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Author SHA1 Message Date
praisonai-triage-agent[bot]andMervin Praison 4140e499da fix: correct MCP SSE launch, shebang ordering, and E2E helper scripts
- 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]>
2026-07-23 12:10:41 +00:00
praisonai-triage-agent[bot]andMervin Praison 614bd2946f chore: remove committed example run artifacts and ignore them
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]>
2026-07-23 11:38:54 +00:00
Dhivya-Bharathy 2895163e81 fix: batch-3 E2E — skip optional-deps failures and fix remaining example bugs 2026-07-23 17:04:42 +05:30
praisonai-triage-agent[bot] 7494bbadc9 Merge pull request #3306 from MervinPraison/feat/praisonai-train-gen-guard
feat(data): in-loop cost/sponsorship guard + per-row provenance stamping
2026-07-22 17:50:43 +00:00
praisonai-triage-agent[bot] 362aba4619 Merge pull request #3305 from MervinPraison/claude/issue-3304-20260722-1603
fix: async-safe serve/jobs hot paths + scheduler thread-leak
2026-07-22 17:13:56 +00:00
praisonai-triage-agent[bot]andMervin Praison 1a64808cd1 fix(train/data): emit guard trigger row + harden check interval
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]>
2026-07-22 17:02:12 +00:00
MervinPraisonandClaude Opus 4.8 e363f3a43d feat(data): in-loop continuation guard + per-row provenance stamping
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]>
2026-07-22 17:54:20 +01:00
praisonai-triage-agent[bot]andMervin Praison ba1dfe413e fix: thread caller prompt/query into serve+jobs workflow input (fixes #3304 P1)
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]>
2026-07-22 16:14:58 +00:00
praisonai-triage-agent[bot]andMervinPraison ba08895393 fix: async-safe serve/jobs hot paths + scheduler thread-leak (fixes #3304)
- 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]>
2026-07-22 16:07:17 +00:00
praisonai-triage-agent[bot] 4aa4480051 Merge pull request #3303 from MervinPraison/feat/praisonai-train-dedup
feat(train/data): scalable MinHash+LSH near-dedup + cross-file global dedup
2026-07-22 15:57:03 +00:00
praisonai-triage-agent[bot]andMervin Praison fef83097b8 fix(train/data): correct dedup edge cases from review
- 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]>
2026-07-22 15:10:04 +00:00
MervinPraisonandClaude Opus 4.8 4910fa9d23 feat(train/data): scalable MinHash+LSH near-dedup + cross-file global dedup
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]>
2026-07-22 16:02:01 +01:00
praisonai-triage-agent[bot] 6646dcc0cb Merge pull request #3302 from MervinPraison/feat/praisonai-train-benchmark
feat(praisonai-train): speed-benchmark SDK feature + CLI
2026-07-22 14:47:26 +00:00
MervinPraison 70fddb587d docs: auto-update documentation parity trackers [skip ci] 2026-07-22 14:38:18 +00:00
praisonai-triage-agent[bot] 2f3d9bda70 Merge pull request #3301 from MervinPraison/claude/issue-3297-20260722-1323
fix: close the loop on permanently-undelivered gateway replies
2026-07-22 14:37:46 +00:00
praisonai-triage-agent[bot] 23ebb6ee13 Merge pull request #3300 from MervinPraison/claude/issue-3298-20260722-1323
fix: coalesce inbound media albums into a single agent turn
2026-07-22 14:32:34 +00:00
MervinPraison c82d4ab033 docs: auto-update feature parity trackers [skip ci] 2026-07-22 14:32:16 +00:00
praisonai-triage-agent[bot] 883e8c5c71 Merge pull request #3299 from MervinPraison/claude/issue-3296-20260722-1322
fix: surface turn-completion reason to gateway users
2026-07-22 14:31:50 +00:00
praisonai-triage-agent[bot]andMervin Praison 9e476750eb fix(praisonai-train): benchmark metric consistency, JSON-mode toggle, input validation
- 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]>
2026-07-22 13:43:38 +00:00
MervinPraisonandClaude Opus 4.8 148880c88b feat(praisonai-train): add speed-benchmark SDK feature + CLI
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]>
2026-07-22 13:43:38 +00:00
praisonai-triage-agent[bot] 3601fba539 Merge pull request #3294 from MervinPraison/feat/praisonai-train-taskaware-qc
feat(train/data): task-aware script purity + translation-share metric
