Praxia

Praxia

[](https://pypi.org/project/praxia/) [](https://pypi.org/project/praxia/) [](https://github.com/praxia-dev/praxia/actions/workflows/test.yml) [](https://x.com/praxia_dev)

🌐 Live: praxia.tools (primary, Cloudflare) · praxia-dev.github.io/praxia (mirror, GitHub Pages) · @praxia_dev on X

[](https://youtu.be/Z3DFa2saHJg "▶ Click to watch the full 4-min demo on YouTube")

📺 Watch the 4-minute walkthrough · 🚀 [Quickstart](docs/quickstart.md) · 💬 Discussions

Install Praxia Desktop (Windows 10 / 11 x64)

[](https://apps.microsoft.com/detail/9P9LSR34HZF3)

> Specialized Multi-Agent Orchestrator with Cyclic Personal/Organizational Memory > > A workflow-specific multi-agent orchestrator that automatically promotes individual tacit knowledge into organizational know-how. Built on a 5-layer memory stack with three independent promotion paths.

[](LICENSE) [](https://www.python.org) []() []() []() []() []()

> 🔍 Complete feature reference: [docs/FEATURES.md](docs/FEATURES.md) > 📊 Concrete Before/After tables: [docs/use-cases.md](docs/use-cases.md)

---

⬇ Try Praxia in 30 seconds — no Python, no setup

👉 📦 Download Praxia Desktop for Windows (.exe, 174 MB)

Windows 10 / 11 x64 alpha · Tauri + embedded Python sidecar · zero pip install, zero praxia serve. Paste an LLM API key (Anthropic / OpenAI / Azure / Google / Qwen / HF / Ollama) and you're chatting. Unsigned alpha — SmartScreen will warn on first launch, click "More info" → "Run anyway".

Other downloads · .msi for managed deployment · all releases & notes · macOS / Linux coming in Phase 1b.

---

🎯 Why Praxia?

General-purpose multi-agent frameworks (CrewAI, AutoGen, LangGraph, …) are powerful but stop short on these four problems:

| Problem with existing frameworks | Praxia's approach | |---|---| | Setup is complex; production deployment is hard | Workflow-specific templates (sales prep / logic check / RAG optimization) that run in 5 minutes | | Senior-engineer "magic prompts" stay locked in one person's editor | Personal-to-org auto-promotion pipeline built in | | "It works" doesn't prove "it works well" | Hallucination detection + retrieval evals shipped by default | | Agents stagnate after launch | Sleep-time consolidation distills your past flows nightly |

Praxia turns "one expert's drawer" into "everyone's best practices."

---

👥 Who Praxia is for

| Persona | What they need | How Praxia fits | Typical year-1 result | |---|---|---|---| | 🏢 Information Systems / Platform team (300–5,000 employees) | Roll out AI tools without paywalled SSO/RBAC/audit, on-prem option | Auth + RBAC + ACL + per-user OAuth + audit log all in OSS, self-hostable | 100 KW × ~$1.25M net benefit, full audit trail | | 🏗️ Engineering / Product VP (50–500 in scope) | Senior architect bottleneck; junior PM 12–18mo ramp | DesignSkill + sleep-time consolidation distills senior review patterns; Markdown+git frozen layer fits PR workflow | Senior load 16h/wk → 4h/wk; junior ramp 6–9mo | | ⚖️ Legal / Compliance lead (regulated industry) | 50–100 contracts/mo bottleneck; need auditable AI workflow without lock-in | LegalSkill (RACE) + read-only memory mode + per-user OAuth + every action audited; Apache 2.0 source for auditors | 60–90min → 10–15min/contract; throughput 50–80/mo → 200–300/mo | | 🧪 OSS / Research integrator | Build domain agent system without re-implementing auth, memory cycling, exporters | 7 plugin types (~50 LoC each); use as library, run praxia serve as backend, embed in LangGraph | Day-30: domain skill + custom connector + memory cycling working — ~3 weeks ahead of from-scratch |

Detailed Before/After by industry: [docs/use-cases.md](docs/use-cases.md).

