2026-02-09 21:51:42 -08:00
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"""Reasoning engine: chain-of-thought, tree-of-thought, and native symbolic reasoning."""
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from fusionagi.reasoning.cot import (
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build_cot_messages,
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run_chain_of_thought,
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)
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from fusionagi.reasoning.decomposition import decompose_recursive
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fix: deep GPU integration, fix all ruff/mypy issues, add .dockerignore
- Integrate GPU scoring inline into reasoning/multi_path.py (auto-uses GPU when available)
- Integrate GPU deduplication into multi_agent/consensus_engine.py
- Add semantic_search() method to memory/semantic_graph.py with GPU acceleration
- Integrate GPU training into self_improvement/training.py AutoTrainer
- Fix all 758 ruff lint issues (whitespace, import sorting, unused imports, ambiguous vars, undefined names)
- Fix all 40 mypy type errors across the codebase (no-any-return, union-attr, arg-type, etc.)
- Fix deprecated ruff config keys (select/ignore -> [tool.ruff.lint])
- Add .dockerignore to exclude .venv/, tests/, docs/ from Docker builds
- Add type hints and docstrings to verification/outcome.py
- Fix E402 import ordering in witness_agent.py
- Fix F821 undefined names in vector_pgvector.py and native.py
- Fix E741 ambiguous variable names in reflective.py and recommender.py
All 276 tests pass. 0 ruff errors. 0 mypy errors.
Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
2026-04-28 05:48:37 +00:00
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from fusionagi.reasoning.gpu_scoring import (
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deduplicate_claims_gpu,
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generate_and_score_gpu,
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score_claims_gpu,
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)
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2026-04-28 09:43:47 +00:00
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from fusionagi.reasoning.insight_bus import Insight, InsightBus
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feat: consequence engine, causal world model, metacognition, interpretability, claim verification
Choice → Consequence → Learning:
- ConsequenceEngine tracks every decision point with alternatives,
risk/reward estimates, and actual outcomes
- Consequences feed into AdaptiveEthics for experience-based learning
- FusionAGILoop now wires ethics + consequences into task lifecycle
Causal World Model:
- CausalWorldModel learns state-transition patterns from execution history
- Predicts outcomes based on observed action→effect patterns
- Uncertainty estimates decrease as more evidence accumulates
Metacognition:
- assess_head_outputs() evaluates reasoning quality from head outputs
- Detects knowledge gaps, measures head agreement, identifies uncertainty
- Actively recommends whether to seek more information
Interpretability:
- ReasoningTracer captures full prompt→answer reasoning traces
- Each step records stage, component, input/output, timing
- explain() generates human-readable reasoning explanations
Claim Verification:
- ClaimVerifier cross-checks claims for evidence, consistency, grounding
- Flags high-confidence claims lacking evidence support
- Detects contradictions between claims from different heads
325 tests passing, 0 ruff errors, 0 mypy errors.
Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
2026-04-28 06:25:35 +00:00
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from fusionagi.reasoning.interpretability import (
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ReasoningTrace,
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ReasoningTracer,
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TraceStep,
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)
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2026-02-09 21:51:42 -08:00
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from fusionagi.reasoning.meta_reasoning import (
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challenge_assumptions,
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detect_contradictions,
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revisit_node,
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)
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feat: consequence engine, causal world model, metacognition, interpretability, claim verification
Choice → Consequence → Learning:
- ConsequenceEngine tracks every decision point with alternatives,
risk/reward estimates, and actual outcomes
- Consequences feed into AdaptiveEthics for experience-based learning
- FusionAGILoop now wires ethics + consequences into task lifecycle
Causal World Model:
- CausalWorldModel learns state-transition patterns from execution history
- Predicts outcomes based on observed action→effect patterns
- Uncertainty estimates decrease as more evidence accumulates
Metacognition:
- assess_head_outputs() evaluates reasoning quality from head outputs
- Detects knowledge gaps, measures head agreement, identifies uncertainty
- Actively recommends whether to seek more information
Interpretability:
- ReasoningTracer captures full prompt→answer reasoning traces
- Each step records stage, component, input/output, timing
- explain() generates human-readable reasoning explanations
Claim Verification:
- ClaimVerifier cross-checks claims for evidence, consistency, grounding
- Flags high-confidence claims lacking evidence support
- Detects contradictions between claims from different heads
325 tests passing, 0 ruff errors, 0 mypy errors.
Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
2026-04-28 06:25:35 +00:00
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from fusionagi.reasoning.metacognition import (
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KnowledgeGap,
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MetacognitiveAssessment,
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assess_head_outputs,
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)
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fix: deep GPU integration, fix all ruff/mypy issues, add .dockerignore
- Integrate GPU scoring inline into reasoning/multi_path.py (auto-uses GPU when available)
- Integrate GPU deduplication into multi_agent/consensus_engine.py
- Add semantic_search() method to memory/semantic_graph.py with GPU acceleration
- Integrate GPU training into self_improvement/training.py AutoTrainer
- Fix all 758 ruff lint issues (whitespace, import sorting, unused imports, ambiguous vars, undefined names)
- Fix all 40 mypy type errors across the codebase (no-any-return, union-attr, arg-type, etc.)
- Fix deprecated ruff config keys (select/ignore -> [tool.ruff.lint])
- Add .dockerignore to exclude .venv/, tests/, docs/ from Docker builds
- Add type hints and docstrings to verification/outcome.py
- Fix E402 import ordering in witness_agent.py
- Fix F821 undefined names in vector_pgvector.py and native.py
- Fix E741 ambiguous variable names in reflective.py and recommender.py
All 276 tests pass. 0 ruff errors. 0 mypy errors.
Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
2026-04-28 05:48:37 +00:00
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from fusionagi.reasoning.multi_path import generate_and_score_parallel
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from fusionagi.reasoning.native import (
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NativeReasoningProvider,
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PromptAnalysis,
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analyze_prompt,
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produce_head_output,
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)
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from fusionagi.reasoning.recomposition import RecomposedResponse, recompose
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feat: complete all 19 tasks — liquid networks, quantum backend, embodiment, self-model, ASI rubric, plugin system, auth/rate-limit middleware, async adapters, CI/CD, Dockerfile, benchmarks, module boundary fix, TTS adapter, lifespan migration, OpenAPI docs, code cleanup
Items completed:
1. Merged PR #2 (starlette/httpx deps)
2. Fixed async race condition in multimodal_ui.py
3. Wired TTSAdapter (ElevenLabs, Azure) in API routes
4. Moved super_big_brain.py from core/ to reasoning/ (backward compat shim)
5. Added API authentication middleware (Bearer token via FUSIONAGI_API_KEY)
6. Added async adapter interface (acomplete/acomplete_structured)
7. Migrated FastAPI on_event to lifespan (fixes 20 deprecation warnings)
8. Liquid Neural Networks (continuous-time adaptive weights)
9. Quantum-AI Hybrid compute backend (simulator + optimization)
10. Embodied Intelligence / Robotics bridge (actuator + sensor protocols)
11. Consciousness Engineering (formal self-model with introspection)
12. ASI Scoring Rubric (C/A/L/N/R self-assessment harness)
13. GPU integration tests for TensorFlow backend
14. Multi-stage production Dockerfile
15. Gitea CI/CD pipeline (lint, test matrix, Docker build)
16. API rate limiting middleware (per-IP sliding window)
17. OpenAPI docs cleanup (auth + rate limiting descriptions)
18. Benchmarking suite (decomposition, multi-path, recomposition, e2e)
19. Plugin system (head registry for custom heads)
427 tests passing, 0 ruff errors, 0 mypy errors.
Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
2026-04-28 08:32:05 +00:00
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from fusionagi.reasoning.super_big_brain import (
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SuperBigBrainConfig,
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SuperBigBrainReasoningProvider,
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run_super_big_brain,
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)
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fix: deep GPU integration, fix all ruff/mypy issues, add .dockerignore
- Integrate GPU scoring inline into reasoning/multi_path.py (auto-uses GPU when available)
- Integrate GPU deduplication into multi_agent/consensus_engine.py
- Add semantic_search() method to memory/semantic_graph.py with GPU acceleration
- Integrate GPU training into self_improvement/training.py AutoTrainer
- Fix all 758 ruff lint issues (whitespace, import sorting, unused imports, ambiguous vars, undefined names)
- Fix all 40 mypy type errors across the codebase (no-any-return, union-attr, arg-type, etc.)