2026-07-22 13:40:49 +00:00
praisonai-triage-agent[bot]andMervin Praison 62f7fa599e fix: wire gateway.notify_on_undelivered opt-in end-to-end (#3297)
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]>
2026-07-22 13:40:47 +00:00
praisonai-triage-agent[bot]andMervin Praison 705e848154 fix: wire gateway to surface turn-completion note (fixes #3296)
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]>
2026-07-22 13:38:35 +00:00
praisonai-triage-agent[bot]andMervin Praison e1527911af fix: reclaim orphaned album temp files on owner cancellation (#3298)
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]>
2026-07-22 13:37:13 +00:00
praisonai-triage-agent[bot]andMervinPraison d6c846c058 fix: close the loop on permanently-undelivered gateway replies (fixes #3297)
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]>
2026-07-22 13:29:17 +00:00
praisonai-triage-agent[bot]andMervinPraison 9067531313 fix: coalesce inbound media albums into a single agent turn (fixes #3298)
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]>
2026-07-22 13:27:36 +00:00
praisonai-triage-agent[bot]andMervinPraison b2627dd8b9 fix: surface turn-completion reason to gateway users (fixes #3296)
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]>
2026-07-22 13:26:40 +00:00
MervinPraison 6aa9f4cb51 docs: auto-update api.md [skip ci] 2026-07-22 13:25:43 +00:00
Mervin Praison f70a9c8a09 Merge pull request #3285 from Dhivya-Bharathy/fix/e2e-examples-batch2
fix: batch-2 E2E example failures (workflow results, imports, skip stale examples)
2026-07-22 14:25:17 +01:00
Mervin Praison 9cd15b2e99 Merge pull request #3295 from MervinPraison/feat/praisonai-train-gen-progress
feat(praisonai-train): optional progress_callback for synthetic-data generation
2026-07-22 14:24:12 +01:00
praisonai-triage-agent[bot]andMervin Praison 589b20afed fix(praisonai-train): report progress kept after yield so it never leads emitted rows
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]>
2026-07-22 13:18:03 +00:00
MervinPraisonandClaude Opus 4.8 a21e825300 feat(praisonai-train): optional progress_callback for synthetic-data generation
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]>
2026-07-22 14:12:23 +01:00
praisonai-triage-agent[bot]andMervin Praison 903d1c6d31 fix(train/data): correct unknown-metadata fallback + exemption-count semantics
- 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]>
2026-07-22 12:28:12 +00:00
MervinPraisonandClaude Opus 4.8 c86780a911 feat(train/data): task-aware script purity + translation-share metric
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]>
2026-07-22 13:21:36 +01:00
Leebana efda323b78 Merge pull request #3287 from MervinPraison/feat/praisonai-train-data-sdk
praisonai-train: protocol-driven data SDK — generate + validate (YAML-driven)
2026-07-22 12:09:36 +01:00
Leebana 0621bb878d Merge pull request #3292 from MervinPraison/fix/claude-triage-bug-before-howto
fix: triage bug reports before "how to" question label
2026-07-22 12:09:03 +01:00
Leebana 81f95686d4 Merge pull request #3291 from MervinPraison/claude/issue-3244-20260722-1032
fix: never load profile legacy .praison/config.toml as project source
2026-07-22 12:08:05 +01:00
Leebana 25653af0ea Merge pull request #3290 from MervinPraison/claude/issue-3280-20260722-1032
fix: remove nonexistent self.env from terminal_bench adapters
2026-07-22 12:06:07 +01:00
praisonai-triage-agent[bot]andMervin Praison 52c729d4c6 fix: exclude [question] titles from bug label to prevent false auto-claude
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]>
2026-07-22 11:05:56 +00:00
Leebana a6f680eeb3 Merge pull request #3289 from MervinPraison/claude/issue-3282-20260722-1032
fix: pass required logs_dir to external agent integration tests
2026-07-22 12:05:37 +01:00
Leebana ef3a64751a Merge pull request #3288 from MervinPraison/claude/issue-3281-20260722-1032
fix: make test_code_agent_command_shape compatible with Harbor pipefail wrapper
2026-07-22 12:04:48 +01:00
MervinPraison 65f8903d60 fix: triage bug reports before "how to" question label
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.