---

💎 Why OSS matters here

The capabilities you typically pay enterprise tier for — already in the Apache 2.0 package:

• SSO + RBAC + audit are not paywalled. OIDC SSO (Google / Microsoft / Okta / GitHub / Keycloak) is in the OSS. Most agent frameworks ship without it; most agent platforms paywall it. Praxia treats it as table stakes.
• Memory format is not locked in. Layer 4 is plain Markdown in your git repo. Layer 3 exports to JSONL. Layer 1 is your chosen backend's native format. Leaving costs nothing.
• You can read every line. Apache 2.0. Show the source to your auditors, your security team, your customers.
• Multi-LTM ensembles, not single-vendor. Run Mem0 + Zep + HindSight in parallel, fuse with RRF, or route per query. No commercial agent platform exposes this — they pick a backend and lock you in.
• Per-user OAuth respects external ACL. When Alice pulls from Box, Box's own ACL applies — Alice only sees what Alice can see. Service-account designs (typical SaaS shortcut) leak data across users.
• Air-gapped operation. PRAXIA_LOCAL_MODEL=gemma, Ollama, backend=json — no cloud LLM, no cloud vector DB, no telemetry. Same code as cloud customers.
• Production-grade OAuth + KMS in OSS. Multi-worker safe state cache, 5 KMS adapters (AWS / Azure / GCP / Vault / local). Most agent platforms paywall this; Praxia ships it.
• A/B experiments + quality eval included. Test prompt variants on real users with deterministic assignment; catch LLM output quality regressions in CI.

---

🏗 Architecture — 5-Layer Memory Stack

The same picture as ASCII art:

┌──────────────────────────────────────────────────────────┐
│  AI Agents (Skills + MCP)                                │
└──────────────┬───────────────────────────────────────────┘
               │ Users just have normal conversations
               ▼
╔═══════════════════════════════════════════════════════════╗
║ Layer 1: Personal memory (auto-extracted)                 ║
║   Mem0 / LangMem / HindSight / Letta / Zep / JSON         ║
║   namespace = user_id                                     ║
║   ★ Zero-effort tacit-knowledge capture                   ║
╚══════════════╤════════════════════════════════════════════╝
               │ Sleep-time Consolidation (nightly batch)
               ▼
╔═══════════════════════════════════════════════════════════╗
║ Layer 2: Distillation & promotion engine                  ║
║   Three parallel "validity tests":                        ║
║     ① Frequency  (recurring across N+ users)              ║
║     ② Outcome    (correlated with wins/losses)            ║
║     ③ Self-eval  (LLM scored)                             ║
╚══════════════╤════════════════════════════════════════════╝
               │ Auto-promote above threshold; queue otherwise
               ▼
╔═══════════════════════════════════════════════════════════╗
║ Layer 3: Shared memory (living organizational knowledge)  ║
║   Letta-style shared blocks; all agents read/write        ║
╚══════════════╤════════════════════════════════════════════╝
               │ PR review for high-impact items
               ▼
╔═══════════════════════════════════════════════════════════╗
║ Layer 4: Frozen layer (git-managed best practices)        ║
║   Markdown + git + PR review                              ║
║   GitHub Copilot / Cursor Rules-compatible format         ║
╚══════════════╤════════════════════════════════════════════╝
               │ (optional)
               ▼
╔═══════════════════════════════════════════════════════════╗
║ Layer 5: Graph layer (only relationship-heavy domains)    ║
║   Zep / Graphiti — decisions, customer 360, incident DAG  ║
╚═══════════════════════════════════════════════════════════╝

Parallel Layer 6: Skills registry
  Personal skills get promoted to the organizational catalog.
  MCP / Claude Skills / Cursor Skills compatible.

Three promotion paths (auto / statistical / manual) run side by side — never depending on a single mechanism.

