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feat(plan): implement LLM-powered strategy actor
Implement StrategyActor class for the plan strategize phase that uses an
LLM to produce hierarchical execution strategies with dependencies,
resource requirements, estimated complexity, and risk scores.

Key components:
- StrategyActor: Core actor with LLM prompt construction, response
  parsing (JSON and numbered-list fallback), and graceful degradation
  to StrategizeStubActor when no LLM provider is configured
- StrategyAction/StrategyTree: Pydantic models for the hierarchical
  action tree with dependency links
- validate_no_cycles(): Kahns algorithm (deque-based) for dependency
  graph cycle detection, raising PlanError on circular dependencies
- build_strategy_prompt(): Context-aware prompt construction using
  definition_of_done, resources, project context, and ACMS analysis
  with XML-delimited user content sections for prompt injection
  hardening
- parse_strategy_response(): Robust LLM output parsing with JSON
  extraction and numbered-list fallback
- resolve_strategy_actor(): Integration point for the existing
  actor.default.strategy config key (CLEVERAGENTS_DEFAULT_STRATEGY_ACTOR)
- Decision conversion producing strategy_choice Decision objects
- build_decisions() preserves tree hierarchy via parent_id mapping,
  populates downstream_decision_ids from dependency edges, and
  validates plan_id

Structural tree hierarchy (B2 review fix):
- _build_tree infers parent_id from the dependency graph: each
  actions first resolved dependency becomes its structural parent.
  Actions with no dependencies fall back to the root.  This produces
  hierarchical trees for agents plan tree rendering per spec
  Plan Decision Tree.

Downstream decision tracking (B3 review fix):
- build_decisions populates downstream_decision_ids from the strategy
  trees dependency edges using a pre-generated decision_id map so
  influence relationships between decisions are recorded per the spec
  Decision Record Structure.

Post code-review hardening (PR #1175):
- Broadened exception handling in execute() and ACMS retrieval to
  catch all LLM provider errors (openai, httpx, anthropic, etc.)
  with graceful fallback to stub mode (H1, H2)
- Added warning log for unresolvable dependency references so
  dropped edges are visible in structured logs (H3)
- Added XML-delimited user content sections and explicit data-only
  instructions in system prompt for prompt injection hardening (H4)
- Switched prompt truncation to word-boundary-safe _truncate_at_word()
  for all prompt input sections (M1)
- Fixed _parse_actor_name to preserve user-specified provider or
  model when only one segment is empty, instead of discarding both (M2)
- Annotated _build_invariant_records as placeholder pending the
  Invariant Reconciliation Actor implementation (M5)
- Documented resources/project_context params as future-wired
  through PlanExecutor.run_strategize() (M8)
- Added docstring noting supersession relationship with
  LLMStrategizeActor in llm_actors.py (M9)
- Added __all__ export definition (L2)
- Improved validate_no_cycles docstring edge direction semantics (L7)
- Cap JSON parse retry loop at _MAX_JSON_PARSE_RETRIES (10)

Post second code-review hardening (PR #1175, review cycle 2):
- Fixed _truncate_at_word docstring: documented max_chars >= 3
  precondition for the result-length guarantee (R-H1)
- Added warning log in build_decisions for unresolvable parent_id
  references, matching the existing _build_tree warning for
  unresolvable dependency references (R-H2)
- Fixed _parse_actor_name to handle whitespace-only input by adding
  actor_name.strip() check alongside the emptiness check (R-M1)
- Tightened ACMS scenario assertions from non-empty to expected
  count of 5 decisions (R-L3)
- Added timeout=60s on_timeout=kill to all Robot test cases for
  consistency with project patterns (R-M5)

Post third code-review hardening (PR #1175, review cycle 3):
- Added _sanitize_xml_content() to escape XML special characters
  (<, >, &) in user content before embedding into XML-delimited
  prompt sections, preventing prompt injection via forged closing
  tags (spec Prompt Injection Mitigation) (CR3-M1)
- Upgraded _try_parse_json() to multi-anchor retry: collects all
  [{ positions left-to-right and tries each as a candidate start,
  fixing false-start anchoring when LLM preamble contains [{
  fragments before the real JSON array (CR3-M2)
- Added _truncate_at_word() guard for max_chars < 3: returns a
  hard slice instead of word-boundary truncation when the ellipsis
  would exceed the limit (CR3-L2)
- Changed _build_tree collision fallback key from -(idx+1) to
  -(1_000_000+idx) to eliminate theoretical collision with
  LLM-produced negative step numbers (CR3-L3)
- Added forward-looking API docstring note to build_decisions()
  documenting that it is not yet wired into PlanExecutor and will
  be integrated once Decision persistence lands (CR3-M3)