- Fix deprecated ruff config keys (select/ignore -> [tool.ruff.lint])
- Add .dockerignore to exclude .venv/, tests/, docs/ from Docker builds
- Add type hints and docstrings to verification/outcome.py
- Fix E402 import ordering in witness_agent.py
- Fix F821 undefined names in vector_pgvector.py and native.py
- Fix E741 ambiguous variable names in reflective.py and recommender.py
All 276 tests pass. 0 ruff errors. 0 mypy errors.
Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
2026-04-28 05:48:37 +00:00
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from fusionagi.reasoning.tot import (
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ThoughtBranch,
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ThoughtNode,
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ToTResult,
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expand_node,
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merge_subtrees,
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prune_subtree,
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run_tree_of_thought,
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run_tree_of_thought_detailed,
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feat: GPU/TensorCore integration — TensorFlow backend, GPU-accelerated reasoning, training, and memory
- New fusionagi/gpu/ module with TensorBackend protocol abstraction
- TensorFlowBackend: GPU-accelerated ops with TensorCore mixed-precision
- NumPyBackend: CPU fallback (always available, no extra deps)
- Auto-selects best available backend at runtime
- GPU-accelerated operations:
- Cosine similarity matrix (batched, XLA-compiled)
- Multi-head attention for consensus scoring
- Batch hypothesis scoring on GPU
- Semantic similarity search (pairwise, nearest-neighbor, deduplication)
- New TensorFlowAdapter (fusionagi/adapters/):
- LLMAdapter for local TF/Keras model inference
- TensorCore mixed-precision support
- GPU-accelerated embedding synthesis fallback
- Reasoning pipeline integration:
- gpu_scoring.py: drop-in GPU replacement for multi_path scoring
- Super Big Brain: use_gpu config flag, GPU scoring when available
- Memory integration:
- gpu_search.py: GPU-accelerated semantic search for SemanticGraphMemory
- Self-improvement integration:
- gpu_training.py: gradient-based heuristic weight optimization
- Reflective memory training loop with loss tracking
- Dependencies: gpu extra (tensorflow>=2.16, numpy>=1.26)
- 64 new tests (276 total), all passing
- Architecture spec: docs/gpu_tensorcore_integration.md
Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
2026-04-28 05:05:50 +00:00
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)
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2026-02-09 21:51:42 -08:00
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__all__ = [
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"build_cot_messages",
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"run_chain_of_thought",
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"run_tree_of_thought",
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"run_tree_of_thought_detailed",
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"ThoughtBranch",
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"ThoughtNode",
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"ToTResult",
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"expand_node",
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"prune_subtree",
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"merge_subtrees",
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"NativeReasoningProvider",
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"analyze_prompt",
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"produce_head_output",
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"PromptAnalysis",
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"decompose_recursive",
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"load_context_for_reasoning",
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"build_compact_prompt",
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"generate_and_score_parallel",
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"recompose",
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"RecomposedResponse",
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"challenge_assumptions",
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"detect_contradictions",
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"revisit_node",
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feat: GPU/TensorCore integration — TensorFlow backend, GPU-accelerated reasoning, training, and memory
- New fusionagi/gpu/ module with TensorBackend protocol abstraction
- TensorFlowBackend: GPU-accelerated ops with TensorCore mixed-precision
- NumPyBackend: CPU fallback (always available, no extra deps)
- Auto-selects best available backend at runtime
- GPU-accelerated operations:
- Cosine similarity matrix (batched, XLA-compiled)
- Multi-head attention for consensus scoring
- Batch hypothesis scoring on GPU
- Semantic similarity search (pairwise, nearest-neighbor, deduplication)