For details, see [docs/architecture.md](docs/architecture.md).

---

✨ What's Bundled

Autonomous agent (LLM-driven tool-use loop)

praxia.agent.AutonomousAgent runs an LLM-driven tool-use loop over the full Praxia stack — personal/org memory, skills, frozen layer, connectors — with ACL checks and audit logging built in. The LLM picks tools on its own until it has the information it needs, mirroring how modern code-editing assistants drives its own tool use.

from praxia.agent import AutonomousAgent
from praxia.core.llm import LLM

agent = AutonomousAgent(user_id="alice", org_id="acme", llm=LLM("claude"))
result = agent.run("Tell me what we know about Acme and draft a proposal.")
print(result.final_text)

praxia agent run "Summarize where we stand with Acme this quarter and draft a proposal"
praxia agent tools     # list the 15 built-in tools

The agent is also exposed as a single MCP meta-tool (autonomous_agent) so remote clients (Claude Desktop, Cursor) can delegate an entire investigation without orchestrating individual tools by hand. See [FEATURES § 38](docs/FEATURES.md#38-autonomous-agent-llm-driven-tool-use-loop).

CommandedAgent — autonomous agent with external verification

AutonomousAgent is a free-running tool-use loop — perfect when the environment is the answer key (tests pass / fail, commands exit 0 / non-zero). For workloads where the environment doesn't give you that free check — private-corpus fact QA, SOP / compliance, customer support over manuals, technical-knowledge transfer — CommandedAgent wraps it with three guards: pre-retrieval + grounding verification + bounded retry, with an explicit abstain path when the sources don't support a confident answer.

Calibrated against an in-house multi-hop RAG harness (HotpotQA / SQuAD v2 / JEMHopQA) — see [docs/VERIFICATION_FINDINGS.md](docs/VERIFICATION_FINDINGS.md) — and the resulting defaults:

• Task-type router — default_task_classifier sends coding /

command / tool prompts straight through (the environment is the verifier for those), and routes knowledge-QA through the grounding gate. Bilingual (EN/JA) keyword classifier; pluggable.

• Query decomposition — wire a QueryDecomposer into the

retriever and multi-hop questions are split into sub-questions, retrieved per hop, and unioned (deduped). +12pt on HotpotQA-distractor 40q in the source benchmark; single-hop queries pass through unchanged.

• No-improvement early stop — a redraft that fails to lift

groundedness by min_groundedness_improvement (default 0.05) aborts to abstain instead of burning the whole round budget on a loop that isn't making progress.

• Promotion engine hardened — PromotionEngine blends

0.5·freq + 0.4·outcome + 0.1·self_eval and alpha26+ adds two hard pre-condition gates that short-circuit the score blend: (1) an optional external verifier(text, verdict) → (passed, reason) callback — passed=False forces decision="skip" no matter how high the blend scored, closing the self-eval reward- hacking path identified in the L0–L3 verification bench; (2) min_independent_sources=2 — single-source candidates cap at "review" and never auto-promote, blocking the contamination-from-one-user path. require_verification=True fails closed when no verifier is wired in.

from praxia.agent import AutonomousAgent, CommandedAgent
from praxia.agent.decomposer import LLMQueryDecomposer
from praxia.agent.commander import DefaultMemoryRetriever
from praxia.core.llm import LLM

llm = LLM("claude")
inner = AutonomousAgent(user_id="alice", org_id="acme", llm=llm)
retriever = DefaultMemoryRetriever(
    personal=inner.memory,                  # L1
    decomposer=LLMQueryDecomposer(llm=llm), # K2: multi-hop split
)
agent = CommandedAgent(inner, retriever=retriever, max_verify_rounds=3)

result = agent.run(
    "How do we handle Customer X's stamping-press alarm code E-204?"
)
# result.answer carries [L1#0, L3#2, ...] citations
# result.verdict.decision ∈ {"accept", "redraft", "abstain"}
# result.stopped_reason ∈ {"accept", "abstain", "no_improvement",
#                          "max_rounds", "bypass_action"}
# result.task_kind         ∈ {"knowledge", "action"}
# result.rounds            is the per-round draft + verdict trace