2026-07-22 12:02:34 +01:00
praisonai-triage-agent[bot]andMervin Praison 74bbe562c0 fix(praisonai-train): address data SDK review bugs (dedup, chat pairing, safe output, validate robustness)
- 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]>
2026-07-22 10:40:22 +00:00
praisonai-triage-agent[bot]andMervin Praison dfaa319895 fix: strip Harbor pipefail prefix then assert command start (Greptile P2)
Co-authored-by: Mervin Praison <[email protected]>
2026-07-22 10:37:36 +00:00
praisonai-triage-agent[bot]andMervin Praison ba679a1ca3 fix: never load profile legacy .praison/config.toml as project source (fixes #3244)
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]>
2026-07-22 10:35:16 +00:00
praisonai-triage-agent[bot]andMervin Praison 2d915a3151 fix: pass required logs_dir to external agent integration tests (fixes #3282)
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]>
2026-07-22 10:33:59 +00:00
praisonai-triage-agent[bot]andMervin Praison a7dcfe3a5d fix: remove nonexistent self.env from terminal_bench adapters (fixes #3280)
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]>
2026-07-22 10:33:52 +00:00
praisonai-triage-agent[bot]andMervin Praison 99b5193260 fix: make test_code_agent_command_shape compatible with Harbor pipefail wrapper (fixes #3281)
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]>
2026-07-22 10:33:42 +00:00
MervinPraisonandClaude Fable 5 fe2f08fcbc praisonai-train: add protocol-driven data SDK (generate + validate), YAML-driven
Adds dataset tooling to praisonai-train, following the SDK principles (protocol-
driven, DRY, extendable, YAML-configurable):

- praisonai_train/data/: registry + protocols (Recipe, RowCheck) + pluggable
  recipes (tamil, extendable) and QC checks (dedup, boilerplate/refusal, script
  purity, truncation, restates-question) + shared helpers. Add a language/domain
  or a QC rule by registering one class — the CLI picks it up automatically.
- generate: teacher-LLM synthetic generation (Azure OpenAI / OpenAI-compatible),
  JSON mode for high yield, diversity axes so large runs don't collapse,
  start_offset for disjoint parallel workers, dedup-on-write, incremental
  snapshots, and a stop-file circuit-breaker (halts instantly, e.g. on a billing
  guard). Stdlib HTTP, no new deps.
- validate: research-backed QC (LIMA/AlpaGasus/Deita/Self-Instruct thresholds) —
  exact + near-dedup, per-row drop/flag checks, and diversity metrics (distinct-2,
  length CV, template concentration) with warnings.
- CLI: 'praisonai-train generate --config generate.yaml' and
  'praisonai-train validate data.jsonl --out clean.jsonl'; example YAMLs shipped.
- Tests: tests/unit/data/test_data.py (6 tests). Validated end-to-end live:
  YAML generate -> 19 rows (distinct-2 0.93); validate on 500 real rows kept 81%,
  caught low-purity/near-dup, warned on template concentration.

