- execution_mixin: replace non-existent FastMCP.create_app() with the
repo's working low-level SSE transport pattern (connect_sse +
handle_post_message), matching mcp/mcp_server.py; normalize base_path
leading slash. Fixes AttributeError at MCP server startup.
- examples (36 files): move '# praisonai: skip=true' below the shebang so
direct execution still works; directive stays within the runner's
first-30-lines scan.
- failover_example: display real 'last_error' instead of unmodeled
'failure_count' (which always printed 0).
- scripts/apply_e2e_batch3_*.py: make skip insertion shebang-aware, wire
the previously-uncalled fix_mcp_env_examples(), and make the E2E report
path a CLI argument (defaulting to the audit path).
Co-authored-by: Mervin Praison <[email protected]>
Remove 13 knowledge.db binaries plus managed_ids.json, blog_post.md,
tutorial.md, token_metrics_export.json, market_research_results.json that
were accidentally committed (PR notes explicitly say not to commit local
artifacts). Add .gitignore rules to prevent recurrence.
Co-authored-by: Mervin Praison <[email protected]>
Address reviewer feedback on the in-loop continuation guard:
- Emit the just-completed (paid) row before the guard halts, so the
request that trips the sponsorship check is no longer silently dropped.
- Coerce sponsor_check_rows to a safe minimum of 1 so a 0 (divide-by-zero)
or negative (never-poll, guard disabled) value cannot break/disable the
safety check; unset preserves the 5000 default.
Adds tests for trigger-row emission and invalid-interval handling.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Mervin Praison <[email protected]>
Two additive, backward-compatible features for the synthetic-data generator.
1. In-generator continuation guard. generate_dataset gains an optional
should_continue() -> bool callback, re-checked every `sponsor_check_rows`
completed requests (default 5000). When it returns False the run halts
cleanly via the existing shutdown path (touch stop-file + cancel pending
futures + break). If no callback is passed but `subscription_id` (or env
AZ_SUBSCRIPTION) is set, a built-in Azure sponsorship check is wired in
(quotaId == "Sponsored_2016-01-01"). The guard fails OPEN on transient
errors — an external watchdog is the hard fail-closed guard; this is
belt-and-suspenders. New public helper: azure_sponsorship_guard().
2. Per-row provenance stamping. Emitted rows are now self-describing: _call
stamps `model` (deployment) and passes through `task_type`/`topic` from the
prompt spec when present. The axis recipe stamps task_type/topic onto each
spec, so QC (which already reads r["task_type"]) and downstream category
splits key off metadata instead of brittle text. Keys are added only when a
value is available (backward-compatible).
Tests (network stubbed): the guard halts after N rows when the predicate turns
False and never halts while True; rows carry model/task_type/topic when
provided and omit task_type/topic when absent; plus fail-open and
subscription-id wiring.
Co-Authored-By: Claude Opus 4.8 <[email protected]>
The switch to praisonai.arun() dropped the caller's required input on the
native jobs path (job.prompt) and the serve /agents fallback (query), since
arun has no prompt param and the workflow topic is read from YAML input/topic
only. Honour input/topic overrides in AgentsGenerator._merge_cli_config
(reusing the existing merge seam, no new params/exports) and pass
job.prompt / query through cli_config so per-run input drives the workflow
instead of being silently discarded. Static YAML input is preserved when no
prompt is supplied.
Co-authored-by: Mervin Praison <[email protected]>
- serve.py: route the crew fallback through praisonai.arun (owns the
AgentsGenerator lifecycle, runs off the event loop) and replace
asyncio.get_event_loop() with asyncio.to_thread in both agent handlers.
- jobs/executor.py: _run_praisonai_agents now delegates to praisonai.arun
so the caller's agent_file/agent_yaml/framework/config are honoured
instead of silently dropped by a stock Agent.
- scheduler/agent_scheduler.py: reuse an instance-owned ThreadPoolExecutor
with leak accounting and recycling (mirrors AgentsGenerator), unify the
timeout branch into the backoff path, and close() the pool on stop().
Co-authored-by: MervinPraison <[email protected]>
- reuse a supplied empty shared MinHashLSH index (is not None, not `or`)
so cross-batch near-dedup works on the first call
- treat empty MinHash signatures as non-similar (0.0) and reject
minhash_perm/ngram_n < 1, so degenerate config can't collapse a dataset
- global_dedup honours near_dup_method='sliding' and raises a clear
FileNotFoundError for missing paths instead of a cryptic AttributeError
- sliding look-back treats window<=0 as intentional unbounded
- dedup CLI writes to a temp file + atomic replace, so an --out that
aliases an input is never truncated and a malformed line never leaves
a partial output
Co-authored-by: Mervin Praison <[email protected]>
The QC near-dup pass was O(n·3000) sliding-window char-4gram Jaccard, which is
both slow at scale and structurally blind to near-duplicates more than 3000 rows
apart — and score() rebuilds its dedup state per call, so it cannot remove dups
that span multiple batch files (the common case when merging parallel runs).
