CSL-Core (Chimera Specification Language) brings mathematical rigor to AI agent governance.

Instead of relying on "please don't do that" prompts, CSL enforces:

• 🛡️ Deterministic Safety: Rules are enforced by a runtime engine, not the LLM itself.
• 📐 Formally Verified: Policies are compiled into Z3 constraints to mathematically prove they have no loopholes.
• 🔌 Model Agnostic: Works with OpenAI, Anthropic, Llama, or custom agents. Independent of training data.
• ⚖️ Auditable & Verifiable: Every decision generates a proof of compliance. Allows third-party auditing of AI behavior without exposing model weights or proprietary data.

⚠️ Alpha (0.2.x). Interfaces may change. Use in production only with thorough testing.

Create my_policy.csl:

CONFIG {
  ENFORCEMENT_MODE: BLOCK
  CHECK_LOGICAL_CONSISTENCY: TRUE
}

DOMAIN MyGuard {
  VARIABLES {
    action: {"READ", "WRITE", "DELETE"}
    user_level: 0..5
  }

  STATE_CONSTRAINT strict_delete {
    WHEN action == "DELETE"
    THEN user_level >= 4
  }
}

CSL-Core provides a powerful CLI for testing policies without writing any Python code:

# 1. Verify policy (syntax + Z3 formal verification)
cslcore verify my_policy.csl

# 2. Test with single input
cslcore simulate my_policy.csl --input '{"action": "DELETE", "user_level": 2}'

# 3. Interactive REPL for rapid testing
cslcore repl my_policy.csl

> {"action": "DELETE", "user_level": 2}
allowed=False violations=1 warnings=0

> {"action": "DELETE", "user_level": 5}
allowed=True violations=0 warnings=0

from chimera_core import load_guard

# Factory method - handles parsing, compilation, and Z3 verification
guard = load_guard("my_policy.csl")

# This will pass
result = guard.verify({"action": "READ", "user_level": 1})
print(result.allowed)  # True

# This will be blocked
try:
    guard.verify({"action": "DELETE", "user_level": 2})
except ChimeraError as e:
    print(f"Blocked: {e}")

• Why CSL-Core?
• The Problem
• Key Features
• Quick Start
• Learning Path
  • Step 1: Quickstart
  • Step 2: Real-World Examples
  • Step 3: Production Deployment
• Architecture
• Documentation
• CLI Tools
• LangChain Integration
• API Quick Reference
• Testing
• Plugin Architecture
• Use Cases
• Roadmap
• Contributing
• License
• Contact

Scenario: You're building a LangChain or any AI agent for a fintech app. The agent can transfer funds, query databases, and send emails. You want to ensure:

• ❌ Junior users cannot transfer more than $1,000
• ❌ PII cannot be sent to external email domains
• ❌ The secrets table cannot be queried by anyone

Traditional Approach (Prompt Engineering):

prompt = """You are a helpful assistant. IMPORTANT RULES:
- Never transfer more than $1000 for junior users
- Never send PII to external emails
- Never query the secrets table
[10 more pages of rules...]"""

Problems:

• ⚠️ LLM can be prompt-injected ("Ignore previous instructions...")
• ⚠️ Rules are probabilistic (99% compliance ≠ 100%)
• ⚠️ No auditability (which rule was violated?)
• ⚠️ Fragile (adding a rule might break existing behavior)

CSL-Core Approach:

CONFIG {
  ENFORCEMENT_MODE: BLOCK
  CHECK_LOGICAL_CONSISTENCY: TRUE
}
DOMAIN AgentGuard {
  VARIABLES { 
    user_tier: {"JUNIOR", "SENIOR"}
    amount: 0..100000
  }
  STATE_CONSTRAINT junior_limit {
    WHEN user_tier == "JUNIOR"
    THEN amount <= 1000
  }
}

guard = load_guard("my_policy.csl")
safe_tools = guard_tools(tools, guard, inject={"user_tier": "JUNIOR"})
agent = create_openai_tools_agent(llm, safe_tools, prompt)

• Mathematically proven consistent (Z3)
• LLM cannot bypass (enforcement is external)
• Every violation logged with constraint name

Modern AI is inherently probabilistic. While this enables creativity, it makes systems fundamentally unreliable for critical constraints:

• ❌ Prompts are suggestions, not rules
• ❌ Fine-tuning biases behavior but guarantees nothing
• ❌ Post-hoc classifiers add another probabilistic layer (more AI watching AI)

CSL-Core flips this model: Instead of asking AI to behave, you force it to comply using an external, deterministic logic layer.