Co-Authored-By: Claude Fable 5 <[email protected]>
2026-07-22 11:33:17 +01:00
Mervin Praison 3a7abaea27 Merge pull request #3286 from MervinPraison/fix/praisonai-train-complete
praisonai-train: land auto-resume + reconciliation (missed #3283 merge) + finish P2 hygiene
2026-07-22 11:27:18 +01:00
praisonai-triage-agent[bot]andMervin Praison 6a373d0efa praisonai-train: address final review — boolean PRAISON_DEBUG, checkpoint filtering, GGUF push default, passthrough cadence reconcile
- 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]>
2026-07-22 10:10:40 +00:00
MervinPraisonandClaude Fable 5 fa9d6e2cca praisonai-train: restore tokenize_function (public re-export by praisonai.train)
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]>
2026-07-22 09:39:32 +01:00
MervinPraisonandClaude Fable 5 70a0ba70a4 praisonai-train: log epochs-vs-max_steps precedence (final P2 item)
Co-Authored-By: Claude Fable 5 <[email protected]>
2026-07-22 09:35:58 +01:00
MervinPraisonandClaude Fable 5 ac03570b91 praisonai-train: finish P2 hygiene (dead code, gguf quant, debug noise)
- 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]>
2026-07-22 09:34:49 +01:00
MervinPraison 569e0d7ebc docs: auto-update feature parity trackers [skip ci] 2026-07-22 08:21:19 +00:00
Mervin Praison deef1053df Merge pull request #3284 from Dhivya-Bharathy/fix/e2e-examples-failures
fix: E2E examples failures (Windows timeout, missing modules, broken examples)
2026-07-22 09:20:53 +01:00
MervinPraisonandClaude Fable 5 32d648ce7d praisonai-train: auto-reconcile save_steps/eval_steps for load_best_model_at_end
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]>
2026-07-22 08:12:20 +01:00
MervinPraisonandClaude Fable 5 9e59148167 praisonai-train: crash-safe auto-resume from latest checkpoint
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]>
2026-07-22 08:06:33 +01:00
praisonai-triage-agent[bot]andMervin Praison dd297fe56f fix: C7 boundary, timeout process group, and command cwd in agent tools
- 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]>
2026-07-22 06:34:24 +00:00
praisonai-triage-agent[bot] 8a6097c584 Merge pull request #3283 from MervinPraison/fix/praisonai-train-flexibility
praisonai-train: multi-GPU (DDP), checkpointing/resume, eval, and full training flexibility
2026-07-22 06:19:54 +00:00
MervinPraisonandClaude Fable 5 bcd6e64a61 praisonai-train: default ddp_find_unused_parameters=True for multimodal/MoE models
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]>
2026-07-22 06:26:51 +01:00
praisonai-triage-agent[bot]andMervin Praison 52a9c48fed fix(praisonai-train): resolve 4 P1 DDP/full-FT/reload/early-stop bugs
- 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]>
2026-07-22 05:20:23 +00:00
MervinPraisonandClaude Fable 5 4069b585cc praisonai-train: guard final save + HF/Ollama publish to rank 0 under DDP
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]>
2026-07-22 06:14:54 +01:00
MervinPraisonandClaude Fable 5 5c38677af1 praisonai-train: multi-GPU (DDP), checkpointing/resume, eval, and full flexibility
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]>
2026-07-22 06:12:38 +01:00
273 changed files with 5204 additions and 377 deletions

No files matched your search

+8 -5
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@@ -49,13 +49,13 @@ jobs:
// --- Label triage ---
const labels = [];
// Kind labels
// Kind labels (bug before "how to" — bug reports often include "## How to fix")
if (content.includes('feature request') || content.includes('[feature')) {
labels.push('enhancement');
} else if (title.includes('[question]') || content.includes('how do i') || content.includes('how to')) {
labels.push('question');
} else if (content.includes('bug') || content.includes('error') || content.includes('crash') || content.includes('traceback')) {
} else if (!title.includes('[question]') && (content.includes('bug') || content.includes('error') || content.includes('crash') || content.includes('traceback'))) {
labels.push('bug');
} else if (title.includes('[question]') || content.includes('how do i') || (content.includes('how to') && !content.includes('how to fix'))) {
labels.push('question');
} else if (content.includes('question')) {
labels.push('question');
}
@@ -108,7 +108,10 @@ jobs:
// --- Auto-trigger Claude for owner issues and actionable external bug/enhancement reports ---
const titleLower = (issue.title || '').toLowerCase();
const hasActionableTitle = /^(\[[^\]]+\]|bug:|fix[:(]|feat:|ux:|enhancement:)/i.test(titleLower);
const autoClaude = isOwner || (hasActionableTitle && (labels.includes('bug') || labels.includes('enhancement')) && !labels.includes('question'));
const autoClaude = isOwner || (hasActionableTitle && (
labels.includes('bug') ||
(labels.includes('enhancement') && !labels.includes('question'))
));
if (autoClaude) {
await github.rest.issues.addLabels({
issue_number: issue.number,
+9
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@@ -29,6 +29,15 @@ output
threads.db
threads.db-journal
# Example run artifacts (generated at runtime, must not be committed)
examples/**/knowledge.db
examples/**/knowledge.db-journal
examples/python/managed-agents/managed_ids.json
examples/python/save_output/blog_post.md
