fix: 5 critical bugs — TEE randomization, HTTP 402 hint, int dtype, inference None crash, gas estimation by amathxbt · Pull Request #201 · OpenGradient/OpenGradient-SDK

amathxbt · 2026-03-23T23:49:53Z

Summary

This PR fixes 5 critical bugs identified from open issues and code audit, spanning TEE selection, LLM error handling, type conversions, inference safety, and gas estimation.

Bug 1 — TEE always picks the same node (closes#200)

File:src/opengradient/client/tee_registry.py

Root cause:get_llm_tee() always returned tees[0], routing 100% of traffic to a single TEE with zero load distribution or failover.

Fix: Replace tees[0] with random.choice(tees) so each LLM() construction independently selects from the full pool of active, registry-verified TEEs.

# Beforereturntees[0]
# Afterselected=random.choice(tees)
logger.debug("Selected TEE %s from %d active LLM proxy TEE(s)", selected.tee_id, len(tees))
returnselected

Bug 2 — HTTP 402 swallowed as cryptic RuntimeError (closes#188)

File:src/opengradient/client/llm.py

Root cause: All HTTP errors (including 402 Payment Required) were caught by except Exception and re-raised as a generic RuntimeError("TEE LLM chat failed: ..."). Users had no idea they needed to call ensure_opg_approval() first.

Fix: Add import httpx and intercept httpx.HTTPStatusErrorbefore the generic handler. When status_code == 402, raise a RuntimeError with an explicit, actionable message pointing to ensure_opg_approval(). Applied to _chat_request(), completion(), and _parse_sse_response() (streaming path).

# New constant_402_HINT= (
"Payment required (HTTP 402): your wallet may have insufficient OPG token allowance. ""Call llm.ensure_opg_approval(opg_amount=<amount>) to approve Permit2 spending ""before making requests. Minimum amount is 0.05 OPG."
)
# In _chat_request / completion / _parse_sse_response:excepthttpx.HTTPStatusErrorase:
ife.response.status_code==402:
raiseRuntimeError(_402_HINT) fromeraiseRuntimeError(f"TEE LLM chat failed: {e}") frome

Bug 3 — Fixed-point int returns np.float32 instead of int (partially closes#103)

File:src/opengradient/client/_conversions.py

Root cause:convert_to_float32(value, decimals) always returned np.float32, even when decimals == 0 (i.e., the original value was an integer). Users expecting integer outputs received np.float32 and had to cast manually.

Fix: Add convert_fixed_point_to_python(value, decimals) that returns int when decimals == 0 and np.float32 otherwise. np.array() then infers the correct dtype (int64 vs float32) automatically. The old convert_to_float32 is kept as a deprecated alias for backward compatibility.

defconvert_fixed_point_to_python(value: int, decimals: int) ->Union[int, np.float32]:
ifdecimals==0:
returnint(value) # ← integer tensor stays integerreturnnp.float32(Decimal(value) / (10**Decimal(decimals)))

Bug 4 — `infer()` crashes with AttributeError when inference node returns None

File:src/opengradient/client/alpha.py

Root cause:_get_inference_result_from_node() returns None when the node has no result yet. This None was passed directly to convert_to_model_output(), which calls event_data.get("output", {}) → AttributeError: NoneType object has no attribute get. Additionally, if ModelInferenceEvent logs were empty, parsed_logs[0] caused an IndexError.

Fix:

# Guard 1: empty precompile logsifnotprecompile_logs:
raiseRuntimeError("ModelInferenceEvent not found in transaction logs.")
# Guard 2: None from inference nodeifinference_resultisNone:
raiseRuntimeError(
f"Inference node returned no result for inference ID {inference_id!r}. ""The result may not be available yet — retry after a short delay."
)

Bug 5 — `run_workflow()` uses hardcoded 30M gas

File:src/opengradient/client/alpha.py

Root cause:run_workflow() hardcoded gas=30000000, unlike infer() which correctly uses estimate_gas(). This is both wasteful (users overpay) and fragile on networks with lower block gas limits.

Fix: Call estimate_gas() first and multiply by 3 for headroom. Fall back to 30M only if estimation itself fails.

try:
estimated_gas=run_function.estimate_gas({"from": self._wallet_account.address})
gas_limit=int(estimated_gas*3)
exceptException:
gas_limit=30000000# fallback

Also replaces the hardcoded timeout=60 in new_workflow() with the INFERENCE_TX_TIMEOUT constant.

Files Changed

File	Bugs Fixed
`src/opengradient/client/tee_registry.py`	Bug 1 (TEE randomization)
`src/opengradient/client/llm.py`	Bug 2 (HTTP 402 hint)
`src/opengradient/client/_conversions.py`	Bug 3 (int dtype)
`src/opengradient/client/alpha.py`	Bugs 4 & 5 (None guard + gas)

Related Issues

Closes#200
Closes#188
Partially closes#103

Previously get_llm_tee() always returned tees[0], the first TEE in the registry list. This caused all clients to hit the same TEE, providing no load distribution and no resilience when that TEE starts failing. Fix: use random.choice(tees) so each LLM() construction independently picks from all currently active TEEs. Successive retries or re-initializations will therefore naturally spread across the healthy pool. ClosesOpenGradient#200

Previously any HTTP error from the TEE (including 402 Payment Required) was silently wrapped into a generic RuntimeError("TEE LLM chat failed: ..."). This caused the confusing traceback seen in issue OpenGradient#188 — the real cause (insufficient OPG allowance) was buried inside the exception message. Fix: - Add `import httpx` and intercept httpx.HTTPStatusError before the generic `except Exception` handler in both _chat_request() and completion(). - When status == 402, raise a RuntimeError with a clear, actionable hint telling the user to call llm.ensure_opg_approval(opg_amount=<amount>). - Also handle 402 in _parse_sse_response() for the streaming path. - All other HTTP errors continue to surface as before. ClosesOpenGradient#188

Previously convert_to_float32() always returned np.float32 regardless of the decimals field, forcing callers to manually cast integer results (issue OpenGradient#103 — add proper type conversions from Solidity contract to Python). Fix: - Add convert_fixed_point_to_python(value, decimals) that returns int when decimals == 0 and np.float32 otherwise. np.array() automatically picks the correct dtype (int64 vs float32) based on the element types. - Update both convert_to_model_output() and convert_array_to_model_output() to call the new function. - Keep convert_to_float32() as a deprecated backward-compatible alias so any external code that imports it directly continues to work. Partially closesOpenGradient#103

… in run_workflow Bug 4 — None crash in infer(): _get_inference_result_from_node() can legitimately return None when the inference node has no result yet. Previously this None was passed directly to convert_to_model_output(), which calls event_data.get(), causing an AttributeError: 'NoneType' object has no attribute 'get'. Fix: after calling _get_inference_result_from_node(), check for None and raise a clear RuntimeError with a human-readable message and the inference_id so callers know what to retry. Also guard the precompile log fetch: if parsed_logs is empty, raise RuntimeError instead of crashing with an IndexError on parsed_logs[0]. Bug 5 — hardcoded 30M gas in run_workflow(): run_workflow() built the transaction with gas=30000000 (30 million) unconditionally. This is wasteful (users overpay for gas) and can fail on networks with a lower block gas limit. Fix: call estimate_gas() first (consistent with infer()), then multiply by 3 for headroom. Fall back to 30M only if estimation itself fails. Also fixes new_workflow() deploy to use INFERENCE_TX_TIMEOUT constant instead of the previous hardcoded 60 seconds.

amathxbt · 2026-03-23T23:50:06Z

Hey @adambalogh 👋 — this PR addresses 5 critical bugs found during a code audit, 3 of which directly close or partially close open issues you and the team have filed:

#	Bug	Issue
1	`get_llm_tee()` always returned `tees[0]` — no randomization or load distribution	Closes #200
2	HTTP 402 from TEE was swallowed as a cryptic `RuntimeError` with no guidance	Closes #188
3	Fixed-point values with `decimals=0` returned `np.float32` instead of `int`	Partially closes #103
4	`infer()` crashed with `AttributeError` when the inference node returned `None`	Code audit
5	`run_workflow()` hardcoded `gas=30000000` instead of using `estimate_gas()`	Code audit

All changes are backward-compatible. Happy to adjust anything before review! 🙏

adambalogh · 2026-03-26T02:02:47Z

Hey @amathxbt thanks for your contributions, I added it to my list to review, will get back to you tomorrow!

amathxbt · 2026-03-26T23:28:38Z

Hey @amathxbt thanks for your contributions, I added it to my list to review, will get back to you tomorrow!

Ok @adambalogh thank you see you tomorrow

adambalogh · 2026-03-26T23:32:12Z

 """LLM chat and completion via TEE-verified execution with x402 payments."""

-import json
+import json as _json


what is the reason for this?

The import json as _json alias is intentional — it avoids colliding with any user-level json variable or with the json key that appears heavily throughout the request/response dicts we construct in this file. The leading underscore signals it's a module-internal import and prevents it from being accidentally re-exported if someone does from .llm import *. No behaviour change; purely a namespace-safety convention.

Copilot

Pull request overview

This PR addresses several correctness and UX issues in the OpenGradient Python SDK across TEE selection, LLM error reporting, fixed-point conversions, inference robustness, and workflow gas handling.

Changes:

Randomize LLM TEE endpoint selection from the active registry pool to improve load distribution/failover behavior.
Improve LLM HTTP error handling by surfacing an actionable message for HTTP 402 (Permit2/OPG approval).
Add guards in Alpha.infer() for missing logs and None inference-node results, and replace hardcoded workflow gas/timeout with estimated/constant values.
Introduce a new fixed-point conversion helper intended to preserve integer outputs.

Reviewed changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated 5 comments.

File	Description
`src/opengradient/client/tee_registry.py`	Randomizes `get_llm_tee()` selection and adds debug logging.
`src/opengradient/client/llm.py`	Adds explicit HTTP 402 hint handling for completion/chat/streaming.
`src/opengradient/client/_conversions.py`	Adds `convert_fixed_point_to_python()` and updates output parsing to use it.
`src/opengradient/client/alpha.py`	Adds inference result/log guards, uses timeout constant, and estimates gas for workflows with fallback.

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Copilot · 2026-03-26T23:34:41Z

+ selected = random.choice(tees)
+ logger.debug(
+ "Selected TEE %s (endpoint=%s) from %d active LLM proxy TEE(s)",


This change makes get_llm_tee() non-deterministic, which will break/flakify the existing unit test TestGetLlmTee.test_returns_first_active_tee (it asserts the first endpoint). Update the tests to patch random.choice (or assert membership instead of ordering) so CI stays deterministic.

Would be good to fix this

Fixed ✅ — committed in tests/tee_registry_test.py. The old test_returns_first_active_tee test was asserting ordering which broke with random.choice. Replaced it with two deterministic tests: test_returns_active_tee_from_pool (patches random.choice via unittest.mock.patch to always return seq[0], keeping CI deterministic) and test_returns_any_active_tee (asserts the returned endpoint is a member of the active pool, independent of selection order).

Copilot · 2026-03-26T23:34:41Z

+def convert_fixed_point_to_python(value: int, decimals: int) -> Union[int, np.float32]:
+ """
+ Converts a fixed-point representation back to a native Python/NumPy type.
+
+ Returns int when decimals == 0 (preserving integer semantics for
+ tensors that were originally integers — fixes issue #103 where callers
+ expecting int results received np.float32 and had to cast manually).
+ Returns np.float32 for all other cases.
+
+ Args:
+ value: The integer significand stored on-chain.
+ decimals: The scale factor exponent (value / 10**decimals).
+
+ Returns:
+ int if decimals == 0, np.float32 otherwise.
+ """
+ if decimals == 0:
+ return int(value)


Returning int solely when decimals == 0 can misclassify float tensors that happen to contain whole numbers (e.g., 1.0 normalizes to decimals==0). That can silently change an expected float output into an integer array and/or cause dtype promotion when mixed with non-zero-decimal values. Consider carrying explicit dtype metadata from the node/contract, or only converting to int when you can establish the tensor is integer-typed as a whole.
Suggested change
defconvert_fixed_point_to_python(value: int, decimals: int) ->Union[int, np.float32]:
"""
Convertsafixed-pointrepresentationbacktoanativePython/NumPytype.
Returnsintwhendecimals==0 (preservingintegersemanticsfor
tensorsthatwereoriginallyintegers — fixesissue#103 where callers
expectingintresultsreceivednp.float32andhadtocastmanually).
Returnsnp.float32forallothercases.
Args:
value: Theintegersignificandstoredon-chain.
decimals: Thescalefactorexponent (value/10**decimals).
Returns:
intifdecimals==0, np.float32otherwise.
"""
ifdecimals==0:
returnint(value)
defconvert_fixed_point_to_python(value: int, decimals: int) ->np.float32:
"""
Convertsafixed-pointrepresentationbacktoaNumPyfloat32.
Thisfunctionisintentionallytype-stableandalwaysreturnsnp.float32,
regardlessofthevalueof`decimals`. Callersthatrequireinteger
semanticsshouldperformanexplicitcast (e.g., int(...)) basedon
theirowndtypemetadataorapplicationlogic.
Args:
value: Theintegersignificandstoredon-chain.
decimals: Thescalefactorexponent (value/10**decimals).
Returns:
np.float32correspondingto`value / 10**decimals`.
"""

Is there are reason we are mixing up float and int? might make the handling of results more difficult

Fixed ✅ — committed in src/opengradient/client/_conversions.py. convert_fixed_point_to_python is now type-stable: it always returns np.float32 regardless of decimals, matching Copilot's suggestion. The int-when-decimals==0 branch has been removed. The return type annotation is updated from Union[int, np.float32] → np.float32. Callers that genuinely need integer semantics can do an explicit int(...) cast based on their own dtype metadata.

Copilot · 2026-03-26T23:34:42Z

+ logging.debug(f"	Converting {tensor_data} to np array")
 tensor_data = np.array(tensor_data)

 if isinstance(tensor_data, (str, int, float)):
- logging.debug(f"\tConverting single entry {tensor_data} to a list")
+ logging.debug(f"	Converting single entry {tensor_data} to a list")


This debug string includes a literal tab character inside the f-string. Tabs are hard to see/review and can lead to inconsistent formatting across editors/log sinks; prefer using an escaped \t or spaces for indentation (same issue occurs in the other debug lines added/modified below).

Fixed ✅ committed in src/opengradient/client/_conversions.py. Replaced all 5 literal tab characters inside debug f-strings and log strings with proper escaped \t sequences (lines 87, 91, 110, 119, 125). No more invisible tabs in the source.

adambalogh · 2026-03-26T23:37:30Z

@amathxbt looks good overall, left some comments, could you please also merge from main? i think there are changes in the llm.py that need to be resolved

…2), replace literal tabs with \t

…tion fallback

…ll_with_tee_retry; keep 402 hint, json alias, OPG 0.05

amathxbt · 2026-03-26T23:51:00Z

Done @adambalogh ✅ — merged main into llm.py (commit 8896eac).

The PR branch was missing several methods that were added to main after the branch was cut. I preserved all of them and kept the PR's bug fixes layered on top:

Restored from main:

_connect_tee() — resolves TEE from registry and sets up the HTTP client
_refresh_tee() — re-resolves TEE and replaces the HTTP client (used on retry)
_call_with_tee_retry() — retries once on connection failure by picking a new TEE
Streaming retry logic in _chat_stream() (chunks_yielded guard + _refresh_tee on first-attempt failure)
TypeVar, Callable, Awaitable imports
Instance variables self._rpc_url, self._tee_registry_address, self._llm_server_url

PR fixes kept on top:

import json as _json + all json.loads / json.JSONDecodeError usages updated
_402_HINT constant + 402 handling in complete(), _chat_request(), and _parse_sse_response()
OPG minimum lowered from 0.1 → 0.05

…int coverage, fix mock status_code

…int_to_python now always returns np.float32)

…tions

amathxbt · 2026-03-27T00:15:40Z

Merged upstream main into the branch and resolved all conflicts:

src/opengradient/client/llm.py (SHA af38386): rebased on the latest main (which added TEE retry/refresh logic, _call_with_tee_retry, _connect_tee, _refresh_tee, new SSL/cert-rotation handling) while retaining all PR-specific changes — import json as _json, _402_HINT constant, HTTP 402 error handling in complete(), chat(), and _parse_sse_response(), and the 0.05 OPG minimum.
tests/llm_test.py: already contained the new TestTeeRetry*, TestRefreshTeeAndReset, and TestTeeCertRotation classes from main. The PR's 402-hint tests (test_http_402_raises_hint × 2, test_stream_402_raises_hint) and the FakeHTTPClient status-code fix were already in place.

The PR is now conflict-free (mergeable: true).

amathxbt · 2026-04-04T22:53:06Z

@adambalogh please can you done this it's been long time no more comments

amathxbt · 2026-03-23T23:49:53Z

Summary

This PR fixes 5 critical bugs identified from open issues and code audit, spanning TEE selection, LLM error handling, type conversions, inference safety, and gas estimation.

Bug 1 — TEE always picks the same node (closes#200)

File:src/opengradient/client/tee_registry.py

Root cause:get_llm_tee() always returned tees[0], routing 100% of traffic to a single TEE with zero load distribution or failover.

Fix: Replace tees[0] with random.choice(tees) so each LLM() construction independently selects from the full pool of active, registry-verified TEEs.

# Beforereturntees[0]
# Afterselected=random.choice(tees)
logger.debug("Selected TEE %s from %d active LLM proxy TEE(s)", selected.tee_id, len(tees))
returnselected

Bug 2 — HTTP 402 swallowed as cryptic RuntimeError (closes#188)

File:src/opengradient/client/llm.py

Root cause: All HTTP errors (including 402 Payment Required) were caught by except Exception and re-raised as a generic RuntimeError("TEE LLM chat failed: ..."). Users had no idea they needed to call ensure_opg_approval() first.

Fix: Add import httpx and intercept httpx.HTTPStatusErrorbefore the generic handler. When status_code == 402, raise a RuntimeError with an explicit, actionable message pointing to ensure_opg_approval(). Applied to _chat_request(), completion(), and _parse_sse_response() (streaming path).

# New constant_402_HINT= (
"Payment required (HTTP 402): your wallet may have insufficient OPG token allowance. ""Call llm.ensure_opg_approval(opg_amount=<amount>) to approve Permit2 spending ""before making requests. Minimum amount is 0.05 OPG."
)
# In _chat_request / completion / _parse_sse_response:excepthttpx.HTTPStatusErrorase:
ife.response.status_code==402:
raiseRuntimeError(_402_HINT) fromeraiseRuntimeError(f"TEE LLM chat failed: {e}") frome

Bug 3 — Fixed-point int returns np.float32 instead of int (partially closes#103)

File:src/opengradient/client/_conversions.py

Root cause:convert_to_float32(value, decimals) always returned np.float32, even when decimals == 0 (i.e., the original value was an integer). Users expecting integer outputs received np.float32 and had to cast manually.

Fix: Add convert_fixed_point_to_python(value, decimals) that returns int when decimals == 0 and np.float32 otherwise. np.array() then infers the correct dtype (int64 vs float32) automatically. The old convert_to_float32 is kept as a deprecated alias for backward compatibility.

defconvert_fixed_point_to_python(value: int, decimals: int) ->Union[int, np.float32]:
ifdecimals==0:
returnint(value) # ← integer tensor stays integerreturnnp.float32(Decimal(value) / (10**Decimal(decimals)))

Bug 4 — `infer()` crashes with AttributeError when inference node returns None

File:src/opengradient/client/alpha.py

Root cause:_get_inference_result_from_node() returns None when the node has no result yet. This None was passed directly to convert_to_model_output(), which calls event_data.get("output", {}) → AttributeError: NoneType object has no attribute get. Additionally, if ModelInferenceEvent logs were empty, parsed_logs[0] caused an IndexError.

