Upload folder using huggingface_hub

README.md

@@ -1,6 +1,6 @@
 ---
 title: Logic Engine
-emoji: 🧠
+emoji: 🤖
 colorFrom: blue
 colorTo: indigo
 sdk: docker
@@ -8,227 +8,6 @@ pinned: false
 app_port: 7860
 ---
 
-<a href="https://kayba.ai"><img src="assets/kayba-banner.png" alt="Kayba - Stop fixing agents by hand" width="1080"/></a>
+# Logic Engine - Node 2
 
-# Agentic Context Engine (ACE)
-
-[![GitHub stars](https://img.shields.io/github/stars/kayba-ai/agentic-context-engine)](https://github.com/kayba-ai/agentic-context-engine/stargazers)
-[![Kayba Website](https://img.shields.io/badge/kayba.ai-6B8BA8?style=flat&logo=data:image/png;base64,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&logoColor=white)](https://kayba.ai)
-[![Discord](https://img.shields.io/discord/1429935408145236131?label=Discord&logo=discord&logoColor=white&color=5865F2)](https://discord.gg/mqCqH7sTyK)
-[![Twitter Follow](https://img.shields.io/twitter/follow/kaybaai?style=social)](https://twitter.com/kaybaai)
-[![Documentation](https://img.shields.io/badge/docs-latest-blue.svg)](https://kayba-ai.github.io/agentic-context-engine/latest/)
-
-> [!TIP]
-> ACE is the open-source engine behind [Kayba](https://kayba.ai). If you'd rather have the whole loop managed for you, from failure investigation to fixes shipped as PRs, [get a demo](https://kayba.ai).
-
----
-
-**AI agents don't learn from experience.** They repeat the same mistakes every session, forget what worked, and ignore what failed. ACE is the open-source engine that adds a persistent learning loop. It also powers [Kayba](https://kayba.ai), the managed service that does this for your production agents automatically.
-
-<img src="examples/seahorse-emoji-ace.gif" alt="ACE learns from mistakes in real time" width="70%"/>
-
-> The agent claims a seahorse emoji exists. ACE reflects on the error, and on the next attempt, the agent responds correctly — without human intervention.
-
----
-
-## Proven Results
-
-| Metric | Result | Context |
-|:-------|:-------|:--------|
-| **2x consistency** | Doubles pass^4 on Tau2 airline benchmark | 15 learned strategies, no reward signals |
-| **49% token reduction** | Browser automation costs cut nearly in half | 10-run learning curve |
-| **$1.50 learning cost** | Claude Code translated 14k lines to TypeScript | Zero build errors, all tests passing |
-
----
-
-## Quick Start
-
-```bash
-uv add ace-framework
-```
-
-**Option A** — Interactive setup (recommended):
-
-```bash
-ace setup            # Walks you through model selection, API keys, and connection validation
-```
-
-**Option B** — Manual configuration:
-
-```bash
-export OPENAI_API_KEY="your-key"    # or ANTHROPIC_API_KEY, or any of 100+ supported providers
-```
-
-Then use it:
-
-```python
-from ace import ACELiteLLM
-
-agent = ACELiteLLM(model="gpt-4o-mini")
-
-# First attempt — the agent may hallucinate
-answer = agent.ask("Is there a seahorse emoji?")
-
-# Feed a correction — ACE extracts a strategy and updates the Skillbook
-agent.learn_from_feedback("There is no seahorse emoji in Unicode.")
-
-# Subsequent calls benefit from the learned strategy
-answer = agent.ask("Is there a seahorse emoji?")
-
-# Inspect what the agent has learned
-print(agent.get_strategies())
-```
-
-No fine-tuning, no training data, no vector database.
-
-[-> Quick Start Guide](https://kayba-ai.github.io/agentic-context-engine/latest/getting-started/quick-start/) | [-> Setup Guide](https://kayba-ai.github.io/agentic-context-engine/latest/getting-started/setup/) | [-> Hosted API: Where Do Traces Come From?](https://kayba-ai.github.io/agentic-context-engine/latest/integrations/hosted-api/#where-do-traces-come-from)
-
----
-
-## How It Works
-
-ACE maintains a **Skillbook** — a persistent collection of strategies that evolves with every task. Three specialized roles manage the learning loop:
-
-| Role | Responsibility |
-|:-----|:---------------|
-| **Agent** | Executes tasks, enhanced with Skillbook strategies |
-| **Reflector** | Analyzes execution traces to extract what worked and what failed |
-| **SkillManager** | Curates the Skillbook — adds, refines, and removes strategies |
-
-The **Recursive Reflector** is the key innovation: instead of summarizing traces in a single pass, it writes and executes Python code in a sandboxed environment to programmatically search for patterns, isolate errors, and iterate until it finds actionable insights.
-
-```mermaid
-flowchart LR
-    Skillbook[(Skillbook)]
-    Start([Task]) --> Agent[Agent]
-    Agent <--> Environment[Environment]
-    Environment -- Trace --> Reflector[Reflector]
-    Reflector --> SkillManager[SkillManager]
-    SkillManager -- Updates --> Skillbook
-    Skillbook -. Strategies .-> Agent
-```
-
-All roles are backed by [PydanticAI](https://ai.pydantic.dev/) agents with structured output validation. PydanticAI routes to 100+ LLM providers through its LiteLLM integration, with native support for OpenAI, Anthropic, Google, Bedrock, Groq, and more.
