graqle
Connects with Git workflows to trigger automatic intelligence recompilation and maintain audit trails of architectural changes.
Supports Google Gemini as a backend for performing low-cost semantic queries and architectural reasoning.
Extracts architectural metadata, including functions, classes, and call graphs, from JavaScript source files to build the knowledge graph.
Provides integration for JetBrains IDEs through a CLI and Python SDK for developer intelligence and architecture querying.
Parses Markdown documentation to extract decisions, requirements, and stakeholders for integration into the codebase knowledge graph.
Offers an optional Neo4j backend for high-performance graph storage, supporting large-scale codebases with vector search and proximity analysis.
Allows for local, zero-cost LLM reasoning by using Ollama as a backend provider for graph-based queries.
Leverages OpenAI models to provide sophisticated reasoning and context-aware insights about codebase architecture.
Integrates as a pre-commit governance gate using the DRACE framework to verify data quality and reasoning accuracy before code changes.
Deeply analyzes Python codebases to extract module structures and dependencies while offering a native SDK for programmatic graph interaction.
Compatible with the Replit environment via CLI and SDK for managing and querying architecture-level intelligence.
Analyzes Rust source code to identify module hierarchies and dependency patterns for inclusion in the architecture knowledge graph.
Parses TypeScript code to extract detailed architectural insights, including module imports and class relationships.
Uses YAML for flexible server configuration and task-based routing across multiple LLM backends.
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
GraQle
Give your AI agents a memory of how your organisation works - and check every decision before they act.
pip install graqleWebsite · Quickstart · Decision Assurance · Shared memory · Changelog
What GraQle does
Remembers. Turns your code, documents, policies and decisions into one connected knowledge graph that lasts beyond a chat session.
Checks. Before an agent acts, the Decision Assurance Gate checks the action and answers EXECUTE, REPLAN, HOLD, REJECT or ESCALATE - with the reason and a signed record.
Shares. One graph serves a whole team of agents - Claude Code, Cursor, Codex, Copilot - each with its own permissions and its own files.
Works with any model: Anthropic, OpenAI, AWS Bedrock, Ollama, Gemini and more - or fully offline.
Related MCP server: OpenCodeHub MCP Server
Quickstart
pip install graqle
graq scan docs ./policies # documents, policies, ADRs -> graph
graq scan repo . # code -> the same graph
graq learn knowledge "vendor DPA must be signed before any data access"
graq run "what approval is needed for a large refund?"
# -> answer + confidence + the exact sources it usedConnect it to your AI tool (Claude Code, Cursor, Windsurf, VS Code):
graq init # detects your IDE and wires GraQle inPDF, DOCX, PPTX and XLSX: pip install "graqle[docs]".
Check every decision
Most AI tools give you one confidence number. GraQle asks separate questions before an agent acts - and a good answer to one never hides a failed answer to another:
Allowed? Policy valid, actor authorised, evidence current, arguments exactly the approved ones. Any "no" stops the action.
Consistent? Contradicting facts are tracked in the graph, and a change updates every decision that depends on it.
Sure enough? Confidence is measured per dimension, not blended into one score.
On track? Unreliable step sequences are sent back to replan within a retry budget.
Who approves? Authority is checked for the action's impact level.
Answer | Meaning |
| Everything checks out. Impossible after any failed check or error. |
| Make a better plan and try again. |
| Something important is missing; wait for it. |
| Not allowed, or contradicted by the evidence. |
| A human decides. |
from graqle.assurance.gate import DecisionAssuranceGate
verdict = gate.evaluate(candidate, ctx)
verdict.outcome # EXECUTE | REPLAN | HOLD | REJECT | ESCALATE
verdict.decisive_rule # which rule decided
verdict.reason_codes # whyEvery decision leaves a sealed record: a signed, hash-chained provenance event, exportable to W3C PROV-O and checkable with SHACL, so an auditor can verify it with standard tools. The gate is off by default: installing or upgrading GraQle changes nothing until you switch it on.
Turn it on, per project:
Set
GRAQLE_DAG_ENABLED=truein the environment where GraQle runs (or in your.env).Provide the required settings. GraQle ships no thresholds, salts or keys: you choose them. Start from the
GRAQLE_DAG_*section of.env.exampleand fill in every placeholder (salt, thresholds, signing key id and version, calibrator version, and the others listed there). Put the values in the environment, or in a private file atGRAQLE_DAG_SECRETS_PATH(default.graqle/graqle_secrets.yaml); the environment wins.Start as usual. If a required value is missing, GraQle raises a configuration error that names it the first time the gate settings load. There is no silent fallback.