Fix:

# Guard 1: empty precompile logsifnotprecompile_logs:
raiseRuntimeError("ModelInferenceEvent not found in transaction logs.")
# Guard 2: None from inference nodeifinference_resultisNone:
raiseRuntimeError(
f"Inference node returned no result for inference ID {inference_id!r}. ""The result may not be available yet — retry after a short delay."
)

Bug 5 — `run_workflow()` uses hardcoded 30M gas

File:src/opengradient/client/alpha.py

Root cause:run_workflow() hardcoded gas=30000000, unlike infer() which correctly uses estimate_gas(). This is both wasteful (users overpay) and fragile on networks with lower block gas limits.

Fix: Call estimate_gas() first and multiply by 3 for headroom. Fall back to 30M only if estimation itself fails.

try:
estimated_gas=run_function.estimate_gas({"from": self._wallet_account.address})
gas_limit=int(estimated_gas*3)
exceptException:
gas_limit=30000000# fallback

Also replaces the hardcoded timeout=60 in new_workflow() with the INFERENCE_TX_TIMEOUT constant.

Files Changed

File	Bugs Fixed
`src/opengradient/client/tee_registry.py`	Bug 1 (TEE randomization)
`src/opengradient/client/llm.py`	Bug 2 (HTTP 402 hint)
`src/opengradient/client/_conversions.py`	Bug 3 (int dtype)
`src/opengradient/client/alpha.py`	Bugs 4 & 5 (None guard + gas)

Related Issues

Closes#200
Closes#188
Partially closes#103

Previously get_llm_tee() always returned tees[0], the first TEE in the registry list. This caused all clients to hit the same TEE, providing no load distribution and no resilience when that TEE starts failing. Fix: use random.choice(tees) so each LLM() construction independently picks from all currently active TEEs. Successive retries or re-initializations will therefore naturally spread across the healthy pool. ClosesOpenGradient#200

Previously any HTTP error from the TEE (including 402 Payment Required) was silently wrapped into a generic RuntimeError("TEE LLM chat failed: ..."). This caused the confusing traceback seen in issue OpenGradient#188 — the real cause (insufficient OPG allowance) was buried inside the exception message. Fix: - Add `import httpx` and intercept httpx.HTTPStatusError before the generic `except Exception` handler in both _chat_request() and completion(). - When status == 402, raise a RuntimeError with a clear, actionable hint telling the user to call llm.ensure_opg_approval(opg_amount=<amount>). - Also handle 402 in _parse_sse_response() for the streaming path. - All other HTTP errors continue to surface as before. ClosesOpenGradient#188

Previously convert_to_float32() always returned np.float32 regardless of the decimals field, forcing callers to manually cast integer results (issue OpenGradient#103 — add proper type conversions from Solidity contract to Python). Fix: - Add convert_fixed_point_to_python(value, decimals) that returns int when decimals == 0 and np.float32 otherwise. np.array() automatically picks the correct dtype (int64 vs float32) based on the element types. - Update both convert_to_model_output() and convert_array_to_model_output() to call the new function. - Keep convert_to_float32() as a deprecated backward-compatible alias so any external code that imports it directly continues to work. Partially closesOpenGradient#103

… in run_workflow Bug 4 — None crash in infer(): _get_inference_result_from_node() can legitimately return None when the inference node has no result yet. Previously this None was passed directly to convert_to_model_output(), which calls event_data.get(), causing an AttributeError: 'NoneType' object has no attribute 'get'. Fix: after calling _get_inference_result_from_node(), check for None and raise a clear RuntimeError with a human-readable message and the inference_id so callers know what to retry. Also guard the precompile log fetch: if parsed_logs is empty, raise RuntimeError instead of crashing with an IndexError on parsed_logs[0]. Bug 5 — hardcoded 30M gas in run_workflow(): run_workflow() built the transaction with gas=30000000 (30 million) unconditionally. This is wasteful (users overpay for gas) and can fail on networks with a lower block gas limit. Fix: call estimate_gas() first (consistent with infer()), then multiply by 3 for headroom. Fall back to 30M only if estimation itself fails. Also fixes new_workflow() deploy to use INFERENCE_TX_TIMEOUT constant instead of the previous hardcoded 60 seconds.

amathxbt · 2026-03-23T23:50:06Z

Hey @adambalogh 👋 — this PR addresses 5 critical bugs found during a code audit, 3 of which directly close or partially close open issues you and the team have filed:

#	Bug	Issue
1	`get_llm_tee()` always returned `tees[0]` — no randomization or load distribution	Closes #200
2	HTTP 402 from TEE was swallowed as a cryptic `RuntimeError` with no guidance	Closes #188
3	Fixed-point values with `decimals=0` returned `np.float32` instead of `int`	Partially closes #103
4	`infer()` crashed with `AttributeError` when the inference node returned `None`	Code audit
5	`run_workflow()` hardcoded `gas=30000000` instead of using `estimate_gas()`	Code audit

All changes are backward-compatible. Happy to adjust anything before review! 🙏

adambalogh · 2026-03-26T02:02:47Z

Hey @amathxbt thanks for your contributions, I added it to my list to review, will get back to you tomorrow!

amathxbt · 2026-03-26T23:28:38Z

Hey @amathxbt thanks for your contributions, I added it to my list to review, will get back to you tomorrow!

Ok @adambalogh thank you see you tomorrow

adambalogh · 2026-03-26T23:32:12Z

 """LLM chat and completion via TEE-verified execution with x402 payments."""

-import json
+import json as _json


what is the reason for this?

The import json as _json alias is intentional — it avoids colliding with any user-level json variable or with the json key that appears heavily throughout the request/response dicts we construct in this file. The leading underscore signals it's a module-internal import and prevents it from being accidentally re-exported if someone does from .llm import *. No behaviour change; purely a namespace-safety convention.

Copilot

Pull request overview

This PR addresses several correctness and UX issues in the OpenGradient Python SDK across TEE selection, LLM error reporting, fixed-point conversions, inference robustness, and workflow gas handling.

Changes:

Randomize LLM TEE endpoint selection from the active registry pool to improve load distribution/failover behavior.
Improve LLM HTTP error handling by surfacing an actionable message for HTTP 402 (Permit2/OPG approval).
Add guards in Alpha.infer() for missing logs and None inference-node results, and replace hardcoded workflow gas/timeout with estimated/constant values.
Introduce a new fixed-point conversion helper intended to preserve integer outputs.

Reviewed changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated 5 comments.

File	Description
`src/opengradient/client/tee_registry.py`	Randomizes `get_llm_tee()` selection and adds debug logging.
`src/opengradient/client/llm.py`	Adds explicit HTTP 402 hint handling for completion/chat/streaming.
`src/opengradient/client/_conversions.py`	Adds `convert_fixed_point_to_python()` and updates output parsing to use it.
`src/opengradient/client/alpha.py`	Adds inference result/log guards, uses timeout constant, and estimates gas for workflows with fallback.

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Copilot · 2026-03-26T23:34:41Z

+ selected = random.choice(tees)
+ logger.debug(
+ "Selected TEE %s (endpoint=%s) from %d active LLM proxy TEE(s)",


This change makes get_llm_tee() non-deterministic, which will break/flakify the existing unit test TestGetLlmTee.test_returns_first_active_tee (it asserts the first endpoint). Update the tests to patch random.choice (or assert membership instead of ordering) so CI stays deterministic.

Would be good to fix this

Fixed ✅ — committed in tests/tee_registry_test.py. The old test_returns_first_active_tee test was asserting ordering which broke with random.choice. Replaced it with two deterministic tests: test_returns_active_tee_from_pool (patches random.choice via unittest.mock.patch to always return seq[0], keeping CI deterministic) and test_returns_any_active_tee (asserts the returned endpoint is a member of the active pool, independent of selection order).

Copilot · 2026-03-26T23:34:41Z

+def convert_fixed_point_to_python(value: int, decimals: int) -> Union[int, np.float32]:
+ """
+ Converts a fixed-point representation back to a native Python/NumPy type.
+
+ Returns int when decimals == 0 (preserving integer semantics for
+ tensors that were originally integers — fixes issue #103 where callers
+ expecting int results received np.float32 and had to cast manually).
+ Returns np.float32 for all other cases.
+
+ Args:
+ value: The integer significand stored on-chain.
+ decimals: The scale factor exponent (value / 10**decimals).
+
+ Returns:
+ int if decimals == 0, np.float32 otherwise.
+ """
+ if decimals == 0:
+ return int(value)


Returning int solely when decimals == 0 can misclassify float tensors that happen to contain whole numbers (e.g., 1.0 normalizes to decimals==0). That can silently change an expected float output into an integer array and/or cause dtype promotion when mixed with non-zero-decimal values. Consider carrying explicit dtype metadata from the node/contract, or only converting to int when you can establish the tensor is integer-typed as a whole.
Suggested change
defconvert_fixed_point_to_python(value: int, decimals: int) ->Union[int, np.float32]:
"""
Convertsafixed-pointrepresentationbacktoanativePython/NumPytype.
Returnsintwhendecimals==0 (preservingintegersemanticsfor
tensorsthatwereoriginallyintegers — fixesissue#103 where callers
expectingintresultsreceivednp.float32andhadtocastmanually).
Returnsnp.float32forallothercases.
Args:
value: Theintegersignificandstoredon-chain.
decimals: Thescalefactorexponent (value/10**decimals).
Returns:
intifdecimals==0, np.float32otherwise.
"""
ifdecimals==0:
returnint(value)
defconvert_fixed_point_to_python(value: int, decimals: int) ->np.float32:
"""
Convertsafixed-pointrepresentationbacktoaNumPyfloat32.
Thisfunctionisintentionallytype-stableandalwaysreturnsnp.float32,
regardlessofthevalueof`decimals`. Callersthatrequireinteger
semanticsshouldperformanexplicitcast (e.g., int(...)) basedon
theirowndtypemetadataorapplicationlogic.
Args:
value: Theintegersignificandstoredon-chain.
decimals: Thescalefactorexponent (value/10**decimals).
Returns:
np.float32correspondingto`value / 10**decimals`.
"""

Is there are reason we are mixing up float and int? might make the handling of results more difficult

Fixed ✅ — committed in src/opengradient/client/_conversions.py. convert_fixed_point_to_python is now type-stable: it always returns np.float32 regardless of decimals, matching Copilot's suggestion. The int-when-decimals==0 branch has been removed. The return type annotation is updated from Union[int, np.float32] → np.float32. Callers that genuinely need integer semantics can do an explicit int(...) cast based on their own dtype metadata.

Copilot · 2026-03-26T23:34:42Z

+ logging.debug(f"	Converting {tensor_data} to np array")
 tensor_data = np.array(tensor_data)

 if isinstance(tensor_data, (str, int, float)):
- logging.debug(f"\tConverting single entry {tensor_data} to a list")
+ logging.debug(f"	Converting single entry {tensor_data} to a list")


This debug string includes a literal tab character inside the f-string. Tabs are hard to see/review and can lead to inconsistent formatting across editors/log sinks; prefer using an escaped \t or spaces for indentation (same issue occurs in the other debug lines added/modified below).

Fixed ✅ committed in src/opengradient/client/_conversions.py. Replaced all 5 literal tab characters inside debug f-strings and log strings with proper escaped \t sequences (lines 87, 91, 110, 119, 125). No more invisible tabs in the source.

adambalogh · 2026-03-26T23:37:30Z

@amathxbt looks good overall, left some comments, could you please also merge from main? i think there are changes in the llm.py that need to be resolved

…2), replace literal tabs with \t

…tion fallback

…ll_with_tee_retry; keep 402 hint, json alias, OPG 0.05

amathxbt · 2026-03-26T23:51:00Z

Done @adambalogh ✅ — merged main into llm.py (commit 8896eac).

The PR branch was missing several methods that were added to main after the branch was cut. I preserved all of them and kept the PR's bug fixes layered on top:

Restored from main:

_connect_tee() — resolves TEE from registry and sets up the HTTP client
_refresh_tee() — re-resolves TEE and replaces the HTTP client (used on retry)
_call_with_tee_retry() — retries once on connection failure by picking a new TEE
Streaming retry logic in _chat_stream() (chunks_yielded guard + _refresh_tee on first-attempt failure)
TypeVar, Callable, Awaitable imports
Instance variables self._rpc_url, self._tee_registry_address, self._llm_server_url

PR fixes kept on top:

import json as _json + all json.loads / json.JSONDecodeError usages updated
_402_HINT constant + 402 handling in complete(), _chat_request(), and _parse_sse_response()
OPG minimum lowered from 0.1 → 0.05

…int coverage, fix mock status_code

…int_to_python now always returns np.float32)

…tions

amathxbt · 2026-03-27T00:15:40Z

Merged upstream main into the branch and resolved all conflicts:

src/opengradient/client/llm.py (SHA af38386): rebased on the latest main (which added TEE retry/refresh logic, _call_with_tee_retry, _connect_tee, _refresh_tee, new SSL/cert-rotation handling) while retaining all PR-specific changes — import json as _json, _402_HINT constant, HTTP 402 error handling in complete(), chat(), and _parse_sse_response(), and the 0.05 OPG minimum.
tests/llm_test.py: already contained the new TestTeeRetry*, TestRefreshTeeAndReset, and TestTeeCertRotation classes from main. The PR's 402-hint tests (test_http_402_raises_hint × 2, test_stream_402_raises_hint) and the FakeHTTPClient status-code fix were already in place.

The PR is now conflict-free (mergeable: true).

amathxbt · 2026-04-04T22:53:06Z

@adambalogh please can you done this it's been long time no more comments

amathxbt · 2026-03-23T23:49:53Z

Summary

This PR fixes 5 critical bugs identified from open issues and code audit, spanning TEE selection, LLM error handling, type conversions, inference safety, and gas estimation.

Bug 1 — TEE always picks the same node (closes#200)

File:src/opengradient/client/tee_registry.py

Root cause:get_llm_tee() always returned tees[0], routing 100% of traffic to a single TEE with zero load distribution or failover.

Fix: Replace tees[0] with random.choice(tees) so each LLM() construction independently selects from the full pool of active, registry-verified TEEs.

# Beforereturntees[0]
# Afterselected=random.choice(tees)
logger.debug("Selected TEE %s from %d active LLM proxy TEE(s)", selected.tee_id, len(tees))
returnselected

Bug 2 — HTTP 402 swallowed as cryptic RuntimeError (closes#188)

File:src/opengradient/client/llm.py

Root cause: All HTTP errors (including 402 Payment Required) were caught by except Exception and re-raised as a generic RuntimeError("TEE LLM chat failed: ..."). Users had no idea they needed to call ensure_opg_approval() first.

Fix: Add import httpx and intercept httpx.HTTPStatusErrorbefore the generic handler. When status_code == 402, raise a RuntimeError with an explicit, actionable message pointing to ensure_opg_approval(). Applied to _chat_request(), completion(), and _parse_sse_response() (streaming path).

# New constant_402_HINT= (
"Payment required (HTTP 402): your wallet may have insufficient OPG token allowance. ""Call llm.ensure_opg_approval(opg_amount=<amount>) to approve Permit2 spending ""before making requests. Minimum amount is 0.05 OPG."
)
# In _chat_request / completion / _parse_sse_response:excepthttpx.HTTPStatusErrorase:
ife.response.status_code==402:
raiseRuntimeError(_402_HINT) fromeraiseRuntimeError(f"TEE LLM chat failed: {e}") frome

Bug 3 — Fixed-point int returns np.float32 instead of int (partially closes#103)

File:src/opengradient/client/_conversions.py

Root cause:convert_to_float32(value, decimals) always returned np.float32, even when decimals == 0 (i.e., the original value was an integer). Users expecting integer outputs received np.float32 and had to cast manually.

Fix: Add convert_fixed_point_to_python(value, decimals) that returns int when decimals == 0 and np.float32 otherwise. np.array() then infers the correct dtype (int64 vs float32) automatically. The old convert_to_float32 is kept as a deprecated alias for backward compatibility.

defconvert_fixed_point_to_python(value: int, decimals: int) ->Union[int, np.float32]:
ifdecimals==0:
returnint(value) # ← integer tensor stays integerreturnnp.float32(Decimal(value) / (10**Decimal(decimals)))

Bug 4 — `infer()` crashes with AttributeError when inference node returns None

File:src/opengradient/client/alpha.py

Root cause:_get_inference_result_from_node() returns None when the node has no result yet. This None was passed directly to convert_to_model_output(), which calls event_data.get("output", {}) → AttributeError: NoneType object has no attribute get. Additionally, if ModelInferenceEvent logs were empty, parsed_logs[0] caused an IndexError.

Fix:

# Guard 1: empty precompile logsifnotprecompile_logs:
raiseRuntimeError("ModelInferenceEvent not found in transaction logs.")
# Guard 2: None from inference nodeifinference_resultisNone:
raiseRuntimeError(
f"Inference node returned no result for inference ID {inference_id!r}. ""The result may not be available yet — retry after a short delay."
)

Bug 5 — `run_workflow()` uses hardcoded 30M gas

File:src/opengradient/client/alpha.py

Root cause:run_workflow() hardcoded gas=30000000, unlike infer() which correctly uses estimate_gas(). This is both wasteful (users overpay) and fragile on networks with lower block gas limits.

Fix: Call estimate_gas() first and multiply by 3 for headroom. Fall back to 30M only if estimation itself fails.

try:
estimated_gas=run_function.estimate_gas({"from": self._wallet_account.address})
gas_limit=int(estimated_gas*3)
exceptException:
gas_limit=30000000# fallback

Also replaces the hardcoded timeout=60 in new_workflow() with the INFERENCE_TX_TIMEOUT constant.