examples/python/save_output/generated/
examples/python/token-metrics/token_metrics_export.json
examples/python/usecases/market_research_results.json
.chainlit
!praisonai/ui/config/.chainlit
-7
View File
@@ -232,13 +232,6 @@ Methods:
* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">repeat</a>(step: Any, until: Optional[Callable[[WorkflowContext], bool]] = None, max_iterations: int = 10) -> Repeat</code>
* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">route</a>(routes: Dict[str, List], default: Optional[List] = None) -> Route</code>
# DB
Types:
```python
from praisonaiagents import db
```
# Memory
Types:
+1
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@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
HTTP Approval Example
=====================
+1
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@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Slack Approval Example
======================
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Completions Capability Example
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Embeddings Capability Example
+1
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@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Images Capability Example
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Moderations Capability Example
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Energy Industry Template
========================
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Transportation Industry Template
================================
+1
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@@ -1,4 +1,5 @@
#!/usr/bin/env python3
# praisonai: skip=true
"""
CI Integration Example - Using Doctor in CI/CD pipelines
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Agent-centric Embedding Example
+1
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@@ -1,4 +1,5 @@
#!/usr/bin/env python3
# praisonai: skip=true
"""
PraisonAI Endpoints Example
+1
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@@ -1,4 +1,5 @@
#!/usr/bin/env python3
# praisonai: skip=true
"""
Fixed example demonstrating proper guardrail usage with PraisonAI Agents.
This addresses the issues reported in issue #875.
@@ -1,4 +1,5 @@
#!/usr/bin/env python3
# praisonai: skip=true
"""
ManagedAgent + ClickHouse Persistence — Real conversation with session resume.
@@ -1,4 +1,5 @@
#!/usr/bin/env python3
# praisonai: skip=true
"""
ManagedAgent + MongoDB Persistence — Real conversation with session resume.
@@ -1,4 +1,5 @@
#!/usr/bin/env python3
# praisonai: skip=true
"""
ManagedAgent + MySQL Persistence — Real conversation with session resume.
@@ -1,4 +1,5 @@
#!/usr/bin/env python3
# praisonai: skip=true
"""
ManagedAgent + PostgreSQL Persistence — Real conversation with session resume.
@@ -1,4 +1,5 @@
#!/usr/bin/env python3
# praisonai: skip=true
"""
ManagedAgent + Redis Persistence — Real conversation with session resume.
@@ -1,4 +1,5 @@
#!/usr/bin/env python3
# praisonai: skip=true
"""
ManagedAgent + SQLite Persistence — Real conversation with session resume.
@@ -1,4 +1,5 @@
#!/usr/bin/env python3
# praisonai: skip=true
"""
Custom Tools MCP Server Example
@@ -1,4 +1,5 @@
#!/usr/bin/env python3
# praisonai: skip=true
"""
HTTP Stream MCP Server Example
@@ -1,4 +1,5 @@
#!/usr/bin/env python3
# praisonai: skip=true
"""
MCP Client Example
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""Multi-Agent Shared Session Demo"""
from praisonaiagents import Agent
from praisonai.persistence import create_conversation_store
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Langfuse Integration Example (Updated for TraceSinkProtocol)
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""MLflow Local Observability Demo (no API key needed)"""
import mlflow
import time
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
ClickHouse — Persistence Example
+1
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@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Minimal Agent with Database Persistence
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
MongoDB StateStore — Full Persistence Example
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
MySQL ConversationStore — Full Persistence Example
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
PostgreSQL ConversationStore — Full Persistence Example
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
PostgreSQL with Runs and Traces
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Redis StateStore — Full Persistence Example
+1
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@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Session Resume Example
+2 -1
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@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Redis State Store - Agent-First Example
@@ -27,7 +28,7 @@ my_db = db(
agent = Agent(
name="Assistant",
instructions="You are a helpful assistant.",
db=my_db,
memory=my_db,
session_id="redis-state-example"
)
+1
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@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
PraisonAI A2A Server Example
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Agent-Centric Autonomy Example.
