Deterministic, local-first memory and guardrails for AI coding agents — no LLM in the hot path.
Install once. From then on it runs in the background, keeping your agent oriented and its changes safe.
No API key. Same answer every time.
install wires your agent and indexes the repo → orient returns the code a task touches → review catches the change that breaks a contract. All local, no API key.
Install · What you get · Benchmarks · vs. Alternatives · Docs
--- AI coding agents are **amnesiac and ungoverned**. Every task restarts by re-reading the same files, long sessions drift onto stale assumptions, and nothing warns the agent when a change is about to break a contract or cross a boundary. OpenLore fixes both halves, and then gets out of your way. A **one-time static analysis** builds a live knowledge graph of your repo — call structure, types, tests, decisions, IaC. From then on your agent is **already oriented at the start of every task** and its changes are **certified before they land** — automatically, deterministically, with **no LLM in the hot path**, so the same question returns the same grounded answer and the agent is *told when a fact goes stale* instead of served a confident guess. ## Install once, then forget it ```bash npm install -g openlore && openlore install ``` That is the only command most people ever run. It **auto-detects your agent** (Claude Code, Cursor, Cline, Continue, Pi, AGENTS.md), wires it, registers the MCP server, and builds the index — no API key, no config, no prompts. After that, **it's on in the background and you don't think about it:** - 🔄 **Every task starts oriented, automatically** — a compact architecture digest is injected into your agent's context at the start of each session, and your agent calls `orient()` and the other tools itself over MCP. You don't type anything. - 🌱 **Every repo, not just this one** — `install` wires your user account too, so each new git repo you open indexes itself in the background on first touch. "Install once" is literal. - 🩹 **Always fresh** — a file watcher re-indexes as you edit and converges to a full analysis; anything it can't recompute in time is marked explicitly stale, never silently wrong. - 🛡️ **Changes get checked before they land** — the governance tools (and `openlore review` on a PR) run deterministically with no API key. The CLI below exists for humans and CI — `prove`, `review`, `doctor`, `features`. The everyday path is: install, then let your agent use it. > **Zero config.** Run **`openlore features`** to see every opt-in capability (embeddings, the commit gate, the spec store…), whether it's on, and the one command to enable it. `openlore doctor` verifies the wiring. --- ## What you get Two things, both deterministic and local: **🧠 Memory — your agent starts every task already oriented** - **Orient in one call** — `orient(task)` returns the relevant functions, callers, specs, tests, and insertion points (**~430µs p50** on a 15k-node graph) instead of a dozen exploratory file reads. - **Survives refactors** — anchored notes and decisions carry forward when a symbol is renamed or moved, instead of orphaning. - **One graph for everything** — application code, Infrastructure-as-Code, and architectural decisions share one graph, so a single query spans all three. - **Told when a fact is stale** — every response carries a brief, **factual freshness note** (the Epistemic Lease) once a cached fact has aged or the repo moved, so a long session can't drift onto a confident guess. **🛡️ Governance — guardrails on what the agent changes** - **Breaking-change verdict** — `certify_public_surface` classifies every changed export `breaking / non-breaking / potentially-breaking` and **names the consumers each break hits**. Never silently "safe." - **Sensitive-boundary check** — `change_impact_certificate` flags when a diff **opens a new path into a boundary you declared** (reachable after the change, not before). - **Grounded claims** — `verify_claim` returns `confirmed / refuted / unverifiable` with a citation, before an agent asserts "X is dead" or "Y is safe to change." - **One commit gate** — `openlore enforce` blocks on findings you mark `blocking`. Advisory by default, no API key. **The one line to remember:** an agent's expensive failure mode isn't ignorance — it's *confidence*. A model that doesn't know a function exists will go look. A model that "knows" a stale fact builds on it, and you pay at review time. OpenLore is built so the agent can be told *"that fact