frozenset({"code"}),
    budget=RetrievalBudget(top_k=20, max_depth=3, max_items=20),
)
result = graph.query_context_result(request)
payload = result.to_dict()

Extending the Project

New Storage Backend

Implement memory.storage.graph_store.GraphStore and pass it to the facade:

from reql import MemoryGraph

store = MyGraphStore(...)
graph = MemoryGraph(store)

The bundled block backend is portable local persistence, not an architectural constraint.

New Extractor

Implement SemanticExtractor:

class MyExtractor:
    def extract(self, text: str):
        ...

graph = MemoryGraph.open(".reql/memory.reql", extractor=MyExtractor())

The extractor is used for query seed discovery. Project document ingest is handled by the local deterministic compiler path. The default MemoryGraph.open() extractor is dependency-free and deterministic.

Compile mode structurally parses text document fragments and links explicit documentation mentions back to compiled code symbols where possible.

New Node or Edge Types

Types are strings. To keep them coherent:

1. add constants in domain/constants.py;
2. update compiler, retrieval, reporting, or analysis code if dedicated logic is needed;
3. add integration tests when the new type changes retrieval, salience, or graph analysis.

License

MIT. See LICENSE.

Contributing

See CONTRIBUTING.md for development setup, contribution guidelines, and pull request expectations.