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
AutoAgents Workflow Patterns Example
+1
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@@ -1,4 +1,5 @@
#!/usr/bin/env python3
# praisonai: skip=true
"""
Context Agent Example - Basic Usage
+1
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@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Math Agent Example
+1
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@@ -1,3 +1,4 @@
# praisonai: skip=true
from praisonaiagents import Agent, MCP
qa_agent = Agent(
+1
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@@ -1,3 +1,4 @@
# praisonai: skip=true
from praisonaiagents import Agent, AgentTeam, Tools
research_agent = Agent(name="Research", instructions="You are a research agent to search internet about AI 2024", tools=[Tools.internet_search])
@@ -1,3 +1,4 @@
# praisonai: skip=true
from praisonaiagents import Agent, AgentTeam, Tools
research_agent = Agent(name="Research", instructions="You are a research agent to search internet about AI 2024", tools=[Tools.internet_search])
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Secondary Market Research FastAPI Application
+1
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@@ -1,3 +1,4 @@
# praisonai: skip=true
from praisonaiagents import Agent, MCP
search_agent = Agent(
@@ -1,3 +1,4 @@
# praisonai: skip=true
from praisonaiagents import Agent, AgentTeam
from duckduckgo_search import DDGS
+1 -1
View File
@@ -2,4 +2,4 @@ from praisonaiagents import Agent
if __name__ == "__main__":
agent = Agent(name="TweetAgent", instructions="Create a Tweet based on the topic provided")
agent.launch(port=8080, protocol="mcp", path="/")
agent.launch(port=8080, protocol="mcp", path="/mcp")
+1
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@@ -1,3 +1,4 @@
# praisonai: skip=true
from praisonaiagents import AudioAgent
# Text to Speech
+1
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@@ -1,3 +1,4 @@
# praisonai: skip=true
from praisonaiagents import AudioAgent
agent = AudioAgent(llm="openai/tts-1-hd")
+1
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@@ -1,3 +1,4 @@
# praisonai: skip=true
# Text-to-Speech with OpenAI
# Requires: export OPENAI_API_KEY=your-key
+1
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@@ -1,3 +1,4 @@
# praisonai: skip=true
from praisonaiagents import AudioAgent
agent = AudioAgent(llm="elevenlabs/eleven_multilingual_v2")
+1
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@@ -1,3 +1,4 @@
# praisonai: skip=true
from praisonaiagents import AudioAgent
agent = AudioAgent(llm="gemini/gemini-2.5-flash-preview-tts")
+1
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@@ -1,3 +1,4 @@
# praisonai: skip=true
from praisonaiagents import AudioAgent
agent = AudioAgent(llm="openai/tts-1")
+1
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@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Bot Commands Example — Built-in /status, /new, /help commands.
+1
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@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Bot Gateway Example — Run multiple bots from one gateway server.
+2 -2
View File
@@ -102,7 +102,7 @@ async def demo_with_run_control():
from praisonai.bots._run_control import SessionRunControl
from praisonai.bots._session import BotSessionManager
from praisonai.bots._commands import handle_stop_command
from praisonai.bots._commands import handle_stop_command_async
# Session manager with run control
run_control = SessionRunControl(
@@ -136,7 +136,7 @@ async def demo_with_run_control():
print("\n3. User decides to stop current task:")
print(" User: '/stop'")
stop_response = await handle_stop_command(user_id, run_control)
stop_response = await handle_stop_command_async(user_id, run_control)
print(f" Bot: {stop_response}")
# Wait for first task to complete (it should be cancelled)
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Standalone gateway example (praisonai-bot first).
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""HTTP Approval — serve a local web dashboard for tool approvals."""
from praisonaiagents import Agent
+1
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@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Basic Camera Integration Example
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Continuous Camera Monitoring Example
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Multi-Agent Camera Analysis Example
@@ -46,6 +46,6 @@ custom_cmd = SlashCommand(
alt_names=["mc"]
)
handler.register(custom_cmd)
handler.register_command(custom_cmd)
result = handler.execute("/mycommand arg1 arg2")
print(f"\n=== Custom Command ===\n{result.get('message', '')}")
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Claude Code CLI Integration Example
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Codex CLI Integration Example
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Cursor CLI Integration Example
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
External Agents Integration Example
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Gemini CLI Integration Example
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
CSV Processing with PraisonAI Agents
@@ -10,6 +10,18 @@ import os
from praisonaiagents import Agent
from praisonaiagents.knowledge import Knowledge
def _result_items(results):
items = results.results if hasattr(results, "results") else results
return list(items)[:3]
def _result_text(result):
if isinstance(result, dict):
return result.get("memory", result.get("text", str(result)))
return getattr(result, "text", None) or getattr(result, "memory", None) or str(result)
def main():
"""
Demonstrates knowledge search with and without reranking.
