openlore

Note

spec-gen has been renamed to OpenLore. The npm package is now openlore and the CLI command is openlore. Existing projects: rename your .spec-gen/ directory to .openlore/ and reinstall (npm i -g openlore). See docs/RENAME-TO-OPENLORE.md for the full migration checklist.

Persistent architectural memory and structural cognition for AI coding agents.

openlore turns any evolving codebase into a navigable knowledge graph backed by OpenSpec living specifications. It maintains persistent architectural context across agent sessions: graph structure, specs, decisions, drift state, and semantic retrieval — so agents start each task already oriented instead of re-discovering the system from file reads.

---

Why It Exists

AI agents are powerful but amnesiac. On every new task:

• They re-read the same source files to understand structure
• They forget architectural decisions made two sessions ago
• They have no link between specs and code — drift is invisible
• File-by-file navigation often burns 15,000–50,000 tokens per orientation pass, before a single line of useful code is written
• In long sessions, they drift from authoritative retrieval toward internally cached reasoning — producing subtly wrong architectural assumptions that compound silently until a refactor breaks

openlore closes this loop. Run a full analysis once, then keep the graph incrementally updated as the codebase evolves. Even greenfield projects become cognitively "brownfield" after only a few agent sessions — architectural context fragments, decisions disappear, and agents repeatedly reconstruct the same understanding from scratch.

openlore persists that context continuously: structure, specs, decisions, drift state, and graph relationships remain queryable across sessions.

---

How It Works

Three layers, each usable independently:

Layer	What it does	API key?
1. Static Analysis	Call graph, clusters, McCabe CC, external deps → CODEBASE.md digest	No
2. Spec Layer	LLM-generated living specs, ADRs, drift detection, decision gates	For generation
3. Agent Runtime	45 MCP tools — orient(), semantic search, graph expansion	No

You can use layer 1 alone to give agents structural context. Add layer 2 for semantic intent and architectural governance through OpenSpec-compatible living specifications. Layer 3 keeps that context continuously accessible through graph-native MCP tools once openlore mcp is running.

---

openlore vs. Alternatives

	Cursor / Claude Code	Sourcegraph	openlore
Graph-aware MCP context	❌ file-based reads	Partial	✓ call graph + clusters
Spec drift detection	❌	❌	✓ milliseconds, no API
Architectural decision gates	❌	❌	✓ pre-commit hook
Offline structural analysis	❌	❌	✓
Token-efficient orient()	❌	❌	✓ ~1–3k vs 15–50k tokens
Living spec generation	❌	❌	✓
Persistent cross-session architectural memory	❌	Partial	✓
Long-session confidence decay (Epistemic Lease)	❌	❌	✓

Traditional coding agents reconstruct architecture from repeated file reads every session. openlore persists it as a queryable graph.

---

5-Minute Quickstart

Minimum to see value — no API key needed:

npm install -g openlore
cd /path/to/your-project

openlore analyze          # build call graph, clusters, CODEBASE.md
openlore install          # auto-configure your agent (Claude Code, Cursor, …)
openlore mcp              # start MCP server

openlore install auto-detects which agent surfaces are present (Claude Code, Cursor, Cline, Continue, AGENTS.md) and wires each one to call orient() automatically — no manual CLAUDE.md editing needed. See docs/install.md.

Then ask your agent: orient("add a new payment method")

That single call returns the relevant functions, their call neighbours, matching spec sections, and insertion-point candidates — preserving architectural continuity across sessions instead of forcing the agent to repeatedly reconstruct context from raw file reads. In practice, this often reduces orientation cost from ~30,000 exploratory tokens to ~1,000 targeted tokens.

Full pipeline (specs + decisions — optional and additive):

openlore generate         # generate living specs (requires API key)
openlore drift            # detect spec/code drift
openlore decisions        # manage architectural decisions

Install from source

git clone https://github.com/clay-good/openlore
cd openlore
npm install && npm run build && npm link

Nix / NixOS

nix run github:clay-good/openlore -- analyze
nix shell github:clay-good/openlore

System flake:

environment.systemPackages = [ openlore.packages.x86_64-linux.default ];

---

See It In Action

Example: orient("add a payment method")

