PraisonAI

AI Agents Framework with Self Reflection and MCP support

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "praisonai": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

PraisonAI Logo

PraisonAI 🦞

PraisonAI 🦞 — Hire a 24/7 AI Workforce. Stop writing boilerplate and start shipping autonomous, self-improving agents that research, plan, and execute tasks across your apps. From one agent to an entire organization, deployed in 5 lines of code.

Terminal

curl -fsSL https://praison.ai/install.sh | bash

PraisonAI Dashboard

Code

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 pip install praisonai

🎯 Use Cases

AI agents solving real-world problems across industries:

Use Case	Description
🔍 Research & Analysis	Conduct deep research, gather information, and generate insights from multiple sources automatically
💻 Code Generation	Write, debug, and refactor code with AI agents that understand your codebase and requirements
✍️ Content Creation	Generate blog posts, documentation, marketing copy, and technical writing with multi-agent teams
📊 Data Pipelines	Extract, transform, and analyze data from APIs, databases, and web sources automatically
🤖 Customer Support	Deploy 24/7 support bots on Telegram, Discord, Slack with memory and knowledge-backed responses
⚙️ Workflow Automation	Automate multi-step business processes with agents that hand off tasks, verify results, and self-correct

🚀 Meet your first Agent (Under 1 Minute)

Install the lightweight core SDK:

Terminal

pip install praisonaiagents
export OPENAI_API_KEY="your-api-key"

Run your first autonomous agent:

server.ts

from praisonaiagents import Agent

# Give your agent a goal, and watch it work.
agent = Agent(instructions="You are a senior data analyst.")
agent.start("Analyze the top 3 tech trends of 2026 and format as a markdown table.")

🧬 The Five-Layer Agent Stack

Most frameworks hand you one or two layers and leave the rest as homework. PraisonAI covers all five — plus the outer layer that decides where your agent actually runs.

Each layer wraps the one inside it. When an agent misbehaves, the layer tells you where to look.

Code

┌─────────────────────────────────────────────────────────────────┐
│ ⬡ MANAGED AGENTS — Where does it actually run?                  │
│ ┌─────────────────────────────────────────────────────────────┐ │
│ │ 5 · GRAPH — Who runs when, and who checks whom?             │ │
│ │ ┌─────────────────────────────────────────────────────────┐ │ │
│ │ │ 4 · LOOP — When do we stop?                             │ │ │
│ │ │ ┌─────────────────────────────────────────────────────┐ │ │ │
│ │ │ │ 3 · HARNESS — Can it act, and be checked?           │ │ │ │
│ │ │ │ ┌─────────────────────────────────────────────────┐ │ │ │ │
│ │ │ │ │ 2 · CONTEXT — Is the right thing in the window? │ │ │ │ │
│ │ │ │ │ ┌─────────────────────────────────────────────┐ │ │ │ │ │
│ │ │ │ │ │ 1 · PROMPT — Did I say it clearly?          │ │ │ │ │ │
│ │ │ │ │ └─────────────────────────────────────────────┘ │ │ │ │ │
│ │ │ │ └─────────────────────────────────────────────────┘ │ │ │ │
│ │ │ └─────────────────────────────────────────────────────┘ │ │ │
│ │ └─────────────────────────────────────────────────────────┘ │ │
│ └─────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────┘

Layer	The question it answers	PraisonAI
1 · Prompt	Did I say it clearly?	`instructions=`, `role`/`goal`/`backstory`, `output=`, `templates=`
2 · Context	Is the right thing in the window?	`memory=`, `knowledge=`, `context=`, handoff `ContextPolicy`
3 · Harness	Can it act, and be checked?	`tools=`, `MCP()`, `guardrails=`, `approval=`, `hooks=`, `sandbox=`
4 · Loop	When do we stop?	`execution=ExecutionConfig(...)`, `reflection=`, `autonomy=`, doom-loop detection
5 · Graph	Who runs when, and who checks whom?	`AgentFlow`, `route()`, `parallel()`, `loop()`, `repeat()`
⬡ Managed	Where does it actually run?	`tools_run_on="docker"` — one shared sandbox for the tools, or `run_on="anthropic"` for the whole agent

Layer 1 · Prompt — Did I say it clearly?