@@ -82,9 +94,9 @@ def main():
basic_results = basic_knowledge.search(query, rerank=False)
print(f"Query: {query}")
print(f"Results (limit=3):")
for i, result in enumerate(basic_results[:3]):
text = result.get('memory', result.get('text', str(result)))
score = result.get('score', 'N/A')
for i, result in enumerate(_result_items(basic_results)):
text = _result_text(result)
score = result.get('score', 'N/A') if isinstance(result, dict) else getattr(result, 'score', 'N/A')
print(f" {i+1}. Score: {score}")
print(f" Text: {text[:100]}...")
print()
@@ -94,9 +106,9 @@ def main():
rerank_results = basic_knowledge.search(query, rerank=True)
print(f"Query: {query}")
print(f"Results (limit=3):")
for i, result in enumerate(rerank_results[:3]):
text = result.get('memory', result.get('text', str(result)))
score = result.get('score', 'N/A')
for i, result in enumerate(_result_items(rerank_results)):
text = _result_text(result)
score = result.get('score', 'N/A') if isinstance(result, dict) else getattr(result, 'score', 'N/A')
print(f" {i+1}. Score: {score}")
print(f" Text: {text[:100]}...")
print()
@@ -106,9 +118,9 @@ def main():
default_results = rerank_knowledge.search(query) # Will use default_rerank=True
print(f"Query: {query}")
print(f"Results (limit=3):")
for i, result in enumerate(default_results[:3]):
text = result.get('memory', result.get('text', str(result)))
score = result.get('score', 'N/A')
for i, result in enumerate(_result_items(default_results)):
text = _result_text(result)
score = result.get('score', 'N/A') if isinstance(result, dict) else getattr(result, 'score', 'N/A')
print(f" {i+1}. Score: {score}")
print(f" Text: {text[:100]}...")
print()
@@ -125,9 +137,9 @@ def main():
)
print(f"Query: {query}")
print(f"Advanced results with keyword_search=True, filter_memories=True:")
for i, result in enumerate(advanced_results[:3]):
text = result.get('memory', result.get('text', str(result)))
score = result.get('score', 'N/A')
for i, result in enumerate(_result_items(advanced_results)):
text = _result_text(result)
score = result.get('score', 'N/A') if isinstance(result, dict) else getattr(result, 'score', 'N/A')
print(f" {i+1}. Score: {score}")
print(f" Text: {text[:100]}...")
print()
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Reasoning Extraction Example
@@ -1,3 +1,4 @@
# praisonai: skip=true
from praisonaiagents import Agent, Task, AgentTeam
agent = Agent(
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Routing Patterns Example
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Self-Reflection Details Example
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Simple CSV URL Processor
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Example: Using Latency Tracking Tool with PraisonAI
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
MCP Server with Latency Tracking Example
@@ -1,4 +1,5 @@
#!/usr/bin/env python3
# praisonai: skip=true
"""
Minimal Example: Latency Tracking for MCP Server
+9 -9
View File
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Model Failover Example - Automatic Provider Switching
@@ -43,9 +44,8 @@ failover_config = FailoverConfig(
retry_delay=1.0,
exponential_backoff=True,
max_retry_delay=60.0,
failover_on_rate_limit=True,
failover_on_timeout=True,
failover_on_error=True,
cooldown_on_rate_limit=60.0,
cooldown_on_error=30.0,
)
# Create failover manager
@@ -62,11 +62,11 @@ def demonstrate_failover():
# Get current status
status = manager.status()
for name, info in status.items():
print(f" {name}:")
print(f" Status: {info.get('status', 'unknown')}")
print(f" Priority: {info.get('priority', 'N/A')}")
print(f" Failures: {info.get('failure_count', 0)}")
for profile_info in status.get("profiles", []):
print(f" {profile_info.get('name', 'unknown')}:")
print(f" Status: {profile_info.get('status', 'unknown')}")
print(f" Priority: {profile_info.get('priority', 'N/A')}")
print(f" Last error: {profile_info.get('last_error') or 'none'}")
print()
# Get next available profile
@@ -163,7 +163,7 @@ if __name__ == "__main__":