{
  "functions": [
    {
      "name": "processPayment",
      "file": "src/payments/processor.ts",
      "risk": "medium",
      "fanIn": 4,
      "callers": ["handleCheckout", "retryFailedCharge"],
      "callType": "direct"
    },
    {
      "name": "validateCard",
      "file": "src/payments/validator.ts",
      "risk": "low",
      "fanIn": 1,
      "testedBy": [{ "name": "validateCard.test.ts", "confidence": "called" }]
    }
  ],
  "specDomains": ["payments — §CardValidation, §PaymentFlow"],
  "insertionPoints": [
    "src/payments/processor.ts:87 — after existing charge logic"
  ],
  "callPath": "POST /charge → handleCheckout → processPayment → validateCard → stripeClient.charge"
}

One graph query replaces most exploratory file reads. The agent knows exactly where to look and what risks to consider.

---

Use OpenLore as a Claude Code Skill

OpenLore ships a canonical Claude Code Skill at skills/openlore-orient/. Install it once and Claude Code will automatically call orient() at the start of every task — no CLAUDE.md editing required.

# From the OpenLore repo root:
npm run skill:install-local           # → ~/.claude/skills/openlore-orient/

# Or copy into a single project's .claude/skills/:
cp -R skills/openlore-orient /path/to/your-project/.claude/skills/

The skill bundle ships a SKILL.md manifest, POSIX + PowerShell wrappers, a worked example, and a redacted real orient() JSON output so the model knows the response shape. See skills/openlore-orient/README.md for details.

---

Core Features

Analyze (no API key)

Continuously maintains a structural representation of your codebase using pure static analysis. Builds a full call graph persisted to SQLite, runs label-propagation community detection to cluster tightly coupled functions, computes McCabe cyclomatic complexity for every function, and extracts DB schemas, HTTP routes, UI components, middleware chains, and environment variables. Outputs .openlore/analysis/CODEBASE.md — a ~600-token structural digest that compresses the equivalent of tens of thousands of exploratory tokens into a small, queryable summary.

With --watch-auto, the call graph updates incrementally on every file save: changed file and its direct callers are re-parsed and the graph is atomically swapped. Orient and BFS queries remain live between full analyze runs.

Generate (API key required)

Sends the analysis to an LLM in 6 structured stages: project survey → entity extraction → service analysis → API extraction → architecture synthesis → ADR enrichment. Produces openspec/specs/ living specifications in RFC 2119 format with Given/When/Then scenarios.

Drift (no API key)

Compares git changes against spec mappings in milliseconds. Detects: Gap (code changed, spec not updated), Uncovered (new file, no spec), Stale (spec references deleted files), ADR gap (code changed in an ADR-referenced domain). Installs as a pre-commit hook.

Install (no API key)

openlore install auto-wires the popular agent surfaces (Claude Code, Cursor, Cline, Continue, AGENTS.md) so they call orient() automatically — no CLAUDE.md editing required. Each integration uses a fingerprinted managed block so re-runs are idempotent and hand-edits are detected. --dry-run previews diffs; --uninstall cleanly removes everything. See docs/install.md.

Preflight (no API key)

openlore preflight is a CI staleness gate: any pull request that edits files in the graph fails the check until the graph is refreshed. Drop-in templates for GitHub Actions, GitLab CI, and generic shell live in examples/ci/. Weighted scoring surfaces hubs first so a one-line leaf edit doesn't fail the same way a refactor of a top-of-stack module does. See docs/preflight.md.

MCP (no API key)

45 graph-native tools exposed over stdio. Together they act as a persistent architectural runtime for coding agents: orientation, graph traversal, semantic retrieval, drift awareness, decision context, and structural risk analysis. orient() is the main entry point — one call replaces 10+ file reads. detect_changes risk-scores changed functions using call graph centrality × change type multiplier. See docs/mcp-tools.md.

orient() runs in ~430µs p50 against a 15k-node codebase (TypeScript compiler, ~79k edges). Full benchmark results: scripts/BENCHMARKS.md.

Epistemic Lease (no API key)

Core principle: EpistemicLease models architectural drift as a behavioral navigation phenomenon rather than a semantic understanding problem. Context decay is driven by where the agent goes (cross-module trajectory), not what it knows.

As a session grows longer, agents naturally shift from authoritative graph retrieval toward internally cached reasoning. This is useful for fluency but dangerous for architectural correctness — cross-module assumptions go stale, dependency hallucinations accumulate, and delegation prompts embed incorrect repository understanding that cannot easily be corrected downstream.

