agentscaffold 0.7.0

Structured AI-assisted development framework with plan lifecycle, review gates, and continuous improvement.

AgentScaffold

Persistent institutional memory and governance enforcement for AI coding agents.

AgentScaffold gives your AI agent two things it lacks by default: a durable knowledge graph that remembers your codebase, plans, contracts, and review findings across every session — and a governance framework that enforces your development workflow before a single line of code is written.

The efficiency gains (fewer file reads, lower token spend) are downstream of this. When an agent carries real memory and follows real process, it spends less time rebuilding context and more time doing the work.

The Two Problems

Memory: Agents start from zero every session

Every time you start a new session with Cursor, Claude Code, Codex, or any AI coding agent, it starts from zero. It reads your files. It greps for imports. It traces call chains. It burns through your token budget and subscription quota just to understand what it already understood yesterday.

On a moderately complex codebase, a single "understand this module" task can cost 12 file reads + 2 grep searches before the agent even starts working. A full plan review pulls in 10+ files. Getting oriented in a new codebase means reading 38+ files.

AgentScaffold indexes your codebase once and serves it via MCP tools in a single call. The graph persists across sessions, grows incrementally, and includes governance artifacts — so the agent knows not just what the code does but why decisions were made and what review findings remain open.

Governance: Agents skip process unless you enforce it

AI agents left to themselves skip reviews, ignore contracts, and build solutions that diverge from your architecture. They work fast until they work wrong.

AgentScaffold enforces a plan lifecycle with adversarial reviews before implementation, interface contracts that survive across plans, and retrospectives that feed learning back into the process. Review findings are written into the knowledge graph and surfaced in every subsequent review of the same plan — the agent cannot forget a finding because it is persisted, not held in context.

When to Use AgentScaffold

Situation	Benefit
Large or complex codebase (>10K LOC)	Graph retrieval replaces expensive context-building reads
Plans that span multiple sessions	Persistent findings, decisions, and state survive context resets
Domain-sensitive work (trading, ML, infra)	Domain pack reviewers enforce domain-specific standards before code is written
Multiple AI agents or platforms	MCP layer works uniformly across Cursor, Claude Code, Windsurf, and any MCP-compatible agent
Teams managing architectural integrity	Interface contracts and ADRs linked to code prevent drift detection gaps
Post-incident retrospectives	Review findings remain queryable; resolved findings keep their history

When you probably don't need it

A well-tuned CLAUDE.md or Cursor rules file gets a solo developer most of the way on a small, short-lived project. If you are working alone on a few thousand lines over a few days, native tooling is enough and AgentScaffold's indexing and governance overhead may not pay for itself. The value compounds with scale (transitive impact analysis past the point where grep breaks down), time (cross-session memory that accumulates instead of resetting), and team (a shared graph and contract lifecycle that per-developer rules files cannot provide).

What It Does

AgentScaffold combines two capabilities:

1. Persistent Knowledge Graph

A DuckDB + DuckPGQ-backed graph that indexes your codebase once and serves it to agents instantly:

• Code structure: Functions, classes, methods, interfaces, import chains, call graphs — across Python, TypeScript, Go, Rust, Java, C, and C++
• Governance artifacts: Plans, contracts, learnings, and review findings linked to the code they reference
• Community detection: Leiden algorithm clustering identifies tightly coupled modules
• Semantic search: Hybrid search combining structural graph queries with vector embeddings (fused via reciprocal-rank fusion), with explicit available/degraded/unavailable status when embeddings or sentence-transformers are missing (graceful keyword fallback). Embeddings are enriched with each definition's docstring/leading comment (read from source at index time) and L2-normalized at store time, so matches reflect intent rather than identifiers alone
• Incremental indexing: SHA-256 content hashing means only changed files are re-processed
• Contract drift detection: Automatically surfaces methods declared in contracts but missing from code
• Review finding write-back: Findings recorded during plan reviews are persisted as graph nodes and surfaced in every future review of the same plan

The graph is exposed via MCP tools that any compatible agent can call, or through the CLI for direct use.