is stale"* and *"this change opens a path you said was sensitive"* — deterministically, with no second model guessing about the first. --- ## Value Scorecard — does it pay for itself? OpenLore only earns its place if an agent **with** it reaches a correct answer for less total cost than the same agent **without** it. We measure that and publish it — wins **and** losses. Numbers from the Spec 14 benchmark (`claude -p`, sonnet, N=4 medians, pinned SHAs), measured **2026-06-01**. | Scenario | Cost Δ | Round-trips Δ | Verdict | |---|---|---|---| | **Large/unfamiliar repo · deep "how does X flow through Y"** *(its target)* | **−7% to −21%** | **−26%** | ✅ helps — and the win grows with repo size | | Small/familiar repo · shallow "who calls X" | task-dependent *(Round 1: +43%)* | +38% | ❌ often **adds overhead** — measure first | The win scales with codebase size (round-trips without → with): | Repo (size) | Cost Δ | Round-trips | |---|---|---| | excalidraw (~640 files) | **−21%** | 25 → 16 | | tokio (~790 files) | **−21%** | 17 → 13 | | okhttp | **−13%** | 13 → 11 | | django (~3k files) | **−7%** | 21 → 15 | | gin (110 files, smallest) | +4% *(≈even)* | 10 → 9 | > **Re-confirmed live 2026-06-03:** the deep-task win reproduces (okhttp **−13%**); the small/familiar case is task-dependent, not a flat loss (chalk **−32%** win vs. express **+59%** loss). Don't guess from our repos — run **`openlore prove --estimate`** on yours (seconds, no API key). > **Honesty contract.** We never publish a savings number the benchmark didn't produce, we always show the losses next to the wins, and every claim traces to a command you can run. Methodology: [docs/AGENT-BENCHMARKS.md](docs/AGENT-BENCHMARKS.md). --- ## Is OpenLore for you? | | | |---|---| | ✅ **Strong fit** | A codebase too big to hold in your head — and the model's. Private or niche code the model never memorized. Long sessions where stale assumptions compound. Polyglot repos, or code plus the IaC that deploys it. | | 🤔 **Try it, measure** | Mid-size repos and mixed workloads. Run **`openlore prove --estimate`** (seconds, no key) before you commit. | | ❌ **Probably not** | A small repo the model already knows, answering shallow questions — your agent's built-in search is cheaper, and we [publish the measurement that says so](#value-scorecard--does-it-pay-for-itself). Also: OpenLore locates and certifies, it never edits your code. | --- ## What your agent reaches for (you rarely type these) Once wired, your agent calls these over MCP on its own. The default surface is the **`substrate`** preset — **15 tools**: the navigation core, the highest-value governance reads (`recall`, `verify_claim`, `blast_radius`), and the spec composites. The lean **`navigation`** preset (10 tools) and the full **80 tools** (`--preset full`) are one flag away. | When the agent needs to… | Tool | |-----------|------| | Start any task | `orient(task)` — functions, callers, specs, insertion points in one call | | Find which file/function handles X | `search_code` | | Know the blast radius of a change | `analyze_impact` — risk + up/downstream chain + governing decisions | | Trace how request X reaches function Y | `trace_execution_path` | | Pick the tests a change should run | `select_tests` — backward reachability to the reaching tests | | Find dead code / what dies if X is deleted | `find_dead_code` — cross-language reachability | | Check a whole diff before commit | `blast_radius` — callers, tests, specs that drift | | Tell if a diff opens a sensitive boundary | `change_impact_certificate` | | Tell if a change breaks a consumer's API | `certify_public_surface` — verdict, consumers named | | Verify a fact before asserting it | `verify_claim` — deterministic verdict + citation | | Record an architectural choice | `record_decision` **before** writing the code | Full reference — all 80 tools: [docs/mcp-tools.md](docs/mcp-tools.md). Ships as a Claude Code [Skill](https://docs.claude.com/en/docs/claude-code/skills) at [`skills/openlore-orient/`](skills/openlore-orient/). --- ## OpenLore vs. alternatives Everyone in this category answers the same first question: **how does the agent see the codebase without reading it file by file?** LSP toolkits answer with symbols, graph MCP servers with a parsed graph, search platforms with an index. OpenLore answers it too — then keeps going into the **second question almost nobody answers: what happens when the agent starts writing?