The Epistemic Lease models this decay explicitly. Every MCP tool response carries a freshness signal when the agent's architectural context has degraded or expired. Decay is triggered by any of: time elapsed since orient(), git hash divergence from the orient baseline, weighted cognitive load accumulation (heavier tools count more), or cross-module file access breadth.

The signal escalates through three levels to resist warning blindness:

Level	Trigger	Signal style
Degraded	load ≥ 30, age ≥ 15min, or cross-module density ≥ 0.15	Advisory signal appended
Stale	load ≥ 60, age ≥ 30min, git hash divergence, or density ≥ 0.30	Procedural block prepended: what NOT to do
Stale [Elevated]	load ≥ 85 or age ≥ 45min	Risk-framing: names downstream consequences
Stale [Critical]	load ≥ 110 or age ≥ 60min	Imperative: STOP. Call orient(). — minimal, hardest to skim

Cross-module density is computed as a sliding-window trajectory model: switches_in_last_15_calls / 15. The fixed denominator prevents false positives during session warmup. Each module switch adds +5 cognitive debt; a high-density window adds +15; a burst (density ≥ 0.60) adds +20. A 5s dampening window prevents back-and-forth from double-counting.

An oscillation coefficient (repeated_bigram_transitions / total_transitions) separately distinguishes confusion loops (A→B→A→B scores 1.0) from genuine exploration (A→B→C→D scores 0.0). When already stale, a heavy architectural tool (weight ≥ 8) or density burst (≥ 0.60) triggers immediate escalation to Stale [Critical].

When fresh, injection is zero-overhead. Calling orient() resets the tracker. Unlike governance systems, the lease never blocks — it modulates the agent's confidence in its own cached reasoning rather than constraining its actions.

Decisions (API key for consolidation)

Agents call record_decision before writing code. Consolidation runs immediately in the background. At commit time, a pre-commit hook gates the commit until all verified decisions are reviewed and written back as requirements in spec.md files. Decisions are classified by scope (local / component / cross-domain / system); only cross-domain and system decisions produce ADR files, keeping the decision log signal-dense.

Telemetry (opt-in, no API key)

Cognitive telemetry for empirical measurement of EpistemicLease behavior. Gated by OPENLORE_TELEMETRY=1 — disabled by default. Writes append-only JSONL to .openlore/telemetry/ per domain. Agent identity is captured from the MCP initialize handshake, enabling per-agent behavioral comparison.

.openlore/telemetry/
  mcp.jsonl              # every tool call: latency, errors, agent name
  orient.jsonl           # orient quality: function/file/insertion_point counts
  cache.jsonl            # readCachedContext hit/miss
  epistemic-lease.jsonl  # state transitions: degraded, stale, depth escalation

Analyze with openlore telemetry:

openlore telemetry [directory]   # summary: latency, cache hit rate, obstinacy index
openlore telemetry --live        # stream events in real time as they occur

Key metrics: obstinacy index (tool calls after stale before orient — measures whether agents act on warnings), recovery efficiency (stale→orient latency), trajectory dynamics (avg cross-module density, burst frequency). These turn EpistemicLease from a tuning-by-intuition system into an empirically measurable one.

---

Architecture

OpenSpec provides semantic intent and workflow structure. openlore maintains the evolving implementation as a continuously queryable architectural graph for agents.

Codebase
   │
   ▼
openlore analyze ──► SQLite graph store (.openlore/analysis/call-graph.db)
                          │                      │
                          │              MCP tools (orient, BFS, search…)
                          │                      │
                     Artifact Generator        Agent
                          │
                    ┌─────┴──────┐
                    ▼            ▼
              CODEBASE.md   (optional)
                         openlore generate ──► openspec/specs/*.md
                         openlore drift   ──► drift report
                         openlore decisions ► ADR gates

The graph and the OpenSpec spec layer are co-equal: the graph makes orientation fast, the specs make it semantically grounded. Drift detection and decision gates connect both. See docs/ARCHITECTURE.md for the full pipeline diagram.

---

Interop

OpenLore exports SCIP (Source Code Intelligence Protocol). Plug it into Sourcegraph code nav, GitHub stack graphs, Glean importers, or any SCIP-aware tool:

openlore analyze            # build the graph (if you haven't already)
openlore export scip        # writes ./index.scip

The SQLite graph stays canonical; SCIP is a one-way export of the subset SCIP can model (functions → symbols, call edges → occurrences). See docs/scip-export.md for what is and isn't exported and how to consume it.