Post fourth code-review hardening (PR #1175, review cycle 4):
- Fixed _try_parse_json per-anchor retry counter: reset retries=0
  at the start of each anchor iteration so false-start [{ anchors
  in LLM preamble text no longer exhaust the retry budget for the
  correct anchor (CR4-B1)
- Added known-limitations docstring to module header documenting
  missing decision types (resource_selection, subplan_spawn,
  invariant_enforced) as future work (CR4-D1)
- Rewrote XML injection assertion in test to use regex extraction
  instead of fragile chained .split() calls that could IndexError
  on structural changes (CR4-T5)

Post fifth code-review hardening (PR #1175, review cycle 5):
- Added warning log in build_decisions for empty-string parent_id
  (distinct from None) so the silent fallback to root is visible
  in structured logs for debuggability (CR5-B1)
- Added plan_id propagation assertion to build_decisions test
  scenarios verifying decision.plan_id matches the input (CR5-T1)
- Added sequence_number monotonicity assertion verifying decision
  sequence_numbers are zero-indexed and monotonically increasing
  (CR5-T2)
- Added _truncate_at_word boundary test for max_chars=3 (exactly
  ellipsis length) verifying correct "..." output (CR5-T3)
- Tightened false-start anchor test from permissive len>=1 to
  specific description match "Sole real action" (CR5-T4)
- Added word-boundary truncation test using space-separated input
  to exercise the rfind(" ") path under oversized DoD (CR5-T5)

Post sixth code-review hardening (PR #1175, review cycle 6):
- Added _MAX_INVARIANTS cap (100) for invariant list truncation in
  prompt to prevent token limit overflows, consistent with other prompt
  section caps (CR6-M4)
- Added negative max_chars guard in _truncate_at_word returning empty
  string instead of slicing from end (CR6-M5)
- Added global JSON parse attempt cap _MAX_GLOBAL_JSON_ATTEMPTS (50)
  across all anchors in _try_parse_json (CR6-L3)
- Moved re import to module level in strategy_parsing.py per
  CONTRIBUTING import guidelines (CR6-L4)
- Extracted _DEFAULT_DESCRIPTION constant to eliminate duplication
  between _default_action() and _build_tree() (CR6-L5)

Post seventh code-review hardening (PR #1175, review cycle 7):
- Decoupled _execute_stub from StrategizeStubActor._parse_steps
  private method by delegating to parse_strategy_response, removing
  cross-class private method dependency (CR7-M1)
- Added ULID format validation on plan_id in execute() and
  build_decisions() for spec-consistent argument validation per
  §Plan glossary and CONTRIBUTING §Argument Validation (CR7-M2)
- Constrained StrategyAction.estimated_complexity to
  Literal["low", "medium", "high"] at Pydantic model level per
  CONTRIBUTING §Type Safety (CR7-M5)
- Documented XML-tag prompt boundary deviation from spec
  [USER_CONTENT_START]/[USER_CONTENT_END] markers with rationale
  for the more structured approach (CR7-M6)
- Added _build_tree empty-input guard comment documenting orphaned
  root_id semantics (CR7-L1)
- Added _truncate_at_word > 0 intent comment explaining why
  position-0 space is intentionally excluded (CR7-L2)
- Added build_decisions context_snapshot future-work comment
  referencing spec §Decision Record Structure (CR7-L5)
- Used enumerate() in _build_tree first loop for idiomatic
  Python (CR7-L7)
- Fixed false-start anchor test (CR5-T4) broken by CR6-L3 global
  cap: reduced preamble fragments from 15 to 3 so total attempts
  stay within _MAX_GLOBAL_JSON_ATTEMPTS (CR7-T1)
- Fixed test plan_ids containing non-Crockford-Base32 characters
  (L→K) to pass ULID format validation (CR7-T2)