Check it: python -c "from graqle.assurance.settings import is_dag_enabled; print(is_dag_enabled())" prints True.
To switch it off again, unset the flag or set it to false. Details: operating the gate.
One memory for a team of agents
Several agents on one codebase usually means several copies of the same graph in memory. GraQle can hold it once:
# where the graph lives
graq mcp serve --transport http --port 8765 --config graqle.yaml --allowed-roots /path/to/projects
# in each agent session (.mcp.json)
{ "mcpServers": { "graqle": { "command": "graq", "args": ["mcp", "relay", "--url", "http://127.0.0.1:8765"] } } }Less memory. Each session runs a small relay instead of a full copy (measured on a 20,000-node graph: 3 separate servers 687 MB, shared server + 3 relays 493 MB).
Learning is shared at once across all sessions.
Each session keeps its own gates and its own files - file work happens on each user's own machine.
Teammates connect over an SSH tunnel, or HTTPS with a token.
Several projects, separate graphs on one server (Team and Enterprise).
Turn on the decision gate (opt-in, baseline tier)
The decision gate is off unless GRAQLE_DAG_ENABLED is set; with it off nothing changes. Turn it on:
Create an ed25519 signing key (PEM, mode
0600) and a key manifest that lists its public key (graqle.assurance.key_manifest_store.save_manifest), plus a coverage policy file.Set
GRAQLE_DAG_ENABLED=1and the requiredGRAQLE_DAG_*settings:SIGNING_KEY_ID,SIGNING_KEY_VERSION,SIGNING_KEY_PATH,COVERAGE_POLICY_PATH,CONFIG_SALT,CAP_VALUE,HG05_POISONING_THRESHOLD,HG07_MATERIALITY_THRESHOLD,CALIBRATOR_VERSION,TRAJECTORY_ESTIMATOR,MAX_RETRY_BUDGET,CIRCUIT_BREAKER_WINDOW,SCOPE_KEY_FIELDS_REF,MERGE_PRIORITY_REF. There are no built-in values for these; you choose them.Register the actors that may take MEDIUM-risk calls (for example
GRAQLE_RBAC_ACTORS_JSON). An actor with no registered role is closed by hard gate HG-06.Start your process. The packaged default runtime provider is registered at start-up. If it cannot be built the process fails at start-up with an error naming
register_runtime; supply your own provider withgraqle.assurance.adapter.register_runtime(provider)if you need one.
Baseline-tier decisions are governed by rule DAG-RT-BASELINE-ADMIT. No projection score,
reliability or residual risk is computed or recorded for them. EXECUTE only when risk is LOW,
impact is zero and all hard gates pass; anything else is HOLD (DAG-RT-NO-EVIDENCE) or, when a
hard gate fails, REJECT. MEDIUM and above routing requires explicit configuration. Every decision
is written as a signed, hash-chained provenance event; if it cannot be written the call is not
executed.
This is a policy-scoped authorisation at evidence tier BASELINE. It is not informed by the graph
and is not a safety verification. Evidence-fed scoring arrives in 0.88 (CR-DAG-088-1).
python scripts/dag_wheel_e2e.py runs these steps against the built wheel in a clean virtualenv.
Trust
Local-first, no telemetry. The graph is a file you control; nothing phones home.
Secure by default. The server needs authentication unless you explicitly switch on local development mode; graph uploads can never silently overwrite each other.
Signed releases. Sigstore-signed wheels, CycloneDX SBOM, reproducible builds.
Regulated work. Audit trail and evidence exports for your own EU AI Act file - GraQle provides evidence, it does not claim to make you compliant. Details
Report vulnerabilities to security@quantamixsolutions.com · SECURITY.md
Editions
What you get | |
Free | Knowledge graph, all core tools, governance gates, Decision Assurance Gate, shared server for one project |
Pro — $19/mo | Cloud sync, priority models, hosted transparency-log relay |
Team — $29/dev/mo | Shared graphs across projects (multi-project server), team lessons, audit-log retention |
Enterprise | On-premise, custom backends, support SLAs - contact us |
Recent releases
v0.87.0 — Decision Assurance Gate: five-answer decisions, contradiction tracking, confidence per dimension, replanning, signed provenance records.
v0.86.0 — One memory for a team of agents: shared server, thin relay, per-session gates, file work on each user's machine; secure-by-default server.
Patent & license
Core methods are patent-pending: EP26167849.4, EP26162901.8 and EP26166054.2. Source is fully auditable under the GraQle License - see LICENSE.
Built by Quantamix Solutions · github.com/quantamixsol/graqle
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