Pluggable everything: Verifier / Retriever / QueryDecomposer / TaskClassifier are all protocols or callables, so you can drop in TiDB Vector / pgvector / hybrid BM25+ANN / GraphRAG or your own grounding scorer / decomposer without touching core. See [FEATURES § 38b](docs/FEATURES.md#38b-commandedagent--autonomous-agent-with-external-grounding-commander) and [docs/COMMANDED_AGENT.md](docs/COMMANDED_AGENT.md).

3 Specialized Multi-Agent Flows

| Flow | What it does | |---|---| | SalesAgentFlow | Reads customer IR, past minutes, RAG context → generates hypotheses → FAQ → proposal outline | | LogicCheckerFlow | Three agents (structure / contradiction / reader) review long documents for logical consistency | | RAGOptimizationFlow | Self-correcting RAG: query expansion → retrieval → relevance eval → hallucination check loop |

6 Default Business-Domain Skills

| Skill | Domain | Use cases | |---|---|---| | InvestmentSkill | Investment | Equity research, due diligence, portfolio decisions | | SalesSkill | Sales | Account research, proposal drafting, FAQ prep | | DesignSkill | Engineering Design | System design review, requirements engineering | | PurchasingSkill | Procurement | Supplier evaluation, RFQ analysis, TCO, BCP risk | | PatentSkill | IP / Patent | Prior-art search, claims drafting, patent maps | | LegalSkill | Legal | Contract review, compliance, M&A diligence |

Each skill serializes to Claude-Skills / MCP-compatible SKILL.md.

Plus four utility skills:

| Skill | What it does | |---|---| | PromptDesignerSkill | Take a one-line task description → produce a production-grade prompt template (system + user + 2-3 few-shot examples + 5-criterion rubric) tuned for the target LLM (Claude / OpenAI / DeepSeek / Mistral / Llama / …). Save to PromptStore, A/B-test via praxia.experiments. | | OutputFormatSkill | Detect "export as PowerPoint" / "as Word doc" / etc. in natural language (English + JA / ZH / KO / ES / FR / DE / PT-BR phrases also recognized) and dispatch to the matching exporter (PPTX / DOCX / HTML / MD / JSON). | | PptxDesignerSkill | Code-gen path (Claude-Skills-style): the LLM authors python-pptx code that runs in a sandbox to produce a design-rich .pptx — multi-column layouts, matrix slides, embedded charts, themed branding. Themes (colors / fonts / logo / footer) live under .praxia/themes// and are managed in Admin → 🎨 Themes. | | DocxDesignerSkill | Same approach for Word documents — LLM-authored python-docx code, sandbox execution, themed .docx output (heading hierarchy, page footer, tables, callouts, embedded charts). |

# Generate a prompt template for any task
praxia skill run prompt_designer "Have in-house legal score contract risk on a 5-point scale"

# Generate a design-rich slide deck via code-gen
praxia skill run pptx_designer "Q4 sales review for Acme — cover, exec summary, top 3 customers, 2x2 challenges matrix, next actions. 10 slides."

All Major LLMs

LiteLLM-powered single-line provider switching:

| Provider | Aliases (current default) | Auth env var(s) | |---|---|---| | Anthropic Claude | claude (Opus 4.7) · claude-sonnet (4.6) · claude-haiku (4.5) | ANTHROPIC_API_KEY | | OpenAI ChatGPT | chatgpt (GPT-5.5) · gpt-5.5 · gpt-5.4 · gpt-5 · o4-mini · o3 · gpt-4o | OPENAI_API_KEY |

…

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: praxia-dev
• Source: praxia-dev/praxia
• License: Apache-2.0
• Homepage: https://praxia.tools

Install and usage instructions live in the source repository linked above.