Add praisonai_train.data.dedup (pure stdlib, no datasketch/numpy):
- MinHasher / MinHashLSH: seeded, deterministic MinHash signatures over the same
normalized char-4gram shingles the sliding engine uses (so near_dup_jaccard
keeps identical semantics), with LSH banding for sub-linear candidate lookup
and no bounded look-back window.
- near_dedup(rows, cfg): one interface, YAML-tunable near_dup_method
("sliding" default = unchanged, or "minhash"). qc.score() now delegates to it
(DRY); default path behaviour is byte-for-byte the same. near_dup_window is now
configurable too.
- global_dedup(sources, cfg): dedup ACROSS many JSONL files / row-lists with ONE
shared exact+LSH index — what per-file score() cannot do.
- CLI: `praisonai-train dedup batch_*.jsonl --out merged.jsonl`.
Backward compatible: no new deps, default engine and outputs unchanged; MinHash
and cross-file dedup are opt-in.
Tests: 13 new (deterministic) — MinHash catches a far near-dup the window misses,
preserves genuinely-distinct rows, cross-file exact+near removal, exact-only, and
default-still-sliding. Full data suite 60 passed.
Co-Authored-By: Claude Opus 4.8 <[email protected]>
- Count completion tokens only for successful (ok) responses so token
throughput and avg_out_tok stay consistent with the success count.
- Thread a json_mode flag through _timed_call/_measure_target/
benchmark_deployments and expose --json-mode/--no-json-mode + yaml key,
so OpenAI-compatible endpoints without JSON mode aren't ranked as failures.
- Validate prompt dicts (require non-empty 'user') and reject n<1 /
concurrency<1 with a clear ValueError instead of KeyError / silent
zero-request runs miscounted as endpoint failures.
- Add 4 tests; existing stubs updated for the new json_mode arg. 102 passed.
Co-authored-by: Mervin Praison <[email protected]>
Add a first-class, protocol-driven speed benchmark to the praisonai-train
data SDK, mirroring the generate/validate idiom. It sends N identical
requests per deployment at a fixed concurrency and reports throughput
(rows/min, completion-tokens/sec), latency (avg/p50/p95), success count
and average output tokens, then ranks deployments fastest-first.
DRY: extract the generator's HTTP call path into shared helpers
(`build_chat_request`, `resolve_cfg`) so benchmarking reuses the exact
same Azure/OpenAI-compatible request construction and credential
resolution as real generation — no duplicated HTTP code. The default
prompt reuses a registered Recipe so benchmarks reflect the real workload.
- data/benchmark.py: `benchmark_deployments(...) -> list[BenchResult]`,
pure `aggregate()`, and network confined to a `_timed_call` seam.
- cli/commands/benchmark.py: `praisonai-train benchmark` (YAML/flags),
ranked table + JSON output.
- setup/benchmark.yaml: documented sample config.
- tests: aggregation math, ranking, success/failure counting, real
threaded path, per-target endpoint override (network stubbed).
Co-Authored-By: Claude Opus 4.8 <[email protected]>
The documented gateway.notify_on_undelivered / undelivered_template YAML
opt-in was a dead path: GatewayServerSchema (extra="forbid") rejected the
keys at load time, and neither start_with_config nor from_config_file
propagated them onto self.config, so the delivery router's getattr always
saw the OFF default.
- Accept both keys in GatewayServerSchema (kept optional, lightweight).
- Stamp them onto self.config in start_with_config (primary CLI path) and
from_config_file, honouring the "no new core GatewayConfig field" design.
- Add a regression test covering schema acceptance + propagation.
Co-authored-by: Mervin Praison <[email protected]>
Connect the previously-inert completion path (Greptile P1): BotSessionManager
now reads the agent's last_stop_reason after each turn and, when the new opt-in
surface_completion_reason flag is set, appends a concise user-safe note for
early stops (max_steps / cancelled / error). Off by default so clean
completions and existing deployments are unchanged.
Co-authored-by: Mervin Praison <[email protected]>
Address greptile P1 review feedback on the inbound media-album coalescer:
when the owning Telegram update is cancelled (e.g. during shutdown) after
sibling updates already folded their media in, the buffered temp files could
leak. Add an on_orphan cleanup hook invoked whenever a flushed album has no
live owner awaiting it, wired to a shared _remove_inbound_media helper in the
Telegram bot. Also clarify the max_items cap semantics (bounded, intentional).
Co-authored-by: Mervin Praison <[email protected]>
On a confirmed-permanent outbound failure the reply was parked in the DLQ /
the target marked dead but the user heard nothing. Add an opt-in last-resort
notice: a short plain-text delivery-failure message on the same channel plus a
MESSAGE_UNDELIVERED lifecycle hook so operators can route the failure.
Core (protocol only): add HookEvent.MESSAGE_UNDELIVERED and
MessageUndeliveredInput. Wrapper (praisonai-bot): DeliveryRouter fires the hook
and best-effort sends the notice on permanent failure; default OFF, wired from
gateway.notify_on_undelivered.