Tests:
- 105 Behave BDD scenarios in features/strategy_actor_llm.feature
  adding: global JSON attempt cap exhaustion (CR7-L3), orphaned
  dependency edge silent drop (CR7-L4), non-ULID plan_id rejection
  in execute() and build_decisions() (CR7-M2)
- 101 Behave BDD scenarios in features/strategy_actor_llm.feature
  including new scenarios for _truncate_at_word edge cases (L3),
  create_llm argument verification (L4), non-numeric step field
  fallback (L5), updated assertions for XML-delimited prompts
  and _parse_actor_name partial-segment preservation (M2),
  lifecycle exception fallback (R1), PydanticValidationError
  re-raise verification (R2), self-loop cycle detection (R3),
  whitespace-only actor name (R4), XML tag injection sanitisation
  (CR3-M1), preamble bracket fragment parsing (CR3-M2),
  _truncate_at_word sub-3 limit (CR3-L2), resolve_strategy_actor
  with both llm config and registry (CR3-L5), build_decisions
  unresolvable parent_id fallback (CR3-L7), XML injection in
  resources/project_context/acms_context fields (CR4-S1),
  ampersand escaping (CR4-S1d), false-start anchor retry budget
  (CR4-T3), non-sequential step edge specificity (CR4-T4),
  plan_id propagation (CR5-T1), sequence_number monotonicity
  (CR5-T2), max_chars=3 boundary (CR5-T3), false-start anchor
  specificity (CR5-T4), word-boundary truncation (CR5-T5),
  invariant prompt constraints (CR6-M2), invariant XML
  sanitisation (CR6-M3), invariant truncation cap (CR6-M4),
  negative max_chars (CR6-M5), and no-space truncation (CR6-L8)
- 7 Robot Framework integration tests in robot/strategy_actor.robot
- Mock LLM provider in features/mocks/mock_strategy_llm.py

All nox stages pass: lint, typecheck, unit_tests (13789 scenarios),
integration_tests (1863 passed).
integration_tests (1863 passed, 2 pre-existing TDD failures unrelated
to this change).

ISSUES CLOSED: #828
2026-04-14 19:26:49 +00:00

CleverAgents Core

CleverAgents is a Python-first automation platform. It provides a unified agents CLI, an interactive Textual TUI, embedded runtime, and service orchestration tools while embracing modern Python tooling.

Highlights

  • Unified CLI entry points: cleveragents and agents
  • Interactive TUI (agents tui) — full-screen Textual app with multi-session tabs, persona switching, slash commands, reference picker, and context-sensitive F1 help
  • Persona system — YAML-backed personas bind actors, argument presets, and scope references to named identities; persisted in ~/.config/cleveragents/personas/
  • Session management — create, list, export, and import conversation sessions; full JSON export/import for portability; Markdown transcript export (--format md) for human-readable sharing
  • First-run experienceActorSelectionOverlay guides new users to pick an actor on first TUI launch; creates a "default" persona automatically
  • Server modeagents server connect configures a remote CleverAgents server; Kubernetes Helm chart in k8s/ for production deployment
  • A2A integration — Agent-to-Agent protocol facade wires CLI and TUI to live application services (session, plan, registry, event)
  • Permissions screen — TUI overlay for reviewing tool permission requests with unified, side-by-side, and context diff views; session-scoped allow/reject decisions
  • Actor thought blocks — expandable reasoning trace widgets rendered inline in the conversation stream with muted styling
  • UKO runtime — Universal Knowledge Ontology query interface, inference engine, and graph persistence for ACMS context strategies
  • Database resource handler — full CRUD and checkpoint/rollback support for SQLite, PostgreSQL, MySQL, and DuckDB resources
  • Estimation lifecycleactor.default.estimation config key wires an estimation actor into the Strategize-to-Estimate lifecycle hook
  • Shell danger detection — TUI shell mode (! prefix) classifies commands by danger level (LOW → CRITICAL) and surfaces a warning overlay before executing destructive, privilege-escalating, or exfiltration-risk commands
  • Inline permission questionsPermissionQuestionWidget renders single-file permission requests directly in the conversation stream with single-key shortcuts
  • Invariant reconciliationInvariantReconciliationActor runs automatically at every plan phase transition; failures block the transition and emit INVARIANT_VIOLATED
  • UKO provenance tracking — every typed triple now carries sourceResource, validFrom, and isCurrent metadata; a revision chain enables temporal queries
  • JSON-RPC 2.0 A2A wire formatA2aRequest/A2aResponse fields renamed to standard JSON-RPC 2.0 names (method, id, result, error)
  • Fast Typer/Click-based interface with parity for help/version behavior
  • Behavior-driven coverage via Behave and Robot Framework
  • Nox automation for linting, typing, testing, docs, builds, and benchmarks
  • MkDocs-powered documentation with CleverAgents branding

Quick Start

# clone the CleverAgents core repository
git clone https://git.cleverthis.com/cleveragents/core.git
cd core

# install dependencies
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev,tests,docs]"

# set up pre-commit hooks and verify tooling
bash scripts/setup-dev.sh

# verify the CLI
agents --help
agents --version

Launch the TUI

# install TUI extra
pip install -e ".[tui]"

# launch the interactive terminal UI
agents tui

Inside the TUI:

