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Agent Farm

An orchestration platform for deploying parallel swarms of tool-enabled AI agents to perform complex tasks like code generation, system monitoring, and multi-perspective analysis. It features a unique chunked write pattern that enables the creation of large-scale documents and code files by assembling parallel agent outputs.

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4
Forks
1
Updated
Jan 23, 2026
Validated
Feb 4, 2026

Agent Farm v3.4 - Chunked Write Edition

AI organism evolution and parallel task execution with tool-enabled agents. Now with Chunked Write Pattern for generating large documents and code files!

What's New in v3.4

  • Chunked Write Pattern: Bugs write sections in parallel, Python assembles directly
  • chunked_write: Generate large markdown/text documents (unlimited size)
  • chunked_code_gen: Generate multi-function code files in parallel
  • chunked_analysis: Multi-perspective analysis with synthesis
  • Bypasses 500-char limit: Each bug writes small chunks, combined output is unlimited

Performance

  • 8.6x faster than v3.0 (103s -> 12s for 4-task swarm)
  • 1 iteration per task (was 3-5)
  • 100% success rate with real tool data
  • Local synthesis - qwen2.5:14b synthesizes results (no cloud tokens!)

Models

RoleModelVRAMPurpose
Scoutqwen3:4b2.5GBReconnaissance
Workerqwen3:4b2.5GBTask execution
Memoryqwen3:4b2.5GBContext retention
Guardianqwen3:4b2.5GBSystem monitoring
Learnerqwen3:4b2.5GBPattern acquisition
Synthesizerqwen2.5:14b8.99GBResult synthesis

MCP Tools (30)

Colony Management

  • spawn_colony - Create bug colony (standard/fast/heavy/hybrid)
  • list_colonies - List active colonies
  • colony_status - Detailed colony info
  • quick_colony - Quick health check
  • dissolve_colony - Remove colony
  • cleanup_idle - Remove idle colonies
  • farm_stats - Comprehensive statistics

Swarm Deployment

  • deploy_swarm - Deploy tasks to colony
  • quick_swarm - One-shot spawn + deploy

Specialized Swarms

  • code_review_swarm - 4-perspective code review
  • code_gen_swarm - Generate code + tests + docs
  • file_swarm - Parallel file operations
  • exec_swarm - Parallel shell commands
  • api_swarm - Parallel HTTP requests
  • kmkb_swarm - Multi-angle knowledge queries

Tool-Enabled Agents

  • tool_swarm - Deploy bugs with real system tools
  • system_health_swarm - Quick system health check
  • recon_swarm - Directory/codebase reconnaissance
  • deep_analysis_swarm - Deep disk/file analysis
  • worker_task - Single worker with full tools

Direct Operations

  • heavy_write - Direct file write (bypasses LLM for large content)
  • synthesize - Standalone synthesis of any JSON results

Chunked Write Pattern (NEW)

  • chunked_write - Generate large documents via parallel section writing
  • chunked_code_gen - Generate code files with functions written in parallel
  • chunked_analysis - Multi-perspective analysis with synthesis

Bug Tool Permissions

RoleTools
Scoutread_file, list_dir, file_exists, system_status, process_list, disk_usage, check_service, exec_cmd
Workerread_file, write_file, list_dir, exec_cmd, http_get, http_post, system_status, disk_usage, check_service
Memoryread_file, kmkb_search, kmkb_ask, list_dir, system_status, process_list, disk_usage, check_service, exec_cmd
Guardiansystem_status, process_list, disk_usage, check_service, read_file, list_dir, exec_cmd
Learnerread_file, analyze_code, list_dir, kmkb_search, system_status, process_list, disk_usage, check_service, exec_cmd

Structured Output Details

Agent Farm v3.3 uses Ollama's structured output feature to enforce JSON schemas on model responses:

# Bug responds with guaranteed-valid JSON:
{"tool": "system_status", "arg": ""}
{"tool": "exec_cmd", "arg": "df -h"}
{"tool": "check_service", "arg": "ollama"}

The constrained decoding (GBNF grammar) masks invalid tokens during generation, ensuring:

  • Always valid JSON
  • Correct tool names
  • Proper argument structure
  • No parsing failures

Results now include a mode field showing which method was used:

  • structured - JSON schema enforced
  • structured+autoformat - JSON + simple result formatting
  • structured+deep - JSON with multi-step reasoning
  • regex - Fallback regex parsing
  • regex+autoformat - Regex + simple result formatting

