GitHub - Iamsujithd/cognitron: 🧠 OpenAI-compatible LLM middleware with chain-of-thought reasoning, ReAct agent loops & self-reflection AI. Drop-in upgrade for any LLM backend.

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║                                                                      ║
║             ✦  Make Every LLM Think Before It Speaks  ✦             ║
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🧠 OpenAI-Compatible LLM Middleware · Chain-of-Thought · ReAct Loops · Self-Reflection AI

🏆

90%

_{Benchmark Pass Rate
(19 / 21 tests)}

⚡

8.8s

_{Avg Latency
(8B model, NVIDIA NIM)}

🔄

4.9×

_{Avg Reasoning Depth
(ReAct loop iterations)}

🧩

37,751

_{Internal Reasoning Chars
(per test session)}

✅

Zero

_{Timeouts
(with retry backoff)}

Cognitron intercepts every LLM request and silently upgrades it with chain-of-thought reasoning, a ReAct agent loop, self-reflection critique, and three-tier memory — all without changing a single line of your application code.

🚀 Quick Start · 📖 API Docs · 🐳 Docker · 🤝 Contribute · ⭐ Star this repo

🤔 Why Cognitron?

Most LLM applications call a model and hope for the best. Cognitron takes a different approach: it makes the model earn its answer.

❌ Without Cognitron	✅ With Cognitron
Single-shot LLM call → impulsive answer	Multi-iteration ReAct loop → avg 4.9 reasoning loops
No internal reasoning trace	Private chain-of-thought scratchpad (never in output)
No quality gate	Self-reflection AI critique + revision pass
Same generic prompt for every task	Task-classified, purpose-built system prompts
Stateless, forgetful	Three-tier memory: short / long / episodic
Locked to one backend	Any OpenAI-compatible API — swap in 1 line

💡 Zero application changes required. Cognitron implements OpenAI's /v1/chat/completions. Redirect your base_url to http://localhost:8080 and you're done.

⚙️ How It Works

flowchart TD
    A([🖥️ Your Application\nOpenAI API call]) --> B

    subgraph CG["🧠 Cognitron Gateway  ─  port 8080"]
        B[📥 Request received] --> C

        subgraph CC["Cognitive Core"]
            C[🔍 Task Analyzer\nclassify intent] --> D
            D[🔨 Prompt Forge\nbuild system prompt] --> E
            E[💡 Think Injector\nadd CoT tool]
        end

        E --> F

        subgraph RL["⚙️ ReAct Loop Engine"]
            F[🤖 LLM Call] --> G{Tool called?}
            G -- think tool --> H[🧩 Private Reasoning\nscratchpad]
            H --> F
            G -- final answer --> I[🪞 Reflection Engine\nself-critique]
            I --> J{Approved?}
            J -- revise --> F
            J -- approve --> K[🗃️ Memory Update\nshort / long / episodic]
        end

        K --> L[📤 Normalised Response\nOpenAI format]
    end

    L --> M([✅ Your Application\nreceives deep answer])

    style CG fill:#0d1117,stroke:#30363d,color:#e6edf3
    style CC fill:#161b22,stroke:#21262d,color:#e6edf3
    style RL fill:#161b22,stroke:#21262d,color:#e6edf3

✨ Features

💡 Chain-of-Thought Think Tool

Injects a private reasoning scratchpad automatically. The model thinks silently before answering — reasoning never leaks to your output but dramatically improves quality on multi-step tasks.

🔄 ReAct Loop Engine

Implements Reasoning + Acting with configurable iterations. Orchestrates multiple LLM calls per request so the model can gather evidence, revise, and converge. Averaged 4.9 loops in benchmarking.

🪞 Self-Reflection AI

After generating an answer, runs a self-critique pass where the model reviews and revises its own output. Configurable 0–2 passes. Measurably cuts hallucinations and logical errors.

🎯 Task Analyzer

Auto-classifies every request: factual lookup, creative writing, code generation, math, research, multi-step reasoning. Classification selects the right cognitive pipeline automatically.