- New TensorFlowAdapter (fusionagi/adapters/):
- LLMAdapter for local TF/Keras model inference
- TensorCore mixed-precision support
- GPU-accelerated embedding synthesis fallback
- Reasoning pipeline integration:
- gpu_scoring.py: drop-in GPU replacement for multi_path scoring
- Super Big Brain: use_gpu config flag, GPU scoring when available
- Memory integration:
- gpu_search.py: GPU-accelerated semantic search for SemanticGraphMemory
- Self-improvement integration:
- gpu_training.py: gradient-based heuristic weight optimization
- Reflective memory training loop with loss tracking
- Dependencies: gpu extra (tensorflow>=2.16, numpy>=1.26)
- 64 new tests (276 total), all passing
- Architecture spec: docs/gpu_tensorcore_integration.md
Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
2026-04-28 05:05:50 +00:00
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"generate_and_score_gpu",
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"score_claims_gpu",
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"deduplicate_claims_gpu",
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feat: consequence engine, causal world model, metacognition, interpretability, claim verification
Choice → Consequence → Learning:
- ConsequenceEngine tracks every decision point with alternatives,
risk/reward estimates, and actual outcomes
- Consequences feed into AdaptiveEthics for experience-based learning
- FusionAGILoop now wires ethics + consequences into task lifecycle
Causal World Model:
- CausalWorldModel learns state-transition patterns from execution history
- Predicts outcomes based on observed action→effect patterns
- Uncertainty estimates decrease as more evidence accumulates
Metacognition:
- assess_head_outputs() evaluates reasoning quality from head outputs
- Detects knowledge gaps, measures head agreement, identifies uncertainty
- Actively recommends whether to seek more information
Interpretability:
- ReasoningTracer captures full prompt→answer reasoning traces
- Each step records stage, component, input/output, timing
- explain() generates human-readable reasoning explanations
Claim Verification:
- ClaimVerifier cross-checks claims for evidence, consistency, grounding
- Flags high-confidence claims lacking evidence support
- Detects contradictions between claims from different heads
325 tests passing, 0 ruff errors, 0 mypy errors.
Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
2026-04-28 06:25:35 +00:00
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"MetacognitiveAssessment",
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"KnowledgeGap",
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"assess_head_outputs",
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"ReasoningTrace",
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"ReasoningTracer",
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"TraceStep",
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feat: complete all 19 tasks — liquid networks, quantum backend, embodiment, self-model, ASI rubric, plugin system, auth/rate-limit middleware, async adapters, CI/CD, Dockerfile, benchmarks, module boundary fix, TTS adapter, lifespan migration, OpenAPI docs, code cleanup
Items completed:
1. Merged PR #2 (starlette/httpx deps)
2. Fixed async race condition in multimodal_ui.py
3. Wired TTSAdapter (ElevenLabs, Azure) in API routes
4. Moved super_big_brain.py from core/ to reasoning/ (backward compat shim)
5. Added API authentication middleware (Bearer token via FUSIONAGI_API_KEY)
6. Added async adapter interface (acomplete/acomplete_structured)
7. Migrated FastAPI on_event to lifespan (fixes 20 deprecation warnings)
8. Liquid Neural Networks (continuous-time adaptive weights)
9. Quantum-AI Hybrid compute backend (simulator + optimization)
10. Embodied Intelligence / Robotics bridge (actuator + sensor protocols)
11. Consciousness Engineering (formal self-model with introspection)
12. ASI Scoring Rubric (C/A/L/N/R self-assessment harness)
13. GPU integration tests for TensorFlow backend
14. Multi-stage production Dockerfile
15. Gitea CI/CD pipeline (lint, test matrix, Docker build)
16. API rate limiting middleware (per-IP sliding window)
17. OpenAPI docs cleanup (auth + rate limiting descriptions)
18. Benchmarking suite (decomposition, multi-path, recomposition, e2e)
19. Plugin system (head registry for custom heads)
427 tests passing, 0 ruff errors, 0 mypy errors.
Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
2026-04-28 08:32:05 +00:00
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"run_super_big_brain",
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"SuperBigBrainConfig",
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"SuperBigBrainReasoningProvider",
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2026-04-28 09:43:47 +00:00
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"Insight",
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"InsightBus",
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2026-02-09 21:51:42 -08:00
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]
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