How should problem-solving-agent be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this skill.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

Crawler Summary

problem-solving-agent answer-first brief

Autonomous multi-agent problem-solving system with LangGraph orchestration, CrewAI + AutoGen agents, Docker-sandboxed code execution, FAISS memory, and real-time WebSocket streaming. Multi-Agent Problem-Solving System An autonomous multi-agent system that plans, researches, codes, validates, and self-corrects to solve programming problems — built with LangGraph orchestration, CrewAI, AutoGen, and a local LLM (Ollama), featuring secure sandboxed execution, persistent memory, and real-time observability. Architecture Features - **Autonomous multi-agent pipeline** — Planner and Researcher (CrewAI), Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

Freshness

Last checked 10/9/2026

Best For

problem-solving-agent is best for crewai, multi-agent workflows where OpenClaw compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, GITHUB REPOS, runtime-metrics, public facts pack

Card Facts Snapshot Contract Trust

Agent DossierGITHUB REPOSSafety: 66/100

problem-solving-agent

OpenClawself-declared

Public facts

Change events

Artifacts

Freshness

Oct 9, 2026

Verifiededitorial-contentNo verified compatibility signals

Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

Trust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Oct 9, 2026

Vendor

Theprakashv

Artifacts

Benchmarks

Last release

Unpublished

Executive Summary

Key links, install path, and a quick operational read before the deeper crawl record.

Verifiededitorial-content

Summary

Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

View Source

Setup snapshot

1
Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
2
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Evidence Ledger

Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.

Verifiededitorial-content

Vendor (1)

Vendor

Theprakashv

profilemedium

Observed Oct 9, 2026Source link Provenance

Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium

Observed Oct 9, 2026Source link Provenance

Security (1)

Handshake status

UNKNOWN

trustmedium

Observed unknownSource link Provenance

Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium

Observed Apr 15, 2026Source link Provenance

Release & Crawl Timeline

Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.

Self-declaredagent-index

Docs Update

Docs refreshed: Sign in to GitHub · GitHub

search_documentmedium

Fresh crawlable documentation was indexed for the official domain.

Observed Apr 15, 2026

Artifacts Archive

Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.

Self-declaredGITHUB REPOS

Extracted files

Examples

Snippets

Languages

python

Executable Examples

text

User ↔ FastAPI/WebSocket (real-time streaming)
        │
   Agent Orchestrator (LangGraph — streaming, conditional loops)
        │
   Planner (CrewAI) / Researcher (CrewAI) / Memory (FAISS)
        │
      Coder (AutoGen)
        │
     Reviewer (Docker-sandboxed real code execution)
        │
   Pass ── Fail
        │
    Debugger (AutoGen) ── loops back to Reviewer
        │
   MLflow tracking ── Plotly Dashboard
        │
       END

text

├── agents/              # Individual agent implementations (Planner, Researcher, Coder, Reviewer, Debugger)
├── orchestrator/         # LangGraph state graph, node definitions, state schema
├── memory/                # FAISS vector memory (persisted index + metadata)
├── api/                    # FastAPI app: WebSocket endpoint, dashboard, unified frontend
├── config/                  # LLM configs (CrewAI + AutoGen), MLflow tracker, .env loading
├── docker/                   # Dockerfile for the code-execution sandbox
├── docs/                       # Phase-by-phase build documentation
├── tests/                        # Test files
├── requirements.txt
└── .env / .gitignore

bash

# Clone the repo
git clone <your-repo-url>
cd multi-agent-problem-solver

# Create virtual environment
python -m venv venv
venv\Scripts\activate        # Windows
# source venv/bin/activate   # macOS/Linux

# Install dependencies
pip install --upgrade pip
pip install -r requirements.txt --prefer-binary

# Install Ollama and pull a model
# https://ollama.com
ollama pull llama3.1

# Build the Docker sandbox image
docker build -t agent-sandbox -f docker/Dockerfile.sandbox .

# Configure environment
# Copy the example values into a .env file (see docs/PHASE_1_Project_Setup.md)

# Run the server
uvicorn api.main:app --reload

Docs & README

Full documentation captured from public sources, including the complete README when available.

Self-declaredGITHUB REPOS

Docs source

GITHUB REPOS

Editorial quality

ready

Full README

Multi-Agent Problem-Solving System

An autonomous multi-agent system that plans, researches, codes, validates, and self-corrects to solve programming problems — built with LangGraph orchestration, CrewAI, AutoGen, and a local LLM (Ollama), featuring secure sandboxed execution, persistent memory, and real-time observability.

Architecture

User ↔ FastAPI/WebSocket (real-time streaming)
        │
   Agent Orchestrator (LangGraph — streaming, conditional loops)
        │
   Planner (CrewAI) / Researcher (CrewAI) / Memory (FAISS)
        │
      Coder (AutoGen)
        │
     Reviewer (Docker-sandboxed real code execution)
        │
   Pass ── Fail
        │
    Debugger (AutoGen) ── loops back to Reviewer
        │
   MLflow tracking ── Plotly Dashboard
        │
       END

Features

Autonomous multi-agent pipeline — Planner and Researcher (CrewAI), Coder and Debugger (AutoGen) collaborate to solve problems end-to-end
Real code execution & self-correction — the Reviewer agent actually runs generated code in an isolated Docker sandbox and routes failures back to the Debugger in a self-healing loop
Secure sandboxed execution — AI-generated code runs in a network-isolated, resource-limited, non-root Docker container, never on the host machine
Long-term memory — a FAISS vector store recalls semantically similar past tasks and solutions across sessions
Real-time streaming — a WebSocket API streams each agent's progress live as it happens, instead of waiting for the full pipeline to finish
Experiment tracking & analytics — every run is logged via MLflow (duration, debug attempts, pass/fail) and visualized on a live Plotly dashboard
Unified web UI — a single-page frontend combines the live "Solve Task" view and the analytics dashboard in one interface
100% local LLM inference — runs entirely on a local Ollama model (no external API costs, full data privacy)