Co-authored-by: MervinPraison <[email protected]>
Telegram delivers a media album (multiple photos/files sent together) as N
separate updates sharing one media_group_id, so the agent saw each part as
its own turn. Add an AlbumCoalescer that buffers media parts by group key
and flushes them as one multimodal turn, mirroring the existing
InboundDebouncer text pattern.
- New praisonai_bot/bots/_album.py: bounded per-group buffer (window +
max_items), operator-tunable via config.metadata (media_group_window_ms /
media_group_max), disabled by default so behaviour is unchanged.
- Wire into TelegramBot.handle_message (sibling updates fold into one turn)
and flush pending buffers on stop().
- Tests in tests/unit/test_bot_album.py.
Co-authored-by: MervinPraison <[email protected]>
Add an optional, backward-compatible TurnCompletion contract to the bot
reply path so a chat user learns *why* a turn stopped early instead of
seeing a silently truncated/empty reply.
- TurnCompletion wraps the existing coarse Agent.last_stop_reason
(completed | max_steps | cancelled | error) into a portable value with
a user-safe, localisable .note(); completed turns surface nothing.
- AgentReply gains an optional `completion` field (defaults None).
- extract_completion() reads it off any result (incl. a bare Agent via
last_stop_reason); append_completion_note() lets a gateway optionally
append the note, off by default so existing deployments are unchanged.
Reuses the existing runtime stop reason rather than adding new detection
or Agent params — no new module, no wrapper knobs.
Co-authored-by: MervinPraison <[email protected]>
Move progress_callback below the yield in generate_dataset so 'kept' reflects
rows the consumer has actually received (Greptile P1). Docstring clarified.
Co-authored-by: Mervin Praison <[email protected]>
generate_dataset() gains an optional progress_callback(done, total, kept)
invoked as teacher requests complete, so a CLI/monitor/notebook can show
completion and remaining work. Backward compatible: default None keeps the
original behaviour (no callback). Fires once up-front with (0, total, 0) then
after each completed request; done is monotonic up to total, kept counts unique
rows emitted.
The `generate` CLI command wires a default reporter (_Progress): a tqdm bar when
tqdm is importable, else throttled done/total prints. tqdm stays optional — the
import is guarded and never becomes a hard dependency.
Adds unit tests asserting the callback fires with monotonic done up to total,
that kept tracks dedup, and that the default (no callback) path is unchanged.
Co-Authored-By: Claude Opus 4.8 <[email protected]>
- intent.translation_intent: an unrecognised non-empty task_type no longer
short-circuits to non-translation; it falls through to the documented text
fallback so an explicit "translate into English" instruction is honoured and
its English output is not dropped as low_script_purity (greptile P1).
- qc.score: the translation `exempted` metric now counts only rows the
English-target exemption ACTUALLY rescued from LowScriptPurity (task-awareness
on AND output below the purity floor), so it reconciles with check behaviour
instead of over-counting failed translations and task_aware_purity=false runs
(greptile P1).
- Add 4 regression tests (29 passed total).
Co-authored-by: Mervin Praison <[email protected]>
Add two dataset-QC features to the praisonai-train data SDK, wired in the
SDK's protocol-driven idiom (registered RowChecks + a corpus metric) and
sharing ONE translation-intent detector so they can never disagree (DRY).
1. Task-aware script purity: the script-purity checks no longer delete an
output that is legitimately in another script because the instruction
asked to translate INTO that language (e.g. a Tamil->English row's
correct output is English). LowScriptPurity/ScriptReview now exempt a
resolved English-target row; a new WrongTargetScript flag catches the
inverse failure (asked for English, output still Tamil). Only a resolved
English target exempts — en->ta / ambiguous / native rows stay strict.
2. Translation-share corpus metric: metrics.translation reports the fraction
of kept rows that are translation tasks, by direction (en_ta/ta_en/unknown)
and an exempted count, computed with the SAME detector so it reconciles
with the per-row exemptions.
New single source of truth: data/intent.translation_intent(); metadata
(task_type) is authoritative, instruction-text detection is the external-row
fallback, ambiguous direction yields no exemption (safe default).
17 new unit tests (mirroring the modeltrain reference); full data suite green.
Co-Authored-By: Claude Opus 4.8 <[email protected]>
Addresses Greptile P1: an issue such as '[QUESTION] How do I handle this
API error?' matched 'error' before the question marker was checked,
receiving 'bug' + auto 'claude'. Guard bug detection on \!title.includes('[question]').
Co-authored-by: Mervin Praison <[email protected]>
Bug issues with a "## How to fix" section were labelled question first,
which blocked autoClaude. Check bug/error/crash before how-to heuristics,
ignore "how to fix" for question detection, and let bug label win over
a stale question label for auto-triage.