Role, instructions, examples, output format.

server.ts

from praisonaiagents import Agent

agent = Agent(
    role="Senior Data Analyst",
    goal="Turn raw numbers into decisions",
    output="verbose",              # markdown-formatted output
)
agent.start("Summarise Q3 revenue trends")

Layer 2 · Context — Is the right thing in the window?

Write, select, compress, isolate — the four context operations, one parameter each.

server.ts

from praisonaiagents import Agent

agent = Agent(
    instructions="You are a support engineer.",
    memory={"user_id": "u-42"},    # write    — persists across runs (needs a user_id)
    knowledge=["docs/"],           # select   — retrieves only what's relevant
    context="summarize",           # compress — auto-compacts before the limit
)

Isolate is handoffs=[specialist] — a sub-agent inherits the last few messages and the intersection of your tools, not your whole transcript. 📖 Handoffs

Layer 3 · Harness — Can it act, and be checked?

Agent = Model + Harness. Tool dispatch, plus the guides that steer before acting and the sensors that observe after.

server.ts

from praisonaiagents import Agent, MCP, tool

@tool
def deploy(env: str) -> str:
    """Deploy the current build to an environment."""
    return f"Deployed to {env}"

agent = Agent(
    name="ReleaseEngineer",
    instructions="You are a release engineer.",
    tools=[deploy, MCP("npx -y @modelcontextprotocol/server-filesystem /tmp")],
    approval=True,                 # guide — human gate before risky tools run
)
agent.start("Deploy to staging, then list the files you can read")

Layer 4 · Loop — When do we stop?

Hard iteration caps, budget ceilings, no-progress detection and completion checks — every brake is explicit.

server.ts

from praisonaiagents import Agent, ExecutionConfig

agent = Agent(
    instructions="Fix the failing tests.",
    execution=ExecutionConfig(max_iter=30, max_budget=0.50, on_budget_exceeded="stop"),
    autonomy=True,                 # required to drive the loop with run_autonomous()
)
result = agent.run_autonomous("Refactor the auth module", max_iterations=5)

print(result.completion_reason)
# goal | no_tool_calls | max_iterations | timeout | doom_loop | needs_help | error
# (with on_budget_exceeded="stop", hitting the cap raises BudgetExceededError,
#  surfaced here as completion_reason="error")

Doom-loop detection is on by default. Repeated identical tool calls and A→B→A→B oscillation get caught — while a poller whose output keeps changing does not. 📖 Doom Loop Detection

Layer 5 · Graph — Who runs when, and who checks whom?

Topology as a versionable artifact: prompt chaining, routing, parallelisation, orchestrator-worker.

server.ts

from praisonaiagents import AgentFlow
from praisonaiagents.workflows import route, parallel, repeat

Read the full README →View source on GitHub →

Related MCP Servers

View all in Developer Tools View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

9.1k

Stargazers on the source repository.

npm downloads

2.9M

Package downloads in the last 30 days.

Last commit

2d ago

Most recent push to the default branch.

Directory activity

1 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about PraisonAI

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "praisonai": { "command": "npx", "args": ["-y","@modelcontextprotocol/server-filesystem"] } }

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Technical Specs & Signals

CategoryDeveloper Tools

11/16 checks healthy over the last 45d

48Quality signal: Fair · 48/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership7/20

Documentation & tools15/30

Adoption & activity14/15

Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

Supply-chain signal

2 high-severity advisories on record for this package. Most advisories affect transitive dependencies and may not be exploitable in this server's actual usage — this is a directional signal, not a security audit.

Critical 0High 2Medium 0Low 0

Scanned 1d ago via OSV.dev · @modelcontextprotocol/server-filesystem (npm)

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More in 💻 Developer Tools →Best MCP servers for Developers →Alternatives to PraisonAI →Install in Claude Desktop Install in Cursor Install in VS Code Setup guides for all 13 MCP clients

PraisonAI

AI Agents Framework with Self Reflection and MCP support

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "praisonai": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