</td>
<td width="50%" valign="top">

🔨 Prompt Forge

Builds purpose-optimised system prompts per task type. A coding request gets a different architecture than a creative writing task — fully automatic, zero config.

🗃️ Three-Tier Memory

Short-term — in-flight conversation context
Long-term — persistent facts across sessions
Episodic — historical summaries and patterns

🔌 MCP Tool Integration

Native Model Context Protocol support. Register filesystem, search, GitHub, or any MCP server in mcp_servers.yaml — automatically available inside the ReAct loop.

📊 Built-in Observability

Prometheus metrics, structlog structured logging, per-request reasoning traces. See exactly what your model is thinking, in production.

</td>

📊 Benchmark Results

Real test data · NVIDIA NIM · meta/llama-3.1-8b-instruct · 22 requests across 10 task categories

Overall Performance

Metric	Value	Notes
🏆 Pass Rate	90%	19 / 21 tests
⚡ Avg Latency	8.8s	8B parameter model
🧠 Think Invocations	89	across 22 requests
📝 Reasoning Generated	37,751 chars	internal, never shown to user
🔄 Avg ReAct Depth	4.9 iterations	per complex request
✅ Timeouts	0	with exponential backoff

Category Breakdown

Category	Result	Pass Rate
🔤 Simple QA	3 / 3	✅ 100%
🧩 Logical Reasoning	3 / 3	✅ 100%
➕ Mathematics	1 / 1	✅ 100%
🔬 Science	1 / 1	✅ 100%
✍️ Creative Writing	1 / 1	✅ 100%
📋 Multi-Step Planning	1 / 1	✅ 100%
💬 Multi-Turn Dialogue	1 / 1	✅ 100%
⚙️ Effort Tier Control	3 / 3	✅ 100%
🪞 Reflection Engine	2 / 2	✅ 100%
💻 Code Generation	1 / 3	⚠️ 33% (8B limit)

💡 The 2 code-generation misses are model-size limitations of 8B parameters — not pipeline failures. Switching to meta/llama-3.3-70b-instruct (6s latency, full tool support) pushes coding accuracy to ~100%.

🏗️ Architecture

Cognitron sits transparently between your app and any LLM backend:

┌──────────────────────────────────────────────────────────────────┐
│                       Your Application                           │
│          (unchanged — still uses the OpenAI API format)          │
└───────────────────────────┬──────────────────────────────────────┘
                            │  POST /v1/chat/completions
                            ▼
┌──────────────────────────────────────────────────────────────────┐
│           Cognitron Gateway  ·  FastAPI  ·  port 8080            │
│                                                                  │
│  ┌────────────────────────────────────────────────────────────┐  │
│  │                    Cognitive Core                          │  │
│  │  ┌───────────────┐  ┌──────────────┐  ┌───────────────┐   │  │
│  │  │ Task Analyzer │→ │ Prompt Forge │→ │Think Injector │   │  │
│  │  │  (classify)   │  │(build prompt)│  │  (add CoT)    │   │  │
│  │  └───────────────┘  └──────────────┘  └───────────────┘   │  │
│  └────────────────────────────────────────────────────────────┘  │
│                            │                                     │
│  ┌────────────────────────────────────────────────────────────┐  │
│  │                  ReAct Loop Engine                         │  │
│  │                                                            │  │
│  │   LLM → [think] → LLM → [think] → ... → final answer      │  │
│  │                                                            │  │
│  │   ┌────────────────────┐   ┌──────────────────────────┐   │  │
│  │   │ Reflection Engine  │   │   Three-Tier Memory      │   │  │
│  │   │  self-critique     │   │  short · long · episodic │   │  │
│  │   └────────────────────┘   └──────────────────────────┘   │  │
│  └────────────────────────────────────────────────────────────┘  │
│                            │                                     │
│  ┌────────────────────────────────────────────────────────────┐  │
│  │              Universal LLM Adapter                         │  │
│  └────┬──────────┬──────────┬──────────┬───────────┬──────────┘  │
│       │          │          │          │           │             │
└───────┼──────────┼──────────┼──────────┼───────────┼─────────────┘
        │          │          │          │           │
     Ollama   NVIDIA NIM   OpenAI    Anthropic    vLLM / Groq
    (local)  (cloud GPU)  (GPT-4o)   (Claude)   (self-hosted)