-
-*Based on the [ACE paper](https://arxiv.org/abs/2510.04618) (Stanford & SambaNova) and [Dynamic Cheatsheet](https://arxiv.org/abs/2504.07952).*
-
----
-
-## Runners
-
-| Runner | Class | Description |
-|:-------|:------|:------------|
-| **LiteLLM** | `ACELiteLLM` | Batteries-included agent with `.ask()`, `.learn()`, `.save()` — accepts any [LiteLLM model string](https://docs.litellm.ai/docs/providers) |
-| **Core** | `ACE` | Full learning loop with batch epochs and evaluation |
-| **Trace Analyser** | `TraceAnalyser` | Learn from pre-recorded traces without re-running tasks |
-| **browser-use** | `BrowserUse` | Browser automation that improves with each run |
-| **LangChain** | `LangChain` | Wrap any LangChain chain or agent with learning |
-| **Claude Code** | `ClaudeCode` | Claude Code CLI tasks with learning |
-
-```bash
-uv add 'ace-framework[browser-use]'    # Browser automation
-uv add 'ace-framework[langchain]'      # LangChain
-uv add 'ace-framework[logfire]'        # Observability (auto-instruments PydanticAI)
-uv add 'ace-framework[mcp]'            # MCP server for IDE integration
-uv add 'ace-framework[deduplication]'  # Embedding-based skill deduplication
-```
-
-Have existing agent logs? Extract strategies from them directly:
-
-```python
-from ace import ACELiteLLM
-
-agent = ACELiteLLM(model="gpt-4o-mini")
-agent.learn_from_traces(your_existing_traces)
-print(agent.get_strategies())
-```
-
-[-> Examples](examples/)
-
----
-
-## Benchmarks
-
-### Tau2 — Multi-Step Agentic Tasks
-
-[tau2-bench](https://github.com/sierra-research/tau2-bench) by Sierra Research: airline domain tasks requiring tool use and policy adherence. Claude Haiku 4.5 agent, strategies learned on the train split with no reward signals, evaluated on the held-out test split.
-
-<img src="benchmarks/tasks/tau_bench/Tau2Benchmark Result Haiku4.5.png" alt="Tau2 Benchmark — ACE doubles consistency at pass^4" width="35%"/>
-
-*pass^k = probability all k independent attempts succeed. ACE doubles consistency at pass^4 with 15 learned strategies.*
-
-### Claude Code — Autonomous Translation
-
-ACE + Claude Code translated this library from Python to TypeScript with zero supervision:
-
-| Metric | Result |
-|:-------|:-------|
-| Duration | ~4 hours |
-| Commits | 119 |
-| Lines written | ~14,000 |
-| Build errors | 0 |
-| Tests | All passing |
-| Learning cost | ~$1.50 |
-
----
-
-## Pipeline Architecture
-
-ACE is built on a composable pipeline engine. Each step declares what it requires and what it produces:
-
-```
-AgentStep -> EvaluateStep -> ReflectStep -> UpdateStep -> DeduplicateStep
-```
-
-Use `learning_tail()` for the standard learning sequence, or compose custom pipelines:
-
-```python
-from ace import Pipeline, AgentStep, EvaluateStep, learning_tail
-
-steps = [AgentStep(agent, skillbook), EvaluateStep(env)] + learning_tail(reflector, skill_manager, skillbook)
-pipeline = Pipeline(steps)
-```
-
-The pipeline engine ([`pipeline/`](pipeline/)) is framework-agnostic with `requires`/`provides` contracts, immutable context, and error isolation. See [Pipeline Design](docs/design/PIPELINE_DESIGN.md) and [Architecture](docs/design/ACE_ARCHITECTURE.md).
-
----
-
-## CLI
-
-| Command | Description |
-|:--------|:------------|
-| `ace setup` | Interactive setup — model selection, API keys, connection validation |
-| `ace models <query>` | Search available models with pricing |
-| `ace validate <model>` | Test a model connection |
-| `ace config` | Show current configuration |
-| `kayba` | Cloud CLI — upload traces, fetch insights, manage prompts |
-| `ace-mcp` | MCP server for IDE integration |
-
----
-
-## Documentation
-
-- [Full Documentation](https://kayba-ai.github.io/agentic-context-engine/latest/) — Guides, API reference, examples
-- [Quick Start](https://kayba-ai.github.io/agentic-context-engine/latest/getting-started/quick-start/) — 5-minute setup
-- [Setup Guide](https://kayba-ai.github.io/agentic-context-engine/latest/getting-started/setup/) — Configuration and providers
-- [Hosted API Guide](https://kayba-ai.github.io/agentic-context-engine/latest/integrations/hosted-api/) — Hosted CLI, trace upload, prompt install
-- [Architecture](docs/design/ACE_ARCHITECTURE.md) — Core concepts and system design
-- [Code Reference](docs/design/ACE_REFERENCE.md) — Implementations, API, usage examples
-- [Design Decisions](docs/design/ACE_DECISIONS.md) — Rejected alternatives and rationale
-- [Pipeline Engine](docs/design/PIPELINE_DESIGN.md) — Step composition and context flow
-- [Examples](examples/) — Runnable demos
-- [Changelog](CHANGELOG.md) — Version history
-
----
-
-## Contributing
-
-Contributions are welcome. See [Contributing Guidelines](CONTRIBUTING.md).
-
----
-
-<div align="center">
-
-**Built by [Kayba](https://kayba.ai) and the open-source community.**
-
-</div>
+FastAPI backend for the Manus UI Clone. Exposes a /chat endpoint.