PraisonAI Logo

PraisonAI 🦞

Terminal

curl -fsSL https://praison.ai/install.sh | bash

PraisonAI Dashboard

Code

 ██████╗ ██████╗  █████╗ ██╗███████╗ ██████╗ ███╗   ██╗     █████╗ ██╗
 ██╔══██╗██╔══██╗██╔══██╗██║██╔════╝██╔═══██╗████╗  ██║    ██╔══██╗██║
 ██████╔╝██████╔╝███████║██║███████╗██║   ██║██╔██╗ ██║    ███████║██║
 ██╔═══╝ ██╔══██╗██╔══██║██║╚════██║██║   ██║██║╚██╗██║    ██╔══██║██║
 ██║     ██║  ██║██║  ██║██║███████║╚██████╔╝██║ ╚████║    ██║  ██║██║
 ╚═╝     ╚═╝  ╚═╝╚═╝  ╚═╝╚═╝╚══════╝ ╚═════╝ ╚═╝  ╚═══╝    ╚═╝  ╚═╝╚═╝

 pip install praisonai

🎯 Use Cases

AI agents solving real-world problems across industries:

Use Case	Description
🔍 Research & Analysis	Conduct deep research, gather information, and generate insights from multiple sources automatically
💻 Code Generation	Write, debug, and refactor code with AI agents that understand your codebase and requirements
✍️ Content Creation	Generate blog posts, documentation, marketing copy, and technical writing with multi-agent teams
📊 Data Pipelines	Extract, transform, and analyze data from APIs, databases, and web sources automatically
🤖 Customer Support	Deploy 24/7 support bots on Telegram, Discord, Slack with memory and knowledge-backed responses
⚙️ Workflow Automation	Automate multi-step business processes with agents that hand off tasks, verify results, and self-correct

🚀 Meet your first Agent (Under 1 Minute)

Install the lightweight core SDK:

Terminal

pip install praisonaiagents
export OPENAI_API_KEY="your-api-key"

Run your first autonomous agent:

server.ts

from praisonaiagents import Agent

# Give your agent a goal, and watch it work.
agent = Agent(instructions="You are a senior data analyst.")
agent.start("Analyze the top 3 tech trends of 2026 and format as a markdown table.")

🧬 The Five-Layer Agent Stack

Most frameworks hand you one or two layers and leave the rest as homework. PraisonAI covers all five — plus the outer layer that decides where your agent actually runs.

Each layer wraps the one inside it. When an agent misbehaves, the layer tells you where to look.

Code

┌─────────────────────────────────────────────────────────────────┐
│ ⬡ MANAGED AGENTS — Where does it actually run?                  │
│ ┌─────────────────────────────────────────────────────────────┐ │
│ │ 5 · GRAPH — Who runs when, and who checks whom?             │ │
│ │ ┌─────────────────────────────────────────────────────────┐ │ │
│ │ │ 4 · LOOP — When do we stop?                             │ │ │
│ │ │ ┌─────────────────────────────────────────────────────┐ │ │ │
│ │ │ │ 3 · HARNESS — Can it act, and be checked?           │ │ │ │
│ │ │ │ ┌─────────────────────────────────────────────────┐ │ │ │ │
│ │ │ │ │ 2 · CONTEXT — Is the right thing in the window? │ │ │ │ │
│ │ │ │ │ ┌─────────────────────────────────────────────┐ │ │ │ │ │
│ │ │ │ │ │ 1 · PROMPT — Did I say it clearly?          │ │ │ │ │ │
│ │ │ │ │ └─────────────────────────────────────────────┘ │ │ │ │ │
│ │ │ │ └─────────────────────────────────────────────────┘ │ │ │ │
│ │ │ └─────────────────────────────────────────────────────┘ │ │ │
│ │ └─────────────────────────────────────────────────────────┘ │ │
│ └─────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────┘

Layer	The question it answers	PraisonAI
1 · Prompt	Did I say it clearly?	`instructions=`, `role`/`goal`/`backstory`, `output=`, `templates=`
2 · Context	Is the right thing in the window?	`memory=`, `knowledge=`, `context=`, handoff `ContextPolicy`
3 · Harness	Can it act, and be checked?	`tools=`, `MCP()`, `guardrails=`, `approval=`, `hooks=`, `sandbox=`
4 · Loop	When do we stop?	`execution=ExecutionConfig(...)`, `reflection=`, `autonomy=`, doom-loop detection
5 · Graph	Who runs when, and who checks whom?	`AgentFlow`, `route()`, `parallel()`, `loop()`, `repeat()`
⬡ Managed	Where does it actually run?	`tools_run_on="docker"` — one shared sandbox for the tools, or `run_on="anthropic"` for the whole agent

Layer 1 · Prompt — Did I say it clearly?