Files Changed

File	Bugs Fixed
`src/opengradient/client/tee_registry.py`	Bug 1 (TEE randomization)
`src/opengradient/client/llm.py`	Bug 2 (HTTP 402 hint)
`src/opengradient/client/_conversions.py`	Bug 3 (int dtype)
`src/opengradient/client/alpha.py`	Bugs 4 & 5 (None guard + gas)

Related Issues

Closes#200
Closes#188
Partially closes#103

Previously get_llm_tee() always returned tees[0], the first TEE in the registry list. This caused all clients to hit the same TEE, providing no load distribution and no resilience when that TEE starts failing. Fix: use random.choice(tees) so each LLM() construction independently picks from all currently active TEEs. Successive retries or re-initializations will therefore naturally spread across the healthy pool. ClosesOpenGradient#200

Previously any HTTP error from the TEE (including 402 Payment Required) was silently wrapped into a generic RuntimeError("TEE LLM chat failed: ..."). This caused the confusing traceback seen in issue OpenGradient#188 — the real cause (insufficient OPG allowance) was buried inside the exception message. Fix: - Add `import httpx` and intercept httpx.HTTPStatusError before the generic `except Exception` handler in both _chat_request() and completion(). - When status == 402, raise a RuntimeError with a clear, actionable hint telling the user to call llm.ensure_opg_approval(opg_amount=<amount>). - Also handle 402 in _parse_sse_response() for the streaming path. - All other HTTP errors continue to surface as before. ClosesOpenGradient#188

Previously convert_to_float32() always returned np.float32 regardless of the decimals field, forcing callers to manually cast integer results (issue OpenGradient#103 — add proper type conversions from Solidity contract to Python). Fix: - Add convert_fixed_point_to_python(value, decimals) that returns int when decimals == 0 and np.float32 otherwise. np.array() automatically picks the correct dtype (int64 vs float32) based on the element types. - Update both convert_to_model_output() and convert_array_to_model_output() to call the new function. - Keep convert_to_float32() as a deprecated backward-compatible alias so any external code that imports it directly continues to work. Partially closesOpenGradient#103

… in run_workflow Bug 4 — None crash in infer(): _get_inference_result_from_node() can legitimately return None when the inference node has no result yet. Previously this None was passed directly to convert_to_model_output(), which calls event_data.get(), causing an AttributeError: 'NoneType' object has no attribute 'get'. Fix: after calling _get_inference_result_from_node(), check for None and raise a clear RuntimeError with a human-readable message and the inference_id so callers know what to retry. Also guard the precompile log fetch: if parsed_logs is empty, raise RuntimeError instead of crashing with an IndexError on parsed_logs[0]. Bug 5 — hardcoded 30M gas in run_workflow(): run_workflow() built the transaction with gas=30000000 (30 million) unconditionally. This is wasteful (users overpay for gas) and can fail on networks with a lower block gas limit. Fix: call estimate_gas() first (consistent with infer()), then multiply by 3 for headroom. Fall back to 30M only if estimation itself fails. Also fixes new_workflow() deploy to use INFERENCE_TX_TIMEOUT constant instead of the previous hardcoded 60 seconds.

amathxbt · 2026-03-23T23:50:06Z

Hey @adambalogh 👋 — this PR addresses 5 critical bugs found during a code audit, 3 of which directly close or partially close open issues you and the team have filed:

#	Bug	Issue
1	`get_llm_tee()` always returned `tees[0]` — no randomization or load distribution	Closes #200
2	HTTP 402 from TEE was swallowed as a cryptic `RuntimeError` with no guidance	Closes #188
3	Fixed-point values with `decimals=0` returned `np.float32` instead of `int`	Partially closes #103
4	`infer()` crashed with `AttributeError` when the inference node returned `None`	Code audit
5	`run_workflow()` hardcoded `gas=30000000` instead of using `estimate_gas()`	Code audit

All changes are backward-compatible. Happy to adjust anything before review! 🙏

adambalogh · 2026-03-26T02:02:47Z

Hey @amathxbt thanks for your contributions, I added it to my list to review, will get back to you tomorrow!

amathxbt · 2026-03-26T23:28:38Z

Hey @amathxbt thanks for your contributions, I added it to my list to review, will get back to you tomorrow!

Ok @adambalogh thank you see you tomorrow

adambalogh · 2026-03-26T23:32:12Z

 """LLM chat and completion via TEE-verified execution with x402 payments."""

-import json
+import json as _json


what is the reason for this?

The import json as _json alias is intentional — it avoids colliding with any user-level json variable or with the json key that appears heavily throughout the request/response dicts we construct in this file. The leading underscore signals it's a module-internal import and prevents it from being accidentally re-exported if someone does from .llm import *. No behaviour change; purely a namespace-safety convention.

Copilot

Pull request overview

This PR addresses several correctness and UX issues in the OpenGradient Python SDK across TEE selection, LLM error reporting, fixed-point conversions, inference robustness, and workflow gas handling.

Changes:

Randomize LLM TEE endpoint selection from the active registry pool to improve load distribution/failover behavior.
Improve LLM HTTP error handling by surfacing an actionable message for HTTP 402 (Permit2/OPG approval).
Add guards in Alpha.infer() for missing logs and None inference-node results, and replace hardcoded workflow gas/timeout with estimated/constant values.
Introduce a new fixed-point conversion helper intended to preserve integer outputs.

Reviewed changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated 5 comments.

File	Description
`src/opengradient/client/tee_registry.py`	Randomizes `get_llm_tee()` selection and adds debug logging.
`src/opengradient/client/llm.py`	Adds explicit HTTP 402 hint handling for completion/chat/streaming.
`src/opengradient/client/_conversions.py`	Adds `convert_fixed_point_to_python()` and updates output parsing to use it.
`src/opengradient/client/alpha.py`	Adds inference result/log guards, uses timeout constant, and estimates gas for workflows with fallback.

💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.

Copilot · 2026-03-26T23:34:41Z

+ selected = random.choice(tees)
+ logger.debug(
+ "Selected TEE %s (endpoint=%s) from %d active LLM proxy TEE(s)",


This change makes get_llm_tee() non-deterministic, which will break/flakify the existing unit test TestGetLlmTee.test_returns_first_active_tee (it asserts the first endpoint). Update the tests to patch random.choice (or assert membership instead of ordering) so CI stays deterministic.

Would be good to fix this

Fixed ✅ — committed in tests/tee_registry_test.py. The old test_returns_first_active_tee test was asserting ordering which broke with random.choice. Replaced it with two deterministic tests: test_returns_active_tee_from_pool (patches random.choice via unittest.mock.patch to always return seq[0], keeping CI deterministic) and test_returns_any_active_tee (asserts the returned endpoint is a member of the active pool, independent of selection order).

Copilot · 2026-03-26T23:34:41Z

+def convert_fixed_point_to_python(value: int, decimals: int) -> Union[int, np.float32]:
+ """
+ Converts a fixed-point representation back to a native Python/NumPy type.
+
+ Returns int when decimals == 0 (preserving integer semantics for
+ tensors that were originally integers — fixes issue #103 where callers
+ expecting int results received np.float32 and had to cast manually).
+ Returns np.float32 for all other cases.
+
+ Args:
+ value: The integer significand stored on-chain.
+ decimals: The scale factor exponent (value / 10**decimals).
+
+ Returns:
+ int if decimals == 0, np.float32 otherwise.
+ """
+ if decimals == 0:
+ return int(value)


Returning int solely when decimals == 0 can misclassify float tensors that happen to contain whole numbers (e.g., 1.0 normalizes to decimals==0). That can silently change an expected float output into an integer array and/or cause dtype promotion when mixed with non-zero-decimal values. Consider carrying explicit dtype metadata from the node/contract, or only converting to int when you can establish the tensor is integer-typed as a whole.
Suggested change
defconvert_fixed_point_to_python(value: int, decimals: int) ->Union[int, np.float32]:
"""
Convertsafixed-pointrepresentationbacktoanativePython/NumPytype.
Returnsintwhendecimals==0 (preservingintegersemanticsfor
tensorsthatwereoriginallyintegers — fixesissue#103 where callers
expectingintresultsreceivednp.float32andhadtocastmanually).
Returnsnp.float32forallothercases.
Args:
value: Theintegersignificandstoredon-chain.
decimals: Thescalefactorexponent (value/10**decimals).
Returns:
intifdecimals==0, np.float32otherwise.
"""
ifdecimals==0:
returnint(value)
defconvert_fixed_point_to_python(value: int, decimals: int) ->np.float32:
"""
Convertsafixed-pointrepresentationbacktoaNumPyfloat32.
Thisfunctionisintentionallytype-stableandalwaysreturnsnp.float32,
regardlessofthevalueof`decimals`. Callersthatrequireinteger
semanticsshouldperformanexplicitcast (e.g., int(...)) basedon
theirowndtypemetadataorapplicationlogic.
Args:
value: Theintegersignificandstoredon-chain.
decimals: Thescalefactorexponent (value/10**decimals).
Returns:
np.float32correspondingto`value / 10**decimals`.
"""

Is there are reason we are mixing up float and int? might make the handling of results more difficult

Fixed ✅ — committed in src/opengradient/client/_conversions.py. convert_fixed_point_to_python is now type-stable: it always returns np.float32 regardless of decimals, matching Copilot's suggestion. The int-when-decimals==0 branch has been removed. The return type annotation is updated from Union[int, np.float32] → np.float32. Callers that genuinely need integer semantics can do an explicit int(...) cast based on their own dtype metadata.

Copilot · 2026-03-26T23:34:42Z

+ logging.debug(f"	Converting {tensor_data} to np array")
 tensor_data = np.array(tensor_data)

 if isinstance(tensor_data, (str, int, float)):
- logging.debug(f"\tConverting single entry {tensor_data} to a list")
+ logging.debug(f"	Converting single entry {tensor_data} to a list")


This debug string includes a literal tab character inside the f-string. Tabs are hard to see/review and can lead to inconsistent formatting across editors/log sinks; prefer using an escaped \t or spaces for indentation (same issue occurs in the other debug lines added/modified below).

Fixed ✅ committed in src/opengradient/client/_conversions.py. Replaced all 5 literal tab characters inside debug f-strings and log strings with proper escaped \t sequences (lines 87, 91, 110, 119, 125). No more invisible tabs in the source.

adambalogh · 2026-03-26T23:37:30Z

@amathxbt looks good overall, left some comments, could you please also merge from main? i think there are changes in the llm.py that need to be resolved

…2), replace literal tabs with \t

…tion fallback

…ll_with_tee_retry; keep 402 hint, json alias, OPG 0.05

amathxbt · 2026-03-26T23:51:00Z

Done @adambalogh ✅ — merged main into llm.py (commit 8896eac).

The PR branch was missing several methods that were added to main after the branch was cut. I preserved all of them and kept the PR's bug fixes layered on top:

Restored from main:

_connect_tee() — resolves TEE from registry and sets up the HTTP client
_refresh_tee() — re-resolves TEE and replaces the HTTP client (used on retry)
_call_with_tee_retry() — retries once on connection failure by picking a new TEE
Streaming retry logic in _chat_stream() (chunks_yielded guard + _refresh_tee on first-attempt failure)
TypeVar, Callable, Awaitable imports
Instance variables self._rpc_url, self._tee_registry_address, self._llm_server_url

PR fixes kept on top:

import json as _json + all json.loads / json.JSONDecodeError usages updated
_402_HINT constant + 402 handling in complete(), _chat_request(), and _parse_sse_response()
OPG minimum lowered from 0.1 → 0.05

…int coverage, fix mock status_code

…int_to_python now always returns np.float32)

…tions

amathxbt · 2026-03-27T00:15:40Z

Merged upstream main into the branch and resolved all conflicts:

src/opengradient/client/llm.py (SHA af38386): rebased on the latest main (which added TEE retry/refresh logic, _call_with_tee_retry, _connect_tee, _refresh_tee, new SSL/cert-rotation handling) while retaining all PR-specific changes — import json as _json, _402_HINT constant, HTTP 402 error handling in complete(), chat(), and _parse_sse_response(), and the 0.05 OPG minimum.
tests/llm_test.py: already contained the new TestTeeRetry*, TestRefreshTeeAndReset, and TestTeeCertRotation classes from main. The PR's 402-hint tests (test_http_402_raises_hint × 2, test_stream_402_raises_hint) and the FakeHTTPClient status-code fix were already in place.

The PR is now conflict-free (mergeable: true).

amathxbt · 2026-04-04T22:53:06Z

@adambalogh please can you done this it's been long time no more comments

amathxbt · 2026-03-23T23:49:53Z

Summary

This PR fixes 5 critical bugs identified from open issues and code audit, spanning TEE selection, LLM error handling, type conversions, inference safety, and gas estimation.

Bug 1 — TEE always picks the same node (closes#200)

File:src/opengradient/client/tee_registry.py

Root cause:get_llm_tee() always returned tees[0], routing 100% of traffic to a single TEE with zero load distribution or failover.

Fix: Replace tees[0] with random.choice(tees) so each LLM() construction independently selects from the full pool of active, registry-verified TEEs.

# Beforereturntees[0]
# Afterselected=random.choice(tees)
logger.debug("Selected TEE %s from %d active LLM proxy TEE(s)", selected.tee_id, len(tees))
returnselected

Bug 2 — HTTP 402 swallowed as cryptic RuntimeError (closes#188)

File:src/opengradient/client/llm.py

Root cause: All HTTP errors (including 402 Payment Required) were caught by except Exception and re-raised as a generic RuntimeError("TEE LLM chat failed: ..."). Users had no idea they needed to call ensure_opg_approval() first.

Fix: Add import httpx and intercept httpx.HTTPStatusErrorbefore the generic handler. When status_code == 402, raise a RuntimeError with an explicit, actionable message pointing to ensure_opg_approval(). Applied to _chat_request(), completion(), and _parse_sse_response() (streaming path).

# New constant_402_HINT= (
"Payment required (HTTP 402): your wallet may have insufficient OPG token allowance. ""Call llm.ensure_opg_approval(opg_amount=<amount>) to approve Permit2 spending ""before making requests. Minimum amount is 0.05 OPG."
)
# In _chat_request / completion / _parse_sse_response:excepthttpx.HTTPStatusErrorase:
ife.response.status_code==402:
raiseRuntimeError(_402_HINT) fromeraiseRuntimeError(f"TEE LLM chat failed: {e}") frome

Bug 3 — Fixed-point int returns np.float32 instead of int (partially closes#103)

File:src/opengradient/client/_conversions.py

Root cause:convert_to_float32(value, decimals) always returned np.float32, even when decimals == 0 (i.e., the original value was an integer). Users expecting integer outputs received np.float32 and had to cast manually.

Fix: Add convert_fixed_point_to_python(value, decimals) that returns int when decimals == 0 and np.float32 otherwise. np.array() then infers the correct dtype (int64 vs float32) automatically. The old convert_to_float32 is kept as a deprecated alias for backward compatibility.

defconvert_fixed_point_to_python(value: int, decimals: int) ->Union[int, np.float32]:
ifdecimals==0:
returnint(value) # ← integer tensor stays integerreturnnp.float32(Decimal(value) / (10**Decimal(decimals)))

Bug 4 — `infer()` crashes with AttributeError when inference node returns None

File:src/opengradient/client/alpha.py

Root cause:_get_inference_result_from_node() returns None when the node has no result yet. This None was passed directly to convert_to_model_output(), which calls event_data.get("output", {}) → AttributeError: NoneType object has no attribute get. Additionally, if ModelInferenceEvent logs were empty, parsed_logs[0] caused an IndexError.

Fix:

# Guard 1: empty precompile logsifnotprecompile_logs:
raiseRuntimeError("ModelInferenceEvent not found in transaction logs.")
# Guard 2: None from inference nodeifinference_resultisNone:
raiseRuntimeError(
f"Inference node returned no result for inference ID {inference_id!r}. ""The result may not be available yet — retry after a short delay."
)

Bug 5 — `run_workflow()` uses hardcoded 30M gas

File:src/opengradient/client/alpha.py

Root cause:run_workflow() hardcoded gas=30000000, unlike infer() which correctly uses estimate_gas(). This is both wasteful (users overpay) and fragile on networks with lower block gas limits.

Fix: Call estimate_gas() first and multiply by 3 for headroom. Fall back to 30M only if estimation itself fails.

try:
estimated_gas=run_function.estimate_gas({"from": self._wallet_account.address})
gas_limit=int(estimated_gas*3)
exceptException:
gas_limit=30000000# fallback

Also replaces the hardcoded timeout=60 in new_workflow() with the INFERENCE_TX_TIMEOUT constant.

Files Changed

File	Bugs Fixed
`src/opengradient/client/tee_registry.py`	Bug 1 (TEE randomization)
`src/opengradient/client/llm.py`	Bug 2 (HTTP 402 hint)
`src/opengradient/client/_conversions.py`	Bug 3 (int dtype)
`src/opengradient/client/alpha.py`	Bugs 4 & 5 (None guard + gas)

Related Issues

Closes#200
Closes#188
Partially closes#103

Previously get_llm_tee() always returned tees[0], the first TEE in the registry list. This caused all clients to hit the same TEE, providing no load distribution and no resilience when that TEE starts failing. Fix: use random.choice(tees) so each LLM() construction independently picks from all currently active TEEs. Successive retries or re-initializations will therefore naturally spread across the healthy pool. ClosesOpenGradient#200

Previously any HTTP error from the TEE (including 402 Payment Required) was silently wrapped into a generic RuntimeError("TEE LLM chat failed: ..."). This caused the confusing traceback seen in issue OpenGradient#188 — the real cause (insufficient OPG allowance) was buried inside the exception message. Fix: - Add `import httpx` and intercept httpx.HTTPStatusError before the generic `except Exception` handler in both _chat_request() and completion(). - When status == 402, raise a RuntimeError with a clear, actionable hint telling the user to call llm.ensure_opg_approval(opg_amount=<amount>). - Also handle 402 in _parse_sse_response() for the streaming path. - All other HTTP errors continue to surface as before. ClosesOpenGradient#188

Previously convert_to_float32() always returned np.float32 regardless of the decimals field, forcing callers to manually cast integer results (issue OpenGradient#103 — add proper type conversions from Solidity contract to Python). Fix: - Add convert_fixed_point_to_python(value, decimals) that returns int when decimals == 0 and np.float32 otherwise. np.array() automatically picks the correct dtype (int64 vs float32) based on the element types. - Update both convert_to_model_output() and convert_array_to_model_output() to call the new function. - Keep convert_to_float32() as a deprecated backward-compatible alias so any external code that imports it directly continues to work. Partially closesOpenGradient#103

… in run_workflow Bug 4 — None crash in infer(): _get_inference_result_from_node() can legitimately return None when the inference node has no result yet. Previously this None was passed directly to convert_to_model_output(), which calls event_data.get(), causing an AttributeError: 'NoneType' object has no attribute 'get'. Fix: after calling _get_inference_result_from_node(), check for None and raise a clear RuntimeError with a human-readable message and the inference_id so callers know what to retry. Also guard the precompile log fetch: if parsed_logs is empty, raise RuntimeError instead of crashing with an IndexError on parsed_logs[0]. Bug 5 — hardcoded 30M gas in run_workflow(): run_workflow() built the transaction with gas=30000000 (30 million) unconditionally. This is wasteful (users overpay for gas) and can fail on networks with a lower block gas limit. Fix: call estimate_gas() first (consistent with infer()), then multiply by 3 for headroom. Fall back to 30M only if estimation itself fails. Also fixes new_workflow() deploy to use INFERENCE_TX_TIMEOUT constant instead of the previous hardcoded 60 seconds.

amathxbt · 2026-03-23T23:50:06Z

Hey @adambalogh 👋 — this PR addresses 5 critical bugs found during a code audit, 3 of which directly close or partially close open issues you and the team have filed:

#	Bug	Issue
1	`get_llm_tee()` always returned `tees[0]` — no randomization or load distribution	Closes #200
2	HTTP 402 from TEE was swallowed as a cryptic `RuntimeError` with no guidance	Closes #188
3	Fixed-point values with `decimals=0` returned `np.float32` instead of `int`	Partially closes #103
4	`infer()` crashed with `AttributeError` when the inference node returned `None`	Code audit
5	`run_workflow()` hardcoded `gas=30000000` instead of using `estimate_gas()`	Code audit

All changes are backward-compatible. Happy to adjust anything before review! 🙏

adambalogh · 2026-03-26T02:02:47Z

Hey @amathxbt thanks for your contributions, I added it to my list to review, will get back to you tomorrow!

amathxbt · 2026-03-26T23:28:38Z

Hey @amathxbt thanks for your contributions, I added it to my list to review, will get back to you tomorrow!

Ok @adambalogh thank you see you tomorrow

adambalogh · 2026-03-26T23:32:12Z

 """LLM chat and completion via TEE-verified execution with x402 payments."""

-import json
+import json as _json


what is the reason for this?

The import json as _json alias is intentional — it avoids colliding with any user-level json variable or with the json key that appears heavily throughout the request/response dicts we construct in this file. The leading underscore signals it's a module-internal import and prevents it from being accidentally re-exported if someone does from .llm import *. No behaviour change; purely a namespace-safety convention.

Copilot

Pull request overview

This PR addresses several correctness and UX issues in the OpenGradient Python SDK across TEE selection, LLM error reporting, fixed-point conversions, inference robustness, and workflow gas handling.