Chunked Write Pattern

The chunked write pattern solves the ~500 char output limitation of small models by decomposing large tasks:

1. PLANNER BUG (qwen2.5:14b)
   |-- Creates structured JSON outline
   |-- {"sections": [{"title": "...", "description": "..."}]}

2. WORKER BUGS (qwen3:4b) - IN PARALLEL
   |-- Each writes one section (~300-500 chars)
   |-- 4 workers = 4 sections simultaneously

3. PYTHON CONCATENATION (NO LLM)
   |-- header + separator.join(sections)
   |-- Zero token cost, instant assembly

4. DIRECT FILE WRITE (NO LLM)
   |-- tool_write_file() saves result
   |-- Bypasses any output corruption

Performance

ToolOutput SizeSectionsTime
chunked_write9.6 KB578s
chunked_code_gen1.9 KB4 functions88s
chunked_analysisVaries4 perspectives~60s

Why It Works

  • Small models excel at focused, short outputs
  • Each section is within the "safe zone" (<500 chars)
  • Python handles assembly (no LLM token cost)
  • Parallel execution via ThreadPoolExecutor
  • Structured output ensures reliable planning

Usage Examples

System Health Check

agent-farm:system_health_swarm

Custom Task Swarm

agent-farm:tool_swarm
  colony_type: "heavy"
  tasks: [
    {"prompt": "Check CPU temperature"},
    {"prompt": "List top 5 memory processes"},
    {"prompt": "Check if docker is running"}
  ]

Large File Write (Direct)

agent-farm:heavy_write
  path: "/tmp/large_output.txt"
  content: "... large content ..."

Codebase Reconnaissance

agent-farm:recon_swarm
  target_path: "/home/kyle/repos/my-project"

Generate Large Document (Chunked)

agent-farm:chunked_write
  output_path: "/tmp/security_guide.md"
  spec: "Linux server security hardening guide"
  num_sections: 5
  doc_type: "markdown"

Output: 9KB+ document with 5 coherent sections

Generate Code File (Chunked)

agent-farm:chunked_code_gen
  output_path: "/tmp/utils.py"
  spec: "File utilities: read, write, copy, delete"
  language: "python"
  num_functions: 4

Output: Complete Python module with 4 functions

Multi-Perspective Analysis

agent-farm:chunked_analysis
  target: "/home/kyle/repos/project"
  question: "What are the architectural patterns?"
  num_perspectives: 4

Output: Analysis from Structure, Patterns, Quality, Performance perspectives

Installation

cd ~/repos/agent-farm
uv venv
uv pip install -e .

Claude Desktop Config

{
  "mcpServers": {
    "agent-farm": {
      "command": "/home/kyle/repos/agent-farm/.venv/bin/python",
      "args": ["-m", "agent_farm.server"]
    }
  }
}

Changelog

v3.4.0 (2026-01-23)

  • Chunked Write Pattern - Bugs write sections in parallel, Python assembles
  • chunked_write - Generate unlimited-size documents (tested: 9.6KB in 78s)
  • chunked_code_gen - Generate multi-function code files in parallel
  • chunked_analysis - Multi-perspective analysis with synthesis
  • Bypasses 500-char bug limitation via task decomposition
  • Planner uses structured JSON output for reliable outlines

v3.3.0 (2026-01-23)

  • Ollama Structured Output - JSON schema enforcement via constrained decoding
  • Reliable tool parsing - No more regex failures
  • Mode tracking - Results show parsing method used
  • Regex fallback - Legacy parsing still available as backup
  • All roles get exec_cmd for complex shell queries

v3.2.0 (2026-01-22)

  • Synthesizer role - qwen2.5:14b for accurate result synthesis
  • synthesize parameter - Added to tool_swarm, system_health_swarm, recon_swarm, deep_analysis_swarm
  • synthesize tool - Standalone synthesis of any JSON results
  • No more Claude synthesis tax - bugs do ALL the work locally

v3.1.0 (2026-01-20)

  • 8.6x speed improvement (103s -> 12s)
  • Auto-format results skip redundant LLM calls
  • Reject invalid tools instantly
  • Force tool usage before answers
  • Complex shell command support fixed
  • All roles upgraded to qwen3:4b minimum

v3.0.0 (2026-01-19)

  • Linux rebuild from Windows version
  • Tool-enabled agents with role permissions
  • System health, recon, worker swarms
  • TRUE PARALLEL via ThreadPoolExecutor

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