2. Agent Governance Framework

A structured development workflow that teaches your AI agent to follow a plan lifecycle with quality gates:

• Plan lifecycle: Draft → Review → Ready → In Progress → Complete
• Adversarial reviews: Devil's advocate, expansion analysis, domain-specific reviews — all run before a single line of code is written
• Interface contracts: Formal declarations of module boundaries, versioned and tracked
• Retrospectives: Post-execution learning that feeds back into the process
• Session tracking: State files that persist context across chat sessions
• Lifecycle hooks: Pre-edit validation and post-edit incremental re-indexing enforced at the platform level

Think of it as a virtual sprint team. Most AI agents work alone. AgentScaffold puts your agent on a team. Before it writes a single line of code, the plan faces a devil's advocate who asks "what if this breaks?", an expansion reviewer who asks "what did you miss?", and a domain expert — a quant architect, a UX designer, a security engineer — who pressure-tests the approach through the lens of your specific domain.

After implementation, a post-implementation review verifies what was built against what was planned. A retrospective captures what worked, what didn't, and what to do differently. Those findings flow into the learnings tracker, which feeds back into the agent's rules and templates — so the next sprint starts sharper than the last.

Measured Efficiency Gains

These numbers are downstream of governance and memory — not the lead story, but real.

From the eval harness (108 scenarios):

Task	Without AgentScaffold	With AgentScaffold	Savings
Understand a module and its dependents	12 reads + 2 greps	1 tool call	~89% fewer tokens, ~93% fewer calls
Codebase orientation	38 file reads	2 tool calls	~74% fewer tokens, ~95% fewer calls
Impact analysis (blast radius)	12 file reads	1 tool call	~50% fewer tokens, ~92% fewer calls
Find all code matching a concept	8 file reads	1 tool call	~51% fewer tokens, ~88% fewer calls
Full plan review with evidence	10 file reads	1 tool call	~90% fewer calls (denser output; see note)

Capability aggregate (raw): ~87% average call reduction, ~38% average token reduction.

Full plan review is the honest outlier: it replaces ten file reads with one call but returns more tokens, not fewer (a token increase in our harness), because the single response carries the brief, adversarial challenges, gaps, verification, and retro together — plus any open findings and config consumers. The win there is calls and completeness, not token count; it is gated on call reduction and kept in the average rather than dropped. Note also that token reduction fell as the tool outputs grew richer over recent releases — the trade is denser, more useful single responses for a smaller token margin.

We report three views so the headline is not the optimistic one:

View	Token Reduction	Call Reduction
Raw capability (tool routes correctly)	~38%	~87%
Behavioral (replay-adjusted)	~30%	~70%
Quality-adjusted behavioral	~27%	~63%

Behavioral and quality-adjusted values come from replay traces (observed tool-call sequences + quality parity checks), not phrase-level intent matching. They are lower because agents do not always route to the tool — the graph does not help if the agent reads files directly instead. Replay-observed tool-first adherence in the latest run was ~80% (20% bypass), and the Cursor/Claude rule taxonomy added in this release is aimed at closing that gap.

Note: Numbers above are from the most recent evaluation run (eval/reports/latest.md). Run python -m pytest eval/ -q from the package root to reproduce against the current codebase.

Quick Start

pip install agentscaffold
cd my-project
scaffold init           # Scaffolds docs + generates the full rule set
scaffold index          # Build the knowledge graph
scaffold agents generate-all  # Re-generate rules with graph context (after indexing)
scaffold mcp            # Start MCP server for tool access

The init command scaffolds your project and, on a fresh init, generates the complete rule set for every supported platform:

• docs/ai/ — templates, prompts, standards, state files
• scaffold.yaml — your project's framework configuration
• AGENTS.md — rules your AI agent follows automatically
• .cursor/rules.md + .cursor/rules/agentscaffold.md — Cursor process rules and the MCP routing / graph trust-discipline policy
• .cursor/mcp.json — Cursor MCP server registration
• CLAUDE.md and .claude/agents/ — Claude Code rules and one subagent file per configured reviewer
• .windsurfrules — Windsurf rules
• Lifecycle hooks for each enabled platform

Re-running scaffold init is idempotent and never overwrites hand-edited rules. Run scaffold agents generate-all to regenerate the rule set on an existing project — for example after scaffold index so AGENTS.md picks up graph context (hot spots, volatile modules, active contracts), or after editing scaffold.yaml.