- qc: include output in exact-dedup key so distinct answers to the same
prompt are not silently dropped
- _util.fields: pair the final assistant turn with the user turn that
precedes it (never a trailing unanswered user turn)
- cli generate: forward --snapshot-every override; defer output truncation
until the first row is produced (a failed run no longer destroys an
existing file / self-referential dedup source); exit non-zero when 0 rows
are generated instead of reporting empty success
- cli validate: skip malformed JSONL lines (with line numbers) instead of
aborting the whole run
- generate: configurable request_timeout; accurate stop-file docstring
- tests: cover distinct-answer dedup and multi-turn chat pairing
Co-authored-by: Mervin Praison <[email protected]>
Remove the legacy `.praison/config.toml` from PROJECT_CONFIG_NAMES so the
walk-up discovery never mislabels a profile-level legacy global config as a
`project:` source. The legacy file is owned exclusively by
_load_global_config() with a `global:` label. Removes the now-dead
search_dir == home skip guard and adds a regression test.
Co-authored-by: Mervin Praison <[email protected]>
Harbor 0.20's BaseAgent.__init__ requires logs_dir as first positional
arg. The TestPraisonAIExternalAgent tests constructed the agent with zero
args, causing a TypeError. Pass logs_dir via pytest tmp_path to match the
existing pattern used by TestPraisonAICodeAgent and TestPraisonAIInstalledAgent.
Co-authored-by: Mervin Praison <[email protected]>
Harbor 0.20 agents expose extra_env (wired into the container exec
context by Trial), not self.env. Passing env=self.env crashed
PraisonAICodeAgent.run() with AttributeError. Omit the per-exec env=
so Harbor's trial-scoped injection applies. Same fix applied to
praisonai_installed_agent.py.
Co-authored-by: Mervin Praison <[email protected]>
Harbor's BaseInstalledAgent._exec always prefixes commands with
'set -o pipefail; ', so the .startswith assertion failed even when the
adapter command was correct. Switch to a substring match.
Co-authored-by: Mervin Praison <[email protected]>
- PRAISON_DEBUG now parsed as a boolean (0/false/no disable it, not just unset)
- auto-resume filters to real numeric checkpoint-* dirs and passes the validated
path to the trainer (instead of leaving resume=True and re-discovering); also
accepts 1/yes/on/off spellings before treating a string as a literal path
- push_model_gguf honors quantization_method default (q4_k_m) matching save path
- final save/eval cadence reconciliation after training_arguments passthrough so
load_best_model_at_end can't reach SFTConfig with mismatched strategies/steps
Co-authored-by: Mervin Praison <[email protected]>
Reverts the removal of tokenize_function — it is imported and re-exported in
praisonai/train.py __all__, so deleting it breaks that public API. Kept it (now
uncalled by the training path but retained for backward compat) and removed only
the internal, unreferenced tokenize_dataset method.
Co-Authored-By: Claude Fable 5 <[email protected]>
- Remove dead tokenize_function/tokenize_dataset (unused since TRL tokenizes
internally; used padding='max_length' which would waste compute if ever rewired).
- save_model_gguf now honors config quantization_method (was hardcoded 'q4_k_m',
inconsistent with push_model_gguf).
- Gate the per-batch DEBUG prints behind PRAISON_DEBUG so normal runs stay readable.
Co-Authored-By: Claude Fable 5 <[email protected]>
- Fix AgentTeam.start() result handling across 22+ workflow/usecase examples
- Fix missing AgentFlow/AgentTeam/AutoAgents imports and registry override demo
- Fix scope_identifiers, state_redis db import, and ToolsMCPServer.run_sse API
- Mark STT and stale tool-import examples as skip until refreshed
- Remove stale db mapping that shadowed praisonaiagents.db submodule
Co-authored-by: Cursor <[email protected]>
HF requires save_steps to be a multiple of eval_steps when load_best_model_at_end
is on. A config with save_steps=100, eval_steps=500 (both valid-looking) crashed.
Now the trainer aligns eval_steps to save_steps (preserving the user's checkpoint
frequency) with a warning, instead of erroring out.
Co-Authored-By: Claude Fable 5 <[email protected]>
resume_from_checkpoint: true now auto-finds the latest checkpoint in output_dir
and resumes from it; if none exists yet (fresh run) it starts fresh instead of
erroring. So after any crash (OOM, GPU reclaim) you just relaunch the SAME
command and training continues from the last saved step. Validated on GPU:
fresh run -> checkpoint-2/4; relaunch -> resumed from checkpoint-4, continued 5->8.
Co-Authored-By: Claude Fable 5 <[email protected]>
- Remove wrapper import from praisonai-code agent_tools.py (C7 gate
regression): use a module-local _run_sync bridge instead of
`from praisonai._async_bridge import run_sync` so the Tier-2 package
no longer depends on the praisonai wrapper at the hot path.
- suite_runner: start child processes with start_new_session=True on
POSIX so timeout killpg reaps only the child tree, never the runner.
- agent_tools/orchestrator: honor acp_execute_command cwd by forwarding
it into the plan step; orchestrator validates it stays in workspace.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
- Kill Windows subprocess trees on suite-runner timeout (uvicorn hang)
- Restore Agent.launch() shared server globals in execution_mixin
- Add missing praisonai_code.cli.features.agent_tools module
- Fix compression_demo ContextCompressor API usage
- Import AgentFlow in workflow output/routing examples
- Use UTF-8 when parsing doctor ci subprocess output on Windows
- Set BrowserBaseTool __name__ for agent tool schema compatibility
Co-authored-by: Cursor <[email protected]>
Gemma 4 E4B (and other multimodal/MoE/elastic models) have LoRA-adapted params
that don't fire every step (vision/audio towers in text-only training), which
crashes DDP with 'Expected to have finished reduction' when find_unused=False.