Policies are mathematically proven consistent at compile-time. Contradictions, unreachable rules, and logic errors are caught before deployment.

Compiled policies execute as lightweight Python functors. No heavy parsing, no API calls — just pure deterministic evaluation.

Drop-in protection for LangChain agents with 3 lines of code:

• Context Injection: Pass runtime context (user roles, environment) that the LLM cannot override
• Optional via tool_field: Tool names auto-injected into policy evaluation
• Custom Context Mappers: Map complex LangChain inputs to policy variables
• Zero Boilerplate: Wrap tools, chains, or entire agents with a single function call

One-line policy loading with automatic compilation and verification:

guard = load_guard("policy.csl")  # Parse + Compile + Verify in one call

If something goes wrong (missing data, type mismatch, evaluation error), the system blocks by default. Safety over convenience.

Native support for:

• LangChain (Tools, Runnables, LCEL chains)
• Python Functions (any callable)
• REST APIs (via plugins)

Every decision produces an audit trail with:

• Triggered rules
• Violations (if any)
• Latency metrics
• Optional Rich terminal visualization

• ✅ Smoke tests (parser, compiler)
• ✅ Logic verification (Z3 engine integrity)
• ✅ Runtime decisions (allow vs block)
• ✅ Framework integrations (LangChain)
• ✅ CLI end-to-end tests
• ✅ Real-world example policies with full test coverage

Run the entire test suite:

pytest  # tests covering all components

pip install csl-core

Create my_policy.csl:

CONFIG {
  ENFORCEMENT_MODE: BLOCK
  CHECK_LOGICAL_CONSISTENCY: TRUE
}

DOMAIN MyGuard {
  VARIABLES {
    action: {"READ", "WRITE", "DELETE"}
    user_level: 0..5
  }
  
  STATE_CONSTRAINT strict_delete {
    WHEN action == "DELETE"
    THEN user_level >= 4
  }
}

CSL-Core provides a powerful CLI for testing policies without writing any Python code:

# 1. Verify policy (syntax + Z3 formal verification)
cslcore verify my_policy.csl

# 2. Test with single input
cslcore simulate my_policy.csl --input '{"action": "DELETE", "user_level": 2}'

# 3. Interactive REPL for rapid testing
cslcore repl my_policy.csl

> {"action": "DELETE", "user_level": 2}
allowed=False violations=1 warnings=0

> {"action": "DELETE", "user_level": 5}
allowed=True violations=0 warnings=0

from chimera_core import load_guard

# Factory method - handles parsing, compilation, and Z3 verification
guard = load_guard("my_policy.csl")

# This will pass
result = guard.verify({"action": "READ", "user_level": 1})
print(result.allowed)  # True

# This will be blocked
try:
    guard.verify({"action": "DELETE", "user_level": 2})
except ChimeraError as e:
    print(f"Blocked: {e}")

from chimera_core import load_guard
from chimera_core.plugins.langchain import guard_tools

# 1. Load policy (auto-compile with Z3 verification)
guard = load_guard("my_policy.csl")

# 2. Wrap tools with policy enforcement
safe_tools = guard_tools(
    tools=[search_tool, delete_tool, transfer_tool],
    guard=guard,
    inject={"user_level": 2, "environment": "prod"},  # Runtime context the LLM can't override
    tool_field="tool",  # Auto-inject tool name into policy context
    enable_dashboard=True  # Optional: Rich terminal visualization
)