Changes:

Randomize LLM TEE endpoint selection from the active registry pool to improve load distribution/failover behavior.
Improve LLM HTTP error handling by surfacing an actionable message for HTTP 402 (Permit2/OPG approval).
Add guards in Alpha.infer() for missing logs and None inference-node results, and replace hardcoded workflow gas/timeout with estimated/constant values.
Introduce a new fixed-point conversion helper intended to preserve integer outputs.

Reviewed changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated 5 comments.

File	Description
`src/opengradient/client/tee_registry.py`	Randomizes `get_llm_tee()` selection and adds debug logging.
`src/opengradient/client/llm.py`	Adds explicit HTTP 402 hint handling for completion/chat/streaming.
`src/opengradient/client/_conversions.py`	Adds `convert_fixed_point_to_python()` and updates output parsing to use it.
`src/opengradient/client/alpha.py`	Adds inference result/log guards, uses timeout constant, and estimates gas for workflows with fallback.

💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.

Copilot · 2026-03-26T23:34:41Z

+ selected = random.choice(tees)
+ logger.debug(
+ "Selected TEE %s (endpoint=%s) from %d active LLM proxy TEE(s)",


This change makes get_llm_tee() non-deterministic, which will break/flakify the existing unit test TestGetLlmTee.test_returns_first_active_tee (it asserts the first endpoint). Update the tests to patch random.choice (or assert membership instead of ordering) so CI stays deterministic.

Would be good to fix this

Fixed ✅ — committed in tests/tee_registry_test.py. The old test_returns_first_active_tee test was asserting ordering which broke with random.choice. Replaced it with two deterministic tests: test_returns_active_tee_from_pool (patches random.choice via unittest.mock.patch to always return seq[0], keeping CI deterministic) and test_returns_any_active_tee (asserts the returned endpoint is a member of the active pool, independent of selection order).

Copilot · 2026-03-26T23:34:41Z

+def convert_fixed_point_to_python(value: int, decimals: int) -> Union[int, np.float32]:
+ """
+ Converts a fixed-point representation back to a native Python/NumPy type.
+
+ Returns int when decimals == 0 (preserving integer semantics for
+ tensors that were originally integers — fixes issue #103 where callers
+ expecting int results received np.float32 and had to cast manually).
+ Returns np.float32 for all other cases.
+
+ Args:
+ value: The integer significand stored on-chain.
+ decimals: The scale factor exponent (value / 10**decimals).
+
+ Returns:
+ int if decimals == 0, np.float32 otherwise.
+ """
+ if decimals == 0:
+ return int(value)


Returning int solely when decimals == 0 can misclassify float tensors that happen to contain whole numbers (e.g., 1.0 normalizes to decimals==0). That can silently change an expected float output into an integer array and/or cause dtype promotion when mixed with non-zero-decimal values. Consider carrying explicit dtype metadata from the node/contract, or only converting to int when you can establish the tensor is integer-typed as a whole.
Suggested change
defconvert_fixed_point_to_python(value: int, decimals: int) ->Union[int, np.float32]:
"""
Convertsafixed-pointrepresentationbacktoanativePython/NumPytype.
Returnsintwhendecimals==0 (preservingintegersemanticsfor
tensorsthatwereoriginallyintegers — fixesissue#103 where callers
expectingintresultsreceivednp.float32andhadtocastmanually).
Returnsnp.float32forallothercases.
Args:
value: Theintegersignificandstoredon-chain.
decimals: Thescalefactorexponent (value/10**decimals).
Returns:
intifdecimals==0, np.float32otherwise.
"""
ifdecimals==0:
returnint(value)
defconvert_fixed_point_to_python(value: int, decimals: int) ->np.float32:
"""
Convertsafixed-pointrepresentationbacktoaNumPyfloat32.
Thisfunctionisintentionallytype-stableandalwaysreturnsnp.float32,
regardlessofthevalueof`decimals`. Callersthatrequireinteger
semanticsshouldperformanexplicitcast (e.g., int(...)) basedon
theirowndtypemetadataorapplicationlogic.
Args:
value: Theintegersignificandstoredon-chain.
decimals: Thescalefactorexponent (value/10**decimals).
Returns:
np.float32correspondingto`value / 10**decimals`.
"""

Is there are reason we are mixing up float and int? might make the handling of results more difficult

Fixed ✅ — committed in src/opengradient/client/_conversions.py. convert_fixed_point_to_python is now type-stable: it always returns np.float32 regardless of decimals, matching Copilot's suggestion. The int-when-decimals==0 branch has been removed. The return type annotation is updated from Union[int, np.float32] → np.float32. Callers that genuinely need integer semantics can do an explicit int(...) cast based on their own dtype metadata.

Copilot · 2026-03-26T23:34:42Z

+ logging.debug(f"	Converting {tensor_data} to np array")
 tensor_data = np.array(tensor_data)

 if isinstance(tensor_data, (str, int, float)):
- logging.debug(f"\tConverting single entry {tensor_data} to a list")
+ logging.debug(f"	Converting single entry {tensor_data} to a list")


This debug string includes a literal tab character inside the f-string. Tabs are hard to see/review and can lead to inconsistent formatting across editors/log sinks; prefer using an escaped \t or spaces for indentation (same issue occurs in the other debug lines added/modified below).

Fixed ✅ committed in src/opengradient/client/_conversions.py. Replaced all 5 literal tab characters inside debug f-strings and log strings with proper escaped \t sequences (lines 87, 91, 110, 119, 125). No more invisible tabs in the source.

adambalogh · 2026-03-26T23:37:30Z

@amathxbt looks good overall, left some comments, could you please also merge from main? i think there are changes in the llm.py that need to be resolved

…2), replace literal tabs with \t

…tion fallback

…ll_with_tee_retry; keep 402 hint, json alias, OPG 0.05

amathxbt · 2026-03-26T23:51:00Z

Done @adambalogh ✅ — merged main into llm.py (commit 8896eac).

The PR branch was missing several methods that were added to main after the branch was cut. I preserved all of them and kept the PR's bug fixes layered on top:

Restored from main:

_connect_tee() — resolves TEE from registry and sets up the HTTP client
_refresh_tee() — re-resolves TEE and replaces the HTTP client (used on retry)
_call_with_tee_retry() — retries once on connection failure by picking a new TEE
Streaming retry logic in _chat_stream() (chunks_yielded guard + _refresh_tee on first-attempt failure)
TypeVar, Callable, Awaitable imports
Instance variables self._rpc_url, self._tee_registry_address, self._llm_server_url

PR fixes kept on top:

import json as _json + all json.loads / json.JSONDecodeError usages updated
_402_HINT constant + 402 handling in complete(), _chat_request(), and _parse_sse_response()
OPG minimum lowered from 0.1 → 0.05

…int coverage, fix mock status_code

…int_to_python now always returns np.float32)

…tions

amathxbt · 2026-03-27T00:15:40Z

Merged upstream main into the branch and resolved all conflicts:

src/opengradient/client/llm.py (SHA af38386): rebased on the latest main (which added TEE retry/refresh logic, _call_with_tee_retry, _connect_tee, _refresh_tee, new SSL/cert-rotation handling) while retaining all PR-specific changes — import json as _json, _402_HINT constant, HTTP 402 error handling in complete(), chat(), and _parse_sse_response(), and the 0.05 OPG minimum.
tests/llm_test.py: already contained the new TestTeeRetry*, TestRefreshTeeAndReset, and TestTeeCertRotation classes from main. The PR's 402-hint tests (test_http_402_raises_hint × 2, test_stream_402_raises_hint) and the FakeHTTPClient status-code fix were already in place.

The PR is now conflict-free (mergeable: true).

amathxbt · 2026-04-04T22:53:06Z

@adambalogh please can you done this it's been long time no more comments

amathxbt · 2026-03-23T23:49:53Z

Summary

This PR fixes 5 critical bugs identified from open issues and code audit, spanning TEE selection, LLM error handling, type conversions, inference safety, and gas estimation.

Bug 1 — TEE always picks the same node (closes#200)

File:src/opengradient/client/tee_registry.py

Root cause:get_llm_tee() always returned tees[0], routing 100% of traffic to a single TEE with zero load distribution or failover.

Fix: Replace tees[0] with random.choice(tees) so each LLM() construction independently selects from the full pool of active, registry-verified TEEs.

# Beforereturntees[0]
# Afterselected=random.choice(tees)
logger.debug("Selected TEE %s from %d active LLM proxy TEE(s)", selected.tee_id, len(tees))
returnselected

Bug 2 — HTTP 402 swallowed as cryptic RuntimeError (closes#188)

File:src/opengradient/client/llm.py

Root cause: All HTTP errors (including 402 Payment Required) were caught by except Exception and re-raised as a generic RuntimeError("TEE LLM chat failed: ..."). Users had no idea they needed to call ensure_opg_approval() first.

Fix: Add import httpx and intercept httpx.HTTPStatusErrorbefore the generic handler. When status_code == 402, raise a RuntimeError with an explicit, actionable message pointing to ensure_opg_approval(). Applied to _chat_request(), completion(), and _parse_sse_response() (streaming path).

# New constant_402_HINT= (
"Payment required (HTTP 402): your wallet may have insufficient OPG token allowance. ""Call llm.ensure_opg_approval(opg_amount=<amount>) to approve Permit2 spending ""before making requests. Minimum amount is 0.05 OPG."
)
# In _chat_request / completion / _parse_sse_response:excepthttpx.HTTPStatusErrorase:
ife.response.status_code==402:
raiseRuntimeError(_402_HINT) fromeraiseRuntimeError(f"TEE LLM chat failed: {e}") frome

Bug 3 — Fixed-point int returns np.float32 instead of int (partially closes#103)

File:src/opengradient/client/_conversions.py

Root cause:convert_to_float32(value, decimals) always returned np.float32, even when decimals == 0 (i.e., the original value was an integer). Users expecting integer outputs received np.float32 and had to cast manually.

Fix: Add convert_fixed_point_to_python(value, decimals) that returns int when decimals == 0 and np.float32 otherwise. np.array() then infers the correct dtype (int64 vs float32) automatically. The old convert_to_float32 is kept as a deprecated alias for backward compatibility.

defconvert_fixed_point_to_python(value: int, decimals: int) ->Union[int, np.float32]:
ifdecimals==0:
returnint(value) # ← integer tensor stays integerreturnnp.float32(Decimal(value) / (10**Decimal(decimals)))

Bug 4 — `infer()` crashes with AttributeError when inference node returns None

File:src/opengradient/client/alpha.py

Root cause:_get_inference_result_from_node() returns None when the node has no result yet. This None was passed directly to convert_to_model_output(), which calls event_data.get("output", {}) → AttributeError: NoneType object has no attribute get. Additionally, if ModelInferenceEvent logs were empty, parsed_logs[0] caused an IndexError.

Fix:

# Guard 1: empty precompile logsifnotprecompile_logs:
raiseRuntimeError("ModelInferenceEvent not found in transaction logs.")
# Guard 2: None from inference nodeifinference_resultisNone:
raiseRuntimeError(
f"Inference node returned no result for inference ID {inference_id!r}. ""The result may not be available yet — retry after a short delay."
)

Bug 5 — `run_workflow()` uses hardcoded 30M gas

File:src/opengradient/client/alpha.py

Root cause:run_workflow() hardcoded gas=30000000, unlike infer() which correctly uses estimate_gas(). This is both wasteful (users overpay) and fragile on networks with lower block gas limits.

Fix: Call estimate_gas() first and multiply by 3 for headroom. Fall back to 30M only if estimation itself fails.

try:
estimated_gas=run_function.estimate_gas({"from": self._wallet_account.address})
gas_limit=int(estimated_gas*3)
exceptException:
gas_limit=30000000# fallback

Also replaces the hardcoded timeout=60 in new_workflow() with the INFERENCE_TX_TIMEOUT constant.

Files Changed

File	Bugs Fixed
`src/opengradient/client/tee_registry.py`	Bug 1 (TEE randomization)
`src/opengradient/client/llm.py`	Bug 2 (HTTP 402 hint)
`src/opengradient/client/_conversions.py`	Bug 3 (int dtype)
`src/opengradient/client/alpha.py`	Bugs 4 & 5 (None guard + gas)

Related Issues

Closes#200
Closes#188
Partially closes#103

Previously get_llm_tee() always returned tees[0], the first TEE in the registry list. This caused all clients to hit the same TEE, providing no load distribution and no resilience when that TEE starts failing. Fix: use random.choice(tees) so each LLM() construction independently picks from all currently active TEEs. Successive retries or re-initializations will therefore naturally spread across the healthy pool. ClosesOpenGradient#200

Previously any HTTP error from the TEE (including 402 Payment Required) was silently wrapped into a generic RuntimeError("TEE LLM chat failed: ..."). This caused the confusing traceback seen in issue OpenGradient#188 — the real cause (insufficient OPG allowance) was buried inside the exception message. Fix: - Add `import httpx` and intercept httpx.HTTPStatusError before the generic `except Exception` handler in both _chat_request() and completion(). - When status == 402, raise a RuntimeError with a clear, actionable hint telling the user to call llm.ensure_opg_approval(opg_amount=<amount>). - Also handle 402 in _parse_sse_response() for the streaming path. - All other HTTP errors continue to surface as before. ClosesOpenGradient#188

Previously convert_to_float32() always returned np.float32 regardless of the decimals field, forcing callers to manually cast integer results (issue OpenGradient#103 — add proper type conversions from Solidity contract to Python). Fix: - Add convert_fixed_point_to_python(value, decimals) that returns int when decimals == 0 and np.float32 otherwise. np.array() automatically picks the correct dtype (int64 vs float32) based on the element types. - Update both convert_to_model_output() and convert_array_to_model_output() to call the new function. - Keep convert_to_float32() as a deprecated backward-compatible alias so any external code that imports it directly continues to work. Partially closesOpenGradient#103

… in run_workflow Bug 4 — None crash in infer(): _get_inference_result_from_node() can legitimately return None when the inference node has no result yet. Previously this None was passed directly to convert_to_model_output(), which calls event_data.get(), causing an AttributeError: 'NoneType' object has no attribute 'get'. Fix: after calling _get_inference_result_from_node(), check for None and raise a clear RuntimeError with a human-readable message and the inference_id so callers know what to retry. Also guard the precompile log fetch: if parsed_logs is empty, raise RuntimeError instead of crashing with an IndexError on parsed_logs[0]. Bug 5 — hardcoded 30M gas in run_workflow(): run_workflow() built the transaction with gas=30000000 (30 million) unconditionally. This is wasteful (users overpay for gas) and can fail on networks with a lower block gas limit. Fix: call estimate_gas() first (consistent with infer()), then multiply by 3 for headroom. Fall back to 30M only if estimation itself fails. Also fixes new_workflow() deploy to use INFERENCE_TX_TIMEOUT constant instead of the previous hardcoded 60 seconds.

amathxbt · 2026-03-23T23:50:06Z

Hey @adambalogh 👋 — this PR addresses 5 critical bugs found during a code audit, 3 of which directly close or partially close open issues you and the team have filed:

#	Bug	Issue
1	`get_llm_tee()` always returned `tees[0]` — no randomization or load distribution	Closes #200
2	HTTP 402 from TEE was swallowed as a cryptic `RuntimeError` with no guidance	Closes #188
3	Fixed-point values with `decimals=0` returned `np.float32` instead of `int`	Partially closes #103
4	`infer()` crashed with `AttributeError` when the inference node returned `None`	Code audit
5	`run_workflow()` hardcoded `gas=30000000` instead of using `estimate_gas()`	Code audit

All changes are backward-compatible. Happy to adjust anything before review! 🙏

adambalogh · 2026-03-26T02:02:47Z

Hey @amathxbt thanks for your contributions, I added it to my list to review, will get back to you tomorrow!

amathxbt · 2026-03-26T23:28:38Z

Hey @amathxbt thanks for your contributions, I added it to my list to review, will get back to you tomorrow!

Ok @adambalogh thank you see you tomorrow

adambalogh · 2026-03-26T23:32:12Z

 """LLM chat and completion via TEE-verified execution with x402 payments."""

-import json
+import json as _json


what is the reason for this?

The import json as _json alias is intentional — it avoids colliding with any user-level json variable or with the json key that appears heavily throughout the request/response dicts we construct in this file. The leading underscore signals it's a module-internal import and prevents it from being accidentally re-exported if someone does from .llm import *. No behaviour change; purely a namespace-safety convention.

Copilot

Pull request overview

This PR addresses several correctness and UX issues in the OpenGradient Python SDK across TEE selection, LLM error reporting, fixed-point conversions, inference robustness, and workflow gas handling.

Changes:

Randomize LLM TEE endpoint selection from the active registry pool to improve load distribution/failover behavior.
Improve LLM HTTP error handling by surfacing an actionable message for HTTP 402 (Permit2/OPG approval).
Add guards in Alpha.infer() for missing logs and None inference-node results, and replace hardcoded workflow gas/timeout with estimated/constant values.
Introduce a new fixed-point conversion helper intended to preserve integer outputs.

Reviewed changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated 5 comments.

File	Description
`src/opengradient/client/tee_registry.py`	Randomizes `get_llm_tee()` selection and adds debug logging.
`src/opengradient/client/llm.py`	Adds explicit HTTP 402 hint handling for completion/chat/streaming.
`src/opengradient/client/_conversions.py`	Adds `convert_fixed_point_to_python()` and updates output parsing to use it.
`src/opengradient/client/alpha.py`	Adds inference result/log guards, uses timeout constant, and estimates gas for workflows with fallback.

💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.

Copilot · 2026-03-26T23:34:41Z

+ selected = random.choice(tees)
+ logger.debug(
+ "Selected TEE %s (endpoint=%s) from %d active LLM proxy TEE(s)",


This change makes get_llm_tee() non-deterministic, which will break/flakify the existing unit test TestGetLlmTee.test_returns_first_active_tee (it asserts the first endpoint). Update the tests to patch random.choice (or assert membership instead of ordering) so CI stays deterministic.

Would be good to fix this

Fixed ✅ — committed in tests/tee_registry_test.py. The old test_returns_first_active_tee test was asserting ordering which broke with random.choice. Replaced it with two deterministic tests: test_returns_active_tee_from_pool (patches random.choice via unittest.mock.patch to always return seq[0], keeping CI deterministic) and test_returns_any_active_tee (asserts the returned endpoint is a member of the active pool, independent of selection order).

Copilot · 2026-03-26T23:34:41Z

+def convert_fixed_point_to_python(value: int, decimals: int) -> Union[int, np.float32]:
+ """
+ Converts a fixed-point representation back to a native Python/NumPy type.
+
+ Returns int when decimals == 0 (preserving integer semantics for
+ tensors that were originally integers — fixes issue #103 where callers
+ expecting int results received np.float32 and had to cast manually).
+ Returns np.float32 for all other cases.
+
+ Args:
+ value: The integer significand stored on-chain.
+ decimals: The scale factor exponent (value / 10**decimals).
+
+ Returns:
+ int if decimals == 0, np.float32 otherwise.
+ """
+ if decimals == 0:
+ return int(value)


Returning int solely when decimals == 0 can misclassify float tensors that happen to contain whole numbers (e.g., 1.0 normalizes to decimals==0). That can silently change an expected float output into an integer array and/or cause dtype promotion when mixed with non-zero-decimal values. Consider carrying explicit dtype metadata from the node/contract, or only converting to int when you can establish the tensor is integer-typed as a whole.
Suggested change
defconvert_fixed_point_to_python(value: int, decimals: int) ->Union[int, np.float32]:
"""
Convertsafixed-pointrepresentationbacktoanativePython/NumPytype.
Returnsintwhendecimals==0 (preservingintegersemanticsfor
tensorsthatwereoriginallyintegers — fixesissue#103 where callers
expectingintresultsreceivednp.float32andhadtocastmanually).
Returnsnp.float32forallothercases.
Args:
value: Theintegersignificandstoredon-chain.
decimals: Thescalefactorexponent (value/10**decimals).
Returns:
intifdecimals==0, np.float32otherwise.
"""
ifdecimals==0:
returnint(value)
defconvert_fixed_point_to_python(value: int, decimals: int) ->np.float32:
"""
Convertsafixed-pointrepresentationbacktoaNumPyfloat32.
Thisfunctionisintentionallytype-stableandalwaysreturnsnp.float32,
regardlessofthevalueof`decimals`. Callersthatrequireinteger
semanticsshouldperformanexplicitcast (e.g., int(...)) basedon
theirowndtypemetadataorapplicationlogic.
Args:
value: Theintegersignificandstoredon-chain.
decimals: Thescalefactorexponent (value/10**decimals).
Returns:
np.float32correspondingto`value / 10**decimals`.
"""

Is there are reason we are mixing up float and int? might make the handling of results more difficult

Fixed ✅ — committed in src/opengradient/client/_conversions.py. convert_fixed_point_to_python is now type-stable: it always returns np.float32 regardless of decimals, matching Copilot's suggestion. The int-when-decimals==0 branch has been removed. The return type annotation is updated from Union[int, np.float32] → np.float32. Callers that genuinely need integer semantics can do an explicit int(...) cast based on their own dtype metadata.