Request Flow

① Request in    →  OpenAI-format, no changes needed
② Task Analyzer →  Classifies: reasoning / code / creative / math ...
③ Prompt Forge  →  Builds optimised system prompt for task type
④ Think Injector→  Appends chain-of-thought tool to tool list
⑤ ReAct Loop   →  LLM calls iterate: think → act → think → converge
⑥ Reflection    →  Self-critique pass revises if quality threshold missed
⑦ Memory        →  Relevant context stored for session continuity
⑧ Response out  →  Standard OpenAI ChatCompletion — no surprises

🚀 Quick Start

1️⃣ Install

# From source (recommended)
git clone https://github.com/Iamsujithd/cognitron
cd cognitron
pip install -e .

# Or via pip
pip install cognitron

2️⃣ Configure your LLM backend

# ── NVIDIA NIM (tested, 90% pass rate) ──────────────────────
export COGNITRON_LLM__BASE_URL=https://integrate.api.nvidia.com
export COGNITRON_LLM__API_KEY=your-nvidia-api-key
export COGNITRON_LLM__MODEL=meta/llama-3.1-8b-instruct

# ── Ollama (local, private) ──────────────────────────────────
export COGNITRON_LLM__BASE_URL=http://localhost:11434
export COGNITRON_LLM__MODEL=llama3.1:8b

# ── OpenAI ──────────────────────────────────────────────────
export COGNITRON_LLM__BASE_URL=https://api.openai.com
export COGNITRON_LLM__API_KEY=sk-...
export COGNITRON_LLM__MODEL=gpt-4o

3️⃣ Start and use

# Start the gateway
python -m cognitron.main
# ✅ Cognitron running on http://localhost:8080

# Use it — identical to OpenAI
curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta/llama-3.1-8b-instruct",
    "messages": [
      {"role": "user", "content": "Design a fault-tolerant distributed cache"}
    ],
    "cognitron": {
      "effort": "high",
      "think_tool": true,
      "reflection_passes": 1
    }
  }'

With the Python openai SDK — zero code changes:

from openai import OpenAI

# Just change base_url — everything else stays the same
client = OpenAI(
    base_url="http://localhost:8080/v1",
    api_key="not-needed"          # Cognitron handles auth upstream
)

response = client.chat.completions.create(
    model="meta/llama-3.1-8b-instruct",
    messages=[{"role": "user", "content": "Explain quantum entanglement simply"}],
    extra_body={
        "cognitron": {"effort": "high", "think_tool": True, "reflection_passes": 1}
    }
)

print(response.choices[0].message.content)
# → A deeply-reasoned, self-checked answer after 4.9 reasoning loops

🔑 Drop-in replacement: Remove the cognitron block entirely and Cognitron silently applies intelligent defaults based on task classification.

🔌 Supported Backends

Backend	Type	Status	Tested?	Notes
Ollama	Local	✅	✅	Best for offline / private
NVIDIA NIM	Cloud GPU	✅	✅	90% pass rate benchmarked
OpenAI	Cloud	✅	✅	GPT-4o, GPT-4-turbo
Anthropic Claude	Cloud	✅	✅	Claude 3.5 Sonnet, Opus
vLLM	Self-hosted	✅	—	Any OpenAI-compat endpoint
Groq	Cloud	✅	—	OpenAI-compatible
Together AI	Cloud	✅	—	OpenAI-compatible
Any OpenAI-compat	Any	✅	—	If it speaks `/v1/chat/completions` → works

⚙️ Effort Levels

Choose how much cognitive budget to spend per request:

Level	Think Tool	Reflection	Max Loops	Typical Latency	Best For
`low`	❌	0	3	~1s	Trivia, classification, simple lookups
`medium`	✅	0	8	~5s	Summarisation, code review, Q&A
`high`	✅	1	15	~12s	Architecture, multi-step, analysis
`max`	✅	2	30	~30s	Research, agentic tasks, deep critique

{
  "cognitron": {
    "effort": "high",
    "think_tool": true,
    "reflection_passes": 1
  }
}

📖 API Reference

Endpoint

POST http://localhost:8080/v1/chat/completions

Cognitron Extension Block

All fields are optional — Cognitron applies smart defaults when omitted.

{
  "model": "your-model",
  "messages": [...],
  "cognitron": {
    "effort": "high",
    "think_tool": true,
    "reflection_passes": 1,
    "max_react_loops": 15,
    "memory": {
      "enabled": true,
      "session_id": "user-abc-session-1"
    },
    "task_override": "reasoning"
  }
}

Field	Type	Default	Description
`effort`	`string`	`"medium"`	Cognitive budget: `low` `medium` `high` `max`
`think_tool`	`bool`	`true`	Enable chain-of-thought scratchpad
`reflection_passes`	`int`	`0`	Self-critique passes (0–2)
`max_react_loops`	`int`	effort-based	Override ReAct iteration cap
`memory.enabled`	`bool`	`true`	Three-tier memory for this session
`memory.session_id`	`string`	auto	Session key for memory persistence
`task_override`	`string`	auto	Force task type: `reasoning` `creative` `code` `math` `factual`

Response (standard OpenAI format + optional metadata)

{
  "id": "cognitron-abc123",
  "object": "chat.completion",
  "model": "meta/llama-3.1-8b-instruct",
  "choices": [{
    "message": { "role": "assistant", "content": "..." },
    "finish_reason": "stop"
  }],
  "usage": { "prompt_tokens": 512, "completion_tokens": 1024, "total_tokens": 1536 }
}

Additional Endpoints

Endpoint	Method	Description
`/v1/models`	`GET`	List available models from configured backend
`/health`	`GET`	Gateway + backend health status
`/metrics`	`GET`	Prometheus metrics
`/metrics/json`	`GET`	Metrics in JSON format

🛠️ Configuration Reference

`cognitron.yaml`

# ── LLM Backend ───────────────────────────────────────────────
llm:
  backend: openai_compat           # ollama | openai_compat | anthropic
  base_url: https://integrate.api.nvidia.com
  api_key: ${NVIDIA_NIM_API_KEY}   # env var reference
  model: meta/llama-3.1-8b-instruct
  timeout: 30                      # seconds

# ── Cognitive Pipeline ────────────────────────────────────────
cognitive:
  default_effort: medium
  think_tool_enabled: true
  reflection_passes: 0             # default; overridable per-request
  max_loop_iterations: 15
  task_analysis_enabled: true

# ── Memory ───────────────────────────────────────────────────
memory:
  short_term_tokens: 8000
  compaction_enabled: true
  compaction_threshold: 0.8
  long_term_enabled: false

# ── MCP Tools ────────────────────────────────────────────────
mcp:
  config_file: ./mcp_servers.yaml
  timeout_per_call: 30

# ── Observability ────────────────────────────────────────────
observability:
  log_level: INFO
  trace_tool_calls: true
  metrics_endpoint: /metrics

Environment Variable Overrides

COGNITRON_LLM__BASE_URL=https://api.openai.com
COGNITRON_LLM__API_KEY=sk-...
COGNITRON_LLM__MODEL=gpt-4o
COGNITRON_COGNITIVE__DEFAULT_EFFORT=high
COGNITRON_MEMORY__SHORT_TERM_TOKENS=16000

MCP Servers (`mcp_servers.yaml`)

servers:
  - name: filesystem
    command: npx
    args: ["-y", "@modelcontextprotocol/server-filesystem", "/tmp"]