# 3. Use in agent - enforcement is automatic and transparent
agent = create_openai_tools_agent(llm, safe_tools, prompt)
executor = AgentExecutor(agent=agent, tools=safe_tools)

What happens under the hood:

• Every tool call is intercepted before execution
• Policy is evaluated with injected context + tool inputs
• Violations block execution with detailed error messages
• Allowed actions pass through with zero overhead

CSL-Core provides a structured learning journey from beginner to production:

No-code exploration of CSL basics:

cd quickstart/
cslcore verify 01_hello_world.csl
cslcore simulate 01_hello_world.csl --input '{"amount": 500, "destination": "EXTERNAL"}'

What's included:

• 01_hello_world.csl - Simplest possible policy (1 rule)
• 02_age_verification.csl - Multi-rule logic with numeric comparisons
• 03_langchain_template.py - Copy-paste LangChain integration

Goal: Understand CSL syntax and CLI workflow in 5 minutes.

Use-ready policies with comprehensive test coverage:

cd examples/
python run_examples.py  # Run all examples with test suites
python run_examples.py agent_tool_guard  # Run specific example

Available Examples:

Interactive Demos:

# See LangChain integration with visual dashboard
python examples/integrations/langchain_agent_demo.py

Goal: Explore production patterns and run comprehensive test suites.

Once you understand the patterns, integrate into your application:

1. Write your policy (or adapt from examples)
2. Test thoroughly using CLI batch simulation
3. Integrate with 3-line LangChain wrapper
4. Deploy with CI/CD verification (policy as code)

See Getting Started Guide for detailed walkthrough.

CSL-Core separates Policy Definition from Runtime Enforcement through a clean 3-stage architecture:

┌─────────────────────────────────────────────────────────────────┐
│                     1. COMPILER (compiler.py)                   │
│  .csl file → AST → Intermediate Representation (IR) → Artifact   │
│  • Syntax validation                                            │
│  • Semantic validation                                          │
│  • Optimized functor generation                                 │
└─────────────────────────────────────────────────────────────────┘
                                ↓
┌─────────────────────────────────────────────────────────────────┐
│                    2. VERIFIER (verifier.py)                    │
│              Z3 Theorem Prover - Static Analysis                │
│  • Reachability analysis                                        │
│  • Contradiction detection                                      │
│  • Rule shadowing detection                                     │
│  ✅ If verification fails → Policy WILL NOT compile             │
└─────────────────────────────────────────────────────────────────┘
                                ↓
┌─────────────────────────────────────────────────────────────────┐
│                    3. RUNTIME GUARD (runtime.py)                │
│                 Deterministic Policy Enforcement                │
│  • Fail-closed evaluation                                       │
│  • Zero dependencies (pure Python functors)                     │
│  • Audit trail generation                                       │
│  • <1ms latency for typical policies                            │
└─────────────────────────────────────────────────────────────────┘

Key Insight: Heavy computation (parsing, Z3 verification) happens once at compile-time. Runtime is pure evaluation — no symbolic solver, no heavy libraries.

The examples/ directory contains policies with comprehensive test suites. Each example demonstrates real-world patterns and includes:

• ✅ Complete .csl policy file
• ✅ JSON test cases (allow + block scenarios)
• ✅ Automated test runner with visual reports
• ✅ Expected violations for each blocked case

Run all examples with the test runner:

python examples/run_examples.py

Run specific example:

python examples/run_examples.py agent_tool_guard
python examples/run_examples.py banking

Show detailed failures:

python examples/run_examples.py --details

Common patterns extracted from examples for reuse:

Pattern 1: Role-Based Access Control (RBAC)

STATE_CONSTRAINT admin_only {
    WHEN operation == "SENSITIVE_ACTION"
    THEN user_role MUST BE "ADMIN"
}

Source: agent_tool_guard.csl (lines 30-33)

Pattern 2: PII Protection

STATE_CONSTRAINT no_external_pii {
    WHEN pii_present == "YES"
    THEN destination MUST NOT BE "EXTERNAL"
}