Copilot · 2026-03-26T23:34:42Z

+ logging.debug(f"	Converting {tensor_data} to np array")
 tensor_data = np.array(tensor_data)

 if isinstance(tensor_data, (str, int, float)):
- logging.debug(f"\tConverting single entry {tensor_data} to a list")
+ logging.debug(f"	Converting single entry {tensor_data} to a list")


This debug string includes a literal tab character inside the f-string. Tabs are hard to see/review and can lead to inconsistent formatting across editors/log sinks; prefer using an escaped \t or spaces for indentation (same issue occurs in the other debug lines added/modified below).

Fixed ✅ committed in src/opengradient/client/_conversions.py. Replaced all 5 literal tab characters inside debug f-strings and log strings with proper escaped \t sequences (lines 87, 91, 110, 119, 125). No more invisible tabs in the source.

adambalogh · 2026-03-26T23:37:30Z

@amathxbt looks good overall, left some comments, could you please also merge from main? i think there are changes in the llm.py that need to be resolved

…2), replace literal tabs with \t

…tion fallback

…ll_with_tee_retry; keep 402 hint, json alias, OPG 0.05

amathxbt · 2026-03-26T23:51:00Z

Done @adambalogh ✅ — merged main into llm.py (commit 8896eac).

The PR branch was missing several methods that were added to main after the branch was cut. I preserved all of them and kept the PR's bug fixes layered on top:

Restored from main:

_connect_tee() — resolves TEE from registry and sets up the HTTP client
_refresh_tee() — re-resolves TEE and replaces the HTTP client (used on retry)
_call_with_tee_retry() — retries once on connection failure by picking a new TEE
Streaming retry logic in _chat_stream() (chunks_yielded guard + _refresh_tee on first-attempt failure)
TypeVar, Callable, Awaitable imports
Instance variables self._rpc_url, self._tee_registry_address, self._llm_server_url

PR fixes kept on top:

import json as _json + all json.loads / json.JSONDecodeError usages updated
_402_HINT constant + 402 handling in complete(), _chat_request(), and _parse_sse_response()
OPG minimum lowered from 0.1 → 0.05

…int coverage, fix mock status_code

…int_to_python now always returns np.float32)

…tions

amathxbt · 2026-03-27T00:15:40Z

Merged upstream main into the branch and resolved all conflicts:

src/opengradient/client/llm.py (SHA af38386): rebased on the latest main (which added TEE retry/refresh logic, _call_with_tee_retry, _connect_tee, _refresh_tee, new SSL/cert-rotation handling) while retaining all PR-specific changes — import json as _json, _402_HINT constant, HTTP 402 error handling in complete(), chat(), and _parse_sse_response(), and the 0.05 OPG minimum.
tests/llm_test.py: already contained the new TestTeeRetry*, TestRefreshTeeAndReset, and TestTeeCertRotation classes from main. The PR's 402-hint tests (test_http_402_raises_hint × 2, test_stream_402_raises_hint) and the FakeHTTPClient status-code fix were already in place.

The PR is now conflict-free (mergeable: true).

amathxbt · 2026-04-04T22:53:06Z

@adambalogh please can you done this it's been long time no more comments

amathxbt · 2026-03-23T23:49:53Z

Summary

This PR fixes 5 critical bugs identified from open issues and code audit, spanning TEE selection, LLM error handling, type conversions, inference safety, and gas estimation.

Bug 1 — TEE always picks the same node (closes#200)

File:src/opengradient/client/tee_registry.py

Root cause:get_llm_tee() always returned tees[0], routing 100% of traffic to a single TEE with zero load distribution or failover.

Fix: Replace tees[0] with random.choice(tees) so each LLM() construction independently selects from the full pool of active, registry-verified TEEs.

# Beforereturntees[0]
# Afterselected=random.choice(tees)
logger.debug("Selected TEE %s from %d active LLM proxy TEE(s)", selected.tee_id, len(tees))
returnselected

Bug 2 — HTTP 402 swallowed as cryptic RuntimeError (closes#188)

File:src/opengradient/client/llm.py

Root cause: All HTTP errors (including 402 Payment Required) were caught by except Exception and re-raised as a generic RuntimeError("TEE LLM chat failed: ..."). Users had no idea they needed to call ensure_opg_approval() first.

Fix: Add import httpx and intercept httpx.HTTPStatusErrorbefore the generic handler. When status_code == 402, raise a RuntimeError with an explicit, actionable message pointing to ensure_opg_approval(). Applied to _chat_request(), completion(), and _parse_sse_response() (streaming path).

# New constant_402_HINT= (
"Payment required (HTTP 402): your wallet may have insufficient OPG token allowance. ""Call llm.ensure_opg_approval(opg_amount=<amount>) to approve Permit2 spending ""before making requests. Minimum amount is 0.05 OPG."
)
# In _chat_request / completion / _parse_sse_response:excepthttpx.HTTPStatusErrorase:
ife.response.status_code==402:
raiseRuntimeError(_402_HINT) fromeraiseRuntimeError(f"TEE LLM chat failed: {e}") frome

Bug 3 — Fixed-point int returns np.float32 instead of int (partially closes#103)

File:src/opengradient/client/_conversions.py

Root cause:convert_to_float32(value, decimals) always returned np.float32, even when decimals == 0 (i.e., the original value was an integer). Users expecting integer outputs received np.float32 and had to cast manually.

Fix: Add convert_fixed_point_to_python(value, decimals) that returns int when decimals == 0 and np.float32 otherwise. np.array() then infers the correct dtype (int64 vs float32) automatically. The old convert_to_float32 is kept as a deprecated alias for backward compatibility.

defconvert_fixed_point_to_python(value: int, decimals: int) ->Union[int, np.float32]:
ifdecimals==0:
returnint(value) # ← integer tensor stays integerreturnnp.float32(Decimal(value) / (10**Decimal(decimals)))

Bug 4 — `infer()` crashes with AttributeError when inference node returns None

File:src/opengradient/client/alpha.py

Root cause:_get_inference_result_from_node() returns None when the node has no result yet. This None was passed directly to convert_to_model_output(), which calls event_data.get("output", {}) → AttributeError: NoneType object has no attribute get. Additionally, if ModelInferenceEvent logs were empty, parsed_logs[0] caused an IndexError.

Fix:

# Guard 1: empty precompile logsifnotprecompile_logs:
raiseRuntimeError("ModelInferenceEvent not found in transaction logs.")
# Guard 2: None from inference nodeifinference_resultisNone:
raiseRuntimeError(
f"Inference node returned no result for inference ID {inference_id!r}. ""The result may not be available yet — retry after a short delay."
)

Bug 5 — `run_workflow()` uses hardcoded 30M gas

File:src/opengradient/client/alpha.py

Root cause:run_workflow() hardcoded gas=30000000, unlike infer() which correctly uses estimate_gas(). This is both wasteful (users overpay) and fragile on networks with lower block gas limits.

Fix: Call estimate_gas() first and multiply by 3 for headroom. Fall back to 30M only if estimation itself fails.

try:
estimated_gas=run_function.estimate_gas({"from": self._wallet_account.address})
gas_limit=int(estimated_gas*3)
exceptException:
gas_limit=30000000# fallback

Also replaces the hardcoded timeout=60 in new_workflow() with the INFERENCE_TX_TIMEOUT constant.

Files Changed

File	Bugs Fixed
`src/opengradient/client/tee_registry.py`	Bug 1 (TEE randomization)
`src/opengradient/client/llm.py`	Bug 2 (HTTP 402 hint)
`src/opengradient/client/_conversions.py`	Bug 3 (int dtype)
`src/opengradient/client/alpha.py`	Bugs 4 & 5 (None guard + gas)

Related Issues

Closes#200
Closes#188
Partially closes#103

Previously get_llm_tee() always returned tees[0], the first TEE in the registry list. This caused all clients to hit the same TEE, providing no load distribution and no resilience when that TEE starts failing. Fix: use random.choice(tees) so each LLM() construction independently picks from all currently active TEEs. Successive retries or re-initializations will therefore naturally spread across the healthy pool. ClosesOpenGradient#200

Previously any HTTP error from the TEE (including 402 Payment Required) was silently wrapped into a generic RuntimeError("TEE LLM chat failed: ..."). This caused the confusing traceback seen in issue OpenGradient#188 — the real cause (insufficient OPG allowance) was buried inside the exception message. Fix: - Add `import httpx` and intercept httpx.HTTPStatusError before the generic `except Exception` handler in both _chat_request() and completion(). - When status == 402, raise a RuntimeError with a clear, actionable hint telling the user to call llm.ensure_opg_approval(opg_amount=<amount>). - Also handle 402 in _parse_sse_response() for the streaming path. - All other HTTP errors continue to surface as before. ClosesOpenGradient#188

Previously convert_to_float32() always returned np.float32 regardless of the decimals field, forcing callers to manually cast integer results (issue OpenGradient#103 — add proper type conversions from Solidity contract to Python). Fix: - Add convert_fixed_point_to_python(value, decimals) that returns int when decimals == 0 and np.float32 otherwise. np.array() automatically picks the correct dtype (int64 vs float32) based on the element types. - Update both convert_to_model_output() and convert_array_to_model_output() to call the new function. - Keep convert_to_float32() as a deprecated backward-compatible alias so any external code that imports it directly continues to work. Partially closesOpenGradient#103

… in run_workflow Bug 4 — None crash in infer(): _get_inference_result_from_node() can legitimately return None when the inference node has no result yet. Previously this None was passed directly to convert_to_model_output(), which calls event_data.get(), causing an AttributeError: 'NoneType' object has no attribute 'get'. Fix: after calling _get_inference_result_from_node(), check for None and raise a clear RuntimeError with a human-readable message and the inference_id so callers know what to retry. Also guard the precompile log fetch: if parsed_logs is empty, raise RuntimeError instead of crashing with an IndexError on parsed_logs[0]. Bug 5 — hardcoded 30M gas in run_workflow(): run_workflow() built the transaction with gas=30000000 (30 million) unconditionally. This is wasteful (users overpay for gas) and can fail on networks with a lower block gas limit. Fix: call estimate_gas() first (consistent with infer()), then multiply by 3 for headroom. Fall back to 30M only if estimation itself fails. Also fixes new_workflow() deploy to use INFERENCE_TX_TIMEOUT constant instead of the previous hardcoded 60 seconds.

amathxbt · 2026-03-23T23:50:06Z

Hey @adambalogh 👋 — this PR addresses 5 critical bugs found during a code audit, 3 of which directly close or partially close open issues you and the team have filed:

#	Bug	Issue
1	`get_llm_tee()` always returned `tees[0]` — no randomization or load distribution	Closes #200
2	HTTP 402 from TEE was swallowed as a cryptic `RuntimeError` with no guidance	Closes #188
3	Fixed-point values with `decimals=0` returned `np.float32` instead of `int`	Partially closes #103
4	`infer()` crashed with `AttributeError` when the inference node returned `None`	Code audit
5	`run_workflow()` hardcoded `gas=30000000` instead of using `estimate_gas()`	Code audit

All changes are backward-compatible. Happy to adjust anything before review! 🙏

adambalogh · 2026-03-26T02:02:47Z

Hey @amathxbt thanks for your contributions, I added it to my list to review, will get back to you tomorrow!

amathxbt · 2026-03-26T23:28:38Z

Hey @amathxbt thanks for your contributions, I added it to my list to review, will get back to you tomorrow!

Ok @adambalogh thank you see you tomorrow

adambalogh · 2026-03-26T23:32:12Z

 """LLM chat and completion via TEE-verified execution with x402 payments."""

-import json
+import json as _json


what is the reason for this?

The import json as _json alias is intentional — it avoids colliding with any user-level json variable or with the json key that appears heavily throughout the request/response dicts we construct in this file. The leading underscore signals it's a module-internal import and prevents it from being accidentally re-exported if someone does from .llm import *. No behaviour change; purely a namespace-safety convention.

Copilot

Pull request overview

This PR addresses several correctness and UX issues in the OpenGradient Python SDK across TEE selection, LLM error reporting, fixed-point conversions, inference robustness, and workflow gas handling.

Changes:

Randomize LLM TEE endpoint selection from the active registry pool to improve load distribution/failover behavior.
Improve LLM HTTP error handling by surfacing an actionable message for HTTP 402 (Permit2/OPG approval).
Add guards in Alpha.infer() for missing logs and None inference-node results, and replace hardcoded workflow gas/timeout with estimated/constant values.
Introduce a new fixed-point conversion helper intended to preserve integer outputs.

Reviewed changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated 5 comments.

File	Description
`src/opengradient/client/tee_registry.py`	Randomizes `get_llm_tee()` selection and adds debug logging.
`src/opengradient/client/llm.py`	Adds explicit HTTP 402 hint handling for completion/chat/streaming.
`src/opengradient/client/_conversions.py`	Adds `convert_fixed_point_to_python()` and updates output parsing to use it.
`src/opengradient/client/alpha.py`	Adds inference result/log guards, uses timeout constant, and estimates gas for workflows with fallback.

💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.

Copilot · 2026-03-26T23:34:41Z

+ selected = random.choice(tees)
+ logger.debug(
+ "Selected TEE %s (endpoint=%s) from %d active LLM proxy TEE(s)",


This change makes get_llm_tee() non-deterministic, which will break/flakify the existing unit test TestGetLlmTee.test_returns_first_active_tee (it asserts the first endpoint). Update the tests to patch random.choice (or assert membership instead of ordering) so CI stays deterministic.

Would be good to fix this

Fixed ✅ — committed in tests/tee_registry_test.py. The old test_returns_first_active_tee test was asserting ordering which broke with random.choice. Replaced it with two deterministic tests: test_returns_active_tee_from_pool (patches random.choice via unittest.mock.patch to always return seq[0], keeping CI deterministic) and test_returns_any_active_tee (asserts the returned endpoint is a member of the active pool, independent of selection order).

Copilot · 2026-03-26T23:34:41Z

+def convert_fixed_point_to_python(value: int, decimals: int) -> Union[int, np.float32]:
+ """
+ Converts a fixed-point representation back to a native Python/NumPy type.
+
+ Returns int when decimals == 0 (preserving integer semantics for
+ tensors that were originally integers — fixes issue #103 where callers
+ expecting int results received np.float32 and had to cast manually).
+ Returns np.float32 for all other cases.
+
+ Args:
+ value: The integer significand stored on-chain.
+ decimals: The scale factor exponent (value / 10**decimals).
+
+ Returns:
+ int if decimals == 0, np.float32 otherwise.
+ """
+ if decimals == 0:
+ return int(value)


Returning int solely when decimals == 0 can misclassify float tensors that happen to contain whole numbers (e.g., 1.0 normalizes to decimals==0). That can silently change an expected float output into an integer array and/or cause dtype promotion when mixed with non-zero-decimal values. Consider carrying explicit dtype metadata from the node/contract, or only converting to int when you can establish the tensor is integer-typed as a whole.
Suggested change
defconvert_fixed_point_to_python(value: int, decimals: int) ->Union[int, np.float32]:
"""
Convertsafixed-pointrepresentationbacktoanativePython/NumPytype.
Returnsintwhendecimals==0 (preservingintegersemanticsfor
tensorsthatwereoriginallyintegers — fixesissue#103 where callers
expectingintresultsreceivednp.float32andhadtocastmanually).
Returnsnp.float32forallothercases.
Args:
value: Theintegersignificandstoredon-chain.
decimals: Thescalefactorexponent (value/10**decimals).
Returns:
intifdecimals==0, np.float32otherwise.
"""
ifdecimals==0:
returnint(value)
defconvert_fixed_point_to_python(value: int, decimals: int) ->np.float32:
"""
Convertsafixed-pointrepresentationbacktoaNumPyfloat32.
Thisfunctionisintentionallytype-stableandalwaysreturnsnp.float32,
regardlessofthevalueof`decimals`. Callersthatrequireinteger
semanticsshouldperformanexplicitcast (e.g., int(...)) basedon
theirowndtypemetadataorapplicationlogic.
Args:
value: Theintegersignificandstoredon-chain.
decimals: Thescalefactorexponent (value/10**decimals).
Returns:
np.float32correspondingto`value / 10**decimals`.
"""

Is there are reason we are mixing up float and int? might make the handling of results more difficult

Fixed ✅ — committed in src/opengradient/client/_conversions.py. convert_fixed_point_to_python is now type-stable: it always returns np.float32 regardless of decimals, matching Copilot's suggestion. The int-when-decimals==0 branch has been removed. The return type annotation is updated from Union[int, np.float32] → np.float32. Callers that genuinely need integer semantics can do an explicit int(...) cast based on their own dtype metadata.

Copilot · 2026-03-26T23:34:42Z

+ logging.debug(f"	Converting {tensor_data} to np array")
 tensor_data = np.array(tensor_data)

 if isinstance(tensor_data, (str, int, float)):
- logging.debug(f"\tConverting single entry {tensor_data} to a list")
+ logging.debug(f"	Converting single entry {tensor_data} to a list")


This debug string includes a literal tab character inside the f-string. Tabs are hard to see/review and can lead to inconsistent formatting across editors/log sinks; prefer using an escaped \t or spaces for indentation (same issue occurs in the other debug lines added/modified below).

Fixed ✅ committed in src/opengradient/client/_conversions.py. Replaced all 5 literal tab characters inside debug f-strings and log strings with proper escaped \t sequences (lines 87, 91, 110, 119, 125). No more invisible tabs in the source.

adambalogh · 2026-03-26T23:37:30Z

@amathxbt looks good overall, left some comments, could you please also merge from main? i think there are changes in the llm.py that need to be resolved

…2), replace literal tabs with \t

…tion fallback

…ll_with_tee_retry; keep 402 hint, json alias, OPG 0.05

amathxbt · 2026-03-26T23:51:00Z

Done @adambalogh ✅ — merged main into llm.py (commit 8896eac).