File safety. AgentScaffold never silently clobbers agent or skill files you already own. Project-owned docs (AGENTS.md, CLAUDE.md, .windsurfrules, .cursor/rules.md) receive generated guidance inside a delimited managed block — existing files are appended to (or the block is refreshed in place), never overwritten, and anything outside the block is always preserved. User-authored skills (SKILL.md without a managed_by: agentscaffold marker) are left untouched. Only machine-owned policy files (.cursor/rules/agentscaffold.md, reviewer rules, enforcement hooks) are regenerated each run. Pass --force to rewrite a file whole; a .bak snapshot is always kept. See File Safety.

The index command builds the knowledge graph (a DuckDB + DuckPGQ database at .scaffold/graph.duckdb), enabling search, reviews, impact analysis, and session memory.

Async freshness (low-latency graph updates for MCP)

AgentScaffold supports async freshness mode for MCP usage. Instead of blocking a tool call to re-index, the request path runs a cheap freshness check and returns immediately. If the graph looks stale, a background incremental refresh is scheduled (with debounce and single-flight locking) while the agent continues working.

Why this design matters:

• Keeps MCP interactions in milliseconds/seconds instead of minutes on large repos
• Avoids duplicate refresh jobs under parallel tool usage
• Surfaces explicit freshness metadata (fresh, stale, unknown, refreshing) so agents can reason about confidence
• Preserves strict governance by allowing gate transitions to defer when freshness is required and not yet restored

Configure in scaffold.yaml:

freshness:
  async_enabled: true
  debounce_seconds: 120
  gate_strict: false
  background_queue_enabled: true

Single-writer model (teams): the graph is one DuckDB file (.scaffold/graph.duckdb) and only one process may write it at a time. The async refresh serializes refresh scheduling per workspace, but it does not make concurrent writers safe. Each developer should keep their own local graph rather than sharing a single file over a network mount; running scaffold index while the MCP server holds the graph open raises a clear GraphLockError (after a short retry) and MCP tool calls return {"graph_locked": true} instead of crashing.

Collaboration ergonomics (opt-in): for teams where several people (or agents) work the same repo, two features reduce git contention on shared governance files. With collab.sharded: true, the high-churn workflow_state.md / backlog.md can be stored as per-entry fragments so concurrent writers touch different files (scaffold state split shards an existing file reversibly; scaffold state render reassembles the canonical file deterministically). Advisory plan claims (scaffold plan claim <n> --owner <who> / scaffold plan release) record git-backed, visible ownership of an in-flight plan — visibility, not an enforced lock. Both default off, so existing repos are unaffected.

Multi-Project Workspaces

Several projects can share one knowledge-graph cache — useful for a monorepo of services or a set of related repos you want an agent to reason across. A workspace.yaml at the workspace root lists the member projects:

projects:
  - name: api
    path: services/api
  - name: web
    path: apps/web

scaffold workspace onboard services/api        # register a project (creates workspace.yaml)
scaffold workspace onboard apps/web             # second project -> workspace is now multi-project
scaffold workspace list                         # show projects + mode

Once a workspace has more than one project, every node is namespaced by project ({project}::{raw_id}) and stamped with a project column. Reads default to the current project, so an agent working in api never misreads web's plans, findings, or learnings (even when both have a plan 12 or a src/utils.py). Widen explicitly when you want to:

scaffold graph search "auth flow"                  # current project only (default)
scaffold graph search "auth flow" --project web    # target a sibling
scaffold graph search "auth flow" --all-projects   # federate (results carry project provenance)
scaffold graph duplicates --table Function         # cross-project near-duplicates (reuse candidates)

How Agents Resolve The Current Project

In normal agent work you do not need to mention the workspace. AgentScaffold resolves the active project from the current working directory.

Example:

~/dev/trading-stack/
  workspace.yaml
  market-data-service/
    scaffold.yaml
  strategy-engine/
    scaffold.yaml

If Cursor, Claude Code, or another agent is working from ~/dev/trading-stack/market-data-service, then plain graph/governance reads default to the market-data-service project:

cd ~/dev/trading-stack/market-data-service
scaffold graph search "symbol normalization"     # market-data-service only

If the agent is working from ~/dev/trading-stack/strategy-engine, the same command defaults to strategy-engine:

cd ~/dev/trading-stack/strategy-engine
scaffold graph search "symbol normalization"     # strategy-engine only

Only widen scope when the task is explicitly cross-project:

scaffold graph search "symbol normalization" --project market-data-service
scaffold graph search "symbol normalization" --all-projects

The generated agent rules teach this behavior: default to the current project, treat plan numbers and file paths as project-scoped, and preserve project provenance when using federated results.