Source: agent_tool_guard.csl (lines 55-58)

Pattern 3: Progressive Limits by Tier

STATE_CONSTRAINT basic_tier_limit {
    WHEN tier == "BASIC"
    THEN amount <= 1000
}

STATE_CONSTRAINT premium_tier_limit {
    WHEN tier == "PREMIUM"
    THEN amount <= 50000
}

Source: chimera_banking_case_study.csl (lines 28-38)

Pattern 4: Hard Sanctions (Fail-Closed)

STATE_CONSTRAINT sanctions {
    ALWAYS True  // Always enforced
    THEN country MUST NOT BE "SANCTIONED_COUNTRY"
}

Source: chimera_banking_case_study.csl (lines 22-25)

Pattern 5: Emergency Bypass

// Normal rule with bypass
STATE_CONSTRAINT normal_with_bypass {
    WHEN condition AND action != "EMERGENCY"
    THEN requirement
}

// Emergency gate (higher threshold)
STATE_CONSTRAINT emergency_gate {
    WHEN action == "EMERGENCY"
    THEN approval_count >= 10
}

Source: dao_treasury_guard.csl (lines 60-67)

See examples/README.md for the complete policy catalog.

CSL-Core includes a comprehensive test suite following the Testing Pyramid:

# Run all tests
pytest

# Run specific categories
pytest tests/integration          # LangChain plugin tests
pytest tests/test_cli_e2e.py      # End-to-end CLI tests
pytest -k "verifier"              # Z3 verification tests

Test Coverage:

• ✅ Smoke tests (parser, compiler)
• ✅ Logic verification (Z3 engine integrity)
• ✅ Runtime decisions (allow vs block scenarios)
• ✅ LangChain integration (tool wrapping, LCEL gates)
• ✅ CLI end-to-end (subprocess simulation)

See tests/README.md for detailed test architecture.

CSL-Core provides the easiest way to add deterministic safety to LangChain agents. No prompting required, no fine-tuning needed — just wrap and run.

from chimera_core import load_guard
from chimera_core.plugins.langchain import guard_tools

# Your existing tools
from langchain.tools import DuckDuckGoSearchRun, ShellTool
tools = [DuckDuckGoSearchRun(), ShellTool()]

# Load policy
guard = load_guard("agent_policy.csl")

# Wrap tools (one line)
safe_tools = guard_tools(tools, guard)

# Use in agent - that's it!
agent = create_openai_tools_agent(llm, safe_tools, prompt)

The inject parameter lets you pass runtime context that the LLM cannot override:

safe_tools = guard_tools(
    tools=tools,
    guard=guard,
    inject={
        "user_role": current_user.role,           # From your auth system
        "environment": os.getenv("ENV"),          # prod/dev/staging
        "tenant_id": session.tenant_id,           # Multi-tenancy
        "rate_limit_remaining": quota.remaining   # Dynamic limits
    }
)

Policy Example (agent_policy.csl):

CONFIG {
  ENFORCEMENT_MODE: BLOCK
  CHECK_LOGICAL_CONSISTENCY: TRUE
  ENABLE_FORMAL_VERIFICATION: FALSE
  ENABLE_CAUSAL_INFERENCE: FALSE
  INTEGRATION: "native"
}

DOMAIN AgentGuard {
  VARIABLES {
    tool: String
    user_role: {"ADMIN", "USER", "ANALYST"}
    environment: {"prod", "dev"}
  }
  
  // Block shell access in production
  STATE_CONSTRAINT no_shell_in_prod {
    WHEN environment == "prod"
    THEN tool MUST NOT BE "ShellTool"
  }
  
  // Only admins can delete
  STATE_CONSTRAINT admin_only_delete {
    WHEN tool == "DeleteRecordTool"
    THEN user_role MUST BE "ADMIN"
  }
}

Map complex LangChain inputs to your policy variables:

def my_context_mapper(tool_input: Dict) -> Dict:
    """
    LangChain tools receive kwargs like:
      {"query": "...", "limit": 10, "metadata": {...}}
    