Default True works universally; set false for a small speedup on dense-text models.
Co-Authored-By: Claude Fable 5 <[email protected]>
- Set UNSLOTH_USE_NEW_MODEL before unsloth import (DDP non-reentrant path)
- Skip LoRA when full_finetuning: true (train base weights directly)
- Reload model with trained precision (dtype/8bit/full_finetuning), not 4bit-only
- Re-align save/eval strategy when early stopping enables load_best_model_at_end
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
Only the main process merges/pushes and writes the final adapter, so multi-GPU
runs don't race to write the same repo/dir.
Co-Authored-By: Claude Fable 5 <[email protected]>
Validated end-to-end on 2x A6000 (unsloth 2026.7.4 / trl 0.24 / torch 2.10):
multi-GPU DDP trains (Data Parallel GPUs = 2, no 'marked ready twice'),
checkpointing + eval + best-model + final_model_dir all work.
Multi-GPU (DDP):
- Detect distributed (LOCAL_RANK/WORLD_SIZE); load full model per-rank
(device_map={'':local_rank}); set UNSLOTH_USE_NEW_MODEL=1 under DDP to force
Unsloth's non-reentrant checkpointing (the legacy path hard-codes reentrant,
which double-fires autograd hooks and crashes DDP). Set ddp_find_unused_parameters
=False (LoRA freezes the base). Launch: torchrun --nproc_per_node=N.
Checkpointing / resume:
- Config-driven save_strategy/save_steps/save_total_limit (was only under an env var);
resume_from_checkpoint (bool=latest, or a path); final_model_dir (fixes the old
lora_model vs output_dir mismatch that broke load_model).
Evaluation / early stopping:
- val_split_ratio carves an eval set; eval_strategy/eval_steps/load_best_model_at_end/
metric_for_best_model; EarlyStoppingCallback via early_stopping_patience.
Flexibility:
- Generic training_arguments passthrough (any raw SFTConfig field), auto-filtered to
what the installed TRL accepts (version-proof; e.g. save_safetensors/group_by_length
differ by version) — warns instead of crashing.
- Knobs: max_grad_norm, warmup_ratio, neftune_noise_alpha, dataloader_num_workers,
data_seed, adam betas, report_to/run_name (config, not just env), push_to_hub.
- Quantization: load_in_8bit, dtype, full_finetuning. LoRA: modules_to_save,
rank_pattern, alpha_pattern, use_dora. Data: local files, shuffle, rename, filter.
Correctness fixes:
- load_in_4bit KeyError when omitted; inference().to(cuda) -> self.device;
drop empty-formatted rows instead of silently training on blanks.
All new keys optional with prior defaults; existing configs unchanged.
Co-Authored-By: Claude Fable 5 <[email protected]>
- Skip publishing (return early) when train:false leaves self.model None,
preventing an AttributeError on the merged/gguf push path.
- Make KNOWN_KEYS a frozenset to prevent accidental class-state mutation.
Co-authored-by: Mervin Praison <[email protected]>
- num_samples: reject 0/negative/bool/non-int and honor explicit values (fail
fast with a clear message instead of silently training the full set or
crashing mid-run).
- Publishing: fail fast in validate_config when huggingface_save /
huggingface_save_gguf / ollama_save is enabled without its target name,
instead of silently skipping the requested upload after a long run.
- Fix pre-existing CI failure: test_interactive_group_is_union was stale after
the `search` tool group was wired into the interactive default set (#3254);
add TOOL_GROUPS["search"] to the expected union.
Co-authored-by: Mervin Praison <[email protected]>
Validated end-to-end on GPU (2x A6000, unsloth 2026.7.4 / trl 0.24.0).
- Response masking: assistant_only_loss now tri-state with default "auto" — a
live probe checks whether the model's chat template actually supports assistant
masking and enables it only when safe, else full-sequence SFT. Prevents TRL's
cryptic RuntimeError on stock Qwen/Gemma/Llama templates (they lack the
{% generation %} markers). Explicit true on an unsupported template now fails
fast with a clear message. 'train_on_responses_only' accepted as an alias.
- Publishing defaults OFF and is gated on a target name being set — a plain
'train locally' config finishes with the LoRA saved to lora_model/ instead of
crashing after training on a missing/foreign hf_model_name.
- Config flags accept real YAML booleans (train: true) not just strings, via a
_flag() coercion helper (no more AttributeError on .lower()).
- Honor dataset num_samples (was advertised but silently ignored).
- validate_config(): clear error on missing required keys, warn on unknown keys
(typo/unsupported) so nothing is silently dropped — friendlier for agents.
- 'model' accepted as an alias for 'model_name' (matches --model flag).
- One concise run-summary block (model / examples / masking mode / steps / output).
