Three follow-ups to the R3 wrapper-chain retirement (80d61de94),
addressing review-pass findings on the Python estimator path:
1. **``_wrap_for_estimator`` drops the triple-backtick fence**
around the body. The fence had turned the body's
``## Pre-fetched …`` headers into code-block content, which the
estimator's section-finder logic might miss (subtle tokenizer
ambiguity). Post-refinement the body is emitted at the top
level under a short directive — the estimator's existing
"Look for the following sections in your prompt" logic sees
the headers exactly where it expects them.
2. **``(pr_number, head_sha)`` cache for estimator results**, with
1 h default TTL (``IMPLEMENTER_ESTIMATOR_CACHE_TTL_S`` env
override). Defense against the run-15 doom-spiral failure mode
(2026-05-16): when the ``auto/last-attempt-tier-N`` label
mechanism falls open, the dispatcher's strict-walk doesn't seed
``start_tier > 0`` and every cycle re-runs the estimator on the
same PR + same commit to confirm the same answer. The cache
short-circuits that. Both confident and no-confidence outcomes
are cached so the null-result case doesn't re-burn the estimator
either. Transport / timeout failures are NOT cached
(environmental — retry next cycle). New commit (different
head_sha) implicitly invalidates the cache entry.
3. **``estimator-implementation.md`` prose updated** to reflect
post-R3 reality: the estimator runs as a top-level OpenCode
session, no intervening ``task`` hops, prefetched sections
survive intact in the prompt. The pre-R3 prose said summarisation
stripped most sections by depth 2 and instructed the LLM to
compensate by always calling ``handoff_fetch_pr_context`` to
recover the digest. That defensive call is now wasted on the
normal path; the prose marks it as the canonical fallback for
genuine missing-section cases (``PREFETCH=0`` rollback, upstream
prefetch failure) but states the digest is normally present.
Tests
-----
8 new tests in ``TestEstimatorResultCache``:
- cache hit skips the session call
- new head_sha invalidates the entry
- no-confidence outcome is cached (avoids re-burn on null-result)
- transport-error is NOT cached
- callers without pr_number / head_sha bypass cache entirely
- ``_estimator_cache_pr_key`` / ``_estimator_cache_head_sha``
helpers handle PR vs issue items correctly
``TestEstimatorPromptShape`` updated for the unfenced wrapper —
pins that the directive leads the prompt, the body appears
verbatim at the top level, and no ``\`\`\`fence`` surrounds the
body (the regression mode the refinement addresses).
Full auto_agents suite: 2280 passing (+18 from new estimator-cache
tests).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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:
cleveragentsandagents - 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 experience —
ActorSelectionOverlayguides new users to pick an actor on first TUI launch; creates a"default"persona automatically - Server mode —
agents server connectconfigures a remote CleverAgents server; Kubernetes Helm chart ink8s/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 lifecycle —
actor.default.estimationconfig 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 questions —
PermissionQuestionWidgetrenders single-file permission requests directly in the conversation stream with single-key shortcuts - Invariant reconciliation —
InvariantReconciliationActorruns automatically at every plan phase transition; failures block the transition and emitINVARIANT_VIOLATED - UKO provenance tracking — every typed triple now carries
sourceResource,validFrom, andisCurrentmetadata; a revision chain enables temporal queries - JSON-RPC 2.0 A2A wire format —
A2aRequest/A2aResponsefields 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
Enterto 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
F1to toggle the context-sensitive help panel - Press
Ctrl+Tto cycle through argument presets for the active persona - Press
Ctrl+Qto 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_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 diagnosticsprints whether the registry can see your credentials and which actor/provider is selected.agents tellandagents buildrequire--actor <name>unless a default actor is set; useagents actor set-default <name>to configure one.- Built-in actors (
<provider>/<model>) are immutable, custom actors must be namedlocal/<id>, and the default actor cannot be removed. Use--unsafewhen adding/updating configs marked unsafe; runtime only warns when invoking unsafe actors. CLEVERAGENTS_TESTING_USE_MOCK_AI=trueforces 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.