    Your policy expects:
      {"search_query": "...", "result_limit": 10, "source": "..."}
    """
    return {
        "search_query": tool_input.get("query"),
        "result_limit": tool_input.get("limit"),
        "source": tool_input.get("metadata", {}).get("source", "unknown")
    }

safe_tools = guard_tools(
    tools=tools,
    guard=guard,
    context_mapper=my_context_mapper
)

Insert a policy gate into LCEL chains:

from chimera_core.plugins.langchain import gate

chain = (
    {"query": RunnablePassthrough()}
    | gate(guard, inject={"user_role": "USER"})  # Policy checkpoint
    | prompt
    | llm
    | StrOutputParser()
)

# If policy blocks, chain stops with ChimeraError
result = chain.invoke({"query": "DELETE * FROM users"})  # Blocked!

See a complete working example in examples/integrations/langchain_agent_demo.py:

• Simulated financial agent with transfer tools
• Role-based access control (USER vs ADMIN)
• PII protection rules
• Rich terminal visualization

python examples/integrations/langchain_agent_demo.py

CSL-Core provides a universal plugin system for integrating with AI frameworks.

Available Plugins:

• ✅ LangChain (chimera_core.plugins.langchain)
• 🚧 LlamaIndex (coming soon)
• 🚧 AutoGen (coming soon)

Create Your Own Plugin:

from chimera_core.plugins.base import ChimeraPlugin

class MyFrameworkPlugin(ChimeraPlugin):
    def process(self, input_data):
        # Enforce policy
        self.run_guard(input_data)
        
        # Continue framework execution
        return input_data

All lifecycle behavior (fail-closed semantics, visualization, context mapping) is inherited automatically from ChimeraPlugin.

See chimera_core/plugins/README.md for the integration guide.

from chimera_core import load_guard, create_guard_from_string

# From file (recommended - handles paths automatically)
guard = load_guard("policies/my_policy.csl")

# From string (useful for testing or dynamic policies)
policy_code = """
CONFIG {
  ENFORCEMENT_MODE: BLOCK
  CHECK_LOGICAL_CONSISTENCY: TRUE
}

DOMAIN Test {
  VARIABLES { x: 0..10 }
  STATE_CONSTRAINT limit { ALWAYS True THEN x <= 5 }
}
"""
guard = create_guard_from_string(policy_code)

# Basic verification
result = guard.verify({"x": 3})
print(result.allowed)  # True
print(result.violations)  # []

# Error handling
from chimera_core import ChimeraError

try:
    guard.verify({"x": 15})
except ChimeraError as e:
    print(f"Blocked: {e}")
    print(f"Violations: {e.violations}")

from chimera_core.plugins.langchain import guard_tools, gate

# Tool wrapping
safe_tools = guard_tools(
    tools=[tool1, tool2],
    guard=guard,
    inject={"user": "alice"},
    tool_field="tool_name",
    enable_dashboard=True
)

# LCEL gate
chain = prompt | gate(guard) | llm

from chimera_core import RuntimeConfig

config = RuntimeConfig(
    raise_on_block=True,           # Raise ChimeraError on violations
    collect_all_violations=True,   # Report all violations, not just first
    missing_key_behavior="block",  # "block", "warn", or "ignore"
    evaluation_error_behavior="block"
)

guard = load_guard("policy.csl", config=config)

CSL-Core's CLI is not just a utility — it's a complete development environment for policies. Test, debug, and deploy without writing a single line of Python.

• ⚡ Instant Feedback: Test policy changes in milliseconds
• 🔍 Interactive Debugging: REPL for exploring edge cases
• 🤖 CI/CD Ready: Integrate verification into your pipeline
• 📊 Batch Testing: Run hundreds of test cases with visual reports
• 🎨 Rich Visualization: See exactly which rules triggered

The verify command is your first line of defense. It checks syntax, semantics, and mathematical consistency using Z3.