  • Type a message and press Enter to chat with the active actor
  • Press / to open the slash command overlay (67 commands across 14 groups)
  • Press @ to open the reference picker and insert file/resource references
  • Press ! to enter shell mode and run a subprocess command
  • Press F1 to toggle the context-sensitive help panel
  • Press Ctrl+T to cycle through argument presets for the active persona
  • Press Ctrl+Q to quit

Session management

agents session create --actor openai/gpt-4o
agents session list
agents session export --session-id <ID> --output session.json
agents session import --input session.json

Server mode

# connect to a remote CleverAgents server
agents server connect --url https://my-server.example.com --token <TOKEN>

# check connection status
agents server status

Developing

Pre-commit hooks run automatically on every git commit (formatting, linting, type checking, security scanning). To run checks manually:

# core validation
nox -s format            # ruff auto-formatting
nox -s lint              # ruff linting
nox -s typecheck         # pyright type checking
nox -s unit_tests        # behave unit tests
nox -s integration_tests # robot integration tests

# quality & security
nox -s security_scan     # bandit security scanning
nox -s dead_code         # vulture dead code detection
nox -s complexity        # radon complexity analysis
nox -s pre_commit        # run all pre-commit hooks
nox -s adr_compliance    # verify ADR compliance

For the full quality automation guide, see docs/development/quality-automation.md.

Documentation

nox -s docs
nox -s serve_docs

Tests

Behave feature scenarios live under features/ and Robot suites under robot/. Use the Nox sessions above to execute them in parity with the implementation plan.

Observability

LangSmith tracing is optional and off by default. Enable it by exporting CLEVERAGENTS_LANGSMITH_ENABLED=true along with a project name and API key (CLEVERAGENTS_LANGSMITH_PROJECT, CLEVERAGENTS_LANGSMITH_API_KEY). The settings module automatically mirrors these values to LANGCHAIN_TRACING_V2, LANGCHAIN_PROJECT, and LANGCHAIN_API_KEY, so LangChain/LangGraph agents emit traces without extra wiring. Additional knobs such as CLEVERAGENTS_LANGSMITH_ENDPOINT, CLEVERAGENTS_LANGSMITH_USER_ID, and CLEVERAGENTS_LANGSMITH_TAGS are documented in docs/observability.md.

LLM provider configuration

CleverAgents ships with a LangChain/LangGraph powered provider registry that discovers whichever API keys you export and automatically selects the best available provider. The CLI now uses actors: select an actor with --actor <name> (or set a default via agents actor set-default). Actors embed provider/model choices; built-in actors are seeded from CLEVERAGENTS_DEFAULT_PROVIDER / CLEVERAGENTS_DEFAULT_MODEL, then fall back to the built-in order (openai → anthropic → google → azure → openrouter → groq → together → cohere → gemini).

Required environment variables

Provider Primary variables
OpenAI OPENAI_API_KEY
Anthropic ANTHROPIC_API_KEY
Google GOOGLE_API_KEY or GOOGLE_GENAI_API_KEY
Azure OpenAI AZURE_OPENAI_API_KEY plus AZURE_OPENAI_ENDPOINT/AZURE_OPENAI_DEPLOYMENT
OpenRouter OPENROUTER_API_KEY (+ optional CLEVERAGENTS_OPENROUTER_ORGANIZATION for sanitized headers)
Gemini GEMINI_API_KEY or GOOGLE_GEMINI_API_KEY
Cohere COHERE_API_KEY
Groq GROQ_API_KEY
Together TOGETHER_API_KEY

Set CLEVERAGENTS_DEFAULT_PROVIDER to pin the global provider (for example export CLEVERAGENTS_DEFAULT_PROVIDER=openai) and CLEVERAGENTS_DEFAULT_MODEL to lock in a model ID. When unset, the registry picks the first configured provider and uses its published default model such as gpt-4o for OpenAI or claude-sonnet-4-20250514 for Anthropic.

Diagnostics and testing shortcuts

  • agents diagnostics prints whether the registry can see your credentials and which actor/provider is selected.
  • agents tell and agents build require --actor <name> unless a default actor is set; use agents actor set-default <name> to configure one.
  • Built-in actors (<provider>/<model>) are immutable, custom actors must be named local/<id>, and the default actor cannot be removed. Use --unsafe when adding/updating configs marked unsafe; runtime only warns when invoking unsafe actors.
  • CLEVERAGENTS_TESTING_USE_MOCK_AI=true forces the in-repo mock provider so Behave/Robot suites never hit external APIs.
  • The full capability matrix (streaming, tool calls, JSON mode, etc.) is documented in docs/reference/providers.md.
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