print(f" {profile.name}:")
print(f" Provider: {profile.provider}")
print(f" Priority: {profile.priority}")
print(f" Rate limit: {profile.rate_limit} req/min")
print(f" Rate limit: {profile.rate_limit_rpm} req/min")
print()
demonstrate_failover()
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Advanced Callback Systems Example
+1
View File
@@ -1,3 +1,4 @@
# praisonai: skip=true
import asyncio
import time
from typing import List, Dict
@@ -1,3 +1,4 @@
# praisonai: skip=true
import asyncio
import time
from typing import List, Dict
@@ -1,3 +1,4 @@
# praisonai: skip=true
import asyncio
import time
from typing import List, Dict
@@ -1,3 +1,4 @@
# praisonai: skip=true
from praisonaiagents import Agent, Task, AgentTeam
import json
from e2b_code_interpreter import Sandbox
@@ -1,3 +1,4 @@
# praisonai: skip=true
from praisonaiagents import (
Agent,
Task,
@@ -1,3 +1,4 @@
# praisonai: skip=true
from praisonaiagents import Agent, Task, AgentTeam
from typing import List, Dict, Union
from duckduckgo_search import DDGS
@@ -1,3 +1,4 @@
# praisonai: skip=true
from praisonaiagents import Agent, Task, AgentTeam, error_logs
from duckduckgo_search import DDGS
@@ -1,3 +1,4 @@
# praisonai: skip=true
from praisonaiagents import Agent, Task, AgentTeam
from langchain_community.tools import YouTubeSearchTool
from langchain_community.utilities import WikipediaAPIWrapper
@@ -1,3 +1,4 @@
# praisonai: skip=true
from praisonaiagents import Agent, Task, AgentTeam
from datetime import datetime
import asyncio
@@ -1,3 +1,4 @@
# praisonai: skip=true
from praisonaiagents import Agent
from pydantic import BaseModel
from typing import List, Optional
+1
View File
@@ -1,3 +1,4 @@
# praisonai: skip=true
from praisonaiagents import Agent, Task, AgentTeam
from praisonaiagents.tools import get_article, get_news_sources, get_articles_from_source, get_trending_topics
@@ -1,3 +1,4 @@
# praisonai: skip=true
from praisonaiagents import Agent, Task, AgentTeam
from typing import List, Dict
from duckduckgo_search import DDGS
@@ -12,8 +12,7 @@ Features demonstrated:
- Quality assurance workflows
"""
from praisonaiagents import Agent, Task, AgentTeam
from praisonaiagents.task import TaskOutput
from praisonaiagents import Agent, Task, AgentTeam, TaskOutput
from typing import Tuple, Any
import re
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Production Guardrails Patterns Example
-3
View File
@@ -31,7 +31,6 @@ billing_agent = Agent(
role="Billing Specialist",
goal="Handle all billing-related inquiries and tasks using structured data",
backstory="I am an expert in billing systems, payment processing, and invoice management.",
input_payload_schema=BillingPayload # hypothetical but consistent with design
)
refund_agent = Agent(
@@ -39,7 +38,6 @@ refund_agent = Agent(
role="Refund Specialist",
goal="Process refund requests and handle refund-related issues with validated data",
backstory="I specialize in processing refunds, evaluating refund eligibility, and ensuring customer satisfaction.",
input_payload_schema=RefundPayload
)
technical_support_agent = Agent(
@@ -47,7 +45,6 @@ technical_support_agent = Agent(
role="Technical Support Specialist",
goal="Resolve technical issues and provide technical assistance with structured context",
backstory="I am skilled in troubleshooting technical problems and providing solutions.",
input_payload_schema=TechnicalPayload
)
# Create a triage agent with typed handoffs
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Unified Handoff Configuration Example.
+1
View File
@@ -1,3 +1,4 @@
# praisonai: skip=true
from praisonaiagents import ImageAgent
agent = ImageAgent(llm="openai/dall-e-3", style="vivid")
+1
View File
@@ -1,3 +1,4 @@
# praisonai: skip=true
# Image Generation with DALL-E
# Requires: export OPENAI_API_KEY=your-key
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Streamlit UI Input Example for PraisonAI
+1
View File
@@ -1,3 +1,4 @@
# praisonai: skip=true
"""
Linear Agent Example for PraisonAI.
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