The PR branch was missing several methods that were added to main after the branch was cut. I preserved all of them and kept the PR's bug fixes layered on top:

Restored from main:

_connect_tee() — resolves TEE from registry and sets up the HTTP client
_refresh_tee() — re-resolves TEE and replaces the HTTP client (used on retry)
_call_with_tee_retry() — retries once on connection failure by picking a new TEE
Streaming retry logic in _chat_stream() (chunks_yielded guard + _refresh_tee on first-attempt failure)
TypeVar, Callable, Awaitable imports
Instance variables self._rpc_url, self._tee_registry_address, self._llm_server_url

PR fixes kept on top:

import json as _json + all json.loads / json.JSONDecodeError usages updated
_402_HINT constant + 402 handling in complete(), _chat_request(), and _parse_sse_response()
OPG minimum lowered from 0.1 → 0.05

…int coverage, fix mock status_code

…int_to_python now always returns np.float32)

…tions

amathxbt · 2026-03-27T00:15:40Z

Merged upstream main into the branch and resolved all conflicts:

src/opengradient/client/llm.py (SHA af38386): rebased on the latest main (which added TEE retry/refresh logic, _call_with_tee_retry, _connect_tee, _refresh_tee, new SSL/cert-rotation handling) while retaining all PR-specific changes — import json as _json, _402_HINT constant, HTTP 402 error handling in complete(), chat(), and _parse_sse_response(), and the 0.05 OPG minimum.
tests/llm_test.py: already contained the new TestTeeRetry*, TestRefreshTeeAndReset, and TestTeeCertRotation classes from main. The PR's 402-hint tests (test_http_402_raises_hint × 2, test_stream_402_raises_hint) and the FakeHTTPClient status-code fix were already in place.

The PR is now conflict-free (mergeable: true).

amathxbt · 2026-04-04T22:53:06Z

@adambalogh please can you done this it's been long time no more comments

amathxbt · 2026-03-23T23:49:53Z

Summary

This PR fixes 5 critical bugs identified from open issues and code audit, spanning TEE selection, LLM error handling, type conversions, inference safety, and gas estimation.

Bug 1 — TEE always picks the same node (closes#200)

File:src/opengradient/client/tee_registry.py

Root cause:get_llm_tee() always returned tees[0], routing 100% of traffic to a single TEE with zero load distribution or failover.

Fix: Replace tees[0] with random.choice(tees) so each LLM() construction independently selects from the full pool of active, registry-verified TEEs.

# Beforereturntees[0]
# Afterselected=random.choice(tees)
logger.debug("Selected TEE %s from %d active LLM proxy TEE(s)", selected.tee_id, len(tees))
returnselected

Bug 2 — HTTP 402 swallowed as cryptic RuntimeError (closes#188)

File:src/opengradient/client/llm.py

Root cause: All HTTP errors (including 402 Payment Required) were caught by except Exception and re-raised as a generic RuntimeError("TEE LLM chat failed: ..."). Users had no idea they needed to call ensure_opg_approval() first.

Fix: Add import httpx and intercept httpx.HTTPStatusErrorbefore the generic handler. When status_code == 402, raise a RuntimeError with an explicit, actionable message pointing to ensure_opg_approval(). Applied to _chat_request(), completion(), and _parse_sse_response() (streaming path).

# New constant_402_HINT= (
"Payment required (HTTP 402): your wallet may have insufficient OPG token allowance. ""Call llm.ensure_opg_approval(opg_amount=<amount>) to approve Permit2 spending ""before making requests. Minimum amount is 0.05 OPG."
)
# In _chat_request / completion / _parse_sse_response:excepthttpx.HTTPStatusErrorase:
ife.response.status_code==402:
raiseRuntimeError(_402_HINT) fromeraiseRuntimeError(f"TEE LLM chat failed: {e}") frome

Bug 3 — Fixed-point int returns np.float32 instead of int (partially closes#103)

File:src/opengradient/client/_conversions.py

Root cause:convert_to_float32(value, decimals) always returned np.float32, even when decimals == 0 (i.e., the original value was an integer). Users expecting integer outputs received np.float32 and had to cast manually.

Fix: Add convert_fixed_point_to_python(value, decimals) that returns int when decimals == 0 and np.float32 otherwise. np.array() then infers the correct dtype (int64 vs float32) automatically. The old convert_to_float32 is kept as a deprecated alias for backward compatibility.

defconvert_fixed_point_to_python(value: int, decimals: int) ->Union[int, np.float32]:
ifdecimals==0:
returnint(value) # ← integer tensor stays integerreturnnp.float32(Decimal(value) / (10**Decimal(decimals)))

Bug 4 — `infer()` crashes with AttributeError when inference node returns None

File:src/opengradient/client/alpha.py

Root cause:_get_inference_result_from_node() returns None when the node has no result yet. This None was passed directly to convert_to_model_output(), which calls event_data.get("output", {}) → AttributeError: NoneType object has no attribute get. Additionally, if ModelInferenceEvent logs were empty, parsed_logs[0] caused an IndexError.

Fix:

# Guard 1: empty precompile logsifnotprecompile_logs:
raiseRuntimeError("ModelInferenceEvent not found in transaction logs.")
# Guard 2: None from inference nodeifinference_resultisNone:
raiseRuntimeError(
f"Inference node returned no result for inference ID {inference_id!r}. ""The result may not be available yet — retry after a short delay."
)

Bug 5 — `run_workflow()` uses hardcoded 30M gas

File:src/opengradient/client/alpha.py

Root cause:run_workflow() hardcoded gas=30000000, unlike infer() which correctly uses estimate_gas(). This is both wasteful (users overpay) and fragile on networks with lower block gas limits.

Fix: Call estimate_gas() first and multiply by 3 for headroom. Fall back to 30M only if estimation itself fails.

try:
estimated_gas=run_function.estimate_gas({"from": self._wallet_account.address})
gas_limit=int(estimated_gas*3)
exceptException:
gas_limit=30000000# fallback

Also replaces the hardcoded timeout=60 in new_workflow() with the INFERENCE_TX_TIMEOUT constant.

Files Changed

File	Bugs Fixed
`src/opengradient/client/tee_registry.py`	Bug 1 (TEE randomization)
`src/opengradient/client/llm.py`	Bug 2 (HTTP 402 hint)
`src/opengradient/client/_conversions.py`	Bug 3 (int dtype)
`src/opengradient/client/alpha.py`	Bugs 4 & 5 (None guard + gas)

Related Issues

Closes#200
Closes#188
Partially closes#103

Previously get_llm_tee() always returned tees[0], the first TEE in the registry list. This caused all clients to hit the same TEE, providing no load distribution and no resilience when that TEE starts failing. Fix: use random.choice(tees) so each LLM() construction independently picks from all currently active TEEs. Successive retries or re-initializations will therefore naturally spread across the healthy pool. ClosesOpenGradient#200

Previously any HTTP error from the TEE (including 402 Payment Required) was silently wrapped into a generic RuntimeError("TEE LLM chat failed: ..."). This caused the confusing traceback seen in issue OpenGradient#188 — the real cause (insufficient OPG allowance) was buried inside the exception message. Fix: - Add `import httpx` and intercept httpx.HTTPStatusError before the generic `except Exception` handler in both _chat_request() and completion(). - When status == 402, raise a RuntimeError with a clear, actionable hint telling the user to call llm.ensure_opg_approval(opg_amount=<amount>). - Also handle 402 in _parse_sse_response() for the streaming path. - All other HTTP errors continue to surface as before. ClosesOpenGradient#188

Previously convert_to_float32() always returned np.float32 regardless of the decimals field, forcing callers to manually cast integer results (issue OpenGradient#103 — add proper type conversions from Solidity contract to Python). Fix: - Add convert_fixed_point_to_python(value, decimals) that returns int when decimals == 0 and np.float32 otherwise. np.array() automatically picks the correct dtype (int64 vs float32) based on the element types. - Update both convert_to_model_output() and convert_array_to_model_output() to call the new function. - Keep convert_to_float32() as a deprecated backward-compatible alias so any external code that imports it directly continues to work. Partially closesOpenGradient#103

… in run_workflow Bug 4 — None crash in infer(): _get_inference_result_from_node() can legitimately return None when the inference node has no result yet. Previously this None was passed directly to convert_to_model_output(), which calls event_data.get(), causing an AttributeError: 'NoneType' object has no attribute 'get'. Fix: after calling _get_inference_result_from_node(), check for None and raise a clear RuntimeError with a human-readable message and the inference_id so callers know what to retry. Also guard the precompile log fetch: if parsed_logs is empty, raise RuntimeError instead of crashing with an IndexError on parsed_logs[0]. Bug 5 — hardcoded 30M gas in run_workflow(): run_workflow() built the transaction with gas=30000000 (30 million) unconditionally. This is wasteful (users overpay for gas) and can fail on networks with a lower block gas limit. Fix: call estimate_gas() first (consistent with infer()), then multiply by 3 for headroom. Fall back to 30M only if estimation itself fails. Also fixes new_workflow() deploy to use INFERENCE_TX_TIMEOUT constant instead of the previous hardcoded 60 seconds.

amathxbt · 2026-03-23T23:50:06Z

Hey @adambalogh 👋 — this PR addresses 5 critical bugs found during a code audit, 3 of which directly close or partially close open issues you and the team have filed:

#	Bug	Issue
1	`get_llm_tee()` always returned `tees[0]` — no randomization or load distribution	Closes #200
2	HTTP 402 from TEE was swallowed as a cryptic `RuntimeError` with no guidance	Closes #188
3	Fixed-point values with `decimals=0` returned `np.float32` instead of `int`	Partially closes #103
4	`infer()` crashed with `AttributeError` when the inference node returned `None`	Code audit
5	`run_workflow()` hardcoded `gas=30000000` instead of using `estimate_gas()`	Code audit

All changes are backward-compatible. Happy to adjust anything before review! 🙏

adambalogh · 2026-03-26T02:02:47Z

Hey @amathxbt thanks for your contributions, I added it to my list to review, will get back to you tomorrow!

amathxbt · 2026-03-26T23:28:38Z

Hey @amathxbt thanks for your contributions, I added it to my list to review, will get back to you tomorrow!

Ok @adambalogh thank you see you tomorrow

adambalogh · 2026-03-26T23:32:12Z

 """LLM chat and completion via TEE-verified execution with x402 payments."""

-import json
+import json as _json


what is the reason for this?

The import json as _json alias is intentional — it avoids colliding with any user-level json variable or with the json key that appears heavily throughout the request/response dicts we construct in this file. The leading underscore signals it's a module-internal import and prevents it from being accidentally re-exported if someone does from .llm import *. No behaviour change; purely a namespace-safety convention.

Copilot

Pull request overview

This PR addresses several correctness and UX issues in the OpenGradient Python SDK across TEE selection, LLM error reporting, fixed-point conversions, inference robustness, and workflow gas handling.

Changes:

Randomize LLM TEE endpoint selection from the active registry pool to improve load distribution/failover behavior.
Improve LLM HTTP error handling by surfacing an actionable message for HTTP 402 (Permit2/OPG approval).
Add guards in Alpha.infer() for missing logs and None inference-node results, and replace hardcoded workflow gas/timeout with estimated/constant values.
Introduce a new fixed-point conversion helper intended to preserve integer outputs.

Reviewed changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated 5 comments.

File	Description
`src/opengradient/client/tee_registry.py`	Randomizes `get_llm_tee()` selection and adds debug logging.
`src/opengradient/client/llm.py`	Adds explicit HTTP 402 hint handling for completion/chat/streaming.
`src/opengradient/client/_conversions.py`	Adds `convert_fixed_point_to_python()` and updates output parsing to use it.
`src/opengradient/client/alpha.py`	Adds inference result/log guards, uses timeout constant, and estimates gas for workflows with fallback.

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Copilot · 2026-03-26T23:34:41Z

+ selected = random.choice(tees)
+ logger.debug(
+ "Selected TEE %s (endpoint=%s) from %d active LLM proxy TEE(s)",


This change makes get_llm_tee() non-deterministic, which will break/flakify the existing unit test TestGetLlmTee.test_returns_first_active_tee (it asserts the first endpoint). Update the tests to patch random.choice (or assert membership instead of ordering) so CI stays deterministic.

Would be good to fix this

Fixed ✅ — committed in tests/tee_registry_test.py. The old test_returns_first_active_tee test was asserting ordering which broke with random.choice. Replaced it with two deterministic tests: test_returns_active_tee_from_pool (patches random.choice via unittest.mock.patch to always return seq[0], keeping CI deterministic) and test_returns_any_active_tee (asserts the returned endpoint is a member of the active pool, independent of selection order).

Copilot · 2026-03-26T23:34:41Z

+def convert_fixed_point_to_python(value: int, decimals: int) -> Union[int, np.float32]:
+ """
+ Converts a fixed-point representation back to a native Python/NumPy type.
+
+ Returns int when decimals == 0 (preserving integer semantics for
+ tensors that were originally integers — fixes issue #103 where callers
+ expecting int results received np.float32 and had to cast manually).
+ Returns np.float32 for all other cases.
+
+ Args:
+ value: The integer significand stored on-chain.
+ decimals: The scale factor exponent (value / 10**decimals).
+
+ Returns:
+ int if decimals == 0, np.float32 otherwise.
+ """
+ if decimals == 0:
+ return int(value)


Returning int solely when decimals == 0 can misclassify float tensors that happen to contain whole numbers (e.g., 1.0 normalizes to decimals==0). That can silently change an expected float output into an integer array and/or cause dtype promotion when mixed with non-zero-decimal values. Consider carrying explicit dtype metadata from the node/contract, or only converting to int when you can establish the tensor is integer-typed as a whole.
Suggested change
defconvert_fixed_point_to_python(value: int, decimals: int) ->Union[int, np.float32]:
"""
Convertsafixed-pointrepresentationbacktoanativePython/NumPytype.
Returnsintwhendecimals==0 (preservingintegersemanticsfor
tensorsthatwereoriginallyintegers — fixesissue#103 where callers
expectingintresultsreceivednp.float32andhadtocastmanually).
Returnsnp.float32forallothercases.
Args:
value: Theintegersignificandstoredon-chain.
decimals: Thescalefactorexponent (value/10**decimals).
Returns:
intifdecimals==0, np.float32otherwise.
"""
ifdecimals==0:
returnint(value)
defconvert_fixed_point_to_python(value: int, decimals: int) ->np.float32:
"""
Convertsafixed-pointrepresentationbacktoaNumPyfloat32.
Thisfunctionisintentionallytype-stableandalwaysreturnsnp.float32,
regardlessofthevalueof`decimals`. Callersthatrequireinteger
semanticsshouldperformanexplicitcast (e.g., int(...)) basedon
theirowndtypemetadataorapplicationlogic.
Args:
value: Theintegersignificandstoredon-chain.
decimals: Thescalefactorexponent (value/10**decimals).
Returns:
np.float32correspondingto`value / 10**decimals`.
"""

Is there are reason we are mixing up float and int? might make the handling of results more difficult

Fixed ✅ — committed in src/opengradient/client/_conversions.py. convert_fixed_point_to_python is now type-stable: it always returns np.float32 regardless of decimals, matching Copilot's suggestion. The int-when-decimals==0 branch has been removed. The return type annotation is updated from Union[int, np.float32] → np.float32. Callers that genuinely need integer semantics can do an explicit int(...) cast based on their own dtype metadata.

Copilot · 2026-03-26T23:34:42Z

+ logging.debug(f"	Converting {tensor_data} to np array")
 tensor_data = np.array(tensor_data)

 if isinstance(tensor_data, (str, int, float)):
- logging.debug(f"\tConverting single entry {tensor_data} to a list")
+ logging.debug(f"	Converting single entry {tensor_data} to a list")


This debug string includes a literal tab character inside the f-string. Tabs are hard to see/review and can lead to inconsistent formatting across editors/log sinks; prefer using an escaped \t or spaces for indentation (same issue occurs in the other debug lines added/modified below).

Fixed ✅ committed in src/opengradient/client/_conversions.py. Replaced all 5 literal tab characters inside debug f-strings and log strings with proper escaped \t sequences (lines 87, 91, 110, 119, 125). No more invisible tabs in the source.

adambalogh · 2026-03-26T23:37:30Z

@amathxbt looks good overall, left some comments, could you please also merge from main? i think there are changes in the llm.py that need to be resolved

…2), replace literal tabs with \t

…tion fallback

…ll_with_tee_retry; keep 402 hint, json alias, OPG 0.05

amathxbt · 2026-03-26T23:51:00Z

Done @adambalogh ✅ — merged main into llm.py (commit 8896eac).

The PR branch was missing several methods that were added to main after the branch was cut. I preserved all of them and kept the PR's bug fixes layered on top:

Restored from main:

_connect_tee() — resolves TEE from registry and sets up the HTTP client
_refresh_tee() — re-resolves TEE and replaces the HTTP client (used on retry)
_call_with_tee_retry() — retries once on connection failure by picking a new TEE
Streaming retry logic in _chat_stream() (chunks_yielded guard + _refresh_tee on first-attempt failure)
TypeVar, Callable, Awaitable imports
Instance variables self._rpc_url, self._tee_registry_address, self._llm_server_url

PR fixes kept on top:

import json as _json + all json.loads / json.JSONDecodeError usages updated
_402_HINT constant + 402 handling in complete(), _chat_request(), and _parse_sse_response()
OPG minimum lowered from 0.1 → 0.05

…int coverage, fix mock status_code

…int_to_python now always returns np.float32)

…tions

amathxbt · 2026-03-27T00:15:40Z

Merged upstream main into the branch and resolved all conflicts:

src/opengradient/client/llm.py (SHA af38386): rebased on the latest main (which added TEE retry/refresh logic, _call_with_tee_retry, _connect_tee, _refresh_tee, new SSL/cert-rotation handling) while retaining all PR-specific changes — import json as _json, _402_HINT constant, HTTP 402 error handling in complete(), chat(), and _parse_sse_response(), and the 0.05 OPG minimum.
tests/llm_test.py: already contained the new TestTeeRetry*, TestRefreshTeeAndReset, and TestTeeCertRotation classes from main. The PR's 402-hint tests (test_http_402_raises_hint × 2, test_stream_402_raises_hint) and the FakeHTTPClient status-code fix were already in place.

The PR is now conflict-free (mergeable: true).

amathxbt · 2026-04-04T22:53:06Z

@adambalogh please can you done this it's been long time no more comments

amathxbt · 2026-03-23T23:49:53Z

Summary

This PR fixes 5 critical bugs identified from open issues and code audit, spanning TEE selection, LLM error handling, type conversions, inference safety, and gas estimation.

Bug 1 — TEE always picks the same node (closes#200)

File:src/opengradient/client/tee_registry.py

Root cause:get_llm_tee() always returned tees[0], routing 100% of traffic to a single TEE with zero load distribution or failover.

Fix: Replace tees[0] with random.choice(tees) so each LLM() construction independently selects from the full pool of active, registry-verified TEEs.

# Beforereturntees[0]
# Afterselected=random.choice(tees)
logger.debug("Selected TEE %s from %d active LLM proxy TEE(s)", selected.tee_id, len(tees))
returnselected

Bug 2 — HTTP 402 swallowed as cryptic RuntimeError (closes#188)

File:src/opengradient/client/llm.py

Root cause: All HTTP errors (including 402 Payment Required) were caught by except Exception and re-raised as a generic RuntimeError("TEE LLM chat failed: ..."). Users had no idea they needed to call ensure_opg_approval() first.

Fix: Add import httpx and intercept httpx.HTTPStatusErrorbefore the generic handler. When status_code == 402, raise a RuntimeError with an explicit, actionable message pointing to ensure_opg_approval(). Applied to _chat_request(), completion(), and _parse_sse_response() (streaming path).

# New constant_402_HINT= (
"Payment required (HTTP 402): your wallet may have insufficient OPG token allowance. ""Call llm.ensure_opg_approval(opg_amount=<amount>) to approve Permit2 spending ""before making requests. Minimum amount is 0.05 OPG."
)
# In _chat_request / completion / _parse_sse_response:excepthttpx.HTTPStatusErrorase:
ife.response.status_code==402:
raiseRuntimeError(_402_HINT) fromeraiseRuntimeError(f"TEE LLM chat failed: {e}") frome

Bug 3 — Fixed-point int returns np.float32 instead of int (partially closes#103)

File:src/opengradient/client/_conversions.py

Root cause:convert_to_float32(value, decimals) always returned np.float32, even when decimals == 0 (i.e., the original value was an integer). Users expecting integer outputs received np.float32 and had to cast manually.