Tech Stack

| Layer | Technology | |---|---| | Orchestration | LangGraph | | Agent Frameworks | CrewAI (Planner, Researcher), AutoGen (Coder, Debugger) | | LLM Backend | Ollama (local inference) | | API / Real-time | FastAPI, WebSocket | | Memory | FAISS + Sentence Transformers | | Sandboxing | Docker | | Experiment Tracking | MLflow | | Visualization | Plotly | | Validation | Pydantic | | Language | Python 3.11 |

How It Works

A user submits a problem/task through the web UI or WebSocket API
The system checks FAISS memory for similar past tasks
Planner (CrewAI) breaks the problem into a step-by-step plan
Researcher (CrewAI) gathers the technical approach and concepts needed
Coder (AutoGen) generates working Python code based on the plan and research
Reviewer executes the code inside a sandboxed Docker container to validate it actually works
If it fails, Debugger (AutoGen) reads the error and fixes the code, looping back to the Reviewer (up to a safety limit)
On success, the solution is saved to memory for future reuse, and the run is logged to MLflow
All of this streams live to the frontend over WebSocket, and is visualized on the analytics dashboard

Project Structure

├── agents/              # Individual agent implementations (Planner, Researcher, Coder, Reviewer, Debugger)
├── orchestrator/         # LangGraph state graph, node definitions, state schema
├── memory/                # FAISS vector memory (persisted index + metadata)
├── api/                    # FastAPI app: WebSocket endpoint, dashboard, unified frontend
├── config/                  # LLM configs (CrewAI + AutoGen), MLflow tracker, .env loading
├── docker/                   # Dockerfile for the code-execution sandbox
├── docs/                       # Phase-by-phase build documentation
├── tests/                        # Test files
├── requirements.txt
└── .env / .gitignore

Setup

# Clone the repo
git clone <your-repo-url>
cd multi-agent-problem-solver

# Create virtual environment
python -m venv venv
venv\Scripts\activate        # Windows
# source venv/bin/activate   # macOS/Linux

# Install dependencies
pip install --upgrade pip
pip install -r requirements.txt --prefer-binary

# Install Ollama and pull a model
# https://ollama.com
ollama pull llama3.1

# Build the Docker sandbox image
docker build -t agent-sandbox -f docker/Dockerfile.sandbox .

# Configure environment
# Copy the example values into a .env file (see docs/PHASE_1_Project_Setup.md)

# Run the server
uvicorn api.main:app --reload

Then open http://127.0.0.1:8000/ in your browser.

Development Journey

This project was built incrementally across 10 documented phases — from basic environment setup through to a fully unified, production-style platform. Full phase-by-phase documentation (including errors encountered and how they were resolved) is available in the docs/ folder.

| Phase | Focus | |---|---| | 1 | Project setup, environment, FastAPI health-check | | 2 | LangGraph orchestrator skeleton | | 3 | CrewAI integration (Planner, Researcher) | | 4 | AutoGen integration (Coder) | | 5 | Reviewer & Debugger (real execution + self-correction loop) | | 6 | FAISS memory + MLflow tracking | | 7 | WebSocket real-time streaming | | 8 | Docker sandboxing for secure code execution | | 9 | Plotly analytics dashboard | | 10 | Unified single-page frontend |

Contract & API

Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.

MissingGITHUB REPOS

Endpoints

Dossier API Snapshot API Contract API Trust API

Contract coverage

Status

missing

Auth

None

Streaming

Data region

Unspecified

Protocol support

OpenClaw: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.

Invocation examples

curl -s "https://www.xpersona.co/api/v1/agents/crewai-theprakashv-problem-solving-agent/snapshot"

curl -s "https://www.xpersona.co/api/v1/agents/crewai-theprakashv-problem-solving-agent/contract"

curl -s "https://www.xpersona.co/api/v1/agents/crewai-theprakashv-problem-solving-agent/trust"

Reliability & Benchmarks

Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.

Missingruntime-metrics

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.

No benchmark suites or observed failure patterns are available.

Media & Demo

Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.

Missingno-media

No screenshots, media assets, or demo links are available.

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Machine Appendix

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://www.xpersona.co/api/v1/agents/crewai-theprakashv-problem-solving-agent/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-theprakashv-problem-solving-agent/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-theprakashv-problem-solving-agent/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-theprakashv-problem-solving-agent/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-theprakashv-problem-solving-agent/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-theprakashv-problem-solving-agent/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_REPOS",
      "generatedAt": "2026-10-09T23:49:35.954Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [
    {
      "key": "OPENCLEW",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    },
    {
      "key": "crewai",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "multi-agent",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    }
  ],
  "flattenedTokens": "protocol:OPENCLEW|unknown|profile capability:crewai|supported|profile capability:multi-agent|supported|profile"
}

Facts JSON

[
  {
    "factKey": "vendor",
    "category": "vendor",
    "label": "Vendor",
    "value": "Theprakashv",
    "href": "https://github.com/ThePrakashV/problem-solving-agent",
    "sourceUrl": "https://github.com/ThePrakashV/problem-solving-agent",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-10-09T15:57:33.648Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-theprakashv-problem-solving-agent/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-theprakashv-problem-solving-agent/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-10-09T15:57:33.648Z",
    "isPublic": true
  },
  {
    "factKey": "docs_crawl",
    "category": "integration",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-theprakashv-problem-solving-agent/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-theprakashv-problem-solving-agent/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub · GitHub",
    "description": "Fresh crawlable documentation was indexed for the official domain.",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  }
]