# Basic verification
cslcore verify my_policy.csl

# Output:
# ⚙️  Compiling Domain: MyGuard
#    • Validating Syntax... ✅ OK
#    ├── Verifying Logic Model (Z3 Engine)... ✅ Mathematically Consistent
#    • Generating IR... ✅ OK

Advanced Debugging:

# Show Z3 trace on verification failures
cslcore verify complex_policy.csl --debug-z3

Skip verification (not recommended for production):

cslcore verify policy.csl --skip-verify

The simulate command is your policy test harness. Pass inputs, see decisions, validate behavior.

Single Input Testing:

# Test one scenario
cslcore simulate agent_policy.csl \
  --input '{"tool": "TRANSFER_FUNDS", "user_role": "ADMIN", "amount": 5000}'

# Output:
# ✅ ALLOWED

Batch Testing with JSON Files:

Create test_cases.json:

[
  {
    "name": "Junior user tries transfer",
    "input": {"tool": "TRANSFER_FUNDS", "user_role": "JUNIOR", "amount": 100},
    "expected": "BLOCK"
  },
  {
    "name": "Admin transfers within limit",
    "input": {"tool": "TRANSFER_FUNDS", "user_role": "ADMIN", "amount": 4000},
    "expected": "ALLOW"
  }
]

Run all tests:

cslcore simulate agent_policy.csl --input-file test_cases.json --dashboard

Machine-Readable Output (CI/CD):

# JSON output for automated testing
cslcore simulate policy.csl --input-file tests.json --json --quiet

# Output to file (JSON Lines format)
cslcore simulate policy.csl --input-file tests.json --json-out results.jsonl

Runtime Behavior Flags:

# Dry-run: Report what WOULD be blocked without actually blocking
cslcore simulate policy.csl --input-file tests.json --dry-run

# Fast-fail: Stop at first violation
cslcore simulate policy.csl --input-file tests.json --fast-fail

# Lenient mode: Missing keys warn instead of block
cslcore simulate policy.csl \
  --input '{"incomplete": "data"}' \
  --missing-key-behavior warn

The REPL (Read-Eval-Print Loop) is the fastest way to explore policy behavior. Load a policy once, then test dozens of scenarios interactively.

cslcore repl my_policy.csl --dashboard

Interactive Session:

cslcore> {"action": "DELETE", "user_level": 2}
🛡️ BLOCKED: Constraint 'strict_delete' violated.
  Rule: user_level >= 4 (got: 2)

cslcore> {"action": "DELETE", "user_level": 5}
✅ ALLOWED

cslcore> {"action": "READ", "user_level": 0}
✅ ALLOWED

cslcore> exit

Use Cases:

• 🧪 Rapid Prototyping: Test edge cases without reloading
• 🐛 Debugging: Explore why a specific input is blocked
• 📚 Learning: Understand policy behavior interactively
• 🎓 Demos: Show stakeholders real-time policy decisions

Example: GitHub Actions

name: Verify Policies
on: [push, pull_request]

jobs:
  verify:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Install CSL-Core
        run: pip install csl-core
      
      - name: Verify all policies
        run: |
          for policy in policies/*.csl; do
            cslcore verify "$policy" || exit 1
          done
      
      - name: Run test suites
        run: |
          cslcore simulate policies/prod_policy.csl \
            --input-file tests/prod_tests.json \
            --json --quiet > results.json
      
      - name: Check for violations
        run: |
          if grep -q '"allowed": false' results.json; then
            echo "❌ Policy tests failed"
            exit 1
          fi

Exit Codes for Automation:

Debug Z3 Solver Issues:

# When verification fails with internal errors
cslcore verify complex_policy.csl --debug-z3 > z3_trace.log

Skip Validation Steps:

# Skip semantic validation (not recommended)
cslcore verify policy.csl --skip-validate

# Skip Z3 verification (DANGEROUS - only for development)
cslcore verify policy.csl --skip-verify

Custom Runtime Behavior:

# Block on missing keys (default)
cslcore simulate policy.csl --input '{"incomplete": "data"}' --missing-key-behavior block

# Warn on evaluation errors instead of blocking
cslcore simulate policy.csl --input '{"bad": "type"}' --evaluation-error-behavior warn

See CLI Reference for complete documentation.