A lone repo with no workspace.yaml is completely unaffected: it behaves as a single synthesized project, nothing is ID-prefixed, and every scope predicate is a no-op. An existing single-project cache can be re-keyed into a named project in place with scaffold workspace onboard <dir> --migrate-existing <name> (an atomic, rollback-safe rebuild verified by an integrity check); otherwise just re-index.

Trust model: a workspace is a single trust domain (all projects belong to the same user/org). Project scoping is a relevance and correctness boundary that prevents cross-project misorientation — not a security isolation boundary. Writes are project-scoped at the storage choke point with a check-before-insert collision guard, so re-indexing one project can never corrupt or wipe a sibling.

Install with language support

pip install agentscaffold[graph]              # Python, JS, TS
pip install agentscaffold[graph-all-languages] # + Go, Rust, Java, C, C++
pip install "agentscaffold[benchmark]"        # Optional live benchmark runner deps
pip install agentscaffold[all]                # Everything

Benchmarking

AgentScaffold includes an opt-in scaffold benchmark command group for comparing a baseline plain-tools agent arm against an AgentScaffold-equipped arm. The initial implementation is preflight/dry-run only:

scaffold benchmark models
scaffold benchmark doctor
scaffold benchmark run --dry-run --model claude-haiku --task-slice 0:1
scaffold benchmark compare path/to/summary.json
scaffold benchmark report path/to/results-dir

Live benchmark runs can spend model-provider credits and will require agentscaffold[benchmark], Docker, an API key, --max-cost-usd, and --confirm-live. See Benchmarking.

How Agents Use It

MCP Tools (for AI agents)

When you run scaffold mcp, these tools become available to your agent.

Interaction Modes

AgentScaffold supports two complementary ways of working:

• Natural-language + MCP (interactive): describe intent conversationally and let the agent route to the right governance/graph workflow.
• Structural CLI commands (explicit/automation): use direct scaffold commands for deterministic setup, verification, CI, and fallback.

Teams usually get best UX with NL+MCP for day-to-day flow, then use explicit CLI commands for verification (scaffold validate, scaffold graph verify, scaffold index --incremental).

You don't need to memorize tool names. AgentScaffold teaches the agent how to interpret user intent in natural conversation, map that intent to the right MCP workflow, and only fall back to direct reads/search when tool output is insufficient.

Composite tools — single calls that replace entire multi-step workflows:

Tool	What It Replaces
scaffold_prepare_review	Reading plan, contracts, learnings, and source to prepare a full adversarial review
scaffold_prepare_implementation	Tracing dependencies, checking contracts, and verifying readiness before coding
scaffold_orient	Reading 38+ files to understand project state, blockers, and next steps
scaffold_decision_context	Tracing the full decision chain (ADRs, spikes, studies) behind a plan
scaffold_staleness_check	Manually comparing plan dates, file changes, and overlapping completed work
scaffold_compare_plans	Reading two plans and their file impacts to identify conflicts
scaffold_prepare_retro	Gathering verification results, study outcomes, and retro insights
scaffold_find_studies	Searching study files by topic, tags, or outcome
scaffold_find_adrs	Searching architecture decision records by topic or status

Write tools — close the review loop by persisting findings into the graph:

Tool	Purpose	Latency
scaffold_record_finding	Persist a review finding (severity, category, affected files)	< 200 ms
scaffold_resolve_finding	Mark a finding resolved with resolution text	< 200 ms

Findings recorded via scaffold_record_finding appear in all future scaffold_prepare_review calls for the same plan, ordered by severity. Resolved findings are retained for retrospectives but filtered from active review output.

Finding lifecycle, before and after:

Without AgentScaffold — A reviewer flags that libs/data/router.py has 8 transitive importers and changing its signature is risky. The session ends. Three sessions later, a different agent reviews the same plan, re-reads the same files, re-traces the same imports, and re-derives the same risk from scratch — roughly 2,000 tokens of reasoning per session, repeated every time, because the conclusion lived only in a chat transcript no one reloads.