Fix: Add convert_fixed_point_to_python(value, decimals) that returns int when decimals == 0 and np.float32 otherwise. np.array() then infers the correct dtype (int64 vs float32) automatically. The old convert_to_float32 is kept as a deprecated alias for backward compatibility.

defconvert_fixed_point_to_python(value: int, decimals: int) ->Union[int, np.float32]:
ifdecimals==0:
returnint(value) # ← integer tensor stays integerreturnnp.float32(Decimal(value) / (10**Decimal(decimals)))

Bug 4 — `infer()` crashes with AttributeError when inference node returns None

File:src/opengradient/client/alpha.py

Root cause:_get_inference_result_from_node() returns None when the node has no result yet. This None was passed directly to convert_to_model_output(), which calls event_data.get("output", {}) → AttributeError: NoneType object has no attribute get. Additionally, if ModelInferenceEvent logs were empty, parsed_logs[0] caused an IndexError.

Fix:

# Guard 1: empty precompile logsifnotprecompile_logs:
raiseRuntimeError("ModelInferenceEvent not found in transaction logs.")
# Guard 2: None from inference nodeifinference_resultisNone:
raiseRuntimeError(
f"Inference node returned no result for inference ID {inference_id!r}. ""The result may not be available yet — retry after a short delay."
)

Bug 5 — `run_workflow()` uses hardcoded 30M gas

File:src/opengradient/client/alpha.py

Root cause:run_workflow() hardcoded gas=30000000, unlike infer() which correctly uses estimate_gas(). This is both wasteful (users overpay) and fragile on networks with lower block gas limits.

Fix: Call estimate_gas() first and multiply by 3 for headroom. Fall back to 30M only if estimation itself fails.

try:
estimated_gas=run_function.estimate_gas({"from": self._wallet_account.address})
gas_limit=int(estimated_gas*3)
exceptException:
gas_limit=30000000# fallback

Also replaces the hardcoded timeout=60 in new_workflow() with the INFERENCE_TX_TIMEOUT constant.

Files Changed

File	Bugs Fixed
`src/opengradient/client/tee_registry.py`	Bug 1 (TEE randomization)
`src/opengradient/client/llm.py`	Bug 2 (HTTP 402 hint)
`src/opengradient/client/_conversions.py`	Bug 3 (int dtype)
`src/opengradient/client/alpha.py`	Bugs 4 & 5 (None guard + gas)

Related Issues

Closes#200
Closes#188
Partially closes#103

Previously get_llm_tee() always returned tees[0], the first TEE in the registry list. This caused all clients to hit the same TEE, providing no load distribution and no resilience when that TEE starts failing. Fix: use random.choice(tees) so each LLM() construction independently picks from all currently active TEEs. Successive retries or re-initializations will therefore naturally spread across the healthy pool. ClosesOpenGradient#200

Previously any HTTP error from the TEE (including 402 Payment Required) was silently wrapped into a generic RuntimeError("TEE LLM chat failed: ..."). This caused the confusing traceback seen in issue OpenGradient#188 — the real cause (insufficient OPG allowance) was buried inside the exception message. Fix: - Add `import httpx` and intercept httpx.HTTPStatusError before the generic `except Exception` handler in both _chat_request() and completion(). - When status == 402, raise a RuntimeError with a clear, actionable hint telling the user to call llm.ensure_opg_approval(opg_amount=<amount>). - Also handle 402 in _parse_sse_response() for the streaming path. - All other HTTP errors continue to surface as before. ClosesOpenGradient#188

Previously convert_to_float32() always returned np.float32 regardless of the decimals field, forcing callers to manually cast integer results (issue OpenGradient#103 — add proper type conversions from Solidity contract to Python). Fix: - Add convert_fixed_point_to_python(value, decimals) that returns int when decimals == 0 and np.float32 otherwise. np.array() automatically picks the correct dtype (int64 vs float32) based on the element types. - Update both convert_to_model_output() and convert_array_to_model_output() to call the new function. - Keep convert_to_float32() as a deprecated backward-compatible alias so any external code that imports it directly continues to work. Partially closesOpenGradient#103

… in run_workflow Bug 4 — None crash in infer(): _get_inference_result_from_node() can legitimately return None when the inference node has no result yet. Previously this None was passed directly to convert_to_model_output(), which calls event_data.get(), causing an AttributeError: 'NoneType' object has no attribute 'get'. Fix: after calling _get_inference_result_from_node(), check for None and raise a clear RuntimeError with a human-readable message and the inference_id so callers know what to retry. Also guard the precompile log fetch: if parsed_logs is empty, raise RuntimeError instead of crashing with an IndexError on parsed_logs[0]. Bug 5 — hardcoded 30M gas in run_workflow(): run_workflow() built the transaction with gas=30000000 (30 million) unconditionally. This is wasteful (users overpay for gas) and can fail on networks with a lower block gas limit. Fix: call estimate_gas() first (consistent with infer()), then multiply by 3 for headroom. Fall back to 30M only if estimation itself fails. Also fixes new_workflow() deploy to use INFERENCE_TX_TIMEOUT constant instead of the previous hardcoded 60 seconds.

amathxbt · 2026-03-23T23:50:06Z

Hey @adambalogh 👋 — this PR addresses 5 critical bugs found during a code audit, 3 of which directly close or partially close open issues you and the team have filed:

#	Bug	Issue
1	`get_llm_tee()` always returned `tees[0]` — no randomization or load distribution	Closes #200
2	HTTP 402 from TEE was swallowed as a cryptic `RuntimeError` with no guidance	Closes #188
3	Fixed-point values with `decimals=0` returned `np.float32` instead of `int`	Partially closes #103
4	`infer()` crashed with `AttributeError` when the inference node returned `None`	Code audit
5	`run_workflow()` hardcoded `gas=30000000` instead of using `estimate_gas()`	Code audit

All changes are backward-compatible. Happy to adjust anything before review! 🙏

adambalogh · 2026-03-26T02:02:47Z

Hey @amathxbt thanks for your contributions, I added it to my list to review, will get back to you tomorrow!

amathxbt · 2026-03-26T23:28:38Z

Hey @amathxbt thanks for your contributions, I added it to my list to review, will get back to you tomorrow!

Ok @adambalogh thank you see you tomorrow

adambalogh · 2026-03-26T23:32:12Z

 """LLM chat and completion via TEE-verified execution with x402 payments."""

-import json
+import json as _json


what is the reason for this?

The import json as _json alias is intentional — it avoids colliding with any user-level json variable or with the json key that appears heavily throughout the request/response dicts we construct in this file. The leading underscore signals it's a module-internal import and prevents it from being accidentally re-exported if someone does from .llm import *. No behaviour change; purely a namespace-safety convention.

Copilot

Pull request overview

This PR addresses several correctness and UX issues in the OpenGradient Python SDK across TEE selection, LLM error reporting, fixed-point conversions, inference robustness, and workflow gas handling.

Changes:

Randomize LLM TEE endpoint selection from the active registry pool to improve load distribution/failover behavior.
Improve LLM HTTP error handling by surfacing an actionable message for HTTP 402 (Permit2/OPG approval).
Add guards in Alpha.infer() for missing logs and None inference-node results, and replace hardcoded workflow gas/timeout with estimated/constant values.
Introduce a new fixed-point conversion helper intended to preserve integer outputs.

Reviewed changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated 5 comments.

File	Description
`src/opengradient/client/tee_registry.py`	Randomizes `get_llm_tee()` selection and adds debug logging.
`src/opengradient/client/llm.py`	Adds explicit HTTP 402 hint handling for completion/chat/streaming.
`src/opengradient/client/_conversions.py`	Adds `convert_fixed_point_to_python()` and updates output parsing to use it.
`src/opengradient/client/alpha.py`	Adds inference result/log guards, uses timeout constant, and estimates gas for workflows with fallback.

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Copilot · 2026-03-26T23:34:41Z

+ selected = random.choice(tees)
+ logger.debug(
+ "Selected TEE %s (endpoint=%s) from %d active LLM proxy TEE(s)",


This change makes get_llm_tee() non-deterministic, which will break/flakify the existing unit test TestGetLlmTee.test_returns_first_active_tee (it asserts the first endpoint). Update the tests to patch random.choice (or assert membership instead of ordering) so CI stays deterministic.

Would be good to fix this

Fixed ✅ — committed in tests/tee_registry_test.py. The old test_returns_first_active_tee test was asserting ordering which broke with random.choice. Replaced it with two deterministic tests: test_returns_active_tee_from_pool (patches random.choice via unittest.mock.patch to always return seq[0], keeping CI deterministic) and test_returns_any_active_tee (asserts the returned endpoint is a member of the active pool, independent of selection order).

Copilot · 2026-03-26T23:34:41Z

+def convert_fixed_point_to_python(value: int, decimals: int) -> Union[int, np.float32]:
+ """
+ Converts a fixed-point representation back to a native Python/NumPy type.
+
+ Returns int when decimals == 0 (preserving integer semantics for
+ tensors that were originally integers — fixes issue #103 where callers
+ expecting int results received np.float32 and had to cast manually).
+ Returns np.float32 for all other cases.
+
+ Args:
+ value: The integer significand stored on-chain.
+ decimals: The scale factor exponent (value / 10**decimals).
+
+ Returns:
+ int if decimals == 0, np.float32 otherwise.
+ """
+ if decimals == 0:
+ return int(value)


Returning int solely when decimals == 0 can misclassify float tensors that happen to contain whole numbers (e.g., 1.0 normalizes to decimals==0). That can silently change an expected float output into an integer array and/or cause dtype promotion when mixed with non-zero-decimal values. Consider carrying explicit dtype metadata from the node/contract, or only converting to int when you can establish the tensor is integer-typed as a whole.
Suggested change
defconvert_fixed_point_to_python(value: int, decimals: int) ->Union[int, np.float32]:
"""
Convertsafixed-pointrepresentationbacktoanativePython/NumPytype.
Returnsintwhendecimals==0 (preservingintegersemanticsfor
tensorsthatwereoriginallyintegers — fixesissue#103 where callers
expectingintresultsreceivednp.float32andhadtocastmanually).
Returnsnp.float32forallothercases.
Args:
value: Theintegersignificandstoredon-chain.
decimals: Thescalefactorexponent (value/10**decimals).
Returns:
intifdecimals==0, np.float32otherwise.
"""
ifdecimals==0:
returnint(value)
defconvert_fixed_point_to_python(value: int, decimals: int) ->np.float32:
"""
Convertsafixed-pointrepresentationbacktoaNumPyfloat32.
Thisfunctionisintentionallytype-stableandalwaysreturnsnp.float32,
regardlessofthevalueof`decimals`. Callersthatrequireinteger
semanticsshouldperformanexplicitcast (e.g., int(...)) basedon
theirowndtypemetadataorapplicationlogic.
Args:
value: Theintegersignificandstoredon-chain.
decimals: Thescalefactorexponent (value/10**decimals).
Returns:
np.float32correspondingto`value / 10**decimals`.
"""

Is there are reason we are mixing up float and int? might make the handling of results more difficult

Fixed ✅ — committed in src/opengradient/client/_conversions.py. convert_fixed_point_to_python is now type-stable: it always returns np.float32 regardless of decimals, matching Copilot's suggestion. The int-when-decimals==0 branch has been removed. The return type annotation is updated from Union[int, np.float32] → np.float32. Callers that genuinely need integer semantics can do an explicit int(...) cast based on their own dtype metadata.

Copilot · 2026-03-26T23:34:42Z

+ logging.debug(f"	Converting {tensor_data} to np array")
 tensor_data = np.array(tensor_data)

 if isinstance(tensor_data, (str, int, float)):
- logging.debug(f"\tConverting single entry {tensor_data} to a list")
+ logging.debug(f"	Converting single entry {tensor_data} to a list")


This debug string includes a literal tab character inside the f-string. Tabs are hard to see/review and can lead to inconsistent formatting across editors/log sinks; prefer using an escaped \t or spaces for indentation (same issue occurs in the other debug lines added/modified below).

Fixed ✅ committed in src/opengradient/client/_conversions.py. Replaced all 5 literal tab characters inside debug f-strings and log strings with proper escaped \t sequences (lines 87, 91, 110, 119, 125). No more invisible tabs in the source.

adambalogh · 2026-03-26T23:37:30Z

@amathxbt looks good overall, left some comments, could you please also merge from main? i think there are changes in the llm.py that need to be resolved

…2), replace literal tabs with \t

…tion fallback

…ll_with_tee_retry; keep 402 hint, json alias, OPG 0.05

amathxbt · 2026-03-26T23:51:00Z

Done @adambalogh ✅ — merged main into llm.py (commit 8896eac).

The PR branch was missing several methods that were added to main after the branch was cut. I preserved all of them and kept the PR's bug fixes layered on top:

Restored from main:

_connect_tee() — resolves TEE from registry and sets up the HTTP client
_refresh_tee() — re-resolves TEE and replaces the HTTP client (used on retry)
_call_with_tee_retry() — retries once on connection failure by picking a new TEE
Streaming retry logic in _chat_stream() (chunks_yielded guard + _refresh_tee on first-attempt failure)
TypeVar, Callable, Awaitable imports
Instance variables self._rpc_url, self._tee_registry_address, self._llm_server_url

PR fixes kept on top:

import json as _json + all json.loads / json.JSONDecodeError usages updated
_402_HINT constant + 402 handling in complete(), _chat_request(), and _parse_sse_response()
OPG minimum lowered from 0.1 → 0.05

…int coverage, fix mock status_code

…int_to_python now always returns np.float32)

…tions

amathxbt · 2026-03-27T00:15:40Z

Merged upstream main into the branch and resolved all conflicts:

src/opengradient/client/llm.py (SHA af38386): rebased on the latest main (which added TEE retry/refresh logic, _call_with_tee_retry, _connect_tee, _refresh_tee, new SSL/cert-rotation handling) while retaining all PR-specific changes — import json as _json, _402_HINT constant, HTTP 402 error handling in complete(), chat(), and _parse_sse_response(), and the 0.05 OPG minimum.
tests/llm_test.py: already contained the new TestTeeRetry*, TestRefreshTeeAndReset, and TestTeeCertRotation classes from main. The PR's 402-hint tests (test_http_402_raises_hint × 2, test_stream_402_raises_hint) and the FakeHTTPClient status-code fix were already in place.

The PR is now conflict-free (mergeable: true).

amathxbt · 2026-04-04T22:53:06Z

@adambalogh please can you done this it's been long time no more comments

amathxbt added 4 commits March 24, 2026 00:44

adambalogh requested a review from Copilot March 26, 2026 23:29

Copilotstarted reviewing on behalf of adambalogh March 26, 2026 23:29 View session

adambalogh reviewed Mar 26, 2026
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CopilotAI reviewed Mar 26, 2026
View reviewed changes

amathxbt added 4 commits March 27, 2026 00:41

fix: make convert_fixed_point_to_python type-stable (always np.float3…
7c786b1
…2), replace literal tabs with \t

fix: separate ContractLogicError from generic Exception in gas estima…
deff27c
…tion fallback

test: fix TestGetLlmTee to patch random.choice for deterministic CI
5dcecd1

fix: merge main into llm.py — restore _connect_tee, _refresh_tee, _ca…
6cfbb68
…ll_with_tee_retry; keep 402 hint, json alias, OPG 0.05

amathxbt added 13 commits March 27, 2026 01:01

test: add HTTP 402 hint coverage for completion, chat, and streaming
7b2157b

fix: merge main into llm_test.py — restore retry/SSL tests, add 402 h…
23f06db
…int coverage, fix mock status_code

fix: remove stale Union import from _conversions.py (convert_fixed_po…
488c7ce
…int_to_python now always returns np.float32)

chore: sync .github/workflows/release.yml from upstream main
f5f5c70

chore: sync README.md from upstream main
7708dec

chore: sync WINDOWS_INSTALL.md from upstream main
dece88b

chore: sync docs/opengradient/client/llm.md from upstream main
7fbc758

chore: sync docs/opengradient/index.md from upstream main
fc972b8

chore: sync integrationtest/llm/test_llm_chat.py from upstream main
e0927b1

chore: sync pyproject.toml from upstream main
de224f5

chore: sync src/opengradient/client/_utils.py from upstream main
1769a0e

chore: sync src/opengradient/client/opg_token.py from upstream main
1f7aa52

chore: sync tests/opg_token_test.py from upstream main
7373919

amathxbt added 6 commits March 27, 2026 01:08

chore: sync stresstest/llm.py from upstream main
3450911

chore: sync stresstest/utils.py from upstream main
74fa273

chore: sync uv.lock from upstream main
6786190

chore: remove stresstest/infer.py (deleted in upstream main)
67542a9

Merge main into fix branch: rebased llm.py with all PR changes
bd147fb

Merge main into fix branch: rebased llm_test.py with all PR test addi…
258e08c
…tions

This was referenced Mar 30, 2026
fix(alpha): add missing chainId to inference transaction in _send_tx_with_revert_handling #217
Open
fix: remove arbitrary 0.1 OPG floor from LLM.ensure_opg_approval() — validate positive only #222
Open
docs: add community Contributors section to README #230
Closed

amathxbt requested a review from adambalogh April 4, 2026 22:53

amathxbt added 4 commits March 24, 2026 00:44

adambalogh requested a review from Copilot March 26, 2026 23:29

Copilotstarted reviewing on behalf of adambalogh March 26, 2026 23:29 View session

adambalogh reviewed Mar 26, 2026
View reviewed changes

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amathxbt added 4 commits March 27, 2026 00:41

fix: make convert_fixed_point_to_python type-stable (always np.float3…
7c786b1
…2), replace literal tabs with \t

fix: separate ContractLogicError from generic Exception in gas estima…
deff27c
…tion fallback

test: fix TestGetLlmTee to patch random.choice for deterministic CI
5dcecd1

fix: merge main into llm.py — restore _connect_tee, _refresh_tee, _ca…
6cfbb68
…ll_with_tee_retry; keep 402 hint, json alias, OPG 0.05

amathxbt added 13 commits March 27, 2026 01:01

test: add HTTP 402 hint coverage for completion, chat, and streaming
7b2157b

fix: merge main into llm_test.py — restore retry/SSL tests, add 402 h…
23f06db
…int coverage, fix mock status_code

fix: remove stale Union import from _conversions.py (convert_fixed_po…
488c7ce
…int_to_python now always returns np.float32)

chore: sync .github/workflows/release.yml from upstream main
f5f5c70

chore: sync README.md from upstream main
7708dec

chore: sync WINDOWS_INSTALL.md from upstream main
dece88b

chore: sync docs/opengradient/client/llm.md from upstream main
7fbc758

chore: sync docs/opengradient/index.md from upstream main
fc972b8

chore: sync integrationtest/llm/test_llm_chat.py from upstream main
e0927b1

chore: sync pyproject.toml from upstream main
de224f5

chore: sync src/opengradient/client/_utils.py from upstream main
1769a0e

chore: sync src/opengradient/client/opg_token.py from upstream main
1f7aa52

chore: sync tests/opg_token_test.py from upstream main
7373919

amathxbt added 6 commits March 27, 2026 01:08

chore: sync stresstest/llm.py from upstream main
3450911

chore: sync stresstest/utils.py from upstream main
74fa273

chore: sync uv.lock from upstream main
6786190

chore: remove stresstest/infer.py (deleted in upstream main)
67542a9

Merge main into fix branch: rebased llm.py with all PR changes
bd147fb

Merge main into fix branch: rebased llm_test.py with all PR test addi…
258e08c
…tions