CSL-Core is ready for:

• Transaction limits by user tier
• Sanctions enforcement
• Risk-based blocking
• Fraud prevention rules

• Tool permission management
• PII protection
• Rate limiting
• Dangerous operation blocking

• Multi-sig requirements
• Timelock enforcement
• Reputation-based access
• Treasury protection

• HIPAA compliance rules
• Patient data access control
• Treatment protocol validation
• Audit trail requirements

• Regulatory rule enforcement
• Contract validation
• Policy adherence verification
• Automated compliance checks

** CSL-Core is currently in Alpha, provided 'as-is' without any warranties; the developers accept no liability for any direct or indirect damages resulting from its use. **

• Core language (CSL syntax, parser, AST)
• Z3 formal verification engine
• Python runtime with fail-closed semantics
• LangChain integration (Tools, LCEL, Runnables)
• Factory pattern for easy policy loading
• CLI tools (verify, simulate, repl)
• Rich terminal visualization
• Comprehensive test suite
• Custom context mappers for framework integration

• Policy versioning & migration tools
• Web-based policy editor
• LangGraph integration

• LlamaIndex integration
• AutoGen integration
• Haystack integration
• Policy marketplace (community-contributed policies)
• Cloud deployment templates (AWS Lambda, GCP Functions, Azure Functions)
• Policy analytics dashboard
• Multi-policy composition
• Hot-reload support for development

• TLA+ temporal logic verification
• Causal inference engine
• Multi-tenancy support
• Advanced policy migration tooling
• Priority support & SLA

We welcome contributions! CSL-Core is open-source and community-driven.

Ways to Contribute:

• 🐛 Report bugs via GitHub Issues
• 💡 Suggest features or improvements
• 📝 Improve documentation
• 🧪 Add test cases
• 🎓 Create example policies for new domains
• 🔌 Build framework integrations (LlamaIndex, AutoGen, Haystack)
• 🌟 Share your LangChain use cases and integration patterns

High-Impact Contributions We'd Love:

• 📚 More real-world example policies (healthcare, legal, supply chain)
• 🔗 Framework integrations (see chimera_core/plugins/base.py for the pattern)
• 🎨 Web-based policy editor
• 📊 Policy analytics and visualization tools
• 🧪 Additional test coverage for edge cases

Contribution Process:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes with tests
4. Run the test suite (pytest)
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

See CONTRIBUTING.md for detailed guidelines.

CSL-Core is released under the Apache License 2.0. See LICENSE for details.

What's included in the open-source core:

• ✅ Complete CSL language (parser, compiler, runtime)
• ✅ Z3-based formal verification
• ✅ LangChain integration
• ✅ CLI tools (verify, simulate, repl)
• ✅ Rich terminal visualization
• ✅ All example policies and test suites

Advanced capabilities for large-scale deployments:

• 🔒 TLA+ Temporal Logic Verification: Beyond Z3, full temporal model checking
• 🔒 Causal Inference Engine: Counterfactual analysis and causal reasoning
• 🔒 Multi-tenancy Support: Policy isolation and tenant-scoped enforcement
• 🔒 Policy Migration Tools: Version control and backward compatibility
• 🔒 Cloud Deployment Templates: Production-ready Kubernetes/Lambda configs
• 🔒 Priority Support: SLA-backed engineering support

CSL-Core is built on the shoulders of giants:

• Z3 Theorem Prover - Microsoft Research (Leonardo de Moura, Nikolaj Bjørner)
• LangChain - Harrison Chase and contributors
• Rich - Will McGugan (terminal visualization)

• GitHub Issues: Report bugs or request features
• Discussions: Ask questions, share use cases
• Email: [email protected]

If you find CSL-Core useful, please consider giving it a star on GitHub! It helps others discover the project.

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Making AI systems mathematically safe, one policy at a time.