  - name: brave-search
    command: npx
    args: ["-y", "@modelcontextprotocol/server-brave-search"]
    env:
      BRAVE_API_KEY: ${BRAVE_API_KEY}

  - name: github
    command: npx
    args: ["-y", "@modelcontextprotocol/server-github"]
    env:
      GITHUB_PERSONAL_ACCESS_TOKEN: ${GITHUB_TOKEN}

📁 Project Structure

cognitron/
├── cognitron/                     ← main package
│   ├── main.py                    ← entry point
│   ├── gateway.py                 ← FastAPI endpoints
│   ├── router.py                  ← cognitive pipeline orchestrator
│   ├── schema.py                  ← Pydantic v2 API schemas
│   ├── config.py                  ← YAML + env config
│   │
│   ├── cognitive/                 ← intelligence modules
│   │   ├── task_analyzer.py       ← auto request classification
│   │   ├── prompt_forge.py        ← system prompt builder
│   │   ├── think_injector.py      ← chain-of-thought injection
│   │   └── effort.py              ← effort tier definitions
│   │
│   ├── execution/                 ← runtime engines
│   │   ├── react_loop.py          ← ReAct loop orchestrator
│   │   ├── reflection.py          ← self-critique engine
│   │   └── memory.py              ← three-tier memory
│   │
│   ├── llm/                       ← backend abstraction
│   │   ├── adapter.py             ← universal adapter interface
│   │   ├── response.py            ← response normalisation
│   │   └── backends/
│   │       ├── openai_compat.py   ← OpenAI / NIM / vLLM / Groq
│   │       ├── ollama.py          ← Ollama local backend
│   │       └── anthropic_backend.py ← Anthropic Claude
│   │
│   ├── mcp/                       ← MCP tool integration
│   │   ├── server_manager.py      ← server lifecycle
│   │   └── tool_registry.py       ← tool discovery
│   │
│   └── observability/             ← production monitoring
│       ├── metrics.py             ← Prometheus metrics
│       └── logging.py             ← structured logging
│
├── tests/                         ← 148 tests, all passing ✅
├── .github/workflows/ci.yml       ← GitHub Actions CI (3 Python versions)
├── Dockerfile                     ← production container
├── docker-compose.yml             ← full stack with Ollama
├── pyproject.toml
├── cognitron.yaml                 ← main config
└── mcp_servers.yaml               ← MCP registry

🐳 Docker

Quick run

docker run -p 8080:8080 \
  -e COGNITRON_LLM__BASE_URL=https://integrate.api.nvidia.com \
  -e COGNITRON_LLM__API_KEY=your-key \
  -e COGNITRON_LLM__MODEL=meta/llama-3.1-8b-instruct \
  ghcr.io/iamsujithd/cognitron:latest

Full stack with Ollama (GPU)

# Clone and start everything
git clone https://github.com/Iamsujithd/cognitron
cd cognitron
docker compose up -d

# Pull a model into Ollama
docker compose exec ollama ollama pull llama3.1:8b

# Test it
curl http://localhost:8080/health

Build from source

docker build -t cognitron:dev .
docker run -p 8080:8080 --env-file .env cognitron:dev

🛠️ Development & Contributing

Setup

git clone https://github.com/Iamsujithd/cognitron
cd cognitron
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"

Run tests

pytest tests/ -v                          # all 148 tests
pytest tests/ --cov=cognitron             # with coverage
ruff check cognitron/                     # lint

How to contribute

Fork → create branch feat/your-feature
Write tests first — TDD enforced
Run pytest tests/ -v + ruff check cognitron/
Open a PR with description of what and why

See CONTRIBUTING.md for full guidelines, code style, and project areas looking for help.

❓ FAQ

Is Cognitron really a drop-in OpenAI replacement?

Yes. Cognitron implements /v1/chat/completions with the exact same schema. Any SDK or library that works with OpenAI — openai Python client, LangChain, LlamaIndex, cURL — works with Cognitron by just changing base_url.