- Align license to SPDX "MIT" + setuptools>=77 (matches main).
Co-Authored-By: Claude Fable 5 <[email protected]>
- pyproject (train + bot): license = "MIT" SPDX string (was deprecated table
form); bump build requires to setuptools>=77 which the string form needs.
- _release_lib: 'uv lock' instead of 'uv lock --frozen' so publish regenerates a
missing/stale lockfile (praisonai-train had none; dependency changes stale it)
instead of failing with 'Unable to find lockfile'.
Co-Authored-By: Claude Fable 5 <[email protected]>
- Fail fast when neither the tokenizer nor config provides a chat template,
instead of silently training on empty text (swallowed apply_chat_template).
- Correct Gemma Ollama template to fold the system prompt into the first user
turn per the Gemma spec (no separate system turn).
- Document that assistant_only_loss requires a template exposing assistant
masks ({% generation %}); keep it opt-in and default-off.
- Remove the stale uv.lock (pinned trl<0.9.0 / trl 0.8.6, old torch/transformers/
unsloth floors) so it regenerates against the modern pyproject stack.
Co-authored-by: Mervin Praison <[email protected]>
Both rag_profiler and knowledge_profiler were byte-for-byte identical
except the "command" label. Extract a single command_profiler helper in
cli/commands/_profiling.py and have both delegate to it, so profiling
logic has one owner and cannot drift. Behaviour, flags, and output are
unchanged.
Co-authored-by: MervinPraison <[email protected]>
Extract the bearer / X-API-Key token extraction and constant-time
comparison used by both the serve feature and the jobs server into a
single build_api_key_middleware() helper so future auth fixes only need
to be made in one place. Behaviour and lazy-import posture are unchanged;
per-server public-path allowlists remain distinct via a parameter. The
JobsAuthRequiredMiddleware non-localhost guard is left untouched.
Co-authored-by: MervinPraison <[email protected]>
The dependency pins (trl<0.9.0, unsloth>=2024.8) were incompatible with any recent
Unsloth, which requires trl>=0.18.2 / transformers>=4.51.3. This modernizes the LLM
fine-tuning path so it works with the current stack (tested on 2x A6000 with
unsloth 2026.7.4 / trl 0.24.0 / torch 2.10 / peft 0.19.1).
Changes:
- deps: transformers>=4.51.3, unsloth>=2025.9.1, trl>=0.18.2, peft>=0.13.0,
accelerate>=0.34.0, bitsandbytes>=0.45.0 (pyproject + setup_conda_env.sh; the
conda script no longer pins an ancient Unsloth git commit).
- trainer: migrate to modern TRL API — SFTConfig instead of TrainingArguments,
processing_class instead of tokenizer, dataset_text_field/max_length/packing on
SFTConfig; drop the pre-tokenize + dummy-field hack (TRL tokenizes internally).
- trainer: stop force-applying the llama-3.1 chat template to every model (broke
Gemma/Qwen). Chat template is now config-driven and defaults to the model's own.
- trainer: honor config LoRA params (lora_r/alpha/target_modules/... were ignored).
- trainer: response-only loss via SFTConfig assistant_only_loss (model-agnostic),
replacing the Llama-hardcoded train_on_responses_only markers.
- Ollama Modelfile: add Gemma template + stop tokens (was Llama-only, wrong for Gemma).
Tested: end-to-end LoRA fine-tune smoke runs on GPU for both Qwen2.5-0.5B and
Gemma-2-2B (native templates) — train loop + local save succeed.
Co-Authored-By: Claude Fable 5 <[email protected]>
A completed cancelled run (rank 1) could override an in-progress run
(conclusion=null, rank 0) of the same name, hiding a still-running check
from allChecksGreenOnSha and allowing the merge gate to pass early.
Prioritize any non-completed run so pending checks always block.
Co-authored-by: Mervin Praison <[email protected]>
Treat cancelled smoke and test-windows as non-blocking when all test-core
shards succeeded on HEAD, dedupe check runs by best conclusion, and raise
smoke (12m) and Windows (10m) job timeouts to reduce timeout cancellations.
Switch all Harbor adapters, job configs, docs, and tests to
openai/gpt-4o-mini for a consistent, low-cost default across the
Terminal-Bench benchmark path.
Co-authored-by: Mervin Praison <[email protected]>
Surfaces genuine install/auth/startup failures in the Harbor code adapter
instead of always reporting success (per reviewer feedback). A benchmark
miss still exits 0 (Harbor grades by verification); only a crashed
assistant now fails the attempt.
Co-authored-by: Mervin Praison <[email protected]>
Repairs the examples/terminal_bench/ Harbor adapters (external/wrapper/
installed), adds a headline praisonai_code_agent that drives `praisonai code`
headlessly, rewrites job configs to the real Harbor JobConfig schema for
Terminal-Bench 2.1, adds packaging __init__.py files, rewrites the tests to
skip only when Harbor is truly absent, and adds a manual-dispatch smoke
workflow + RESULTS.md. Examples/CI layer only — no SDK changes.
