Building reliable Agent runtimes, typed semantic harnesses, tool and knowledge boundaries,
evaluation pipelines, failure recovery, and full-stack systems that make model behavior verifiable.
🛡️ OperCerta — Controlled Agent Runtime
A recoverable, evaluable, and auditable Agent runtime for operational workflows.
- Single-root LangGraph Agent Loop: Model → ToolPolicy → MCP Observation → Model
- Typed Context/State, PostgreSQL Checkpoint/Replay, HITL approval binding, fact re-fetch, Verifier, and idempotent writes
- FastAPI, FastMCP, PostgreSQL/pgvector, Redis, React/SSE, OpenTelemetry, Docker, and CI
- Evidence: 42/42 fixed business contracts, 9/9 safe recovery traces, and 0 duplicate work orders across 20 × 10 concurrent writes
- Redis reduced repeated MCP reads from 10 to 2 (-80%); fixed local scenarios reduced P50 by 58%–61%
- 🌐 Engineering showcase · 💻 Source code
🧭 FieldPilot — Typed Semantic Harness
A cross-city field-mission Agent that isolates LLM uncertainty at the natural-language boundary.
- A no-tool PydanticAI Harness converts untrusted text into a strict, confirmable MissionDraft
- Deterministic postchecks recompute clarifications, safety labels, and date normalization
- Bounded Beam Planner, Policy Engine, Independent Verifier, ExecutionCheckpoint, and suffix-only replanning
- Idempotent AgentRun, input fingerprints, usage limits, auditable traces, and no-fallback live evaluation gates
- Evidence: 15/15 Kimi K2.6 live, state/safety 100%, field precision 94.87%, clarification precision 93.33%
- 🌐 Project showcase · 💻 Source code
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Agent Loop, Context/State, Checkpoint/Replay, human approval, failure recovery, concurrency control, and idempotent side effects. |
Tool Calling, MCP, RAG, hybrid retrieval, citations, permission boundaries, policy checks, caching, and external provider ports. |
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Versioned datasets, deterministic contracts, live-model gates, Trace, failure replay, latency/cost analysis, and regression tests. |
FastAPI, PostgreSQL, Redis, React/Vue, TypeScript, Docker, CI, cloud deployment, and operational documentation. |
Agent / AI
Languages / Application
Data / Delivery
Nine focused contributions are merged in established Agent and developer-tool projects. Personal repository work is excluded.
| Project | Stars | Engineering contribution | Status |
|---|---|---|---|
| Cherry Studio #18475 | 50.4k | Removed a duplicate workspace-list request after creation while preserving the existing mutation-owned refresh and workspace selection flow. | ✅ Merged |
| AgentScope #2178 | 28.9k | Kept tool-response error states sticky across later interrupted or denied chunks and added regression coverage. | ✅ Merged |
| Mastra #21154 | 27.2k | Preserved the provider-selected response model on inference spans without overwriting the requested model. | ✅ Merged |
| Microsoft Agent Framework #7324 | 12.8k | Preserved table binding across consecutive declarative EditTable operations and added regression coverage. |
✅ Merged |
| OpenSandbox #1519 | 12.6k | Replenished stale idle sandbox slots immediately after asynchronous cleanup and added deterministic Kotlin regression coverage. | ✅ Merged |
| local-deep-research #5269 | 8.9k | Unified Zotero progress and error feedback while preserving accessibility and XSS-safe rendering. | ✅ Merged |
| Strands Agents Harness SDK #3627 | 6.9k | Preserved token usage from hook-retried model calls so cost reporting and invocation limits remain accurate. | ✅ Merged |
| Langroid #1072 | 4.1k | Fixed concurrent result selection so a fast empty result cannot cancel a slower valid result. | ✅ Merged |
| A2A Java #999 | 0.5k | Corrected interoperability test topology while preserving valid JSON-RPC and gRPC coverage. | ✅ Merged |
- 🧠 The model handles ambiguity; deterministic code decides correctness and side effects.
- 🧾 Metrics need datasets, scripts, runtime conditions, and commits—not just screenshots.
- 🔒 Public demos use synthetic or redacted data and expose no credentials or private systems.
- 🧪 Failure recovery, idempotency, policy checks, and evaluation are product behavior.
- 🧱 Agent engineering includes APIs, state, data, UI, deployment, and operations—not only model calls.
Beyond Agent systems, repositories include C# serial/device communication tools, MATLAB signal-processing and imaging utilities, PDF report generation, and full-stack Web applications. These projects provide the systems, data, debugging, and delivery foundation behind the Agent work.