Role, instructions, examples, output format.

server.ts

from praisonaiagents import Agent

agent = Agent(
    role="Senior Data Analyst",
    goal="Turn raw numbers into decisions",
    output="verbose",              # markdown-formatted output
)
agent.start("Summarise Q3 revenue trends")

Layer 2 · Context — Is the right thing in the window?

Write, select, compress, isolate — the four context operations, one parameter each.

server.ts

from praisonaiagents import Agent

agent = Agent(
    instructions="You are a support engineer.",
    memory={"user_id": "u-42"},    # write    — persists across runs (needs a user_id)
    knowledge=["docs/"],           # select   — retrieves only what's relevant
    context="summarize",           # compress — auto-compacts before the limit
)

Isolate is handoffs=[specialist] — a sub-agent inherits the last few messages and the intersection of your tools, not your whole transcript. 📖 Handoffs

Layer 3 · Harness — Can it act, and be checked?

Agent = Model + Harness. Tool dispatch, plus the guides that steer before acting and the sensors that observe after.

server.ts

from praisonaiagents import Agent, MCP, tool

@tool
def deploy(env: str) -> str:
    """Deploy the current build to an environment."""
    return f"Deployed to {env}"

agent = Agent(
    name="ReleaseEngineer",
    instructions="You are a release engineer.",
    tools=[deploy, MCP("npx -y @modelcontextprotocol/server-filesystem /tmp")],
    approval=True,                 # guide — human gate before risky tools run
)
agent.start("Deploy to staging, then list the files you can read")

Layer 4 · Loop — When do we stop?

Hard iteration caps, budget ceilings, no-progress detection and completion checks — every brake is explicit.

server.ts

from praisonaiagents import Agent, ExecutionConfig

agent = Agent(
    instructions="Fix the failing tests.",
    execution=ExecutionConfig(max_iter=30, max_budget=0.50, on_budget_exceeded="stop"),
    autonomy=True,                 # required to drive the loop with run_autonomous()
)
result = agent.run_autonomous("Refactor the auth module", max_iterations=5)

print(result.completion_reason)
# goal | no_tool_calls | max_iterations | timeout | doom_loop | needs_help | error
# (with on_budget_exceeded="stop", hitting the cap raises BudgetExceededError,
#  surfaced here as completion_reason="error")

Doom-loop detection is on by default. Repeated identical tool calls and A→B→A→B oscillation get caught — while a poller whose output keeps changing does not. 📖 Doom Loop Detection

Layer 5 · Graph — Who runs when, and who checks whom?

Topology as a versionable artifact: prompt chaining, routing, parallelisation, orchestrator-worker.

server.ts

from praisonaiagents import AgentFlow
from praisonaiagents.workflows import route, parallel, repeat

Read the full README →View source on GitHub →

Related MCP Servers

View all in Developer Tools View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

9.1k

Stargazers on the source repository.

npm downloads

2.9M

Package downloads in the last 30 days.

Last commit

2d ago

Most recent push to the default branch.

Directory activity

1 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about PraisonAI

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "praisonai": { "command": "npx", "args": ["-y","@modelcontextprotocol/server-filesystem"] } }

AllMCPs Directory Badge

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Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:

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Markdown (GitHub README)

[![AllMCPs](https://allmcps.com/api/badge/praisonai?style=directory)](https://allmcps.com/mcp/praisonai)

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<a href="https://allmcps.com/mcp/praisonai"><img src="https://allmcps.com/api/badge/praisonai?style=directory" alt="PraisonAI on AllMCPs" /></a>

Technical Specs & Signals

CategoryDeveloper Tools

11/16 checks healthy over the last 45d

48Quality signal: Fair · 48/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership7/20

Documentation & tools15/30

Adoption & activity14/15

Community engagement0/10

Supply-chain signal

Critical 0High 2Medium 0Low 0

Scanned 1d ago via OSV.dev · @modelcontextprotocol/server-filesystem (npm)

★ Featured

AllMCPs Server

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More in 💻 Developer Tools →Best MCP servers for Developers →Alternatives to PraisonAI →Install in Claude Desktop Install in Cursor Install in VS Code Setup guides for all 13 MCP clients