** A retrieval layer makes an agent *informed*; it doesn't make it *safe*. That second half — **governance on the same graph, no LLM in the loop** — is what OpenLore is for. | | Agent built-ins | LSP toolkits | Graph MCP | Search platforms | **OpenLore** | |---|---|---|---|---|---| | Structural context instead of file reads | ❌ grep | ✓ symbols | ✓ graph | ✓ index | ✓ call graph + **IaC + decisions on one graph** | | Local, no API key, deterministic | Partial | ✓ | ✓ | ❌ hosted | ✓ no LLM in the hot path | | Cross-session memory anchored to code | ❌ | Partial | ✓ notes | ❌ | ✓ **carried across renames** | | Told when a cached fact goes **stale** | ❌ | ❌ | ❌ | ❌ | ✓ Epistemic Lease | | Breaking-change **verdict** over a diff | ❌ | ❌ | Partial | ❌ | ✓ per export, **consumers named** | | "Did this diff open a sensitive path?" | ❌ | ❌ | ❌ | ❌ | ✓ differential, pre-commit | | Cost effect **published with the losses** | ❌ | ❌ | ❌ | ❌ | ✓ −26% round-trips on deep tasks | **Use the other tool when it's the right one:** symbol-level *edits* (rename across files) are an LSP toolkit's home turf — OpenLore is deliberately read-only, and the two compose. Org-wide search across hundreds of repos is a search platform. Just-fast-graph-retrieval is a smaller graph MCP server. And a small, familiar repo with shallow questions is cheaper with your agent's built-in search. OpenLore also [exports SCIP](docs/scip-export.md), so it sits alongside these tools, not against them. *Comparisons reflect each project's publicly documented capabilities as of July 2026; a correction PR is always welcome.* --- ## What it costs Measured on a fresh clone of [ripgrep](https://github.com/BurntSushi/ripgrep) with published `openlore@2.1.6`: | What it costs | On ripgrep (232 files) | |---|---| | **One-time index build** | **13.6 s**, entirely local — no API key, no network | | **Disk** | **27 MB** under `.openlore/` (gitignorable; rebuildable from source) | | **Per-query latency** | **~430 µs p50** in-process via the MCP server | | **Your source code** | never leaves the machine — no account, no telemetry (opt-in only) | | **Lock-in** | none — delete `.openlore/` and nothing about your repo has changed | Large monorepos take minutes rather than seconds, stated plainly in [Known limitations](#known-limitations). Platform: Linux and Windows are exercised in CI; macOS is supported. > **Fully local — nothing phones home.** No account, no network call; your source, the index, and every query stay on your machine. The only diagnostics that exist are **opt-in and local-only**, off by default and never transmitted: `OPENLORE_TELEMETRY=1` records tool calls, agent identity, latency, error messages, decision titles, and lease events to the gitignored `.openlore/telemetry/` directory, and `OPENLORE_LLM_LOGS=1` stores prompts and responses (after secret redaction) in the gitignored `.openlore/logs/` directory. → [docs/configuration.md](docs/configuration.md) --- ## Languages & Infrastructure-as-Code **Languages**: TypeScript · JavaScript · Python · Go · Rust · Ruby · Java · C++ · Swift · C# · Kotlin · PHP · C · Scala · Dart · Lua · Elixir · Bash — call graphs ride the same primitives for every language. → [docs/language-support.md](docs/language-support.md) **Infrastructure-as-Code**: Terraform/HCL · Kubernetes · Helm · CloudFormation · Ansible · Pulumi · AWS CDK · CDKTF · Dockerfile · Docker Compose · GitHub Actions · Azure Bicep — IaC resources project onto the **same graph** as application code, so `orient`, `search_code`, and `analyze_impact` answer "what's the blast radius of changing this security group / IAM role / base image / CI job?" with zero new tooling. → [docs/iac.md](docs/iac.md) --- ## Known limitations We'd rather you know these up front. - **Static analysis only.** Polymorphic dispatch, event channels, and route→handler bindings *are* recovered (provenance-labeled `synthesized`). What isn't: reflective invocation with a non-literal target (`getattr(o, name)()`), `eval`, and DI/plugin registries with no visible binding — silently absent from the graph. - **LLM spec quality varies.** Generated spec *prose* reflects the model's understanding — review complex business logic. Structure, format, coverage, and drift are checked deterministically. - **Keyword (BM25) search is the default; semantic is an opt-in upgrade.** `orient`/`search_code` work immediately with no key. Add hybrid dense+BM25 with `openlore embed --local` (bundled, CPU-only, ~23 MB). - **Large monorepos** analyze one package at a time after the first full build (`openlore analyze --shard