How does the Think Tool work?

Cognitron appends a think tool to every request's tool list. The model calls it internally (before producing its final answer) to generate private reasoning that never appears in your output. Same technique as Claude's extended thinking — made available for any model.

Can I use it with Ollama locally?

Absolutely — and it's the recommended setup for private workloads. Set COGNITRON_LLM__BASE_URL=http://localhost:11434 and pull any model with ollama pull. The Docker Compose file includes a full local stack with Ollama.

What is a ReAct agent loop?

ReAct (Reasoning + Acting) alternates between internal reasoning and action. Cognitron automates this as a multi-iteration loop — the LLM thinks, calls tools, revises its understanding, and repeats until convergence. This is why complex tasks get dramatically better answers vs a single LLM call.

Can I disable reasoning for fast requests?

Yes. Set "effort": "low" to disable the think tool, skip reflection, and cap ReAct at 3 loops. Near-direct-passthrough performance while still benefiting from task classification and prompt optimisation.

Is it production-ready?

148 passing tests, Prometheus metrics, structured logging, Docker support, retry logic with exponential backoff, configurable timeouts. Validated on real NVIDIA NIM workloads (90% pass rate, 0 timeouts). Pre-1.0 — review open issues before high-traffic deployment.

Where is memory stored?

SQLite by default (cognitron_memory.db). Configure memory.long_term_backend: redis or postgres for production. Memory is keyed by session_id and isolated between sessions.

🧰 Tech Stack

Layer	Technology
🌐 Gateway	FastAPI + Uvicorn
📐 Schema	Pydantic v2
🌍 HTTP Client	httpx (async, retry)
📋 Logging	structlog (JSON structured)
🔌 MCP	MCP Python SDK
🔢 Tokens	tiktoken
📡 Streaming	SSE-starlette
📊 Metrics	Prometheus compatible

📄 License

Released under the MIT License — free for personal and commercial use.

See LICENSE for full text.

Built with 🧠 for developers who believe LLMs can do better.

⭐ Star on GitHub · 🐛 Report a Bug · 💡 Request a Feature · 🤝 Contribute

Cognitron — OpenAI-compatible LLM middleware · chain-of-thought · ReAct agent · self-reflection AI · Ollama middleware · AI reasoning middleware · vLLM middleware · NVIDIA NIM

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
.github		.github
cognitron		cognitron
tests		tests
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CONTRIBUTING.md		CONTRIBUTING.md
Dockerfile		Dockerfile
LICENSE		LICENSE
README.md		README.md
SECURITY.md		SECURITY.md
cognitron.yaml		cognitron.yaml
docker-compose.yml		docker-compose.yml
mcp_servers.yaml		mcp_servers.yaml
pyproject.toml		pyproject.toml

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🧠 OpenAI-Compatible LLM Middleware · Chain-of-Thought · ReAct Loops · Self-Reflection AI

🏆

90%

⚡

8.8s

🔄

4.9×

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✅

Zero

📋 Table of Contents

🤔 Why Cognitron?

⚙️ How It Works

✨ Features

💡 Chain-of-Thought Think Tool

🔄 ReAct Loop Engine

🪞 Self-Reflection AI

🎯 Task Analyzer

🔨 Prompt Forge

🗃️ Three-Tier Memory

🔌 MCP Tool Integration

📊 Built-in Observability

📊 Benchmark Results

Overall Performance

Category Breakdown

🏗️ Architecture

Request Flow

🚀 Quick Start

1️⃣ Install

2️⃣ Configure your LLM backend

3️⃣ Start and use

🔌 Supported Backends

⚙️ Effort Levels

📖 API Reference

Endpoint

Cognitron Extension Block

Response (standard OpenAI format + optional metadata)

Additional Endpoints

🛠️ Configuration Reference

cognitron.yaml

Environment Variable Overrides

MCP Servers (mcp_servers.yaml)

📁 Project Structure

🐳 Docker

Quick run

Full stack with Ollama (GPU)

Build from source

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