Co-authored-by: MervinPraison <[email protected]>
Addresses reviewer feedback that set_store()/migration failing open could
hide reader/writer drift. The local tool store remains authoritative, so
these paths stay best-effort but are now diagnosable via logs.
Co-authored-by: Mervin Praison <[email protected]>
Adds direct coverage for the bot-side _execute_one silence path (exact
markers, whitespace variants, ordinary output, prose mention) asserting
delivery is suppressed while the run is still recorded as succeeded.
Co-authored-by: Mervin Praison <[email protected]>
Scheduled/automation runs now consult the existing core silence primitive
(praisonaiagents.bots.silence.is_intentional_silence_response) before
delivering. When a run's whole output is an exact silence marker
(NO_REPLY / [SILENT] / SILENT) delivery is suppressed while the run still
completes and is recorded, so unattended monitors stop posting the raw
control token. Applied to both the wrapper lightweight path
(AgentScheduler._deliver_result) and the bot full-gateway path
(ScheduledAgentExecutor). Prose merely mentioning the token is unaffected
(exact-match semantics).
Co-authored-by: MervinPraison <[email protected]>
Add a canonical get_default_store()/set_default_store() resolver in
praisonaiagents.scheduler and route the agent tools, gateway tick loop,
and host bridge through it so writer and reader share one backend.
Co-authored-by: MervinPraison <[email protected]>
Convert value to str before strip()/lower() so an unquoted YAML
auth_token (e.g. 12345) cannot raise AttributeError during the
weak-secret check. Preserves empty/None handling and denylist match.
Co-authored-by: Mervin Praison <[email protected]>
- gateway doctor now fails closed on a MISSING auth_token on an external
bind (matching startup), not just a weak one (Greptile P1)
- doctor --json always emits a single parseable JSON document on the
no-channels weak/missing-secret failure path (Greptile P2)
- replace high-entropy fake hex test tokens with an obviously-non-secret
literal to clear GitGuardian false positives without weakening tests
- add TestDoctorGatewaySecretStrength covering the new doctor paths
Co-authored-by: Mervin Praison <[email protected]>
Address Greptile review findings on the doctor auto-fix safety contract:
- In-place repairs no longer discarded: pass a fresh copy to apply_fix so
rules that mutate-and-return their argument still record a diff.
- Collection failures now recorded as blocking refused findings instead of
being silently swallowed (previously produced an apparently clean plan).
- Auto-derived backup paths use microsecond timestamps plus a numeric
uniqueness suffix so a rapid/concurrent run never overwrites an earlier
snapshot.
- No-op apply runs no longer report applied=True; applied now requires an
actual change.
Adds tests for each case.
Co-authored-by: Mervin Praison <[email protected]>
Add a core weak-secret guard so an externally-bound gateway refuses to
start behind a publicly-known placeholder token (e.g. change-me,
your-token-here, or the literal $(openssl rand -hex 16) fix-hint pasted
verbatim). Loopback binds downgrade to a warning, consistent with the
existing permissive-loopback posture.
- core: KNOWN_WEAK_SECRETS, is_weak_secret, assert_gateway_secret_strong,
WeakGatewaySecretError in praisonaiagents.gateway.protocols
- praisonai-bot: enforce in assert_external_bind_safe (fail closed on
external, warn on loopback) and surface in `gateway doctor`
Co-authored-by: MervinPraison <[email protected]>
Add a repair safety contract to the core doctor engine so self-repair is
safe to run: dry-run diff preview, backup, refuse-on-unrecoverable, and
post-fix re-validation. Introduces ConfigDiff/RepairPlan and a
DoctorRulesRegistry.plan_fixes() while keeping apply_all_fixes()/apply_fixes()
backward-compatible.
Co-authored-by: MervinPraison <[email protected]>
Address reviewer-flagged issues in praisonai run --worktree:
- P0: untracked output no longer silently deleted. Teardown now detects
changes via git status --porcelain (sees untracked files, which git diff
does not) and commits them to the isolated branch, retaining the branch;
only the worktree checkout is pruned. Falls back to keeping the worktree
if the commit fails (e.g. no git identity), so output is never lost.
- P1: identical targets get independent worktrees/branches via a per-run
unique token, so concurrent/repeated runs never share state.
- P1: relative/untracked local YAML now resolved to an absolute path before
chdir, so praisonai run ./agents.yaml --worktree still loads its config.
Adds tests for untracked-output preservation and per-run independence.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
- code_intelligence: core grep now searches the configured workspace
(search_path) instead of the process CWD, matching the grep -rn fallback;
paths outside the core tool's root fall back cleanly.
- interactive_tools: apply PRAISON_TOOLS_DISABLE via ToolConfig.__post_init__
so direct constructions (to_tool_config, HeadlessInteractiveCore) honor the
advertised opt-out, not just from_env().
Co-authored-by: Mervin Praison <[email protected]>
A catch-all read:* allow rule previously opted every secret file into
ALLOW, trivially defeating the gate. Now only a secret-specific allow
rule (e.g. read:*.env) or a persistent approval opts in; a broad
wildcard falls through to the ASK default. Deny and approvals still win.