With AgentScaffold — The first reviewer calls scaffold_record_finding (severity high, category dependency, file libs/data/router.py). It is persisted as a ReviewFinding node linked to the file. Every later scaffold_prepare_review for that plan surfaces it in one call, so the reviewer starts from "this risk is known and open" instead of rediscovering it. When the risk is mitigated, scaffold_resolve_finding records the resolution and the finding drops out of active review but stays in the graph for the retrospective. In the eval harness this cut the review's token cost by about a third versus re-deriving — but the real point is that a known issue is never silently forgotten between sessions.

Granular tools — building blocks for custom queries:

Tool	What It Replaces
scaffold_context	Reading 12+ files to understand a symbol, its callers, and its layer
scaffold_impact	Manually tracing imports and grep-searching for consumers
scaffold_search	Multiple grep passes to find code by concept
scaffold_review_context	Reading plan files, contracts, and source to prepare a single review type
scaffold_stats	Scanning the entire directory tree to understand codebase shape
scaffold_validate	Running separate staleness checks and contract verification
scaffold_query	Writing ad-hoc queries against the knowledge graph

CLI (for humans)

scaffold plan create my-feature            # Create a plan from template
scaffold plan lint --plan 001              # Validate plan structure
scaffold plan status                       # Dashboard of all plans
scaffold validate                          # Run all enforcement checks
scaffold retro check                       # Find missing retrospectives
scaffold agents generate-all   # Regenerate all platform agent files
scaffold agents cursor # Cursor rules only
scaffold agents claude # Claude Code agent files only
scaffold agents skills                     # Generate skill disclosure files
scaffold plugins package trading           # Package a domain pack as a wheel
scaffold import chat.json --format chatgpt # Import conversation
scaffold ci setup                          # Generate CI workflows
scaffold metrics                           # Plan analytics
scaffold graph search "data routing"       # Hybrid search (keyword + semantic)
scaffold graph search "data routing" --all-projects  # Federate across a multi-project workspace
scaffold graph duplicates                  # Cross-project near-duplicate definitions
scaffold workspace onboard services/api    # Register a project into the workspace
scaffold workspace list                    # List workspace projects + mode
scaffold graph verify                      # Graph accuracy check
scaffold review brief 42                   # Pre-review brief for plan 42
scaffold review challenges 42              # Adversarial challenges with evidence
scaffold session start --plan 42           # Start a tracked coding session

Execution Profiles

Interactive (default): Human + AI agent in an IDE conversation. The agent follows AGENTS.md, asks questions when uncertain.

Semi-Autonomous (opt-in): Agent invoked from CLI/CI without a human present. Adds session tracking, safety boundaries, notification hooks, structured PR output, and cautious execution rules.

Both profiles coexist in the same AGENTS.md. The agent self-selects based on invocation context.

Rigor Levels

• Minimal: Lightweight gates for prototypes and small projects
• Standard: Full plan lifecycle with reviews, contracts, and retrospectives
• Strict: All gates enforced, all plans require approval

Domain Packs

The governance framework is domain-aware. Domain packs teach the adversarial reviewers to think like specialists in your field — a trading pack adds a quant architect who challenges risk assumptions and position sizing logic, a webapp pack adds a UX reviewer who flags accessibility gaps and performance regressions. Each pack includes tailored review prompts, implementation standards, and approval gates specific to the domain:

Pack	Focus
trading	Quantitative finance, RL, traceability
webapp	UX/UI, accessibility, performance budgets
mlops	Model lifecycle, experiment tracking, drift detection
data-engineering	Pipeline quality, schema evolution, SLAs
api-services	API design, backward compatibility, contract testing
infrastructure	IaC, deployment safety, cost analysis
mobile	Platform guidelines, offline-first, app store compliance
game-dev	Game loops, ECS, frame budgets
embedded	Memory constraints, real-time deadlines, OTA safety
research	Reproducibility, statistical rigor, experiment protocol

This keeps governance strict where risk is high and lightweight where speed matters, without rewriting the core framework.

scaffold domains add trading
scaffold domains add webapp

Documentation

Full documentation is in docs/:

• Getting Started — installation, init, first plan
• User Guide — session workflow, knowledge graph, review patterns
• Platform Integration — Cursor, Claude Code, Windsurf, Cline, aider, Codex, MCP setup
• Configuration Reference — full scaffold.yaml reference
• Domain Packs — available packs and installation
• Semi-Autonomous Guide — CLI/CI agent mode
• CI Integration — GitHub Actions workflows

License

MIT