---

Federation (cross-repo)

The hardest agent-orientation questions cross repo boundaries: who calls BillingService.refund, where is event X consumed, how does data flow from service A to service B. OpenLore's answer is "SBOM-of-cognition" — every repo publishes a small, public, deterministic manifest describing what it exposes:

openlore manifest emit        # writes ./.well-known/openlore.json
openlore manifest validate .well-known/openlore.json

The manifest captures the public API surface, HTTP routes, stats, dependencies, and spec state in a versioned schema. A future OpenLore federation index will read these manifests across many repos to answer cross-repo orient() questions, staying a thin merger rather than a giant analyzer. See docs/federation.md.

---

Documentation

Topic	Doc
MCP tools reference (45 tools + parameters)	docs/mcp-tools.md
Agent setup (Claude Code, Cline, OpenCode, Vibe…)	docs/agent-setup.md
openlore install — auto-configure agent surfaces	docs/install.md
LLM providers + embedding config	docs/providers.md
Drift detection in depth	docs/drift-detection.md
Spec-driven tests + spec digest	docs/spec-tests.md
CI/CD integration	docs/ci-cd.md
Preflight CI staleness gate	docs/preflight.md
SCIP export (Sourcegraph/Glean interop)	docs/scip-export.md
Federation manifest (cross-repo)	docs/federation.md
CLI command reference	docs/cli-reference.md
Interactive graph viewer	docs/viewer.md
Analysis output files	docs/output.md
Configuration reference	docs/configuration.md
Programmatic API	docs/api.md
Pipeline architecture	docs/pipeline.md
Internal design	docs/ARCHITECTURE.md
Algorithms	docs/ALGORITHMS.md
Agentic workflows (BMAD, Vibe, GSD, spec-kit)	docs/agentic-workflows.md
Troubleshooting	docs/TROUBLESHOOTING.md
Philosophy	docs/PHILOSOPHY.md
Telemetry & cognitive metrics	docs/telemetry.md

---

Known Limitations

• Incremental call graph updates are depth-1 only: --watch-auto re-indexes signatures and edges on save for the changed file and its direct callers. Transitive callers (A→B→C, C changes, A stays stale) are only refreshed by the next analyze --force. For hub files with 100+ callerFiles, re-parse may take several seconds.
• Static analysis only: dynamic dispatch, runtime metaprogramming, and eval-based patterns are not captured in the call graph.
• LLM spec quality varies: generated specs reflect the model's understanding. Review sections covering complex business logic before treating them as authoritative.
• Embedding is optional: plain openlore analyze (no --embed, no EMBED_*) builds a keyword (BM25) search index out of the box, so orient, search_code, suggest_insertion_points, and search_specs work immediately. Configure an embedding endpoint (EMBED_BASE_URL/EMBED_MODEL or an embedding block in .openlore/config.json) to upgrade to hybrid dense+BM25 search, which is more accurate for semantic queries.
• Large monorepos: openlore analyze on large codebases may take several minutes. Graph storage itself has no practical limit — the pipeline (AST parsing, symbol extraction) is the bottleneck.
• node:sqlite experimental warning on Node 22: Node.js 22 prints ExperimentalWarning: SQLite is an experimental feature to stderr. The warning is gone on Node 24+. Suppress on Node 22 with NODE_NO_WARNINGS=1 openlore analyze.

---

Requirements

• Node.js 22.5+
• API key for generate, verify, and drift --use-llm:

  export ANTHROPIC_API_KEY=sk-ant-...    # default provider
  export OPENAI_API_KEY=sk-...           # OpenAI
  export GEMINI_API_KEY=...              # Google Gemini

  Or use a CLI-based provider (claude-code, gemini-cli, mistral-vibe, cursor-agent) — no API key, just the CLI on your PATH.
• analyze, drift, mcp, and init require no API key

Languages supported: TypeScript · JavaScript · Python · Go · Rust · Ruby · Java · C++ · Swift

---

Development

npm install
npm run build
npm test          # 2660+ unit tests
npm run typecheck

---

Links

• OpenSpec — spec-driven development framework
• AGENTS.md — system prompt for direct LLM prompting
• Examples — BMAD, Vibe, GSD, drift-demo, spec-kit integrations