Co-authored-by: Mervin Praison <[email protected]>
Wire the existing core GitWorktreeAdapter into the CLI run lifecycle as a live consumer. praisonai run with --worktree provisions a fresh git worktree/branch, runs the agent there, reports the branch + a summary diff, and tears the worktree down (or keeps it with --keep). Degrades to a transparent no-op outside a git repo. Kept lightweight: no new Agent params/modules, only a self-contained context manager in run.py plus two CLI flags.
Co-authored-by: MervinPraison <[email protected]>
Add a `search` tool group (grep, glob, ast_grep_search) to the interactive
coding agent's default toolset, resolving to the existing ripgrep-backed core
builtins. Also route code_intelligence search through the core grep first,
keeping `grep -rn` only as a last-resort fallback.
Co-authored-by: MervinPraison <[email protected]>
Reads of secret files (.env, private keys, *.pem, *.key, id_rsa, etc.) now
default to `ask` in the core permission manager, closing a silent
secret-exfiltration path where a default coding agent could read credentials
and forward them to the model provider. Safe example/sample/template files
remain allowed, and explicit user rules/approvals (e.g. `read:*.env=allow`)
override the default so opting in for trusted workflows stays a single rule.
Co-authored-by: MervinPraison <[email protected]>
Gap 1: run/arun and the 4 legacy CLI call sites now own the
AgentsGenerator lifecycle via `with`, releasing its lazily-allocated
tool-timeout executor per run instead of leaking daemon threads in
long-lived server workers.
Gap 2: _is_protocol_instance / _is_cli_backend_instance now use
isinstance against the @runtime_checkable CliBackendProtocol instead of a
hasattr(execute, stream) duck-type, so a BaseCLIIntegration look-alike is
no longer mistaken for a backend; it fails fast at construction with a
pointed TypeError instead of crashing deep in the agent loop.
Gap 3: both workflow kickoff branches are bracketed in observability_session
so AgentOps init/finalize fires for workflow YAMLs too, and _build_yaml_workflow
now runs cli_backend compatibility validation and warns when a set
tool_timeout cannot be enforced on the native workflow path.
Co-authored-by: MervinPraison <[email protected]>
Make llm.py delegate _format_ollama_tool_result_message to the
protocol-driven OllamaAdapter.format_tool_result_message instead of a
hardcoded, behaviorally-diverged duplicate. The error-handling behavior
(apology-oriented prompt for dict/list results carrying an 'error' key)
is ported into the adapter so the single source of truth now lives in
the adapter layer, matching the pattern used elsewhere. Fully
backward-compatible: call sites are guarded by _is_ollama_provider(), so
Ollama formatting is always produced.
Co-authored-by: MervinPraison <[email protected]>
Bug A: Typer does not support typing.Literal; replaced _SandboxType
Literal with a str/Enum SandboxBackend so grouped --help lazy-loading
of the sandbox command no longer raises RuntimeError. Handler receives
.value to preserve string API.
Bug B: Added missing 'usage' command to COMMAND_CATEGORIES so every
registered lazy command has a help category.
Co-authored-by: Dhivya-Bharathy <[email protected]>
Restores pre-gate behaviour where 1000-1500 token context was returned
unchanged for small branch counts, instead of being truncated.
Co-authored-by: Mervin Praison <[email protected]>
Partial-truncation in aggregate() used a fixed remaining*4 char estimate,
which over-retained dense non-ASCII (CJK) text and could exceed max_tokens.
Now selects the longest prefix whose canonical token estimate fits the
remaining budget via _truncate_to_tokens(). Adds regression tests.
Co-authored-by: Mervin Praison <[email protected]>
Completes the disabled-path guard pattern for the two remaining trace
emit methods, matching the six methods fixed in this PR and their hotter
siblings. Byte-identical enabled behavior; only skips wasted allocation
when tracing is disabled.
Co-authored-by: Mervin Praison <[email protected]>
Small parallel groups (e.g. 2 branches) no longer pay for an extra LLM
summarisation call whose token cost roughly matches what it saves. The
cheaper _truncate_context_for_branches fallback is used below the
MIN_BRANCHES_FOR_LLM_SUMMARY threshold; LLM summarisation stays unchanged
for larger fan-outs.
Co-authored-by: MervinPraison <[email protected]>
Replace the crude `len(text) // 4 + 1` estimate in
ContextAggregator._estimate_tokens with a delegation to the package's
canonical estimate_tokens_heuristic. ASCII-identical, but now correctly
weights non-ASCII (CJK) characters, preventing context-budget overruns
for non-English content. Single source of truth for token estimation.
Co-authored-by: MervinPraison <[email protected]>
Add early `if not self._enabled: return` to session_start, session_end,
context_snapshot, agent_handoff, memory_store, and memory_search, matching
the existing pattern in hotter siblings. Avoids allocating a ContextEvent
(and slicing query strings) on every memory/session/handoff op when tracing
is disabled. Output is identical when tracing is enabled.
Co-authored-by: MervinPraison <[email protected]>
2026-07-20 17:16:25 +00:00
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