main.py

@@ -16,44 +16,58 @@ app.add_middleware(
 
 class ChatRequest(BaseModel):
     prompt: str
-    model: str = "gpt-4o-mini"
+    model: str = "llama-3.1-8b-instant"
+    api_key: str = ""
+    provider: str = "Groq"
+
+PROVIDER_BASES = {
+    "OpenAI":      "https://api.openai.com/v1",
+    "Anthropic":   "https://api.anthropic.com/v1",
+    "Groq":        "https://api.groq.com/openai/v1",
+    "HuggingFace": "https://api-inference.huggingface.co/v1",
+    "OpenRouter":  "https://openrouter.ai/api/v1",
+    "Nvidia":      "https://integrate.api.nvidia.com/v1",
+}
 
 @app.get("/")
 def root():
     return {"status": "Logic Engine is running", "endpoints": ["/chat", "/health"]}
 
-
 @app.post("/chat")
 async def chat_endpoint(request: ChatRequest):
-    api_key = os.environ.get("OPENAI_API_KEY") or os.environ.get("GROQ_API_KEY")
+    # Priority: key from frontend → env vars
+    api_key = (
+        request.api_key.strip()
+        or os.environ.get("GROQ_API_KEY", "")
+        or os.environ.get("OPENAI_API_KEY", "")
+    )
 
     if not api_key:
-        # Graceful fallback when no API key is configured
         return {
             "response": (
-                f"[Logic Engine] Received your message: \"{request.prompt}\"\n\n"
-                "No LLM API key is configured yet. Please add OPENAI_API_KEY or GROQ_API_KEY "
-                "via the Providers panel in the UI or in the Space Secrets settings on Hugging Face."
+                f'[Logic Engine] Received: "{request.prompt}"\n\n'
+                "No API key configured. Please add your key via the Providers panel."
             ),
             "doc": True
         }
 
-    try:
-        # Use OpenAI-compatible API via httpx (works with OpenAI & Groq)
-        base_url = "https://api.groq.com/openai/v1" if os.environ.get("GROQ_API_KEY") else "https://api.openai.com/v1"
-        chosen_key = os.environ.get("GROQ_API_KEY") or os.environ.get("OPENAI_API_KEY")
-        model = "llama-3.1-8b-instant" if os.environ.get("GROQ_API_KEY") else request.model
+    base_url = PROVIDER_BASES.get(request.provider, PROVIDER_BASES["Groq"])
 
-        async with httpx.AsyncClient(timeout=30) as client:
+    try:
+        async with httpx.AsyncClient(timeout=60) as client:
             resp = await client.post(
                 f"{base_url}/chat/completions",
-                headers={"Authorization": f"Bearer {chosen_key}", "Content-Type": "application/json"},
+                headers={
+                    "Authorization": f"Bearer {api_key}",
+                    "Content-Type": "application/json",
+                },
                 json={
-                    "model": model,
+                    "model": request.model,
                     "messages": [
-                        {"role": "system", "content": "You are Manus, a helpful autonomous AI agent."},
-                        {"role": "user", "content": request.prompt}
-                    ]
+                        {"role": "system", "content": "You are Manus, a helpful and concise autonomous AI agent."},
+                        {"role": "user", "content": request.prompt},
+                    ],
+                    "temperature": 0.7,
                 }
             )
             resp.raise_for_status()
@@ -61,6 +75,8 @@ async def chat_endpoint(request: ChatRequest):
             reply = data["choices"][0]["message"]["content"]
             return {"response": reply, "doc": True}
 
+    except httpx.HTTPStatusError as e:
+        raise HTTPException(status_code=e.response.status_code, detail=f"Provider error: {e.response.text}")
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))