This was referenced Mar 30, 2026
fix(alpha): add missing chainId to inference transaction in _send_tx_with_revert_handling #217
Open
fix: remove arbitrary 0.1 OPG floor from LLM.ensure_opg_approval() — validate positive only #222
Open
docs: add community Contributors section to README #230
Closed

amathxbt requested a review from adambalogh April 4, 2026 22:53

amathxbt added 4 commits March 24, 2026 00:44

adambalogh requested a review from Copilot March 26, 2026 23:29

Copilotstarted reviewing on behalf of adambalogh March 26, 2026 23:29 View session

adambalogh reviewed Mar 26, 2026
View reviewed changes

CopilotAI reviewed Mar 26, 2026
View reviewed changes

amathxbt added 4 commits March 27, 2026 00:41

fix: make convert_fixed_point_to_python type-stable (always np.float3…
7c786b1
…2), replace literal tabs with \t

fix: separate ContractLogicError from generic Exception in gas estima…
deff27c
…tion fallback

test: fix TestGetLlmTee to patch random.choice for deterministic CI
5dcecd1

fix: merge main into llm.py — restore _connect_tee, _refresh_tee, _ca…
6cfbb68
…ll_with_tee_retry; keep 402 hint, json alias, OPG 0.05

amathxbt added 13 commits March 27, 2026 01:01

test: add HTTP 402 hint coverage for completion, chat, and streaming
7b2157b

fix: merge main into llm_test.py — restore retry/SSL tests, add 402 h…
23f06db
…int coverage, fix mock status_code

fix: remove stale Union import from _conversions.py (convert_fixed_po…
488c7ce
…int_to_python now always returns np.float32)

chore: sync .github/workflows/release.yml from upstream main
f5f5c70

chore: sync README.md from upstream main
7708dec

chore: sync WINDOWS_INSTALL.md from upstream main
dece88b

chore: sync docs/opengradient/client/llm.md from upstream main
7fbc758

chore: sync docs/opengradient/index.md from upstream main
fc972b8

chore: sync integrationtest/llm/test_llm_chat.py from upstream main
e0927b1

chore: sync pyproject.toml from upstream main
de224f5

chore: sync src/opengradient/client/_utils.py from upstream main
1769a0e

chore: sync src/opengradient/client/opg_token.py from upstream main
1f7aa52

chore: sync tests/opg_token_test.py from upstream main
7373919

amathxbt added 6 commits March 27, 2026 01:08

chore: sync stresstest/llm.py from upstream main
3450911

chore: sync stresstest/utils.py from upstream main
74fa273

chore: sync uv.lock from upstream main
6786190

chore: remove stresstest/infer.py (deleted in upstream main)
67542a9

Merge main into fix branch: rebased llm.py with all PR changes
bd147fb

Merge main into fix branch: rebased llm_test.py with all PR test addi…
258e08c
…tions

This was referenced Mar 30, 2026
fix(alpha): add missing chainId to inference transaction in _send_tx_with_revert_handling #217
Open
fix: remove arbitrary 0.1 OPG floor from LLM.ensure_opg_approval() — validate positive only #222
Open
docs: add community Contributors section to README #230
Closed

amathxbt requested a review from adambalogh April 4, 2026 22:53

amathxbt added 4 commits March 24, 2026 00:44

adambalogh requested a review from Copilot March 26, 2026 23:29

Copilotstarted reviewing on behalf of adambalogh March 26, 2026 23:29 View session

adambalogh reviewed Mar 26, 2026
View reviewed changes

CopilotAI reviewed Mar 26, 2026
View reviewed changes

amathxbt added 4 commits March 27, 2026 00:41

fix: make convert_fixed_point_to_python type-stable (always np.float3…
7c786b1
…2), replace literal tabs with \t

fix: separate ContractLogicError from generic Exception in gas estima…
deff27c
…tion fallback

test: fix TestGetLlmTee to patch random.choice for deterministic CI
5dcecd1

fix: merge main into llm.py — restore _connect_tee, _refresh_tee, _ca…
6cfbb68
…ll_with_tee_retry; keep 402 hint, json alias, OPG 0.05

amathxbt added 13 commits March 27, 2026 01:01

test: add HTTP 402 hint coverage for completion, chat, and streaming
7b2157b

fix: merge main into llm_test.py — restore retry/SSL tests, add 402 h…
23f06db
…int coverage, fix mock status_code

fix: remove stale Union import from _conversions.py (convert_fixed_po…
488c7ce
…int_to_python now always returns np.float32)

chore: sync .github/workflows/release.yml from upstream main
f5f5c70

chore: sync README.md from upstream main
7708dec

chore: sync WINDOWS_INSTALL.md from upstream main
dece88b

chore: sync docs/opengradient/client/llm.md from upstream main
7fbc758

chore: sync docs/opengradient/index.md from upstream main
fc972b8

chore: sync integrationtest/llm/test_llm_chat.py from upstream main
e0927b1

chore: sync pyproject.toml from upstream main
de224f5

chore: sync src/opengradient/client/_utils.py from upstream main
1769a0e

chore: sync src/opengradient/client/opg_token.py from upstream main
1f7aa52

chore: sync tests/opg_token_test.py from upstream main
7373919

amathxbt added 6 commits March 27, 2026 01:08

chore: sync stresstest/llm.py from upstream main
3450911

chore: sync stresstest/utils.py from upstream main
74fa273

chore: sync uv.lock from upstream main
6786190

chore: remove stresstest/infer.py (deleted in upstream main)
67542a9

Merge main into fix branch: rebased llm.py with all PR changes
bd147fb

Merge main into fix branch: rebased llm_test.py with all PR test addi…
258e08c
…tions

This was referenced Mar 30, 2026
fix(alpha): add missing chainId to inference transaction in _send_tx_with_revert_handling #217
Open
fix: remove arbitrary 0.1 OPG floor from LLM.ensure_opg_approval() — validate positive only #222
Open
docs: add community Contributors section to README #230
Closed

amathxbt requested a review from adambalogh April 4, 2026 22:53

amathxbt added 4 commits March 24, 2026 00:44

adambalogh requested a review from Copilot March 26, 2026 23:29

Copilotstarted reviewing on behalf of adambalogh March 26, 2026 23:29 View session

adambalogh reviewed Mar 26, 2026
View reviewed changes

CopilotAI reviewed Mar 26, 2026
View reviewed changes

amathxbt added 4 commits March 27, 2026 00:41

fix: make convert_fixed_point_to_python type-stable (always np.float3…
7c786b1
…2), replace literal tabs with \t

fix: separate ContractLogicError from generic Exception in gas estima…
deff27c
…tion fallback

test: fix TestGetLlmTee to patch random.choice for deterministic CI
5dcecd1

fix: merge main into llm.py — restore _connect_tee, _refresh_tee, _ca…
6cfbb68
…ll_with_tee_retry; keep 402 hint, json alias, OPG 0.05

amathxbt added 13 commits March 27, 2026 01:01

test: add HTTP 402 hint coverage for completion, chat, and streaming
7b2157b

fix: merge main into llm_test.py — restore retry/SSL tests, add 402 h…
23f06db
…int coverage, fix mock status_code

fix: remove stale Union import from _conversions.py (convert_fixed_po…
488c7ce
…int_to_python now always returns np.float32)

chore: sync .github/workflows/release.yml from upstream main
f5f5c70

chore: sync README.md from upstream main
7708dec

chore: sync WINDOWS_INSTALL.md from upstream main
dece88b

chore: sync docs/opengradient/client/llm.md from upstream main
7fbc758

chore: sync docs/opengradient/index.md from upstream main
fc972b8

chore: sync integrationtest/llm/test_llm_chat.py from upstream main
e0927b1

chore: sync pyproject.toml from upstream main
de224f5

chore: sync src/opengradient/client/_utils.py from upstream main
1769a0e

chore: sync src/opengradient/client/opg_token.py from upstream main
1f7aa52

chore: sync tests/opg_token_test.py from upstream main
7373919

amathxbt added 6 commits March 27, 2026 01:08

chore: sync stresstest/llm.py from upstream main
3450911

chore: sync stresstest/utils.py from upstream main
74fa273

chore: sync uv.lock from upstream main
6786190

chore: remove stresstest/infer.py (deleted in upstream main)
67542a9

Merge main into fix branch: rebased llm.py with all PR changes
bd147fb

Merge main into fix branch: rebased llm_test.py with all PR test addi…
258e08c
…tions

This was referenced Mar 30, 2026
fix(alpha): add missing chainId to inference transaction in _send_tx_with_revert_handling #217
Open
fix: remove arbitrary 0.1 OPG floor from LLM.ensure_opg_approval() — validate positive only #222
Open
docs: add community Contributors section to README #230
Closed

amathxbt requested a review from adambalogh April 4, 2026 22:53

amathxbt added 4 commits March 24, 2026 00:44

adambalogh requested a review from Copilot March 26, 2026 23:29

Copilotstarted reviewing on behalf of adambalogh March 26, 2026 23:29 View session

adambalogh reviewed Mar 26, 2026
View reviewed changes

CopilotAI reviewed Mar 26, 2026
View reviewed changes

amathxbt added 4 commits March 27, 2026 00:41

fix: make convert_fixed_point_to_python type-stable (always np.float3…
7c786b1
…2), replace literal tabs with \t

fix: separate ContractLogicError from generic Exception in gas estima…
deff27c
…tion fallback

test: fix TestGetLlmTee to patch random.choice for deterministic CI
5dcecd1

fix: merge main into llm.py — restore _connect_tee, _refresh_tee, _ca…
6cfbb68
…ll_with_tee_retry; keep 402 hint, json alias, OPG 0.05

amathxbt added 13 commits March 27, 2026 01:01

test: add HTTP 402 hint coverage for completion, chat, and streaming
7b2157b

fix: merge main into llm_test.py — restore retry/SSL tests, add 402 h…
23f06db
…int coverage, fix mock status_code

fix: remove stale Union import from _conversions.py (convert_fixed_po…
488c7ce
…int_to_python now always returns np.float32)

chore: sync .github/workflows/release.yml from upstream main
f5f5c70

chore: sync README.md from upstream main
7708dec

chore: sync WINDOWS_INSTALL.md from upstream main
dece88b

chore: sync docs/opengradient/client/llm.md from upstream main
7fbc758

chore: sync docs/opengradient/index.md from upstream main
fc972b8

chore: sync integrationtest/llm/test_llm_chat.py from upstream main
e0927b1

chore: sync pyproject.toml from upstream main
de224f5

chore: sync src/opengradient/client/_utils.py from upstream main
1769a0e

chore: sync src/opengradient/client/opg_token.py from upstream main
1f7aa52

chore: sync tests/opg_token_test.py from upstream main
7373919

amathxbt added 6 commits March 27, 2026 01:08

chore: sync stresstest/llm.py from upstream main
3450911

chore: sync stresstest/utils.py from upstream main
74fa273

chore: sync uv.lock from upstream main
6786190

chore: remove stresstest/infer.py (deleted in upstream main)
67542a9

Merge main into fix branch: rebased llm.py with all PR changes
bd147fb

Merge main into fix branch: rebased llm_test.py with all PR test addi…
258e08c
…tions

This was referenced Mar 30, 2026
fix(alpha): add missing chainId to inference transaction in _send_tx_with_revert_handling #217
Open
fix: remove arbitrary 0.1 OPG floor from LLM.ensure_opg_approval() — validate positive only #222
Open
docs: add community Contributors section to README #230
Closed

amathxbt requested a review from adambalogh April 4, 2026 22:53

amathxbt added 4 commits March 24, 2026 00:44

adambalogh requested a review from Copilot March 26, 2026 23:29

Copilotstarted reviewing on behalf of adambalogh March 26, 2026 23:29 View session

adambalogh reviewed Mar 26, 2026
View reviewed changes

CopilotAI reviewed Mar 26, 2026
View reviewed changes

amathxbt added 4 commits March 27, 2026 00:41

fix: make convert_fixed_point_to_python type-stable (always np.float3…
7c786b1
…2), replace literal tabs with \t

fix: separate ContractLogicError from generic Exception in gas estima…
deff27c
…tion fallback

test: fix TestGetLlmTee to patch random.choice for deterministic CI
5dcecd1

fix: merge main into llm.py — restore _connect_tee, _refresh_tee, _ca…
6cfbb68
…ll_with_tee_retry; keep 402 hint, json alias, OPG 0.05

amathxbt added 13 commits March 27, 2026 01:01

test: add HTTP 402 hint coverage for completion, chat, and streaming
7b2157b

fix: merge main into llm_test.py — restore retry/SSL tests, add 402 h…
23f06db
…int coverage, fix mock status_code

fix: remove stale Union import from _conversions.py (convert_fixed_po…
488c7ce
…int_to_python now always returns np.float32)

chore: sync .github/workflows/release.yml from upstream main
f5f5c70

chore: sync README.md from upstream main
7708dec

chore: sync WINDOWS_INSTALL.md from upstream main
dece88b

chore: sync docs/opengradient/client/llm.md from upstream main
7fbc758

chore: sync docs/opengradient/index.md from upstream main
fc972b8

chore: sync integrationtest/llm/test_llm_chat.py from upstream main
e0927b1

chore: sync pyproject.toml from upstream main
de224f5

chore: sync src/opengradient/client/_utils.py from upstream main
1769a0e

chore: sync src/opengradient/client/opg_token.py from upstream main
1f7aa52

chore: sync tests/opg_token_test.py from upstream main
7373919

amathxbt added 6 commits March 27, 2026 01:08

chore: sync stresstest/llm.py from upstream main
3450911

chore: sync stresstest/utils.py from upstream main
74fa273

chore: sync uv.lock from upstream main
6786190

chore: remove stresstest/infer.py (deleted in upstream main)
67542a9

Merge main into fix branch: rebased llm.py with all PR changes
bd147fb

Merge main into fix branch: rebased llm_test.py with all PR test addi…
258e08c
…tions

This was referenced Mar 30, 2026
fix(alpha): add missing chainId to inference transaction in _send_tx_with_revert_handling #217
Open
fix: remove arbitrary 0.1 OPG floor from LLM.ensure_opg_approval() — validate positive only #222
Open
docs: add community Contributors section to README #230
Closed

amathxbt requested a review from adambalogh April 4, 2026 22:53

amathxbt added 4 commits March 24, 2026 00:44

adambalogh requested a review from Copilot March 26, 2026 23:29

Copilotstarted reviewing on behalf of adambalogh March 26, 2026 23:29 View session

adambalogh reviewed Mar 26, 2026
View reviewed changes

CopilotAI reviewed Mar 26, 2026
View reviewed changes

amathxbt added 4 commits March 27, 2026 00:41

fix: make convert_fixed_point_to_python type-stable (always np.float3…
7c786b1
…2), replace literal tabs with \t

fix: separate ContractLogicError from generic Exception in gas estima…
deff27c
…tion fallback

test: fix TestGetLlmTee to patch random.choice for deterministic CI
5dcecd1

fix: merge main into llm.py — restore _connect_tee, _refresh_tee, _ca…
6cfbb68
…ll_with_tee_retry; keep 402 hint, json alias, OPG 0.05

amathxbt added 13 commits March 27, 2026 01:01

test: add HTTP 402 hint coverage for completion, chat, and streaming
7b2157b

fix: merge main into llm_test.py — restore retry/SSL tests, add 402 h…
23f06db
…int coverage, fix mock status_code

fix: remove stale Union import from _conversions.py (convert_fixed_po…
488c7ce
…int_to_python now always returns np.float32)

chore: sync .github/workflows/release.yml from upstream main
f5f5c70

chore: sync README.md from upstream main
7708dec

chore: sync WINDOWS_INSTALL.md from upstream main
dece88b

chore: sync docs/opengradient/client/llm.md from upstream main
7fbc758

chore: sync docs/opengradient/index.md from upstream main
fc972b8

chore: sync integrationtest/llm/test_llm_chat.py from upstream main
e0927b1

chore: sync pyproject.toml from upstream main
de224f5

chore: sync src/opengradient/client/_utils.py from upstream main
1769a0e

chore: sync src/opengradient/client/opg_token.py from upstream main
1f7aa52

chore: sync tests/opg_token_test.py from upstream main
7373919

amathxbt added 6 commits March 27, 2026 01:08

chore: sync stresstest/llm.py from upstream main
3450911

chore: sync stresstest/utils.py from upstream main
74fa273

chore: sync uv.lock from upstream main
6786190

chore: remove stresstest/infer.py (deleted in upstream main)
67542a9

Merge main into fix branch: rebased llm.py with all PR changes
bd147fb

Merge main into fix branch: rebased llm_test.py with all PR test addi…
258e08c
…tions

This was referenced Mar 30, 2026
fix(alpha): add missing chainId to inference transaction in _send_tx_with_revert_handling #217
Open
fix: remove arbitrary 0.1 OPG floor from LLM.ensure_opg_approval() — validate positive only #222
Open
docs: add community Contributors section to README #230
Closed

amathxbt requested a review from adambalogh April 4, 2026 22:53

-defconvert_fixed_point_to_python(value: int, decimals: int) ->Union[int, np.float32]:
- """
-Convertsafixed-pointrepresentationbacktoanativePython/NumPytype.
-Returnsintwhendecimals==0 (preservingintegersemanticsfor
-tensorsthatwereoriginallyintegers — fixesissue#103 where callers
-expectingintresultsreceivednp.float32andhadtocastmanually).
-Returnsnp.float32forallothercases.
-Args:
-value: Theintegersignificandstoredon-chain.
-decimals: Thescalefactorexponent (value/10**decimals).
-Returns:
-intifdecimals==0, np.float32otherwise.
- """
-ifdecimals==0:
-returnint(value)
+defconvert_fixed_point_to_python(value: int, decimals: int) ->np.float32:
+ """
+Convertsafixed-pointrepresentationbacktoaNumPyfloat32.
+Thisfunctionisintentionallytype-stableandalwaysreturnsnp.float32,
+regardlessofthevalueof`decimals`. Callersthatrequireinteger
+semanticsshouldperformanexplicitcast (e.g., int(...)) basedon
+theirowndtypemetadataorapplicationlogic.
+Args:
+value: Theintegersignificandstoredon-chain.
+decimals: Thescalefactorexponent (value/10**decimals).
+Returns:
+np.float32correspondingto`value / 10**decimals`.
+ """

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amathxbt commented Mar 23, 2026

Summary

Bug 1 — TEE always picks the same node (closes#200)

Bug 2 — HTTP 402 swallowed as cryptic RuntimeError (closes#188)

Bug 3 — Fixed-point int returns np.float32 instead of int (partially closes#103)

Bug 4 — infer() crashes with AttributeError when inference node returns None

Bug 5 — run_workflow() uses hardcoded 30M gas

Files Changed

Related Issues

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amathxbt commented Mar 23, 2026

Summary

Bug 1 — TEE always picks the same node (closes#200)

Bug 2 — HTTP 402 swallowed as cryptic RuntimeError (closes#188)

Bug 3 — Fixed-point int returns np.float32 instead of int (partially closes#103)

Bug 4 — infer() crashes with AttributeError when inference node returns None

Bug 5 — run_workflow() uses hardcoded 30M gas

Files Changed

Related Issues

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Bug 4 — `infer()` crashes with AttributeError when inference node returns None

Bug 5 — `run_workflow()` uses hardcoded 30M gas

amathxbtMar 26, 2026•
edited
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Bug 4 — `infer()` crashes with AttributeError when inference node returns None

Bug 5 — `run_workflow()` uses hardcoded 30M gas

amathxbtMar 26, 2026•
edited
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